matchms package

class matchms.Fingerprints(fingerprint_generator, *, ignore_stereochemistry: bool = False, count: bool = False, folded: bool = True, return_csr: bool = False, invalid_policy: str = 'raise', **config_kwargs)[source]

Bases: object

Compute and store an InChIKey-to-fingerprint mapping for a collection of spectra.

This class is a container for molecular fingerprints keyed by InChIKey. Fingerprints are computed for unique compounds only and stored either as a dense NumPy array or as a SciPy CSR sparse matrix.

Compared to the older implementation, this refactor is designed for larger scale use cases and delegates fingerprint computation to chemap.

Example

import numpy as np
from rdkit.Chem import rdFingerprintGenerator
from matchms import Fingerprints, Spectrum

spectrum_1 = Spectrum(
    mz=np.array([100, 150, 200.]),
    intensities=np.array([0.7, 0.2, 0.1]),
    metadata={
        "inchikey": "OTMSDBZUPAUEDD-UHFFFAOYSA-N",
        "smiles": "CC",
        "precursor_mz": 150.0,
    },
)
spectrum_2 = Spectrum(
    mz=np.array([100, 150, 200.]),
    intensities=np.array([0.7, 0.2, 0.1]),
    metadata={
        "inchikey": "UGFAIRIUMAVXCW-UHFFFAOYSA-N",
        "smiles": "[C-]#[O+]",
        "precursor_mz": 150.0,
    },
)

spectra = [spectrum_1, spectrum_2]

generator = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=256)

fpgen = Fingerprints(
    fingerprint_generator=generator,
    count=False,
    folded=True,
    return_csr=False,
)
fpgen.compute_fingerprints(spectra)

print(fpgen.fingerprint_count)
print(type(fpgen.get_fingerprint_by_inchikey("OTMSDBZUPAUEDD-UHFFFAOYSA-N")))

Should output

2
<class 'numpy.ndarray'>

fingerprints: The computed fingerprints as either a NumPy array or SciPy CSR matrix.

inchikeys: Ordered list of unique InChIKeys corresponding to fingerprint rows.

fingerprint_count: Number of unique fingerprints currently stored.

config: Dictionary with configuration used for fingerprint computation.

to_dataframe: DataFrame containing InChIKeys and fingerprints.

__init__(fingerprint_generator, *, ignore_stereochemistry: bool = False, count: bool = False, folded: bool = True, return_csr: bool = False, invalid_policy: str = 'raise', **config_kwargs)[source]

Parameters:

fingerprint_generator – A chemap-compatible fingerprint generator, for example an RDKit fingerprint generator or a scikit-fingerprints object.
ignore_stereochemistry – If True, the first 14 characters of the InChIKey are used.
count – Whether count fingerprints should be computed.
folded – Whether fingerprints should be folded.
return_csr – If True, fingerprints are stored as a SciPy CSR matrix. Otherwise they are stored as a dense NumPy array.
invalid_policy – Policy passed to chemap for invalid molecular inputs.
**config_kwargs – Additional keyword arguments passed into FingerprintConfig.

compute_fingerprint(spectrum: Spectrum)[source]

Compute one fingerprint for a given spectrum.

This does not add the fingerprint to the internal storage. It only computes and returns the fingerprint.

Parameters:: spectrum – A spectrum for which a fingerprint is to be calculated.
Returns:: Fingerprint row, or None if fingerprint could not be computed.
Return type:: Optional[np.ndarray | scipy.sparse.csr_matrix]

compute_fingerprints(spectra: list[Spectrum])[source]

Compute fingerprints for a list of spectra.

Fingerprints are computed only for unique compounds, keyed by InChIKey. Existing stored fingerprints are replaced.

Parameters:: spectra – List of spectra.

property config: dict: Return configuration used for fingerprint computation.

property fingerprint_count: int: Return the number of stored fingerprints.

property fingerprints: ndarray | csr_matrix | None: Return the stored fingerprint matrix.

get_fingerprint_by_inchikey(inchikey: str)[source]

Get fingerprint by InChIKey.

Parameters:: inchikey – InChIKey of a compound.
Returns:: The corresponding fingerprint row, or None if not present.
Return type:: Optional[np.ndarray | scipy.sparse.csr_matrix]

get_fingerprint_by_spectrum(spectrum: Spectrum)[source]

Get fingerprint by spectrum.

Parameters:: spectrum – Spectrum with an InChIKey.
Returns:: The corresponding fingerprint row, or None if not present.
Return type:: Optional[np.ndarray | scipy.sparse.csr_matrix]

property inchikeys: list[str]: Return ordered list of stored InChIKeys.

property is_sparse: bool: Return True if fingerprints are stored as CSR sparse matrix.

property to_dataframe: DataFrame: Return fingerprints as a pandas DataFrame indexed by InChIKey.

class matchms.Fragments(mz=None, intensities=None)[source]

Bases: object

Stores arrays of intensities and M/z values, with some checks on their internal consistency.

For example

import numpy as np
from matchms import Fragments

mz = np.array([10, 20, 30], dtype="float")
intensities = np.array([100, 20, 300], dtype="float")

peaks = Fragments(mz=mz, intensities=intensities)
print(peaks[2])

Should output

[ 30. 300.]

mz: Numpy array of m/z values.

intensities: Numpy array of peak intensity values.

__init__(mz=None, intensities=None)[source]

clone()[source]: Return a copy of the Fragments instance.

copy()[source]: Return a deepcopy of the Fragments instance.

property intensities: getter method for intensities private variable

property mz: getter method for mz private variable

property to_numpy: getter method to return stacked numpy array of both peak mz and intensities

class matchms.Metadata(metadata: dict = None, matchms_key_style: bool = True)[source]

Bases: object

Class to handle spectrum metadata in matchms.

Metadata entries will be stored as PickyDict dictionary in metadata.data. Unlike normal Python dictionaries, not all key names will be accepted. Key names will be forced to be lower-case to avoid confusions between key such as “Precursor_MZ” and “precursor_mz”.

To avoid the default harmonization of the metadata dictionary use the option matchms_key_style=False.

Code example:

metadata = Metadata({"Precursor_MZ": 201.5, "Compound Name": "SuperStuff"})
print(metadata["precursor_mz"])  # => 201.5
print(metadata["compound_name"])  # => SuperStuff

Or if the matchms default metadata harmonization should not take place:

metadata = Metadata({"Precursor_MZ": 201.5, "Compound Name": "SuperStuff"}, matchms_key_style=False)
print(metadata["precursor_mz"])  # => 201.5
print(metadata["compound_name"])  # => None (now you need to use "compound name")

__init__(metadata: dict = None, matchms_key_style: bool = True)[source]

Parameters:

metadata – Spectrum metadata as a dictionary.
matchms_key_style – Set to False if metadata harmonization to default keys is not desired. The default is True.

get(key: str, default=None)[source]: Retrieve value from metadata dict.

harmonize_keys()[source]

Runs default harmonization of metadata.

Method harmonized metadata field names which include setting them to lower-case and running a series of regex replacements followed by the default field name replacements (such as precursor_mass –> precursor_mz).

harmonize_values()[source]

Runs default harmonization of metadata.

This includes harmonizing entries for ionmode, retention time and index, charge, as well as the removal of invalid entries (“”, “NA”, “N/A”, “NaN”).

items()[source]: Retrieve all items (key, value pairs) of metadata dict.

keys()[source]: Retrieve all keys of metadata dict.

set(key: str, value)[source]: Set value in metadata dict.

static set_key_replacements(keys: dict)[source]

Set key replacements for metadata harmonization.

Parameters:: keys – Dictionary with key replacements.

to_dict(export_style: str = 'matchms')[source]

Returns a regular Python dictionary containing the metadata entries.

Parameters:: export_style – Specifies the naming style of the metadata fields. Default is “matchms”.

values()[source]: Retrieve all values of metadata dict.

class matchms.MetadataCollection(data, collection=None, *args, **kwargs)[source]

Bases: DataFrame

Metadata proxy class. Used for filter directly on metadata and synchronize fragments.

property T: DataFrame

The transpose of the DataFrame.

Returns:: The transposed DataFrame.
Return type:: DataFrame

See also

DataFrame.transpose: Transpose index and columns.

Examples

>>> df = pd.DataFrame({"col1": [1, 2], "col2": [3, 4]})
>>> df
   col1  col2
0     1     3
1     2     4

>>> df.T
      0  1
col1  1  2
col2  3  4

__init__(data, collection=None, *args, **kwargs)[source]

abs() → Self

Return a Series/DataFrame with absolute numeric value of each element.

This function only applies to elements that are all numeric.

Returns:: Series/DataFrame containing the absolute value of each element.
Return type:: abs

See also

numpy.absolute: Calculate the absolute value element-wise.

Notes

For complex inputs, 1.2 + 1j, the absolute value is \(\sqrt{ a^2 + b^2 }\).

Examples

Absolute numeric values in a Series.

>>> s = pd.Series([-1.10, 2, -3.33, 4])
>>> s.abs()
0    1.10
1    2.00
2    3.33
3    4.00
dtype: float64

Absolute numeric values in a Series with complex numbers.

>>> s = pd.Series([1.2 + 1j])
>>> s.abs()
0    1.56205
dtype: float64

Absolute numeric values in a Series with a Timedelta element.

>>> s = pd.Series([pd.Timedelta("1 days")])
>>> s.abs()
0   1 days
dtype: timedelta64[us]

Select rows with data closest to certain value using argsort (from StackOverflow).

>>> df = pd.DataFrame(
...     {"a": [4, 5, 6, 7], "b": [10, 20, 30, 40], "c": [100, 50, -30, -50]}
... )
>>> df
     a    b    c
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50
>>> df.loc[(df.c - 43).abs().argsort()]
     a    b    c
1    5   20   50
0    4   10  100
2    6   30  -30
3    7   40  -50

add(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Addition of dataframe and other, element-wise (binary operator add).

Equivalent to dataframe + other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, radd.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

add_prefix(prefix: str, axis: int | Literal['index', 'columns', 'rows'] | None = None) → Self

Prefix labels with string prefix.

For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed.

Parameters:

prefix (str) – The string to add before each label.
axis ({0 or 'index', 1 or 'columns', None}, default None) –
Axis to add prefix on

Added in version 2.0.0.

Returns:

New Series or DataFrame with updated labels.

Return type:

Series or DataFrame

See also

Series.add_suffix: Suffix row labels with string suffix.
DataFrame.add_suffix: Suffix column labels with string suffix.

Examples

>>> s = pd.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
dtype: int64

>>> s.add_prefix("item_")
item_0    1
item_1    2
item_2    3
item_3    4
dtype: int64

>>> df = pd.DataFrame({"A": [1, 2, 3, 4], "B": [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6

>>> df.add_prefix("col_")
     col_A  col_B
0       1       3
1       2       4
2       3       5
3       4       6

add_suffix(suffix: str, axis: int | Literal['index', 'columns', 'rows'] | None = None) → Self

Suffix labels with string suffix.

For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed.

Parameters:

suffix (str) – The string to add after each label.
axis ({0 or 'index', 1 or 'columns', None}, default None) –
Axis to add suffix on

Added in version 2.0.0.

Returns:

New Series or DataFrame with updated labels.

Return type:

Series or DataFrame

See also

Series.add_prefix: Prefix row labels with string prefix.
DataFrame.add_prefix: Prefix column labels with string prefix.

Examples

>>> s = pd.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
dtype: int64

>>> s.add_suffix("_item")
0_item    1
1_item    2
2_item    3
3_item    4
dtype: int64

>>> df = pd.DataFrame({"A": [1, 2, 3, 4], "B": [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6

>>> df.add_suffix("_col")
     A_col  B_col
0       1       3
1       2       4
2       3       5
3       4       6

agg(func=None, axis: Axis = 0, *args, **kwargs)

Aggregate using one or more operations over the specified axis.

Parameters:

func (function, str, list or dict) –
Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply.

Accepted combinations are:
- function
- string function name
- list of functions and/or function names, e.g. [np.sum, 'mean']
- dict of axis labels -> functions, function names or list of such.
axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.
*args – Positional arguments to pass to func.
**kwargs – Keyword arguments to pass to func.

Returns:

The return can be:

scalar : when Series.agg is called with single function
Series : when DataFrame.agg is called with a single function
DataFrame : when DataFrame.agg is called with several functions

Return type:

scalar, Series or DataFrame

See also

DataFrame.apply: Perform any type of operations.
DataFrame.transform: Perform transformation type operations.
DataFrame.groupby: Perform operations over groups.
DataFrame.resample: Perform operations over resampled bins.
DataFrame.rolling: Perform operations over rolling window.
DataFrame.expanding: Perform operations over expanding window.
core.window.ewm.ExponentialMovingWindow: Perform operation over exponential weighted window.

Notes

The aggregation operations are always performed over an axis, either the index (default) or the column axis. This behavior is different from numpy aggregation functions (mean, median, prod, sum, std, var), where the default is to compute the aggregation of the flattened array, e.g., numpy.mean(arr_2d) as opposed to numpy.mean(arr_2d, axis=0).

agg is an alias for aggregate. Use the alias.

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.

A passed user-defined-function will be passed a Series for evaluation.

If func defines an index relabeling, axis must be 0 or index.

Examples

>>> df = pd.DataFrame(
...     [[1, 2, 3], [4, 5, 6], [7, 8, 9], [np.nan, np.nan, np.nan]],
...     columns=["A", "B", "C"],
... )

Aggregate these functions over the rows.

>>> df.agg(["sum", "min"])
        A     B     C
sum  12.0  15.0  18.0
min   1.0   2.0   3.0

Different aggregations per column.

>>> df.agg({"A": ["sum", "min"], "B": ["min", "max"]})
        A    B
sum  12.0  NaN
min   1.0  2.0
max   NaN  8.0

Aggregate different functions over the columns and rename the index of the resulting DataFrame.

>>> df.agg(x=("A", "max"), y=("B", "min"), z=("C", "mean"))
     A    B    C
x  7.0  NaN  NaN
y  NaN  2.0  NaN
z  NaN  NaN  6.0

Aggregate over the columns.

>>> df.agg("mean", axis="columns")
0    2.0
1    5.0
2    8.0
3    NaN
dtype: float64

aggregate(func=None, axis: Axis = 0, *args, **kwargs)

Aggregate using one or more operations over the specified axis.

Parameters:

func (function, str, list or dict) –
Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply.

Accepted combinations are:
- function
- string function name
- list of functions and/or function names, e.g. [np.sum, 'mean']
- dict of axis labels -> functions, function names or list of such.
axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.
*args – Positional arguments to pass to func.
**kwargs – Keyword arguments to pass to func.

Returns:

The return can be:

scalar : when Series.agg is called with single function
Series : when DataFrame.agg is called with a single function
DataFrame : when DataFrame.agg is called with several functions

Return type:

scalar, Series or DataFrame

See also

DataFrame.apply: Perform any type of operations.
DataFrame.transform: Perform transformation type operations.
DataFrame.groupby: Perform operations over groups.
DataFrame.resample: Perform operations over resampled bins.
DataFrame.rolling: Perform operations over rolling window.
DataFrame.expanding: Perform operations over expanding window.
core.window.ewm.ExponentialMovingWindow: Perform operation over exponential weighted window.

Notes

The aggregation operations are always performed over an axis, either the index (default) or the column axis. This behavior is different from numpy aggregation functions (mean, median, prod, sum, std, var), where the default is to compute the aggregation of the flattened array, e.g., numpy.mean(arr_2d) as opposed to numpy.mean(arr_2d, axis=0).

agg is an alias for aggregate. Use the alias.

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.

A passed user-defined-function will be passed a Series for evaluation.

If func defines an index relabeling, axis must be 0 or index.

Examples

>>> df = pd.DataFrame(
...     [[1, 2, 3], [4, 5, 6], [7, 8, 9], [np.nan, np.nan, np.nan]],
...     columns=["A", "B", "C"],
... )

Aggregate these functions over the rows.

>>> df.agg(["sum", "min"])
        A     B     C
sum  12.0  15.0  18.0
min   1.0   2.0   3.0

Different aggregations per column.

>>> df.agg({"A": ["sum", "min"], "B": ["min", "max"]})
        A    B
sum  12.0  NaN
min   1.0  2.0
max   NaN  8.0

Aggregate different functions over the columns and rename the index of the resulting DataFrame.

>>> df.agg(x=("A", "max"), y=("B", "min"), z=("C", "mean"))
     A    B    C
x  7.0  NaN  NaN
y  NaN  2.0  NaN
z  NaN  NaN  6.0

Aggregate over the columns.

>>> df.agg("mean", axis="columns")
0    2.0
1    5.0
2    8.0
3    NaN
dtype: float64

align(other: NDFrameT, join: AlignJoin = 'outer', axis: Axis | None = None, level: Level | None = None, copy: bool | lib.NoDefault = <no_default>, fill_value: Hashable | None = None) → tuple[Self, NDFrameT]

Align two objects on their axes with the specified join method.

Join method is specified for each axis Index.

Parameters:

other (DataFrame or Series) – The object to align with.
join ({'outer', 'inner', 'left', 'right'}, default 'outer') –
Type of alignment to be performed.
- left: use only keys from left frame, preserve key order.
- right: use only keys from right frame, preserve key order.
- outer: use union of keys from both frames, sort keys lexicographically.
- inner: use intersection of keys from both frames, preserve the order of the left keys.
axis (allowed axis of the other object, default None) – Align on index (0), columns (1), or both (None).
level (int or level name, default None) – Broadcast across a level, matching Index values on the passed MultiIndex level.
copy (bool, default False) –
This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.
fill_value (scalar, default np.nan) – Value to use for missing values. Defaults to NaN, but can be any “compatible” value.

Returns:

Aligned objects.

Return type:

tuple of (Series/DataFrame, type of other)

See also

Series.align: Align two objects on their axes with specified join method.
DataFrame.align: Align two objects on their axes with specified join method.

Examples

>>> df = pd.DataFrame(
...     [[1, 2, 3, 4], [6, 7, 8, 9]], columns=["D", "B", "E", "A"], index=[1, 2]
... )
>>> other = pd.DataFrame(
...     [[10, 20, 30, 40], [60, 70, 80, 90], [600, 700, 800, 900]],
...     columns=["A", "B", "C", "D"],
...     index=[2, 3, 4],
... )
>>> df
   D  B  E  A
1  1  2  3  4
2  6  7  8  9
>>> other
    A    B    C    D
2   10   20   30   40
3   60   70   80   90
4  600  700  800  900

Align on columns:

>>> left, right = df.align(other, join="outer", axis=1)
>>> left
   A  B   C  D  E
1  4  2 NaN  1  3
2  9  7 NaN  6  8
>>> right
    A    B    C    D   E
2   10   20   30   40 NaN
3   60   70   80   90 NaN
4  600  700  800  900 NaN

We can also align on the index:

>>> left, right = df.align(other, join="outer", axis=0)
>>> left
    D    B    E    A
1  1.0  2.0  3.0  4.0
2  6.0  7.0  8.0  9.0
3  NaN  NaN  NaN  NaN
4  NaN  NaN  NaN  NaN
>>> right
    A      B      C      D
1    NaN    NaN    NaN    NaN
2   10.0   20.0   30.0   40.0
3   60.0   70.0   80.0   90.0
4  600.0  700.0  800.0  900.0

Finally, the default axis=None will align on both index and columns:

>>> left, right = df.align(other, join="outer", axis=None)
>>> left
     A    B   C    D    E
1  4.0  2.0 NaN  1.0  3.0
2  9.0  7.0 NaN  6.0  8.0
3  NaN  NaN NaN  NaN  NaN
4  NaN  NaN NaN  NaN  NaN
>>> right
       A      B      C      D   E
1    NaN    NaN    NaN    NaN NaN
2   10.0   20.0   30.0   40.0 NaN
3   60.0   70.0   80.0   90.0 NaN
4  600.0  700.0  800.0  900.0 NaN

all(*, axis: Axis | None = 0, bool_only: bool = False, skipna: bool = True, **kwargs) → Series | bool

Return whether all elements are True, potentially over an axis.

Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).

Parameters:

axis ({0 or 'index', 1 or 'columns', None}, default 0) –
Indicate which axis or axes should be reduced. For Series this parameter is unused and defaults to 0.
- 0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.
- 1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
- None : reduce all axes, return a scalar.
bool_only (bool, default False) – Include only boolean columns. Not implemented for Series.
skipna (bool, default True) – Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be True, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.
**kwargs (any, default None) – Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

If axis=None, then a scalar boolean is returned. Otherwise a Series is returned with index matching the index argument.

Return type:

Series or scalar

See also

Series.all: Return True if all elements are True.
DataFrame.any: Return True if one (or more) elements are True.

Examples

Series

>>> pd.Series([True, True]).all()
True
>>> pd.Series([True, False]).all()
False
>>> pd.Series([], dtype="float64").all()
True
>>> pd.Series([np.nan]).all()
True
>>> pd.Series([np.nan]).all(skipna=False)
True

DataFrames

Create a DataFrame from a dictionary.

>>> df = pd.DataFrame({"col1": [True, True], "col2": [True, False]})
>>> df
   col1   col2
0  True   True
1  True  False

Default behaviour checks if values in each column all return True.

>>> df.all()
col1     True
col2    False
dtype: bool

Specify axis='columns' to check if values in each row all return True.

>>> df.all(axis="columns")
0     True
1    False
dtype: bool

Or axis=None for whether every value is True.

>>> df.all(axis=None)
False

any(*, axis: Axis | None = 0, bool_only: bool = False, skipna: bool = True, **kwargs) → Series | bool

Return whether any element is True, potentially over an axis.

Returns False unless there is at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).

Parameters:

axis ({0 or 'index', 1 or 'columns', None}, default 0) –
Indicate which axis or axes should be reduced. For Series this parameter is unused and defaults to 0.
- 0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.
- 1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
- None : reduce all axes, return a scalar.
bool_only (bool, default False) – Include only boolean columns. Not implemented for Series.
skipna (bool, default True) – Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be False, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.
**kwargs (any, default None) – Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

If axis=None, then a scalar boolean is returned. Otherwise a Series is returned with index matching the index argument.

Return type:

Series or scalar

See also

numpy.any: Numpy version of this method.
Series.any: Return whether any element is True.
Series.all: Return whether all elements are True.
DataFrame.any: Return whether any element is True over requested axis.
DataFrame.all: Return whether all elements are True over requested axis.

Examples

Series

For Series input, the output is a scalar indicating whether any element is True.

>>> pd.Series([False, False]).any()
False
>>> pd.Series([True, False]).any()
True
>>> pd.Series([], dtype="float64").any()
False
>>> pd.Series([np.nan]).any()
False
>>> pd.Series([np.nan]).any(skipna=False)
True

DataFrame

Whether each column contains at least one True element (the default).

>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
>>> df
   A  B  C
0  1  0  0
1  2  2  0

>>> df.any()
A     True
B     True
C    False
dtype: bool

Aggregating over the columns.

>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
>>> df
       A  B
0   True  1
1  False  2

>>> df.any(axis="columns")
0    True
1    True
dtype: bool

>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
>>> df
       A  B
0   True  1
1  False  0

>>> df.any(axis="columns")
0    True
1    False
dtype: bool

Aggregating over the entire DataFrame with axis=None.

>>> df.any(axis=None)
True

any for an empty DataFrame is an empty Series.

>>> pd.DataFrame([]).any()
Series([], dtype: bool)

apply(func: AggFuncType, axis: Axis = 0, raw: bool = False, result_type: Literal['expand', 'reduce', 'broadcast'] | None = None, args=(), by_row: Literal[False, 'compat'] = 'compat', engine: Callable | None | Literal['python', 'numba'] = None, engine_kwargs: dict[str, bool] | None = None, **kwargs)

Apply a function along an axis of the DataFrame.

Objects passed to the function are Series objects whose index is either the DataFrame’s index (axis=0) or the DataFrame’s columns (axis=1). By default (result_type=None), the final return type is inferred from the return type of the applied function. Otherwise, it depends on the result_type argument. The return type of the applied function is inferred based on the first computed result obtained after applying the function to a Series object.

Parameters:

func (function) – Function to apply to each column or row.
axis ({0 or 'index', 1 or 'columns'}, default 0) –
Axis along which the function is applied:
- 0 or ‘index’: apply function to each column.
- 1 or ‘columns’: apply function to each row.
raw (bool, default False) –
Determines if row or column is passed as a Series or ndarray object:
- False : passes each row or column as a Series to the function.
- True : the passed function will receive ndarray objects instead. If you are just applying a NumPy reduction function this will achieve much better performance.
Note

When raw=True, the result dtype is inferred from the first returned value.
result_type ({'expand', 'reduce', 'broadcast', None}, default None) –
These only act when axis=1 (columns):
- ’expand’ : list-like results will be turned into columns.
- ’reduce’ : returns a Series if possible rather than expanding list-like results. This is the opposite of ‘expand’.
- ’broadcast’ : results will be broadcast to the original shape of the DataFrame, the original index and columns will be retained.
The default behaviour (None) depends on the return value of the applied function: list-like results will be returned as a Series of those. However if the apply function returns a Series these are expanded to columns.
args (tuple) – Positional arguments to pass to func in addition to the array/series.
by_row (False or "compat", default "compat") –
Only has an effect when func is a listlike or dictlike of funcs and the func isn’t a string. If “compat”, will if possible first translate the func into pandas methods (e.g. Series().apply(np.sum) will be translated to Series().sum()). If that doesn’t work, will try call to apply again with by_row=True and if that fails, will call apply again with by_row=False (backward compatible). If False, the funcs will be passed the whole Series at once.

Added in version 2.1.0.
engine (decorator or {'python', 'numba'}, optional) –
Choose the execution engine to use. If not provided the function will be executed by the regular Python interpreter.

Other options include JIT compilers such Numba and Bodo, which in some cases can speed up the execution. To use an executor you can provide the decorators numba.jit, numba.njit or bodo.jit. You can also provide the decorator with parameters, like numba.jit(nogit=True).

Not all functions can be executed with all execution engines. In general, JIT compilers will require type stability in the function (no variable should change data type during the execution). And not all pandas and NumPy APIs are supported. Check the engine documentation [1]_ and [2]_ for limitations.

Warning

String parameters will stop being supported in a future pandas version.

Added in version 2.2.0.
engine_kwargs (dict) – Pass keyword arguments to the engine. This is currently only used by the numba engine, see the documentation for the engine argument for more information.
**kwargs – Additional keyword arguments to pass as keywords arguments to func.

Returns:

Result of applying func along the given axis of the DataFrame.

Return type:

Series or DataFrame

See also

DataFrame.map: For elementwise operations.
DataFrame.aggregate: Only perform aggregating type operations.
DataFrame.transform: Only perform transforming type operations.

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.

References

Examples

>>> df = pd.DataFrame([[4, 9]] * 3, columns=["A", "B"])
>>> df
   A  B
0  4  9
1  4  9
2  4  9

Using a numpy universal function (in this case the same as np.sqrt(df)):

>>> df.apply(np.sqrt)
     A    B
0  2.0  3.0
1  2.0  3.0
2  2.0  3.0

Using a reducing function on either axis

>>> df.apply(np.sum, axis=0)
A    12
B    27
dtype: int64

>>> df.apply(np.sum, axis=1)
0    13
1    13
2    13
dtype: int64

Returning a list-like will result in a Series

>>> df.apply(lambda x: [1, 2], axis=1)
0    [1, 2]
1    [1, 2]
2    [1, 2]
dtype: object

Passing result_type='expand' will expand list-like results to columns of a Dataframe

>>> df.apply(lambda x: [1, 2], axis=1, result_type="expand")
1
1  2
1  2
1  2

Returning a Series inside the function is similar to passing result_type='expand'. The resulting column names will be the Series index.

>>> df.apply(lambda x: pd.Series([1, 2], index=["foo", "bar"]), axis=1)
   foo  bar
0    1    2
1    1    2
2    1    2

Passing result_type='broadcast' will ensure the same shape result, whether list-like or scalar is returned by the function, and broadcast it along the axis. The resulting column names will be the originals.

>>> df.apply(lambda x: [1, 2], axis=1, result_type="broadcast")
   A  B
0  1  2
1  1  2
2  1  2

Advanced users can speed up their code by using a Just-in-time (JIT) compiler with apply. The main JIT compilers available for pandas are Numba and Bodo. In general, JIT compilation is only possible when the function passed to apply has type stability (variables in the function do not change their type during the execution).

>>> import bodo
>>> df.apply(lambda x: x.A + x.B, axis=1, engine=bodo.jit)

Note that JIT compilation is only recommended for functions that take a significant amount of time to run. Fast functions are unlikely to run faster with JIT compilation.

apply_to_rows(func, *args, row_mask=None, inplace: bool = False, drop_missing_updates: bool = True, **kwargs)[source]

Apply a metadata function to selected rows and merge the result back.

The function receives a pandas DataFrame containing either all metadata rows or the rows selected by row_mask. It must return a DataFrame with metadata updates.

The returned update DataFrame may contain fewer rows and fewer columns than the input subset. Its index must be a subset of the selected input rows. Missing values in the update DataFrame are treated as “no update” and do not overwrite existing metadata values.

This method only updates metadata. It does not modify fragments.

Parameters:

func – Function that receives a MetadataCollection or DataFrame subset as first argument and returns a DataFrame/MetadataCollection or None.
*args – Positional arguments passed to func.
row_mask – Optional boolean mask selecting rows. If None, all rows are passed directly to func.
inplace – If True, update the bound collection metadata in place and return None. If False, return a new MetadataCollection.
drop_missing_updates – If True, missing values in the DataFrame returned by func are treated as “no update” and do not overwrite existing metadata values. If False, missing values are treated as explicit updates and will overwrite existing metadata values.
**kwargs – Keyword arguments passed to func.

Returns:

Updated metadata table if inplace=False. Otherwise None.

Return type:

MetadataCollection or None

asfreq(freq: Frequency, method: FillnaOptions | None = None, how: Literal['start', 'end'] | None = None, normalize: bool = False, fill_value: Hashable | None = None) → Self

Convert time series to specified frequency.

Returns the original data conformed to a new index with the specified frequency.

If the index of this Series/DataFrame is a PeriodIndex, the new index is the result of transforming the original index with PeriodIndex.asfreq (so the original index will map one-to-one to the new index).

Otherwise, the new index will be equivalent to pd.date_range(start, end, freq=freq) where start and end are, respectively, the min and max entries in the original index (see pandas.date_range()). The values corresponding to any timesteps in the new index which were not present in the original index will be null (NaN), unless a method for filling such unknowns is provided (see the method parameter below).

The resample() method is more appropriate if an operation on each group of timesteps (such as an aggregate) is necessary to represent the data at the new frequency.

Parameters:

freq (DateOffset or str) – Frequency DateOffset or string.
method ({'backfill'/'bfill', 'pad'/'ffill'}, default None) –
Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present):
- ’pad’ / ‘ffill’: propagate last valid observation forward to next valid based on the order of the index
- ’backfill’ / ‘bfill’: use NEXT valid observation to fill.
how ({'start', 'end'}, default end) – For PeriodIndex only (see PeriodIndex.asfreq).
normalize (bool, default False) – Whether to reset output index to midnight.
fill_value (scalar, optional) – Value to use for missing values, applied during upsampling (note this does not fill NaNs that already were present).

Returns:

Series/DataFrame object reindexed to the specified frequency.

Return type:

Series/DataFrame

See also

reindex: Conform DataFrame to new index with optional filling logic.

Notes

To learn more about the frequency strings, please see this link.

Examples

Start by creating a series with 4 one minute timestamps.

>>> index = pd.date_range("1/1/2000", periods=4, freq="min")
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({"s": series})
>>> df
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:01:00    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:03:00    3.0

Upsample the series into 30 second bins.

>>> df.asfreq(freq="30s")
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    NaN
2000-01-01 00:03:00    3.0

Upsample again, providing a fill value.

>>> df.asfreq(freq="30s", fill_value=9.0)
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    9.0
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    9.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    9.0
2000-01-01 00:03:00    3.0

Upsample again, providing a method.

>>> df.asfreq(freq="30s", method="bfill")
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    2.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    3.0
2000-01-01 00:03:00    3.0

asof(where, subset=None)

Return the last row(s) without any NaNs before where.

The last row (for each element in where, if list) without any NaN is taken. In case of a DataFrame, the last row without NaN considering only the subset of columns (if not None)

If there is no good value, NaN is returned for a Series or a Series of NaN values for a DataFrame

Parameters:

where (date or array-like of dates) – Date(s) before which the last row(s) are returned.
subset (str or array-like of str, default None) – For DataFrame, if not None, only use these columns to check for NaNs.

Returns:

The return can be:

scalar : when self is a Series and where is a scalar
Series: when self is a Series and where is an array-like, or when self is a DataFrame and where is a scalar
DataFrame : when self is a DataFrame and where is an array-like

Return type:

scalar, Series, or DataFrame

See also

merge_asof: Perform an asof merge. Similar to left join.

Notes

Dates are assumed to be sorted. Raises if this is not the case.

Examples

A Series and a scalar where.

>>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
>>> s
10    1.0
20    2.0
30    NaN
40    4.0
dtype: float64

>>> s.asof(20)
np.float64(2.0)

For a sequence where, a Series is returned. The first value is NaN, because the first element of where is before the first index value.

>>> s.asof([5, 20])
5     NaN
20    2.0
dtype: float64

Missing values are not considered. The following is 2.0, not NaN, even though NaN is at the index location for 30.

>>> s.asof(30)
np.float64(2.0)

Take all columns into consideration

>>> df = pd.DataFrame(
...     {
...         "a": [10.0, 20.0, 30.0, 40.0, 50.0],
...         "b": [None, None, None, None, 500],
...     },
...     index=pd.DatetimeIndex(
...         [
...             "2018-02-27 09:01:00",
...             "2018-02-27 09:02:00",
...             "2018-02-27 09:03:00",
...             "2018-02-27 09:04:00",
...             "2018-02-27 09:05:00",
...         ]
...     ),
... )
>>> df.asof(pd.DatetimeIndex(["2018-02-27 09:03:30", "2018-02-27 09:04:30"]))
                      a   b
2018-02-27 09:03:30 NaN NaN
2018-02-27 09:04:30 NaN NaN

Take a single column into consideration

>>> df.asof(
...     pd.DatetimeIndex(["2018-02-27 09:03:30", "2018-02-27 09:04:30"]),
...     subset=["a"],
... )
                        a   b
2018-02-27 09:03:30  30.0 NaN
2018-02-27 09:04:30  40.0 NaN

assign(**kwargs) → DataFrame

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

Parameters:: **kwargs (callable or Series) – The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.
Returns:: A new DataFrame with the new columns in addition to all the existing columns.
Return type:: DataFrame

See also

DataFrame.loc: Select a subset of a DataFrame by labels.
DataFrame.iloc: Select a subset of a DataFrame by positions.

Notes

Assigning multiple columns within the same assign is possible. Later items in ‘**kwargs’ may refer to newly created or modified columns in ‘df’; items are computed and assigned into ‘df’ in order.

Examples

>>> df = pd.DataFrame({"temp_c": [17.0, 25.0]}, index=["Portland", "Berkeley"])
>>> df
          temp_c
Portland    17.0
Berkeley    25.0

Where the value is a callable, evaluated on df:

>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence:

>>> df.assign(temp_f=df["temp_c"] * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

or by using pandas.col():

>>> df.assign(temp_f=pd.col("temp_c") * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign:

>>> df.assign(
...     temp_f=lambda x: x["temp_c"] * 9 / 5 + 32,
...     temp_k=lambda x: (x["temp_f"] + 459.67) * 5 / 9,
... )
          temp_c  temp_f  temp_k
Portland    17.0    62.6  290.15
Berkeley    25.0    77.0  298.15

astype(dtype, copy: bool | Literal[_NoDefault.no_default] = <no_default>, errors: Literal['ignore', 'raise']='raise') → Self

Cast a pandas object to a specified dtype dtype.

This method allows the conversion of the data types of pandas objects, including DataFrames and Series, to the specified dtype. It supports casting entire objects to a single data type or applying different data types to individual columns using a mapping.

Parameters:

dtype (str, data type, Series or Mapping of column name -> data type) – Use a str, numpy.dtype, pandas.ExtensionDtype or Python type to cast entire pandas object to the same type. Alternatively, use a mapping, e.g. {col: dtype, …}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame’s columns to column-specific types.
copy (bool, default False) –
This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.
errors ({'raise', 'ignore'}, default 'raise') –
Control raising of exceptions on invalid data for provided dtype.
- raise : allow exceptions to be raised
- ignore : suppress exceptions. On error return original object.

Returns:

The pandas object casted to the specified dtype.

Return type:

same type as caller

See also

to_datetime: Convert argument to datetime.
to_timedelta: Convert argument to timedelta.
to_numeric: Convert argument to a numeric type.
numpy.ndarray.astype: Cast a numpy array to a specified type.

Notes

Changed in version 2.0.0: Using astype to convert from timezone-naive dtype to timezone-aware dtype will raise an exception. Use Series.dt.tz_localize() instead.

Examples

Create a DataFrame:

>>> d = {"col1": [1, 2], "col2": [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df.dtypes
col1    int64
col2    int64
dtype: object

Cast all columns to int32:

>>> df.astype("int32").dtypes
col1    int32
col2    int32
dtype: object

Cast col1 to int32 using a dictionary:

>>> df.astype({"col1": "int32"}).dtypes
col1    int32
col2    int64
dtype: object

Create a series:

>>> ser = pd.Series([1, 2], dtype="int32")
>>> ser
0    1
1    2
dtype: int32
>>> ser.astype("int64")
0    1
1    2
dtype: int64

Convert to categorical type:

>>> ser.astype("category")
0    1
1    2
dtype: category
Categories (2, int32): [1, 2]

Convert to ordered categorical type with custom ordering:

>>> from pandas.api.types import CategoricalDtype
>>> cat_dtype = CategoricalDtype(categories=[2, 1], ordered=True)
>>> ser.astype(cat_dtype)
0    1
1    2
dtype: category
Categories (2, int64): [2 < 1]

Create a series of dates:

>>> ser_date = pd.Series(pd.date_range("20200101", periods=3))
>>> ser_date
0   2020-01-01
1   2020-01-02
2   2020-01-03
dtype: datetime64[us]

property at: _AtIndexer

Access a single value for a row/column label pair.

Similar to loc, in that both provide label-based lookups. Use at if you only need to get or set a single value in a DataFrame or Series.

Raises:

KeyError – If getting a value and ‘label’ does not exist in a DataFrame or Series.
ValueError – If row/column label pair is not a tuple or if any label from the pair is not a scalar for DataFrame. If label is list-like (excluding NamedTuple) for Series.

See also

DataFrame.at: Access a single value for a row/column pair by label.
DataFrame.iat: Access a single value for a row/column pair by integer position.
DataFrame.loc: Access a group of rows and columns by label(s).
DataFrame.iloc: Access a group of rows and columns by integer position(s).
Series.at: Access a single value by label.
Series.iat: Access a single value by integer position.
Series.loc: Access a group of rows by label(s).
Series.iloc: Access a group of rows by integer position(s).

Notes

See Fast scalar value getting and setting for more details.

Examples

>>> df = pd.DataFrame(
...     [[0, 2, 3], [0, 4, 1], [10, 20, 30]],
...     index=[4, 5, 6],
...     columns=["A", "B", "C"],
... )
>>> df
    A   B   C
4   0   2   3
5   0   4   1
6  10  20  30

Get value at specified row/column pair

>>> df.at[4, "B"]
np.int64(2)

Set value at specified row/column pair

>>> df.at[4, "B"] = 10
>>> df.at[4, "B"]
np.int64(10)

Get value within a Series

>>> df.loc[5].at["B"]
np.int64(4)

at_time(time, asof: bool = False, axis: int | Literal['index', 'columns', 'rows'] | None = None) → Self

Select values at particular time of day (e.g., 9:30AM).

Parameters:

time (datetime.time or str) – The values to select.
asof (bool, default False) – This parameter is currently not supported.
axis ({0 or 'index', 1 or 'columns'}, default 0) – For Series this parameter is unused and defaults to 0.

Returns:

The values with the specified time.

Return type:

Series or DataFrame

Raises:

TypeError – If the index is not a DatetimeIndex

See also

between_time: Select values between particular times of the day.
first: Select initial periods of time series based on a date offset.
last: Select final periods of time series based on a date offset.
DatetimeIndex.indexer_at_time: Get just the index locations for values at particular time of the day.

Examples

>>> i = pd.date_range("2018-04-09", periods=4, freq="12h")
>>> ts = pd.DataFrame({"A": [1, 2, 3, 4]}, index=i)
>>> ts
                     A
2018-04-09 00:00:00  1
2018-04-09 12:00:00  2
2018-04-10 00:00:00  3
2018-04-10 12:00:00  4

>>> ts.at_time("12:00")
                     A
2018-04-09 12:00:00  2
2018-04-10 12:00:00  4

property attrs: dict[Hashable, Any]

Dictionary of global attributes of this dataset.

Warning

attrs is experimental and may change without warning.

See also

DataFrame.flags: Global flags applying to this object.

Notes

Many operations that create new datasets will copy attrs. Copies are always deep so that changing attrs will only affect the present dataset. pandas.concat() and pandas.merge() will only copy attrs if all input datasets have the same attrs.

Examples

For Series:

>>> ser = pd.Series([1, 2, 3])
>>> ser.attrs = {"A": [10, 20, 30]}
>>> ser.attrs
{'A': [10, 20, 30]}

For DataFrame:

>>> df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
>>> df.attrs = {"A": [10, 20, 30]}
>>> df.attrs
{'A': [10, 20, 30]}

property axes: list[Index]

Return a list representing the axes of the DataFrame.

It has the row axis labels and column axis labels as the only members. They are returned in that order.

See also

DataFrame.index: The index (row labels) of the DataFrame.
DataFrame.columns: The column labels of the DataFrame.

Examples

>>> df = pd.DataFrame({"col1": [1, 2], "col2": [3, 4]})
>>> df.axes
[RangeIndex(start=0, stop=2, step=1), Index(['col1', 'col2'], dtype='str')]

between_time(start_time, end_time, inclusive: Literal['left', 'right', 'both', 'neither'] = 'both', axis: int | Literal['index', 'columns', 'rows'] | None = None) → Self

Select values between particular times of the day (e.g., 9:00-9:30 AM).

By setting start_time to be later than end_time, you can get the times that are not between the two times.

Parameters:

start_time (datetime.time or str) – Initial time as a time filter limit.
end_time (datetime.time or str) – End time as a time filter limit.
inclusive ({"both", "neither", "left", "right"}, default "both") – Include boundaries; whether to set each bound as closed or open.
axis ({0 or 'index', 1 or 'columns'}, default 0) – Determine range time on index or columns value. For Series this parameter is unused and defaults to 0.

Returns:

Data from the original object filtered to the specified dates range.

Return type:

Series or DataFrame

Raises:

TypeError – If the index is not a DatetimeIndex

See also

at_time: Select values at a particular time of the day.
first: Select initial periods of time series based on a date offset.
last: Select final periods of time series based on a date offset.
DatetimeIndex.indexer_between_time: Get just the index locations for values between particular times of the day.

Examples

>>> i = pd.date_range("2018-04-09", periods=4, freq="1D20min")
>>> ts = pd.DataFrame({"A": [1, 2, 3, 4]}, index=i)
>>> ts
                     A
2018-04-09 00:00:00  1
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3
2018-04-12 01:00:00  4

>>> ts.between_time("0:15", "0:45")
                     A
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3

You get the times that are not between two times by setting start_time later than end_time:

>>> ts.between_time("0:45", "0:15")
                     A
2018-04-09 00:00:00  1
2018-04-12 01:00:00  4

bfill(*, axis: None | int | Literal['index', 'columns', 'rows'] = None, inplace: bool = False, limit: None | int = None, limit_area: Literal['inside', 'outside'] | None = None) → Self

Fill NA/NaN values by using the next valid observation to fill the gap.

This method fills missing values in a backward direction along the specified axis, propagating non-null values from later positions to earlier positions containing NaN.

Parameters:

axis ({0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame) – Axis along which to fill missing values. For Series this parameter is unused and defaults to 0.
inplace (bool, default False) – If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).
limit (int, default None) – If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.
limit_area ({None, ‘inside’, ‘outside’}, default None) –
If limit is specified, consecutive NaNs will be filled with this restriction.
- None: No fill restriction.
- ’inside’: Only fill NaNs surrounded by valid values (interpolate).
- ’outside’: Only fill NaNs outside valid values (extrapolate).
Added in version 2.2.0.

Returns:

Object with missing values filled.

Return type:

Series/DataFrame

See also

DataFrame.ffill: Fill NA/NaN values by propagating the last valid observation to next valid.

Examples

For Series:

>>> s = pd.Series([1, None, None, 2])
>>> s.bfill()
0    1.0
1    2.0
2    2.0
3    2.0
dtype: float64
>>> s.bfill(limit=1)
0    1.0
1    NaN
2    2.0
3    2.0
dtype: float64

With DataFrame:

>>> df = pd.DataFrame({"A": [1, None, None, 4], "B": [None, 5, None, 7]})
>>> df
      A     B
0   1.0   NaN
1   NaN   5.0
2   NaN   NaN
3   4.0   7.0
>>> df.bfill()
      A     B
0   1.0   5.0
1   4.0   5.0
2   4.0   7.0
3   4.0   7.0
>>> df.bfill(limit=1)
      A     B
0   1.0   5.0
1   NaN   5.0
2   4.0   7.0
3   4.0   7.0

boxplot(column=None, by=None, ax=None, fontsize: int | None = None, rot: int = 0, grid: bool = True, figsize: tuple[float, float] | None = None, layout=None, return_type=None, backend=None, **kwargs)

Make a box plot from DataFrame columns.

Make a box-and-whisker plot from DataFrame columns, optionally grouped by some other columns. A box plot is a method for graphically depicting groups of numerical data through their quartiles. The box extends from the Q1 to Q3 quartile values of the data, with a line at the median (Q2). The whiskers extend from the edges of box to show the range of the data. By default, they extend no more than 1.5 * IQR (IQR = Q3 - Q1) from the edges of the box, ending at the farthest data point within that interval. Outliers are plotted as separate dots.

For further details see Wikipedia’s entry for boxplot.

Parameters:

column (str or list of str, optional) – Column name or list of names, or vector. Can be any valid input to pandas.DataFrame.groupby().
by (str or array-like, optional) – Column in the DataFrame to pandas.DataFrame.groupby(). One box-plot will be done per value of columns in by.
ax (object of class matplotlib.axes.Axes, optional) – The matplotlib axes to be used by boxplot.
fontsize (float or str) – Tick label font size in points or as a string (e.g., large).
rot (float, default 0) – The rotation angle of labels (in degrees) with respect to the screen coordinate system.
grid (bool, default True) – Setting this to True will show the grid.
figsize (A tuple (width, height) in inches) – The size of the figure to create in matplotlib.
layout (tuple (rows, columns), optional) – For example, (3, 5) will display the subplots using 3 rows and 5 columns, starting from the top-left.
return_type ({'axes', 'dict', 'both'} or None, default 'axes') –
The kind of object to return. The default is axes.
- ’axes’ returns the matplotlib axes the boxplot is drawn on.
- ’dict’ returns a dictionary whose values are the matplotlib lines of the boxplot.
- ’both’ returns a namedtuple with the axes and dict.
- when grouping with by, a Series mapping columns to return_type is returned.
If return_type is None, a NumPy array of axes with the same shape as layout is returned.
backend (str, default None) – Backend to use instead of the backend specified in the option plotting.backend. For instance, ‘matplotlib’. Alternatively, to specify the plotting.backend for the whole session, set pd.options.plotting.backend.
**kwargs – All other plotting keyword arguments to be passed to matplotlib.pyplot.boxplot().

Returns:

See Notes.

Return type:

result

See also

Series.plot.hist: Make a histogram.
matplotlib.pyplot.boxplot: Matplotlib equivalent plot.

Notes

The return type depends on the return_type parameter:

‘axes’ : object of class matplotlib.axes.Axes
‘dict’ : dict of matplotlib.lines.Line2D objects
‘both’ : a namedtuple with structure (ax, lines)

For data grouped with by, return a Series of the above or a numpy array:

Series
array (for return_type = None)

Use return_type='dict' when you want to tweak the appearance of the lines after plotting. In this case a dict containing the Lines making up the boxes, caps, fliers, medians, and whiskers is returned.

Examples

Boxplots can be created for every column in the dataframe by df.boxplot() or indicating the columns to be used:

Boxplots of variables distributions grouped by the values of a third variable can be created using the option by. For instance:

A list of strings (i.e. ['X', 'Y']) can be passed to boxplot in order to group the data by combination of the variables in the x-axis:

The layout of boxplot can be adjusted giving a tuple to layout:

Additional formatting can be done to the boxplot, like suppressing the grid (grid=False), rotating the labels in the x-axis (i.e. rot=45) or changing the fontsize (i.e. fontsize=15):

The parameter return_type can be used to select the type of element returned by boxplot. When return_type='axes' is selected, the matplotlib axes on which the boxplot is drawn are returned:

When grouping with by, a Series mapping columns to return_type is returned:

If return_type is None, a NumPy array of axes with the same shape as layout is returned:

clip(lower=None, upper=None, *, axis: int | Literal['index', 'columns', 'rows'] | None = None, inplace: bool = False, **kwargs) → Self

Trim values at input threshold(s).

Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the specified axis.

Parameters:

lower (float or array-like, default None) – Minimum threshold value. All values below this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.
upper (float or array-like, default None) – Maximum threshold value. All values above this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.
axis ({0 or 'index', 1 or 'columns', None}, default None) – Align object with lower and upper along the given axis. For Series this parameter is unused and defaults to None.
inplace (bool, default False) – Whether to perform the operation in place on the data.
**kwargs – Additional keywords have no effect but might be accepted for compatibility with numpy.

Returns:

Same type as calling object with the values outside the clip boundaries replaced.

Return type:

Series or DataFrame

See also

Series.clip: Trim values at input threshold in series.
DataFrame.clip: Trim values at input threshold in DataFrame.
numpy.clip: Clip (limit) the values in an array.

Examples

>>> data = {"col_0": [9, -3, 0, -1, 5], "col_1": [-2, -7, 6, 8, -5]}
>>> df = pd.DataFrame(data)
>>> df
   col_0  col_1
0      9     -2
1     -3     -7
2      0      6
3     -1      8
4      5     -5

Clips per column using lower and upper thresholds:

>>> df.clip(-4, 6)
   col_0  col_1
    6     -2
   -3     -4
    0      6
   -1      6
    5     -4

Clips using specific lower and upper thresholds per column:

>>> df.clip([-2, -1], [4, 5])
    col_0  col_1
    4     -1
   -2     -1
    0      5
   -1      5
    4     -1

Clips using specific lower and upper thresholds per column element:

>>> t = pd.Series([2, -4, -1, 6, 3])
>>> t
0    2
1   -4
2   -1
3    6
4    3
dtype: int64

>>> df.clip(t, t + 4, axis=0)
   col_0  col_1
    6      2
   -3     -4
    0      3
    6      8
    5      3

Clips using specific lower threshold per column element, with missing values:

>>> t = pd.Series([2, -4, np.nan, 6, 3])
>>> t
0    2.0
1   -4.0
2    NaN
3    6.0
4    3.0
dtype: float64

>>> df.clip(t, axis=0)
col_0  col_1
    9.0    2.0
   -3.0   -4.0
    0.0    6.0
    6.0    8.0
    5.0    3.0

columns

The column labels of the DataFrame.

This property holds the column names as a pandas Index object. It provides an immutable sequence of column labels that can be used for data selection, renaming, and alignment in DataFrame operations.

Returns:: The column labels of the DataFrame.
Return type:: pandas.Index

See also

DataFrame.index: The index (row labels) of the DataFrame.
DataFrame.axes: Return a list representing the axes of the DataFrame.

Examples

>>> df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
>>> df
        A  B
0    1  3
1    2  4
>>> df.columns
Index(['A', 'B'], dtype='str')

combine(other: DataFrame, func: Callable[[Series, Series], Series | Hashable], fill_value=None, overwrite: bool = True) → DataFrame

Perform column-wise combine with another DataFrame.

Combines a DataFrame with other DataFrame using func to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two.

Parameters:

other (DataFrame) – The DataFrame to merge column-wise.
func (function) – Function that takes two series as inputs and return a Series or a scalar. Used to merge the two dataframes column by columns.
fill_value (scalar value, default None) – The value to fill NaNs with prior to passing any column to the merge func.
overwrite (bool, default True) – If True, columns in self that do not exist in other will be overwritten with NaNs.

Returns:

Combination of the provided DataFrames.

Return type:

DataFrame

See also

DataFrame.combine_first: Combine two DataFrame objects and default to non-null values in frame calling the method.

Examples

Combine using a simple function that chooses the smaller column.

>>> df1 = pd.DataFrame({"A": [0, 0], "B": [4, 4]})
>>> df2 = pd.DataFrame({"A": [1, 1], "B": [3, 3]})
>>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
>>> df1.combine(df2, take_smaller)
   A  B
0  0  3
1  0  3

Example using a true element-wise combine function.

>>> df1 = pd.DataFrame({"A": [5, 0], "B": [2, 4]})
>>> df2 = pd.DataFrame({"A": [1, 1], "B": [3, 3]})
>>> df1.combine(df2, np.minimum)
   A  B
0  1  2
1  0  3

Using fill_value fills Nones prior to passing the column to the merge function.

>>> df1 = pd.DataFrame({"A": [0, 0], "B": [None, 4]})
>>> df2 = pd.DataFrame({"A": [1, 1], "B": [3, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
   A    B
0  0 -5.0
1  0  4.0

Example that demonstrates the use of overwrite and behavior when the axis differ between the dataframes.

>>> df1 = pd.DataFrame({"A": [0, 0], "B": [4, 4]})
>>> df2 = pd.DataFrame(
...     {
...         "B": [3, 3],
...         "C": [-10, 1],
...     },
...     index=[1, 2],
... )
>>> df1.combine(df2, take_smaller)
     A    B     C
0  NaN  NaN   NaN
1  NaN  3.0 -10.0
2  NaN  3.0   1.0

>>> df1.combine(df2, take_smaller, overwrite=False)
     A    B     C
0  0.0  NaN   NaN
1  0.0  3.0 -10.0
2  NaN  3.0   1.0

Demonstrating the preference of the passed in dataframe.

>>> df2 = pd.DataFrame(
...     {
...         "B": [3, 3],
...         "C": [1, 1],
...     },
...     index=[1, 2],
... )
>>> df2.combine(df1, take_smaller)
     B    C   A
0  NaN  NaN 0.0
1  3.0  NaN 0.0
2  3.0  NaN NaN

>>> df2.combine(df1, take_smaller, overwrite=False)
     B    C   A
0  NaN  NaN 0.0
1  3.0  1.0 0.0
2  3.0  1.0 NaN

combine_first(other: DataFrame) → DataFrame

Update null elements with value in the same location in other.

Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the union of the two. The resulting dataframe contains the ‘first’ dataframe values and overrides the second one values where both first.loc[index, col] and second.loc[index, col] are not missing values, upon calling first.combine_first(second).

Parameters:: other (DataFrame) – Provided DataFrame to use to fill null values.
Returns:: The result of combining the provided DataFrame with the other object.
Return type:: DataFrame

See also

DataFrame.combine: Perform series-wise operation on two DataFrames using a given function.

Examples

>>> df1 = pd.DataFrame({"A": [None, 0], "B": [None, 4]})
>>> df2 = pd.DataFrame({"A": [1, 1], "B": [3, 3]})
>>> df1.combine_first(df2)
     A    B
0  1.0  3.0
1  0.0  4.0

Null values still persist if the location of that null value does not exist in other

>>> df1 = pd.DataFrame({"A": [None, 0], "B": [4, None]})
>>> df2 = pd.DataFrame({"B": [3, 3], "C": [1, 1]}, index=[1, 2])
>>> df1.combine_first(df2)
     A    B    C
0  NaN  4.0  NaN
1  0.0  3.0  1.0
2  NaN  3.0  1.0

compare(other: DataFrame, align_axis: Axis = 1, keep_shape: bool = False, keep_equal: bool = False, result_names: Suffixes = ('self', 'other')) → DataFrame

Compare to another DataFrame and show the differences.

Parameters:

other (DataFrame) – Object to compare with.
align_axis ({0 or 'index', 1 or 'columns'}, default 1) –
Determine which axis to align the comparison on.
- 0, or ‘index’ : Resulting differences are stacked vertically with rows drawn alternately from self and other.
- 1, or ‘columns’ : Resulting differences are aligned horizontally with columns drawn alternately from self and other.
keep_shape (bool, default False) – If true, all rows and columns are kept. Otherwise, only the ones with different values are kept.
keep_equal (bool, default False) – If true, the result keeps values that are equal. Otherwise, equal values are shown as NaNs.
result_names (tuple, default ('self', 'other')) – Set the dataframes names in the comparison.

Returns:

DataFrame that shows the differences stacked side by side.

The resulting index will be a MultiIndex with ‘self’ and ‘other’ stacked alternately at the inner level.

Return type:

DataFrame

Raises:

ValueError – When the two DataFrames don’t have identical labels or shape.

See also

Series.compare: Compare with another Series and show differences.
DataFrame.equals: Test whether two objects contain the same elements.

Notes

Matching NaNs will not appear as a difference.

Can only compare identically-labeled (i.e. same shape, identical row and column labels) DataFrames

Examples

>>> df = pd.DataFrame(
...     {
...         "col1": ["a", "a", "b", "b", "a"],
...         "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
...         "col3": [1.0, 2.0, 3.0, 4.0, 5.0],
...     },
...     columns=["col1", "col2", "col3"],
... )
>>> df
  col1  col2  col3
0    a   1.0   1.0
1    a   2.0   2.0
2    b   3.0   3.0
3    b   NaN   4.0
4    a   5.0   5.0

>>> df2 = df.copy()
>>> df2.loc[0, "col1"] = "c"
>>> df2.loc[2, "col3"] = 4.0
>>> df2
  col1  col2  col3
0    c   1.0   1.0
1    a   2.0   2.0
2    b   3.0   4.0
3    b   NaN   4.0
4    a   5.0   5.0

Align the differences on columns

>>> df.compare(df2)
  col1       col3
  self other self other
0    a     c  NaN   NaN
2  NaN   NaN  3.0   4.0

Assign result_names

>>> df.compare(df2, result_names=("left", "right"))
  col1       col3
  left right left right
0    a     c  NaN   NaN
2  NaN   NaN  3.0   4.0

Stack the differences on rows

>>> df.compare(df2, align_axis=0)
        col1  col3
0 self     a   NaN
  other    c   NaN
2 self   NaN   3.0
  other  NaN   4.0

Keep the equal values

>>> df.compare(df2, keep_equal=True)
  col1       col3
  self other self other
0    a     c  1.0   1.0
2    b     b  3.0   4.0

Keep all original rows and columns

>>> df.compare(df2, keep_shape=True)
  col1       col2       col3
  self other self other self other
0    a     c  NaN   NaN  NaN   NaN
1  NaN   NaN  NaN   NaN  NaN   NaN
2  NaN   NaN  NaN   NaN  3.0   4.0
3  NaN   NaN  NaN   NaN  NaN   NaN
4  NaN   NaN  NaN   NaN  NaN   NaN

Keep all original rows and columns and also all original values

>>> df.compare(df2, keep_shape=True, keep_equal=True)
  col1       col2       col3
  self other self other self other
0    a     c  1.0   1.0  1.0   1.0
1    a     a  2.0   2.0  2.0   2.0
2    b     b  3.0   3.0  3.0   4.0
3    b     b  NaN   NaN  4.0   4.0
4    a     a  5.0   5.0  5.0   5.0

convert_dtypes(infer_objects: bool = True, convert_string: bool = True, convert_integer: bool = True, convert_boolean: bool = True, convert_floating: bool = True, dtype_backend: Literal['pyarrow', 'numpy_nullable'] = 'numpy_nullable') → Self

Convert columns from numpy dtypes to the best dtypes that support pd.NA.

Parameters:

infer_objects (bool, default True) – Whether object dtypes should be converted to the best possible types.
convert_string (bool, default True) – Whether object dtypes should be converted to StringDtype().
convert_integer (bool, default True) – Whether, if possible, conversion can be done to integer extension types.
convert_boolean (bool, defaults True) – Whether object dtypes should be converted to BooleanDtypes().
convert_floating (bool, defaults True) – Whether, if possible, conversion can be done to floating extension types. If convert_integer is also True, preference will be give to integer dtypes if the floats can be faithfully casted to integers.
dtype_backend ({'numpy_nullable', 'pyarrow'}, default 'numpy_nullable') –
Back-end data type applied to the resultant DataFrame or Series (still experimental). Behaviour is as follows:
- "numpy_nullable": returns nullable-dtype-backed DataFrame or Serires.
- "pyarrow": returns pyarrow-backed nullable ArrowDtype DataFrame or Series.
Added in version 2.0.

Returns:

Copy of input object with new dtype.

Return type:

Series or DataFrame

See also

infer_objects: Infer dtypes of objects.
to_datetime: Convert argument to datetime.
to_timedelta: Convert argument to timedelta.
to_numeric: Convert argument to a numeric type.

Notes

By default, convert_dtypes will attempt to convert a Series (or each Series in a DataFrame) to dtypes that support pd.NA. By using the options convert_string, convert_integer, convert_boolean and convert_floating, it is possible to turn off individual conversions to StringDtype, the integer extension types, BooleanDtype or floating extension types, respectively.

For object-dtyped columns, if infer_objects is True, use the inference rules as during normal Series/DataFrame construction. Then, if possible, convert to StringDtype, BooleanDtype or an appropriate integer or floating extension type, otherwise leave as object.

If the dtype is integer, convert to an appropriate integer extension type.

If the dtype is numeric, and consists of all integers, convert to an appropriate integer extension type. Otherwise, convert to an appropriate floating extension type.

In the future, as new dtypes are added that support pd.NA, the results of this method will change to support those new dtypes.

Examples

>>> df = pd.DataFrame(
...     {
...         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
...         "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
...         "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
...         "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
...         "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
...         "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
...     }
... )

Start with a DataFrame with default dtypes.

>>> df
   a  b      c    d     e      f
0  1  x   True    h  10.0    NaN
1  2  y  False    i   NaN  100.5
2  3  z    NaN  NaN  20.0  200.0

>>> df.dtypes
a      int32
b     object
c     object
d     object
e    float64
f    float64
dtype: object

Convert the DataFrame to use best possible dtypes.

>>> dfn = df.convert_dtypes()
>>> dfn
   a  b      c     d     e      f
0  1  x   True     h    10   <NA>
1  2  y  False     i  <NA>  100.5
2  3  z   <NA>  <NA>    20  200.0

>>> dfn.dtypes
a      Int32
b     string
c    boolean
d     string
e      Int64
f    Float64
dtype: object

Start with a Series of strings and missing data represented by np.nan.

>>> s = pd.Series(["a", "b", np.nan])
>>> s
0      a
1      b
2    NaN
dtype: str

Obtain a Series with dtype StringDtype.

>>> s.convert_dtypes()
0       a
1       b
2    <NA>
dtype: string

copy(deep: bool = True) → Self

Make a copy of this object’s indices and data.

When deep=True (default), a new object will be created with a copy of the calling object’s data and indices. Modifications to the data or indices of the copy will not be reflected in the original object (see notes below).

When deep=False, a new object will be created without copying the calling object’s data or index (only references to the data and index are copied). With Copy-on-Write, changes to the original will not be reflected in the shallow copy (and vice versa). The shallow copy uses a lazy (deferred) copy mechanism that copies the data only when any changes to the original or shallow copy are made, ensuring memory efficiency while maintaining data integrity.

Note

In pandas versions prior to 3.0, the default behavior without Copy-on-Write was different: changes to the original were reflected in the shallow copy (and vice versa). See the Copy-on-Write user guide for more information.

Parameters:: deep (bool, default True) – Make a deep copy, including a copy of the data and the indices. With deep=False neither the indices nor the data are copied.
Returns:: Object type matches caller.
Return type:: Series or DataFrame

See also

copy.copy: Return a shallow copy of an object.
copy.deepcopy: Return a deep copy of an object.

Notes

When deep=True, data is copied but actual Python objects will not be copied recursively, only the reference to the object. This is in contrast to copy.deepcopy in the Standard Library, which recursively copies object data (see examples below).

While Index objects are copied when deep=True, the underlying numpy array is not copied for performance reasons. Since Index is immutable, the underlying data can be safely shared and a copy is not needed.

Since pandas is not thread safe, see the gotchas when copying in a threading environment.

Copy-on-Write protects shallow copies against accidental modifications. This means that any changes to the copied data would make a new copy of the data upon write (and vice versa). Changes made to either the original or copied variable would not be reflected in the counterpart. See Copy_on_Write for more information.

Examples

>>> s = pd.Series([1, 2], index=["a", "b"])
>>> s
a    1
b    2
dtype: int64

>>> s_copy = s.copy(deep=True)
>>> s_copy
a    1
b    2
dtype: int64

Due to Copy-on-Write, shallow copies still protect data modifications. Note shallow does not get modified below.

>>> s = pd.Series([1, 2], index=["a", "b"])
>>> shallow = s.copy(deep=False)
>>> s.iloc[1] = 200
>>> shallow
a    1
b    2
dtype: int64

When the data has object dtype, even a deep copy does not copy the underlying Python objects. Updating a nested data object will be reflected in the deep copy.

>>> s = pd.Series([[1, 2], [3, 4]])
>>> deep = s.copy()
>>> s[0][0] = 10
>>> s
0    [10, 2]
1     [3, 4]
dtype: object
>>> deep
0    [10, 2]
1     [3, 4]
dtype: object

corr(method: CorrelationMethod = 'pearson', min_periods: int = 1, numeric_only: bool = False) → DataFrame

Compute pairwise correlation of columns, excluding NA/null values.

Parameters:

method ({'pearson', 'kendall', 'spearman'} or callable) –
Method of correlation:
- pearson : standard correlation coefficient
- kendall : Kendall Tau correlation coefficient
- spearman : Spearman rank correlation
- callable: callable with input two 1d ndarrays
  and returning a float. Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable’s behavior.
min_periods (int, optional) – Minimum number of observations required per pair of columns to have a valid result. Currently only available for Pearson and Spearman correlation.
numeric_only (bool, default False) –
Include only float, int or boolean data.

Changed in version 2.0.0: The default value of numeric_only is now False.

Returns:

Correlation matrix.

Return type:

DataFrame

See also

DataFrame.corrwith: Compute pairwise correlation with another DataFrame or Series.
Series.corr: Compute the correlation between two Series.

Notes

Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.

Examples

>>> def histogram_intersection(a, b):
...     v = np.minimum(a, b).sum().round(decimals=1)
...     return v
>>> df = pd.DataFrame(
...     [(0.2, 0.3), (0.0, 0.6), (0.6, 0.0), (0.2, 0.1)],
...     columns=["dogs", "cats"],
... )
>>> df.corr(method=histogram_intersection)
      dogs  cats
dogs   1.0   0.3
cats   0.3   1.0

>>> df = pd.DataFrame(
...     [(1, 1), (2, np.nan), (np.nan, 3), (4, 4)], columns=["dogs", "cats"]
... )
>>> df.corr(min_periods=3)
      dogs  cats
dogs   1.0   NaN
cats   NaN   1.0

corrwith(other: DataFrame | Series, axis: Axis = 0, drop: bool = False, method: CorrelationMethod = 'pearson', numeric_only: bool = False, min_periods: int | None = None) → Series

Compute pairwise correlation.

Pairwise correlation is computed between rows or columns of DataFrame with rows or columns of Series or DataFrame. DataFrames are first aligned along both axes before computing the correlations.

Parameters:

other (DataFrame, Series) – Object with which to compute correlations.
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to use. 0 or ‘index’ to compute row-wise, 1 or ‘columns’ for column-wise.
drop (bool, default False) – Drop missing indices from result.
method ({'pearson', 'kendall', 'spearman'} or callable) –
Method of correlation:
- pearson : standard correlation coefficient
- kendall : Kendall Tau correlation coefficient
- spearman : Spearman rank correlation
- callable: callable with input two 1d ndarrays
  and returning a float.
numeric_only (bool, default False) – Include only float, int or boolean data.
min_periods (int, optional) –
Minimum number of observations needed to have a valid result.

Changed in version 2.0.0: The default value of numeric_only is now False.

Returns:

Pairwise correlations.

Return type:

Series

See also

DataFrame.corr: Compute pairwise correlation of columns.

Examples

>>> index = ["a", "b", "c", "d", "e"]
>>> columns = ["one", "two", "three", "four"]
>>> df1 = pd.DataFrame(
...     np.arange(20).reshape(5, 4), index=index, columns=columns
... )
>>> df2 = pd.DataFrame(
...     np.arange(16).reshape(4, 4), index=index[:4], columns=columns
... )
>>> df1.corrwith(df2)
one      1.0
two      1.0
three    1.0
four     1.0
dtype: float64

>>> df2.corrwith(df1, axis=1)
a    1.0
b    1.0
c    1.0
d    1.0
e    NaN
dtype: float64

count(axis: Axis = 0, numeric_only: bool = False) → Series

Count non-NA cells for each column or row.

The values None, NaN, NaT, pandas.NA are considered NA.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are generated for each row.
numeric_only (bool, default False) – Include only float, int or boolean data.

Returns:

For each column/row the number of non-NA/null entries.

Return type:

Series

See also

Series.count: Number of non-NA elements in a Series.
DataFrame.value_counts: Count unique combinations of columns.
DataFrame.shape: Number of DataFrame rows and columns (including NA elements).
DataFrame.isna: Boolean same-sized DataFrame showing places of NA elements.

Examples

Constructing DataFrame from a dictionary:

>>> df = pd.DataFrame(
...     {
...         "Person": ["John", "Myla", "Lewis", "John", "Myla"],
...         "Age": [24.0, np.nan, 21.0, 33, 26],
...         "Single": [False, True, True, True, False],
...     }
... )
>>> df
   Person   Age  Single
0    John  24.0   False
1    Myla   NaN    True
2   Lewis  21.0    True
3    John  33.0    True
4    Myla  26.0   False

Notice the uncounted NA values:

>>> df.count()
Person    5
Age       4
Single    5
dtype: int64

Counts for each row:

>>> df.count(axis="columns")
  3
  2
  3
  3
  3
dtype: int64

cov(min_periods: int | None = None, ddof: int | None = 1, numeric_only: bool = False) → DataFrame

Compute pairwise covariance of columns, excluding NA/null values.

Compute the pairwise covariance among the series of a DataFrame. The returned data frame is the covariance matrix of the columns of the DataFrame.

Both NA and null values are automatically excluded from the calculation. (See the note below about bias from missing values.) A threshold can be set for the minimum number of observations for each value created. Comparisons with observations below this threshold will be returned as NaN.

This method is generally used for the analysis of time series data to understand the relationship between different measures across time.

Parameters:

min_periods (int, optional) – Minimum number of observations required per pair of columns to have a valid result.
ddof (int, default 1) – Delta degrees of freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. This argument is applicable only when no nan is in the dataframe.
numeric_only (bool, default False) –
Include only float, int or boolean data.

Changed in version 2.0.0: The default value of numeric_only is now False.

Returns:

The covariance matrix of the series of the DataFrame.

Return type:

DataFrame

See also

Series.cov: Compute covariance with another Series.
core.window.ewm.ExponentialMovingWindow.cov: Exponential weighted sample covariance.
core.window.expanding.Expanding.cov: Expanding sample covariance.
core.window.rolling.Rolling.cov: Rolling sample covariance.

Notes

Returns the covariance matrix of the DataFrame’s time series. The covariance is normalized by N-ddof.

For DataFrames that have Series that are missing data (assuming that data is missing at random) the returned covariance matrix will be an unbiased estimate of the variance and covariance between the member Series.

However, for many applications this estimate may not be acceptable because the estimate covariance matrix is not guaranteed to be positive semi-definite. This could lead to estimate correlations having absolute values which are greater than one, and/or a non-invertible covariance matrix. See Estimation of covariance matrices for more details.

Examples

>>> df = pd.DataFrame(
...     [(1, 2), (0, 3), (2, 0), (1, 1)], columns=["dogs", "cats"]
... )
>>> df.cov()
          dogs      cats
dogs  0.666667 -1.000000
cats -1.000000  1.666667

>>> np.random.seed(42)
>>> df = pd.DataFrame(
...     np.random.randn(1000, 5), columns=["a", "b", "c", "d", "e"]
... )
>>> df.cov()
          a         b         c         d         e
a  0.998438 -0.020161  0.059277 -0.008943  0.014144
b -0.020161  1.059352 -0.008543 -0.024738  0.009826
c  0.059277 -0.008543  1.010670 -0.001486 -0.000271
d -0.008943 -0.024738 -0.001486  0.921297 -0.013692
e  0.014144  0.009826 -0.000271 -0.013692  0.977795

Minimum number of periods

This method also supports an optional min_periods keyword that specifies the required minimum number of non-NA observations for each column pair in order to have a valid result:

>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])
>>> df.loc[df.index[:5], "a"] = np.nan
>>> df.loc[df.index[5:10], "b"] = np.nan
>>> df.cov(min_periods=12)
          a         b         c
a  0.316741       NaN -0.150812
b       NaN  1.248003  0.191417
c -0.150812  0.191417  0.895202

cummax(axis: Axis = 0, skipna: bool = True, numeric_only: bool = False, *args, **kwargs) → Self

Return cumulative maximum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative maximum.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default 0) – The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
numeric_only (bool, default False) – Include only float, int, boolean columns.
*args – Additional keywords have no effect but might be accepted for compatibility with NumPy.
**kwargs – Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

Return cumulative maximum of Series or DataFrame.

Return type:

Series or DataFrame

See also

core.window.expanding.Expanding.max: Similar functionality but ignores NaN values.
DataFrame.max: Return the maximum over DataFrame axis.
DataFrame.cummax: Return cumulative maximum over DataFrame axis.
DataFrame.cummin: Return cumulative minimum over DataFrame axis.
DataFrame.cumsum: Return cumulative sum over DataFrame axis.
DataFrame.cumprod: Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummax()
  2.0
  NaN
  5.0
  5.0
  5.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummax(skipna=False)
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame(
...     [[2.0, 1.0], [3.0, np.nan], [1.0, 0.0]], columns=list("AB")
... )
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the maximum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummax()
     A    B
0  2.0  1.0
1  3.0  NaN
2  3.0  1.0

To iterate over columns and find the maximum in each row, use axis=1

>>> df.cummax(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  1.0

cummin(axis: Axis = 0, skipna: bool = True, numeric_only: bool = False, *args, **kwargs) → Self

Return cumulative minimum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative minimum.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default 0) – The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
numeric_only (bool, default False) – Include only float, int, boolean columns.
*args – Additional keywords have no effect but might be accepted for compatibility with NumPy.
**kwargs – Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

Return cumulative minimum of Series or DataFrame.

Return type:

Series or DataFrame

See also

core.window.expanding.Expanding.min: Similar functionality but ignores NaN values.
DataFrame.min: Return the minimum over DataFrame axis.
DataFrame.cummax: Return cumulative maximum over DataFrame axis.
DataFrame.cummin: Return cumulative minimum over DataFrame axis.
DataFrame.cumsum: Return cumulative sum over DataFrame axis.
DataFrame.cumprod: Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummin()
  2.0
  NaN
  2.0
 -1.0
 -1.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummin(skipna=False)
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame(
...     [[2.0, 1.0], [3.0, np.nan], [1.0, 0.0]], columns=list("AB")
... )
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the minimum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummin()
     A    B
0  2.0  1.0
1  2.0  NaN
2  1.0  0.0

To iterate over columns and find the minimum in each row, use axis=1

>>> df.cummin(axis=1)
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

cumprod(axis: Axis = 0, skipna: bool = True, numeric_only: bool = False, *args, **kwargs) → Self

Return cumulative product over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative product.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default 0) – The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
numeric_only (bool, default False) – Include only float, int, boolean columns.
*args – Additional keywords have no effect but might be accepted for compatibility with NumPy.
**kwargs – Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

Return cumulative product of Series or DataFrame.

Return type:

Series or DataFrame

See also

core.window.expanding.Expanding.prod: Similar functionality but ignores NaN values.
DataFrame.prod: Return the product over DataFrame axis.
DataFrame.cummax: Return cumulative maximum over DataFrame axis.
DataFrame.cummin: Return cumulative minimum over DataFrame axis.
DataFrame.cumsum: Return cumulative sum over DataFrame axis.
DataFrame.cumprod: Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumprod()
   2.0
   NaN
  10.0
 -10.0
  -0.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumprod(skipna=False)
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame(
...     [[2.0, 1.0], [3.0, np.nan], [1.0, 0.0]], columns=list("AB")
... )
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the product in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumprod()
     A    B
0  2.0  1.0
1  6.0  NaN
2  6.0  0.0

To iterate over columns and find the product in each row, use axis=1

>>> df.cumprod(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  0.0

cumsum(axis: Axis = 0, skipna: bool = True, numeric_only: bool = False, *args, **kwargs) → Self

Return cumulative sum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative sum.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default 0) – The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
numeric_only (bool, default False) – Include only float, int, boolean columns.
*args – Additional keywords have no effect but might be accepted for compatibility with NumPy.
**kwargs – Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

Return cumulative sum of Series or DataFrame.

Return type:

Series or DataFrame

See also

core.window.expanding.Expanding.sum: Similar functionality but ignores NaN values.
DataFrame.sum: Return the sum over DataFrame axis.
DataFrame.cummax: Return cumulative maximum over DataFrame axis.
DataFrame.cummin: Return cumulative minimum over DataFrame axis.
DataFrame.cumsum: Return cumulative sum over DataFrame axis.
DataFrame.cumprod: Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumsum()
  2.0
  NaN
  7.0
  6.0
  6.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumsum(skipna=False)
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame(
...     [[2.0, 1.0], [3.0, np.nan], [1.0, 0.0]], columns=list("AB")
... )
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the sum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumsum()
     A    B
0  2.0  1.0
1  5.0  NaN
2  6.0  1.0

To iterate over columns and find the sum in each row, use axis=1

>>> df.cumsum(axis=1)
     A    B
0  2.0  3.0
1  3.0  NaN
2  1.0  1.0

describe(percentiles=None, include=None, exclude=None) → Self

Generate descriptive statistics.

Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.

Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail.

Parameters:

percentiles (list-like of numbers, optional) – The percentiles to include in the output. All should fall between 0 and 1. The default, None, will automatically return the 25th, 50th, and 75th percentiles.
include ('all', list-like of dtypes or None (default), optional) –
A white list of data types to include in the result. Ignored for Series. Here are the options:
- ’all’ : All columns of the input will be included in the output.
- A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To limit it instead to object columns submit the numpy.object data type. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To select pandas categorical columns, use 'category'
- None (default) : The result will include all numeric columns.
exclude (list-like of dtypes or None (default), optional,) –
A black list of data types to omit from the result. Ignored for Series. Here are the options:
- A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit numpy.number. To exclude object columns submit the data type numpy.object. Strings can also be used in the style of select_dtypes (e.g. df.describe(exclude=['O'])). To exclude pandas categorical columns, use 'category'
- None (default) : The result will exclude nothing.

Returns:

Summary statistics of the Series or Dataframe provided.

Return type:

Series or DataFrame

See also

DataFrame.count: Count number of non-NA/null observations.
DataFrame.max: Maximum of the values in the object.
DataFrame.min: Minimum of the values in the object.
DataFrame.mean: Mean of the values.
DataFrame.std: Standard deviation of the observations.
DataFrame.select_dtypes: Subset of a DataFrame including/excluding columns based on their dtype.

Notes

For numeric data, the result’s index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75. The 50 percentile is the same as the median.

For object data (e.g. strings), the result’s index will include count, unique, top, and freq. The top is the most common value. The freq is the most common value’s frequency.

If multiple object values have the highest count, then the count and top results will be arbitrarily chosen from among those with the highest count.

For mixed data types provided via a DataFrame, the default is to return only an analysis of numeric columns. If the DataFrame consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If include='all' is provided as an option, the result will include a union of attributes of each type.

The include and exclude parameters can be used to limit which columns in a DataFrame are analyzed for the output. The parameters are ignored when analyzing a Series.

Examples

Describing a numeric Series.

>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
dtype: float64

Describing a categorical Series.

>>> s = pd.Series(["a", "a", "b", "c"])
>>> s.describe()
count     4
unique    3
top       a
freq      2
dtype: object

Describing a timestamp Series.

>>> s = pd.Series(
...     [
...         np.datetime64("2000-01-01"),
...         np.datetime64("2010-01-01"),
...         np.datetime64("2010-01-01"),
...     ]
... )
>>> s.describe()
count                      3
mean     2006-09-01 08:00:00
min      2000-01-01 00:00:00
25%      2004-12-31 12:00:00
50%      2010-01-01 00:00:00
75%      2010-01-01 00:00:00
max      2010-01-01 00:00:00
dtype: object

Describing a DataFrame. By default only numeric fields are returned.

>>> df = pd.DataFrame(
...     {
...         "categorical": pd.Categorical(["d", "e", "f"]),
...         "numeric": [1, 2, 3],
...         "object": ["a", "b", "c"],
...     }
... )
>>> df.describe()
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Describing all columns of a DataFrame regardless of data type.

>>> df.describe(include="all")
       categorical  numeric object
count            3      3.0      3
unique           3      NaN      3
top              f      NaN      a
freq             1      NaN      1
mean           NaN      2.0    NaN
std            NaN      1.0    NaN
min            NaN      1.0    NaN
25%            NaN      1.5    NaN
50%            NaN      2.0    NaN
75%            NaN      2.5    NaN
max            NaN      3.0    NaN

Describing a column from a DataFrame by accessing it as an attribute.

>>> df.numeric.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
Name: numeric, dtype: float64

Including only numeric columns in a DataFrame description.

>>> df.describe(include=[np.number])
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Including only string columns in a DataFrame description.

>>> df.describe(include=[object])
       object
count       3
unique      3
top         a
freq        1

Including only categorical columns from a DataFrame description.

>>> df.describe(include=["category"])
       categorical
count            3
unique           3
top              d
freq             1

Excluding numeric columns from a DataFrame description.

>>> df.describe(exclude=[np.number])
       categorical object
count            3      3
unique           3      3
top              f      a
freq             1      1

Excluding object columns from a DataFrame description.

>>> df.describe(exclude=[object])
       categorical  numeric
count            3      3.0
unique           3      NaN
top              f      NaN
freq             1      NaN
mean           NaN      2.0
std            NaN      1.0
min            NaN      1.0
25%            NaN      1.5
50%            NaN      2.0
75%            NaN      2.5
max            NaN      3.0

diff(periods: int = 1, axis: Axis = 0) → DataFrame

First discrete difference of element.

Calculates the difference of a DataFrame element compared with another element in the DataFrame (default is element in previous row).

Parameters:

periods (int, default 1) – Periods to shift for calculating difference, accepts negative values.
axis ({0 or 'index', 1 or 'columns'}, default 0) – Take difference over rows (0) or columns (1).

Returns:

First differences of the Series.

Return type:

DataFrame

See also

DataFrame.pct_change: Percent change over given number of periods.
DataFrame.shift: Shift index by desired number of periods with an optional time freq.
Series.diff: First discrete difference of object.

Notes

For boolean dtypes, this uses operator.xor() rather than operator.sub(). The result is calculated according to current dtype in DataFrame, however dtype of the result is always float64.

Examples

Difference with previous row

>>> df = pd.DataFrame(
...     {
...         "a": [1, 2, 3, 4, 5, 6],
...         "b": [1, 1, 2, 3, 5, 8],
...         "c": [1, 4, 9, 16, 25, 36],
...     }
... )
>>> df
   a  b   c
0  1  1   1
1  2  1   4
2  3  2   9
3  4  3  16
4  5  5  25
5  6  8  36
>>> df.diff()
     a    b     c
0  NaN  NaN   NaN
1  1.0  0.0   3.0
2  1.0  1.0   5.0
3  1.0  1.0   7.0
4  1.0  2.0   9.0
5  1.0  3.0  11.0

Difference with previous column

>>> df.diff(axis=1)
    a  b   c
NaN  0   0
NaN -1   3
NaN -1   7
NaN -1  13
NaN  0  20
NaN  2  28

Difference with 3rd previous row

>>> df.diff(periods=3)
     a    b     c
NaN  NaN   NaN
NaN  NaN   NaN
NaN  NaN   NaN
3.0  2.0  15.0
3.0  4.0  21.0
3.0  6.0  27.0

Difference with following row

>>> df.diff(periods=-1)
     a    b     c
-1.0  0.0  -3.0
-1.0 -1.0  -5.0
-1.0 -1.0  -7.0
-1.0 -2.0  -9.0
-1.0 -3.0 -11.0
NaN  NaN   NaN

Overflow in input dtype

>>> df = pd.DataFrame({"a": [1, 0]}, dtype=np.uint8)
>>> df.diff()
       a
0    NaN
1  255.0

div(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Floating division of dataframe and other, element-wise (binary operator truediv).

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

divide(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Floating division of dataframe and other, element-wise (binary operator truediv).

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

dot(other: AnyArrayLike | DataFrame) → DataFrame | Series

Compute the matrix multiplication between the DataFrame and other.

This method computes the matrix product between the DataFrame and the values of an other Series, DataFrame or a numpy array.

It can also be called using self @ other.

Parameters:: other (Series, DataFrame or array-like) – The other object to compute the matrix product with.
Returns:: If other is a Series, return the matrix product between self and other as a Series. If other is a DataFrame or a numpy.array, return the matrix product of self and other in a DataFrame of a np.array.
Return type:: Series or DataFrame

See also

Series.dot: Similar method for Series.

Notes

The dimensions of DataFrame and other must be compatible in order to compute the matrix multiplication. In addition, the column names of DataFrame and the index of other must contain the same values, as they will be aligned prior to the multiplication.

The dot method for Series computes the inner product, instead of the matrix product here.

Examples

Here we multiply a DataFrame with a Series.

>>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = pd.Series([1, 1, 2, 1])
>>> df.dot(s)
0    -4
1     5
dtype: int64

Here we multiply a DataFrame with another DataFrame.

>>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other)
    0   1
0   1   4
1   2   2

Note that the dot method give the same result as @

>>> df @ other
 1
 1   4
 2   2

The dot method works also if other is an np.array.

>>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr)
    0   1
0   1   4
1   2   2

Note how shuffling of the objects does not change the result.

>>> s2 = s.reindex([1, 0, 2, 3])
>>> df.dot(s2)
0    -4
1     5
dtype: int64

Drop specified labels from rows or columns.

Remove rows or columns by specifying label names and corresponding axis, or by directly specifying index or column names. When using a multi-index, labels on different levels can be removed by specifying the level. See the user guide for more information about the now unused levels.

Parameters:

labels (single label or iterable of labels) – Index or column labels to drop. A tuple will be used as a single label and not treated as an iterable.
axis ({0 or 'index', 1 or 'columns'}, default 0) – Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’).
index (single label or iterable of labels) – Alternative to specifying axis (labels, axis=0 is equivalent to index=labels).
columns (single label or iterable of labels) – Alternative to specifying axis (labels, axis=1 is equivalent to columns=labels).
level (int or level name, optional) – For MultiIndex, level from which the labels will be removed.
inplace (bool, default False) – If False, return a copy. Otherwise, do operation in place and return None.
errors ({'ignore', 'raise'}, default 'raise') – If ‘ignore’, suppress error and only existing labels are dropped.

Returns:

Returns DataFrame or None DataFrame with the specified index or column labels removed or None if inplace=True.

Return type:

DataFrame or None

Raises:

KeyError – If any of the labels is not found in the selected axis.

See also

DataFrame.loc: Label-location based indexer for selection by label.
DataFrame.dropna: Return DataFrame with labels on given axis omitted where (all or any) data are missing.
DataFrame.drop_duplicates: Return DataFrame with duplicate rows removed, optionally only considering certain columns.
Series.drop: Return Series with specified index labels removed.

Examples

>>> df = pd.DataFrame(np.arange(12).reshape(3, 4), columns=["A", "B", "C", "D"])
>>> df
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

Drop columns

>>> df.drop(["B", "C"], axis=1)
   A   D
0  0   3
1  4   7
2  8  11

>>> df.drop(columns=["B", "C"])
   A   D
0  0   3
1  4   7
2  8  11

Drop a row by index

>>> df.drop([0, 1])
   A  B   C   D
2  8  9  10  11

Drop columns and/or rows of MultiIndex DataFrame

>>> midx = pd.MultiIndex(
...     levels=[["llama", "cow", "falcon"], ["speed", "weight", "length"]],
...     codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]],
... )
>>> df = pd.DataFrame(
...     index=midx,
...     columns=["big", "small"],
...     data=[
...         [45, 30],
...         [200, 100],
...         [1.5, 1],
...         [30, 20],
...         [250, 150],
...         [1.5, 0.8],
...         [320, 250],
...         [1, 0.8],
...         [0.3, 0.2],
...     ],
... )
>>> df
                big     small
llama   speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        weight  1.0     0.8
        length  0.3     0.2

Drop a specific index combination from the MultiIndex DataFrame, i.e., drop the combination 'falcon' and 'weight', which deletes only the corresponding row

>>> df.drop(index=("falcon", "weight"))
                big     small
llama   speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        length  0.3     0.2

>>> df.drop(index="cow", columns="small")
                big
llama   speed   45.0
        weight  200.0
        length  1.5
falcon  speed   320.0
        weight  1.0
        length  0.3

>>> df.drop(index="length", level=1)
                big     small
llama   speed   45.0    30.0
        weight  200.0   100.0
cow     speed   30.0    20.0
        weight  250.0   150.0
falcon  speed   320.0   250.0
        weight  1.0     0.8

drop_duplicates(subset: Hashable | Iterable[Hashable] | None = None, *, keep: DropKeep = 'first', inplace: bool = False, ignore_index: bool = False) → DataFrame | None

Return DataFrame with duplicate rows removed.

Considering certain columns is optional. Indexes, including time indexes are ignored.

Parameters:

subset (column label or iterable of labels, optional) – Only consider certain columns for identifying duplicates, by default use all of the columns.
keep ({‘first’, ‘last’, False}, default ‘first’) –
Determines which duplicates (if any) to keep.
- ’first’ : Drop duplicates except for the first occurrence.
- ’last’ : Drop duplicates except for the last occurrence.
- False : Drop all duplicates.
inplace (bool, default False) – Whether to modify the DataFrame rather than creating a new one.
ignore_index (bool, default False) – If True, the resulting axis will be labeled 0, 1, …, n - 1.

Returns:

DataFrame with duplicates removed or None if inplace=True.

Return type:

DataFrame or None

See also

DataFrame.value_counts: Count unique combinations of columns.

Notes

This method requires columns specified by subset to be of hashable type. Passing unhashable columns will raise a TypeError.

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame(
...     {
...         "brand": ["Yum Yum", "Yum Yum", "Indomie", "Indomie", "Indomie"],
...         "style": ["cup", "cup", "cup", "pack", "pack"],
...         "rating": [4, 4, 3.5, 15, 5],
...     }
... )
>>> df
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, it removes duplicate rows based on all columns.

>>> df.drop_duplicates()
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

To remove duplicates on specific column(s), use subset.

>>> df.drop_duplicates(subset=["brand"])
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5

To remove duplicates and keep last occurrences, use keep.

>>> df.drop_duplicates(subset=["brand", "style"], keep="last")
    brand style  rating
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
4  Indomie  pack     5.0

droplevel(level: Hashable | Sequence[Hashable], axis: int | Literal['index', 'columns', 'rows'] = 0) → Self

Return Series/DataFrame with requested index / column level(s) removed.

Parameters:

level (int, str, or list-like) – If a string is given, must be the name of a level If list-like, elements must be names or positional indexes of levels.
axis ({0 or 'index', 1 or 'columns'}, default 0) –
Axis along which the level(s) is removed:
- 0 or ‘index’: remove level(s) in column.
- 1 or ‘columns’: remove level(s) in row.
For Series this parameter is unused and defaults to 0.

Returns:

Series/DataFrame with requested index / column level(s) removed.

Return type:

Series/DataFrame

See also

DataFrame.replace: Replace values given in to_replace with value.
DataFrame.pivot: Return reshaped DataFrame organized by given index / column values.

Examples

>>> df = (
...     pd.DataFrame([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
...     .set_index([0, 1])
...     .rename_axis(["a", "b"])
... )

>>> df.columns = pd.MultiIndex.from_tuples(
...     [("c", "e"), ("d", "f")], names=["level_1", "level_2"]
... )

>>> df
level_1   c   d
level_2   e   f
a b
1 2      3   4
5 6      7   8
9 10    11  12

>>> df.droplevel("a")
level_1   c   d
level_2   e   f
b
2        3   4
6        7   8
10      11  12

>>> df.droplevel("level_2", axis=1)
level_1   c   d
a b
1 2      3   4
5 6      7   8
9 10    11  12

Remove missing values.

See the User Guide for more on which values are considered missing, and how to work with missing data.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default 0) –
Determine if rows or columns which contain missing values are removed.
- 0, or ‘index’ : Drop rows which contain missing values.
- 1, or ‘columns’ : Drop columns which contain missing value.
Only a single axis is allowed.
how ({'any', 'all'}, default 'any') –
Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.
- ’any’ : If any NA values are present, drop that row or column.
- ’all’ : If all values are NA, drop that row or column.
thresh (int, optional) – Require that many non-NA values. Cannot be combined with how.
subset (column label or iterable of labels, optional) – Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include.
inplace (bool, default False) – Whether to modify the DataFrame rather than creating a new one.
ignore_index (bool, default False) –
If True, the resulting axis will be labeled 0, 1, …, n - 1.

Added in version 2.0.0.

Returns:

DataFrame with NA entries dropped from it or None if inplace=True.

Return type:

DataFrame or None

See also

DataFrame.isna: Indicate missing values.
DataFrame.notna: Indicate existing (non-missing) values.
DataFrame.fillna: Replace missing values.
Series.dropna: Drop missing values.
Index.dropna: Drop missing indices.

Examples

>>> df = pd.DataFrame(
...     {
...         "name": ["Alfred", "Batman", "Catwoman"],
...         "toy": [np.nan, "Batmobile", "Bullwhip"],
...         "born": [pd.NaT, pd.Timestamp("1940-04-25"), pd.NaT],
...     }
... )
>>> df
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Drop the rows where at least one element is missing.

>>> df.dropna()
     name        toy       born
1  Batman  Batmobile 1940-04-25

Drop the columns where at least one element is missing.

>>> df.dropna(axis="columns")
       name
0    Alfred
1    Batman
2  Catwoman

Drop the rows where all elements are missing.

>>> df.dropna(how="all")
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Keep only the rows with at least 2 non-NA values.

>>> df.dropna(thresh=2)
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Define in which columns to look for missing values.

>>> df.dropna(subset=["name", "toy"])
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

property dtypes

Return the dtypes in the DataFrame.

This returns a Series with the data type of each column. The result’s index is the original DataFrame’s columns. Columns with mixed types are stored with the object dtype. See the User Guide for more.

Returns:: The data type of each column.
Return type:: pandas.Series

See also

Series.dtypes: Return the dtype object of the underlying data.

Examples

>>> df = pd.DataFrame(
...     {
...         "float": [1.0],
...         "int": [1],
...         "datetime": [pd.Timestamp("20180310")],
...         "string": ["foo"],
...     }
... )
>>> df.dtypes
float              float64
int                  int64
datetime    datetime64[us]
string              str
dtype: object

duplicated(subset: Hashable | Iterable[Hashable] | None = None, keep: DropKeep = 'first') → Series

Return boolean Series denoting duplicate rows.

Considering certain columns is optional.

Parameters:

subset (column label or iterable of labels, optional) – Only consider certain columns for identifying duplicates, by default use all of the columns.
keep ({'first', 'last', False}, default 'first') –
Determines which duplicates (if any) to mark.
- first : Mark duplicates as True except for the first occurrence.
- last : Mark duplicates as True except for the last occurrence.
- False : Mark all duplicates as True.

Returns:

Boolean series for each duplicated rows.

Return type:

Series

See also

Index.duplicated: Equivalent method on index.
Series.duplicated: Equivalent method on Series.
Series.drop_duplicates: Remove duplicate values from Series.
DataFrame.drop_duplicates: Remove duplicate values from DataFrame.

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame(
...     {
...         "brand": ["Yum Yum", "Yum Yum", "Indomie", "Indomie", "Indomie"],
...         "style": ["cup", "cup", "cup", "pack", "pack"],
...         "rating": [4, 4, 3.5, 15, 5],
...     }
... )
>>> df
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, for each set of duplicated values, the first occurrence is set on False and all others on True.

>>> df.duplicated()
  False
   True
  False
  False
  False
dtype: bool

By using ‘last’, the last occurrence of each set of duplicated values is set on False and all others on True.

>>> df.duplicated(keep="last")
   True
  False
  False
  False
  False
dtype: bool

By setting keep on False, all duplicates are True.

>>> df.duplicated(keep=False)
   True
   True
  False
  False
  False
dtype: bool

To find duplicates on specific column(s), use subset.

>>> df.duplicated(subset=["brand"])
  False
   True
  False
   True
   True
dtype: bool

property empty: bool

Indicator whether Series/DataFrame is empty.

True if Series/DataFrame is entirely empty (no items), meaning any of the axes are of length 0.

Returns:: If Series/DataFrame is empty, return True, if not return False.
Return type:: bool

See also

Series.dropna: Return series without null values.
DataFrame.dropna: Return DataFrame with labels on given axis omitted where (all or any) data are missing.

Notes

If Series/DataFrame contains only NaNs, it is still not considered empty. See the example below.

Examples

An example of an actual empty DataFrame. Notice the index is empty:

>>> df_empty = pd.DataFrame({"A": []})
>>> df_empty
Empty DataFrame
Columns: [A]
Index: []
>>> df_empty.empty
True

If we only have NaNs in our DataFrame, it is not considered empty! We will need to drop the NaNs to make the DataFrame empty:

>>> df = pd.DataFrame({"A": [np.nan]})
>>> df
    A
0 NaN
>>> df.empty
False
>>> df.dropna().empty
True

>>> ser_empty = pd.Series({"A": []})
>>> ser_empty
A    []
dtype: object
>>> ser_empty.empty
False
>>> ser_empty = pd.Series()
>>> ser_empty.empty
True

eq(other, axis: Axis = 'columns', level=None) → DataFrame

Get Not equal to of dataframe and other, element-wise (binary operator eq).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters:

other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns:

Result of the comparison.

Return type:

DataFrame of bool

See also

DataFrame.eq: Compare DataFrames for equality elementwise.
DataFrame.ne: Compare DataFrames for inequality elementwise.
DataFrame.le: Compare DataFrames for less than inequality or equality elementwise.
DataFrame.lt: Compare DataFrames for strictly less than inequality elementwise.
DataFrame.ge: Compare DataFrames for greater than inequality or equality elementwise.
DataFrame.gt: Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame(
...     {"cost": [250, 150, 100], "revenue": [100, 250, 300]},
...     index=["A", "B", "C"],
... )
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis="index")
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis="index")
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame(
...     {"revenue": [300, 250, 100, 150]}, index=["A", "B", "C", "D"]
... )
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame(
...     {
...         "cost": [250, 150, 100, 150, 300, 220],
...         "revenue": [100, 250, 300, 200, 175, 225],
...     },
...     index=[
...         ["Q1", "Q1", "Q1", "Q2", "Q2", "Q2"],
...         ["A", "B", "C", "A", "B", "C"],
...     ],
... )
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

equals(other: object) → bool

Test whether two objects contain the same elements.

This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal.

The row/column index do not need to have the same type, as long as the values are considered equal. Corresponding columns and index must be of the same dtype.

Parameters:: other (Series or DataFrame) – The other Series or DataFrame to be compared with the first.
Returns:: True if all elements are the same in both objects, False otherwise.
Return type:: bool

See also

Series.eq: Compare two Series objects of the same length and return a Series where each element is True if the element in each Series is equal, False otherwise.
DataFrame.eq: Compare two DataFrame objects of the same shape and return a DataFrame where each element is True if the respective element in each DataFrame is equal, False otherwise.
testing.assert_series_equal: Raises an AssertionError if left and right are not equal. Provides an easy interface to ignore inequality in dtypes, indexes and precision among others.
testing.assert_frame_equal: Like assert_series_equal, but targets DataFrames.
numpy.array_equal: Return True if two arrays have the same shape and elements, False otherwise.

Examples

>>> df = pd.DataFrame({1: [10], 2: [20]})
>>> df
    1   2
0  10  20

DataFrames df and exactly_equal have the same types and values for their elements and column labels, which will return True.

>>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
>>> exactly_equal
    1   2
0  10  20
>>> df.equals(exactly_equal)
True

DataFrames df and different_column_type have the same element types and values, but have different types for the column labels, which will still return True.

>>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
>>> different_column_type
   1.0  2.0
0   10   20
>>> df.equals(different_column_type)
True

DataFrames df and different_data_type have different types for the same values for their elements, and will return False even though their column labels are the same values and types.

>>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
>>> different_data_type
      1     2
0  10.0  20.0
>>> df.equals(different_data_type)
False

DataFrames with NaN in the same locations compare equal.

>>> df_nan1 = pd.DataFrame({"a": [1, np.nan], "b": [3, np.nan]})
>>> df_nan2 = pd.DataFrame({"a": [1, np.nan], "b": [3, np.nan]})
>>> df_nan1.equals(df_nan2)
True

If the NaN values are not in the same locations, they compare unequal.

>>> df_nan3 = pd.DataFrame({"a": [1, np.nan], "b": [3, 4]})
>>> df_nan1.equals(df_nan3)
False

eval(expr: str, *, inplace: bool = False, **kwargs) → Any | None

Evaluate a string describing operations on DataFrame columns.

Warning

This method can run arbitrary code which can make you vulnerable to code injection if you pass user input to this function.

Operates on columns only, not specific rows or elements. This allows eval to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.

Parameters:

expr (str) –
The expression string to evaluate.

You can refer to variables in the environment by prefixing them with an ‘@’ character like @a + b.

You can refer to column names that are not valid Python variable names by surrounding them in backticks. Thus, column names containing spaces or punctuation (besides underscores) or starting with digits must be surrounded by backticks. (For example, a column named “Area (cm^2)” would be referenced as `Area (cm^2)`). Column names which are Python keywords (like “if”, “for”, “import”, etc) cannot be used.

For example, if one of your columns is called a a and you want to sum it with b, your query should be `a a` + b.

See the documentation for eval() for full details of supported operations and functions in the expression string.
inplace (bool, default False) – If the expression contains an assignment, whether to perform the operation inplace and mutate the existing DataFrame. Otherwise, a new DataFrame is returned.
**kwargs – See the documentation for eval() for complete details on the keyword arguments accepted by eval().

Returns:

The result of the evaluation or None if inplace=True.

Return type:

ndarray, scalar, pandas object, or None

See also

DataFrame.query: Evaluates a boolean expression to query the columns of a frame.
DataFrame.assign: Can evaluate an expression or function to create new values for a column.
eval: Evaluate a Python expression as a string using various backends.

Notes

For more details see the API documentation for eval(). For detailed examples see enhancing performance with eval.

Examples

>>> df = pd.DataFrame(
...     {"A": range(1, 6), "B": range(10, 0, -2), "C&C": range(10, 5, -1)}
... )
>>> df
   A   B  C&C
0  1  10   10
1  2   8    9
2  3   6    8
3  4   4    7
4  5   2    6
>>> df.eval("A + B")
0    11
1    10
2     9
3     8
4     7
dtype: int64

Assignment is allowed though by default the original DataFrame is not modified.

>>> df.eval("D = A + B")
   A   B  C&C   D
1  10   10  11
2   8    9  10
3   6    8   9
4   4    7   8
5   2    6   7
>>> df
   A   B  C&C
1  10   10
2   8    9
3   6    8
4   4    7
5   2    6

Multiple columns can be assigned to using multi-line expressions:

>>> df.eval(
...     '''
... D = A + B
... E = A - B
... '''
... )
   A   B  C&C   D  E
0  1  10   10  11 -9
1  2   8    9  10 -6
2  3   6    8   9 -3
3  4   4    7   8  0
4  5   2    6   7  3

For columns with spaces or other disallowed characters in their name, you can use backtick quoting.

>>> df.eval("B * `C&C`")
  100
   72
   48
   28
   12
dtype: int64

Local variables shall be explicitly referenced using @ character in front of the name:

>>> local_var = 2
>>> df.eval("@local_var * A")
0     2
1     4
2     6
3     8
4    10
Name: A, dtype: int64

Provide exponentially weighted (EW) calculations.

Exactly one of com, span, halflife, or alpha must be provided if times is not provided. If times is provided and adjust=True, halflife and one of com, span or alpha may be provided. If times is provided and adjust=False, halflife must be the only provided decay-specification parameter.

Parameters:

com (float, optional) –
Specify decay in terms of center of mass

\(\alpha = 1 / (1 + com)\), for \(com \geq 0\).
span (float, optional) –
Specify decay in terms of span

\(\alpha = 2 / (span + 1)\), for \(span \geq 1\).
halflife (float, str, timedelta, optional) –
Specify decay in terms of half-life

\(\alpha = 1 - \exp\left(-\ln(2) / halflife\right)\), for \(halflife > 0\).

If times is specified, a timedelta convertible unit over which an observation decays to half its value. Only applicable to mean(), and halflife value will not apply to the other functions.
alpha (float, optional) –
Specify smoothing factor \(\alpha\) directly

\(0 < \alpha \leq 1\).
min_periods (int, default 0) – Minimum number of observations in window required to have a value; otherwise, result is np.nan.
adjust (bool, default True) –
Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings (viewing EWMA as a moving average).
- When adjust=True (default), the EW function is calculated using weights \(w_i = (1 - \alpha)^i\). For example, the EW moving average of the series [\(x_0, x_1, ..., x_t\)] would be:
\[y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ... + (1 - \alpha)^t x_0}{1 + (1 - \alpha) + (1 - \alpha)^2 + ... + (1 - \alpha)^t}\]
- When adjust=False, the exponentially weighted function is calculated recursively:
\[\begin{split}\begin{split} y_0 &= x_0\\ y_t &= (1 - \alpha) y_{t-1} + \alpha x_t, \end{split}\end{split}\]
ignore_na (bool, default False) –
Ignore missing values when calculating weights.
- When ignore_na=False (default), weights are based on absolute positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \((1-\alpha)^2\) and \(1\) if adjust=True, and \((1-\alpha)^2\) and \(\alpha\) if adjust=False.
- When ignore_na=True, weights are based on relative positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \(1-\alpha\) and \(1\) if adjust=True, and \(1-\alpha\) and \(\alpha\) if adjust=False.
times (np.ndarray, Series, default None) –
Only applicable to mean().

Times corresponding to the observations. Must be monotonically increasing and datetime64[ns] dtype.

If 1-D array like, a sequence with the same shape as the observations.
method (str {'single', 'table'}, default 'single') –
Execute the rolling operation per single column or row ('single') or over the entire object ('table').

This argument is only implemented when specifying engine='numba' in the method call.

Only applicable to mean()

Returns:

An instance of ExponentialMovingWindow for further exponentially weighted (EW) calculations, e.g. using the mean method.

Return type:

pandas.api.typing.ExponentialMovingWindow

See also

rolling: Provides rolling window calculations.
expanding: Provides expanding transformations.

Notes

See Windowing Operations for further usage details and examples.

Examples

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

>>> df.ewm(com=0.5).mean()
          B
0.000000
0.750000
1.615385
1.615385
3.670213
>>> df.ewm(alpha=2 / 3).mean()
          B
0.000000
0.750000
1.615385
1.615385
3.670213

adjust

>>> df.ewm(com=0.5, adjust=True).mean()
          B
0.000000
0.750000
1.615385
1.615385
3.670213
>>> df.ewm(com=0.5, adjust=False).mean()
          B
0.000000
0.666667
1.555556
1.555556
3.650794

ignore_na

>>> df.ewm(com=0.5, ignore_na=True).mean()
          B
0.000000
0.750000
1.615385
1.615385
3.225000
>>> df.ewm(com=0.5, ignore_na=False).mean()
          B
0.000000
0.750000
1.615385
1.615385
3.670213

times

Exponentially weighted mean with weights calculated with a timedelta halflife relative to times.

>>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15', '2020-01-17']
>>> df.ewm(halflife='4 days', times=pd.DatetimeIndex(times)).mean()
          B
0  0.000000
1  0.585786
2  1.523889
3  1.523889
4  3.233686

expanding(min_periods: int = 1, method: Literal['single', 'table'] = 'single') → Expanding

Provide expanding window calculations.

An expanding window yields the value of an aggregation statistic with all the data available up to that point in time.

Parameters:

min_periods (int, default 1) – Minimum number of observations in window required to have a value; otherwise, result is np.nan.
method (str {'single', 'table'}, default 'single') –
Execute the rolling operation per single column or row ('single') or over the entire object ('table').

This argument is only implemented when specifying engine='numba' in the method call.

Returns:

An instance of Expanding for further expanding window calculations, e.g. using the sum method.

Return type:

pandas.api.typing.Expanding

See also

rolling: Provides rolling window calculations.
ewm: Provides exponential weighted functions.

Notes

See Windowing Operations for further usage details and examples.

Examples

>>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

min_periods

Expanding sum with 1 vs 3 observations needed to calculate a value.

>>> df.expanding(1).sum()
     B
0.0
1.0
3.0
3.0
7.0
>>> df.expanding(3).sum()
     B
NaN
NaN
3.0
3.0
7.0

explode(column: IndexLabel, ignore_index: bool = False) → DataFrame

Transform each element of a list-like to a row, replicating index values.

Parameters:

column (IndexLabel) – Column(s) to explode. For multiple columns, specify a non-empty list with each element be str or tuple, and all specified columns their list-like data on same row of the frame must have matching length.
ignore_index (bool, default False) – If True, the resulting index will be labeled 0, 1, …, n - 1.

Returns:

Exploded lists to rows of the subset columns; index will be duplicated for these rows.

Return type:

DataFrame

Raises:

ValueError : –

If columns of the frame are not unique. * If specified columns to explode is empty list. * If specified columns to explode have not matching count of elements rowwise in the frame.

See also

DataFrame.unstack: Pivot a level of the (necessarily hierarchical) index labels.
DataFrame.melt: Unpivot a DataFrame from wide format to long format.
Series.explode: Explode a DataFrame from list-like columns to long format.

Notes

This routine will explode list-likes including lists, tuples, sets, Series, and np.ndarray. The result dtype of the subset rows will be object. Scalars will be returned unchanged, and empty list-likes will result in a np.nan for that row. In addition, the ordering of rows in the output will be non-deterministic when exploding sets.

Reference the user guide for more examples.

Examples

>>> df = pd.DataFrame(
...     {
...         "A": [[0, 1, 2], "foo", [], [3, 4]],
...         "B": 1,
...         "C": [["a", "b", "c"], np.nan, [], ["d", "e"]],
...     }
... )
>>> df
           A  B          C
0  [0, 1, 2]  1  [a, b, c]
1        foo  1        NaN
2         []  1         []
3     [3, 4]  1     [d, e]

Single-column explode.

>>> df.explode("A")
     A  B          C
  0  1  [a, b, c]
  1  1  [a, b, c]
  2  1  [a, b, c]
foo  1        NaN
NaN  1         []
  3  1     [d, e]
  4  1     [d, e]

Multi-column explode.

>>> df.explode(list("AC"))
     A  B    C
  0  1    a
  1  1    b
  2  1    c
foo  1  NaN
NaN  1  NaN
  3  1    d
  4  1    e

ffill(*, axis: None | int | Literal['index', 'columns', 'rows'] = None, inplace: bool = False, limit: None | int = None, limit_area: Literal['inside', 'outside'] | None = None) → Self

Fill NA/NaN values by propagating the last valid observation to next valid.

Parameters:

axis ({0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame) – Axis along which to fill missing values. For Series this parameter is unused and defaults to 0.
inplace (bool, default False) – If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).
limit (int, default None) – If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.
limit_area ({None, ‘inside’, ‘outside’}, default None) –
If limit is specified, consecutive NaNs will be filled with this restriction.
- None: No fill restriction.
- ’inside’: Only fill NaNs surrounded by valid values (interpolate).
- ’outside’: Only fill NaNs outside valid values (extrapolate).
Added in version 2.2.0.

Returns:

Object with missing values filled.

Return type:

Series/DataFrame

See also

DataFrame.bfill: Fill NA/NaN values by using the next valid observation to fill the gap.

Examples

>>> df = pd.DataFrame(
...     [
...         [np.nan, 2, np.nan, 0],
...         [3, 4, np.nan, 1],
...         [np.nan, np.nan, np.nan, np.nan],
...         [np.nan, 3, np.nan, 4],
...     ],
...     columns=list("ABCD"),
... )
>>> df
     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
2  NaN  NaN NaN  NaN
3  NaN  3.0 NaN  4.0

>>> df.ffill()
     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
2  3.0  4.0 NaN  1.0
3  3.0  3.0 NaN  4.0

>>> ser = pd.Series([1, np.nan, 2, 3])
>>> ser.ffill()
0   1.0
1   1.0
2   2.0
3   3.0
dtype: float64

Fill NA/NaN values with value.

Parameters:

value (scalar, dict, Series, or DataFrame) – Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list.
axis ({0 or 'index'} for Series, {0 or 'index', 1 or 'columns'} for DataFrame) – Axis along which to fill missing values. For Series this parameter is unused and defaults to 0.
inplace (bool, default False) – If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).
limit (int, default None) – This is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

Returns:

Object with missing values filled.

Return type:

Series/DataFrame

See also

ffill: Fill values by propagating the last valid observation to next valid.
bfill: Fill values by using the next valid observation to fill the gap.
interpolate: Fill NaN values using interpolation.
reindex: Conform object to new index.
asfreq: Convert TimeSeries to specified frequency.

Notes

For non-object dtype, value=None will use the NA value of the dtype. See more details in the Filling missing data section.

Examples

>>> df = pd.DataFrame(
...     [
...         [np.nan, 2, np.nan, 0],
...         [3, 4, np.nan, 1],
...         [np.nan, np.nan, np.nan, np.nan],
...         [np.nan, 3, np.nan, 4],
...     ],
...     columns=list("ABCD"),
... )
>>> df
     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
2  NaN  NaN NaN  NaN
3  NaN  3.0 NaN  4.0

Replace all NaN elements with 0s.

>>> df.fillna(0)
     A    B    C    D
0  0.0  2.0  0.0  0.0
1  3.0  4.0  0.0  1.0
2  0.0  0.0  0.0  0.0
3  0.0  3.0  0.0  4.0

Replace all NaN elements in column ‘A’, ‘B’, ‘C’, and ‘D’, with 0, 1, 2, and 3 respectively.

>>> values = {"A": 0, "B": 1, "C": 2, "D": 3}
>>> df.fillna(value=values)
     A    B    C    D
0  0.0  2.0  2.0  0.0
1  3.0  4.0  2.0  1.0
2  0.0  1.0  2.0  3.0
3  0.0  3.0  2.0  4.0

Only replace the first NaN element.

>>> df.fillna(value=values, limit=1)
     A    B    C    D
0  0.0  2.0  2.0  0.0
1  3.0  4.0  NaN  1.0
2  NaN  1.0  NaN  3.0
3  NaN  3.0  NaN  4.0

When filling using a DataFrame, replacement happens along the same column names and same indices

>>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
>>> df.fillna(df2)
     A    B    C    D
0  0.0  2.0  0.0  0.0
1  3.0  4.0  0.0  1.0
2  0.0  0.0  0.0  NaN
3  0.0  3.0  0.0  4.0

Note that column D is not affected since it is not present in df2.

filter(items=None, like: str | None = None, regex: str | None = None, axis: int | Literal['index', 'columns', 'rows'] | None = None) → Self

Subset the DataFrame or Series according to the specified index labels.

For DataFrame, filter rows or columns depending on axis argument. Note that this routine does not filter based on content. The filter is applied to the labels of the index.

Parameters:

items (list-like) – Keep labels from axis which are in items.
like (str) – Keep labels from axis for which “like in label == True”.
regex (str (regular expression)) – Keep labels from axis for which re.search(regex, label) == True.
axis ({0 or 'index', 1 or 'columns', None}, default None) – The axis to filter on, expressed either as an index (int) or axis name (str). By default this is the info axis, ‘columns’ for DataFrame. For Series this parameter is unused and defaults to None.

Returns:

The filtered subset of the DataFrame or Series.

Return type:

Same type as caller

See also

DataFrame.loc: Access a group of rows and columns by label(s) or a boolean array.

Notes

The items, like, and regex parameters are enforced to be mutually exclusive.

axis defaults to the info axis that is used when indexing with [].

Examples

>>> df = pd.DataFrame(
...     np.array(([1, 2, 3], [4, 5, 6])),
...     index=["mouse", "rabbit"],
...     columns=["one", "two", "three"],
... )
>>> df
        one  two  three
mouse     1    2      3
rabbit    4    5      6

>>> # select columns by name
>>> df.filter(items=["one", "three"])
         one  three
mouse     1      3
rabbit    4      6

>>> # select columns by regular expression
>>> df.filter(regex="e$", axis=1)
         one  three
mouse     1      3
rabbit    4      6

>>> # select rows containing 'bbi'
>>> df.filter(like="bbi", axis=0)
         one  two  three
rabbit    4    5      6

first_valid_index() → Hashable

Return index for first non-missing value or None, if no value is found.

See the User Guide for more information on which values are considered missing.

Returns:: Index of first non-missing value.
Return type:: type of index

See also

DataFrame.last_valid_index: Return index for last non-NA value or None, if no non-NA value is found.
Series.last_valid_index: Return index for last non-NA value or None, if no non-NA value is found.
DataFrame.isna: Detect missing values.

Examples

For Series:

>>> s = pd.Series([None, 3, 4])
>>> s.first_valid_index()
1
>>> s.last_valid_index()
2

>>> s = pd.Series([None, None])
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None

If all elements in Series are NA/null, returns None.

>>> s = pd.Series()
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None

If Series is empty, returns None.

For DataFrame:

>>> df = pd.DataFrame({"A": [None, None, 2], "B": [None, 3, 4]})
>>> df
     A      B
0  NaN    NaN
1  NaN    3.0
2  2.0    4.0
>>> df.first_valid_index()
1
>>> df.last_valid_index()
2

>>> df = pd.DataFrame({"A": [None, None, None], "B": [None, None, None]})
>>> df
     A      B
0  None   None
1  None   None
2  None   None
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None

If all elements in DataFrame are NA/null, returns None.

>>> df = pd.DataFrame()
>>> df
Empty DataFrame
Columns: []
Index: []
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None

If DataFrame is empty, returns None.

property flags: Flags

Get the properties associated with this pandas object.

The available flags are

Flags.allows_duplicate_labels

See also

Flags: Flags that apply to pandas objects.
DataFrame.attrs: Global metadata applying to this dataset.

Notes

“Flags” differ from “metadata”. Flags reflect properties of the pandas object (the Series or DataFrame). Metadata refer to properties of the dataset, and should be stored in DataFrame.attrs.

Examples

>>> df = pd.DataFrame({"A": [1, 2]})
>>> df.flags
<Flags(allows_duplicate_labels=True)>

Flags can be get or set using .

>>> df.flags.allows_duplicate_labels
True
>>> df.flags.allows_duplicate_labels = False

Or by slicing with a key

>>> df.flags["allows_duplicate_labels"]
False
>>> df.flags["allows_duplicate_labels"] = True

floordiv(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Integer division of dataframe and other, element-wise (binary operator floordiv).

Equivalent to dataframe // other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rfloordiv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

classmethod from_arrow(data: ArrowArrayExportable | ArrowStreamExportable) → DataFrame

Construct a DataFrame from a tabular Arrow object.

This function accepts any Arrow-compatible tabular object implementing the Arrow PyCapsule Protocol (i.e. having an __arrow_c_array__ or __arrow_c_stream__ method).

This function currently relies on pyarrow to convert the tabular object in Arrow format to pandas.

Added in version 3.0.

Parameters:: data (pyarrow.Table or Arrow-compatible table) – Any tabular object implementing the Arrow PyCapsule Protocol (i.e. has an __arrow_c_array__ or __arrow_c_stream__ method).
Return type:: DataFrame

See also

Series.from_arrow: Construct a Series from an Arrow object.

Examples

>>> import pyarrow as pa
>>> table = pa.table({"a": [1, 2, 3], "b": ["x", "y", "z"]})
>>> pd.DataFrame.from_arrow(table)
   a  b
0  1  x
1  2  y
2  3  z

classmethod from_dict(data: dict, orient: FromDictOrient = 'columns', dtype: Dtype | None = None, columns: Axes | None = None) → DataFrame

Construct DataFrame from dict of array-like or dicts.

Creates DataFrame object from dictionary by columns or by index allowing dtype specification.

Parameters:

data (dict) – Of the form {field : array-like} or {field : dict}.
orient ({'columns', 'index', 'tight'}, default 'columns') – The “orientation” of the data. If the keys of the passed dict should be the columns of the resulting DataFrame, pass ‘columns’ (default). Otherwise if the keys should be rows, pass ‘index’. If ‘tight’, assume a dict with keys [‘index’, ‘columns’, ‘data’, ‘index_names’, ‘column_names’].
dtype (dtype, default None) – Data type to force after DataFrame construction, otherwise infer.
columns (list, default None) – Column labels to use when orient='index'. Raises a ValueError if used with orient='columns' or orient='tight'.

Return type:

DataFrame

See also

DataFrame.from_records: DataFrame from structured ndarray, sequence of tuples or dicts, or DataFrame.
DataFrame: DataFrame object creation using constructor.
DataFrame.to_dict: Convert the DataFrame to a dictionary.

Examples

By default the keys of the dict become the DataFrame columns:

>>> data = {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
>>> pd.DataFrame.from_dict(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Specify orient='index' to create the DataFrame using dictionary keys as rows:

>>> data = {"row_1": [3, 2, 1, 0], "row_2": ["a", "b", "c", "d"]}
>>> pd.DataFrame.from_dict(data, orient="index")
       0  1  2  3
row_1  3  2  1  0
row_2  a  b  c  d

When using the ‘index’ orientation, the column names can be specified manually:

>>> pd.DataFrame.from_dict(data, orient="index", columns=["A", "B", "C", "D"])
       A  B  C  D
row_1  3  2  1  0
row_2  a  b  c  d

Specify orient='tight' to create the DataFrame using a ‘tight’ format:

>>> data = {
...     "index": [("a", "b"), ("a", "c")],
...     "columns": [("x", 1), ("y", 2)],
...     "data": [[1, 3], [2, 4]],
...     "index_names": ["n1", "n2"],
...     "column_names": ["z1", "z2"],
... }
>>> pd.DataFrame.from_dict(data, orient="tight")
z1     x  y
z2     1  2
n1 n2
a  b   1  3
   c   2  4

classmethod from_records(data, index=None, exclude=None, columns=None, coerce_float: bool = False, nrows: int | None = None) → DataFrame

Convert structured or record ndarray to DataFrame.

Creates a DataFrame object from a structured ndarray, or iterable of tuples or dicts.

Parameters:

data (structured ndarray, iterable of tuples or dicts) – Structured input data.
index (str, list of fields, array-like) – Field of array to use as the index, alternately a specific set of input labels to use.
exclude (sequence, default None) – Columns or fields to exclude.
columns (sequence, default None) – Column names to use. If the passed data do not have names associated with them, this argument provides names for the columns. Otherwise, this argument indicates the order of the columns in the result (any names not found in the data will become all-NA columns) and limits the data to these columns if not all column names are provided.
coerce_float (bool, default False) – Attempt to convert values of non-string, non-numeric objects (like decimal.Decimal) to floating point, useful for SQL result sets.
nrows (int, default None) – Number of rows to read if data is an iterator.

Return type:

DataFrame

See also

DataFrame.from_dict: DataFrame from dict of array-like or dicts.
DataFrame: DataFrame object creation using constructor.

Examples

Data can be provided as a structured ndarray:

>>> data = np.array(
...     [(3, "a"), (2, "b"), (1, "c"), (0, "d")],
...     dtype=[("col_1", "i4"), ("col_2", "U1")],
... )
>>> pd.DataFrame.from_records(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Data can be provided as a list of dicts:

>>> data = [
...     {"col_1": 3, "col_2": "a"},
...     {"col_1": 2, "col_2": "b"},
...     {"col_1": 1, "col_2": "c"},
...     {"col_1": 0, "col_2": "d"},
... ]
>>> pd.DataFrame.from_records(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Data can be provided as a list of tuples with corresponding columns:

>>> data = [(3, "a"), (2, "b"), (1, "c"), (0, "d")]
>>> pd.DataFrame.from_records(data, columns=["col_1", "col_2"])
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

ge(other, axis: Axis = 'columns', level=None) → DataFrame

Get Greater than or equal to of dataframe and other, element-wise (binary operator ge).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters:

other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns:

Result of the comparison.

Return type:

DataFrame of bool

See also

DataFrame.eq: Compare DataFrames for equality elementwise.
DataFrame.ne: Compare DataFrames for inequality elementwise.
DataFrame.le: Compare DataFrames for less than inequality or equality elementwise.
DataFrame.lt: Compare DataFrames for strictly less than inequality elementwise.
DataFrame.ge: Compare DataFrames for greater than inequality or equality elementwise.
DataFrame.gt: Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

get(key, default=None)

Get item from object for given key (ex: DataFrame column).

Returns default value if not found.

Parameters:

key (object) – Key for which item should be returned.
default (object, default None) – Default value to return if key is not found.

Returns:

Item for given key or default value, if key is not found.

Return type:

same type as items contained in object

See also

DataFrame.get: Get item from object for given key (ex: DataFrame column).
Series.get: Get item from object for given key (ex: DataFrame column).

Examples

>>> df = pd.DataFrame(
...     [
...         [24.3, 75.7, "high"],
...         [31, 87.8, "high"],
...         [22, 71.6, "medium"],
...         [35, 95, "medium"],
...     ],
...     columns=["temp_celsius", "temp_fahrenheit", "windspeed"],
...     index=pd.date_range(start="2014-02-12", end="2014-02-15", freq="D"),
... )

>>> df
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          24.3             75.7      high
2014-02-13          31.0             87.8      high
2014-02-14          22.0             71.6    medium
2014-02-15          35.0             95.0    medium

>>> df.get(["temp_celsius", "windspeed"])
            temp_celsius windspeed
2014-02-12          24.3      high
2014-02-13          31.0      high
2014-02-14          22.0    medium
2014-02-15          35.0    medium

>>> ser = df["windspeed"]
>>> ser.get("2014-02-13")
'high'

If the key isn’t found, the default value will be used.

>>> df.get(["temp_celsius", "temp_kelvin"], default="default_value")
'default_value'

>>> ser.get("2014-02-10", "[unknown]")
'[unknown]'

groupby(by=None, level: IndexLabel | None = None, *, as_index: bool = True, sort: bool = True, group_keys: bool = True, observed: bool = True, dropna: bool = True) → DataFrameGroupBy

Group DataFrame using a mapper or by a Series of columns.

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Parameters:

by (mapping, function, label, pd.Grouper or list of such) – Used to determine the groups for the groupby. If by is a function, it’s called on each value of the object’s index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series’ values are first aligned; see .align() method). If a list or ndarray of length equal to the number of rows is passed (see the groupby user guide), the values are used as-is to determine the groups. A label or list of labels may be passed to group by the columns in self. Notice that a tuple is interpreted as a (single) key.
level (int, level name, or sequence of such, default None) – If the axis is a MultiIndex (hierarchical), group by a particular level or levels. Do not specify both by and level.
as_index (bool, default True) – Return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output. This argument has no effect on filtrations (see the filtrations in the user guide), such as head(), tail(), nth() and in transformations (see the transformations in the user guide).
sort (bool, default True) –
Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group. If False, the groups will appear in the same order as they did in the original DataFrame. This argument has no effect on filtrations (see the filtrations in the user guide), such as head(), tail(), nth() and in transformations (see the transformations in the user guide).

Changed in version 2.0.0: Specifying sort=False with an ordered categorical grouper will no longer sort the values.
group_keys (bool, default True) –
When calling apply and the by argument produces a like-indexed (i.e. a transform) result, add group keys to index to identify pieces. By default group keys are not included when the result’s index (and column) labels match the inputs, and are included otherwise.

Changed in version 2.0.0: group_keys now defaults to True.
observed (bool, default True) –
This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.

Changed in version 3.0.0: The default value is now True.
dropna (bool, default True) – If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups.

Returns:

Returns a groupby object that contains information about the groups.

Return type:

pandas.api.typing.DataFrameGroupBy

See also

resample: Convenience method for frequency conversion and resampling of time series.

Notes

See the user guide for more detailed usage and examples, including splitting an object into groups, iterating through groups, selecting a group, aggregation, and more.

The implementation of groupby is hash-based, meaning in particular that objects that compare as equal will be considered to be in the same group. An exception to this is that pandas has special handling of NA values: any NA values will be collapsed to a single group, regardless of how they compare. See the user guide linked above for more details.

Examples

>>> df = pd.DataFrame(
...     {
...         "Animal": ["Falcon", "Falcon", "Parrot", "Parrot"],
...         "Max Speed": [380.0, 370.0, 24.0, 26.0],
...     }
... )
>>> df
   Animal  Max Speed
0  Falcon      380.0
1  Falcon      370.0
2  Parrot       24.0
3  Parrot       26.0
>>> df.groupby(["Animal"]).mean()
        Max Speed
Animal
Falcon      375.0
Parrot       25.0

Hierarchical Indexes

We can groupby different levels of a hierarchical index using the level parameter:

>>> arrays = [
...     ["Falcon", "Falcon", "Parrot", "Parrot"],
...     ["Captive", "Wild", "Captive", "Wild"],
... ]
>>> index = pd.MultiIndex.from_arrays(arrays, names=("Animal", "Type"))
>>> df = pd.DataFrame({"Max Speed": [390.0, 350.0, 30.0, 20.0]}, index=index)
>>> df
                Max Speed
Animal Type
Falcon Captive      390.0
       Wild         350.0
Parrot Captive       30.0
       Wild          20.0
>>> df.groupby(level=0).mean()
        Max Speed
Animal
Falcon      370.0
Parrot       25.0
>>> df.groupby(level="Type").mean()
         Max Speed
Type
Captive      210.0
Wild         185.0

We can also choose to include NA in group keys or not by setting dropna parameter, the default setting is True.

>>> arr = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
>>> df = pd.DataFrame(arr, columns=["a", "b", "c"])

>>> df.groupby(by=["b"]).sum()
    a   c
b
1.0 2   3
2.0 2   5

>>> df.groupby(by=["b"], dropna=False).sum()
    a   c
b
1.0 2   3
2.0 2   5
NaN 1   4

>>> arr = [["a", 12, 12], [None, 12.3, 33.0], ["b", 12.3, 123], ["a", 1, 1]]
>>> df = pd.DataFrame(arr, columns=["a", "b", "c"])

>>> df.groupby(by="a").sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0

>>> df.groupby(by="a", dropna=False).sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0
NaN 12.3   33.0

When using .apply(), use group_keys to include or exclude the group keys. The group_keys argument defaults to True (include).

>>> df = pd.DataFrame(
...     {
...         "Animal": ["Falcon", "Falcon", "Parrot", "Parrot"],
...         "Max Speed": [380.0, 370.0, 24.0, 26.0],
...     }
... )
>>> df.groupby("Animal", group_keys=True)[["Max Speed"]].apply(lambda x: x)
          Max Speed
Animal
Falcon 0      380.0
       1      370.0
Parrot 2       24.0
       3       26.0

>>> df.groupby("Animal", group_keys=False)[["Max Speed"]].apply(lambda x: x)
   Max Speed
0      380.0
1      370.0
2       24.0
3       26.0

gt(other, axis: Axis = 'columns', level=None) → DataFrame

Get Greater than of dataframe and other, element-wise (binary operator gt).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters:

other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns:

Result of the comparison.

Return type:

DataFrame of bool

See also

DataFrame.eq: Compare DataFrames for equality elementwise.
DataFrame.ne: Compare DataFrames for inequality elementwise.
DataFrame.le: Compare DataFrames for less than inequality or equality elementwise.
DataFrame.lt: Compare DataFrames for strictly less than inequality elementwise.
DataFrame.ge: Compare DataFrames for greater than inequality or equality elementwise.
DataFrame.gt: Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

harmonize_columns(inplace: bool = False)[source]: Harmonize metadata columns to matchms key style.

head(n: int = 5) → Self

Return the first n rows.

This function exhibits the same behavior as df[:n], returning the first n rows based on position. It is useful for quickly checking if your object has the right type of data in it.

When n is positive, it returns the first n rows. For n equal to 0, it returns an empty object. When n is negative, it returns all rows except the last |n| rows, mirroring the behavior of df[:n].

If n is larger than the number of rows, this function returns all rows.

Parameters:: n (int, default 5) – Number of rows to select.
Returns:: The first n rows of the caller object.
Return type:: same type as caller

See also

DataFrame.tail: Returns the last n rows.

Examples

>>> df = pd.DataFrame(
...     {
...         "animal": [
...             "alligator",
...             "bee",
...             "falcon",
...             "lion",
...             "monkey",
...             "parrot",
...             "shark",
...             "whale",
...             "zebra",
...         ]
...     }
... )
>>> df
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra

Viewing the first 5 lines

>>> df.head()
      animal
alligator
      bee
   falcon
     lion
   monkey

Viewing the first n lines (three in this case)

>>> df.head(3)
      animal
0  alligator
1        bee
2     falcon

For negative values of n

>>> df.head(-3)
      animal
alligator
      bee
   falcon
     lion
   monkey
   parrot

hist(column: IndexLabel | None = None, by=None, grid: bool = True, xlabelsize: int | None = None, xrot: float | None = None, ylabelsize: int | None = None, yrot: float | None = None, ax=None, sharex: bool = False, sharey: bool = False, figsize: tuple[int, int] | None = None, layout: tuple[int, int] | None = None, bins: int | Sequence[int] = 10, backend: str | None = None, legend: bool = False, **kwargs)

Make a histogram of the DataFrame’s columns.

A histogram is a representation of the distribution of data. This function calls matplotlib.pyplot.hist(), on each series in the DataFrame, resulting in one histogram per column.

Parameters:

data (DataFrame) – The pandas object holding the data.
column (str or sequence, optional) – If passed, will be used to limit data to a subset of columns.
by (object, optional) – If passed, then used to form histograms for separate groups.
grid (bool, default True) – Whether to show axis grid lines.
xlabelsize (int, default None) – If specified changes the x-axis label size.
xrot (float, default None) – Rotation of x axis labels. For example, a value of 90 displays the x labels rotated 90 degrees clockwise.
ylabelsize (int, default None) – If specified changes the y-axis label size.
yrot (float, default None) – Rotation of y axis labels. For example, a value of 90 displays the y labels rotated 90 degrees clockwise.
ax (Matplotlib axes object, default None) – The axes to plot the histogram on.
sharex (bool, default True if ax is None else False) – In case subplots=True, share x axis and set some x axis labels to invisible; defaults to True if ax is None otherwise False if an ax is passed in. Note that passing in both an ax and sharex=True will alter all x axis labels for all subplots in a figure.
sharey (bool, default False) – In case subplots=True, share y axis and set some y axis labels to invisible.
figsize (tuple, optional) – The size in inches of the figure to create. Uses the value in matplotlib.rcParams by default.
layout (tuple, optional) – Tuple of (rows, columns) for the layout of the histograms.
bins (int or sequence, default 10) – Number of histogram bins to be used. If an integer is given, bins + 1 bin edges are calculated and returned. If bins is a sequence, gives bin edges, including left edge of first bin and right edge of last bin. In this case, bins is returned unmodified.
backend (str, default None) – Backend to use instead of the backend specified in the option plotting.backend. For instance, ‘matplotlib’. Alternatively, to specify the plotting.backend for the whole session, set pd.options.plotting.backend.
legend (bool, default False) – Whether to show the legend.
**kwargs – All other plotting keyword arguments to be passed to matplotlib.pyplot.hist().

Returns:

2D NumPy Array of matplotlib.axes.Axes.

Return type:

np.ndarray

See also

matplotlib.pyplot.hist: Plot a histogram using matplotlib.

Examples

This example draws a histogram based on the length and width of some animals, displayed in three bins

property iat: _iAtIndexer

Access a single value for a row/column pair by integer position.

Similar to iloc, in that both provide integer-based lookups. Use iat if you only need to get or set a single value in a DataFrame or Series.

Raises:: IndexError – When integer position is out of bounds.

See also

DataFrame.at: Access a single value for a row/column label pair.
DataFrame.loc: Access a group of rows and columns by label(s).
DataFrame.iloc: Access a group of rows and columns by integer position(s).

Examples

>>> df = pd.DataFrame(
...     [[0, 2, 3], [0, 4, 1], [10, 20, 30]], columns=["A", "B", "C"]
... )
>>> df
    A   B   C
0   0   2   3
1   0   4   1
2  10  20  30

Get value at specified row/column pair

>>> df.iat[1, 2]
np.int64(1)

Set value at specified row/column pair

>>> df.iat[1, 2] = 10
>>> df.iat[1, 2]
np.int64(10)

Get value within a series

>>> df.loc[0].iat[1]
np.int64(2)

idxmax(axis: Axis = 0, skipna: bool = True, numeric_only: bool = False) → Series

Return index of first occurrence of maximum over requested axis.

NA/null values are excluded.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
skipna (bool, default True) – Exclude NA/null values. If the entire DataFrame is NA, or if skipna=False and there is an NA value, this method will raise a ValueError.
numeric_only (bool, default False) – Include only float, int or boolean data.

Returns:

Indexes of maxima along the specified axis.

Return type:

Series

Raises:

ValueError –

If the row/column is empty

See also

Series.idxmax: Return index of the maximum element.

Notes

This method is the DataFrame version of ndarray.argmax.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame(
...     {
...         "consumption": [10.51, 103.11, 55.48],
...         "co2_emissions": [37.2, 19.66, 1712],
...     },
...     index=["Pork", "Wheat Products", "Beef"],
... )

>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the maximum value in each column.

>>> df.idxmax()
consumption      Wheat Products
co2_emissions              Beef
dtype: str

To return the index for the maximum value in each row, use axis="columns".

>>> df.idxmax(axis="columns")
Pork              co2_emissions
Wheat Products     consumption
Beef              co2_emissions
dtype: str

idxmin(axis: Axis = 0, skipna: bool = True, numeric_only: bool = False) → Series

Return index of first occurrence of minimum over requested axis.

NA/null values are excluded.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
skipna (bool, default True) – Exclude NA/null values. If the entire DataFrame is NA, or if skipna=False and there is an NA value, this method will raise a ValueError.
numeric_only (bool, default False) – Include only float, int or boolean data.

Returns:

Indexes of minima along the specified axis.

Return type:

Series

Raises:

ValueError –

If the row/column is empty

See also

Series.idxmin: Return index of the minimum element.

Notes

This method is the DataFrame version of ndarray.argmin.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame(
...     {
...         "consumption": [10.51, 103.11, 55.48],
...         "co2_emissions": [37.2, 19.66, 1712],
...     },
...     index=["Pork", "Wheat Products", "Beef"],
... )

>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the minimum value in each column.

>>> df.idxmin()
consumption                Pork
co2_emissions    Wheat Products
dtype: str

To return the index for the minimum value in each row, use axis="columns".

>>> df.idxmin(axis="columns")
Pork                consumption
Wheat Products    co2_emissions
Beef                consumption
dtype: str

property iloc: _iLocIndexer

Purely integer-location based indexing for selection by position.

Changed in version 3.0: Callables which return a tuple are deprecated as input.

.iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.

Allowed inputs are:

An integer, e.g. 5.
A list or array of integers, e.g. [4, 3, 0].
A slice object with ints, e.g. 1:7.
A boolean array.
A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don’t have a reference to the calling object, but would like to base your selection on some value.
A tuple of row and column indexes. The tuple elements consist of one of the above inputs, e.g. (0, 1).

.iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing (this conforms with python/numpy slice semantics).

See more at Selection by Position.

See also

DataFrame.iat: Fast integer location scalar accessor.
DataFrame.loc: Purely label-location based indexer for selection by label.
Series.iloc: Purely integer-location based indexing for selection by position.

Examples

>>> mydict = [
...     {"a": 1, "b": 2, "c": 3, "d": 4},
...     {"a": 100, "b": 200, "c": 300, "d": 400},
...     {"a": 1000, "b": 2000, "c": 3000, "d": 4000},
... ]
>>> df = pd.DataFrame(mydict)
>>> df
      a     b     c     d
0     1     2     3     4
1   100   200   300   400
2  1000  2000  3000  4000

Indexing just the rows

With a scalar integer.

>>> type(df.iloc[0])
<class 'pandas.Series'>
>>> df.iloc[0]
a    1
b    2
c    3
d    4
Name: 0, dtype: int64

With a list of integers.

>>> df.iloc[[0]]
   a  b  c  d
0  1  2  3  4
>>> type(df.iloc[[0]])
<class 'pandas.DataFrame'>

>>> df.iloc[[0, 1]]
     a    b    c    d
0    1    2    3    4
1  100  200  300  400

With a slice object.

>>> df.iloc[:3]
      a     b     c     d
0     1     2     3     4
1   100   200   300   400
2  1000  2000  3000  4000

With a boolean mask the same length as the index.

>>> df.iloc[[True, False, True]]
      a     b     c     d
0     1     2     3     4
2  1000  2000  3000  4000

With a callable, useful in method chains. The x passed to the lambda is the DataFrame being sliced. This selects the rows whose index label even.

>>> df.iloc[lambda x: x.index % 2 == 0]
      a     b     c     d
0     1     2     3     4
2  1000  2000  3000  4000

Indexing both axes

You can mix the indexer types for the index and columns. Use : to select the entire axis.

With scalar integers.

>>> df.iloc[0, 1]
np.int64(2)

With lists of integers.

>>> df.iloc[[0, 2], [1, 3]]
      b     d
0     2     4
2  2000  4000

With slice objects.

>>> df.iloc[1:3, 0:3]
      a     b     c
1   100   200   300
2  1000  2000  3000

With a boolean array whose length matches the columns.

>>> df.iloc[:, [True, False, True, False]]
      a     c
0     1     3
1   100   300
2  1000  3000

With a callable function that expects the Series or DataFrame.

>>> df.iloc[:, lambda df: [0, 2]]
      a     c
0     1     3
1   100   300
2  1000  3000

index

The index (row labels) of the DataFrame.

The index of a DataFrame is a series of labels that identify each row. The labels can be integers, strings, or any other hashable type. The index is used for label-based access and alignment, and can be accessed or modified using this attribute.

Returns:: The index labels of the DataFrame.
Return type:: pandas.Index

See also

DataFrame.columns: The column labels of the DataFrame.
DataFrame.to_numpy: Convert the DataFrame to a NumPy array.

Examples

>>> df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Aritra'],
...                    'Age': [25, 30, 35],
...                    'Location': ['Seattle', 'New York', 'Kona']},
...                   index=([10, 20, 30]))
>>> df.index
Index([10, 20, 30], dtype='int64')

In this example, we create a DataFrame with 3 rows and 3 columns, including Name, Age, and Location information. We set the index labels to be the integers 10, 20, and 30. We then access the index attribute of the DataFrame, which returns an Index object containing the index labels.

>>> df.index = [100, 200, 300]
>>> df
    Name  Age Location
100  Alice   25  Seattle
200    Bob   30 New York
300  Aritra  35    Kona

In this example, we modify the index labels of the DataFrame by assigning a new list of labels to the index attribute. The DataFrame is then updated with the new labels, and the output shows the modified DataFrame.

infer_objects(copy: bool | Literal[_NoDefault.no_default] = <no_default>) → Self

Attempt to infer better dtypes for object columns.

Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction.

Parameters:

copy (bool, default False) –

This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.

Returns:

Returns an object of the same type as the input object.

Return type:

same type as input object

See also

to_datetime: Convert argument to datetime.
to_timedelta: Convert argument to timedelta.
to_numeric: Convert argument to numeric type.
convert_dtypes: Convert argument to best possible dtype.

Examples

>>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})
>>> df = df.iloc[1:]
>>> df
   A
1  1
2  2
3  3

>>> df.dtypes
A    object
dtype: object

>>> df.infer_objects().dtypes
A    int64
dtype: object

Print a concise summary of a DataFrame.

This method prints information about a DataFrame including the index dtype and columns, non-NA values and memory usage.

Parameters:

verbose (bool, optional) – Whether to print the full summary. By default, the setting in pandas.options.display.max_info_columns is followed.
buf (writable buffer, defaults to sys.stdout) – Where to send the output. By default, the output is printed to sys.stdout. Pass a writable buffer if you need to further process the output.
max_cols (int, optional) – When to switch from the verbose to the truncated output. If the DataFrame has more than max_cols columns, the truncated output is used. By default, the setting in pandas.options.display.max_info_columns is used.
memory_usage (bool, str, optional) –
Specifies whether total memory usage of the DataFrame elements (including the index) should be displayed. By default, this follows the pandas.options.display.memory_usage setting.

True always show memory usage. False never shows memory usage. A value of ‘deep’ is equivalent to “True with deep introspection”. Memory usage is shown in human-readable units (base-2 representation). Without deep introspection a memory estimation is made based in column dtype and number of rows assuming values consume the same memory amount for corresponding dtypes. With deep memory introspection, a real memory usage calculation is performed at the cost of computational resources. See the Frequently Asked Questions for more details.
show_counts (bool, optional) – Whether to show the non-null counts. By default, this is shown only if the DataFrame is smaller than pandas.options.display.max_info_rows and pandas.options.display.max_info_columns. A value of True always shows the counts, and False never shows the counts.

Returns:

This method prints a summary of a DataFrame and returns None.

Return type:

None

See also

DataFrame.describe: Generate descriptive statistics of DataFrame columns.
DataFrame.memory_usage: Memory usage of DataFrame columns.

Examples

>>> int_values = [1, 2, 3, 4, 5]
>>> text_values = ["alpha", "beta", "gamma", "delta", "epsilon"]
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = pd.DataFrame(
...     {
...         "int_col": int_values,
...         "text_col": text_values,
...         "float_col": float_values,
...     }
... )
>>> df
    int_col text_col  float_col
0        1    alpha       0.00
1        2     beta       0.25
2        3    gamma       0.50
3        4    delta       0.75
4        5  epsilon       1.00

Prints information of all columns:

>>> df.info(verbose=True)
<class 'pandas.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
 #   Column     Non-Null Count  Dtype
---  ------     --------------  -----
 0   int_col    5 non-null      int64
 1   text_col   5 non-null      str
 2   float_col  5 non-null      float64
dtypes: float64(1), int64(1), str(1)
memory usage: 278.0 bytes

Prints a summary of columns count and its dtypes but not per column information:

>>> df.info(verbose=False)
<class 'pandas.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), str(1)
memory usage: 278.0 bytes

Pipe output of DataFrame.info to buffer instead of sys.stdout, get buffer content and writes to a text file:

>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open("df_info.txt", "w", encoding="utf-8") as f:
...     f.write(s)
260

The memory_usage parameter allows deep introspection mode, specially useful for big DataFrames and fine-tune memory optimization:

>>> random_strings_array = np.random.choice(["a", "b", "c"], 10**6)
>>> df = pd.DataFrame(
...     {
...         "column_1": np.random.choice(["a", "b", "c"], 10**6),
...         "column_2": np.random.choice(["a", "b", "c"], 10**6),
...         "column_3": np.random.choice(["a", "b", "c"], 10**6),
...     }
... )
>>> df.info()
<class 'pandas.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  str
 1   column_2  1000000 non-null  str
 2   column_3  1000000 non-null  str
dtypes: str(3)
memory usage: 25.7 MB

>>> df.info(memory_usage="deep")
<class 'pandas.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  str
 1   column_2  1000000 non-null  str
 2   column_3  1000000 non-null  str
dtypes: str(3)
memory usage: 25.7 MB

insert(loc: int, column: Hashable, value: object, allow_duplicates: bool | Literal[_NoDefault.no_default] = <no_default>) → None

Insert column into DataFrame at specified location.

Raises a ValueError if column is already contained in the DataFrame, unless allow_duplicates is set to True.

Parameters:

loc (int) – Insertion index. Must verify 0 <= loc <= len(columns).
column (str, number, or hashable object) – Label of the inserted column.
value (Scalar, Series, or array-like) – Content of the inserted column.
allow_duplicates (bool, optional, default lib.no_default) – Allow duplicate column labels to be created.

See also

Index.insert: Insert new item by index.

Examples

>>> df = pd.DataFrame({"col1": [1, 2], "col2": [3, 4]})
>>> df
   col1  col2
0     1     3
1     2     4
>>> df.insert(1, "newcol", [99, 99])
>>> df
   col1  newcol  col2
0     1      99     3
1     2      99     4
>>> df.insert(0, "col1", [100, 100], allow_duplicates=True)
>>> df
   col1  col1  newcol  col2
0   100     1      99     3
1   100     2      99     4

Notice that pandas uses index alignment in case of value from type Series:

>>> df.insert(0, "col0", pd.Series([5, 6], index=[1, 2]))
>>> df
   col0  col1  col1  newcol  col2
0   NaN   100     1      99     3
1   5.0   100     2      99     4

interpolate(method: Literal['linear', 'time', 'index', 'values', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric', 'polynomial', 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima', 'cubicspline', 'from_derivatives'] = 'linear', *, axis: int | Literal['index', 'columns', 'rows'] = 0, limit: int | None = None, inplace: bool = False, limit_direction: Literal['forward', 'backward', 'both'] | None = None, limit_area: Literal['inside', 'outside'] | None = None, **kwargs) → Self

Fill NaN values using an interpolation method.

Please note that only method='linear' is supported for DataFrame/Series with a MultiIndex.

Parameters:

method (str, default 'linear') –
Interpolation technique to use. One of:
- ’linear’: Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes.
- ’time’: Works on daily and higher resolution data to interpolate given length of interval. This interpolates values based on time interval between observations.
- ’index’: The interpolation uses the numerical values of the DataFrame’s index to linearly calculate missing values.
- ’values’: Interpolation based on the numerical values in the DataFrame, treating them as equally spaced along the index.
- ’nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘barycentric’, ‘polynomial’: Passed to scipy.interpolate.interp1d, whereas ‘spline’ is passed to scipy.interpolate.UnivariateSpline. These methods use the numerical values of the index. Both ‘polynomial’ and ‘spline’ require that you also specify an order (int), e.g. df.interpolate(method='polynomial', order=5). Note that, slinear method in Pandas refers to the Scipy first order spline instead of Pandas first order spline.
- ’krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’, ‘akima’, ‘cubicspline’: Wrappers around the SciPy interpolation methods of similar names. See Notes.
- ’from_derivatives’: Refers to scipy.interpolate.BPoly.from_derivatives.
axis ({0 or 'index', 1 or 'columns', None}, default None) – Axis to interpolate along. For Series this parameter is unused and defaults to 0.
limit (int, optional) – Maximum number of consecutive NaNs to fill. Must be greater than 0.
inplace (bool, default False) – Update the data in place if possible.
limit_direction ({'forward', 'backward', 'both'}, optional, default 'forward') – Consecutive NaNs will be filled in this direction.
limit_area ({None, ‘inside’, ‘outside’}, default None) –
If limit is specified, consecutive NaNs will be filled with this restriction.
- None: No fill restriction.
- ’inside’: Only fill NaNs surrounded by valid values (interpolate).
- ’outside’: Only fill NaNs outside valid values (extrapolate).
**kwargs (optional) – Keyword arguments to pass on to the interpolating function.

Returns:

Returns the same object type as the caller, interpolated at some or all NaN values.

Return type:

Series or DataFrame

See also

fillna: Fill missing values using different methods.
scipy.interpolate.Akima1DInterpolator: Piecewise cubic polynomials (Akima interpolator).
scipy.interpolate.BPoly.from_derivatives: Piecewise polynomial in the Bernstein basis.
scipy.interpolate.interp1d: Interpolate a 1-D function.
scipy.interpolate.KroghInterpolator: Interpolate polynomial (Krogh interpolator).
scipy.interpolate.PchipInterpolator: PCHIP 1-d monotonic cubic interpolation.
scipy.interpolate.CubicSpline: Cubic spline data interpolator.

Notes

The ‘krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’ and ‘akima’ methods are wrappers around the respective SciPy implementations of similar names. These use the actual numerical values of the index. For more information on their behavior, see the SciPy documentation.

Examples

Filling in NaN in a Series via linear interpolation.

>>> s = pd.Series([0, 1, np.nan, 3])
>>> s
0    0.0
1    1.0
2    NaN
3    3.0
dtype: float64
>>> s.interpolate()
0    0.0
1    1.0
2    2.0
3    3.0
dtype: float64

Filling in NaN in a Series via polynomial interpolation or splines: Both ‘polynomial’ and ‘spline’ methods require that you also specify an order (int).

>>> s = pd.Series([0, 2, np.nan, 8])
>>> s.interpolate(method="polynomial", order=2)
0    0.000000
1    2.000000
2    4.666667
3    8.000000
dtype: float64

Fill the DataFrame forward (that is, going down) along each column using linear interpolation.

Note how the last entry in column ‘a’ is interpolated differently, because there is no entry after it to use for interpolation. Note how the first entry in column ‘b’ remains NaN, because there is no entry before it to use for interpolation.

>>> df = pd.DataFrame(
...     [
...         (0.0, np.nan, -1.0, 1.0),
...         (np.nan, 2.0, np.nan, np.nan),
...         (2.0, 3.0, np.nan, 9.0),
...         (np.nan, 4.0, -4.0, 16.0),
...     ],
...     columns=list("abcd"),
... )
>>> df
     a    b    c     d
0  0.0  NaN -1.0   1.0
1  NaN  2.0  NaN   NaN
2  2.0  3.0  NaN   9.0
3  NaN  4.0 -4.0  16.0
>>> df.interpolate(method="linear", limit_direction="forward", axis=0)
     a    b    c     d
0  0.0  NaN -1.0   1.0
1  1.0  2.0 -2.0   5.0
2  2.0  3.0 -3.0   9.0
3  2.0  4.0 -4.0  16.0

Using polynomial interpolation.

>>> df["d"].interpolate(method="polynomial", order=2)
0     1.0
1     4.0
2     9.0
3    16.0
Name: d, dtype: float64

isetitem(loc, value) → None

Set the given value in the column with position loc.

This is a positional analogue to __setitem__.

Parameters:

loc (int or sequence of ints) – Index position for the column.
value (scalar or arraylike) – Value(s) for the column.

See also

DataFrame.iloc: Purely integer-location based indexing for selection by position.

Notes

frame.isetitem(loc, value) is an in-place method as it will modify the DataFrame in place (not returning a new object). In contrast to frame.iloc[:, i] = value which will try to update the existing values in place, frame.isetitem(loc, value) will not update the values of the column itself in place, it will instead insert a new array.

In cases where frame.columns is unique, this is equivalent to frame[frame.columns[i]] = value.

Examples

>>> df = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
>>> df.isetitem(1, [5, 6])
>>> df
      A  B
0     1  5
1     2  6

isin(values: Series | DataFrame | Sequence | Mapping) → DataFrame

Whether each element in the DataFrame is contained in values.

Parameters:: values (iterable, Series, DataFrame or dict) – The result will only be true at a location if all the labels match. If values is a Series, that’s the index. If values is a dict, the keys must be the column names, which must match. If values is a DataFrame, then both the index and column labels must match.
Returns:: DataFrame of booleans showing whether each element in the DataFrame is contained in values.
Return type:: DataFrame

See also

DataFrame.eq: Equality test for DataFrame.
Series.isin: Equivalent method on Series.
Series.str.contains: Test if pattern or regex is contained within a string of a Series or Index.

Notes

__iter__ is used (and not __contains__) to iterate over values when checking if it contains the elements in DataFrame.

Examples

>>> df = pd.DataFrame(
...     {"num_legs": [2, 4], "num_wings": [2, 0]}, index=["falcon", "dog"]
... )
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0

When values is a list check whether every value in the DataFrame is present in the list (which animals have 0 or 2 legs or wings)

>>> df.isin([0, 2])
        num_legs  num_wings
falcon      True       True
dog        False       True

To check if values is not in the DataFrame, use the ~ operator:

>>> ~df.isin([0, 2])
        num_legs  num_wings
falcon     False      False
dog         True      False

When values is a dict, we can pass values to check for each column separately:

>>> df.isin({"num_wings": [0, 3]})
        num_legs  num_wings
falcon     False      False
dog        False       True

When values is a Series or DataFrame the index and column must match. Note that ‘falcon’ does not match based on the number of legs in other.

>>> other = pd.DataFrame(
...     {"num_legs": [8, 3], "num_wings": [0, 2]}, index=["spider", "falcon"]
... )
>>> df.isin(other)
        num_legs  num_wings
falcon     False       True
dog        False      False

isna() → DataFrame

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values.

Returns:: Mask of bool values for each element in Series/DataFrame that indicates whether an element is an NA value.
Return type:: Series/DataFrame

See also

Series.isnull: Alias of isna.
DataFrame.isnull: Alias of isna.
Series.notna: Boolean inverse of isna.
DataFrame.notna: Boolean inverse of isna.
Series.dropna: Omit axes labels with missing values.
DataFrame.dropna: Omit axes labels with missing values.
isna: Top-level isna.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame(
...     dict(
...         age=[5, 6, np.nan],
...         born=[
...             pd.NaT,
...             pd.Timestamp("1939-05-27"),
...             pd.Timestamp("1940-04-25"),
...         ],
...         name=["Alfred", "Batman", ""],
...         toy=[None, "Batmobile", "Joker"],
...     )
... )
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred        NaN
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.isna()
0    False
1    False
2     True
dtype: bool

isnull() → DataFrame

DataFrame.isnull is an alias for DataFrame.isna.

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values.

Returns:: Mask of bool values for each element in Series/DataFrame that indicates whether an element is an NA value.
Return type:: Series/DataFrame

See also

Series.isnull: Alias of isna.
DataFrame.isnull: Alias of isna.
Series.notna: Boolean inverse of isna.
DataFrame.notna: Boolean inverse of isna.
Series.dropna: Omit axes labels with missing values.
DataFrame.dropna: Omit axes labels with missing values.
isna: Top-level isna.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame(
...     dict(
...         age=[5, 6, np.nan],
...         born=[
...             pd.NaT,
...             pd.Timestamp("1939-05-27"),
...             pd.Timestamp("1940-04-25"),
...         ],
...         name=["Alfred", "Batman", ""],
...         toy=[None, "Batmobile", "Joker"],
...     )
... )
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred        NaN
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.isna()
0    False
1    False
2     True
dtype: bool

items() → Iterable[tuple[Hashable, Series]]

Iterate over (column name, Series) pairs.

Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.

Yields:

label (object) – The column names for the DataFrame being iterated over.
content (Series) – The column entries belonging to each label, as a Series.

See also

DataFrame.iterrows: Iterate over DataFrame rows as (index, Series) pairs.
DataFrame.itertuples: Iterate over DataFrame rows as namedtuples of the values.

Examples

>>> df = pd.DataFrame(
...     {
...         "species": ["bear", "bear", "marsupial"],
...         "population": [1864, 22000, 80000],
...     },
...     index=["panda", "polar", "koala"],
... )
>>> df
        species   population
panda   bear      1864
polar   bear      22000
koala   marsupial 80000
>>> for label, content in df.items():
...     print(f"label: {label}")
...     print(f"content: {content}", sep="\n")
label: species
content:
panda         bear
polar         bear
koala    marsupial
Name: species, dtype: str
label: population
content:
panda     1864
polar    22000
koala    80000
Name: population, dtype: int64

iterrows() → Iterable[tuple[Hashable, Series]]

Iterate over DataFrame rows as (index, Series) pairs.

Yields:

index (label or tuple of label) – The index of the row. A tuple for a MultiIndex.
data (Series) – The data of the row as a Series.

See also

DataFrame.itertuples: Iterate over DataFrame rows as namedtuples of the values.
DataFrame.items: Iterate over (column name, Series) pairs.

Notes

Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames).

To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally faster than iterrows.
You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.

Examples

>>> df = pd.DataFrame([[1, 1.5]], columns=["int", "float"])
>>> row = next(df.iterrows())[1]
>>> row
int      1.0
float    1.5
Name: 0, dtype: float64
>>> print(row["int"].dtype)
float64
>>> print(df["int"].dtype)
int64

itertuples(index: bool = True, name: str | None = 'Pandas') → Iterable[tuple[Any, ...]]

Iterate over DataFrame rows as namedtuples.

Parameters:

index (bool, default True) – If True, return the index as the first element of the tuple.
name (str or None, default "Pandas") – The name of the returned namedtuples or None to return regular tuples.

Returns:

An object to iterate over namedtuples for each row in the DataFrame with the first field possibly being the index and following fields being the column values.

Return type:

iterator

See also

DataFrame.iterrows: Iterate over DataFrame rows as (index, Series) pairs.
DataFrame.items: Iterate over (column name, Series) pairs.

Notes

The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore.

Examples

>>> df = pd.DataFrame(
...     {"num_legs": [4, 2], "num_wings": [0, 2]}, index=["dog", "hawk"]
... )
>>> df
      num_legs  num_wings
dog          4          0
hawk         2          2
>>> for row in df.itertuples():
...     print(row)
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)

By setting the index parameter to False we can remove the index as the first element of the tuple:

>>> for row in df.itertuples(index=False):
...     print(row)
Pandas(num_legs=4, num_wings=0)
Pandas(num_legs=2, num_wings=2)

With the name parameter set we set a custom name for the yielded namedtuples:

>>> for row in df.itertuples(name="Animal"):
...     print(row)
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2)

Join columns of another DataFrame.

Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.

Parameters:

other (DataFrame, Series, or a list containing any combination of them) – Index should be similar to one of the columns in this one. If a Series is passed, its name attribute must be set, and that will be used as the column name in the resulting joined DataFrame.
on (str, list of str, or array-like, optional) – Column or index level name(s) in the caller to join on the index in other, otherwise joins index-on-index. If multiple values given, the other DataFrame must have a MultiIndex. Can pass an array as the join key if it is not already contained in the calling DataFrame. Like an Excel VLOOKUP operation.
how ({'left', 'right', 'outer', 'inner', 'cross', 'left_anti', 'right_anti'},) –
default ‘left’ How to handle the operation of the two objects.
- left: use calling frame’s index (or column if on is specified)
- right: use other’s index.
- outer: form union of calling frame’s index (or column if on is specified) with other’s index, and sort it lexicographically.
- inner: form intersection of calling frame’s index (or column if on is specified) with other’s index, preserving the order of the calling’s one.
- cross: creates the cartesian product from both frames, preserves the order of the left keys.
- left_anti: use set difference of calling frame’s index and other’s index.
- right_anti: use set difference of other’s index and calling frame’s index.
lsuffix (str, default '') – Suffix to use from left frame’s overlapping columns.
rsuffix (str, default '') – Suffix to use from right frame’s overlapping columns.
sort (bool, default False) – Order result DataFrame lexicographically by the join key. If False, the order of the join key depends on the join type (how keyword).
validate (str, optional) –
If specified, checks if join is of specified type.
- ”one_to_one” or “1:1”: check if join keys are unique in both left and right datasets.
- ”one_to_many” or “1:m”: check if join keys are unique in left dataset.
- ”many_to_one” or “m:1”: check if join keys are unique in right dataset.
- ”many_to_many” or “m:m”: allowed, but does not result in checks.

Returns:

A dataframe containing columns from both the caller and other.

Return type:

DataFrame

See also

DataFrame.merge: For column(s)-on-column(s) operations.

Notes

Parameters on, lsuffix, and rsuffix are not supported when passing a list of DataFrame objects.

Examples

>>> df = pd.DataFrame(
...     {
...         "key": ["K0", "K1", "K2", "K3", "K4", "K5"],
...         "A": ["A0", "A1", "A2", "A3", "A4", "A5"],
...     }
... )

>>> df
  key   A
K0  A0
K1  A1
K2  A2
K3  A3
K4  A4
K5  A5

>>> other = pd.DataFrame({"key": ["K0", "K1", "K2"], "B": ["B0", "B1", "B2"]})

>>> other
  key   B
0  K0  B0
1  K1  B1
2  K2  B2

Join DataFrames using their indexes.

>>> df.join(other, lsuffix="_caller", rsuffix="_other")
  key_caller   A key_other    B
       K0  A0        K0   B0
       K1  A1        K1   B1
       K2  A2        K2   B2
       K3  A3       NaN  NaN
       K4  A4       NaN  NaN
       K5  A5       NaN  NaN

If we want to join using the key columns, we need to set key to be the index in both df and other. The joined DataFrame will have key as its index.

>>> df.set_index("key").join(other.set_index("key"))
      A    B
key
K0   A0   B0
K1   A1   B1
K2   A2   B2
K3   A3  NaN
K4   A4  NaN
K5   A5  NaN

Another option to join using the key columns is to use the on parameter. DataFrame.join always uses other’s index but we can use any column in df. This method preserves the original DataFrame’s index in the result.

>>> df.join(other.set_index("key"), on="key")
  key   A    B
K0  A0   B0
K1  A1   B1
K2  A2   B2
K3  A3  NaN
K4  A4  NaN
K5  A5  NaN

Using non-unique key values shows how they are matched.

>>> df = pd.DataFrame(
...     {
...         "key": ["K0", "K1", "K1", "K3", "K0", "K1"],
...         "A": ["A0", "A1", "A2", "A3", "A4", "A5"],
...     }
... )

>>> df
  key   A
K0  A0
K1  A1
K1  A2
K3  A3
K0  A4
K1  A5

>>> df.join(other.set_index("key"), on="key", validate="m:1")
  key   A    B
K0  A0   B0
K1  A1   B1
K1  A2   B1
K3  A3  NaN
K0  A4   B0
K1  A5   B1

keys() → Index

Get the ‘info axis’ (see Indexing for more).

This is index for Series, columns for DataFrame.

Returns:: Info axis.
Return type:: Index

See also

DataFrame.index: The index (row labels) of the DataFrame.
DataFrame.columns: The column labels of the DataFrame.

Examples

>>> d = pd.DataFrame(
...     data={"A": [1, 2, 3], "B": [0, 4, 8]}, index=["a", "b", "c"]
... )
>>> d
   A  B
a  1  0
b  2  4
c  3  8
>>> d.keys()
Index(['A', 'B'], dtype='str')

kurt(*, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs) → Series | Any

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Parameters:

axis ({index (0), columns (1)}) –
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.
skipna (bool, default True) – Exclude NA/null values when computing the result.
numeric_only (bool, default False) – Include only float, int, boolean columns.
**kwargs – Additional keyword arguments to be passed to the function.

Returns:

Unbiased kurtosis over requested axis.

Return type:

Series or scalar

See also

Dataframe.kurtosis: Returns unbiased kurtosis over requested axis.

Examples

>>> s = pd.Series([1, 2, 2, 3], index=["cat", "dog", "dog", "mouse"])
>>> s
cat    1
dog    2
dog    2
mouse  3
dtype: int64
>>> s.kurt()
1.5

With a DataFrame

>>> df = pd.DataFrame(
...     {"a": [1, 2, 2, 3], "b": [3, 4, 4, 4]},
...     index=["cat", "dog", "dog", "mouse"],
... )
>>> df
       a   b
  cat  1   3
  dog  2   4
  dog  2   4
mouse  3   4
>>> df.kurt()
a   1.5
b   4.0
dtype: float64

With axis=None

>>> df.kurt(axis=None)
-0.9886927196984727

Using axis=1

>>> df = pd.DataFrame(
...     {"a": [1, 2], "b": [3, 4], "c": [3, 4], "d": [1, 2]},
...     index=["cat", "dog"],
... )
>>> df.kurt(axis=1)
cat   -6.0
dog   -6.0
dtype: float64

kurtosis(*, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs) → Series | Any

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Parameters:

axis ({index (0), columns (1)}) –
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.
skipna (bool, default True) – Exclude NA/null values when computing the result.
numeric_only (bool, default False) – Include only float, int, boolean columns.
**kwargs – Additional keyword arguments to be passed to the function.

Returns:

Unbiased kurtosis over requested axis.

Return type:

Series or scalar

See also

Dataframe.kurtosis: Returns unbiased kurtosis over requested axis.

Examples

>>> s = pd.Series([1, 2, 2, 3], index=["cat", "dog", "dog", "mouse"])
>>> s
cat    1
dog    2
dog    2
mouse  3
dtype: int64
>>> s.kurt()
1.5

With a DataFrame

>>> df = pd.DataFrame(
...     {"a": [1, 2, 2, 3], "b": [3, 4, 4, 4]},
...     index=["cat", "dog", "dog", "mouse"],
... )
>>> df
       a   b
  cat  1   3
  dog  2   4
  dog  2   4
mouse  3   4
>>> df.kurt()
a   1.5
b   4.0
dtype: float64

With axis=None

>>> df.kurt(axis=None)
-0.9886927196984727

Using axis=1

>>> df = pd.DataFrame(
...     {"a": [1, 2], "b": [3, 4], "c": [3, 4], "d": [1, 2]},
...     index=["cat", "dog"],
... )
>>> df.kurt(axis=1)
cat   -6.0
dog   -6.0
dtype: float64

last_valid_index() → Hashable

Return index for last non-missing value or None, if no value is found.

See the User Guide for more information on which values are considered missing.

Returns:: Index of last non-missing value.
Return type:: type of index

See also

DataFrame.first_valid_index: Return index for first non-NA value or None, if no non-NA value is found.
Series.first_valid_index: Return index for first non-NA value or None, if no non-NA value is found.
DataFrame.isna: Detect missing values.

Examples

For Series:

>>> s = pd.Series([None, 3, 4])
>>> s.first_valid_index()
1
>>> s.last_valid_index()
2

>>> s = pd.Series([None, None])
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None

If all elements in Series are NA/null, returns None.

>>> s = pd.Series()
>>> print(s.first_valid_index())
None
>>> print(s.last_valid_index())
None

If Series is empty, returns None.

For DataFrame:

>>> df = pd.DataFrame({"A": [None, None, 2], "B": [None, 3, 4]})
>>> df
     A      B
0  NaN    NaN
1  NaN    3.0
2  2.0    4.0
>>> df.first_valid_index()
1
>>> df.last_valid_index()
2

>>> df = pd.DataFrame({"A": [None, None, None], "B": [None, None, None]})
>>> df
     A      B
0  None   None
1  None   None
2  None   None
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None

If all elements in DataFrame are NA/null, returns None.

>>> df = pd.DataFrame()
>>> df
Empty DataFrame
Columns: []
Index: []
>>> print(df.first_valid_index())
None
>>> print(df.last_valid_index())
None

If DataFrame is empty, returns None.

le(other, axis: Axis = 'columns', level=None) → DataFrame

Get Greater than or equal to of dataframe and other, element-wise (binary operator le).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters:

other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns:

Result of the comparison.

Return type:

DataFrame of bool

See also

DataFrame.eq: Compare DataFrames for equality elementwise.
DataFrame.ne: Compare DataFrames for inequality elementwise.
DataFrame.le: Compare DataFrames for less than inequality or equality elementwise.
DataFrame.lt: Compare DataFrames for strictly less than inequality elementwise.
DataFrame.ge: Compare DataFrames for greater than inequality or equality elementwise.
DataFrame.gt: Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

property loc: _LocIndexer

Access a group of rows and columns by label(s) or a boolean array.

.loc[] is primarily label based, but may also be used with a boolean array.

Allowed inputs are:

A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer position along the index).
A list or array of labels, e.g. ['a', 'b', 'c'].
A slice object with labels, e.g. 'a':'f'.

Warning

Note that contrary to usual python slices, both the start and the stop are included
A boolean array of the same length as the axis being sliced, e.g. [True, False, True].
An alignable boolean Series. The index of the key will be aligned before masking.
An alignable Index. The Index of the returned selection will be the input.
A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above)

See more at Selection by Label.

Raises:

KeyError – If any items are not found.
IndexingError – If an indexed key is passed and its index is unalignable to the frame index.

See also

DataFrame.at: Access a single value for a row/column label pair.
DataFrame.iloc: Access group of rows and columns by integer position(s).
DataFrame.xs: Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.
Series.loc: Access group of values using labels.

Examples

Getting values

>>> df = pd.DataFrame(
...     [[1, 2], [4, 5], [7, 8]],
...     index=["cobra", "viper", "sidewinder"],
...     columns=["max_speed", "shield"],
... )
>>> df
            max_speed  shield
cobra               1       2
viper               4       5
sidewinder          7       8

Single label. Note this returns the row as a Series.

>>> df.loc["viper"]
max_speed    4
shield       5
Name: viper, dtype: int64

List of labels. Note using [[]] returns a DataFrame.

>>> df.loc[["viper", "sidewinder"]]
            max_speed  shield
viper               4       5
sidewinder          7       8

Single label for row and column

>>> df.loc["cobra", "shield"]
np.int64(2)

Slice with labels for row and single label for column. As mentioned above, note that both the start and stop of the slice are included.

>>> df.loc["cobra":"viper", "max_speed"]
cobra    1
viper    4
Name: max_speed, dtype: int64

Boolean list with the same length as the row axis

>>> df.loc[[False, False, True]]
            max_speed  shield
sidewinder          7       8

Alignable boolean Series:

>>> df.loc[
...     pd.Series([False, True, False], index=["viper", "sidewinder", "cobra"])
... ]
                     max_speed  shield
sidewinder          7       8

Index (same behavior as df.reindex)

>>> df.loc[pd.Index(["cobra", "viper"], name="foo")]
       max_speed  shield
foo
cobra          1       2
viper          4       5

Conditional that returns a boolean Series

>>> df.loc[df["shield"] > 6]
            max_speed  shield
sidewinder          7       8

Conditional that returns a boolean Series with column labels specified

>>> df.loc[df["shield"] > 6, ["max_speed"]]
            max_speed
sidewinder          7

Multiple conditional using & that returns a boolean Series

>>> df.loc[(df["max_speed"] > 1) & (df["shield"] < 8)]
            max_speed  shield
viper          4       5

Multiple conditional using | that returns a boolean Series

>>> df.loc[(df["max_speed"] > 4) | (df["shield"] < 5)]
            max_speed  shield
cobra               1       2
sidewinder          7       8

Please ensure that each condition is wrapped in parentheses (). See the user guide for more details and explanations of Boolean indexing.

Note

If you find yourself using 3 or more conditionals in .loc[], consider using advanced indexing.

See below for using .loc[] on MultiIndex DataFrames.

Callable that returns a boolean Series

>>> df.loc[lambda df: df["shield"] == 8]
            max_speed  shield
sidewinder          7       8

Setting values

Set value for all items matching the list of labels

>>> df.loc[["viper", "sidewinder"], ["shield"]] = 50
>>> df
            max_speed  shield
cobra               1       2
viper               4      50
sidewinder          7      50

Set value for an entire row

>>> df.loc["cobra"] = 10
>>> df
            max_speed  shield
cobra              10      10
viper               4      50
sidewinder          7      50

Set value for an entire column

>>> df.loc[:, "max_speed"] = 30
>>> df
            max_speed  shield
cobra              30      10
viper              30      50
sidewinder         30      50

Set value for rows matching callable condition

>>> df.loc[df["shield"] > 35] = 0
>>> df
            max_speed  shield
cobra              30      10
viper               0       0
sidewinder          0       0

Add value matching location

>>> df.loc["viper", "shield"] += 5
>>> df
            max_speed  shield
cobra              30      10
viper               0       5
sidewinder          0       0

Setting using a Series or a DataFrame sets the values matching the index labels, not the index positions.

>>> shuffled_df = df.loc[["viper", "cobra", "sidewinder"]]
>>> df.loc[:] += shuffled_df
>>> df
            max_speed  shield
cobra              60      20
viper               0      10
sidewinder          0       0

Getting values on a DataFrame with an index that has integer labels

Another example using integers for the index

>>> df = pd.DataFrame(
...     [[1, 2], [4, 5], [7, 8]],
...     index=[7, 8, 9],
...     columns=["max_speed", "shield"],
... )
>>> df
   max_speed  shield
7          1       2
8          4       5
9          7       8

Slice with integer labels for rows. As mentioned above, note that both the start and stop of the slice are included.

>>> df.loc[7:9]
   max_speed  shield
7          1       2
8          4       5
9          7       8

Getting values with a MultiIndex

A number of examples using a DataFrame with a MultiIndex

>>> tuples = [
...     ("cobra", "mark i"),
...     ("cobra", "mark ii"),
...     ("sidewinder", "mark i"),
...     ("sidewinder", "mark ii"),
...     ("viper", "mark ii"),
...     ("viper", "mark iii"),
... ]
>>> index = pd.MultiIndex.from_tuples(tuples)
>>> values = [[12, 2], [0, 4], [10, 20], [1, 4], [7, 1], [16, 36]]
>>> df = pd.DataFrame(values, columns=["max_speed", "shield"], index=index)
>>> df
                     max_speed  shield
cobra      mark i           12       2
           mark ii           0       4
sidewinder mark i           10      20
           mark ii           1       4
viper      mark ii           7       1
           mark iii         16      36

Single label. Note this returns a DataFrame with a single index.

>>> df.loc["cobra"]
         max_speed  shield
mark i          12       2
mark ii          0       4

Single index tuple. Note this returns a Series.

>>> df.loc[("cobra", "mark ii")]
max_speed    0
shield       4
Name: (cobra, mark ii), dtype: int64

Single label for row and column. Similar to passing in a tuple, this returns a Series.

>>> df.loc["cobra", "mark i"]
max_speed    12
shield        2
Name: (cobra, mark i), dtype: int64

Single tuple. Note using [[]] returns a DataFrame.

>>> df.loc[[("cobra", "mark ii")]]
               max_speed  shield
cobra mark ii          0       4

Single tuple for the index with a single label for the column

>>> df.loc[("cobra", "mark i"), "shield"]
np.int64(2)

Slice from index tuple to single label

>>> df.loc[("cobra", "mark i") : "viper"]
                     max_speed  shield
cobra      mark i           12       2
           mark ii           0       4
sidewinder mark i           10      20
           mark ii           1       4
viper      mark ii           7       1
           mark iii         16      36

Slice from index tuple to index tuple

>>> df.loc[("cobra", "mark i") : ("viper", "mark ii")]
                    max_speed  shield
cobra      mark i          12       2
           mark ii          0       4
sidewinder mark i          10      20
           mark ii          1       4
viper      mark ii          7       1

Please see the user guide for more details and explanations of advanced indexing.

Assignment with Series

When assigning a Series to .loc[row_indexer, col_indexer], pandas aligns the Series by index labels, not by order or position.

Series assignment with .loc and index alignment:

>>> df = pd.DataFrame({"A": [1, 2, 3]}, index=[0, 1, 2])
>>> s = pd.Series([10, 20], index=[1, 0])  # Note reversed order
>>> df.loc[:, "B"] = s  # Aligns by index, not order
>>> df
   A   B
0  1  20.0
1  2  10.0
2  3 NaN

lt(other, axis: Axis = 'columns', level=None) → DataFrame

Get Greater than of dataframe and other, element-wise (binary operator lt).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters:

other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns:

Result of the comparison.

Return type:

DataFrame of bool

See also

DataFrame.eq: Compare DataFrames for equality elementwise.
DataFrame.ne: Compare DataFrames for inequality elementwise.
DataFrame.le: Compare DataFrames for less than inequality or equality elementwise.
DataFrame.lt: Compare DataFrames for strictly less than inequality elementwise.
DataFrame.ge: Compare DataFrames for greater than inequality or equality elementwise.
DataFrame.gt: Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},
...                      index=['A', 'B', 'C', 'D'])
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

map(func: PythonFuncType, na_action: Literal['ignore'] | None = None, **kwargs) → DataFrame

Apply a function to a Dataframe elementwise.

Added in version 2.1.0: DataFrame.applymap was deprecated and renamed to DataFrame.map.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Parameters:

func (callable) – Python function, returns a single value from a single value.
na_action ({None, 'ignore'}, default None) – If ‘ignore’, propagate NaN values, without passing them to func.
**kwargs – Additional keyword arguments to pass as keywords arguments to func.

Returns:

Transformed DataFrame.

Return type:

DataFrame

See also

DataFrame.apply: Apply a function along input axis of DataFrame.
DataFrame.replace: Replace values given in to_replace with value.
Series.map: Apply a function elementwise on a Series.

Examples

>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
>>> df
       0      1
0  1.000  2.120
1  3.356  4.567

>>> df.map(lambda x: len(str(x)))
1
3  4
5  5

Like Series.map, NA values can be ignored:

>>> df_copy = df.copy()
>>> df_copy.iloc[0, 0] = pd.NA
>>> df_copy.map(lambda x: len(str(x)), na_action="ignore")
     0  1
0  NaN  4
1  5.0  5

It is also possible to use map with functions that are not lambda functions:

>>> df.map(round, ndigits=1)
  1
1.0  2.1
3.4  4.6

Note that a vectorized version of func often exists, which will be much faster. You could square each number elementwise.

>>> df.map(lambda x: x**2)
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

But it’s better to avoid map in that case.

>>> df**2
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

mask(cond, other=<no_default>, *, inplace: bool = False, axis: int | ~typing.Literal['index', 'columns', 'rows'] | None=None, level: Hashable | None = None) → Self

Replace values where the condition is True.

Parameters:

cond (bool Series/DataFrame, array-like, or callable) – Where cond is False, keep the original value. Where True, replace with corresponding value from other. If cond is callable, it is computed on the Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn’t check it).
other (scalar, Series/DataFrame, or callable) – Entries where cond is True are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn’t check it). If not specified, entries will be filled with the corresponding NULL value (np.nan for numpy dtypes, pd.NA for extension dtypes).
inplace (bool, default False) – Whether to perform the operation in place on the data.
axis (int, default None) – Alignment axis if needed. For Series this parameter is unused and defaults to 0.
level (int, default None) – Alignment level if needed.

Returns:

When applied to a Series, the function will return a Series, and when applied to a DataFrame, it will return a DataFrame.

Return type:

Series or DataFrame

See also

DataFrame.where(): Return an object of same shape as caller.
Series.where(): Return an object of same shape as caller.

Notes

The mask method is an application of the if-then idiom. For each element in the caller, if cond is False the element is used; otherwise the corresponding element from other is used. If the axis of other does not align with axis of cond Series/DataFrame, the values of cond on misaligned index positions will be filled with True.

The signature for Series.where() or DataFrame.where() differs from numpy.where(). Roughly df1.where(m, df2) is equivalent to np.where(m, df1, df2).

For further details and examples see the mask documentation in indexing.

The dtype of the object takes precedence. The fill value is casted to the object’s dtype, if this can be done losslessly.

Examples

>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0    NaN
1    1.0
2    2.0
3    3.0
4    4.0
dtype: float64
>>> s.mask(s > 0)
0    0.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

>>> s = pd.Series(range(5))
>>> t = pd.Series([True, False])
>>> s.where(t, 99)
0     0
1    99
2    99
3    99
4    99
dtype: int64
>>> s.mask(t, 99)
0    99
1     1
2    99
3    99
4    99
dtype: int64

>>> s.where(s > 1, 10)
  10
  10
  2
  3
  4
dtype: int64
>>> s.mask(s > 1, 10)
   0
   1
  10
  10
  10
dtype: int64

>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=["A", "B"])
>>> df
   A  B
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9
>>> m = df % 3 == 0
>>> df.where(m, -df)
   A  B
0  0 -1
1 -2  3
2 -4 -5
3  6 -7
4 -8  9
>>> df.where(m, -df) == np.where(m, df, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
>>> df.where(m, -df) == df.mask(~m, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True

max(*, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs) → Series | Any

Return the maximum of the values over the requested axis.

If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax.

Parameters:

axis ({index (0), columns (1)}) –
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.
skipna (bool, default True) – Exclude NA/null values when computing the result.
numeric_only (bool, default False) – Include only float, int, boolean columns.
**kwargs – Additional keyword arguments to be passed to the function.

Returns:

Value containing the calculation referenced in the description.

Return type:

Series or scalar

See also

Series.sum: Return the sum.
Series.min: Return the minimum.
Series.max: Return the maximum.
Series.idxmin: Return the index of the minimum.
Series.idxmax: Return the index of the maximum.
DataFrame.sum: Return the sum over the requested axis.
DataFrame.min: Return the minimum over the requested axis.
DataFrame.max: Return the maximum over the requested axis.
DataFrame.idxmin: Return the index of the minimum over the requested axis.
DataFrame.idxmax: Return the index of the maximum over the requested axis.

Examples

>>> idx = pd.MultiIndex.from_arrays(
...     [["warm", "warm", "cold", "cold"], ["dog", "falcon", "fish", "spider"]],
...     names=["blooded", "animal"],
... )
>>> s = pd.Series([4, 2, 0, 8], name="legs", index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.max()
8

mean(*, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs) → Series | Any

Return the mean of the values over the requested axis.

Parameters:

axis ({index (0), columns (1)}) –
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.
skipna (bool, default True) – Exclude NA/null values when computing the result.
numeric_only (bool, default False) – Include only float, int, boolean columns.
**kwargs – Additional keyword arguments to be passed to the function.

Returns:

Value containing the calculation referenced in the description.

Return type:

Series or scalar

See also

Series.sum: Return the sum.
Series.min: Return the minimum.
Series.max: Return the maximum.
Series.idxmin: Return the index of the minimum.
Series.idxmax: Return the index of the maximum.
DataFrame.sum: Return the sum over the requested axis.
DataFrame.min: Return the minimum over the requested axis.
DataFrame.max: Return the maximum over the requested axis.
DataFrame.idxmin: Return the index of the minimum over the requested axis.
DataFrame.idxmax: Return the index of the maximum over the requested axis.

Examples

>>> s = pd.Series([1, 2, 3])
>>> s.mean()
2.0

With a DataFrame

>>> df = pd.DataFrame({"a": [1, 2], "b": [2, 3]}, index=["tiger", "zebra"])
>>> df
       a   b
tiger  1   2
zebra  2   3
>>> df.mean()
a   1.5
b   2.5
dtype: float64

Using axis=1

>>> df.mean(axis=1)
tiger   1.5
zebra   2.5
dtype: float64

In this case, numeric_only should be set to True to avoid getting an error.

>>> df = pd.DataFrame({"a": [1, 2], "b": ["T", "Z"]}, index=["tiger", "zebra"])
>>> df.mean(numeric_only=True)
a   1.5
dtype: float64

median(*, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs) → Series | Any

Return the median of the values over the requested axis.

Parameters:

axis ({index (0), columns (1)}) –
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.
skipna (bool, default True) – Exclude NA/null values when computing the result.
numeric_only (bool, default False) – Include only float, int, boolean columns.
**kwargs – Additional keyword arguments to be passed to the function.

Returns:

Value containing the calculation referenced in the description.

Return type:

Series or scalar

See also

Series.sum: Return the sum.
Series.min: Return the minimum.
Series.max: Return the maximum.
Series.idxmin: Return the index of the minimum.
Series.idxmax: Return the index of the maximum.
DataFrame.sum: Return the sum over the requested axis.
DataFrame.min: Return the minimum over the requested axis.
DataFrame.max: Return the maximum over the requested axis.
DataFrame.idxmin: Return the index of the minimum over the requested axis.
DataFrame.idxmax: Return the index of the maximum over the requested axis.

Examples

>>> s = pd.Series([1, 2, 3])
>>> s.median()
2.0

With a DataFrame

>>> df = pd.DataFrame({"a": [1, 2], "b": [2, 3]}, index=["tiger", "zebra"])
>>> df
       a   b
tiger  1   2
zebra  2   3
>>> df.median()
a   1.5
b   2.5
dtype: float64

Using axis=1

>>> df.median(axis=1)
tiger   1.5
zebra   2.5
dtype: float64

In this case, numeric_only should be set to True to avoid getting an error.

>>> df = pd.DataFrame({"a": [1, 2], "b": ["T", "Z"]}, index=["tiger", "zebra"])
>>> df.median(numeric_only=True)
a   1.5
dtype: float64

melt(id_vars=None, value_vars=None, var_name=None, value_name: Hashable = 'value', col_level: Level | None = None, ignore_index: bool = True) → DataFrame

Unpivot DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.

Parameters:

id_vars (scalar, tuple, list, or ndarray, optional) – Column(s) to use as identifier variables.
value_vars (scalar, tuple, list, or ndarray, optional) – Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
var_name (scalar, default None) – Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.
value_name (scalar, default 'value') – Name to use for the ‘value’ column, can’t be an existing column label.
col_level (scalar, optional) – If columns are a MultiIndex then use this level to melt.
ignore_index (bool, default True) – If True, original index is ignored. If False, original index is retained. Index labels will be repeated as necessary.

Returns:

Unpivoted DataFrame.

Return type:

DataFrame

See also

melt: Identical method.
pivot_table: Create a spreadsheet-style pivot table as a DataFrame.
DataFrame.pivot: Return reshaped DataFrame organized by given index / column values.
DataFrame.explode: Explode a DataFrame from list-like columns to long format.

Notes

Reference the user guide for more examples.

Examples

>>> df = pd.DataFrame(
...     {
...         "A": {0: "a", 1: "b", 2: "c"},
...         "B": {0: 1, 1: 3, 2: 5},
...         "C": {0: 2, 1: 4, 2: 6},
...     }
... )
>>> df
A  B  C
0  a  1  2
1  b  3  4
2  c  5  6

>>> df.melt(id_vars=["A"], value_vars=["B"])
A variable  value
0  a        B      1
1  b        B      3
2  c        B      5

>>> df.melt(id_vars=["A"], value_vars=["B", "C"])
A variable  value
a        B      1
b        B      3
c        B      5
a        C      2
b        C      4
c        C      6

The names of ‘variable’ and ‘value’ columns can be customized:

>>> df.melt(
...     id_vars=["A"],
...     value_vars=["B"],
...     var_name="myVarname",
...     value_name="myValname",
... )
A myVarname  myValname
0  a         B          1
1  b         B          3
2  c         B          5

Original index values can be kept around:

>>> df.melt(id_vars=["A"], value_vars=["B", "C"], ignore_index=False)
A variable  value
a        B      1
b        B      3
c        B      5
a        C      2
b        C      4
c        C      6

If you have multi-index columns:

>>> df.columns = [list("ABC"), list("DEF")]
>>> df
A  B  C
D  E  F
0  a  1  2
1  b  3  4
2  c  5  6

>>> df.melt(col_level=0, id_vars=["A"], value_vars=["B"])
A variable  value
0  a        B      1
1  b        B      3
2  c        B      5

>>> df.melt(id_vars=[("A", "D")], value_vars=[("B", "E")])
(A, D) variable_0 variable_1  value
0      a          B          E      1
1      b          B          E      3
2      c          B          E      5

memory_usage(index: bool = True, deep: bool = False) → Series

Return the memory usage of each column in bytes.

The memory usage can optionally include the contribution of the index and elements of object dtype.

This value is displayed in DataFrame.info by default. This can be suppressed by setting pandas.options.display.memory_usage to False.

Parameters:

index (bool, default True) – Specifies whether to include the memory usage of the DataFrame’s index in returned Series. If index=True, the memory usage of the index is the first item in the output.
deep (bool, default False) – If True, introspect the data deeply by interrogating object dtypes for system-level memory consumption, and include it in the returned values.

Returns:

A Series whose index is the original column names and whose values is the memory usage of each column in bytes.

Return type:

Series

See also

numpy.ndarray.nbytes: Total bytes consumed by the elements of an ndarray.
Series.memory_usage: Bytes consumed by a Series.
Categorical: Memory-efficient array for string values with many repeated values.
DataFrame.info: Concise summary of a DataFrame.

Notes

See the Frequently Asked Questions for more details.

Examples

>>> dtypes = ["int64", "float64", "complex128", "object", "bool"]
>>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t)) for t in dtypes])
>>> df = pd.DataFrame(data)
>>> df.head()
   int64  float64            complex128  object  bool
0      1      1.0              1.0+0.0j       1  True
1      1      1.0              1.0+0.0j       1  True
2      1      1.0              1.0+0.0j       1  True
3      1      1.0              1.0+0.0j       1  True
4      1      1.0              1.0+0.0j       1  True

>>> df.memory_usage()
Index           132
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64

>>> df.memory_usage(index=False)
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64

The memory footprint of object dtype columns is ignored by default:

>>> df.memory_usage(deep=True)
Index            132
int64          40000
float64        40000
complex128     80000
object        180000
bool            5000
dtype: int64

Use a Categorical for efficient storage of an object-dtype column with many repeated values.

>>> df["object"].astype("category").memory_usage(deep=True)
5140

Merge DataFrame or named Series objects with a database-style join.

A named Series object is treated as a DataFrame with a single named column.

The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross merge, no column specifications to merge on are allowed.

Warning

If both key columns contain rows where the key is a null value, those rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results.

Parameters:

right (DataFrame or named Series) – Object to merge with.
how ({'left', 'right', 'outer', 'inner', 'cross', 'left_anti', 'right_anti'},) –
default ‘inner’ Type of merge to be performed.
- left: use only keys from left frame, similar to a SQL left outer join; preserve key order.
- right: use only keys from right frame, similar to a SQL right outer join; preserve key order.
- outer: use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically.
- inner: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys.
- cross: creates the cartesian product from both frames, preserves the order of the left keys.
- left_anti: use only keys from left frame that are not in right frame, similar to SQL left anti join; preserve key order.
  
  Added in version 3.0.
- right_anti: use only keys from right frame that are not in left frame, similar to SQL right anti join; preserve key order.
  
  Added in version 3.0.
on (Hashable or a sequence of the previous) – Column or index level names to join on. These must be found in both DataFrames. If on is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames.
left_on (Hashable or a sequence of the previous, or array-like) – Column or index level names to join on in the left DataFrame. Can also be an array or list of arrays of the length of the left DataFrame. These arrays are treated as if they are columns.
right_on (Hashable or a sequence of the previous, or array-like) – Column or index level names to join on in the right DataFrame. Can also be an array or list of arrays of the length of the right DataFrame. These arrays are treated as if they are columns.
left_index (bool, default False) – Use the index from the left DataFrame as the join key(s). If it is a MultiIndex, the number of keys in the other DataFrame (either the index or a number of columns) must match the number of levels.
right_index (bool, default False) – Use the index from the right DataFrame as the join key. Same caveats as left_index.
sort (bool, default False) – Sort the join keys lexicographically in the result DataFrame. If False, the order of the join keys depends on the join type (how keyword).
suffixes (list-like, default is ("_x", "_y")) – A length-2 sequence where each element is optionally a string indicating the suffix to add to overlapping column names in left and right respectively. Pass a value of None instead of a string to indicate that the column name from left or right should be left as-is, with no suffix. At least one of the values must not be None.
copy (bool, default False) –
This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.
indicator (bool or str, default False) – If True, adds a column to the output DataFrame called “_merge” with information on the source of each row. The column can be given a different name by providing a string argument. The column will have a Categorical type with the value of “left_only” for observations whose merge key only appears in the left DataFrame, “right_only” for observations whose merge key only appears in the right DataFrame, and “both” if the observation’s merge key is found in both DataFrames.
validate (str, optional) –
If specified, checks if merge is of specified type.
- ”one_to_one” or “1:1”: check if merge keys are unique in both left and right datasets.
- ”one_to_many” or “1:m”: check if merge keys are unique in left dataset.
- ”many_to_one” or “m:1”: check if merge keys are unique in right dataset.
- ”many_to_many” or “m:m”: allowed, but does not result in checks.

Returns:

A DataFrame of the two merged objects.

Return type:

DataFrame

See also

merge_ordered: Merge with optional filling/interpolation.
merge_asof: Merge on nearest keys.
DataFrame.join: Similar method using indices.

Examples

>>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
...                     'value': [1, 2, 3, 5]})
>>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
...                     'value': [5, 6, 7, 8]})
>>> df1
    lkey value
0   foo      1
1   bar      2
2   baz      3
3   foo      5
>>> df2
    rkey value
0   foo      5
1   bar      6
2   baz      7
3   foo      8

Merge df1 and df2 on the lkey and rkey columns. The value columns have the default suffixes, _x and _y, appended.

>>> df1.merge(df2, left_on='lkey', right_on='rkey')
  lkey  value_x rkey  value_y
foo        1  foo        5
foo        1  foo        8
bar        2  bar        6
baz        3  baz        7
foo        5  foo        5
foo        5  foo        8

Merge DataFrames df1 and df2 with specified left and right suffixes appended to any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey',
...           suffixes=('_left', '_right'))
  lkey  value_left rkey  value_right
0  foo           1  foo            5
1  foo           1  foo            8
2  bar           2  bar            6
3  baz           3  baz            7
4  foo           5  foo            5
5  foo           5  foo            8

Merge DataFrames df1 and df2, but raise an exception if the DataFrames have any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
Traceback (most recent call last):
...
ValueError: columns overlap but no suffix specified:
    Index(['value'], dtype='object')

>>> df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})
>>> df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})
>>> df1
      a  b
0   foo  1
1   bar  2
>>> df2
      a  c
0   foo  3
1   baz  4

>>> df1.merge(df2, how='inner', on='a')
      a  b  c
0   foo  1  3

>>> df1.merge(df2, how='left', on='a')
      a  b  c
0   foo  1  3.0
1   bar  2  NaN

>>> df1 = pd.DataFrame({'left': ['foo', 'bar']})
>>> df2 = pd.DataFrame({'right': [7, 8]})
>>> df1
    left
0   foo
1   bar
>>> df2
    right
0   7
1   8

>>> df1.merge(df2, how='cross')
   left  right
0   foo      7
1   foo      8
2   bar      7
3   bar      8

min(*, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs) → Series | Any

Return the minimum of the values over the requested axis.

If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin.

Parameters:

axis ({index (0), columns (1)}) –
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.
skipna (bool, default True) – Exclude NA/null values when computing the result.
numeric_only (bool, default False) – Include only float, int, boolean columns.
**kwargs – Additional keyword arguments to be passed to the function.

Returns:

Value containing the calculation referenced in the description.

Return type:

Series or scalar

See also

Series.sum: Return the sum.
Series.min: Return the minimum.
Series.max: Return the maximum.
Series.idxmin: Return the index of the minimum.
Series.idxmax: Return the index of the maximum.
DataFrame.sum: Return the sum over the requested axis.
DataFrame.min: Return the minimum over the requested axis.
DataFrame.max: Return the maximum over the requested axis.
DataFrame.idxmin: Return the index of the minimum over the requested axis.
DataFrame.idxmax: Return the index of the maximum over the requested axis.

Examples

>>> idx = pd.MultiIndex.from_arrays(
...     [["warm", "warm", "cold", "cold"], ["dog", "falcon", "fish", "spider"]],
...     names=["blooded", "animal"],
... )
>>> s = pd.Series([4, 2, 0, 8], name="legs", index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.min()
0

mod(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Modulo of dataframe and other, element-wise (binary operator mod).

Equivalent to dataframe % other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmod.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

mode(axis: Axis = 0, numeric_only: bool = False, dropna: bool = True) → DataFrame

Get the mode(s) of each element along the selected axis.

The mode of a set of values is the value that appears most often. It can be multiple values.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default 0) –
The axis to iterate over while searching for the mode:
- 0 or ‘index’ : get mode of each column
- 1 or ‘columns’ : get mode of each row.
numeric_only (bool, default False) – If True, only apply to numeric columns.
dropna (bool, default True) – Don’t consider counts of NaN/NaT.

Returns:

The modes of each column or row.

Return type:

DataFrame

See also

Series.mode: Return the highest frequency value in a Series.
Series.value_counts: Return the counts of values in a Series.

Examples

>>> df = pd.DataFrame(
...     [
...         ("bird", 2, 2),
...         ("mammal", 4, np.nan),
...         ("arthropod", 8, 0),
...         ("bird", 2, np.nan),
...     ],
...     index=("falcon", "horse", "spider", "ostrich"),
...     columns=("species", "legs", "wings"),
... )
>>> df
           species  legs  wings
falcon        bird     2    2.0
horse       mammal     4    NaN
spider   arthropod     8    0.0
ostrich       bird     2    NaN

By default, missing values are not considered, and the mode of wings are both 0 and 2. Because the resulting DataFrame has two rows, the second row of species and legs contains NaN.

>>> df.mode()
  species  legs  wings
0    bird   2.0    0.0
1     NaN   NaN    2.0

Setting dropna=False NaN values are considered and they can be the mode (like for wings).

>>> df.mode(dropna=False)
  species  legs  wings
0    bird     2    NaN

Setting numeric_only=True, only the mode of numeric columns is computed, and columns of other types are ignored.

>>> df.mode(numeric_only=True)
   legs  wings
0   2.0    0.0
1   NaN    2.0

To compute the mode over columns and not rows, use the axis parameter:

>>> df.mode(axis="columns", numeric_only=True)
           0    1
falcon   2.0  NaN
horse    4.0  NaN
spider   0.0  8.0
ostrich  2.0  NaN

mul(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Multiplication of dataframe and other, element-wise (binary operator mul).

Equivalent to dataframe * other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmul.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

multiply(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Multiplication of dataframe and other, element-wise (binary operator mul).

Equivalent to dataframe * other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmul.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

property ndim: int

Return an int representing the number of axes / array dimensions.

Return 1 if Series. Otherwise return 2 if DataFrame.

See also

numpy.ndarray.ndim: Number of array dimensions.

Examples

>>> s = pd.Series({"a": 1, "b": 2, "c": 3})
>>> s.ndim
1

>>> df = pd.DataFrame({"col1": [1, 2], "col2": [3, 4]})
>>> df.ndim
2

ne(other, axis: Axis = 'columns', level=None) → DataFrame

Get Not equal to of dataframe and other, element-wise (binary operator ne).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters:

other (scalar, sequence, Series, or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}, default 'columns') – Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns:

Result of the comparison.

Return type:

DataFrame of bool

See also

DataFrame.eq: Compare DataFrames for equality elementwise.
DataFrame.ne: Compare DataFrames for inequality elementwise.
DataFrame.le: Compare DataFrames for less than inequality or equality elementwise.
DataFrame.lt: Compare DataFrames for strictly less than inequality elementwise.
DataFrame.ge: Compare DataFrames for greater than inequality or equality elementwise.
DataFrame.gt: Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame(
...     {"cost": [250, 150, 100], "revenue": [100, 250, 300]},
...     index=["A", "B", "C"],
... )
>>> df
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis="index")
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis="index")
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame(
...     {"revenue": [300, 250, 100, 150]}, index=["A", "B", "C", "D"]
... )
>>> other
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame(
...     {
...         "cost": [250, 150, 100, 150, 300, 220],
...         "revenue": [100, 250, 300, 200, 175, 225],
...     },
...     index=[
...         ["Q1", "Q1", "Q1", "Q2", "Q2", "Q2"],
...         ["A", "B", "C", "A", "B", "C"],
...     ],
... )
>>> df_multindex
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

nlargest(n: int, columns: IndexLabel, keep: NsmallestNlargestKeep = 'first') → DataFrame

Return the first n rows ordered by columns in descending order.

Return the first n rows with the largest values in columns, in descending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=False).head(n), but more performant.

Parameters:

n (int) – Number of rows to return.
columns (Hashable or a sequence of the previous) – Column label(s) to order by.
keep ({'first', 'last', 'all'}, default 'first') –
Where there are duplicate values:
- first : prioritize the first occurrence(s)
- last : prioritize the last occurrence(s)
- all : keep all the ties of the smallest item even if it means selecting more than n items.

Returns:

The first n rows ordered by the given columns in descending order.

Return type:

DataFrame

See also

DataFrame.nsmallest: Return the first n rows ordered by columns in ascending order.
DataFrame.sort_values: Sort DataFrame by the values.
DataFrame.head: Return the first n rows without re-ordering.

Notes

This function cannot be used with all column types. For example, when specifying columns with object or category dtypes, TypeError is raised.

Examples

>>> df = pd.DataFrame(
...     {
...         "population": [
...             59000000,
...             65000000,
...             434000,
...             434000,
...             434000,
...             337000,
...             11300,
...             11300,
...             11300,
...         ],
...         "GDP": [1937894, 2583560, 12011, 4520, 12128, 17036, 182, 38, 311],
...         "alpha-2": ["IT", "FR", "MT", "MV", "BN", "IS", "NR", "TV", "AI"],
...     },
...     index=[
...         "Italy",
...         "France",
...         "Malta",
...         "Maldives",
...         "Brunei",
...         "Iceland",
...         "Nauru",
...         "Tuvalu",
...         "Anguilla",
...     ],
... )
>>> df
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru          11300      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nlargest to select the three rows having the largest values in column “population”.

>>> df.nlargest(3, "population")
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Malta       434000    12011      MT

When using keep='last', ties are resolved in reverse order:

>>> df.nlargest(3, "population", keep="last")
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN

When using keep='all', the number of element kept can go beyond n if there are duplicate values for the smallest element, all the ties are kept:

>>> df.nlargest(3, "population", keep="all")
          population      GDP alpha-2
France      65000000  2583560      FR
Italy       59000000  1937894      IT
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN

However, nlargest does not keep n distinct largest elements:

>>> df.nlargest(5, "population", keep="all")
          population      GDP alpha-2
France      65000000  2583560      FR
Italy       59000000  1937894      IT
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN

To order by the largest values in column “population” and then “GDP”, we can specify multiple columns like in the next example.

>>> df.nlargest(3, ["population", "GDP"])
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN

notna() → DataFrame

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values. NA values, such as None or numpy.NaN, get mapped to False values.

Returns:: Mask of bool values for each element in Series/DataFrame that indicates whether an element is not an NA value.
Return type:: Series/DataFrame

See also

Series.notnull: Alias of notna.
DataFrame.notnull: Alias of notna.
Series.isna: Boolean inverse of notna.
DataFrame.isna: Boolean inverse of notna.
Series.dropna: Omit axes labels with missing values.
DataFrame.dropna: Omit axes labels with missing values.
notna: Top-level notna.

Examples

Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame(
...     dict(
...         age=[5, 6, np.nan],
...         born=[
...             pd.NaT,
...             pd.Timestamp("1939-05-27"),
...             pd.Timestamp("1940-04-25"),
...         ],
...         name=["Alfred", "Batman", ""],
...         toy=[None, "Batmobile", "Joker"],
...     )
... )
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred        NaN
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.notna()
0     True
1     True
2    False
dtype: bool

notnull() → DataFrame

DataFrame.notnull is an alias for DataFrame.notna.

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values. NA values, such as None or numpy.NaN, get mapped to False values.

Returns:: Mask of bool values for each element in Series/DataFrame that indicates whether an element is not an NA value.
Return type:: Series/DataFrame

See also

Series.notnull: Alias of notna.
DataFrame.notnull: Alias of notna.
Series.isna: Boolean inverse of notna.
DataFrame.isna: Boolean inverse of notna.
Series.dropna: Omit axes labels with missing values.
DataFrame.dropna: Omit axes labels with missing values.
notna: Top-level notna.

Examples

Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame(
...     dict(
...         age=[5, 6, np.nan],
...         born=[
...             pd.NaT,
...             pd.Timestamp("1939-05-27"),
...             pd.Timestamp("1940-04-25"),
...         ],
...         name=["Alfred", "Batman", ""],
...         toy=[None, "Batmobile", "Joker"],
...     )
... )
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred        NaN
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.notnull()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.nan])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.notnull()
0     True
1     True
2    False
dtype: bool

nsmallest(n: int, columns: IndexLabel, keep: NsmallestNlargestKeep = 'first') → DataFrame

Return the first n rows ordered by columns in ascending order.

Return the first n rows with the smallest values in columns, in ascending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=True).head(n), but more performant.

Parameters:

n (int) – Number of items to retrieve.
columns (list or str) – Column name or names to order by.
keep ({'first', 'last', 'all'}, default 'first') –
Where there are duplicate values:
- first : take the first occurrence.
- last : take the last occurrence.
- all : keep all the ties of the largest item even if it means selecting more than n items.

Returns:

DataFrame with the first n rows ordered by columns in ascending order.

Return type:

DataFrame

See also

DataFrame.nlargest: Return the first n rows ordered by columns in descending order.
DataFrame.sort_values: Sort DataFrame by the values.
DataFrame.head: Return the first n rows without re-ordering.

Examples

>>> df = pd.DataFrame(
...     {
...         "population": [
...             59000000,
...             65000000,
...             434000,
...             434000,
...             434000,
...             337000,
...             337000,
...             11300,
...             11300,
...         ],
...         "GDP": [1937894, 2583560, 12011, 4520, 12128, 17036, 182, 38, 311],
...         "alpha-2": ["IT", "FR", "MT", "MV", "BN", "IS", "NR", "TV", "AI"],
...     },
...     index=[
...         "Italy",
...         "France",
...         "Malta",
...         "Maldives",
...         "Brunei",
...         "Iceland",
...         "Nauru",
...         "Tuvalu",
...         "Anguilla",
...     ],
... )
>>> df
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru         337000      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nsmallest to select the three rows having the smallest values in column “population”.

>>> df.nsmallest(3, "population")
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS

When using keep='last', ties are resolved in reverse order:

>>> df.nsmallest(3, "population", keep="last")
          population  GDP alpha-2
Anguilla       11300  311      AI
Tuvalu         11300   38      TV
Nauru         337000  182      NR

When using keep='all', the number of element kept can go beyond n if there are duplicate values for the largest element, all the ties are kept.

>>> df.nsmallest(3, "population", keep="all")
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS
Nauru         337000    182      NR

However, nsmallest does not keep n distinct smallest elements:

>>> df.nsmallest(4, "population", keep="all")
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS
Nauru         337000    182      NR

To order by the smallest values in column “population” and then “GDP”, we can specify multiple columns like in the next example.

>>> df.nsmallest(3, ["population", "GDP"])
          population  GDP alpha-2
Tuvalu         11300   38      TV
Anguilla       11300  311      AI
Nauru         337000  182      NR

nunique(axis: Axis = 0, dropna: bool = True) → Series

Count number of distinct elements in specified axis.

Return Series with number of distinct elements. Can ignore NaN values.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
dropna (bool, default True) – Don’t include NaN in the counts.

Returns:

Series with counts of unique values per row or column, depending on axis.

Return type:

Series

See also

Series.nunique: Method nunique for Series.
DataFrame.count: Count non-NA cells for each column or row.

Examples

>>> df = pd.DataFrame({"A": [4, 5, 6], "B": [4, 1, 1]})
>>> df.nunique()
A    3
B    2
dtype: int64

>>> df.nunique(axis=1)
0    1
1    2
2    2
dtype: int64

pct_change(periods: int = 1, fill_method: None = None, freq=None, **kwargs) → Self

Fractional change between the current and a prior element.

Computes the fractional change from the immediately previous row by default. This is useful in comparing the fraction of change in a time series of elements.

Note

Despite the name of this method, it calculates fractional change (also known as per unit change or relative change) and not percentage change. If you need the percentage change, multiply these values by 100.

Parameters:

periods (int, default 1) – Periods to shift for forming percent change.
fill_method (None) – Must be None. This argument will be removed in a future version of pandas.
freq (DateOffset, timedelta, or str, optional) – Increment to use from time series API (e.g. ‘ME’ or BDay()).
**kwargs – Additional keyword arguments are passed into DataFrame.shift or Series.shift.

Returns:

The same type as the calling object.

Return type:

Series or DataFrame

See also

Series.diff: Compute the difference of two elements in a Series.
DataFrame.diff: Compute the difference of two elements in a DataFrame.
Series.shift: Shift the index by some number of periods.
DataFrame.shift: Shift the index by some number of periods.

Examples

Series

>>> s = pd.Series([90, 91, 85])
>>> s
0    90
1    91
2    85
dtype: int64

>>> s.pct_change()
0         NaN
1    0.011111
2   -0.065934
dtype: float64

>>> s.pct_change(periods=2)
0         NaN
1         NaN
2   -0.055556
dtype: float64

See the percentage change in a Series where filling NAs with last valid observation forward to next valid.

>>> s = pd.Series([90, 91, None, 85])
>>> s
0    90.0
1    91.0
2     NaN
3    85.0
dtype: float64

>>> s.ffill().pct_change()
0         NaN
1    0.011111
2    0.000000
3   -0.065934
dtype: float64

DataFrame

Percentage change in French franc, Deutsche Mark, and Italian lira from 1980-01-01 to 1980-03-01.

>>> df = pd.DataFrame(
...     {
...         "FR": [4.0405, 4.0963, 4.3149],
...         "GR": [1.7246, 1.7482, 1.8519],
...         "IT": [804.74, 810.01, 860.13],
...     },
...     index=["1980-01-01", "1980-02-01", "1980-03-01"],
... )
>>> df
                FR      GR      IT
1980-01-01  4.0405  1.7246  804.74
1980-02-01  4.0963  1.7482  810.01
1980-03-01  4.3149  1.8519  860.13

>>> df.pct_change()
                  FR        GR        IT
1980-01-01       NaN       NaN       NaN
1980-02-01  0.013810  0.013684  0.006549
1980-03-01  0.053365  0.059318  0.061876

Percentage of change in GOOG and APPL stock volume. Shows computing the percentage change between columns.

>>> df = pd.DataFrame(
...     {
...         "2016": [1769950, 30586265],
...         "2015": [1500923, 40912316],
...         "2014": [1371819, 41403351],
...     },
...     index=["GOOG", "APPL"],
... )
>>> df
          2016      2015      2014
GOOG   1769950   1500923   1371819
APPL  30586265  40912316  41403351

>>> df.pct_change(axis="columns", periods=-1)
          2016      2015  2014
GOOG  0.179241  0.094112   NaN
APPL -0.252395 -0.011860   NaN

pipe(func: Callable[Concatenate[Self, P], T] | tuple[Callable[..., T], str], *args: Any, **kwargs: Any) → T

Apply chainable functions that expect Series or DataFrames.

Parameters:

func (function) – Function to apply to the Series/DataFrame. args, and kwargs are passed into func. Alternatively a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects the Series/DataFrame.
*args (iterable, optional) – Positional arguments passed into func.
**kwargs (mapping, optional) – A dictionary of keyword arguments passed into func.

Returns:

The result of applying func to the Series or DataFrame.

Return type:

The return type of func.

See also

DataFrame.apply: Apply a function along input axis of DataFrame.
DataFrame.map: Apply a function elementwise on a whole DataFrame.
Series.map: Apply a mapping correspondence on a Series.

Notes

Use .pipe when chaining together functions that expect Series, DataFrames or GroupBy objects.

Examples

Constructing an income DataFrame from a dictionary.

>>> data = [[8000, 1000], [9500, np.nan], [5000, 2000]]
>>> df = pd.DataFrame(data, columns=["Salary", "Others"])
>>> df
   Salary  Others
0    8000  1000.0
1    9500     NaN
2    5000  2000.0

Functions that perform tax reductions on an income DataFrame.

>>> def subtract_federal_tax(df):
...     return df * 0.9
>>> def subtract_state_tax(df, rate):
...     return df * (1 - rate)
>>> def subtract_national_insurance(df, rate, rate_increase):
...     new_rate = rate + rate_increase
...     return df * (1 - new_rate)

Instead of writing

>>> subtract_national_insurance(
...     subtract_state_tax(subtract_federal_tax(df), rate=0.12),
...     rate=0.05,
...     rate_increase=0.02,
... )

You can write

>>> (
...     df.pipe(subtract_federal_tax)
...     .pipe(subtract_state_tax, rate=0.12)
...     .pipe(subtract_national_insurance, rate=0.05, rate_increase=0.02)
... )
    Salary   Others
0  5892.48   736.56
1  6997.32      NaN
2  3682.80  1473.12

If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose national_insurance takes its data as df in the second argument:

>>> def subtract_national_insurance(rate, df, rate_increase):
...     new_rate = rate + rate_increase
...     return df * (1 - new_rate)
>>> (
...     df.pipe(subtract_federal_tax)
...     .pipe(subtract_state_tax, rate=0.12)
...     .pipe(
...         (subtract_national_insurance, "df"), rate=0.05, rate_increase=0.02
...     )
... )
    Salary   Others
0  5892.48   736.56
1  6997.32      NaN
2  3682.80  1473.12

pivot(*, columns, index=<no_default>, values=<no_default>) → DataFrame

Return reshaped DataFrame organized by given index / column values.

Reshape data (produce a “pivot” table) based on column values. Uses unique values from specified index / columns to form axes of the resulting DataFrame. This function does not support data aggregation, multiple values will result in a MultiIndex in the columns. See the User Guide for more on reshaping.

Parameters:

columns (Hashable or a sequence of the previous) – Column to use to make new frame’s columns.
index (Hashable or a sequence of the previous, optional) – Column to use to make new frame’s index. If not given, uses existing index.
values (Hashable or a sequence of the previous, optional) – Column(s) to use for populating new frame’s values. If not specified, all remaining columns will be used and the result will have hierarchically indexed columns.

Returns:

Returns reshaped DataFrame.

Return type:

DataFrame

Raises:

ValueError: – When there are any index, columns combinations with multiple values. DataFrame.pivot_table when you need to aggregate.

See also

DataFrame.pivot_table: Generalization of pivot that can handle duplicate values for one index/column pair.
DataFrame.unstack: Pivot based on the index values instead of a column.
wide_to_long: Wide panel to long format. Less flexible but more user-friendly than melt.

Notes

For finer-tuned control, see hierarchical indexing documentation along with the related stack/unstack methods.

Reference the user guide for more examples.

Examples

>>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',
...                            'two'],
...                    'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
...                    'baz': [1, 2, 3, 4, 5, 6],
...                    'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
>>> df
    foo   bar  baz  zoo
0   one   A    1    x
1   one   B    2    y
2   one   C    3    z
3   two   A    4    q
4   two   B    5    w
5   two   C    6    t

>>> df.pivot(index='foo', columns='bar', values='baz')
bar  A   B   C
foo
one  1   2   3
two  4   5   6

>>> df.pivot(index='foo', columns='bar')['baz']
bar  A   B   C
foo
one  1   2   3
two  4   5   6

>>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
      baz       zoo
bar   A  B  C   A  B  C
foo
one   1  2  3   x  y  z
two   4  5  6   q  w  t

You could also assign a list of column names or a list of index names.

>>> df = pd.DataFrame({
...                   "lev1": [1, 1, 1, 2, 2, 2],
...                   "lev2": [1, 1, 2, 1, 1, 2],
...                   "lev3": [1, 2, 1, 2, 1, 2],
...                   "lev4": [1, 2, 3, 4, 5, 6],
...                   "values": [0, 1, 2, 3, 4, 5]})
>>> df
    lev1 lev2 lev3 lev4 values
0   1    1    1    1    0
1   1    1    2    2    1
2   1    2    1    3    2
3   2    1    2    4    3
4   2    1    1    5    4
5   2    2    2    6    5

>>> df.pivot(index="lev1", columns=["lev2", "lev3"], values="values")
lev2    1         2
lev3    1    2    1    2
lev1
1     0.0  1.0  2.0  NaN
2     4.0  3.0  NaN  5.0

>>> df.pivot(index=["lev1", "lev2"], columns=["lev3"], values="values")
      lev3    1    2
lev1  lev2
   1     1  0.0  1.0
         2  2.0  NaN
   2     1  4.0  3.0
         2  NaN  5.0

A ValueError is raised if there are any duplicates.

>>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
...                    "bar": ['A', 'A', 'B', 'C'],
...                    "baz": [1, 2, 3, 4]})
>>> df
   foo bar  baz
0  one   A    1
1  one   A    2
2  two   B    3
3  two   C    4

Notice that the first two rows are the same for our index and columns arguments.

>>> df.pivot(index='foo', columns='bar', values='baz')
Traceback (most recent call last):
   ...
ValueError: Index contains duplicate entries, cannot reshape

pivot_table(values=None, index=None, columns=None, aggfunc: AggFuncType = 'mean', fill_value=None, margins: bool = False, dropna: bool = True, margins_name: Level = 'All', observed: bool = True, sort: bool = True, **kwargs) → DataFrame

Create a spreadsheet-style pivot table as a DataFrame.

The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.

Parameters:

values (list-like or scalar, optional) – Column or columns to aggregate.
index (column, Grouper, array, or sequence of the previous) – Keys to group by on the pivot table index. If a list is passed, it can contain any of the other types (except list). If an array is passed, it must be the same length as the data and will be used in the same manner as column values.
columns (column, Grouper, array, or sequence of the previous) – Keys to group by on the pivot table column. If a list is passed, it can contain any of the other types (except list). If an array is passed, it must be the same length as the data and will be used in the same manner as column values.
aggfunc (function, list of functions, dict, default "mean") – If a list of functions is passed, the resulting pivot table will have hierarchical columns whose top level are the function names (inferred from the function objects themselves). If a dict is passed, the key is column to aggregate and the value is function or list of functions. If margin=True, aggfunc will be used to calculate the partial aggregates.
fill_value (scalar, default None) – Value to replace missing values with (in the resulting pivot table, after aggregation).
margins (bool, default False) – If margins=True, special All columns and rows will be added with partial group aggregates across the categories on the rows and columns.
dropna (bool, default True) –
Do not include columns whose entries are all NaN. If True,
- rows with an NA value in any column will be omitted before computing margins,
- index/column keys containing NA values will be dropped (see dropna parameter in DataFrame.groupby()).
margins_name (str, default 'All') – Name of the row / column that will contain the totals when margins is True.
observed (bool, default False) –
This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.

Changed in version 3.0.0: The default value is now True.
sort (bool, default True) – Specifies if the result should be sorted.
**kwargs (dict) – Optional keyword arguments to pass to aggfunc.

Returns:

An Excel style pivot table.

Return type:

DataFrame

See also

DataFrame.pivot: Pivot without aggregation that can handle non-numeric data.
DataFrame.melt: Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
wide_to_long: Wide panel to long format. Less flexible but more user-friendly than melt.

Notes

Reference the user guide for more examples.

Examples

>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
...                          "bar", "bar", "bar", "bar"],
...                    "B": ["one", "one", "one", "two", "two",
...                          "one", "one", "two", "two"],
...                    "C": ["small", "large", "large", "small",
...                          "small", "large", "small", "small",
...                          "large"],
...                    "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
...                    "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
>>> df
     A    B      C  D  E
0  foo  one  small  1  2
1  foo  one  large  2  4
2  foo  one  large  2  5
3  foo  two  small  3  5
4  foo  two  small  3  6
5  bar  one  large  4  6
6  bar  one  small  5  8
7  bar  two  small  6  9
8  bar  two  large  7  9

This first example aggregates values by taking the sum.

>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
...                        columns=['C'], aggfunc="sum")
>>> table
C        large  small
A   B
bar one    4.0    5.0
    two    7.0    6.0
foo one    4.0    1.0
    two    NaN    6.0

We can also fill missing values using the fill_value parameter.

>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
...                        columns=['C'], aggfunc="sum", fill_value=0)
>>> table
C        large  small
A   B
bar one      4      5
    two      7      6
foo one      4      1
    two      0      6

The next example aggregates by taking the mean across multiple columns.

>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
...                        aggfunc={'D': "mean", 'E': "mean"})
>>> table
                D         E
A   C
bar large  5.500000  7.500000
    small  5.500000  8.500000
foo large  2.000000  4.500000
    small  2.333333  4.333333

We can also calculate multiple types of aggregations for any given value column.

>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
...                        aggfunc={'D': "mean",
...                                 'E': ["min", "max", "mean"]})
>>> table
                  D   E
               mean max      mean  min
A   C
bar large  5.500000   9  7.500000    6
    small  5.500000   9  8.500000    8
foo large  2.000000   5  4.500000    4
    small  2.333333   6  4.333333    2

plot: alias of PlotAccessor

pop(item: Hashable) → Series

Return item and drop it from DataFrame. Raise KeyError if not found.

Parameters:: item (label) – Label of column to be popped.
Returns:: Series representing the item that is dropped.
Return type:: Series

See also

DataFrame.drop: Drop specified labels from rows or columns.
DataFrame.drop_duplicates: Return DataFrame with duplicate rows removed.

Examples

>>> df = pd.DataFrame(
...     [
...         ("falcon", "bird", 389.0),
...         ("parrot", "bird", 24.0),
...         ("lion", "mammal", 80.5),
...         ("monkey", "mammal", np.nan),
...     ],
...     columns=("name", "class", "max_speed"),
... )
>>> df
     name   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN

>>> df.pop("class")
0      bird
1      bird
2    mammal
3    mammal
Name: class, dtype: str

>>> df
     name  max_speed
0  falcon      389.0
1  parrot       24.0
2    lion       80.5
3  monkey        NaN

pow(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Exponential power of dataframe and other, element-wise (binary operator pow).

Equivalent to dataframe ** other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rpow.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

prod(*, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, min_count: int = 0, **kwargs) → Series

Return the product of the values over the requested axis.

Parameters:

axis ({index (0), columns (1)}) –
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.prod with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).

Added in version 2.0.0.
skipna (bool, default True) – Exclude NA/null values when computing the result.
numeric_only (bool, default False) – Include only float, int, boolean columns. Not implemented for Series.
min_count (int, default 0) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.
**kwargs – Additional keyword arguments to be passed to the function.

Returns:

The product of the values over the requested axis.

Return type:

Series or scalar

See also

Series.sum: Return the sum.
Series.min: Return the minimum.
Series.max: Return the maximum.
Series.idxmin: Return the index of the minimum.
Series.idxmax: Return the index of the maximum.
DataFrame.sum: Return the sum over the requested axis.
DataFrame.min: Return the minimum over the requested axis.
DataFrame.max: Return the maximum over the requested axis.
DataFrame.idxmin: Return the index of the minimum over the requested axis.
DataFrame.idxmax: Return the index of the maximum over the requested axis.

Examples

By default, the product of an empty or all-NA Series is 1

>>> pd.Series([], dtype="float64").prod()
1.0

This can be controlled with the min_count parameter

>>> pd.Series([], dtype="float64").prod(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).prod()
1.0

>>> pd.Series([np.nan]).prod(min_count=1)
nan

product(*, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, min_count: int = 0, **kwargs) → Series

Return the product of the values over the requested axis.

Parameters:

axis ({index (0), columns (1)}) –
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.prod with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).

Added in version 2.0.0.
skipna (bool, default True) – Exclude NA/null values when computing the result.
numeric_only (bool, default False) – Include only float, int, boolean columns. Not implemented for Series.
min_count (int, default 0) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.
**kwargs – Additional keyword arguments to be passed to the function.

Returns:

The product of the values over the requested axis.

Return type:

Series or scalar

See also

Series.sum: Return the sum.
Series.min: Return the minimum.
Series.max: Return the maximum.
Series.idxmin: Return the index of the minimum.
Series.idxmax: Return the index of the maximum.
DataFrame.sum: Return the sum over the requested axis.
DataFrame.min: Return the minimum over the requested axis.
DataFrame.max: Return the maximum over the requested axis.
DataFrame.idxmin: Return the index of the minimum over the requested axis.
DataFrame.idxmax: Return the index of the maximum over the requested axis.

Examples

By default, the product of an empty or all-NA Series is 1

>>> pd.Series([], dtype="float64").prod()
1.0

This can be controlled with the min_count parameter

>>> pd.Series([], dtype="float64").prod(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).prod()
1.0

>>> pd.Series([np.nan]).prod(min_count=1)
nan

quantile(q: float | AnyArrayLike | Sequence[float] = 0.5, axis: Axis = 0, numeric_only: bool = False, interpolation: QuantileInterpolation = 'linear', method: Literal['single', 'table'] = 'single') → Series | DataFrame

Return values at the given quantile over requested axis.

Parameters:

q (float or array-like, default 0.5 (50% quantile)) – Value between 0 <= q <= 1, the quantile(s) to compute.
axis ({0 or 'index', 1 or 'columns'}, default 0) – Equals 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
numeric_only (bool, default False) –
Include only float, int or boolean data.

Changed in version 2.0.0: The default value of numeric_only is now False.
interpolation ({'linear', 'lower', 'higher', 'midpoint', 'nearest'}) –
This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points i and j:
- linear: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j.
- lower: i.
- higher: j.
- nearest: i or j whichever is nearest.
- midpoint: (i + j) / 2.
method ({'single', 'table'}, default 'single') – Whether to compute quantiles per-column (‘single’) or over all columns (‘table’). When ‘table’, the only allowed interpolation methods are ‘nearest’, ‘lower’, and ‘higher’.

Returns:

If q is an array, a DataFrame will be returned where the: index is q, the columns are the columns of self, and the values are the quantiles.
If q is a float, a Series will be returned where the: index is the columns of self and the values are the quantiles.

Return type:

Series or DataFrame

See also

core.window.rolling.Rolling.quantile: Rolling quantile.
numpy.percentile: Numpy function to compute the percentile.

Examples

>>> df = pd.DataFrame(
...     np.array([[1, 1], [2, 10], [3, 100], [4, 100]]), columns=["a", "b"]
... )
>>> df.quantile(0.1)
a    1.3
b    3.7
Name: 0.1, dtype: float64
>>> df.quantile([0.1, 0.5])
       a     b
0.1  1.3   3.7
0.5  2.5  55.0

Specifying method=’table’ will compute the quantile over all columns.

>>> df.quantile(0.1, method="table", interpolation="nearest")
a    1
b    1
Name: 0.1, dtype: int64
>>> df.quantile([0.1, 0.5], method="table", interpolation="nearest")
     a    b
0.1  1    1
0.5  3  100

Specifying numeric_only=False will compute the quantiles for all columns.

>>> df = pd.DataFrame(
...     {
...         "A": [1, 2],
...         "B": [pd.Timestamp("2010"), pd.Timestamp("2011")],
...         "C": [pd.Timedelta("1 days"), pd.Timedelta("2 days")],
...     }
... )
>>> df.quantile(0.5, numeric_only=False)
A                    1.5
B    2010-07-02 12:00:00
C        1 days 12:00:00
Name: 0.5, dtype: object

query(expr: str, *, parser: Literal['pandas', 'python'] = 'pandas', engine: Literal['python', 'numexpr'] | None = None, local_dict: dict[str, Any] | None = None, global_dict: dict[str, Any] | None = None, resolvers: list[Mapping] | None = None, level: int = 0, inplace: bool = False) → DataFrame | None

Query the columns of a DataFrame with a boolean expression.

Warning

This method can run arbitrary code which can make you vulnerable to code injection if you pass user input to this function.

Parameters:

expr (str) –
The query string to evaluate.

See the documentation for eval() for details of supported operations and functions in the query string.

See the documentation for DataFrame.eval() for details on referring to column names and variables in the query string.
parser ({'pandas', 'python'}, default 'pandas') – The parser to use to construct the syntax tree from the expression. The default of 'pandas' parses code slightly different than standard Python. Alternatively, you can parse an expression using the 'python' parser to retain strict Python semantics. See the enhancing performance documentation for more details.
engine ({'python', 'numexpr'}, default 'numexpr') –
The engine used to evaluate the expression. Supported engines are
- None : tries to use numexpr, falls back to python
- 'numexpr' : This default engine evaluates pandas objects using numexpr for large speed ups in complex expressions with large frames.
- 'python' : Performs operations as if you had eval’d in top level python. This engine is generally not that useful.
More backends may be available in the future.
local_dict (dict or None, optional) – A dictionary of local variables, taken from locals() by default.
global_dict (dict or None, optional) – A dictionary of global variables, taken from globals() by default.
resolvers (list of dict-like or None, optional) – A list of objects implementing the __getitem__ special method that you can use to inject an additional collection of namespaces to use for variable lookup. For example, this is used in the query() method to inject the DataFrame.index and DataFrame.columns variables that refer to their respective DataFrame instance attributes.
level (int, optional) – The number of prior stack frames to traverse and add to the current scope. Most users will not need to change this parameter.
inplace (bool) – Whether to modify the DataFrame rather than creating a new one.

Returns:

DataFrame resulting from the provided query expression or None if inplace=True.

Return type:

DataFrame or None

See also

eval: Evaluate a string describing operations on DataFrame columns.
DataFrame.eval: Evaluate a string describing operations on DataFrame columns.

Notes

The result of the evaluation of this expression is first passed to DataFrame.loc and if that fails because of a multidimensional key (e.g., a DataFrame) then the result will be passed to DataFrame.__getitem__().

This method uses the top-level eval() function to evaluate the passed query.

The query() method uses a slightly modified Python syntax by default. For example, the & and | (bitwise) operators have the precedence of their boolean cousins, and and or. This is syntactically valid Python, however the semantics are different.

You can change the semantics of the expression by passing the keyword argument parser='python'. This enforces the same semantics as evaluation in Python space. Likewise, you can pass engine='python' to evaluate an expression using Python itself as a backend. This is not recommended as it is inefficient compared to using numexpr as the engine.

The DataFrame.index and DataFrame.columns attributes of the DataFrame instance are placed in the query namespace by default, which allows you to treat both the index and columns of the frame as a column in the frame. The identifier index is used for the frame index; you can also use the name of the index to identify it in a query. Please note that Python keywords may not be used as identifiers.

For further details and examples see the query documentation in indexing.

Backtick quoted variables

Backtick quoted variables are parsed as literal Python code and are converted internally to a Python valid identifier. This can lead to the following problems.

During parsing a number of disallowed characters inside the backtick quoted string are replaced by strings that are allowed as a Python identifier. These characters include all operators in Python, the space character, the question mark, the exclamation mark, the dollar sign, and the euro sign.

A backtick can be escaped by double backticks.

See also the Python documentation about lexical analysis in combination with the source code in pandas.core.computation.parsing.

Examples

>>> df = pd.DataFrame(
...     {"A": range(1, 6), "B": range(10, 0, -2), "C&C": range(10, 5, -1)}
... )
>>> df
   A   B  C&C
0  1  10   10
1  2   8    9
2  3   6    8
3  4   4    7
4  5   2    6
>>> df.query("A > B")
   A  B  C&C
4  5  2    6

The previous expression is equivalent to

>>> df[df.A > df.B]
   A  B  C&C
4  5  2    6

For columns with spaces in their name, you can use backtick quoting.

>>> df.query("B == `C&C`")
   A   B  C&C
0  1  10   10

The previous expression is equivalent to

>>> df[df.B == df["C&C"]]
   A   B  C&C
0  1  10   10

Using local variable:

>>> local_var = 2
>>> df.query("A <= @local_var")
A   B  C&C
0  1  10   10
1  2   8    9

radd(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Addition of dataframe and other, element-wise (binary operator radd).

Equivalent to other + dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, add.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

rank(axis: int | Literal['index', 'columns', 'rows'] = 0, method: Literal['average', 'min', 'max', 'first', 'dense'] = 'average', numeric_only: bool = False, na_option: Literal['keep', 'top', 'bottom'] = 'keep', ascending: bool = True, pct: bool = False) → Self

Compute numerical data ranks (1 through n) along axis.

By default, equal values are assigned a rank that is the average of the ranks of those values.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default 0) – Index to direct ranking. For Series this parameter is unused and defaults to 0.
method ({'average', 'min', 'max', 'first', 'dense'}, default 'average') –
How to rank the group of records that have the same value (i.e. ties):
- average: average rank of the group
- min: lowest rank in the group
- max: highest rank in the group
- first: ranks assigned in order they appear in the array
- dense: like ‘min’, but rank always increases by 1 between groups.
numeric_only (bool, default False) –
For DataFrame objects, rank only numeric columns if set to True.

Changed in version 2.0.0: The default value of numeric_only is now False.
na_option ({'keep', 'top', 'bottom'}, default 'keep') –
How to rank NaN values:
- keep: assign NaN rank to NaN values
- top: assign lowest rank to NaN values
- bottom: assign highest rank to NaN values
ascending (bool, default True) – Whether or not the elements should be ranked in ascending order.
pct (bool, default False) – Whether or not to display the returned rankings in percentile form.

Returns:

Return a Series or DataFrame with data ranks as values.

Return type:

same type as caller

See also

core.groupby.DataFrameGroupBy.rank: Rank of values within each group.
core.groupby.SeriesGroupBy.rank: Rank of values within each group.

Examples

>>> df = pd.DataFrame(
...     data={
...         "Animal": ["cat", "penguin", "dog", "spider", "snake"],
...         "Number_legs": [4, 2, 4, 8, np.nan],
...     }
... )
>>> df
    Animal  Number_legs
0      cat          4.0
1  penguin          2.0
2      dog          4.0
3   spider          8.0
4    snake          NaN

Ties are assigned the mean of the ranks (by default) for the group.

>>> s = pd.Series(range(5), index=list("abcde"))
>>> s["d"] = s["b"]
>>> s.rank()
a    1.0
b    2.5
c    4.0
d    2.5
e    5.0
dtype: float64

The following example shows how the method behaves with the above parameters:

default_rank: this is the default behaviour obtained without using any parameter.
max_rank: setting method = 'max' the records that have the same values are ranked using the highest rank (e.g.: since ‘cat’ and ‘dog’ are both in the 2nd and 3rd position, rank 3 is assigned.)
NA_bottom: choosing na_option = 'bottom', if there are records with NaN values they are placed at the bottom of the ranking.
pct_rank: when setting pct = True, the ranking is expressed as percentile rank.

>>> df["default_rank"] = df["Number_legs"].rank()
>>> df["max_rank"] = df["Number_legs"].rank(method="max")
>>> df["NA_bottom"] = df["Number_legs"].rank(na_option="bottom")
>>> df["pct_rank"] = df["Number_legs"].rank(pct=True)
>>> df
    Animal  Number_legs  default_rank  max_rank  NA_bottom  pct_rank
0      cat          4.0           2.5       3.0        2.5     0.625
1  penguin          2.0           1.0       1.0        1.0     0.250
2      dog          4.0           2.5       3.0        2.5     0.625
3   spider          8.0           4.0       4.0        4.0     1.000
4    snake          NaN           NaN       NaN        5.0       NaN

rdiv(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Floating division of dataframe and other, element-wise (binary operator rtruediv).

Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, truediv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

Conform DataFrame to new index with optional filling logic.

Places NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False.

Parameters:

labels (array-like, optional) – New labels / index to conform the axis specified by ‘axis’ to.
index (array-like, optional) – New labels for the index. Preferably an Index object to avoid duplicating data.
columns (array-like, optional) – New labels for the columns. Preferably an Index object to avoid duplicating data.
axis (int or str, optional) – Axis to target. Can be either the axis name (‘index’, ‘columns’) or number (0, 1).
method ({None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}) –
Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.
- None (default): don’t fill gaps
- pad / ffill: Propagate last valid observation forward to next valid.
- backfill / bfill: Use next valid observation to fill gap.
- nearest: Use nearest valid observations to fill gap.
copy (bool, default False) –
This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.
level (int or name) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (scalar, default np.nan) – Value to use for missing values. Defaults to NaN, but can be any “compatible” value.
limit (int, default None) – Maximum number of consecutive elements to forward or backward fill.
tolerance (optional) –
Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation abs(index[indexer] - target) <= tolerance.

Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type.

Returns:

DataFrame with changed index.

Return type:

DataFrame

See also

DataFrame.set_index: Set row labels.
DataFrame.reset_index: Remove row labels or move them to new columns.
DataFrame.reindex_like: Change to same indices as other DataFrame.

Examples

DataFrame.reindex supports two calling conventions

(index=index_labels, columns=column_labels, ...)
(labels, axis={'index', 'columns'}, ...)

We highly recommend using keyword arguments to clarify your intent.

Create a DataFrame with some fictional data.

>>> index = ["Firefox", "Chrome", "Safari", "IE10", "Konqueror"]
>>> columns = ["http_status", "response_time"]
>>> df = pd.DataFrame(
...     [[200, 0.04], [200, 0.02], [404, 0.07], [404, 0.08], [301, 1.0]],
...     columns=columns,
...     index=index,
... )
>>> df
           http_status  response_time
Firefox            200           0.04
Chrome             200           0.02
Safari             404           0.07
IE10               404           0.08
Konqueror          301           1.00

Create a new index and reindex the DataFrame. By default values in the new index that do not have corresponding records in the DataFrame are assigned NaN.

>>> new_index = ["Safari", "Iceweasel", "Comodo Dragon", "IE10", "Chrome"]
>>> df.reindex(new_index)
               http_status  response_time
Safari               404.0           0.07
Iceweasel              NaN            NaN
Comodo Dragon          NaN            NaN
IE10                 404.0           0.08
Chrome               200.0           0.02

We can fill in the missing values by passing a value to the keyword fill_value. Because the index is not monotonically increasing or decreasing, we cannot use arguments to the keyword method to fill the NaN values.

>>> df.reindex(new_index, fill_value=0)
               http_status  response_time
Safari                 404           0.07
Iceweasel                0           0.00
Comodo Dragon            0           0.00
IE10                   404           0.08
Chrome                 200           0.02

>>> df.reindex(new_index, fill_value="missing")
              http_status response_time
Safari                404          0.07
Iceweasel         missing       missing
Comodo Dragon     missing       missing
IE10                  404          0.08
Chrome                200          0.02

We can also reindex the columns.

>>> df.reindex(columns=["http_status", "user_agent"])
           http_status  user_agent
Firefox            200         NaN
Chrome             200         NaN
Safari             404         NaN
IE10               404         NaN
Konqueror          301         NaN

Or we can use “axis-style” keyword arguments

>>> df.reindex(["http_status", "user_agent"], axis="columns")
           http_status  user_agent
Firefox            200         NaN
Chrome             200         NaN
Safari             404         NaN
IE10               404         NaN
Konqueror          301         NaN

To further illustrate the filling functionality in reindex, we will create a DataFrame with a monotonically increasing index (for example, a sequence of dates).

>>> date_index = pd.date_range("1/1/2010", periods=6, freq="D")
>>> df2 = pd.DataFrame(
...     {"prices": [100, 101, np.nan, 100, 89, 88]}, index=date_index
... )
>>> df2
            prices
2010-01-01   100.0
2010-01-02   101.0
2010-01-03     NaN
2010-01-04   100.0
2010-01-05    89.0
2010-01-06    88.0

Suppose we decide to expand the DataFrame to cover a wider date range.

>>> date_index2 = pd.date_range("12/29/2009", periods=10, freq="D")
>>> df2.reindex(date_index2)
            prices
2009-12-29     NaN
2009-12-30     NaN
2009-12-31     NaN
2010-01-01   100.0
2010-01-02   101.0
2010-01-03     NaN
2010-01-04   100.0
2010-01-05    89.0
2010-01-06    88.0
2010-01-07     NaN

The index entries that did not have a value in the original data frame (for example, ‘2009-12-29’) are by default filled with NaN. If desired, we can fill in the missing values using one of several options.

For example, to back-propagate the last valid value to fill the NaN values, pass bfill as an argument to the method keyword.

>>> df2.reindex(date_index2, method="bfill")
            prices
2009-12-29   100.0
2009-12-30   100.0
2009-12-31   100.0
2010-01-01   100.0
2010-01-02   101.0
2010-01-03     NaN
2010-01-04   100.0
2010-01-05    89.0
2010-01-06    88.0
2010-01-07     NaN

Please note that the NaN value present in the original DataFrame (at index value 2010-01-03) will not be filled by any of the value propagation schemes. This is because filling while reindexing does not look at DataFrame values, but only compares the original and desired indexes. If you do want to fill in the NaN values present in the original DataFrame, use the fillna() method.

See the user guide for more.

reindex_like(other, method: Literal['backfill', 'bfill', 'pad', 'ffill', 'nearest'] | None=None, copy: bool | Literal[_NoDefault.no_default] = <no_default>, limit: int | None = None, tolerance=None) → Self

Return an object with matching indices as other object.

Conform the object to the same index on all axes. Optional filling logic, placing NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False.

Parameters:

other (Object of the same data type) – Its row and column indices are used to define the new indices of this object.
method ({None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}) –
Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.

Deprecated since version 3.0.0.
- None (default): don’t fill gaps
- pad / ffill: propagate last valid observation forward to next valid
- backfill / bfill: use next valid observation to fill gap
- nearest: use nearest valid observations to fill gap.
copy (bool, default False) –
This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.
limit (int, default None) – Maximum number of consecutive labels to fill for inexact matches.
tolerance (optional) –
Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation abs(index[indexer] - target) <= tolerance.

Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type.

Returns:

Same type as caller, but with changed indices on each axis.

Return type:

Series or DataFrame

See also

DataFrame.set_index: Set row labels.
DataFrame.reset_index: Remove row labels or move them to new columns.
DataFrame.reindex: Change to new indices or expand indices.

Notes

Same as calling .reindex(index=other.index, columns=other.columns,...).

Examples

>>> df1 = pd.DataFrame(
...     [
...         [24.3, 75.7, "high"],
...         [31, 87.8, "high"],
...         [22, 71.6, "medium"],
...         [35, 95, "medium"],
...     ],
...     columns=["temp_celsius", "temp_fahrenheit", "windspeed"],
...     index=pd.date_range(start="2014-02-12", end="2014-02-15", freq="D"),
... )

>>> df1
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          24.3             75.7      high
2014-02-13          31.0             87.8      high
2014-02-14          22.0             71.6    medium
2014-02-15          35.0             95.0    medium

>>> df2 = pd.DataFrame(
...     [[28, "low"], [30, "low"], [35.1, "medium"]],
...     columns=["temp_celsius", "windspeed"],
...     index=pd.DatetimeIndex(["2014-02-12", "2014-02-13", "2014-02-15"]),
... )

>>> df2
            temp_celsius windspeed
2014-02-12          28.0       low
2014-02-13          30.0       low
2014-02-15          35.1    medium

>>> df2.reindex_like(df1)
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          28.0              NaN       low
2014-02-13          30.0              NaN       low
2014-02-14           NaN              NaN       NaN
2014-02-15          35.1              NaN    medium

Rename columns or index labels.

Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don’t throw an error.

See the user guide for more.

Parameters:

mapper (dict-like or function) – Dict-like or function transformations to apply to that axis’ values. Use either mapper and axis to specify the axis to target with mapper, or index and columns.
index (dict-like or function) – Alternative to specifying axis (mapper, axis=0 is equivalent to index=mapper).
columns (dict-like or function) – Alternative to specifying axis (mapper, axis=1 is equivalent to columns=mapper).
axis ({0 or 'index', 1 or 'columns'}, default 0) – Axis to target with mapper. Can be either the axis name (‘index’, ‘columns’) or number (0, 1). The default is ‘index’.
copy (bool, default False) –
This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.
inplace (bool, default False) – Whether to modify the DataFrame rather than creating a new one. If True then value of copy is ignored.
level (int or level name, default None) – In case of a MultiIndex, only rename labels in the specified level.
errors ({'ignore', 'raise'}, default 'ignore') – If ‘raise’, raise a KeyError when a dict-like mapper, index, or columns contains labels that are not present in the Index being transformed. If ‘ignore’, existing keys will be renamed and extra keys will be ignored.

Returns:

DataFrame with the renamed axis labels or None if inplace=True.

Return type:

DataFrame or None

Raises:

KeyError – If any of the labels is not found in the selected axis and “errors=’raise’”.

See also

DataFrame.rename_axis: Set the name of the axis.

Examples

DataFrame.rename supports two calling conventions

(index=index_mapper, columns=columns_mapper, ...)
(mapper, axis={'index', 'columns'}, ...)

We highly recommend using keyword arguments to clarify your intent.

Rename columns using a mapping:

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
   a  c
0  1  4
1  2  5
2  3  6

Rename index using a mapping:

>>> df.rename(index={0: "x", 1: "y", 2: "z"})
   A  B
x  1  4
y  2  5
z  3  6

Cast index labels to a different type:

>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Index(['0', '1', '2'], dtype='str')

>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
KeyError: ['C'] not found in axis

Using axis-style parameters:

>>> df.rename(str.lower, axis="columns")
   a  b
0  1  4
1  2  5
2  3  6

>>> df.rename({1: 2, 2: 4}, axis="index")
   A  B
0  1  4
2  2  5
4  3  6

rename_axis(mapper: Hashable | Sequence[Hashable] | Literal[_NoDefault.no_default] = <no_default>, *, index=<no_default>, columns=<no_default>, axis: int | ~typing.Literal['index', 'columns', 'rows']=0, copy: bool | Literal[_NoDefault.no_default] = <no_default>, inplace: bool = False) → Self | None

Set the name of the axis for the index or columns.

Parameters:

mapper (scalar, list-like, optional) –
Value to set the axis name attribute.

Use either mapper and axis to specify the axis to target with mapper, or index and/or columns.
index (scalar, list-like, dict-like or function, optional) – A scalar, list-like, dict-like or functions transformations to apply to that axis’ values.
columns (scalar, list-like, dict-like or function, optional) – A scalar, list-like, dict-like or functions transformations to apply to that axis’ values.
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to rename.
copy (bool, default False) –
This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.
inplace (bool, default False) – Modifies the object directly, instead of creating a new Series or DataFrame.

Returns:

The same type as the caller or None if inplace=True.

Return type:

DataFrame, or None

See also

Series.rename: Alter Series index labels or name.
DataFrame.rename: Alter DataFrame index labels or name.
Index.rename: Set new names on index.

Notes

DataFrame.rename_axis supports two calling conventions

(index=index_mapper, columns=columns_mapper, ...)
(mapper, axis={'index', 'columns'}, ...)

The first calling convention will only modify the names of the index and/or the names of the Index object that is the columns. In this case, the parameter copy is ignored.

The second calling convention will modify the names of the corresponding index if mapper is a list or a scalar. However, if mapper is dict-like or a function, it will use the deprecated behavior of modifying the axis labels.

We highly recommend using keyword arguments to clarify your intent.

Examples

DataFrame

>>> df = pd.DataFrame(
...     {"num_legs": [4, 4, 2], "num_arms": [0, 0, 2]}, ["dog", "cat", "monkey"]
... )
>>> df
        num_legs  num_arms
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("animal")
>>> df
        num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("limbs", axis="columns")
>>> df
limbs   num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2

MultiIndex

>>> df.index = pd.MultiIndex.from_product(
...     [["mammal"], ["dog", "cat", "monkey"]], names=["type", "name"]
... )
>>> df
limbs          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2

>>> df.rename_axis(index={"type": "class"})
limbs          num_legs  num_arms
class  name
mammal dog            4         0
       cat            4         0
       monkey         2         2

>>> df.rename_axis(columns=str.upper)
LIMBS          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2

reorder_levels(order: Sequence[int | str], axis: Axis = 0) → DataFrame

Rearrange index or column levels using input order.

May not drop or duplicate levels.

Parameters:

order (list of int or list of str) – List representing new level order. Reference level by number (position) or by key (label).
axis ({0 or 'index', 1 or 'columns'}, default 0) – Where to reorder levels.

Returns:

DataFrame with indices or columns with reordered levels.

Return type:

DataFrame

See also

DataFrame.swaplevel: Swap levels i and j in a MultiIndex.

Examples

>>> data = {
...     "class": ["Mammals", "Mammals", "Reptiles"],
...     "diet": ["Omnivore", "Carnivore", "Carnivore"],
...     "species": ["Humans", "Dogs", "Snakes"],
... }
>>> df = pd.DataFrame(data, columns=["class", "diet", "species"])
>>> df = df.set_index(["class", "diet"])
>>> df
                                  species
class      diet
Mammals    Omnivore                Humans
           Carnivore                 Dogs
Reptiles   Carnivore               Snakes

Let’s reorder the levels of the index:

>>> df.reorder_levels(["diet", "class"])
                                  species
diet      class
Omnivore  Mammals                  Humans
Carnivore Mammals                    Dogs
          Reptiles                 Snakes

replace(to_replace=None, value=<no_default>, *, inplace: bool = False, regex: bool = False) → Self

Replace values given in to_replace with value.

Values of the Series/DataFrame are replaced with other values dynamically. This differs from updating with .loc or .iloc, which require you to specify a location to update with some value.

Parameters:

to_replace (str, regex, list, dict, Series, int, float, or None) –
How to find the values that will be replaced.
- numeric, str or regex:
  - numeric: numeric values equal to to_replace will be replaced with value
  - str: string exactly matching to_replace will be replaced with value
  - regex: regexes matching to_replace will be replaced with value
- list of str, regex, or numeric:
  - First, if to_replace and value are both lists, they must be the same length.
  - Second, if regex=True then all of the strings in both lists will be interpreted as regexes otherwise they will match directly. This doesn’t matter much for value since there are only a few possible substitution regexes you can use.
  - str, regex and numeric rules apply as above.
- dict:
  - Dicts can be used to specify different replacement values for different existing values. For example, {'a': 'b', 'y': 'z'} replaces the value ‘a’ with ‘b’ and ‘y’ with ‘z’. To use a dict in this way, the optional value parameter should not be given.
  - For a DataFrame a dict can specify that different values should be replaced in different columns. For example, {'a': 1, 'b': 'z'} looks for the value 1 in column ‘a’ and the value ‘z’ in column ‘b’ and replaces these values with whatever is specified in value. The value parameter should not be None in this case. You can treat this as a special case of passing two lists except that you are specifying the column to search in.
  - For a DataFrame nested dictionaries, e.g., {'a': {'b': np.nan}}, are read as follows: look in column ‘a’ for the value ‘b’ and replace it with NaN. The optional value parameter should not be specified to use a nested dict in this way. You can nest regular expressions as well. Note that column names (the top-level dictionary keys in a nested dictionary) cannot be regular expressions.
- None:
  - This means that the regex argument must be a string, compiled regular expression, or list, dict, ndarray or Series of such elements. If value is also None then this must be a nested dictionary or Series.
See the examples section for examples of each of these.
value (scalar, dict, list, str, regex, default None) – Value to replace any values matching to_replace with. For a DataFrame a dict of values can be used to specify which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed.
inplace (bool, default False) – If True, performs operation inplace.
regex (bool or same types as to_replace, default False) – Whether to interpret to_replace and/or value as regular expressions. Alternatively, this could be a regular expression or a list, dict, or array of regular expressions in which case to_replace must be None.

Returns:

Object after replacement.

Return type:

Series/DataFrame

Raises:

AssertionError –
- If regex is not a bool and to_replace is not None.
TypeError –
- If to_replace is not a scalar, array-like, dict, or None * If to_replace is a dict and value is not a list, dict, ndarray, or Series * If to_replace is None and regex is not compilable into a regular expression or is a list, dict, ndarray, or Series. * When replacing multiple bool or datetime64 objects and the arguments to to_replace does not match the type of the value being replaced
ValueError –
- If a list or an ndarray is passed to to_replace and value but they are not the same length.

See also

Series.fillna: Fill NA values.
DataFrame.fillna: Fill NA values.
Series.where: Replace values based on boolean condition.
DataFrame.where: Replace values based on boolean condition.
DataFrame.map: Apply a function to a Dataframe elementwise.
Series.map: Map values of Series according to an input mapping or function.
Series.str.replace: Simple string replacement.

Notes

Regex substitution is performed under the hood with re.sub. The rules for substitution for re.sub are the same.
Regular expressions will only substitute on strings, meaning you cannot provide, for example, a regular expression matching floating point numbers and expect the columns in your frame that have a numeric dtype to be matched. However, if those floating point numbers are strings, then you can do this.
This method has a lot of options. You are encouraged to experiment and play with this method to gain intuition about how it works.
When dict is used as the to_replace value, it is like key(s) in the dict are the to_replace part and value(s) in the dict are the value parameter.

Examples

Scalar `to_replace` and `value`

>>> s = pd.Series([1, 2, 3, 4, 5])
>>> s.replace(1, 5)
0    5
1    2
2    3
3    4
4    5
dtype: int64

>>> df = pd.DataFrame(
...     {
...         "A": [0, 1, 2, 3, 4],
...         "B": [5, 6, 7, 8, 9],
...         "C": ["a", "b", "c", "d", "e"],
...     }
... )
>>> df.replace(0, 5)
    A  B  C
0  5  5  a
1  1  6  b
2  2  7  c
3  3  8  d
4  4  9  e

List-like `to_replace`

>>> df.replace([0, 1, 2, 3], 4)
    A  B  C
4  5  a
4  6  b
4  7  c
4  8  d
4  9  e

>>> df.replace([0, 1, 2, 3], [4, 3, 2, 1])
    A  B  C
4  5  a
3  6  b
2  7  c
1  8  d
4  9  e

dict-like `to_replace`

>>> df.replace({0: 10, 1: 100})
        A  B  C
 10  5  a
100  6  b
  2  7  c
  3  8  d
  4  9  e

>>> df.replace({"A": 0, "B": 5}, 100)
        A    B  C
100  100  a
  1    6  b
  2    7  c
  3    8  d
  4    9  e

>>> df.replace({"A": {0: 100, 4: 400}})
        A  B  C
100  5  a
  1  6  b
  2  7  c
  3  8  d
400  9  e

Regular expression `to_replace`

>>> df = pd.DataFrame({"A": ["bat", "foo", "bait"], "B": ["abc", "bar", "xyz"]})
>>> df.replace(to_replace=r"^ba.$", value="new", regex=True)
        A    B
0   new  abc
1   foo  new
2  bait  xyz

>>> df.replace({"A": r"^ba.$"}, {"A": "new"}, regex=True)
        A    B
0   new  abc
1   foo  bar
2  bait  xyz

>>> df.replace(regex=r"^ba.$", value="new")
        A    B
0   new  abc
1   foo  new
2  bait  xyz

>>> df.replace(regex={r"^ba.$": "new", "foo": "xyz"})
        A    B
0   new  abc
1   xyz  new
2  bait  xyz

>>> df.replace(regex=[r"^ba.$", "foo"], value="new")
        A    B
0   new  abc
1   new  new
2  bait  xyz

Compare the behavior of s.replace({'a': None}) and s.replace('a', None) to understand the peculiarities of the to_replace parameter:

>>> s = pd.Series([10, "a", "a", "b", "a"])

When one uses a dict as the to_replace value, it is like the value(s) in the dict are equal to the value parameter. s.replace({'a': None}) is equivalent to s.replace(to_replace={'a': None}, value=None):

>>> s.replace({"a": None})
    10
  None
  None
     b
  None
dtype: object

If None is explicitly passed for value, it will be respected:

>>> s.replace("a", None)
    10
  None
  None
     b
  None
dtype: object

When regex=True, value is not None and to_replace is a string, the replacement will be applied in all columns of the DataFrame.

>>> df = pd.DataFrame(
...     {
...         "A": [0, 1, 2, 3, 4],
...         "B": ["a", "b", "c", "d", "e"],
...         "C": ["f", "g", "h", "i", "j"],
...     }
... )

>>> df.replace(to_replace="^[a-g]", value="e", regex=True)
    A  B  C
0  e  e
1  e  e
2  e  h
3  e  i
4  e  j

If value is not None and to_replace is a dictionary, the dictionary keys will be the DataFrame columns that the replacement will be applied.

>>> df.replace(to_replace={"B": "^[a-c]", "C": "^[h-j]"}, value="e", regex=True)
    A  B  C
0  e  f
1  e  g
2  e  e
3  d  e
4  e  e

resample(rule, closed: Literal['right', 'left'] | None = None, label: Literal['right', 'left'] | None = None, convention: Literal['start', 'end', 's', 'e'] = 'start', on: Level | None = None, level: Level | None = None, origin: str | TimestampConvertibleTypes = 'start_day', offset: TimedeltaConvertibleTypes | None = None, group_keys: bool = False) → Resampler

Resample time-series data.

Convenience method for frequency conversion and resampling of time series. The object must have a datetime-like index (DatetimeIndex, PeriodIndex, or TimedeltaIndex), or the caller must pass the label of a datetime-like series/index to the on/level keyword parameter.

Parameters:

rule (DateOffset, Timedelta or str) – The offset string or object representing target conversion.
closed ({'right', 'left'}, default None) – Which side of bin interval is closed. The default is ‘left’ for all frequency offsets except for ‘ME’, ‘YE’, ‘QE’, ‘BME’, ‘BA’, ‘BQE’, and ‘W’ which all have a default of ‘right’.
label ({'right', 'left'}, default None) – Which bin edge label to label bucket with. The default is ‘left’ for all frequency offsets except for ‘ME’, ‘YE’, ‘QE’, ‘BME’, ‘BA’, ‘BQE’, and ‘W’ which all have a default of ‘right’.
convention ({'start', 'end', 's', 'e'}, default 'start') – For PeriodIndex only, controls whether to use the start or end of rule.
on (str, optional) – For a DataFrame, column to use instead of index for resampling. Column must be datetime-like.
level (str or int, optional) – For a MultiIndex, level (name or number) to use for resampling. level must be datetime-like.
origin (Timestamp or str, default 'start_day') –
The timestamp on which to adjust the grouping. The timezone of origin must match the timezone of the index. If string, must be Timestamp convertible or one of the following:
- ’epoch’: origin is 1970-01-01
- ’start’: origin is the first value of the timeseries
- ’start_day’: origin is the first day at midnight of the timeseries
- ’end’: origin is the last value of the timeseries
- ’end_day’: origin is the ceiling midnight of the last day
Note

Only takes effect for Tick-frequencies (i.e. fixed frequencies like days, hours, and minutes, rather than months or quarters).
offset (Timedelta or str, default is None) – An offset timedelta added to the origin.
group_keys (bool, default False) –
Whether to include the group keys in the result index when using .apply() on the resampled object.

Changed in version 2.0.0: group_keys now defaults to False.

Returns:

Resampler object.

Return type:

pandas.api.typing.Resampler

See also

Series.resample: Resample a Series.
DataFrame.resample: Resample a DataFrame.
groupby: Group Series/DataFrame by mapping, function, label, or list of labels.
asfreq: Reindex a Series/DataFrame with the given frequency without grouping.

Notes

See the user guide for more.

To learn more about the offset strings, please see this link.

Examples

Start by creating a series with 9 one minute timestamps.

>>> index = pd.date_range("1/1/2000", periods=9, freq="min")
>>> series = pd.Series(range(9), index=index)
>>> series
2000-01-01 00:00:00    0
2000-01-01 00:01:00    1
2000-01-01 00:02:00    2
2000-01-01 00:03:00    3
2000-01-01 00:04:00    4
2000-01-01 00:05:00    5
2000-01-01 00:06:00    6
2000-01-01 00:07:00    7
2000-01-01 00:08:00    8
Freq: min, dtype: int64

Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin.

>>> series.resample("3min").sum()
2000-01-01 00:00:00     3
2000-01-01 00:03:00    12
2000-01-01 00:06:00    21
Freq: 3min, dtype: int64

Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. Please note that the value in the bucket used as the label is not included in the bucket, which it labels. For example, in the original series the bucket 2000-01-01 00:03:00 contains the value 3, but the summed value in the resampled bucket with the label 2000-01-01 00:03:00 does not include 3 (if it did, the summed value would be 6, not 3).

>>> series.resample("3min", label="right").sum()
2000-01-01 00:03:00     3
2000-01-01 00:06:00    12
2000-01-01 00:09:00    21
Freq: 3min, dtype: int64

To include this value close the right side of the bin interval, as shown below.

>>> series.resample("3min", label="right", closed="right").sum()
2000-01-01 00:00:00     0
2000-01-01 00:03:00     6
2000-01-01 00:06:00    15
2000-01-01 00:09:00    15
Freq: 3min, dtype: int64

Upsample the series into 30 second bins.

>>> series.resample("30s").asfreq()[0:5]  # Select first 5 rows
2000-01-01 00:00:00   0.0
2000-01-01 00:00:30   NaN
2000-01-01 00:01:00   1.0
2000-01-01 00:01:30   NaN
2000-01-01 00:02:00   2.0
Freq: 30s, dtype: float64

Upsample the series into 30 second bins and fill the NaN values using the ffill method.

>>> series.resample("30s").ffill()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    0
2000-01-01 00:01:00    1
2000-01-01 00:01:30    1
2000-01-01 00:02:00    2
Freq: 30s, dtype: int64

Upsample the series into 30 second bins and fill the NaN values using the bfill method.

>>> series.resample("30s").bfill()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    1
2000-01-01 00:01:00    1
2000-01-01 00:01:30    2
2000-01-01 00:02:00    2
Freq: 30s, dtype: int64

Pass a custom function via apply

>>> def custom_resampler(arraylike):
...     return np.sum(arraylike) + 5
>>> series.resample("3min").apply(custom_resampler)
2000-01-01 00:00:00     8
2000-01-01 00:03:00    17
2000-01-01 00:06:00    26
Freq: 3min, dtype: int64

For a Series with a PeriodIndex, the keyword convention can be used to control whether to use the start or end of rule.

Resample a year by quarter using ‘start’ convention. Values are assigned to the first quarter of the period.

>>> s = pd.Series(
...     [1, 2], index=pd.period_range("2012-01-01", freq="Y", periods=2)
... )
>>> s
2012    1
2013    2
Freq: Y-DEC, dtype: int64
>>> s.resample("Q", convention="start").asfreq()
2012Q1    1.0
2012Q2    NaN
2012Q3    NaN
2012Q4    NaN
2013Q1    2.0
2013Q2    NaN
2013Q3    NaN
2013Q4    NaN
Freq: Q-DEC, dtype: float64

Resample quarters by month using ‘end’ convention. Values are assigned to the last month of the period.

>>> q = pd.Series(
...     [1, 2, 3, 4], index=pd.period_range("2018-01-01", freq="Q", periods=4)
... )
>>> q
2018Q1    1
2018Q2    2
2018Q3    3
2018Q4    4
Freq: Q-DEC, dtype: int64
>>> q.resample("M", convention="end").asfreq()
2018-03    1.0
2018-04    NaN
2018-05    NaN
2018-06    2.0
2018-07    NaN
2018-08    NaN
2018-09    3.0
2018-10    NaN
2018-11    NaN
2018-12    4.0
Freq: M, dtype: float64

For DataFrame objects, the keyword on can be used to specify the column instead of the index for resampling.

>>> df = pd.DataFrame([10, 11, 9, 13, 14, 18, 17, 19], columns=["price"])
>>> df["volume"] = [50, 60, 40, 100, 50, 100, 40, 50]
>>> df["week_starting"] = pd.date_range("01/01/2018", periods=8, freq="W")
>>> df
   price  volume week_starting
0     10      50    2018-01-07
1     11      60    2018-01-14
2      9      40    2018-01-21
3     13     100    2018-01-28
4     14      50    2018-02-04
5     18     100    2018-02-11
6     17      40    2018-02-18
7     19      50    2018-02-25
>>> df.resample("ME", on="week_starting").mean()
               price  volume
week_starting
2018-01-31     10.75    62.5
2018-02-28     17.00    60.0

For a DataFrame with MultiIndex, the keyword level can be used to specify on which level the resampling needs to take place.

>>> days = pd.date_range("1/1/2000", periods=4, freq="D")
>>> df2 = pd.DataFrame(
...     [
...         [10, 50],
...         [11, 60],
...         [9, 40],
...         [13, 100],
...         [14, 50],
...         [18, 100],
...         [17, 40],
...         [19, 50],
...     ],
...     columns=["price", "volume"],
...     index=pd.MultiIndex.from_product([days, ["morning", "afternoon"]]),
... )
>>> df2
                      price  volume
2000-01-01 morning       10      50
           afternoon     11      60
2000-01-02 morning        9      40
           afternoon     13     100
2000-01-03 morning       14      50
           afternoon     18     100
2000-01-04 morning       17      40
           afternoon     19      50
>>> df2.resample("D", level=0).sum()
            price  volume
2000-01-01     21     110
2000-01-02     22     140
2000-01-03     32     150
2000-01-04     36      90

If you want to adjust the start of the bins based on a fixed timestamp:

>>> start, end = "2000-10-01 23:30:00", "2000-10-02 00:30:00"
>>> rng = pd.date_range(start, end, freq="7min")
>>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
>>> ts
2000-10-01 23:30:00     0
2000-10-01 23:37:00     3
2000-10-01 23:44:00     6
2000-10-01 23:51:00     9
2000-10-01 23:58:00    12
2000-10-02 00:05:00    15
2000-10-02 00:12:00    18
2000-10-02 00:19:00    21
2000-10-02 00:26:00    24
Freq: 7min, dtype: int64

>>> ts.resample("17min").sum()
2000-10-01 23:14:00     0
2000-10-01 23:31:00     9
2000-10-01 23:48:00    21
2000-10-02 00:05:00    54
2000-10-02 00:22:00    24
Freq: 17min, dtype: int64

>>> ts.resample("17min", origin="epoch").sum()
2000-10-01 23:18:00     0
2000-10-01 23:35:00    18
2000-10-01 23:52:00    27
2000-10-02 00:09:00    39
2000-10-02 00:26:00    24
Freq: 17min, dtype: int64

>>> ts.resample("17min", origin="2000-01-01").sum()
2000-10-01 23:24:00     3
2000-10-01 23:41:00    15
2000-10-01 23:58:00    45
2000-10-02 00:15:00    45
Freq: 17min, dtype: int64

If you want to adjust the start of the bins with an offset Timedelta, the two following lines are equivalent:

>>> ts.resample("17min", origin="start").sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17min, dtype: int64

>>> ts.resample("17min", offset="23h30min").sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17min, dtype: int64

If you want to take the largest Timestamp as the end of the bins:

>>> ts.resample("17min", origin="end").sum()
2000-10-01 23:35:00     0
2000-10-01 23:52:00    18
2000-10-02 00:09:00    27
2000-10-02 00:26:00    63
Freq: 17min, dtype: int64

In contrast with the start_day, you can use end_day to take the ceiling midnight of the largest Timestamp as the end of the bins and drop the bins not containing data:

>>> ts.resample("17min", origin="end_day").sum()
2000-10-01 23:38:00     3
2000-10-01 23:55:00    15
2000-10-02 00:12:00    45
2000-10-02 00:29:00    45
Freq: 17min, dtype: int64

reset_index(level: IndexLabel | None = None, *, drop: bool = False, inplace: bool = False, col_level: Hashable = 0, col_fill: Hashable = '', allow_duplicates: bool | lib.NoDefault = <no_default>, names: Hashable | Sequence[Hashable] | None = None) → DataFrame | None

Reset the index, or a level of it.

Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels.

Parameters:

level (int, str, tuple, or list, default None) – Only remove the given levels from the index. Removes all levels by default.
drop (bool, default False) – Do not try to insert index into dataframe columns. This resets the index to the default integer index.
inplace (bool, default False) – Whether to modify the DataFrame rather than creating a new one.
col_level (int or str, default 0) – If the columns have multiple levels, determines which level the labels are inserted into. By default it is inserted into the first level.
col_fill (object, default '') – If the columns have multiple levels, determines how the other levels are named. If None then the index name is repeated.
allow_duplicates (bool, optional, default lib.no_default) – Allow duplicate column labels to be created.
names (int, str or 1-dimensional list, default None) – Using the given string, rename the DataFrame column which contains the index data. If the DataFrame has a MultiIndex, this has to be a list with length equal to the number of levels.

Returns:

DataFrame with the new index or None if inplace=True.

Return type:

DataFrame or None

See also

DataFrame.set_index: Opposite of reset_index.
DataFrame.reindex: Change to new indices or expand indices.
DataFrame.reindex_like: Change to same indices as other DataFrame.

Examples

>>> df = pd.DataFrame(
...     [("bird", 389.0), ("bird", 24.0), ("mammal", 80.5), ("mammal", np.nan)],
...     index=["falcon", "parrot", "lion", "monkey"],
...     columns=("class", "max_speed"),
... )
>>> df
         class  max_speed
falcon    bird      389.0
parrot    bird       24.0
lion    mammal       80.5
monkey  mammal        NaN

When we reset the index, the old index is added as a column, and a new sequential index is used:

>>> df.reset_index()
    index   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN

We can use the drop parameter to avoid the old index being added as a column:

>>> df.reset_index(drop=True)
    class  max_speed
0    bird      389.0
1    bird       24.0
2  mammal       80.5
3  mammal        NaN

You can also use reset_index with MultiIndex.

>>> index = pd.MultiIndex.from_tuples(
...     [
...         ("bird", "falcon"),
...         ("bird", "parrot"),
...         ("mammal", "lion"),
...         ("mammal", "monkey"),
...     ],
...     names=["class", "name"],
... )
>>> columns = pd.MultiIndex.from_tuples([("speed", "max"), ("species", "type")])
>>> df = pd.DataFrame(
...     [(389.0, "fly"), (24.0, "fly"), (80.5, "run"), (np.nan, "jump")],
...     index=index,
...     columns=columns,
... )
>>> df
               speed species
                 max    type
class  name
bird   falcon  389.0     fly
       parrot   24.0     fly
mammal lion     80.5     run
       monkey    NaN    jump

Using the names parameter, choose a name for the index column:

>>> df.reset_index(names=["classes", "names"])
  classes   names  speed species
                     max    type
0    bird  falcon  389.0     fly
1    bird  parrot   24.0     fly
2  mammal    lion   80.5     run
3  mammal  monkey    NaN    jump

If the index has multiple levels, we can reset a subset of them:

>>> df.reset_index(level="class")
         class  speed species
                  max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

If we are not dropping the index, by default, it is placed in the top level. We can place it in another level:

>>> df.reset_index(level="class", col_level=1)
                speed species
         class    max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

When the index is inserted under another level, we can specify under which one with the parameter col_fill:

>>> df.reset_index(level="class", col_level=1, col_fill="species")
              species  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump

If we specify a nonexistent level for col_fill, it is created:

>>> df.reset_index(level="class", col_level=1, col_fill="genus")
                genus  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump

rfloordiv(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Integer division of dataframe and other, element-wise (binary operator rfloordiv).

Equivalent to other // dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, floordiv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

rmod(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Modulo of dataframe and other, element-wise (binary operator rmod).

Equivalent to other % dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, mod.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

rmul(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Multiplication of dataframe and other, element-wise (binary operator rmul).

Equivalent to other * dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, mul.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

Provide rolling window calculations.

Parameters:

window (int, timedelta, str, offset, or BaseIndexer subclass) –
Interval of the moving window.

If an integer, the delta between the start and end of each window. The number of points in the window depends on the closed argument.

If a timedelta, str, or offset, the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes. To learn more about the offsets & frequency strings, please see this link.

If a BaseIndexer subclass, the window boundaries based on the defined get_window_bounds method. Additional rolling keyword arguments, namely min_periods, center, closed and step will be passed to get_window_bounds.
min_periods (int, default None) –
Minimum number of observations in window required to have a value; otherwise, result is np.nan.

For a window that is specified by an offset, min_periods will default to 1.

For a window that is specified by an integer, min_periods will default to the size of the window.
center (bool, default False) –
If False, set the window labels as the right edge of the window index.

If True, set the window labels as the center of the window index.
win_type (str, default None) –
If None, all points are evenly weighted.

If a string, it must be a valid scipy.signal window function.

Certain Scipy window types require additional parameters to be passed in the aggregation function. The additional parameters must match the keywords specified in the Scipy window type method signature.
on (str, optional) –
For a DataFrame, a column label or Index level on which to calculate the rolling window, rather than the DataFrame’s index.

Provided integer column is ignored and excluded from result since an integer index is not used to calculate the rolling window.
closed (str, default None) –
Determines the inclusivity of points in the window

If 'right', uses the window (first, last] meaning the last point is included in the calculations.

If 'left', uses the window [first, last) meaning the first point is included in the calculations.

If 'both', uses the window [first, last] meaning all points in the window are included in the calculations.

If 'neither', uses the window (first, last) meaning the first and last points in the window are excluded from calculations.

() and [] are referencing open and closed set notation respetively.

Default None ('right').
step (int, default None) – Evaluate the window at every step result, equivalent to slicing as [::step]. window must be an integer. Using a step argument other than None or 1 will produce a result with a different shape than the input.
method (str {'single', 'table'}, default 'single') –
Execute the rolling operation per single column or row ('single') or over the entire object ('table').

This argument is only implemented when specifying engine='numba' in the method call.

Returns:

An instance of Window is returned if win_type is passed. Otherwise, an instance of Rolling is returned.

Return type:

pandas.api.typing.Window or pandas.api.typing.Rolling

See also

expanding: Provides expanding transformations.
ewm: Provides exponential weighted functions.

Notes

See Windowing Operations for further usage details and examples.

Examples

>>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

window

Rolling sum with a window length of 2 observations.

>>> df.rolling(2).sum()
     B
NaN
1.0
3.0
NaN
NaN

Rolling sum with a window span of 2 seconds.

>>> df_time = pd.DataFrame(
...     {"B": [0, 1, 2, np.nan, 4]},
...     index=[
...         pd.Timestamp("20130101 09:00:00"),
...         pd.Timestamp("20130101 09:00:02"),
...         pd.Timestamp("20130101 09:00:03"),
...         pd.Timestamp("20130101 09:00:05"),
...         pd.Timestamp("20130101 09:00:06"),
...     ],
... )

>>> df_time
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  2.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0

>>> df_time.rolling("2s").sum()
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  3.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0

Rolling sum with forward looking windows with 2 observations.

>>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
>>> df.rolling(window=indexer, min_periods=1).sum()
     B
0  1.0
1  3.0
2  2.0
3  4.0
4  4.0

min_periods

Rolling sum with a window length of 2 observations, but only needs a minimum of 1 observation to calculate a value.

>>> df.rolling(2, min_periods=1).sum()
     B
0.0
1.0
3.0
2.0
4.0

center

Rolling sum with the result assigned to the center of the window index.

>>> df.rolling(3, min_periods=1, center=True).sum()
     B
1.0
3.0
3.0
6.0
4.0

>>> df.rolling(3, min_periods=1, center=False).sum()
     B
0.0
1.0
3.0
3.0
6.0

step

Rolling sum with a window length of 2 observations, minimum of 1 observation to calculate a value, and a step of 2.

>>> df.rolling(2, min_periods=1, step=2).sum()
     B
0  0.0
2  3.0
4  4.0

win_type

Rolling sum with a window length of 2, using the Scipy 'gaussian' window type. std is required in the aggregation function.

>>> df.rolling(2, win_type="gaussian").sum(std=3)
          B
      NaN
 0.986207
 2.958621
      NaN
      NaN

on

Rolling sum with a window length of 2 days.

>>> df = pd.DataFrame(
...     {
...         "A": [
...             pd.to_datetime("2020-01-01"),
...             pd.to_datetime("2020-01-01"),
...             pd.to_datetime("2020-01-02"),
...         ],
...         "B": [1, 2, 3],
...     },
...     index=pd.date_range("2020", periods=3),
... )

>>> df
                    A  B
2020-01-01 2020-01-01  1
2020-01-02 2020-01-01  2
2020-01-03 2020-01-02  3

>>> df.rolling("2D", on="A").sum()
                    A    B
2020-01-01 2020-01-01  1.0
2020-01-02 2020-01-01  3.0
2020-01-03 2020-01-02  6.0

round(decimals: int | dict[IndexLabel, int] | Series = 0, *args, **kwargs) → DataFrame

Round numeric columns in a DataFrame to a variable number of decimal places.

Parameters:

decimals (int, dict, Series) – Number of decimal places to round each column to. If an int is given, round each column to the same number of places. Otherwise dict and Series round to variable numbers of places. Column names should be in the keys if decimals is a dict-like, or in the index if decimals is a Series. Any columns not included in decimals will be left as is. Elements of decimals which are not columns of the input will be ignored.
*args – Additional keywords have no effect but might be accepted for compatibility with numpy.
**kwargs – Additional keywords have no effect but might be accepted for compatibility with numpy.

Returns:

A DataFrame with the affected columns rounded to the specified number of decimal places.

Return type:

DataFrame

See also

numpy.around: Round a numpy array to the given number of decimals.
Series.round: Round a Series to the given number of decimals.

Notes

For values exactly halfway between rounded decimal values, pandas rounds to the nearest even value (e.g. -0.5 and 0.5 round to 0.0, 1.5 and 2.5 round to 2.0, etc.).

Examples

>>> df = pd.DataFrame(
...     [(0.21, 0.32), (0.01, 0.67), (0.66, 0.03), (0.21, 0.18)],
...     columns=["dogs", "cats"],
... )
>>> df
    dogs  cats
0  0.21  0.32
1  0.01  0.67
2  0.66  0.03
3  0.21  0.18

By providing an integer each column is rounded to the same number of decimal places

>>> df.round(1)
    dogs  cats
0   0.2   0.3
1   0.0   0.7
2   0.7   0.0
3   0.2   0.2

With a dict, the number of places for specific columns can be specified with the column names as key and the number of decimal places as value

>>> df.round({"dogs": 1, "cats": 0})
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0

Using a Series, the number of places for specific columns can be specified with the column names as index and the number of decimal places as value

>>> decimals = pd.Series([0, 1], index=["cats", "dogs"])
>>> df.round(decimals)
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0

static row_to_dict(row: Series) → dict[source]

Convert a metadata row to a plain Python metadata dict.

Pandas missing values such as NaN and pd.NA are converted to None. NumPy scalar values such as np.int64 and np.float64 are converted to native Python scalars so reconstructed Spectrum objects can be exported to JSON.

rpow(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Exponential power of dataframe and other, element-wise (binary operator rpow).

Equivalent to other ** dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, pow.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

rsub(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Subtraction of dataframe and other, element-wise (binary operator rsub).

Equivalent to other - dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, sub.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

rtruediv(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Floating division of dataframe and other, element-wise (binary operator rtruediv).

Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, truediv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

Return a random sample of items from an axis of object.

You can use random_state for reproducibility.

Parameters:

n (int, optional) – Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None.
frac (float, optional) – Fraction of axis items to return. Cannot be used with n.
replace (bool, default False) – Allow or disallow sampling of the same row more than once.
weights (str or ndarray-like, optional) – Default None results in equal probability weighting. If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed. When replace = False will not allow (n * max(weights) / sum(weights)) > 1 in order to avoid biased results. See the Notes below for more details.
random_state (int, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional) – If int, array-like, or BitGenerator, seed for random number generator. If np.random.RandomState or np.random.Generator, use as given. Default None results in sampling with the current state of np.random.
axis ({0 or 'index', 1 or 'columns', None}, default None) – Axis to sample. Accepts axis number or name. Default is stat axis for given data type. For Series this parameter is unused and defaults to None.
ignore_index (bool, default False) – If True, the resulting index will be labeled 0, 1, …, n - 1.

Returns:

A new object of same type as caller containing n items randomly sampled from the caller object.

Return type:

Series or DataFrame

See also

DataFrameGroupBy.sample: Generates random samples from each group of a DataFrame object.
SeriesGroupBy.sample: Generates random samples from each group of a Series object.
numpy.random.choice: Generates a random sample from a given 1-D numpy array.

Notes

If frac > 1, replacement should be set to True.

When replace = False will not allow (n * max(weights) / sum(weights)) > 1, since that would cause results to be biased. E.g. sampling 2 items without replacement with weights [100, 1, 1] would yield two last items in 1/2 of cases, instead of 1/102. This is similar to specifying n=4 without replacement on a Series with 3 elements.

Examples

>>> df = pd.DataFrame(
...     {
...         "num_legs": [2, 4, 8, 0],
...         "num_wings": [2, 0, 0, 0],
...         "num_specimen_seen": [10, 2, 1, 8],
...     },
...     index=["falcon", "dog", "spider", "fish"],
... )
>>> df
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
dog            4          0                  2
spider         8          0                  1
fish           0          0                  8

Extract 3 random elements from the Series df['num_legs']: Note that we use random_state to ensure the reproducibility of the examples.

>>> df["num_legs"].sample(n=3, random_state=1)
fish      0
spider    8
falcon    2
Name: num_legs, dtype: int64

A random 50% sample of the DataFrame with replacement:

>>> df.sample(frac=0.5, replace=True, random_state=1)
      num_legs  num_wings  num_specimen_seen
dog          4          0                  2
fish         0          0                  8

An upsample sample of the DataFrame with replacement: Note that replace parameter has to be True for frac parameter > 1.

>>> df.sample(frac=2, replace=True, random_state=1)
        num_legs  num_wings  num_specimen_seen
dog            4          0                  2
fish           0          0                  8
falcon         2          2                 10
falcon         2          2                 10
fish           0          0                  8
dog            4          0                  2
fish           0          0                  8
dog            4          0                  2

Using a DataFrame column as weights. Rows with larger value in the num_specimen_seen column are more likely to be sampled.

>>> df.sample(n=2, weights="num_specimen_seen", random_state=1)
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
fish           0          0                  8

select_dtypes(include=None, exclude=None) → DataFrame

Return a subset of the DataFrame’s columns based on the column dtypes.

This method allows for filtering columns based on their data types. It is useful when working with heterogeneous DataFrames where operations need to be performed on a specific subset of data types.

Parameters:

include (scalar or list-like) – A selection of dtypes or strings to be included/excluded. At least one of these parameters must be supplied.
exclude (scalar or list-like) – A selection of dtypes or strings to be included/excluded. At least one of these parameters must be supplied.

Returns:

The subset of the frame including the dtypes in include and excluding the dtypes in exclude.

Return type:

DataFrame

Raises:

ValueError –
- If both of include and exclude are empty * If include and exclude have overlapping elements
TypeError –
- If any kind of string dtype is passed in.

See also

DataFrame.dtypes: Return Series with the data type of each column.

Notes

To select all numeric types, use np.number or 'number'
To select strings you must use the object dtype, but note that this will return all object dtype columns. With pd.options.future.infer_string enabled, using "str" will work to select all string columns.
See the numpy dtype hierarchy
To select datetimes, use np.datetime64, 'datetime' or 'datetime64'
To select timedeltas, use np.timedelta64, 'timedelta' or 'timedelta64'
To select Pandas categorical dtypes, use 'category'
To select Pandas datetimetz dtypes, use 'datetimetz' or 'datetime64[ns, tz]'

Examples

>>> df = pd.DataFrame(
...     {"a": [1, 2] * 3, "b": [True, False] * 3, "c": [1.0, 2.0] * 3}
... )
>>> df
        a      b  c
0       1   True  1.0
1       2  False  2.0
2       1   True  1.0
3       2  False  2.0
4       1   True  1.0
5       2  False  2.0

>>> df.select_dtypes(include="bool")
   b
True
False
True
False
True
False

>>> df.select_dtypes(include=["float64"])
   c
1.0
2.0
1.0
2.0
1.0
2.0

>>> df.select_dtypes(exclude=["int64"])
       b    c
 True  1.0
False  2.0
 True  1.0
False  2.0
 True  1.0
False  2.0

sem(*, axis: Axis | None = 0, skipna: bool = True, ddof: int = 1, numeric_only: bool = False, **kwargs) → Series | Any

Return unbiased standard error of the mean over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters:

axis ({index (0), columns (1)}) –
For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.sem with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
numeric_only (bool, default False) – Include only float, int, boolean columns. Not implemented for Series.
**kwargs – Additional keywords passed.

Returns:

Unbiased standard error of the mean over requested axis.

Return type:

Series or DataFrame (if level specified)

See also

DataFrame.var: Return unbiased variance over requested axis.
DataFrame.std: Returns sample standard deviation over requested axis.

Examples

>>> s = pd.Series([1, 2, 3])
>>> round(s.sem(), 6)
0.57735

With a DataFrame

>>> df = pd.DataFrame({"a": [1, 2], "b": [2, 3]}, index=["tiger", "zebra"])
>>> df
       a   b
tiger  1   2
zebra  2   3
>>> df.sem()
a   0.5
b   0.5
dtype: float64

Using axis=1

>>> df.sem(axis=1)
tiger   0.5
zebra   0.5
dtype: float64

In this case, numeric_only should be set to True to avoid getting an error.

>>> df = pd.DataFrame({"a": [1, 2], "b": ["T", "Z"]}, index=["tiger", "zebra"])
>>> df.sem(numeric_only=True)
a   0.5
dtype: float64

set_axis(labels, *, axis: Axis = 0, copy: bool | lib.NoDefault = <no_default>) → DataFrame

Assign desired index to given axis.

Indexes for column or row labels can be changed by assigning a list-like or Index.

Parameters:

labels (list-like, Index) – The values for the new index.
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to update. The value 0 identifies the rows. For Series this parameter is unused and defaults to 0.
copy (bool, default False) –
This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.

Returns:

An object of type DataFrame.

Return type:

DataFrame

See also

DataFrame.rename_axis: Alter the name of the index or columns.

Examples

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})

Change the row labels.

>>> df.set_axis(["a", "b", "c"], axis="index")
    A  B
a  1  4
b  2  5
c  3  6

Change the column labels.

>>> df.set_axis(["I", "II"], axis="columns")
    I  II
0  1   4
1  2   5
2  3   6

set_flags(*, copy: bool | Literal[_NoDefault.no_default] = <no_default>, allows_duplicate_labels: bool | None = None) → Self

Return a new object with updated flags.

This method creates a shallow copy of the original object, preserving its underlying data while modifying its global flags. In particular, it allows you to update properties such as whether duplicate labels are permitted. This behavior is especially useful in method chains, where one wishes to adjust DataFrame or Series characteristics without altering the original object.

Parameters:

copy (bool, default False) –
This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.
allows_duplicate_labels (bool, optional) – Whether the returned object allows duplicate labels.

Returns:

The same type as the caller.

Return type:

Series or DataFrame

See also

DataFrame.attrs: Global metadata applying to this dataset.
DataFrame.flags: Global flags applying to this object.

Notes

This method returns a new object that’s a view on the same data as the input. Mutating the input or the output values will be reflected in the other.

This method is intended to be used in method chains.

“Flags” differ from “metadata”. Flags reflect properties of the pandas object (the Series or DataFrame). Metadata refer to properties of the dataset, and should be stored in DataFrame.attrs.

Examples

>>> df = pd.DataFrame({"A": [1, 2]})
>>> df.flags.allows_duplicate_labels
True
>>> df2 = df.set_flags(allows_duplicate_labels=False)
>>> df2.flags.allows_duplicate_labels
False

set_index(keys, *, drop: bool = True, append: bool = False, inplace: bool = False, verify_integrity: bool | Literal[_NoDefault.no_default] = <no_default>) → DataFrame | None

Set the DataFrame index using existing columns.

Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length). The index can replace the existing index or expand on it.

Parameters:

keys (label or array-like or list of labels/arrays) – This parameter can be either a single column key, a single array of the same length as the calling DataFrame, or a list containing an arbitrary combination of column keys and arrays. Here, “array” encompasses Series, Index, np.ndarray, and instances of Iterator.
drop (bool, default True) – Delete columns to be used as the new index.
append (bool, default False) – Whether to append columns to existing index. Setting to True will add the new columns to existing index. When set to False, the current index will be dropped from the DataFrame.
inplace (bool, default False) – Whether to modify the DataFrame rather than creating a new one.
verify_integrity (bool, default False) –
Check the new index for duplicates. Otherwise defer the check until necessary. Setting to False will improve the performance of this method.

Deprecated since version 3.0.0.

Returns:

Changed row labels or None if inplace=True.

Return type:

DataFrame or None

See also

DataFrame.reset_index: Opposite of set_index.
DataFrame.reindex: Change to new indices or expand indices.
DataFrame.reindex_like: Change to same indices as other DataFrame.

Examples

>>> df = pd.DataFrame(
...     {
...         "month": [1, 4, 7, 10],
...         "year": [2012, 2014, 2013, 2014],
...         "sale": [55, 40, 84, 31],
...     }
... )
>>> df
   month  year  sale
0      1  2012    55
1      4  2014    40
2      7  2013    84
3     10  2014    31

Set the index to become the ‘month’ column:

>>> df.set_index("month")
       year  sale
month
1      2012    55
4      2014    40
7      2013    84
10     2014    31

Create a MultiIndex using columns ‘year’ and ‘month’:

>>> df.set_index(["year", "month"])
            sale
year  month
2012  1     55
2014  4     40
2013  7     84
2014  10    31

Create a MultiIndex using an Index and a column:

>>> df.set_index([pd.Index([1, 2, 3, 4]), "year"])
         month  sale
   year
1  2012  1      55
2  2014  4      40
3  2013  7      84
4  2014  10     31

Create a MultiIndex using two Series:

>>> s = pd.Series([1, 2, 3, 4])
>>> df.set_index([s, s**2])
      month  year  sale
1 1       1  2012    55
2 4       4  2014    40
3 9       7  2013    84
4 16     10  2014    31

Append a column to the existing index:

>>> df = df.set_index("month")
>>> df.set_index("year", append=True)
              sale
month  year
1      2012    55
4      2014    40
7      2013    84
10     2014    31

>>> df.set_index("year", append=False)
       sale
year
2012    55
2014    40
2013    84
2014    31

property shape: tuple[int, int]

Return a tuple representing the dimensionality of the DataFrame.

Unlike the len() method, which only returns the number of rows, shape provides both row and column counts, making it a more informative method for understanding dataset size.

See also

numpy.ndarray.shape: Tuple of array dimensions.

Examples

>>> df = pd.DataFrame({"col1": [1, 2], "col2": [3, 4]})
>>> df.shape
(2, 2)

>>> df = pd.DataFrame({"col1": [1, 2], "col2": [3, 4], "col3": [5, 6]})
>>> df.shape
(2, 3)

shift(periods: int | Sequence[int] = 1, freq: Frequency | None = None, axis: Axis = 0, fill_value: Hashable = <no_default>, suffix: str | None = None) → DataFrame

Shift index by desired number of periods with an optional time freq.

When freq is not passed, shift the index without realigning the data. If freq is passed (in this case, the index must be date or datetime, or it will raise a NotImplementedError), the index will be increased using the periods and the freq. freq can be inferred when specified as “infer” as long as either freq or inferred_freq attribute is set in the index.

Parameters:

periods (int or Sequence) – Number of periods to shift. Can be positive or negative. If an iterable of ints, the data will be shifted once by each int. This is equivalent to shifting by one value at a time and concatenating all resulting frames. The resulting columns will have the shift suffixed to their column names. For multiple periods, axis must not be 1.
freq (DateOffset, tseries.offsets, timedelta, or str, optional) – Offset to use from the tseries module or time rule (e.g. ‘EOM’). If freq is specified then the index values are shifted but the data is not realigned. That is, use freq if you would like to extend the index when shifting and preserve the original data. If freq is specified as “infer” then it will be inferred from the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown.
axis ({0 or 'index', 1 or 'columns', None}, default None) – Shift direction. For Series this parameter is unused and defaults to 0.
fill_value (object, optional) – The scalar value to use for newly introduced missing values. the default depends on the dtype of self. For Boolean and numeric NumPy data types, np.nan is used. For datetime, timedelta, or period data, etc. NaT is used. For extension dtypes, self.dtype.na_value is used.
suffix (str, optional) – If str and periods is an iterable, this is added after the column name and before the shift value for each shifted column name. For Series this parameter is unused and defaults to None.

Returns:

Copy of input object, shifted.

Return type:

DataFrame

See also

Index.shift: Shift values of Index.
DatetimeIndex.shift: Shift values of DatetimeIndex.
PeriodIndex.shift: Shift values of PeriodIndex.

Examples

>>> df = pd.DataFrame(
...     [[10, 13, 17], [20, 23, 27], [15, 18, 22], [30, 33, 37], [45, 48, 52]],
...     columns=["Col1", "Col2", "Col3"],
...     index=pd.date_range("2020-01-01", "2020-01-05"),
... )
>>> df
            Col1  Col2  Col3
2020-01-01    10    13    17
2020-01-02    20    23    27
2020-01-03    15    18    22
2020-01-04    30    33    37
2020-01-05    45    48    52

>>> df.shift(periods=3)
            Col1  Col2  Col3
2020-01-01   NaN   NaN   NaN
2020-01-02   NaN   NaN   NaN
2020-01-03   NaN   NaN   NaN
2020-01-04  10.0  13.0  17.0
2020-01-05  20.0  23.0  27.0

>>> df.shift(periods=1, axis="columns")
            Col1  Col2  Col3
2020-01-01   NaN    10    13
2020-01-02   NaN    20    23
2020-01-03   NaN    15    18
2020-01-04   NaN    30    33
2020-01-05   NaN    45    48

>>> df.shift(periods=3, fill_value=0)
            Col1  Col2  Col3
2020-01-01     0     0     0
2020-01-02     0     0     0
2020-01-03     0     0     0
2020-01-04    10    13    17
2020-01-05    20    23    27

>>> df.shift(periods=3, freq="D")
            Col1  Col2  Col3
2020-01-04    10    13    17
2020-01-05    20    23    27
2020-01-06    15    18    22
2020-01-07    30    33    37
2020-01-08    45    48    52

>>> df.shift(periods=3, freq="infer")
            Col1  Col2  Col3
2020-01-04    10    13    17
2020-01-05    20    23    27
2020-01-06    15    18    22
2020-01-07    30    33    37
2020-01-08    45    48    52

>>> df["Col1"].shift(periods=[0, 1, 2])
            Col1_0  Col1_1  Col1_2
2020-01-01      10     NaN     NaN
2020-01-02      20    10.0     NaN
2020-01-03      15    20.0    10.0
2020-01-04      30    15.0    20.0
2020-01-05      45    30.0    15.0

property size: int

Return an int representing the number of elements in this object.

Return the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame.

See also

numpy.ndarray.size: Number of elements in the array.

Examples

>>> s = pd.Series({"a": 1, "b": 2, "c": 3})
>>> s.size
3

>>> df = pd.DataFrame({"col1": [1, 2], "col2": [3, 4]})
>>> df.size
4

skew(*, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, **kwargs) → Series | Any

Return unbiased skew over requested axis.

Normalized by N-1.

Parameters:

axis ({index (0), columns (1)}) –
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

For DataFrames, specifying axis=None will apply the aggregation across both axes.

Added in version 2.0.0.
skipna (bool, default True) – Exclude NA/null values when computing the result.
numeric_only (bool, default False) – Include only float, int, boolean columns.
**kwargs – Additional keyword arguments to be passed to the function.

Returns:

Unbiased skew over requested axis.

Return type:

Series or scalar

See also

Dataframe.kurt: Returns unbiased kurtosis over requested axis.

Examples

>>> s = pd.Series([1, 2, 3])
>>> s.skew()
0.0

With a DataFrame

>>> df = pd.DataFrame(
...     {"a": [1, 2, 3], "b": [2, 3, 4], "c": [1, 3, 5]},
...     index=["tiger", "zebra", "cow"],
... )
>>> df
        a   b   c
tiger   1   2   1
zebra   2   3   3
cow     3   4   5
>>> df.skew()
a   0.0
b   0.0
c   0.0
dtype: float64

Using axis=1

>>> df.skew(axis=1)
tiger   1.732051
zebra  -1.732051
cow     0.000000
dtype: float64

In this case, numeric_only should be set to True to avoid getting an error.

>>> df = pd.DataFrame(
...     {"a": [1, 2, 3], "b": ["T", "Z", "X"]}, index=["tiger", "zebra", "cow"]
... )
>>> df.skew(numeric_only=True)
a   0.0
dtype: float64

sort_index(*, axis: Axis = 0, level: IndexLabel | None = None, ascending: bool | Sequence[bool] = True, inplace: bool = False, kind: SortKind = 'quicksort', na_position: NaPosition = 'last', sort_remaining: bool = True, ignore_index: bool = False, key: IndexKeyFunc | None = None) → DataFrame | None

Sort object by labels (along an axis).

Returns a new DataFrame sorted by label if inplace argument is False, otherwise updates the original DataFrame and returns None.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis along which to sort. The value 0 identifies the rows, and 1 identifies the columns.
level (int or level name or list of ints or list of level names) – If not None, sort on values in specified index level(s).
ascending (bool or list-like of bools, default True) – Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually.
inplace (bool, default False) – Whether to modify the DataFrame rather than creating a new one.
kind ({'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort') – Choice of sorting algorithm. See also numpy.sort() for more information. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.
na_position ({'first', 'last'}, default 'last') – Puts NaNs at the beginning if first; last puts NaNs at the end. Not implemented for MultiIndex.
sort_remaining (bool, default True) – If True and sorting by level and index is multilevel, sort by other levels too (in order) after sorting by specified level.
ignore_index (bool, default False) – If True, the resulting axis will be labeled 0, 1, …, n - 1.
key (callable, optional) – If not None, apply the key function to the index values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect an Index and return an Index of the same shape. For MultiIndex inputs, the key is applied per level.

Returns:

The original DataFrame sorted by the labels or None if inplace=True.

Return type:

DataFrame or None

See also

Series.sort_index: Sort Series by the index.
DataFrame.sort_values: Sort DataFrame by the value.
Series.sort_values: Sort Series by the value.

Examples

>>> df = pd.DataFrame(
...     [1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150], columns=["A"]
... )
>>> df.sort_index()
     A
1    4
29   2
100  1
150  5
234  3

By default, it sorts in ascending order, to sort in descending order, use ascending=False

>>> df.sort_index(ascending=False)
     A
3
5
1
 2
  4

A key function can be specified which is applied to the index before sorting. For a MultiIndex this is applied to each level separately.

>>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=["A", "b", "C", "d"])
>>> df.sort_index(key=lambda x: x.str.lower())
   a
A  1
b  2
C  3
d  4

sort_values(by, inplace=False, **kwargs)[source]

Sort by the values along either axis.

Parameters:

by (str or list of str) –
Name or list of names to sort by.
- if axis is 0 or ‘index’ then by may contain index levels and/or column labels.
- if axis is 1 or ‘columns’ then by may contain column levels and/or index labels.
axis ("{0 or 'index', 1 or 'columns'}", default 0) – Axis to be sorted.
ascending (bool or list of bool, default True) – Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.
inplace (bool, default False) – If True, perform operation in-place.
kind ({'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort') – Choice of sorting algorithm. See also numpy.sort() for more information. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.
na_position ({'first', 'last'}, default 'last') – Puts NaNs at the beginning if first; last puts NaNs at the end.
ignore_index (bool, default False) – If True, the resulting axis will be labeled 0, 1, …, n - 1.
key (callable, optional) – Apply the key function to the values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect a Series and return a Series with the same shape as the input. It will be applied to each column in by independently. The values in the returned Series will be used as the keys for sorting.

Returns:

DataFrame with sorted values or None if inplace=True.

Return type:

DataFrame or None

See also

DataFrame.sort_index: Sort a DataFrame by the index.
Series.sort_values: Similar method for a Series.

Examples

>>> df = pd.DataFrame(
...     {
...         "col1": ["A", "A", "B", np.nan, "D", "C"],
...         "col2": [2, 1, 9, 8, 7, 4],
...         "col3": [0, 1, 9, 4, 2, 3],
...         "col4": ["a", "B", "c", "D", "e", "F"],
...     }
... )
>>> df
  col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F

Sort by a single column

In this case, we are sorting the rows according to values in col1:

>>> df.sort_values(by=["col1"])
  col1  col2  col3 col4
  A     2     0    a
  A     1     1    B
  B     9     9    c
  C     4     3    F
  D     7     2    e
NaN     8     4    D

Sort by multiple columns

You can also provide multiple columns to by argument, as shown below. In this example, the rows are first sorted according to col1, and then the rows that have an identical value in col1 are sorted according to col2.

>>> df.sort_values(by=["col1", "col2"])
  col1  col2  col3 col4
  A     1     1    B
  A     2     0    a
  B     9     9    c
  C     4     3    F
  D     7     2    e
NaN     8     4    D

Sort in a descending order

The sort order can be reversed using ascending argument, as shown below:

>>> df.sort_values(by="col1", ascending=False)
  col1  col2  col3 col4
  D     7     2    e
  C     4     3    F
  B     9     9    c
  A     2     0    a
  A     1     1    B
NaN     8     4    D

Placing any NA first

Note that in the above example, the rows that contain an NA value in their col1 are placed at the end of the dataframe. This behavior can be modified via na_position argument, as shown below:

>>> df.sort_values(by="col1", ascending=False, na_position="first")
  col1  col2  col3 col4
NaN     8     4    D
  D     7     2    e
  C     4     3    F
  B     9     9    c
  A     2     0    a
  A     1     1    B

Customized sort order

The key argument allows for a further customization of sorting behaviour. For example, you may want to ignore the letter’s case when sorting strings:

>>> df.sort_values(by="col4", key=lambda col: col.str.lower())
   col1  col2  col3 col4
  A     2     0    a
  A     1     1    B
  B     9     9    c
NaN     8     4    D
  D     7     2    e
  C     4     3    F

Another typical example is natural sorting. This can be done using natsort package, which provides a function to generate a key to sort data in their natural order:

>>> df = pd.DataFrame(
...     {
...         "hours": ["0hr", "128hr", "0hr", "64hr", "64hr", "128hr"],
...         "mins": [
...             "10mins",
...             "40mins",
...             "40mins",
...             "40mins",
...             "10mins",
...             "10mins",
...         ],
...         "value": [10, 20, 30, 40, 50, 60],
...     }
... )
>>> df
   hours    mins  value
0    0hr  10mins     10
1  128hr  40mins     20
2    0hr  40mins     30
3   64hr  40mins     40
4   64hr  10mins     50
5  128hr  10mins     60
>>> from natsort import natsort_keygen
>>> df.sort_values(
...     by=["hours", "mins"],
...     key=natsort_keygen(),
... )
   hours    mins  value
0    0hr  10mins     10
2    0hr  40mins     30
4   64hr  10mins     50
3   64hr  40mins     40
5  128hr  10mins     60
1  128hr  40mins     20

sparse: alias of SparseFrameAccessor

squeeze(axis: Axis | None = None) → Scalar | Series | DataFrame

Squeeze 1 dimensional axis objects into scalars.

Series or DataFrames with a single element are squeezed to a scalar. DataFrames with a single column or a single row are squeezed to a Series. Otherwise the object is unchanged.

This method is most useful when you don’t know if your object is a Series or DataFrame, but you do know it has just a single column. In that case you can safely call squeeze to ensure you have a Series.

Parameters:: axis ({0 or 'index', 1 or 'columns', None}, default None) – A specific axis to squeeze. By default, all length-1 axes are squeezed. For Series this parameter is unused and defaults to None.
Returns:: The projection after squeezing axis or all the axes.
Return type:: DataFrame, Series, or scalar

See also

Series.iloc: Integer-location based indexing for selecting scalars.
DataFrame.iloc: Integer-location based indexing for selecting Series.
Series.to_frame: Inverse of DataFrame.squeeze for a single-column DataFrame.

Examples

>>> primes = pd.Series([2, 3, 5, 7])

Slicing might produce a Series with a single value:

>>> even_primes = primes[primes % 2 == 0]
>>> even_primes
0    2
dtype: int64

>>> even_primes.squeeze()
np.int64(2)

Squeezing objects with more than one value in every axis does nothing:

>>> odd_primes = primes[primes % 2 == 1]
>>> odd_primes
1    3
2    5
3    7
dtype: int64

>>> odd_primes.squeeze()
1    3
2    5
3    7
dtype: int64

Squeezing is even more effective when used with DataFrames.

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["a", "b"])
>>> df
   a  b
0  1  2
1  3  4

Slicing a single column will produce a DataFrame with the columns having only one value:

>>> df_a = df[["a"]]
>>> df_a
   a
0  1
1  3

So the columns can be squeezed down, resulting in a Series:

>>> df_a.squeeze("columns")
0    1
1    3
Name: a, dtype: int64

Slicing a single row from a single column will produce a single scalar DataFrame:

>>> df_0a = df.loc[df.index < 1, ["a"]]
>>> df_0a
   a
0  1

Squeezing the rows produces a single scalar Series:

>>> df_0a.squeeze("rows")
a    1
Name: 0, dtype: int64

Squeezing all axes will project directly into a scalar:

>>> df_0a.squeeze()
np.int64(1)

stack(level: IndexLabel = -1, dropna: bool | lib.NoDefault = <no_default>, sort: bool | lib.NoDefault = <no_default>, future_stack: bool = True)

Stack the prescribed level(s) from columns to index.

Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe:

if the columns have a single level, the output is a Series;
if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.

Parameters:

level (int, str, list, default -1) – Level(s) to stack from the column axis onto the index axis, defined as one index or label, or a list of indices or labels.
dropna (bool, default True) – Whether to drop rows in the resulting Frame/Series with missing values. Stacking a column level onto the index axis can create combinations of index and column values that are missing from the original dataframe. See Examples section.
sort (bool, default True) – Whether to sort the levels of the resulting MultiIndex.
future_stack (bool, default True) – Whether to use the new implementation that will replace the current implementation in pandas 3.0. When True, dropna and sort have no impact on the result and must remain unspecified. See pandas 2.1.0 Release notes for more details.

Returns:

Stacked dataframe or series.

Return type:

DataFrame or Series

See also

DataFrame.unstack: Unstack prescribed level(s) from index axis onto column axis.
DataFrame.pivot: Reshape dataframe from long format to wide format.
DataFrame.pivot_table: Create a spreadsheet-style pivot table as a DataFrame.

Notes

The function is named by analogy with a collection of books being reorganized from being side by side on a horizontal position (the columns of the dataframe) to being stacked vertically on top of each other (in the index of the dataframe).

Reference the user guide for more examples.

Examples

Single level columns

>>> df_single_level_cols = pd.DataFrame(
...     [[0, 1], [2, 3]], index=["cat", "dog"], columns=["weight", "height"]
... )

Stacking a dataframe with a single level column axis returns a Series:

>>> df_single_level_cols
     weight height
cat       0      1
dog       2      3
>>> df_single_level_cols.stack()
cat  weight    0
     height    1
dog  weight    2
     height    3
dtype: int64

Multi level columns: simple case

>>> multicol1 = pd.MultiIndex.from_tuples(
...     [("weight", "kg"), ("weight", "pounds")]
... )
>>> df_multi_level_cols1 = pd.DataFrame(
...     [[1, 2], [2, 4]], index=["cat", "dog"], columns=multicol1
... )

Stacking a dataframe with a multi-level column axis:

>>> df_multi_level_cols1
     weight
         kg    pounds
cat       1        2
dog       2        4
>>> df_multi_level_cols1.stack()
            weight
cat kg           1
    pounds       2
dog kg           2
    pounds       4

Missing values

>>> multicol2 = pd.MultiIndex.from_tuples([("weight", "kg"), ("height", "m")])
>>> df_multi_level_cols2 = pd.DataFrame(
...     [[1.0, 2.0], [3.0, 4.0]], index=["cat", "dog"], columns=multicol2
... )

It is common to have missing values when stacking a dataframe with multi-level columns, as the stacked dataframe typically has more values than the original dataframe. Missing values are filled with NaNs:

>>> df_multi_level_cols2
    weight height
        kg      m
cat    1.0    2.0
dog    3.0    4.0
>>> df_multi_level_cols2.stack()
        weight  height
cat kg     1.0     NaN
    m      NaN     2.0
dog kg     3.0     NaN
    m      NaN     4.0

Prescribing the level(s) to be stacked

The first parameter controls which level or levels are stacked:

>>> df_multi_level_cols2.stack(0)
             kg    m
cat weight  1.0  NaN
    height  NaN  2.0
dog weight  3.0  NaN
    height  NaN  4.0
>>> df_multi_level_cols2.stack([0, 1])
cat  weight  kg    1.0
     height  m     2.0
dog  weight  kg    3.0
     height  m     4.0
dtype: float64

std(*, axis: Axis | None = 0, skipna: bool = True, ddof: int = 1, numeric_only: bool = False, **kwargs) → Series | Any

Return sample standard deviation over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument.

Parameters:

axis ({index (0), columns (1)}) –
For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.std with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
numeric_only (bool, default False) – Include only float, int, boolean columns. Not implemented for Series.
**kwargs (dict) – Additional keyword arguments to be passed to the function.

Returns:

Standard deviation over requested axis.

Return type:

Series or scalar

See also

Series.std: Return standard deviation over Series values.
DataFrame.mean: Return the mean of the values over the requested axis.
DataFrame.median: Return the median of the values over the requested axis.
DataFrame.mode: Get the mode(s) of each element along the requested axis.
DataFrame.sum: Return the sum of the values over the requested axis.

Notes

To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)

Examples

>>> df = pd.DataFrame(
...     {
...         "person_id": [0, 1, 2, 3],
...         "age": [21, 25, 62, 43],
...         "height": [1.61, 1.87, 1.49, 2.01],
...     }
... ).set_index("person_id")
>>> df
           age  height
person_id
0           21    1.61
1           25    1.87
2           62    1.49
3           43    2.01

The standard deviation of the columns can be found as follows:

>>> df.std()
age       18.786076
height     0.237417
dtype: float64

Alternatively, ddof=0 can be set to normalize by N instead of N-1:

>>> df.std(ddof=0)
age       16.269219
height     0.205609
dtype: float64

property style: Styler

Returns a Styler object.

Contains methods for building a styled HTML representation of the DataFrame.

See also

io.formats.style.Styler: Helps style a DataFrame or Series according to the data with HTML and CSS.

Examples

>>> df = pd.DataFrame({"A": [1, 2, 3]})
>>> df.style

Please see Table Visualization for more examples.

sub(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Subtraction of dataframe and other, element-wise (binary operator sub).

Equivalent to dataframe - other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rsub.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

subtract(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Subtraction of dataframe and other, element-wise (binary operator sub).

Equivalent to dataframe - other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rsub.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

sum(*, axis: Axis | None = 0, skipna: bool = True, numeric_only: bool = False, min_count: int = 0, **kwargs) → Series

Return the sum of the values over the requested axis.

This is equivalent to the method numpy.sum.

Parameters:

axis ({index (0), columns (1)}) –
Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.sum with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).

Added in version 2.0.0.
skipna (bool, default True) – Exclude NA/null values when computing the result.
numeric_only (bool, default False) – Include only float, int, boolean columns. Not implemented for Series.
min_count (int, default 0) – The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.
**kwargs – Additional keyword arguments to be passed to the function.

Returns:

Sum over requested axis.

Return type:

Series or scalar

See also

Series.sum: Return the sum over Series values.
DataFrame.mean: Return the mean of the values over the requested axis.
DataFrame.median: Return the median of the values over the requested axis.
DataFrame.mode: Get the mode(s) of each element along the requested axis.
DataFrame.std: Return the standard deviation of the values over the requested axis.

Examples

>>> idx = pd.MultiIndex.from_arrays(
...     [["warm", "warm", "cold", "cold"], ["dog", "falcon", "fish", "spider"]],
...     names=["blooded", "animal"],
... )
>>> s = pd.Series([4, 2, 0, 8], name="legs", index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.sum()
14

By default, the sum of an empty or all-NA Series is 0.

>>> pd.Series([], dtype="float64").sum()  # min_count=0 is the default
0.0

This can be controlled with the min_count parameter. For example, if you’d like the sum of an empty series to be NaN, pass min_count=1.

>>> pd.Series([], dtype="float64").sum(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).sum()
0.0

>>> pd.Series([np.nan]).sum(min_count=1)
nan

swaplevel(i: Axis = -2, j: Axis = -1, axis: Axis = 0) → DataFrame

Swap levels i and j in a MultiIndex.

Default is to swap the two innermost levels of the index.

Parameters:

i (int or str) – Levels of the indices to be swapped. Can pass level name as string.
j (int or str) – Levels of the indices to be swapped. Can pass level name as string.
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to swap levels on. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.

Returns:

DataFrame with levels swapped in MultiIndex.

Return type:

DataFrame

See also

DataFrame.reorder_levels: Reorder levels of MultiIndex.
DataFrame.sort_index: Sort MultiIndex.

Examples

>>> df = pd.DataFrame(
...     {"Grade": ["A", "B", "A", "C"]},
...     index=[
...         ["Final exam", "Final exam", "Coursework", "Coursework"],
...         ["History", "Geography", "History", "Geography"],
...         ["January", "February", "March", "April"],
...     ],
... )
>>> df
                                    Grade
Final exam  History     January      A
            Geography   February     B
Coursework  History     March        A
            Geography   April        C

In the following example, we will swap the levels of the indices. Here, we will swap the levels column-wise, but levels can be swapped row-wise in a similar manner. Note that column-wise is the default behaviour. By not supplying any arguments for i and j, we swap the last and second to last indices.

>>> df.swaplevel()
                                    Grade
Final exam  January     History         A
            February    Geography       B
Coursework  March       History         A
            April       Geography       C

By supplying one argument, we can choose which index to swap the last index with. We can for example swap the first index with the last one as follows.

>>> df.swaplevel(0)
                                    Grade
January     History     Final exam      A
February    Geography   Final exam      B
March       History     Coursework      A
April       Geography   Coursework      C

We can also define explicitly which indices we want to swap by supplying values for both i and j. Here, we for example swap the first and second indices.

>>> df.swaplevel(0, 1)
                                    Grade
History     Final exam  January         A
Geography   Final exam  February        B
History     Coursework  March           A
Geography   Coursework  April           C

tail(n: int = 5) → Self

Return the last n rows.

This function returns last n rows from the object based on position. It is useful for quickly verifying data, for example, after sorting or appending rows.

For negative values of n, this function returns all rows except the first |n| rows, equivalent to df[|n|:].

If n is larger than the number of rows, this function returns all rows.

Parameters:: n (int, default 5) – Number of rows to select.
Returns:: The last n rows of the caller object.
Return type:: type of caller

See also

DataFrame.head: The first n rows of the caller object.

Examples

>>> df = pd.DataFrame(
...     {
...         "animal": [
...             "alligator",
...             "bee",
...             "falcon",
...             "lion",
...             "monkey",
...             "parrot",
...             "shark",
...             "whale",
...             "zebra",
...         ]
...     }
... )
>>> df
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra

Viewing the last 5 lines

>>> df.tail()
   animal
monkey
parrot
 shark
 whale
 zebra

Viewing the last n lines (three in this case)

>>> df.tail(3)
  animal
6  shark
7  whale
8  zebra

For negative values of n

>>> df.tail(-3)
   animal
  lion
monkey
parrot
 shark
 whale
 zebra

take(indices, axis: int | Literal['index', 'columns', 'rows'] = 0, **kwargs) → Self

Return the elements in the given positional indices along an axis.

This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object.

Parameters:

indices (array-like) – An array of ints indicating which positions to take.
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis on which to select elements. 0 means that we are selecting rows, 1 means that we are selecting columns. For Series this parameter is unused and defaults to 0.
**kwargs – For compatibility with numpy.take(). Has no effect on the output.

Returns:

An array-like containing the elements taken from the object.

Return type:

same type as caller

See also

DataFrame.loc: Select a subset of a DataFrame by labels.
DataFrame.iloc: Select a subset of a DataFrame by positions.
numpy.take: Take elements from an array along an axis.

Examples

>>> df = pd.DataFrame(
...     [
...         ("falcon", "bird", 389.0),
...         ("parrot", "bird", 24.0),
...         ("lion", "mammal", 80.5),
...         ("monkey", "mammal", np.nan),
...     ],
...     columns=["name", "class", "max_speed"],
...     index=[0, 2, 3, 1],
... )
>>> df
     name   class  max_speed
0  falcon    bird      389.0
2  parrot    bird       24.0
3    lion  mammal       80.5
1  monkey  mammal        NaN

Take elements at positions 0 and 3 along the axis 0 (default).

Note how the actual indices selected (0 and 1) do not correspond to our selected indices 0 and 3. That’s because we are selecting the 0th and 3rd rows, not rows whose indices equal 0 and 3.

>>> df.take([0, 3])
     name   class  max_speed
0  falcon    bird      389.0
1  monkey  mammal        NaN

Take elements at indices 1 and 2 along the axis 1 (column selection).

>>> df.take([1, 2], axis=1)
    class  max_speed
0    bird      389.0
2    bird       24.0
3  mammal       80.5
1  mammal        NaN

We may take elements using negative integers for positive indices, starting from the end of the object, just like with Python lists.

>>> df.take([-1, -2])
     name   class  max_speed
1  monkey  mammal        NaN
3    lion  mammal       80.5

to_clipboard(*, excel: bool = True, sep: str | None = None, **kwargs) → None

Copy object to the system clipboard.

Write a text representation of object to the system clipboard. This can be pasted into Excel, for example.

Parameters:

excel (bool, default True) –
Produce output in a csv format for easy pasting into excel.
- True, use the provided separator for csv pasting.
- False, write a string representation of the object to the clipboard.
sep (str, default '\t') – Field delimiter.
**kwargs – These parameters will be passed to DataFrame.to_csv.

See also

DataFrame.to_csv: Write a DataFrame to a comma-separated values (csv) file.
read_clipboard: Read text from clipboard and pass to read_csv.

Notes

Requirements for your platform.

Linux : xclip, or xsel (with PyQt4 modules)

Windows : none

macOS : none

This method uses the processes developed for the package pyperclip. A solution to render any output string format is given in the examples.

Examples

Copy the contents of a DataFrame to the clipboard.

>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"])

>>> df.to_clipboard(sep=",")
... # Wrote the following to the system clipboard:
... # ,A,B,C
... # 0,1,2,3
... # 1,4,5,6

We can omit the index by passing the keyword index and setting it to false.

>>> df.to_clipboard(sep=",", index=False)
... # Wrote the following to the system clipboard:
... # A,B,C
... # 1,2,3
... # 4,5,6

Using the original pyperclip package for any string output format.

import pyperclip

html = df.style.to_html()
pyperclip.copy(html)

to_csv(path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None, *, sep: str = ',', na_rep: str = '', float_format: str | Callable | None = None, columns: Sequence[Hashable] | None = None, header: bool | list[str] = True, index: bool = True, index_label: IndexLabel | None = None, mode: str = 'w', encoding: str | None = None, compression: CompressionOptions = 'infer', quoting: int | None = None, quotechar: str = '"', lineterminator: str | None = None, chunksize: int | None = None, date_format: str | None = None, doublequote: bool = True, escapechar: str | None = None, decimal: str = '.', errors: OpenFileErrors = 'strict', storage_options: StorageOptions | None = None) → str | None

Write object to a comma-separated values (csv) file.

Parameters:

path_or_buf (str, path object, file-like object, or None, default None) – String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string. If a non-binary file object is passed, it should be opened with newline=’’, disabling universal newlines. If a binary file object is passed, mode might need to contain a ‘b’.
sep (str, default ',') – String of length 1. Field delimiter for the output file.
na_rep (str, default '') – Missing data representation.
float_format (str, Callable, default None) – Format string for floating point numbers. If a Callable is given, it takes precedence over other numeric formatting parameters, like decimal.
columns (sequence, optional) – Columns to write.
header (bool or list of str, default True) – Write out the column names. If a list of strings is given it is assumed to be aliases for the column names.
index (bool, default True) – Write row names (index).
index_label (str or sequence, or False, default None) – Column label for index column(s) if desired. If None is given, and header and index are True, then the index names are used. A sequence should be given if the object uses MultiIndex. If False do not print fields for index names. Use index_label=False for easier importing in R.
mode ({'w', 'x', 'a'}, default 'w') –
Forwarded to either open(mode=) or fsspec.open(mode=) to control the file opening. Typical values include:
- ’w’, truncate the file first.
- ’x’, exclusive creation, failing if the file already exists.
- ’a’, append to the end of file if it exists.
encoding (str, optional) – A string representing the encoding to use in the output file, defaults to ‘utf-8’. encoding is not supported if path_or_buf is a non-binary file object.
compression (str or dict, default 'infer') –
For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.

May be a dict with key ‘method’ as compression mode and other entries as additional compression options if compression mode is ‘zip’.

Passing compression options as keys in dict is supported for compression modes ‘gzip’, ‘bz2’, ‘zstd’, and ‘zip’.
quoting (optional constant from csv module) – Defaults to csv.QUOTE_MINIMAL. If you have set a float_format then floats are converted to strings and thus csv.QUOTE_NONNUMERIC will treat them as non-numeric.
quotechar (str, default '"') – String of length 1. Character used to quote fields.
lineterminator (str, optional) – The newline character or character sequence to use in the output file. Defaults to os.linesep, which depends on the OS in which this method is called (’\n’ for linux, ‘\r\n’ for Windows, i.e.).
chunksize (int or None) – Rows to write at a time.
date_format (str, default None) – Format string for datetime objects.
doublequote (bool, default True) – Control quoting of quotechar inside a field.
escapechar (str, default None) – String of length 1. Character used to escape sep and quotechar when appropriate.
decimal (str, default '.') – Character recognized as decimal separator. E.g. use ‘,’ for European data.
errors (str, default 'strict') – Specifies how encoding and decoding errors are to be handled. See the errors argument for open() for a full list of options.
storage_options (dict, optional) – Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

Returns:

If path_or_buf is None, returns the resulting csv format as a string. Otherwise returns None.

Return type:

None or str

See also

read_csv: Load a CSV file into a DataFrame.
to_excel: Write DataFrame to an Excel file.

Examples

Create ‘out.csv’ containing ‘df’ without indices

>>> df = pd.DataFrame(
...     [["Raphael", "red", "sai"], ["Donatello", "purple", "bo staff"]],
...     columns=["name", "mask", "weapon"],
... )
>>> df.to_csv("out.csv", index=False)

Create ‘out.zip’ containing ‘out.csv’

>>> df.to_csv(index=False)
'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'
>>> compression_opts = dict(
...     method="zip", archive_name="out.csv"
... )
>>> df.to_csv(
...     "out.zip", index=False, compression=compression_opts
... )

To write a csv file to a new folder or nested folder you will first need to create it using either Pathlib or os:

>>> from pathlib import Path
>>> filepath = Path("folder/subfolder/out.csv")
>>> filepath.parent.mkdir(parents=True, exist_ok=True)
>>> df.to_csv(filepath)

>>> import os
>>> os.makedirs("folder/subfolder", exist_ok=True)
>>> df.to_csv("folder/subfolder/out.csv")

Format floats to two decimal places:

>>> df.to_csv("out1.csv", float_format="%.2f")

Format floats using scientific notation:

>>> df.to_csv("out2.csv", float_format="{:.2e}".format)

to_dict(orient: Literal['dict', 'list', 'series', 'split', 'tight', 'records', 'index']='dict', *, into: type[MutableMappingT] | MutableMappingT = <class 'dict'>, index: bool = True) → MutableMappingT | list[MutableMappingT]

Convert the DataFrame to a dictionary.

The type of the key-value pairs can be customized with the parameters (see below).

Parameters:

orient (str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}) –
Determines the type of the values of the dictionary.
- ’dict’ (default) : dict like {column -> {index -> value}}
- ’list’ : dict like {column -> [values]}
- ’series’ : dict like {column -> Series(values)}
- ’split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}
- ’tight’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values], ‘index_names’ -> [index.names], ‘column_names’ -> [column.names]}
- ’records’ : list like [{column -> value}, … , {column -> value}]
- ’index’ : dict like {index -> {column -> value}}
into (class, default dict) – The collections.abc.MutableMapping subclass used for all Mappings in the return value. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized.
index (bool, default True) –
Whether to include the index item (and index_names item if orient is ‘tight’) in the returned dictionary. Can only be False when orient is ‘split’ or ‘tight’. Note that when orient is ‘records’, this parameter does not take effect (index item always not included).

Added in version 2.0.0.

Returns:

Return a collections.abc.MutableMapping object representing the DataFrame. The resulting transformation depends on the orient parameter.

Return type:

dict, list or collections.abc.MutableMapping

See also

DataFrame.from_dict: Create a DataFrame from a dictionary.
DataFrame.to_json: Convert a DataFrame to JSON format.

Examples

>>> df = pd.DataFrame(
...     {"col1": [1, 2], "col2": [0.5, 0.75]}, index=["row1", "row2"]
... )
>>> df
      col1  col2
row1     1  0.50
row2     2  0.75
>>> df.to_dict()
{'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}

You can specify the return orientation.

>>> df.to_dict("series")
{'col1': row1    1
         row2    2
Name: col1, dtype: int64,
'col2': row1    0.50
        row2    0.75
Name: col2, dtype: float64}

>>> df.to_dict("split")
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
 'data': [[1, 0.5], [2, 0.75]]}

>>> df.to_dict("records")
[{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]

>>> df.to_dict("index")
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}

>>> df.to_dict("tight")
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
 'data': [[1, 0.5], [2, 0.75]], 'index_names': [None], 'column_names': [None]}

You can also specify the mapping type.

>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
             ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])

If you want a defaultdict, you need to initialize it:

>>> dd = defaultdict(list)
>>> df.to_dict("records", into=dd)
[defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
 defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]

to_excel(excel_writer: FilePath | WriteExcelBuffer | ExcelWriter, *, sheet_name: str = 'Sheet1', na_rep: str = '', float_format: str | None = None, columns: Sequence[Hashable] | None = None, header: Sequence[Hashable] | bool = True, index: bool = True, index_label: IndexLabel | None = None, startrow: int = 0, startcol: int = 0, engine: Literal['openpyxl', 'xlsxwriter'] | None = None, merge_cells: bool = True, inf_rep: str = 'inf', freeze_panes: tuple[int, int] | None = None, storage_options: StorageOptions | None = None, engine_kwargs: dict[str, Any] | None = None, autofilter: bool = False) → None

Write object to an Excel sheet.

To write a single object to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an ExcelWriter object with a target file name, and specify a sheet in the file to write to.

Multiple sheets may be written to by specifying unique sheet_name. With all data written to the file it is necessary to save the changes. Note that creating an ExcelWriter object with a file name that already exists will overwrite the existing file because the default mode is write.

Parameters:

excel_writer (path-like, file-like, or ExcelWriter object) – File path or existing ExcelWriter.
sheet_name (str, default 'Sheet1') – Name of sheet which will contain DataFrame.
na_rep (str, default '') – Missing data representation.
float_format (str, optional) – Format string for floating point numbers. For example float_format="%.2f" will format 0.1234 to 0.12.
columns (sequence or list of str, optional) – Columns to write.
header (bool or list of str, default True) – Write out the column names. If a list of string is given it is assumed to be aliases for the column names.
index (bool, default True) – Write row names (index).
index_label (str or sequence, optional) – Column label for index column(s) if desired. If not specified, and header and index are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.
startrow (int, default 0) – Upper left cell row to dump data frame.
startcol (int, default 0) – Upper left cell column to dump data frame.
engine (str, optional) – Write engine to use, ‘openpyxl’ or ‘xlsxwriter’. You can also set this via the options io.excel.xlsx.writer or io.excel.xlsm.writer.
merge_cells (bool or 'columns', default False) – If True, write MultiIndex index and columns as merged cells. If ‘columns’, merge MultiIndex column cells only.
inf_rep (str, default 'inf') – Representation for infinity (there is no native representation for infinity in Excel).
freeze_panes (tuple of int (length 2), optional) – Specifies the one-based bottommost row and rightmost column that is to be frozen.
storage_options (dict, optional) –
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.
engine_kwargs (dict, optional) – Arbitrary keyword arguments passed to excel engine.
autofilter (bool, default False) – If True, add automatic filters to all columns.

See also

to_csv: Write DataFrame to a comma-separated values (csv) file.
ExcelWriter: Class for writing DataFrame objects into excel sheets.
read_excel: Read an Excel file into a pandas DataFrame.
read_csv: Read a comma-separated values (csv) file into DataFrame.
io.formats.style.Styler.to_excel: Add styles to Excel sheet.

Notes

For compatibility with to_csv(), to_excel serializes lists and dicts to strings before writing.

Once a workbook has been saved it is not possible to write further data without rewriting the whole workbook.

pandas will check the number of rows, columns, and cell character count does not exceed Excel’s limitations. All other limitations must be checked by the user.

Examples

Create, write to and save a workbook:

>>> df1 = pd.DataFrame(
...     [["a", "b"], ["c", "d"]],
...     index=["row 1", "row 2"],
...     columns=["col 1", "col 2"],
... )
>>> df1.to_excel("output.xlsx")

To specify the sheet name:

>>> df1.to_excel("output.xlsx", sheet_name="Sheet_name_1")

If you wish to write to more than one sheet in the workbook, it is necessary to specify an ExcelWriter object:

>>> df2 = df1.copy()
>>> with pd.ExcelWriter("output.xlsx") as writer:
...     df1.to_excel(writer, sheet_name="Sheet_name_1")
...     df2.to_excel(writer, sheet_name="Sheet_name_2")

ExcelWriter can also be used to append to an existing Excel file:

>>> with pd.ExcelWriter("output.xlsx", mode="a") as writer:
...     df1.to_excel(writer, sheet_name="Sheet_name_3")

To set the library that is used to write the Excel file, you can pass the engine keyword (the default engine is automatically chosen depending on the file extension):

>>> df1.to_excel("output1.xlsx", engine="xlsxwriter")

to_feather(path: FilePath | WriteBuffer[bytes], **kwargs) → None

Write a DataFrame to the binary Feather format.

Parameters:

path (str, path object, file-like object) – String, path object (implementing os.PathLike[str]), or file-like object implementing a binary write() function. If a string or a path, it will be used as Root Directory path when writing a partitioned dataset.
**kwargs – Additional keywords passed to pyarrow.feather.write_feather(). This includes the compression, compression_level, chunksize and version keywords.

See also

DataFrame.to_parquet: Write a DataFrame to the binary parquet format.
DataFrame.to_excel: Write object to an Excel sheet.
DataFrame.to_sql: Write to a sql table.
DataFrame.to_csv: Write a csv file.
DataFrame.to_json: Convert the object to a JSON string.
DataFrame.to_html: Render a DataFrame as an HTML table.
DataFrame.to_string: Convert DataFrame to a string.

Notes

This function writes the dataframe as a feather file. Requires a default index. For saving the DataFrame with your custom index use a method that supports custom indices e.g. to_parquet.

Examples

>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]])
>>> df.to_feather("file.feather")

to_hdf(path_or_buf: FilePath | HDFStore, *, key: str, mode: Literal['a', 'w', 'r+'] = 'a', complevel: int | None = None, complib: Literal['zlib', 'lzo', 'bzip2', 'blosc'] | None = None, append: bool = False, format: Literal['fixed', 'table'] | None = None, index: bool = True, min_itemsize: int | dict[str, int] | None = None, nan_rep=None, dropna: bool | None = None, data_columns: Literal[True] | list[str] | None = None, errors: OpenFileErrors = 'strict', encoding: str = 'UTF-8') → None

Write the contained data to an HDF5 file using HDFStore.

Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects.

In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key.

Warning

One can store a subclass of DataFrame or Series to HDF5, but the type of the subclass is lost upon storing.

For more information see the user guide.

Parameters:

path_or_buf (str or pandas.HDFStore) – File path or HDFStore object.
key (str) – Identifier for the group in the store.
mode ({'a', 'w', 'r+'}, default 'a') –
Mode to open file:
- ’w’: write, a new file is created (an existing file with the same name would be deleted).
- ’a’: append, an existing file is opened for reading and writing, and if the file does not exist it is created.
- ’r+’: similar to ‘a’, but the file must already exist.
complevel ({0-9}, default None) – Specifies a compression level for data. A value of 0 or None disables compression.
complib ({'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib') – Specifies the compression library to be used. These additional compressors for Blosc are supported (default if no compressor specified: ‘blosc:blosclz’): {‘blosc:blosclz’, ‘blosc:lz4’, ‘blosc:lz4hc’, ‘blosc:snappy’, ‘blosc:zlib’, ‘blosc:zstd’}. Specifying a compression library which is not available issues a ValueError.
append (bool, default False) – For Table formats, append the input data to the existing.
format ({'fixed', 'table', None}, default 'fixed') –
Possible values:
- ’fixed’: Fixed format. Fast writing/reading. Not-appendable, nor searchable.
- ’table’: Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data.
- If None, pd.get_option(‘io.hdf.default_format’) is checked, followed by fallback to “fixed”.
index (bool, default True) – Write DataFrame index as a column.
min_itemsize (dict or int, optional) – Map column names to minimum string sizes for columns.
nan_rep (Any, optional) – How to represent null values as str. Not allowed with append=True.
dropna (bool, default False, optional) – Remove missing values.
data_columns (list of columns or True, optional) – List of columns to create as indexed data columns for on-disk queries, or True to use all columns. By default only the axes of the object are indexed. See Query via data columns. for more information. Applicable only to format=’table’.
errors (str, default 'strict') – Specifies how encoding and decoding errors are to be handled. See the errors argument for open() for a full list of options.
encoding (str, default "UTF-8") – Set character encoding.

See also

read_hdf: Read from HDF file.
DataFrame.to_orc: Write a DataFrame to the binary orc format.
DataFrame.to_parquet: Write a DataFrame to the binary parquet format.
DataFrame.to_sql: Write to a SQL table.
DataFrame.to_feather: Write out feather-format for DataFrames.
DataFrame.to_csv: Write out to a csv file.

Examples

>>> df = pd.DataFrame(
...     {"A": [1, 2, 3], "B": [4, 5, 6]}, index=["a", "b", "c"]
... )
>>> df.to_hdf("data.h5", key="df", mode="w")

We can add another object to the same file:

>>> s = pd.Series([1, 2, 3, 4])
>>> s.to_hdf("data.h5", key="s")

Reading from HDF file:

>>> pd.read_hdf("data.h5", "df")
A  B
a  1  4
b  2  5
c  3  6
>>> pd.read_hdf("data.h5", "s")
0    1
1    2
2    3
3    4
dtype: int64

Render a DataFrame as an HTML table.

Parameters:

buf (str, Path or StringIO-like, optional, default None) – Buffer to write to. If None, the output is returned as a string.
columns (array-like, optional, default None) – The subset of columns to write. Writes all columns by default.
col_space (str or int, list or dict of int or str, optional) – The minimum width of each column in CSS length units. An int is assumed to be px units.
header (bool, optional) – Whether to print column labels, default True.
index (bool, optional, default True) – Whether to print index (row) labels.
na_rep (str, optional, default 'NaN') – String representation of NaN to use.
formatters (list, tuple or dict of one-param. functions, optional) – Formatter functions to apply to columns’ elements by position or name. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.
float_format (one-parameter function, optional, default None) – Formatter function to apply to columns’ elements if they are floats. This function must return a unicode string and will be applied only to the non-NaN elements, with NaN being handled by na_rep.
sparsify (bool, optional, default True) – Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.
index_names (bool, optional, default True) – Prints the names of the indexes.
justify (str, default None) –
How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), ‘right’ out of the box. Valid values are
- left
- right
- center
- justify
- justify-all
- start
- end
- inherit
- match-parent
- initial
- unset.
max_rows (int, optional) – Maximum number of rows to display in the console.
max_cols (int, optional) – Maximum number of columns to display in the console.
show_dimensions (bool, default False) – Display DataFrame dimensions (number of rows by number of columns).
decimal (str, default '.') – Character recognized as decimal separator, e.g. ‘,’ in Europe.
bold_rows (bool, default True) – Make the row labels bold in the output.
classes (str or list or tuple, default None) – CSS class(es) to apply to the resulting html table.
escape (bool, default True) – Convert the characters <, >, and & to HTML-safe sequences.
notebook ({True, False}, default False) – Whether the generated HTML is for IPython Notebook.
border (int or bool) – When an integer value is provided, it sets the border attribute in the opening tag, specifying the thickness of the border. If False or 0 is passed, the border attribute will not be present in the <table> tag. The default value for this parameter is governed by pd.options.display.html.border.
table_id (str, optional) – A css id is included in the opening <table> tag if specified.
render_links (bool, default False) – Convert URLs to HTML links.
encoding (str, default "utf-8") – Set character encoding.

Returns:

If buf is None, returns the result as a string. Otherwise returns None.

Return type:

str or None

See also

to_string: Convert DataFrame to a string.

Examples

>>> df = pd.DataFrame(data={"col1": [1, 2], "col2": [4, 3]})
>>> html_string = df.to_html()
>>> print(html_string)
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>col1</th>
      <th>col2</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>4</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>3</td>
    </tr>
  </tbody>
</table>

HTML output

	col1	col2
0	1	4
1	2	3

>>> df = pd.DataFrame(data={"col1": [1, 2], "col2": [4, 3]})
>>> html_string = df.to_html(index=False)
>>> print(html_string)
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th>col1</th>
      <th>col2</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>1</td>
      <td>4</td>
    </tr>
    <tr>
      <td>2</td>
      <td>3</td>
    </tr>
  </tbody>
</table>

HTML output

col1	col2
1	4
2	3

to_iceberg(table_identifier: str, catalog_name: str | None = None, *, catalog_properties: dict[str, Any] | None = None, location: str | None = None, append: bool = False, snapshot_properties: dict[str, str] | None = None) → None

Write a DataFrame to an Apache Iceberg table.

Added in version 3.0.0.

Warning

to_iceberg is experimental and may change without warning.

Parameters:

table_identifier (str) – Table identifier.
catalog_name (str, optional) – The name of the catalog.
catalog_properties (dict of {str: str}, optional) – The properties that are used next to the catalog configuration.
location (str, optional) – Location for the table.
append (bool, default False) – If True, append data to the table, instead of replacing the content.
snapshot_properties (dict of {str: str}, optional) – Custom properties to be added to the snapshot summary

See also

read_iceberg: Read an Apache Iceberg table.
DataFrame.to_parquet: Write a DataFrame in Parquet format.

Examples

>>> df = pd.DataFrame(data={"col1": [1, 2], "col2": [4, 3]})
>>> df.to_iceberg("my_table", catalog_name="my_catalog")

to_json(path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None, *, orient: Literal['split', 'records', 'index', 'table', 'columns', 'values'] | None = None, date_format: str | None = None, double_precision: int = 10, force_ascii: bool = True, date_unit: TimeUnit = 'ms', default_handler: Callable[[Any], JSONSerializable] | None = None, lines: bool = False, compression: CompressionOptions = 'infer', index: bool | None = None, indent: int | None = None, storage_options: StorageOptions | None = None, mode: Literal['a', 'w'] = 'w') → str | None

Convert the object to a JSON string.

Note NaN’s and None will be converted to null and datetime objects will be converted to UNIX timestamps.

Parameters:

path_or_buf (str, path object, file-like object, or None, default None) – String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string.
orient (str) –
Indication of expected JSON string format.
- Series:
  - default is ‘index’
  - allowed values are: {‘split’, ‘records’, ‘index’, ‘table’}.
- DataFrame:
  - default is ‘columns’
  - allowed values are: {‘split’, ‘records’, ‘index’, ‘columns’, ‘values’, ‘table’}.
- The format of the JSON string:
  - ’split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}
  - ’records’ : list like [{column -> value}, … , {column -> value}]
  - ’index’ : dict like {index -> {column -> value}}
  - ’columns’ : dict like {column -> {index -> value}}
  - ’values’ : just the values array
  - ’table’ : dict like {‘schema’: {schema}, ‘data’: {data}}
  Describing the data, where data component is like orient='records'.
date_format ({None, 'epoch', 'iso'}) –
Type of date conversion. ‘epoch’ = epoch milliseconds, ‘iso’ = ISO8601. The default depends on the orient. For orient='table', the default is ‘iso’. For all other orients, the default is ‘epoch’.

Deprecated since version 3.0.0: ‘epoch’ date format is deprecated and will be removed in a future version, please use ‘iso’ instead.
double_precision (int, default 10) – The number of decimal places to use when encoding floating point values. The possible maximal value is 15. Passing double_precision greater than 15 will raise a ValueError.
force_ascii (bool, default True) – Force encoded string to be ASCII.
date_unit (str, default 'ms' (milliseconds)) – The time unit to encode to, governs timestamp and ISO8601 precision. One of ‘s’, ‘ms’, ‘us’, ‘ns’ for second, millisecond, microsecond, and nanosecond respectively.
default_handler (callable, default None) – Handler to call if object cannot otherwise be converted to a suitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object.
lines (bool, default False) – If ‘orient’ is ‘records’ write out line-delimited json format. Will throw ValueError if incorrect ‘orient’ since others are not list-like.
compression (str or dict, default 'infer') – For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.
index (bool or None, default None) – The index is only used when ‘orient’ is ‘split’, ‘index’, ‘column’, or ‘table’. Of these, ‘index’ and ‘column’ do not support index=False. The string ‘index’ as a column name with empty Index or if it is ‘index’ will raise a ValueError.
indent (int, optional) – Length of whitespace used to indent each record.
storage_options (dict, optional) –
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.
mode (str, default 'w' (writing)) – Specify the IO mode for output when supplying a path_or_buf. Accepted args are ‘w’ (writing) and ‘a’ (append) only. mode=’a’ is only supported when lines is True and orient is ‘records’.

Returns:

If path_or_buf is None, returns the resulting json format as a string. Otherwise returns None.

Return type:

None or str

See also

read_json: Convert a JSON string to pandas object.

Notes

The behavior of indent=0 varies from the stdlib, which does not indent the output but does insert newlines. Currently, indent=0 and the default indent=None are equivalent in pandas, though this may change in a future release.

orient='table' contains a ‘pandas_version’ field under ‘schema’. This stores the version of pandas used in the latest revision of the schema.

Examples

>>> from json import loads, dumps
>>> df = pd.DataFrame(
...     [["a", "b"], ["c", "d"]],
...     index=["row 1", "row 2"],
...     columns=["col 1", "col 2"],
... )

>>> result = df.to_json(orient="split")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)
{
    "columns": [
        "col 1",
        "col 2"
    ],
    "index": [
        "row 1",
        "row 2"
    ],
    "data": [
        [
            "a",
            "b"
        ],
        [
            "c",
            "d"
        ]
    ]
}

Encoding/decoding a Dataframe using 'records' formatted JSON. Note that index labels are not preserved with this encoding.

>>> result = df.to_json(orient="records")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)
[
    {
        "col 1": "a",
        "col 2": "b"
    },
    {
        "col 1": "c",
        "col 2": "d"
    }
]

Encoding/decoding a Dataframe using 'index' formatted JSON:

>>> result = df.to_json(orient="index")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)
{
    "row 1": {
        "col 1": "a",
        "col 2": "b"
    },
    "row 2": {
        "col 1": "c",
        "col 2": "d"
    }
}

Encoding/decoding a Dataframe using 'columns' formatted JSON:

>>> result = df.to_json(orient="columns")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)
{
    "col 1": {
        "row 1": "a",
        "row 2": "c"
    },
    "col 2": {
        "row 1": "b",
        "row 2": "d"
    }
}

Encoding/decoding a Dataframe using 'values' formatted JSON:

>>> result = df.to_json(orient="values")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)
[
    [
        "a",
        "b"
    ],
    [
        "c",
        "d"
    ]
]

Encoding with Table Schema:

>>> result = df.to_json(orient="table")
>>> parsed = loads(result)
>>> dumps(parsed, indent=4)
{
    "schema": {
        "fields": [
            {
                "name": "index",
                "type": "string"
            },
            {
                "name": "col 1",
                "type": "string"
            },
            {
                "name": "col 2",
                "type": "string"
            }
        ],
        "primaryKey": [
            "index"
        ],
        "pandas_version": "1.4.0"
    },
    "data": [
        {
            "index": "row 1",
            "col 1": "a",
            "col 2": "b"
        },
        {
            "index": "row 2",
            "col 1": "c",
            "col 2": "d"
        }
    ]
}

Render object to a LaTeX tabular, longtable, or nested table.

Requires \usepackage{booktabs}. The output can be copy/pasted into a main LaTeX document or read from an external file with \input{table.tex}.

Changed in version 2.0.0: Refactored to use the Styler implementation via jinja2 templating.

Parameters:

buf (str, Path or StringIO-like, optional, default None) – Buffer to write to. If None, the output is returned as a string.
columns (list of label, optional) – The subset of columns to write. Writes all columns by default.
header (bool or list of str, default True) – Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names. Braces must be escaped.
index (bool, default True) – Write row names (index).
na_rep (str, default 'NaN') – Missing data representation.
formatters (list of functions or dict of {str: function}, optional) – Formatter functions to apply to columns’ elements by position or name. The result of each function must be a unicode string. List must be of length equal to the number of columns.
float_format (one-parameter function or str, optional, default None) – Formatter for floating point numbers. For example float_format="%.2f" and float_format="{:0.2f}".format will both result in 0.1234 being formatted as 0.12.
sparsify (bool, optional) – Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row. By default, the value will be read from the config module.
index_names (bool, default True) – Prints the names of the indexes.
bold_rows (bool, default False) – Make the row labels bold in the output.
column_format (str, optional) – The columns format as specified in LaTeX table format e.g. ‘rcl’ for 3 columns. By default, ‘l’ will be used for all columns except columns of numbers, which default to ‘r’.
longtable (bool, optional) –
Use a longtable environment instead of tabular. Requires adding a usepackage{longtable} to your LaTeX preamble. By default, the value will be read from the pandas config module, and set to True if the option styler.latex.environment is “longtable”.

Changed in version 2.0.0: The pandas option affecting this argument has changed.
escape (bool, optional) –
By default, the value will be read from the pandas config module and set to True if the option styler.format.escape is “latex”. When set to False prevents from escaping latex special characters in column names.

Changed in version 2.0.0: The pandas option affecting this argument has changed, as has the default value to False.
encoding (str, optional) – A string representing the encoding to use in the output file, defaults to ‘utf-8’.
decimal (str, default '.') – Character recognized as decimal separator, e.g. ‘,’ in Europe.
multicolumn (bool, default True) –
Use multicolumn to enhance MultiIndex columns. The default will be read from the config module, and is set as the option styler.sparse.columns.

Changed in version 2.0.0: The pandas option affecting this argument has changed.
multicolumn_format (str, default 'r') –
The alignment for multicolumns, similar to column_format The default will be read from the config module, and is set as the option styler.latex.multicol_align.

Changed in version 2.0.0: The pandas option affecting this argument has changed, as has the default value to “r”.
multirow (bool, default True) –
Use multirow to enhance MultiIndex rows. Requires adding a usepackage{multirow} to your LaTeX preamble. Will print centered labels (instead of top-aligned) across the contained rows, separating groups via clines. The default will be read from the pandas config module, and is set as the option styler.sparse.index.

Changed in version 2.0.0: The pandas option affecting this argument has changed, as has the default value to True.
caption (str or tuple, optional) – Tuple (full_caption, short_caption), which results in \caption[short_caption]{full_caption}; if a single string is passed, no short caption will be set.
label (str, optional) – The LaTeX label to be placed inside \label{} in the output. This is used with \ref{} in the main .tex file.
position (str, optional) – The LaTeX positional argument for tables, to be placed after \begin{} in the output.

Returns:

If buf is None, returns the result as a string. Otherwise returns None.

Return type:

str or None

See also

io.formats.style.Styler.to_latex: Render a DataFrame to LaTeX with conditional formatting.
DataFrame.to_string: Render a DataFrame to a console-friendly tabular output.
DataFrame.to_html: Render a DataFrame as an HTML table.

Notes

As of v2.0.0 this method has changed to use the Styler implementation as part of Styler.to_latex() via jinja2 templating. This means that jinja2 is a requirement, and needs to be installed, for this method to function. It is advised that users switch to using Styler, since that implementation is more frequently updated and contains much more flexibility with the output.

Examples

Convert a general DataFrame to LaTeX with formatting:

>>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'],
...                        age=[26, 45],
...                        height=[181.23, 177.65]))
>>> print(df.to_latex(index=False,
...                   formatters={"name": str.upper},
...                   float_format="{:.1f}".format,
...                   ))
\begin{tabular}{lrr}
\toprule
name & age & height \\
\midrule
RAPHAEL & 26 & 181.2 \\
DONATELLO & 45 & 177.7 \\
\bottomrule
\end{tabular}

to_markdown(buf: FilePath | WriteBuffer[str] | None = None, *, mode: str = 'wt', index: bool = True, storage_options: StorageOptions | None = None, **kwargs) → str | None

Print DataFrame in Markdown-friendly format.

Parameters:

buf (str, Path or StringIO-like, optional, default None) – Buffer to write to. If None, the output is returned as a string.
mode (str, optional) – Mode in which file is opened, “wt” by default.
index (bool, optional, default True) – Add index (row) labels.
storage_options (dict, optional) –
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.
**kwargs – These parameters will be passed to tabulate.

Returns:

DataFrame in Markdown-friendly format.

Return type:

str

See also

DataFrame.to_html: Render DataFrame to HTML-formatted table.
DataFrame.to_latex: Render DataFrame to LaTeX-formatted table.

Notes

Requires the tabulate package.

Examples

>>> df = pd.DataFrame(
...     data={"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]}
... )
>>> print(df.to_markdown())
|    | animal_1   | animal_2   |
|---:|:-----------|:-----------|
|  0 | elk        | dog        |
|  1 | pig        | quetzal    |

Output markdown with a tabulate option.

>>> print(df.to_markdown(tablefmt="grid"))
+----+------------+------------+
|    | animal_1   | animal_2   |
+====+============+============+
|  0 | elk        | dog        |
+----+------------+------------+
|  1 | pig        | quetzal    |
+----+------------+------------+

to_numpy(dtype: npt.DTypeLike | None = None, copy: bool = False, na_value: object = <no_default>) → np.ndarray

Convert the DataFrame to a NumPy array.

By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame. For example, if the dtypes are float16 and float32, the results dtype will be float32. This may require copying data and coercing values, which may be expensive.

Parameters:

dtype (str or numpy.dtype, optional) – The dtype to pass to numpy.asarray().
copy (bool, default False) – Whether to ensure that the returned value is not a view on another array. Note that copy=False does not ensure that to_numpy() is no-copy. Rather, copy=True ensure that a copy is made, even if not strictly necessary.
na_value (Any, optional) – The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns.

Returns:

The NumPy array representing the values in the DataFrame.

Return type:

numpy.ndarray

See also

Series.to_numpy: Similar method for Series.

Examples

>>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()
array([[1, 3],
       [2, 4]])

With heterogeneous data, the lowest common type will have to be used.

>>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
>>> df.to_numpy()
array([[1. , 3. ],
       [2. , 4.5]])

For a mix of numeric and non-numeric types, the output array will have object dtype.

>>> df["C"] = pd.date_range("2000", periods=2)
>>> df.to_numpy()
array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
       [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)

Write a DataFrame to the Optimized Row Columnar (ORC) format.

Parameters:

path (str, file-like object or None, default None) – If a string, it will be used as Root Directory path when writing a partitioned dataset. By file-like object, we refer to objects with a write() method, such as a file handle (e.g. via builtin open function). If path is None, a bytes object is returned.
engine ({'pyarrow'}, default 'pyarrow') – ORC library to use.
index (bool, optional) – If True, include the dataframe’s index(es) in the file output. If False, they will not be written to the file. If None, similar to infer the dataframe’s index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn’t require much space and is faster. Other indexes will be included as columns in the file output.
engine_kwargs (dict[str, Any] or None, default None) – Additional keyword arguments passed to pyarrow.orc.write_table().

Returns:

Bytes object with DataFrame data if path is not specified else None.

Return type:

bytes if no path argument is provided else None

Raises:

NotImplementedError – Dtype of one or more columns is category, unsigned integers, interval, period or sparse.
ValueError – engine is not pyarrow.

See also

read_orc: Read a ORC file.
DataFrame.to_parquet: Write a parquet file.
DataFrame.to_csv: Write a csv file.
DataFrame.to_sql: Write to a sql table.
DataFrame.to_hdf: Write to hdf.

Notes

Find more information on ORC here.
Before using this function you should read the user guide about ORC and install optional dependencies.
This function requires pyarrow library.
For supported dtypes please refer to supported ORC features in Arrow.
Currently timezones in datetime columns are not preserved when a dataframe is converted into ORC files.

Examples

>>> df = pd.DataFrame(data={"col1": [1, 2], "col2": [4, 3]})
>>> df.to_orc("df.orc")
>>> pd.read_orc("df.orc")
   col1  col2
0     1     4
1     2     3

If you want to get a buffer to the orc content you can write it to io.BytesIO

>>> import io
>>> b = io.BytesIO(df.to_orc())
>>> b.seek(0)
0
>>> content = b.read()

to_parquet(path: FilePath | WriteBuffer[bytes] | None = None, *, engine: Literal['auto', 'pyarrow', 'fastparquet'] = 'auto', compression: ParquetCompressionOptions = 'snappy', index: bool | None = None, partition_cols: list[str] | None = None, storage_options: StorageOptions | None = None, filesystem: Any = None, **kwargs) → bytes | None

Write a DataFrame to the binary parquet format.

This function writes the dataframe as a parquet file. You can choose different parquet backends, and have the option of compression. See the user guide for more details.

Parameters:

path (str, path object, file-like object, or None, default None) – String, path object (implementing os.PathLike[str]), or file-like object implementing a binary write() function. If None, the result is returned as bytes. If a string or path, it will be used as Root Directory path when writing a partitioned dataset.
engine ({'auto', 'pyarrow', 'fastparquet'}, default 'auto') – Parquet library to use. If ‘auto’, then the option io.parquet.engine is used. The default io.parquet.engine behavior is to try ‘pyarrow’, falling back to ‘fastparquet’ if ‘pyarrow’ is unavailable.
compression (str or None, default 'snappy') – Name of the compression to use. Use None for no compression. Supported options: ‘snappy’, ‘gzip’, ‘brotli’, ‘lz4’, ‘zstd’.
index (bool, default None) – If True, include the dataframe’s index(es) in the file output. If False, they will not be written to the file. If None, similar to True the dataframe’s index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn’t require much space and is faster. Other indexes will be included as columns in the file output.
partition_cols (list, optional, default None) – Column names by which to partition the dataset. Columns are partitioned in the order they are given. Must be None if path is not a string.
storage_options (dict, optional) –
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.
filesystem (fsspec or pyarrow filesystem, default None) –
Filesystem object to use when reading the parquet file. Only implemented for engine="pyarrow".

Added in version 2.1.0.
**kwargs – Additional arguments passed to the parquet library. See pandas io for more details.

Returns:

Returns the DataFrame converted to the binary parquet format as bytes if no path argument. Returns None and writes the DataFrame to the specified location in the Parquet format if the path argument is provided.

Return type:

bytes if no path argument is provided else None

See also

read_parquet: Read a parquet file.
DataFrame.to_orc: Write an orc file.
DataFrame.to_csv: Write a csv file.
DataFrame.to_sql: Write to a sql table.
DataFrame.to_hdf: Write to hdf.

Notes

This function requires either the fastparquet or pyarrow library.
When saving a DataFrame with categorical columns to parquet, the file size may increase due to the inclusion of all possible categories, not just those present in the data. This behavior is expected and consistent with pandas’ handling of categorical data. To manage file size and ensure a more predictable roundtrip process, consider using Categorical.remove_unused_categories() on the DataFrame before saving.

Examples

>>> df = pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]})
>>> df.to_parquet("df.parquet.gzip", compression="gzip")
>>> pd.read_parquet("df.parquet.gzip")
   col1  col2
0     1     3
1     2     4

If you want to get a buffer to the parquet content you can use a io.BytesIO object, as long as you don’t use partition_cols, which creates multiple files.

>>> import io
>>> f = io.BytesIO()
>>> df.to_parquet(f)
>>> f.seek(0)
0
>>> content = f.read()

to_period(freq: Frequency | None = None, axis: Axis = 0, copy: bool | lib.NoDefault = <no_default>) → DataFrame

Convert DataFrame from DatetimeIndex to PeriodIndex.

Convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed). Either index of columns can be converted, depending on axis argument.

Parameters:

freq (str, default) – Frequency of the PeriodIndex.
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to convert (the index by default).
copy (bool, default False) –
This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.

Returns:

The DataFrame with the converted PeriodIndex.

Return type:

DataFrame

See also

Series.to_period: Equivalent method for Series.
Series.dt.to_period: Convert DateTime column values.

Examples

>>> idx = pd.to_datetime(
...     [
...         "2001-03-31 00:00:00",
...         "2002-05-31 00:00:00",
...         "2003-08-31 00:00:00",
...     ]
... )

>>> idx
DatetimeIndex(['2001-03-31', '2002-05-31', '2003-08-31'],
              dtype='datetime64[us]', freq=None)

>>> idx.to_period("M")
PeriodIndex(['2001-03', '2002-05', '2003-08'], dtype='period[M]')

For the yearly frequency

>>> idx.to_period("Y")
PeriodIndex(['2001', '2002', '2003'], dtype='period[Y-DEC]')

Pickle (serialize) object to file.

Parameters:

path (str, path object, or file-like object) – String, path object (implementing os.PathLike[str]), or file-like object implementing a binary write() function. File path where the pickled object will be stored.
compression (str or dict, default 'infer') – For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.
protocol (int) –
Int which indicates which protocol should be used by the pickler, default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible values are 0, 1, 2, 3, 4, 5. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL.

[1]
https://docs.python.org/3/library/pickle.html.
storage_options (dict, optional) –
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

See also

read_pickle: Load pickled pandas object (or any object) from file.
DataFrame.to_hdf: Write DataFrame to an HDF5 file.
DataFrame.to_sql: Write DataFrame to a SQL database.
DataFrame.to_parquet: Write a DataFrame to the binary parquet format.

Examples

>>> original_df = pd.DataFrame(
...     {"foo": range(5), "bar": range(5, 10)}
... )
>>> original_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
>>> original_df.to_pickle("./dummy.pkl")

>>> unpickled_df = pd.read_pickle("./dummy.pkl")
>>> unpickled_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9

to_records(index: bool = True, column_dtypes=None, index_dtypes=None) → recarray

Convert DataFrame to a NumPy record array.

Index will be included as the first field of the record array if requested.

Parameters:

index (bool, default True) – Include index in resulting record array, stored in ‘index’ field or using the index label, if set.
column_dtypes (str, type, dict, default None) – If a string or type, the data type to store all columns. If a dictionary, a mapping of column names and indices (zero-indexed) to specific data types.
index_dtypes (str, type, dict, default None) –
If a string or type, the data type to store all index levels. If a dictionary, a mapping of index level names and indices (zero-indexed) to specific data types.

This mapping is applied only if index=True.

Returns:

NumPy ndarray with the DataFrame labels as fields and each row of the DataFrame as entries.

Return type:

numpy.rec.recarray

See also

DataFrame.from_records: Convert structured or record ndarray to DataFrame.
numpy.rec.recarray: An ndarray that allows field access using attributes, analogous to typed columns in a spreadsheet.

Examples

>>> df = pd.DataFrame({"A": [1, 2], "B": [0.5, 0.75]}, index=["a", "b"])
>>> df
   A     B
a  1  0.50
b  2  0.75
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])

If the DataFrame index has no label then the recarray field name is set to ‘index’. If the index has a label then this is used as the field name:

>>> df.index = df.index.rename("I")
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])

The index can be excluded from the record array:

>>> df.to_records(index=False)
rec.array([(1, 0.5 ), (2, 0.75)],
          dtype=[('A', '<i8'), ('B', '<f8')])

Data types can be specified for the columns:

>>> df.to_records(column_dtypes={"A": "int32"})
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])

As well as for the index:

>>> df.to_records(index_dtypes="<S2")
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])

>>> index_dtypes = f"<S{df.index.str.len().max()}"
>>> df.to_records(index_dtypes=index_dtypes)
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])

Write records stored in a DataFrame to a SQL database.

Databases supported by SQLAlchemy [1]_ are supported. Tables can be newly created, appended to, or overwritten.

Warning

The pandas library does not attempt to sanitize inputs provided via a to_sql call. Please refer to the documentation for the underlying database driver to see if it will properly prevent injection, or alternatively be advised of a security risk when executing arbitrary commands in a to_sql call.

Parameters:

name (str) – Name of SQL table.
con (ADBC connection, sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection) –
ADBC provides high performance I/O with native type support, where available. Using SQLAlchemy makes it possible to use any DB supported by that library. Legacy support is provided for sqlite3.Connection objects. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable. See here. If passing a sqlalchemy.engine.Connection which is already in a transaction, the transaction will not be committed. If passing a sqlite3.Connection, it will not be possible to roll back the record insertion.
schema (str, optional) – Specify the schema (if database flavor supports this). If None, use default schema.
if_exists ({'fail', 'replace', 'append', 'delete_rows'}, default 'fail') –
How to behave if the table already exists.
- fail: Raise a ValueError.
- replace: Drop the table before inserting new values.
- append: Insert new values to the existing table.
- delete_rows: If a table exists, delete all records and insert data.
index (bool, default True) – Write DataFrame index as a column. Uses index_label as the column name in the table. Creates a table index for this column.
index_label (str or sequence, default None) – Column label for index column(s). If None is given (default) and index is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.
chunksize (int, optional) – Specify the number of rows in each batch to be written to the database connection at a time. By default, all rows will be written at once. Also see the method keyword.
dtype (dict or scalar, optional) – Specifying the datatype for columns. If a dictionary is used, the keys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns.
method ({None, 'multi', callable}, optional) –
Controls the SQL insertion clause used:
- None : Uses standard SQL INSERT clause (one per row).
- ’multi’: Pass multiple values in a single INSERT clause.
- callable with signature (pd_table, conn, keys, data_iter).
Details and a sample callable implementation can be found in the section insert method.

Returns:

Number of rows affected by to_sql. None is returned if the callable passed into method does not return an integer number of rows.

The number of returned rows affected is the sum of the rowcount attribute of sqlite3.Cursor or SQLAlchemy connectable which may not reflect the exact number of written rows as stipulated in the sqlite3 or SQLAlchemy.

Return type:

None or int

Raises:

ValueError – When the table already exists and if_exists is ‘fail’ (the default).

See also

read_sql: Read a DataFrame from a table.

Notes

Timezone aware datetime columns will be written as Timestamp with timezone type with SQLAlchemy if supported by the database. Otherwise, the datetimes will be stored as timezone unaware timestamps local to the original timezone.

Not all datastores support method="multi". Oracle, for example, does not support multi-value insert.

References

Examples

Create an in-memory SQLite database.

>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite://', echo=False)

Create a table from scratch with 3 rows.

>>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
>>> df
     name
0  User 1
1  User 2
2  User 3

>>> df.to_sql(name='users', con=engine)
3
>>> from sqlalchemy import text
>>> with engine.connect() as conn:
...     conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]

An sqlalchemy.engine.Connection can also be passed to con:

>>> with engine.begin() as connection:
...     df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
...     df1.to_sql(name='users', con=connection, if_exists='append')
2

This is allowed to support operations that require that the same DBAPI connection is used for the entire operation.

>>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']})
>>> df2.to_sql(name='users', con=engine, if_exists='append')
2
>>> with engine.connect() as conn:
...     conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
 (0, 'User 4'), (1, 'User 5'), (0, 'User 6'),
 (1, 'User 7')]

Overwrite the table with just df2.

>>> df2.to_sql(name='users', con=engine, if_exists='replace',
...            index_label='id')
2
>>> with engine.connect() as conn:
...     conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 6'), (1, 'User 7')]

Delete all rows before inserting new records with df3

>>> df3 = pd.DataFrame({"name": ['User 8', 'User 9']})
>>> df3.to_sql(name='users', con=engine, if_exists='delete_rows',
...            index_label='id')
2
>>> with engine.connect() as conn:
...     conn.execute(text("SELECT * FROM users")).fetchall()
[(0, 'User 8'), (1, 'User 9')]

Use method to define a callable insertion method to do nothing if there’s a primary key conflict on a table in a PostgreSQL database.

>>> from sqlalchemy.dialects.postgresql import insert
>>> def insert_on_conflict_nothing(table, conn, keys, data_iter):
...     # "a" is the primary key in "conflict_table"
...     data = [dict(zip(keys, row)) for row in data_iter]
...     stmt = insert(table.table).values(data).on_conflict_do_nothing(index_elements=["a"])
...     result = conn.execute(stmt)
...     return result.rowcount
>>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append",  # noqa: F821
...                    method=insert_on_conflict_nothing)
0

For MySQL, a callable to update columns b and c if there’s a conflict on a primary key.

>>> from sqlalchemy.dialects.mysql import insert   # noqa: F811
>>> def insert_on_conflict_update(table, conn, keys, data_iter):
...     # update columns "b" and "c" on primary key conflict
...     data = [dict(zip(keys, row)) for row in data_iter]
...     stmt = (
...         insert(table.table)
...         .values(data)
...     )
...     stmt = stmt.on_duplicate_key_update(b=stmt.inserted.b, c=stmt.inserted.c)
...     result = conn.execute(stmt)
...     return result.rowcount
>>> df_conflict.to_sql(name="conflict_table", con=conn, if_exists="append",  # noqa: F821
...                    method=insert_on_conflict_update)
2

Specify the dtype (especially useful for integers with missing values). Notice that while pandas is forced to store the data as floating point, the database supports nullable integers. When fetching the data with Python, we get back integer scalars.

>>> df = pd.DataFrame({"A": [1, None, 2]})
>>> df
     A
0  1.0
1  NaN
2  2.0

>>> from sqlalchemy.types import Integer
>>> df.to_sql(name='integers', con=engine, index=False,
...           dtype={"A": Integer()})
3

>>> with engine.connect() as conn:
...     conn.execute(text("SELECT * FROM integers")).fetchall()
[(1,), (None,), (2,)]

Added in version 2.2.0: pandas now supports writing via ADBC drivers

>>> df = pd.DataFrame({'name' : ['User 10', 'User 11', 'User 12']})
>>> df
      name
0  User 10
1  User 11
2  User 12

>>> from adbc_driver_sqlite import dbapi
>>> with dbapi.connect("sqlite://") as conn:
...     df.to_sql(name="users", con=conn)
3

to_stata(path: FilePath | WriteBuffer[bytes], *, convert_dates: dict[Hashable, str] | None = None, write_index: bool = True, byteorder: ToStataByteorder | None = None, time_stamp: datetime.datetime | None = None, data_label: str | None = None, variable_labels: dict[Hashable, str] | None = None, version: int | None = 114, convert_strl: Sequence[Hashable] | None = None, compression: CompressionOptions = 'infer', storage_options: StorageOptions | None = None, value_labels: dict[Hashable, dict[float, str]] | None = None) → None

Export DataFrame object to Stata dta format.

Writes the DataFrame to a Stata dataset file. “dta” files contain a Stata dataset.

Parameters:

path (str, path object, or buffer) – String, path object (implementing os.PathLike[str]), or file-like object implementing a binary write() function.
convert_dates (dict) – Dictionary mapping columns containing datetime types to stata internal format to use when writing the dates. Options are ‘tc’, ‘td’, ‘tm’, ‘tw’, ‘th’, ‘tq’, ‘ty’. Column can be either an integer or a name. Datetime columns that do not have a conversion type specified will be converted to ‘tc’. Raises NotImplementedError if a datetime column has timezone information.
write_index (bool) – Write the index to Stata dataset.
byteorder (str) – Can be “>”, “<”, “little”, or “big”. default is sys.byteorder.
time_stamp (datetime) – A datetime to use as file creation date. Default is the current time.
data_label (str, optional) – A label for the data set. Must be 80 characters or smaller.
variable_labels (dict) – Dictionary containing columns as keys and variable labels as values. Each label must be 80 characters or smaller.
version ({114, 117, 118, 119, None}, default 114) –
Version to use in the output dta file. Set to None to let pandas decide between 118 or 119 formats depending on the number of columns in the frame. Version 114 can be read by Stata 10 and later. Version 117 can be read by Stata 13 or later. Version 118 is supported in Stata 14 and later. Version 119 is supported in Stata 15 and later. Version 114 limits string variables to 244 characters or fewer while versions 117 and later allow strings with lengths up to 2,000,000 characters. Versions 118 and 119 support Unicode characters, and version 119 supports more than 32,767 variables.

Version 119 should usually only be used when the number of variables exceeds the capacity of dta format 118. Exporting smaller datasets in format 119 may have unintended consequences, and, as of November 2020, Stata SE cannot read version 119 files.
convert_strl (list, optional) – List of column names to convert to string columns to Stata StrL format. Only available if version is 117. Storing strings in the StrL format can produce smaller dta files if strings have more than 8 characters and values are repeated.
compression (str or dict, default 'infer') – For on-the-fly compression of the output data. If ‘infer’ and ‘path’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.
storage_options (dict, optional) –
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.
value_labels (dict of dicts) – Dictionary containing columns as keys and dictionaries of column value to labels as values. Labels for a single variable must be 32,000 characters or smaller.

Raises:

NotImplementedError –
- If datetimes contain timezone information * Column dtype is not representable in Stata
ValueError –
- Columns listed in convert_dates are neither datetime64[ns] or datetime.datetime * Column listed in convert_dates is not in DataFrame * Categorical label contains more than 32,000 characters

See also

read_stata: Import Stata data files.
io.stata.StataWriter: Low-level writer for Stata data files.
io.stata.StataWriter117: Low-level writer for version 117 files.

Examples

>>> df = pd.DataFrame(
...     [["falcon", 350], ["parrot", 18]], columns=["animal", "parrot"]
... )
>>> df.to_stata("animals.dta")

Render a DataFrame to a console-friendly tabular output.

Parameters:

buf (str, Path or StringIO-like, optional, default None) – Buffer to write to. If None, the output is returned as a string.
columns (array-like, optional, default None) – The subset of columns to write. Writes all columns by default.
col_space (int, list or dict of int, optional) – The minimum width of each column. If a list of ints is given every integers corresponds with one column. If a dict is given, the key references the column, while the value defines the space to use.
header (bool or list of str, optional) – Write out the column names. If a list of columns is given, it is assumed to be aliases for the column names.
index (bool, optional, default True) – Whether to print index (row) labels.
na_rep (str, optional, default 'NaN') – String representation of NaN to use.
formatters (list, tuple or dict of one-param. functions, optional) – Formatter functions to apply to columns’ elements by position or name. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.
float_format (one-parameter function, optional, default None) – Formatter function to apply to columns’ elements if they are floats. This function must return a unicode string and will be applied only to the non-NaN elements, with NaN being handled by na_rep.
sparsify (bool, optional, default True) – Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.
index_names (bool, optional, default True) – Prints the names of the indexes.
justify (str, default None) –
How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), ‘right’ out of the box. Valid values are
- left
- right
- center
- justify
- justify-all
- start
- end
- inherit
- match-parent
- initial
- unset.
max_rows (int, optional) – Maximum number of rows to display in the console.
max_cols (int, optional) – Maximum number of columns to display in the console.
show_dimensions (bool, default False) – Display DataFrame dimensions (number of rows by number of columns).
decimal (str, default '.') – Character recognized as decimal separator, e.g. ‘,’ in Europe.
line_width (int, optional) – Width to wrap a line in characters.
min_rows (int, optional) – The number of rows to display in the console in a truncated repr (when number of rows is above max_rows).
max_colwidth (int, optional) – Max width to truncate each column in characters. By default, no limit.
encoding (str, default "utf-8") – Set character encoding.

Returns:

If buf is None, returns the result as a string. Otherwise returns None.

Return type:

str or None

See also

to_html: Convert DataFrame to HTML.

Examples

>>> d = {"col1": [1, 2, 3], "col2": [4, 5, 6]}
>>> df = pd.DataFrame(d)
>>> print(df.to_string())
   col1  col2
0     1     4
1     2     5
2     3     6

to_timestamp(freq: Frequency | None = None, how: ToTimestampHow = 'start', axis: Axis = 0, copy: bool | lib.NoDefault = <no_default>) → DataFrame

Cast PeriodIndex to DatetimeIndex of timestamps, at beginning of period.

This can be changed to the end of the period, by specifying how=”e”.

Parameters:

freq (str, default frequency of PeriodIndex) – Desired frequency.
how ({'s', 'e', 'start', 'end'}) – Convention for converting period to timestamp; start of period vs. end.
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to convert (the index by default).
copy (bool, default False) –
This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.

Returns:

DataFrame with the PeriodIndex cast to DatetimeIndex.

Return type:

DataFrame with DatetimeIndex

See also

DataFrame.to_period: Inverse method to cast DatetimeIndex to PeriodIndex.
Series.to_timestamp: Equivalent method for Series.

Examples

>>> idx = pd.PeriodIndex(["2023", "2024"], freq="Y")
>>> d = {"col1": [1, 2], "col2": [3, 4]}
>>> df1 = pd.DataFrame(data=d, index=idx)
>>> df1
      col1   col2
2023     1      3
2024     2      4

The resulting timestamps will be at the beginning of the year in this case

>>> df1 = df1.to_timestamp()
>>> df1
            col1   col2
2023-01-01     1      3
2024-01-01     2      4
>>> df1.index
DatetimeIndex(['2023-01-01', '2024-01-01'], dtype='datetime64[us]', freq=None)

Using freq which is the offset that the Timestamps will have

>>> df2 = pd.DataFrame(data=d, index=idx)
>>> df2 = df2.to_timestamp(freq="M")
>>> df2
            col1   col2
2023-01-31     1      3
2024-01-31     2      4
>>> df2.index
DatetimeIndex(['2023-01-31', '2024-01-31'], dtype='datetime64[us]', freq=None)

to_xarray()

Return an xarray object from the pandas object.

Returns:: Data in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series.
Return type:: xarray.DataArray or xarray.Dataset

See also

DataFrame.to_hdf: Write DataFrame to an HDF5 file.
DataFrame.to_parquet: Write a DataFrame to the binary parquet format.

Notes

See the xarray docs

Examples

>>> df = pd.DataFrame(
...     [
...         ("falcon", "bird", 389.0, 2),
...         ("parrot", "bird", 24.0, 2),
...         ("lion", "mammal", 80.5, 4),
...         ("monkey", "mammal", np.nan, 4),
...     ],
...     columns=["name", "class", "max_speed", "num_legs"],
... )
>>> df
     name   class  max_speed  num_legs
0  falcon    bird      389.0         2
1  parrot    bird       24.0         2
2    lion  mammal       80.5         4
3  monkey  mammal        NaN         4

>>> df.to_xarray()
<xarray.Dataset>
Dimensions:    (index: 4)
Coordinates:
  * index      (index) int64 32B 0 1 2 3
Data variables:
    name       (index) object 32B 'falcon' 'parrot' 'lion' 'monkey'
    class      (index) object 32B 'bird' 'bird' 'mammal' 'mammal'
    max_speed  (index) float64 32B 389.0 24.0 80.5 nan
    num_legs   (index) int64 32B 2 2 4 4

>>> df["max_speed"].to_xarray()
<xarray.DataArray 'max_speed' (index: 4)>
array([389. ,  24. ,  80.5,   nan])
Coordinates:
  * index    (index) int64 0 1 2 3

>>> dates = pd.to_datetime(
...     ["2018-01-01", "2018-01-01", "2018-01-02", "2018-01-02"]
... )
>>> df_multiindex = pd.DataFrame(
...     {
...         "date": dates,
...         "animal": ["falcon", "parrot", "falcon", "parrot"],
...         "speed": [350, 18, 361, 15],
...     }
... )
>>> df_multiindex = df_multiindex.set_index(["date", "animal"])

>>> df_multiindex
                   speed
date       animal
2018-01-01 falcon    350
           parrot     18
2018-01-02 falcon    361
           parrot     15

>>> df_multiindex.to_xarray()
<xarray.Dataset>
Dimensions:  (date: 2, animal: 2)
Coordinates:
  * date     (date) datetime64[s] 2018-01-01 2018-01-02
  * animal   (animal) object 'falcon' 'parrot'
Data variables:
    speed    (date, animal) int64 350 18 361 15

Render a DataFrame to an XML document.

Parameters:

path_or_buffer (str, path object, file-like object, or None, default None) – String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string.
index (bool, default True) – Whether to include index in XML document.
root_name (str, default 'data') – The name of root element in XML document.
row_name (str, default 'row') – The name of row element in XML document.
na_rep (str, optional) – Missing data representation.
attr_cols (list-like, optional) – List of columns to write as attributes in row element. Hierarchical columns will be flattened with underscore delimiting the different levels.
elem_cols (list-like, optional) – List of columns to write as children in row element. By default, all columns output as children of row element. Hierarchical columns will be flattened with underscore delimiting the different levels.
namespaces (dict, optional) –
All namespaces to be defined in root element. Keys of dict should be prefix names and values of dict corresponding URIs. Default namespaces should be given empty string key. For example,
```
namespaces = {"": "https://example.com"}
```
prefix (str, optional) – Namespace prefix to be used for every element and/or attribute in document. This should be one of the keys in namespaces dict.
encoding (str, default 'utf-8') – Encoding of the resulting document.
xml_declaration (bool, default True) – Whether to include the XML declaration at start of document.
pretty_print (bool, default True) – Whether output should be pretty printed with indentation and line breaks.
parser ({'lxml','etree'}, default 'lxml') – Parser module to use for building of tree. Only ‘lxml’ and ‘etree’ are supported. With ‘lxml’, the ability to use XSLT stylesheet is supported.
stylesheet (str, path object or file-like object, optional) – A URL, file-like object, or a raw string containing an XSLT script used to transform the raw XML output. Script should use layout of elements and attributes from original output. This argument requires lxml to be installed. Only XSLT 1.0 scripts and not later versions is currently supported.
compression (str or dict, default 'infer') – For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buffer’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, ‘.zst’, ‘.tar’, ‘.tar.gz’, ‘.tar.xz’ or ‘.tar.bz2’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdCompressor, lzma.LZMAFile or tarfile.TarFile, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.
storage_options (dict, optional) –
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

Returns:

If io is None, returns the resulting XML format as a string. Otherwise returns None.

Return type:

None or str

See also

to_json: Convert the pandas object to a JSON string.
to_html: Convert DataFrame to a html.

Examples

>>> df = pd.DataFrame(
...     [["square", 360, 4], ["circle", 360, np.nan], ["triangle", 180, 3]],
...     columns=["shape", "degrees", "sides"],
... )

>>> df.to_xml()
<?xml version='1.0' encoding='utf-8'?>
<data>
  <row>
    <index>0</index>
    <shape>square</shape>
    <degrees>360</degrees>
    <sides>4.0</sides>
  </row>
  <row>
    <index>1</index>
    <shape>circle</shape>
    <degrees>360</degrees>
    <sides/>
  </row>
  <row>
    <index>2</index>
    <shape>triangle</shape>
    <degrees>180</degrees>
    <sides>3.0</sides>
  </row>
</data>

>>> df.to_xml(
...     attr_cols=["index", "shape", "degrees", "sides"]
... )
<?xml version='1.0' encoding='utf-8'?>
<data>
  <row index="0" shape="square" degrees="360" sides="4.0"/>
  <row index="1" shape="circle" degrees="360"/>
  <row index="2" shape="triangle" degrees="180" sides="3.0"/>
</data>

>>> df.to_xml(
...     namespaces={"doc": "https://example.com"}, prefix="doc"
... )
<?xml version='1.0' encoding='utf-8'?>
<doc:data xmlns:doc="https://example.com">
  <doc:row>
    <doc:index>0</doc:index>
    <doc:shape>square</doc:shape>
    <doc:degrees>360</doc:degrees>
    <doc:sides>4.0</doc:sides>
  </doc:row>
  <doc:row>
    <doc:index>1</doc:index>
    <doc:shape>circle</doc:shape>
    <doc:degrees>360</doc:degrees>
    <doc:sides/>
  </doc:row>
  <doc:row>
    <doc:index>2</doc:index>
    <doc:shape>triangle</doc:shape>
    <doc:degrees>180</doc:degrees>
    <doc:sides>3.0</doc:sides>
  </doc:row>
</doc:data>

transform(func: AggFuncType, axis: Axis = 0, *args, **kwargs) → DataFrame

Call func on self producing a DataFrame with the same axis shape as self.

Parameters:

func (function, str, list-like or dict-like) –
Function to use for transforming the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. If func is both list-like and dict-like, dict-like behavior takes precedence.

Accepted combinations are:
- function
- string function name
- list-like of functions and/or function names, e.g. [np.exp, 'sqrt']
- dict-like of axis labels -> functions, function names or list-like of such.
axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.
*args – Positional arguments to pass to func.
**kwargs – Keyword arguments to pass to func.

Returns:

A DataFrame that must have the same length as self.

Return type:

DataFrame

:raises ValueError : If the returned DataFrame has a different length than self.:

See also

DataFrame.agg: Only perform aggregating type operations.
DataFrame.apply: Invoke function on a DataFrame.

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See gotchas.udf-mutation for more details.

Examples

>>> df = pd.DataFrame({"A": range(3), "B": range(1, 4)})
>>> df
   A  B
0  0  1
1  1  2
2  2  3
>>> df.transform(lambda x: x + 1)
   A  B
0  1  2
1  2  3
2  3  4

Even though the resulting DataFrame must have the same length as the input DataFrame, it is possible to provide several input functions:

>>> s = pd.Series(range(3))
>>> s
0    0
1    1
2    2
dtype: int64
>>> s.transform([np.sqrt, np.exp])
       sqrt        exp
0  0.000000   1.000000
1  1.000000   2.718282
2  1.414214   7.389056

You can call transform on a GroupBy object:

>>> df = pd.DataFrame(
...     {
...         "Date": [
...             "2015-05-08",
...             "2015-05-07",
...             "2015-05-06",
...             "2015-05-05",
...             "2015-05-08",
...             "2015-05-07",
...             "2015-05-06",
...             "2015-05-05",
...         ],
...         "Data": [5, 8, 6, 1, 50, 100, 60, 120],
...     }
... )
>>> df
         Date  Data
0  2015-05-08     5
1  2015-05-07     8
2  2015-05-06     6
3  2015-05-05     1
4  2015-05-08    50
5  2015-05-07   100
6  2015-05-06    60
7  2015-05-05   120
>>> df.groupby("Date")["Data"].transform("sum")
0     55
1    108
2     66
3    121
4     55
5    108
6     66
7    121
Name: Data, dtype: int64

>>> df = pd.DataFrame(
...     {
...         "c": [1, 1, 1, 2, 2, 2, 2],
...         "type": ["m", "n", "o", "m", "m", "n", "n"],
...     }
... )
>>> df
   c type
0  1    m
1  1    n
2  1    o
3  2    m
4  2    m
5  2    n
6  2    n
>>> df["size"] = df.groupby("c")["type"].transform(len)
>>> df
   c type size
0  1    m    3
1  1    n    3
2  1    o    3
3  2    m    4
4  2    m    4
5  2    n    4
6  2    n    4

transpose(*args, copy: bool | Literal[_NoDefault.no_default] = <no_default>) → DataFrame

Transpose index and columns.

Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. The property T is an accessor to the method transpose().

Parameters:

*args (tuple, optional) – Accepted for compatibility with NumPy.
copy (bool, default False) –
This keyword is now ignored; changing its value will have no impact on the method.

Note that a copy is always required for mixed dtype DataFrames, or for DataFrames with any extension types.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.

Returns:

The transposed DataFrame.

Return type:

DataFrame

See also

numpy.transpose: Permute the dimensions of a given array.

Notes

Transposing a DataFrame with mixed dtypes will result in a homogeneous DataFrame with the object dtype. In such a case, a copy of the data is always made.

Examples

Square DataFrame with homogeneous dtype

>>> d1 = {"col1": [1, 2], "col2": [3, 4]}
>>> df1 = pd.DataFrame(data=d1)
>>> df1
   col1  col2
0     1     3
1     2     4

>>> df1_transposed = df1.T  # or df1.transpose()
>>> df1_transposed
      0  1
col1  1  2
col2  3  4

When the dtype is homogeneous in the original DataFrame, we get a transposed DataFrame with the same dtype:

>>> df1.dtypes
col1    int64
col2    int64
dtype: object
>>> df1_transposed.dtypes
0    int64
1    int64
dtype: object

Non-square DataFrame with mixed dtypes

>>> d2 = {
...     "name": ["Alice", "Bob"],
...     "score": [9.5, 8],
...     "employed": [False, True],
...     "kids": [0, 0],
... }
>>> df2 = pd.DataFrame(data=d2)
>>> df2
    name  score  employed  kids
0  Alice    9.5     False     0
1    Bob    8.0      True     0

>>> df2_transposed = df2.T  # or df2.transpose()
>>> df2_transposed
              0     1
name      Alice   Bob
score       9.5   8.0
employed  False  True
kids          0     0

When the DataFrame has mixed dtypes, we get a transposed DataFrame with the object dtype:

>>> df2.dtypes
name            str
score       float64
employed       bool
kids          int64
dtype: object
>>> df2_transposed.dtypes
0    object
1    object
dtype: object

truediv(other, axis: Axis = 'columns', level=None, fill_value=None) → DataFrame

Get Floating division of dataframe and other, element-wise (binary operator truediv).

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

other (scalar, sequence, Series, dict or DataFrame) – Any single or multiple element data structure, or list-like object.
axis ({0 or 'index', 1 or 'columns'}) – Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
level (int or label) – Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value (float or None, default None) – Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

Result of the arithmetic operation.

Return type:

DataFrame

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})
            angles  degrees
circle           0      720
triangle         0      360
rectangle        0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
            angles  degrees
circle           0        0
triangle         6      360
rectangle       12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},
...                      index=['circle', 'triangle', 'rectangle'])
>>> other
           angles
circle          0
triangle        3
rectangle       4

>>> df * other
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

>>> df_pow = pd.DataFrame({'A': [2, 3, 4, 5],
...                        'B': [6, 7, 8, 9]})
>>> df_pow.pow(2)
    A   B
0   4  36
1   9  49
2  16  64
3  25  81

truncate(before=None, after=None, axis: int | ~typing.Literal['index', 'columns', 'rows'] | None=None, copy: bool | Literal[_NoDefault.no_default] = <no_default>) → Self

Truncate a Series or DataFrame before and after some index value.

This is a useful shorthand for boolean indexing based on index values above or below certain thresholds.

Parameters:

before (date, str, int) – Truncate all rows before this index value.
after (date, str, int) – Truncate all rows after this index value.
axis ({0 or 'index', 1 or 'columns'}, optional) – Axis to truncate. Truncates the index (rows) by default. For Series this parameter is unused and defaults to 0.
copy (bool, default False) –
This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.

Returns:

The truncated Series or DataFrame.

Return type:

type of caller

See also

DataFrame.loc: Select a subset of a DataFrame by label.
DataFrame.iloc: Select a subset of a DataFrame by position.

Notes

If the index being truncated contains only datetime values, before and after may be specified as strings instead of Timestamps.

Examples

>>> df = pd.DataFrame(
...     {
...         "A": ["a", "b", "c", "d", "e"],
...         "B": ["f", "g", "h", "i", "j"],
...         "C": ["k", "l", "m", "n", "o"],
...     },
...     index=[1, 2, 3, 4, 5],
... )
>>> df
   A  B  C
1  a  f  k
2  b  g  l
3  c  h  m
4  d  i  n
5  e  j  o

>>> df.truncate(before=2, after=4)
   A  B  C
2  b  g  l
3  c  h  m
4  d  i  n

The columns of a DataFrame can be truncated.

>>> df.truncate(before="A", after="B", axis="columns")
   A  B
a  f
b  g
c  h
d  i
e  j

For Series, only rows can be truncated.

>>> df["A"].truncate(before=2, after=4)
2    b
3    c
4    d
Name: A, dtype: str

The index values in truncate can be datetimes or string dates.

>>> dates = pd.date_range("2016-01-01", "2016-02-01", freq="s")
>>> df = pd.DataFrame(index=dates, data={"A": 1})
>>> df.tail()
                     A
2016-01-31 23:59:56  1
2016-01-31 23:59:57  1
2016-01-31 23:59:58  1
2016-01-31 23:59:59  1
2016-02-01 00:00:00  1

>>> df.truncate(
...     before=pd.Timestamp("2016-01-05"), after=pd.Timestamp("2016-01-10")
... ).tail()
                     A
2016-01-09 23:59:56  1
2016-01-09 23:59:57  1
2016-01-09 23:59:58  1
2016-01-09 23:59:59  1
2016-01-10 00:00:00  1

Because the index is a DatetimeIndex containing only dates, we can specify before and after as strings. They will be coerced to Timestamps before truncation.

>>> df.truncate("2016-01-05", "2016-01-10").tail()
                     A
2016-01-09 23:59:56  1
2016-01-09 23:59:57  1
2016-01-09 23:59:58  1
2016-01-09 23:59:59  1
2016-01-10 00:00:00  1

Note that truncate assumes a 0 value for any unspecified time component (midnight). This differs from partial string slicing, which returns any partially matching dates.

>>> df.loc["2016-01-05":"2016-01-10", :].tail()
                     A
2016-01-10 23:59:55  1
2016-01-10 23:59:56  1
2016-01-10 23:59:57  1
2016-01-10 23:59:58  1
2016-01-10 23:59:59  1

tz_convert(tz, axis: int | ~typing.Literal['index', 'columns', 'rows']=0, level=None, copy: bool | Literal[_NoDefault.no_default] = <no_default>) → Self

Convert tz-aware axis to target time zone.

Parameters:

tz (str or tzinfo object or None) – Target time zone. Passing None will convert to UTC and remove the timezone information.
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to convert
level (int, str, default None) – If axis is a MultiIndex, convert a specific level. Otherwise must be None.
copy (bool, default False) –
This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.

Returns:

Object with time zone converted axis.

Return type:

Series/DataFrame

Raises:

TypeError – If the axis is tz-naive.

See also

DataFrame.tz_localize: Localize tz-naive index of DataFrame to target time zone.
Series.tz_localize: Localize tz-naive index of Series to target time zone.

Examples

Change to another time zone:

>>> s = pd.Series(
...     [1],
...     index=pd.DatetimeIndex(["2018-09-15 01:30:00+02:00"]),
... )
>>> s.tz_convert("Asia/Shanghai")
2018-09-15 07:30:00+08:00    1
dtype: int64

Pass None to convert to UTC and get a tz-naive index:

>>> s = pd.Series([1], index=pd.DatetimeIndex(["2018-09-15 01:30:00+02:00"]))
>>> s.tz_convert(None)
2018-09-14 23:30:00    1
dtype: int64

tz_localize(tz, axis: int | ~typing.Literal['index', 'columns', 'rows'] = 0, level=None, copy: bool | ~pandas.api.typing.Literal[_NoDefault.no_default] = <no_default>, ambiguous: ~typing.Literal['infer', 'NaT', 'raise'] | bool | ~numpy.ndarray[tuple[~typing.Any, ...], ~numpy.dtype[~numpy.bool]] = 'raise', nonexistent: ~typing.Literal['shift_forward', 'shift_backward', 'NaT', 'raise'] | ~datetime.timedelta = 'raise') → Self

Localize time zone naive index of a Series or DataFrame to target time zone.

This operation localizes the Index. To localize the values in a time zone naive Series, use Series.dt.tz_localize().

Parameters:

tz (str or tzinfo or None) – Time zone to localize. Passing None will remove the time zone information and preserve local time.
axis ({0 or 'index', 1 or 'columns'}, default 0) – The axis to localize
level (int, str, default None) – If axis ia a MultiIndex, localize a specific level. Otherwise must be None.
copy (bool, default False) –
This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.
ambiguous ('infer', bool, bool-ndarray, 'NaT', default 'raise') –
When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the ambiguous parameter dictates how ambiguous times should be handled.
- ’infer’ will attempt to infer fall dst-transition hours based on order
- bool (or bool-ndarray) where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times)
- ’NaT’ will return NaT where there are ambiguous times
- ’raise’ will raise a ValueError if there are ambiguous times.
nonexistent (str, default 'raise') –
A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. Valid values are:
- ’shift_forward’ will shift the nonexistent time forward to the closest existing time
- ’shift_backward’ will shift the nonexistent time backward to the closest existing time
- ’NaT’ will return NaT where there are nonexistent times
- timedelta objects will shift nonexistent times by the timedelta
- ’raise’ will raise a ValueError if there are nonexistent times.

Returns:

Same type as the input, with time zone naive or aware index, depending on tz.

Return type:

Series/DataFrame

Raises:

TypeError – If the TimeSeries is tz-aware and tz is not None.

See also

Series.dt.tz_localize: Localize the values in a time zone naive Series.
Timestamp.tz_localize: Localize the Timestamp to a timezone.

Examples

Localize local times:

>>> s = pd.Series(
...     [1],
...     index=pd.DatetimeIndex(["2018-09-15 01:30:00"]),
... )
>>> s.tz_localize("CET")
2018-09-15 01:30:00+02:00    1
dtype: int64

Pass None to convert to tz-naive index and preserve local time:

>>> s = pd.Series([1], index=pd.DatetimeIndex(["2018-09-15 01:30:00+02:00"]))
>>> s.tz_localize(None)
2018-09-15 01:30:00    1
dtype: int64

Be careful with DST changes. When there is sequential data, pandas can infer the DST time:

>>> s = pd.Series(
...     range(7),
...     index=pd.DatetimeIndex(
...         [
...             "2018-10-28 01:30:00",
...             "2018-10-28 02:00:00",
...             "2018-10-28 02:30:00",
...             "2018-10-28 02:00:00",
...             "2018-10-28 02:30:00",
...             "2018-10-28 03:00:00",
...             "2018-10-28 03:30:00",
...         ]
...     ),
... )
>>> s.tz_localize("CET", ambiguous="infer")
2018-10-28 01:30:00+02:00    0
2018-10-28 02:00:00+02:00    1
2018-10-28 02:30:00+02:00    2
2018-10-28 02:00:00+01:00    3
2018-10-28 02:30:00+01:00    4
2018-10-28 03:00:00+01:00    5
2018-10-28 03:30:00+01:00    6
dtype: int64

In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly

>>> s = pd.Series(
...     range(3),
...     index=pd.DatetimeIndex(
...         [
...             "2018-10-28 01:20:00",
...             "2018-10-28 02:36:00",
...             "2018-10-28 03:46:00",
...         ]
...     ),
... )
>>> s.tz_localize("CET", ambiguous=np.array([True, True, False]))
2018-10-28 01:20:00+02:00    0
2018-10-28 02:36:00+02:00    1
2018-10-28 03:46:00+01:00    2
dtype: int64

If the DST transition causes nonexistent times, you can shift these dates forward or backward with a timedelta object or ‘shift_forward’ or ‘shift_backward’.

>>> dti = pd.DatetimeIndex(
...     ["2015-03-29 02:30:00", "2015-03-29 03:30:00"], dtype="M8[ns]"
... )
>>> s = pd.Series(range(2), index=dti)
>>> s.tz_localize("Europe/Warsaw", nonexistent="shift_forward")
2015-03-29 03:00:00+02:00    0
2015-03-29 03:30:00+02:00    1
dtype: int64
>>> s.tz_localize("Europe/Warsaw", nonexistent="shift_backward")
2015-03-29 01:59:59.999999999+01:00    0
2015-03-29 03:30:00+02:00              1
dtype: int64
>>> s.tz_localize("Europe/Warsaw", nonexistent=pd.Timedelta("1h"))
2015-03-29 03:30:00+02:00    0
2015-03-29 03:30:00+02:00    1
dtype: int64

unstack(level: IndexLabel = -1, fill_value=None, sort: bool = True) → DataFrame | Series

Pivot a level of the (necessarily hierarchical) index labels.

Returns a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels.

If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex).

Parameters:

level (int, str, or list of these, default -1 (last level)) – Level(s) of index to unstack, can pass level name.
fill_value (scalar) – Replace NaN with this value if the unstack produces missing values.
sort (bool, default True) – Sort the level(s) in the resulting MultiIndex columns.

Returns:

If index is a MultiIndex: DataFrame with pivoted index labels as new inner-most level column labels, else Series.

Return type:

Series or DataFrame

See also

DataFrame.pivot: Pivot a table based on column values.
DataFrame.stack: Pivot a level of the column labels (inverse operation from unstack).

Notes

Reference the user guide for more examples.

Examples

>>> index = pd.MultiIndex.from_tuples(
...     [("one", "a"), ("one", "b"), ("two", "a"), ("two", "b")]
... )
>>> s = pd.Series(np.arange(1.0, 5.0), index=index)
>>> s
one  a   1.0
     b   2.0
two  a   3.0
     b   4.0
dtype: float64

>>> s.unstack(level=-1)
     a   b
one  1.0  2.0
two  3.0  4.0

>>> s.unstack(level=0)
   one  two
a  1.0   3.0
b  2.0   4.0

>>> df = s.unstack(level=0)
>>> df.unstack()
one  a  1.0
     b  2.0
two  a  3.0
     b  4.0
dtype: float64

update(other, join: UpdateJoin = 'left', overwrite: bool = True, filter_func=None, errors: IgnoreRaise = 'ignore') → None

Modify in place using non-NA values from another DataFrame.

Aligns on indices. There is no return value.

Parameters:

other (DataFrame, or object coercible into a DataFrame) – Should have at least one matching index/column label with the original DataFrame. If a Series is passed, its name attribute must be set, and that will be used as the column name to align with the original DataFrame.
join ({'left'}, default 'left') – Only left join is implemented, keeping the index and columns of the original object.
overwrite (bool, default True) –
How to handle non-NA values for overlapping keys:
- True: overwrite original DataFrame’s values with values from other.
- False: only update values that are NA in the original DataFrame.
filter_func (callable(1d-array) -> bool 1d-array, optional) – Can choose to replace values other than NA. Return True for values that should be updated.
errors ({'raise', 'ignore'}, default 'ignore') – If ‘raise’, will raise a ValueError if the DataFrame and other both contain non-NA data in the same place.

Returns:

This method directly changes calling object.

Return type:

None

Raises:

ValueError –
- When errors=’raise’ and there’s overlapping non-NA data. * When errors is not either ‘ignore’ or ‘raise’
NotImplementedError –
- If join != ‘left’

See also

dict.update: Similar method for dictionaries.
DataFrame.merge: For column(s)-on-column(s) operations.

Notes

Duplicate indices on other are not supported and raises ValueError.

Examples

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [400, 500, 600]})
>>> new_df = pd.DataFrame({"B": [4, 5, 6], "C": [7, 8, 9]})
>>> df.update(new_df)
>>> df
   A  B
0  1  4
1  2  5
2  3  6

The DataFrame’s length does not increase as a result of the update, only values at matching index/column labels are updated.

>>> df = pd.DataFrame({"A": ["a", "b", "c"], "B": ["x", "y", "z"]})
>>> new_df = pd.DataFrame({"B": ["d", "e", "f", "g", "h", "i"]})
>>> df.update(new_df)
>>> df
   A  B
0  a  d
1  b  e
2  c  f

>>> df = pd.DataFrame({"A": ["a", "b", "c"], "B": ["x", "y", "z"]})
>>> new_df = pd.DataFrame({"B": ["d", "f"]}, index=[0, 2])
>>> df.update(new_df)
>>> df
   A  B
0  a  d
1  b  y
2  c  f

For Series, its name attribute must be set.

>>> df = pd.DataFrame({"A": ["a", "b", "c"], "B": ["x", "y", "z"]})
>>> new_column = pd.Series(["d", "e", "f"], name="B")
>>> df.update(new_column)
>>> df
   A  B
0  a  d
1  b  e
2  c  f

If other contains NaNs the corresponding values are not updated in the original dataframe.

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [400.0, 500.0, 600.0]})
>>> new_df = pd.DataFrame({"B": [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
   A      B
0  1    4.0
1  2  500.0
2  3    6.0

value_counts(subset: IndexLabel | None = None, normalize: bool = False, sort: bool = True, ascending: bool = False, dropna: bool = True) → Series

Return a Series containing the frequency of each distinct row in the DataFrame.

Parameters:

subset (Hashable or a sequence of the previous, optional) – Columns to use when counting unique combinations.
normalize (bool, default False) – Return proportions rather than frequencies.
sort (bool, default True) –
Stable sort by frequencies when True. Preserve the order of the data when False.

Changed in version 3.0.0: Prior to 3.0.0, sort=False would sort by the columns values.

Changed in version 3.0.0: Prior to 3.0.0, the sort was unstable.
ascending (bool, default False) – Sort in ascending order.
dropna (bool, default True) – Do not include counts of rows that contain NA values.

Returns:

Series containing the frequency of each distinct row in the DataFrame.

Return type:

Series

See also

Series.value_counts: Equivalent method on Series.

Notes

The returned Series will have a MultiIndex with one level per input column but an Index (non-multi) for a single label. By default, rows that contain any NA values are omitted from the result. By default, the resulting Series will be sorted by frequencies in descending order so that the first element is the most frequently-occurring row.

Examples

>>> df = pd.DataFrame(
...     {"num_legs": [2, 4, 4, 6], "num_wings": [2, 0, 0, 0]},
...     index=["falcon", "dog", "cat", "ant"],
... )
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0
cat            4          0
ant            6          0

>>> df.value_counts()
num_legs  num_wings
4         0            2
2         2            1
6         0            1
Name: count, dtype: int64

>>> df.value_counts(sort=False)
num_legs  num_wings
2         2            1
4         0            2
6         0            1
Name: count, dtype: int64

>>> df.value_counts(ascending=True)
num_legs  num_wings
2         2            1
6         0            1
4         0            2
Name: count, dtype: int64

>>> df.value_counts(normalize=True)
num_legs  num_wings
4         0            0.50
2         2            0.25
6         0            0.25
Name: proportion, dtype: float64

With dropna set to False we can also count rows with NA values.

>>> df = pd.DataFrame(
...     {
...         "first_name": ["John", "Anne", "John", "Beth"],
...         "middle_name": ["Smith", pd.NA, pd.NA, "Louise"],
...     }
... )
>>> df
  first_name middle_name
0       John       Smith
1       Anne         NaN
2       John         NaN
3       Beth      Louise

>>> df.value_counts()
first_name  middle_name
John        Smith          1
Beth        Louise         1
Name: count, dtype: int64

>>> df.value_counts(dropna=False)
first_name  middle_name
John        Smith          1
Anne        NaN            1
John        NaN            1
Beth        Louise         1
Name: count, dtype: int64

>>> df.value_counts("first_name")
first_name
John    2
Anne    1
Beth    1
Name: count, dtype: int64

property values: ndarray

Return a Numpy representation of the DataFrame.

Warning

We recommend using DataFrame.to_numpy() instead.

Only the values in the DataFrame will be returned, the axes labels will be removed.

Returns:: The values of the DataFrame.
Return type:: numpy.ndarray

See also

DataFrame.to_numpy: Recommended alternative to this method.
DataFrame.index: Retrieve the index labels.
DataFrame.columns: Retrieving the column names.

Notes

The dtype will be a lower-common-denominator dtype (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. Use this with care if you are not dealing with the blocks.

e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. If dtypes are int32 and uint8, dtype will be upcast to int32. By numpy.find_common_type() convention, mixing int64 and uint64 will result in a float64 dtype.

Examples

A DataFrame where all columns are the same type (e.g., int64) results in an array of the same type.

>>> df = pd.DataFrame(
...     {"age": [3, 29], "height": [94, 170], "weight": [31, 115]}
... )
>>> df
   age  height  weight
0    3      94      31
1   29     170     115
>>> df.dtypes
age       int64
height    int64
weight    int64
dtype: object
>>> df.values
array([[  3,  94,  31],
       [ 29, 170, 115]])

A DataFrame with mixed type columns(e.g., str/object, int64, float32) results in an ndarray of the broadest type that accommodates these mixed types (e.g., object).

>>> df2 = pd.DataFrame(
...     [
...         ("parrot", 24.0, "second"),
...         ("lion", 80.5, 1),
...         ("monkey", np.nan, None),
...     ],
...     columns=("name", "max_speed", "rank"),
... )
>>> df2.dtypes
name             str
max_speed    float64
rank          object
dtype: object
>>> df2.values
array([['parrot', 24.0, 'second'],
       ['lion', 80.5, 1],
       ['monkey', nan, None]], dtype=object)

var(*, axis: Axis | None = 0, skipna: bool = True, ddof: int = 1, numeric_only: bool = False, **kwargs) → Series | Any

Return unbiased variance over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument.

Parameters:

axis ({index (0), columns (1)}) –
For Series this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.var with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To retain the old behavior, pass axis=0 (or do not pass axis).
skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.
ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
numeric_only (bool, default False) – Include only float, int, boolean columns. Not implemented for Series.
**kwargs – Additional keywords passed.

Returns:

Unbiased variance over requested axis.

Return type:

Series or scalaer

See also

numpy.var: Equivalent function in NumPy.
Series.var: Return unbiased variance over Series values.
Series.std: Return standard deviation over Series values.
DataFrame.std: Return standard deviation of the values over the requested axis.

Examples

>>> df = pd.DataFrame(
...     {
...         "person_id": [0, 1, 2, 3],
...         "age": [21, 25, 62, 43],
...         "height": [1.61, 1.87, 1.49, 2.01],
...     }
... ).set_index("person_id")
>>> df
           age  height
person_id
0           21    1.61
1           25    1.87
2           62    1.49
3           43    2.01

>>> df.var()
age       352.916667
height      0.056367
dtype: float64

Alternatively, ddof=0 can be set to normalize by N instead of N-1:

>>> df.var(ddof=0)
age       264.687500
height      0.042275
dtype: float64

where(cond, other=<no_default>, *, inplace: bool = False, axis: int | ~typing.Literal['index', 'columns', 'rows'] | None=None, level: Hashable | None = None) → Self

Replace values where the condition is False.

This method allows conditional replacement of values. Where the condition evaluates to True, the original values are retained; where it evaluates to False, values are replaced with corresponding entries from other.

Parameters:

cond (bool Series/DataFrame, array-like, or callable) – Where cond is True, keep the original value. Where False, replace with corresponding value from other. If cond is callable, it is computed on the Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn’t check it).
other (scalar, Series/DataFrame, or callable) – Entries where cond is False are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn’t check it). If not specified, entries will be filled with the corresponding NULL value (np.nan for numpy dtypes, pd.NA for extension dtypes).
inplace (bool, default False) – Whether to perform the operation in place on the data.
axis (int, default None) – Alignment axis if needed. For Series this parameter is unused and defaults to 0.
level (int, default None) – Alignment level if needed.

Returns:

When applied to a Series, the function will return a Series, and when applied to a DataFrame, it will return a DataFrame.

Return type:

Series or DataFrame

See also

DataFrame.mask(): Return an object of same shape as caller.
Series.mask(): Return an object of same shape as caller.

Notes

The where method is an application of the if-then idiom. For each element in the caller, if cond is True the element is used; otherwise the corresponding element from other is used. If the axis of other does not align with axis of cond Series/DataFrame, the values of cond on misaligned index positions will be filled with False.

The signature for Series.where() or DataFrame.where() differs from numpy.where(). Roughly df1.where(m, df2) is equivalent to np.where(m, df1, df2).

For further details and examples see the where documentation in indexing.

The dtype of the object takes precedence. The fill value is casted to the object’s dtype, if this can be done losslessly.

Examples

>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0    NaN
1    1.0
2    2.0
3    3.0
4    4.0
dtype: float64
>>> s.mask(s > 0)
0    0.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

>>> s = pd.Series(range(5))
>>> t = pd.Series([True, False])
>>> s.where(t, 99)
0     0
1    99
2    99
3    99
4    99
dtype: int64
>>> s.mask(t, 99)
0    99
1     1
2    99
3    99
4    99
dtype: int64

>>> s.where(s > 1, 10)
  10
  10
  2
  3
  4
dtype: int64
>>> s.mask(s > 1, 10)
   0
   1
  10
  10
  10
dtype: int64

>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=["A", "B"])
>>> df
   A  B
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9
>>> m = df % 3 == 0
>>> df.where(m, -df)
   A  B
0  0 -1
1 -2  3
2 -4 -5
3  6 -7
4 -8  9
>>> df.where(m, -df) == np.where(m, df, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
>>> df.where(m, -df) == df.mask(~m, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True

xs(key: Hashable | Sequence[Hashable], axis: int | Literal['index', 'columns', 'rows'] = 0, level: Hashable | Sequence[Hashable] | None = None, drop_level: bool = True) → Self

Return cross-section from the Series/DataFrame.

This method takes a key argument to select data at a particular level of a MultiIndex.

Parameters:

key (label or tuple of label) – Label contained in the index, or partially in a MultiIndex.
axis ({0 or 'index', 1 or 'columns'}, default 0) – Axis to retrieve cross-section on.
level (object, defaults to first n levels (n=1 or len(key))) – In case of a key partially contained in a MultiIndex, indicate which levels are used. Levels can be referred by label or position.
drop_level (bool, default True) – If False, returns object with same levels as self.

Returns:

Cross-section from the original Series or DataFrame corresponding to the selected index levels.

Return type:

Series or DataFrame

See also

DataFrame.loc: Access a group of rows and columns by label(s) or a boolean array.
DataFrame.iloc: Purely integer-location based indexing for selection by position.

Notes

xs can not be used to set values.

MultiIndex Slicers is a generic way to get/set values on any level or levels. It is a superset of xs functionality, see MultiIndex Slicers.

Examples

>>> d = {
...     "num_legs": [4, 4, 2, 2],
...     "num_wings": [0, 0, 2, 2],
...     "class": ["mammal", "mammal", "mammal", "bird"],
...     "animal": ["cat", "dog", "bat", "penguin"],
...     "locomotion": ["walks", "walks", "flies", "walks"],
... }
>>> df = pd.DataFrame(data=d)
>>> df = df.set_index(["class", "animal", "locomotion"])
>>> df
                           num_legs  num_wings
class  animal  locomotion
mammal cat     walks              4          0
       dog     walks              4          0
       bat     flies              2          2
bird   penguin walks              2          2

Get values at specified index

>>> df.xs("mammal")
                   num_legs  num_wings
animal locomotion
cat    walks              4          0
dog    walks              4          0
bat    flies              2          2

Get values at several indexes

>>> df.xs(("mammal", "dog", "walks"))
num_legs     4
num_wings    0
Name: (mammal, dog, walks), dtype: int64

Get values at specified index and level

>>> df.xs("cat", level=1)
                   num_legs  num_wings
class  locomotion
mammal walks              4          0

Get values at several indexes and levels

>>> df.xs(("bird", "walks"), level=[0, "locomotion"])
         num_legs  num_wings
animal
penguin         2          2

Get values at specified column and axis

>>> df.xs("num_wings", axis=1)
class   animal   locomotion
mammal  cat      walks         0
        dog      walks         0
        bat      flies         2
bird    penguin  walks         2
Name: num_wings, dtype: int64

class matchms.Pipeline(workflow: OrderedDict, progress_bar: bool = True, logging_level: str = 'WARNING', logging_file: str | None = None)[source]

Bases: object

Central pipeline class.

The pipeline applies filters to one or two collections of spectra and then executes a sequence of similarity computations and mask steps.

Notes

If only spectra_1 is provided during run(), the pipeline assumes a symmetric all-vs-all computation and sets is_symmetric=True.
If spectra_2 is also provided, the pipeline computes spectra_1 vs spectra_2 and sets is_symmetric=False.

__init__(workflow: OrderedDict, progress_bar: bool = True, logging_level: str = 'WARNING', logging_file: str | None = None)[source]

import_spectra(spectra_1: list[str] | str, spectra_2: list[str] | str | None = None) → None[source]: Import one or two spectra collections from file(s).

run(spectra_1, spectra_2=None, cleaned_spectra_1_file=None, cleaned_spectra_2_file=None, create_report: bool = True)[source]: Execute the pipeline workflow.

class matchms.Scores(data: dict[str, ndarray | coo_array])[source]

Bases: object

Container for computed matchms scores.

The Scores class stores the output of one similarity computation and provides a small, intuitive API that works for both dense and sparse score matrices.

A Scores instance can represent either:

a scalar score matrix with one field, usually "score"
a multi-field score result, for example "score" and "matches"
dense data stored as NumPy arrays
sparse data stored as SciPy COO arrays

Parameters:: data – Dictionary mapping score field names to score data. Each value must be either a 2D NumPy array or a SciPy coo_array. All fields must have the same shape and must all be either dense or sparse.

Notes

The class is designed to offer a consistent API independent of the underlying storage format.

Field access: Score fields can be accessed by name, for example scores["score"] or scores["matches"]. Field selection returns another Scores object containing only the selected field.
Scalar scores: If only one field is present, direct comparisons are supported, for example scores > 0.5. This is equivalent to scores["score"] > 0.5.
Masking: Boolean masking returns a filtered Scores object with the same shape. For example, scores[scores["score"] > 0.5] keeps only entries where the condition is true.
Slicing: Basic slicing is supported, for example scores[3, 4], scores[3, :], or scores[:, 2].
Conversion: Use to_array() to obtain a dense NumPy representation and to_coo() to obtain a sparse COO representation.

Examples

Scalar dense scores:

>>> scores = Scores({"score": np.array([[1.0, 0.0], [0.3, 0.8]])})
>>> scores["score"].to_array()
array([[1. , 0. ],
       [0.3, 0.8]])
>>> filtered = scores[scores > 0.5]
>>> filtered.to_array()
array([[1. , 0. ],
       [0. , 0.8]])

Multi-field scores:

>>> scores = Scores({
...     "score": np.array([[1.0, 0.0], [0.3, 0.8]]),
...     "matches": np.array([[5, 0], [1, 4]])
... })
>>> scores["score"].to_array()
array([[1. , 0. ],
       [0.3, 0.8]])
>>> scores["matches"].to_array()
array([[5, 0],
       [1, 4]])
>>> good = scores[(scores["score"] > 0.2) & (scores["matches"] >= 2)]
>>> good.to_array("score")
array([[1. , 0. ],
       [0. , 0.8]])

__init__(data: dict[str, ndarray | coo_array])[source]

copy() → Scores[source]

Return a copy of the Scores object.

Dense score fields are copied as independent NumPy arrays. Sparse score fields are copied as independent SciPy COO arrays. The returned Scores object preserves the score fields, shape, and storage mode of the original object.

classmethod load(path: str | Path) → Scores[source]

Load a Scores object from a .npz file.

Parameters:: path – Input file path.
Returns:: Reconstructed Scores object.
Return type:: Scores

save(path: str | Path, compressed: bool = True) → None[source]

Save the Scores object to a single .npz file.

Parameters:

path – Output file path.
compressed – If True, use numpy.savez_compressed. Default is True.

class matchms.SpectraCollection(spectra: list[Spectrum] | Generator[Spectrum, None, None], mz_precision=1e-06)[source]

Bases: object

Central collection object for matchms spectra datasets.

A SpectraCollection stores many spectra in a synchronized, table-like representation. It separates spectrum-level metadata from peak data while preserving a shared row order between both components.

This class synchronizes:

metadata, tabular data kept internally as pandas DataFrame
fragments, stored in a fragment backend, currently CSRFragmentCollection

Rows correspond to spectra. Metadata row i and fragment row i always describe the same spectrum. Operations such as slicing, filtering, sorting, dropping, and deduplication are applied to both metadata and fragments so that this alignment is preserved.

Compared with a plain list[Spectrum], this representation is intended to support efficient collection-level operations, including metadata-based filtering, fragment-based filtering, m/z range slicing, sorting, hashing, and summary statistics.

Individual rows can still be accessed as regular Spectrum objects. These objects are reconstructed from the stored metadata row and the corresponding fragment row.

Notes

The fragment backend may use an internal representation that differs from the original input spectra. In particular, the default CSR backend stores fragments as a binned sparse matrix. Reconstructed spectra therefore contain m/z values derived from the backend representation, for example bin centers, rather than necessarily the exact original input m/z values.

The central invariant of this class is:

len(metadata) == len(fragments) == n_spectra

and for every row index i:

metadata.iloc[i] corresponds to fragments.get_row(i).

Direct modifications of internal metadata or fragment storage should be avoided. Use collection-level methods such as filter, sort, drop, and add_metadata to preserve row alignment and invalidate cached values correctly.

__init__(spectra: list[Spectrum] | Generator[Spectrum, None, None], mz_precision=1e-06)[source]

apply_to_metadata_rows(func, *args, row_mask=None, inplace: bool = False, drop_missing_updates: bool = True, **kwargs)[source]

Apply a metadata function to selected rows and merge the result back.

This is a convenience wrapper around self.metadata.apply_to_rows. It only modifies metadata and does not change fragments.

bin_to_mz(bin_idx: ndarray | int) → ndarray[source]

Convert bin indices to mz values.

Uses the mz_precision of SpectraCollection and calculates the mz value at the center of the bin.

Parameters:: bin_idx – Bin indices/columns to convert.
Returns:: The mz values at the center of specified bins.
Return type:: np.ndarray

describe() → DataFrame[source]

Generate descriptive statistics for the spectra collection.

Calculates key metrics for spectra collection, including peak counts, total ion intensity, average m/z, and Shannon entropy based on peak intensities. It then computes summary statistics (count, mean, std, min, max, etc.) for the entire collection.

Returns:

pd.DataFrame: A DataFrame containing summary statistics for the: following columns: - ‘peak_counts’: Number of detected peaks per spectrum. - ‘intensity_sums’: Total ion current (TIC) per spectrum. - ‘intensity_entropy’: Shannon entropy of peak intensities,

quantifying the spectral complexity/information density.

drop(indices: list[int] | ndarray, inplace: bool = False)[source]

Removes specified rows (spectra) from both fragments and metadata.

Parameters:

indiceslist[int] | np.ndarray: Indices of the rows to remove.
inplacebool: Will return a new SpectraCollection, if True and the same if False. Defaults to False.

drop_duplicates(inplace: bool = False)[source]

Drops duplicates by spectra hashes.

Parameters:

inplacebool: Will return a new SpectraCollection, if True and the same if False. Defaults to False.

drop_empty_spectra(inplace: bool = False)[source]

Removes spectra without peaks.

Parameters:

inplacebool: Will return a new SpectraCollection, if True and the same if False. Defaults to False.

filter(mask: ndarray | Series | list[bool], inplace: bool = False)[source]

Filters SpectraCollection by keeping only the spectra where the mask is True.

This method synchronizes the filtering of both fragments and metadata. It uses boolean indexing from NumPy and Pandas.

Parameters:

list[bool]) (mask (np.ndarray | pd.Series |) – of the same length as the collection. Rows where the mask is True will be kept; all others will be removed.
(bool) (inplace) – returns None. If False (default), returns a new filtered SpectraCollection instance.

Returns:

SpectraCollection | None – otherwise None.

Return type:

A new filtered instance if inplace is False,

Raises

ValueError: If the length of the mask does not match the number of spectra in the collection.

Example:

>>> # Filter by metadata
>>> filtered_coll = coll.filter(coll.metadata["ms_level"] == 2)
>>>
>>> # Filter by fragment properties
>>> coll.filter(coll.fragments.sum() > 500, inplace=True)
>>>
>>> # Using an external vectorized filter function
>>> mask = filter_min_peaks(coll, n_required=10)
>>> coll.filter(mask, inplace=True)

harmonize_metadata_columns(inplace: bool = False)[source]: Harmonize metadata column names to matchms key style.

mz_to_bin(mz: ndarray | float) → ndarray[source]

Convert mz values into bins.

Uses the mz_precision of SpectraCollection and maps mz values into integer bins by flooring them.

Parameters:: mz – The mz values to bin.
Returns:: Bin indices as np.int64.
Return type:: np.ndarray

sort(by: str | list[str], on: str = 'metadata', inplace: bool = False, **kwargs)[source]

Sorts SpectraCollection (fragments AND metadata) by either metadata keyword(s) or fragment function.

Parameters:

bystr | list[str]: Either metadata column name or method name in FragmentsProxy (e.g., ‘sum’).
onstr: ‘metadata’ (Standard) or ‘fragments’.
inplacebool: Will return a new, sorted SpectraCollection, if True and the same, sorted if False. Defaults to False.

to_json(file: str, export_style: str = 'matchms', append: bool = False) → None[source]

Export the spectra collection to a JSON file.

Parameters:

file – Path to the output file.
export_style – Metadata key style used during export. One of "matchms", "massbank", "nist", "riken", or "gnps". Default is "matchms".
append – JSON export does not support appending. If True, a ValueError is raised.

to_mgf(file: str, export_style: str = 'matchms', append: bool = False) → None[source]

Export the spectra collection to an MGF file.

Parameters:

file – Path to the output file.
export_style – Metadata key style used during export. One of "matchms", "massbank", "nist", "riken", or "gnps". Default is "matchms".
append – If True, append to an existing file. Default is False.

to_msp(file: str, export_style: str = 'matchms', append: bool = False) → None[source]

Export the spectra collection to an MSP file.

Parameters:

file – Path to the output file.
export_style – Metadata key style used during export. One of "matchms", "massbank", "nist", "riken", or "gnps". Default is "matchms".
append – If True, append to an existing file. Default is False.

class matchms.SpectraCollectionProcessor(filters: Iterable[str | Callable | tuple[Callable | str, dict[str, object]]])[source]

Bases: object

Process a SpectraCollection using a series of filters.

This is the SpectraCollection equivalent of SpectrumProcessor, but it applies each filter to the full collection instead of processing spectra one by one.

Parameters:

filters –

A list of filter functions. Allowed formats are the same as for SpectrumProcessor:

str
(str, dict)
Callable
(Callable, dict)

Examples

Create a SpectraCollection and process it with collection-compatible filters:

import numpy as np

from matchms import Spectrum, SpectraCollection
from matchms.filtering import SpectraCollectionProcessor

spectra = [
    Spectrum(
        mz=np.array([100.0, 150.0, 200.0]),
        intensities=np.array([5.0, 50.0, 500.0]),
        metadata={"smiles": "n/a", "compound_name": "example"},
    ),
    Spectrum(
        mz=np.array([110.0, 160.0, 210.0]),
        intensities=np.array([10.0, 100.0, 1000.0]),
        metadata={"smiles": "CCCO", "compound_name": "other"},
    ),
]

collection = SpectraCollection(spectra)

processor = SpectraCollectionProcessor(
    filters=[
        "harmonize_missing_entries",
        (
            "select_by_relative_intensity",
            {"intensity_from": 0.01, "intensity_to": 1.0},
        ),
    ]
)

processed = processor.process_collection(collection)

assert isinstance(processed, SpectraCollection)

The same processor can also create a SpectraCollection from an iterable of Spectrum objects:

processed = processor.process_spectra(spectra)

__init__(filters: Iterable[str | Callable | tuple[Callable | str, dict[str, object]]])[source]

parse_and_add_filter(filter_description: str | Callable | tuple[Callable | str, dict[str, object]], filter_position: int | None = None)[source]: Add a filter by parsing the allowed filter description formats.

process_collection(collection: SpectraCollection) → SpectraCollection | None[source]

Process a SpectraCollection with all filters in the pipeline.

Parameters:: collection – SpectraCollection to process.
Returns:: The processed collection. If a filter removes all spectra and returns None, processing stops and None is returned.
Return type:: SpectraCollection or None

process_spectra(spectra, cleaned_spectra_file=None) → SpectraCollection | None[source]

Process spectra as a SpectraCollection.

Parameters:

spectra – Either a SpectraCollection or an iterable of Spectrum objects.
cleaned_spectra_file – Optional output path. The processed collection is materialized as Spectrum objects for saving.

Returns:

Processed collection.

Return type:

SpectraCollection or None

class matchms.Spectrum(mz: array, intensities: array, metadata: dict | None = None, metadata_harmonization: bool = True)[source]

Bases: object

Container for a collection of peaks, losses and metadata.

Spectrum peaks are stored as Fragments object which can be addressed calling spectrum.peaks and contains m/z values and the respective peak intensities.

Spectrum metadata is stored as Metadata object which can be addressed by spectrum.metadata.

Code example

import numpy as np
from matchms import Scores, Spectrum
from matchms.similarity import CosineGreedy

spectrum = Spectrum(mz=np.array([100, 150, 200.]),
                    intensities=np.array([0.7, 0.2, 0.1]),
                    metadata={"id": 'spectrum1',
                              "precursor_mz": 222.333,
                              "peak_comments": {200.: "the peak at 200 m/z"}})

print(spectrum)
print(spectrum.peaks.mz[0])
print(spectrum.peaks.intensities[0])
print(spectrum.get('id'))
print(spectrum.peak_comments.get(200))

Should output

Spectrum(precursor m/z=222.33, 3 fragments between 100.0 and 200.0)
100.0
0.7
spectrum1
the peak at 200 m/z

peaks

Peaks of spectrum

Type:: Fragments

losses

Losses of spectrum, the difference between the precursor and all peaks.

Can be filled with

from matchms import Fragments
spectrum.losess = Fragments(mz=np.array([50.]), intensities=np.array([0.1]))

Type:: Fragments or None

metadata

Dict of metadata with for example the scan number of precursor m/z.

Type:: dict

__init__(mz: array, intensities: array, metadata: dict | None = None, metadata_harmonization: bool = True)[source]

Parameters:

mz – Array of m/z for the peaks
intensities – Array of intensities for the peaks
metadata – Dictionary with for example the scan number of precursor m/z.
metadata_harmonization (bool, optional) – Set to False if default metadata filters should not be applied. The default is True.

clone()[source]: Return a deepcopy of the spectrum instance.

compute_losses(loss_mz_from: float = 0.0, loss_mz_to: float = None) → Fragments | None[source]

This will compute the “losses”, i.e. the differences between the precursor_mz and the individual fragment m/z values. Only losses between loss_mz_from and loss_mz_to will be kept.

Parameters:

loss_mz_from – Float value to set the minimum acceptable loss value. Default is 0.0.
loss_mz_to – Float value to set the maximum acceptable loss value. Default is None which means that the los_mz_to will be set to the spectrum’s precursor_mz.

property fragments: Fragments

Return the spectrum fragments.

Alias for peaks.

Notes

peaks is the historic matchms name and remains fully supported. fragments is provided for naming consistency with SpectraCollection.

get(key: str, default=None)[source]

Retrieve value from metadata dict. Shorthand for

val = self.metadata[key]

metadata_dict(export_style: str = 'matchms') → dict[source]

Convert spectrum metadata to Python dictionary.

Parameters:: export_style – Converts the keys to the required export style. One of [“matchms”, “massbank”, “nist”, “riken”, “gnps”]. Default is “matchms”

metadata_hash()[source]: Return a (truncated) sha256-based hash which is generated based on the spectrum metadata. Spectra with same metadata results in same metadata_hash.

plot(figsize=(8, 6), dpi=200, **kwargs)[source]: Plot to visually inspect a spectrum run spectrum.plot()

Example of a spectrum plotted using spectrum.plot() ..

plot_against(other_spectrum, figsize=(8, 6), dpi=200, **spectrum_kws)[source]

Compare two spectra visually in a mirror plot.

To visually compare the peaks of two spectra run spectrum.plot_against(other_spectrum)

spectrum mirror plot function — Example of a mirror plot comparing two spectra `spectrum.plot_against()` ..

set(key: str, value)[source]

Set value in metadata dict. Shorthand for

self.metadata[key] = val

spectrum_hash()[source]: Return a (truncated) sha256-based hash which is generated based on the spectrum peaks (mz:intensity pairs). Spectra with same peaks will results in same spectrum_hash.

to_dict(export_style: str = 'matchms') → dict[source]

Return a dictionary representation of a spectrum.

Parameters:: export_style – Converts the keys to the required export style. One of [“matchms”, “massbank”, “nist”, “riken”, “gnps”]. Default is “matchms”

classmethod update_peak_comments_mz_tolerance(mz_tolerance: float)[source]: Change current peak comment m/z tolerance to mz_tolerance.

class matchms.SpectrumProcessor(filters: Iterable[str | Callable | tuple[Callable | str, dict[str, any]]])[source]

Bases: object

A class to process spectra using a series of filters.

The class enables a user to define a custom spectrum processing workflow by setting multiple flags and parameters.

Parameters:: filters – A list of filter functions, see add_filter for all the allowed formats.

__init__(filters: Iterable[str | Callable | tuple[Callable | str, dict[str, any]]])[source]

parse_and_add_filter(filter_description: str | Callable | tuple[Callable | str, dict[str, any]], filter_position: int | None = None)[source]

Adds a filter, by parsing the different allowed inputs.

filter:: Allowed formats: str (has to be a matchms function name) (str, {str, any} (has to be a matchms function name, followed by parameters) Callable (can be matchms filter or custom made filter) Callable, {str, any} (the dict should be parameters.
filter_position:: If None: Matchms filters are automatically ordered. Custom filters will be added at the end of the filter list. If not None, the filter will be added to the given position in the filter order list.

process_spectra(spectra: Iterable[Spectrum], progress_bar: bool = True, cleaned_spectra_file=None, create_report: bool | None = False)[source]

Process a list of spectra with all filters in the processing pipeline.

Parameters:

spectra (list[Spectrum]) – The spectra to process.
create_report (bool, optional) – Creates and outputs a report of the main changes during processing. The report will be returned as pandas DataFrame. Default is set to False.
progress_bar (bool, optional) – Displays progress bar if set to True. Default is True.
cleaned_spectra_file – Path to where the cleaned spectra should be saved.

Returns:

spectra – List containing the processed spectra.
processing_report – A ProcessingReport containing the effect of the filters.

process_spectrum(spectrum, processing_report: ProcessingReport | None = None)[source]

Process the given spectrum with all filters in the processing pipeline.

Parameters:

spectrum (Spectrum) – The spectrum to process.
processing_report – A ProcessingReport object When passed the progress will be added to the object.

Returns:

The processed spectrum.

Return type:

Spectrum

matchms.calculate_scores(spectra_1: Sequence[Spectrum], spectra_2: Sequence[Spectrum], similarity_function: BaseSimilarity) → Scores[source]

Calculate the similarity between all reference objects versus all query objects.

Example to calculate scores between 2 spectra and iterate over the scores

import numpy as np
from matchms import calculate_scores, Spectrum
from matchms.similarity import CosineGreedy

spectrum_1 = Spectrum(mz=np.array([100, 150, 200.]),
                      intensities=np.array([0.7, 0.2, 0.1]),
                      metadata={'id': 'spectrum1'})
spectrum_2 = Spectrum(mz=np.array([100, 140, 190.]),
                      intensities=np.array([0.4, 0.2, 0.1]),
                      metadata={'id': 'spectrum2'})
spectra = [spectrum_1, spectrum_2]

scores = calculate_scores(spectra, spectra, CosineGreedy())

for (reference, query, score) in scores:
    print(f"Cosine score between {spectrum_1.get('id')} and {spectrum_2.get('id')}" +
          f" is {score[0]:.2f} with {score[1]} matched peaks")

Should output

Cosine score between spectrum1 and spectrum1 is 1.00 with 3 matched peaks
Cosine score between spectrum1 and spectrum2 is 0.83 with 1 matched peaks
Cosine score between spectrum2 and spectrum1 is 0.83 with 1 matched peaks
Cosine score between spectrum2 and spectrum2 is 1.00 with 3 matched peaks

Parameters:

spectra_1 – List of reference objects
spectra_2 – List of query objects
similarity_function – Function which accepts a reference + query object and returns a score or tuple of scores

Return type:

Scores

matchms.set_matchms_logger_level(loglevel: str, logger_name='matchms')[source]

Update logging level to given loglevel.

Parameters:

loglevels – Can be ‘DEBUG’, ‘INFO’, ‘WARNING’, ‘ERROR’, ‘CRITICAL’.
logger_name – Default is “matchms”. Change if logger name should be different.

matchms package

Parameters:

Parameters:

Parameters:

Parameters:

Subpackages

Submodules