pandas.DataFrame.groupby#
- 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 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:
- bymapping, function, label, pd.Grouper or list of such
Used to determine the groups for the groupby. If
byis 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 selected axis 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 inself. 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 parameter is 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, doframe.T.groupby(...)instead.- levelint, 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
byandlevel.- as_indexbool, 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).- sortbool, 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=Falsewith an ordered categorical grouper will no longer sort the values.- group_keysbool, default True
When calling apply and the
byargument 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 1.5.0: Warns that
group_keyswill no longer be ignored when the result fromapplyis a like-indexed Series or DataFrame. Specifygroup_keysexplicitly to include the group keys or not.Changed in version 2.0.0:
group_keysnow defaults toTrue.- observedbool, 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.
Deprecated since version 2.1.0: The default value will change to True in a future version of pandas.
- dropnabool, 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:
- pandas.api.typing.DataFrameGroupBy
Returns a groupby object that contains information about the groups.
See also
resampleConvenience 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.
Examples
>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon', ... 'Parrot', 'Parrot'], ... 'Max Speed': [380., 370., 24., 26.]}) >>> 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., 350., 30., 20.]}, ... 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.
>>> 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 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
>>> 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 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(), usegroup_keysto include or exclude the group keys. Thegroup_keysargument defaults toTrue(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 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