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pandas 1.4.2

Notes Parameters Returns BackRef

groupby(self, by=None, axis: 'Axis' = 0, level: 'Level | None' = None, as_index: 'bool' = True, sort: 'bool' = True, group_keys: 'bool' = True, squeeze: 'bool | lib.NoDefault' = <no_default>, observed: 'bool' = False, dropna: 'bool' = True) -> 'DataFrameGroupBy'

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.

Notes

See the :None:None:`user guide <https://pandas.pydata.org/pandas-docs/stable/groupby.html>` for more detailed usage and examples, including splitting an object into groups, iterating through groups, selecting a group, aggregation, and more.

Parameters

by : mapping, function, label, or list of labels: 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 selected axis is passed (see the :None:None:`groupby user guide <https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#splitting-an-object-into-groups>`), 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.
axis : {0 or 'index', 1 or 'columns'}, default 0: Split along rows (0) or columns (1).
level : int, level name, or sequence of such, default None: If the axis is a MultiIndex (hierarchical), group by a particular level or levels.
as_index : bool, default True: For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively "SQL-style" grouped output.
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.
group_keys : bool, default True: When calling apply, add group keys to index to identify pieces.
squeeze : bool, default False: Reduce the dimensionality of the return type if possible, otherwise return a consistent type.

deprecated
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.
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.

versionadded

Returns

DataFrameGroupBy: Returns a groupby object that contains information about the groups.

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

See Also

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

Examples

This example is valid syntax, but we were not able to check execution

>>> 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

This example is valid syntax, but we were not able to check execution

>>> 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 :None:None:`level` parameter:

This example is valid syntax, but we were not able to check execution

>>> 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

This example is valid syntax, but we were not able to check execution

>>> df.groupby(level=0).mean()
        Max Speed
Animal
Falcon      370.0
Parrot       25.0

This example is valid syntax, but we were not able to check execution

>>> 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 :None:None:`True`.

This example is valid syntax, but we were not able to check execution

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

This example is valid syntax, but we were not able to check execution

>>> df.groupby(by=["b"]).sum()
    a   c
b
1.0 2   3
2.0 2   5

This example is valid syntax, but we were not able to check execution

>>> df.groupby(by=["b"], dropna=False).sum()
    a   c
b
1.0 2   3
2.0 2   5
NaN 1   4

This example is valid syntax, but we were not able to check execution

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

This example is valid syntax, but we were not able to check execution

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

This example is valid syntax, but we were not able to check execution

>>> 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

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.groupby.groupby.GroupBy.mean pandas.core.groupby.groupby.GroupBy.ewm pandas.core.groupby.groupby.GroupBy.rolling pandas.core.groupby.groupby.GroupBy.any pandas.core.groupby.groupby.GroupBy.nth pandas.core.groupby.groupby.GroupBy.cummax pandas.core.groupby.groupby.GroupBy.std pandas.core.groupby.groupby.GroupBy.size pandas.core.groupby.groupby.GroupBy.all pandas.core.groupby.groupby.GroupBy.expanding pandas.core.groupby.groupby.GroupBy.cumsum pandas.core.groupby.groupby.GroupBy.tail pandas.core.groupby.groupby.GroupBy.ohlc pandas.core.groupby.groupby.GroupBy.cummin pandas.core.groupby.groupby.GroupBy.var pandas.core.groupby.groupby.GroupBy.sem pandas.core.groupby.groupby.GroupBy.cumprod pandas.core.groupby.groupby.GroupBy.median pandas.core.groupby.groupby.GroupBy.pct_change pandas.core.groupby.groupby.GroupBy.head pandas.core.groupby.groupby.GroupBy.count pandas.core.groupby.groupby.GroupBy.rank

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