pandas.DataFrame.groupby — pandas 2.2.2 documentation (2024)

DataFrame.groupby(by=None, axis=_NoDefault.no_default, level=None, as_index=True, sort=True, group_keys=True, observed=_NoDefault.no_default, dropna=True)[source]#

Group DataFrame using a mapper or by a Series of columns.

A groupby operation involves some combination of splitting theobject, applying a function, and combining the results. This can beused to group large amounts of data and compute operations on thesegroups.

Parameters:

bymapping, 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’sindex. If a dict or Series is passed, the Series or dict VALUESwill be used to determine the groups (the Series’ values are firstaligned; see .align() method). If a list or ndarray of lengthequal to the selected axis is passed (see the groupby user guide),the values are used as-is to determine the groups. A label or listof labels may be passed to group by the columns in self.Notice that a tuple is interpreted as a (single) key.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Split along rows (0) or columns (1). For Series this parameteris unused and defaults to 0.

Deprecated since version 2.1.0: Will be removed and behave like axis=0 in a future version.For axis=1, do frame.T.groupby(...) instead.

levelint, level name, or sequence of such, default None

If the axis is a MultiIndex (hierarchical), group by a particularlevel or levels. Do not specify both by and level.

as_indexbool, default True

Return object with group labels as theindex. Only relevant for DataFrame input. as_index=False iseffectively “SQL-style” grouped output. This argument has no effecton filtrations (see the filtrations in the user guide),such as head(), tail(), nth() and in transformations(see the transformations in the user guide).

sortbool, default True

Sort group keys. Get better performance by turning this off.Note this does not influence the order of observations within eachgroup. 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 nolonger sort the values.

group_keysbool, default True

When calling apply and the by argument produces a like-indexed(i.e. a transform) result, add group keys toindex to identify pieces. By default group keys are not includedwhen the result’s index (and column) labels match the inputs, andare included otherwise.

Changed in version 1.5.0: Warns that group_keys will no longer be ignored when theresult from apply is a like-indexed Series or DataFrame.Specify group_keys explicitly to include the group keys ornot.

Changed in version 2.0.0: group_keys now defaults to True.

See also

resample: Convenience method for frequency conversion and resampling of time series.

Notes

See the user guide for moredetailed usage and examples, including splitting an object into groups,iterating through groups, selecting a group, aggregation, and more.

Examples

>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',...  'Parrot', 'Parrot'],...  'Max Speed': [380., 370., 24., 26.]})>>> df Animal Max Speed0 Falcon 380.01 Falcon 370.02 Parrot 24.03 Parrot 26.0>>> df.groupby(['Animal']).mean() Max SpeedAnimalFalcon 375.0Parrot 25.0

Hierarchical Indexes

We can groupby different levels of a hierarchical indexusing 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., 350., 30., 20.]},...  index=index)>>> df Max SpeedAnimal TypeFalcon Captive 390.0 Wild 350.0Parrot Captive 30.0 Wild 20.0>>> df.groupby(level=0).mean() Max SpeedAnimalFalcon 370.0Parrot 25.0>>> df.groupby(level="Type").mean() Max SpeedTypeCaptive 210.0Wild 185.0

We can also choose to include NA in group keys or not by settingdropna parameter, the default setting is True.

>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]>>> df = pd.DataFrame(l, columns=["a", "b", "c"])

>>> df.groupby(by=["b"]).sum() a cb1.0 2 32.0 2 5

>>> df.groupby(by=["b"], dropna=False).sum() a cb1.0 2 32.0 2 5NaN 1 4

>>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]>>> df = pd.DataFrame(l, columns=["a", "b", "c"])

>>> df.groupby(by="a").sum() b caa 13.0 13.0b 12.3 123.0

>>> df.groupby(by="a", dropna=False).sum() b caa 13.0 13.0b 12.3 123.0NaN 12.3 33.0

When using .apply(), use group_keys to include or exclude thegroup keys. The group_keys argument defaults to True (include).

>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',...  'Parrot', 'Parrot'],...  'Max Speed': [380., 370., 24., 26.]})>>> df.groupby("Animal", group_keys=True)[['Max Speed']].apply(lambda x: x) Max SpeedAnimalFalcon 0 380.0 1 370.0Parrot 2 24.0 3 26.0

>>> df.groupby("Animal", group_keys=False)[['Max Speed']].apply(lambda x: x) Max Speed0 380.01 370.02 24.03 26.0