pandas.Series.groupby#

Series.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=_NoDefault.no_default, squeeze=_NoDefault.no_default, observed=False, dropna=True)[source]#

Group Series 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, 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 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.
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.
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 by and level.
as_indexbool, 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.
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.
group_keysbool, optional: 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. This argument has no effect if the result produced is not like-indexed with respect to the input.

Changed in version 1.5.0: Warns that group_keys will no longer be ignored when the result from apply is a like-indexed Series or DataFrame. Specify group_keys explicitly to include the group keys or not.
squeezebool, default False: Reduce the dimensionality of the return type if possible, otherwise return a consistent type.

Deprecated since version 1.1.0.
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.
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.

New in version 1.1.0.

Returns

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

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.

Examples

>>> ser = pd.Series([390., 350., 30., 20.],
...                 index=['Falcon', 'Falcon', 'Parrot', 'Parrot'], name="Max Speed")
>>> ser
Falcon    390.0
Falcon    350.0
Parrot     30.0
Parrot     20.0
Name: Max Speed, dtype: float64
>>> ser.groupby(["a", "b", "a", "b"]).mean()
a    210.0
b    185.0
Name: Max Speed, dtype: float64
>>> ser.groupby(level=0).mean()
Falcon    370.0
Parrot     25.0
Name: Max Speed, dtype: float64
>>> ser.groupby(ser > 100).mean()
Max Speed
False     25.0
True     370.0
Name: Max Speed, dtype: float64

Grouping by 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'))
>>> ser = pd.Series([390., 350., 30., 20.], index=index, name="Max Speed")
>>> ser
Animal  Type
Falcon  Captive    390.0
        Wild       350.0
Parrot  Captive     30.0
        Wild        20.0
Name: Max Speed, dtype: float64
>>> ser.groupby(level=0).mean()
Animal
Falcon    370.0
Parrot     25.0
Name: Max Speed, dtype: float64
>>> ser.groupby(level="Type").mean()
Type
Captive    210.0
Wild       185.0
Name: Max Speed, dtype: float64

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

>>> ser = pd.Series([1, 2, 3, 3], index=["a", 'a', 'b', np.nan])
>>> ser.groupby(level=0).sum()
a    3
b    3
dtype: int64

>>> ser.groupby(level=0, dropna=False).sum()
a    3
b    3
NaN  3
dtype: int64

>>> arrays = ['Falcon', 'Falcon', 'Parrot', 'Parrot']
>>> ser = pd.Series([390., 350., 30., 20.], index=arrays, name="Max Speed")
>>> ser.groupby(["a", "b", "a", np.nan]).mean()
a    210.0
b    350.0
Name: Max Speed, dtype: float64

>>> ser.groupby(["a", "b", "a", np.nan], dropna=False).mean()
a    210.0
b    350.0
NaN   20.0
Name: Max Speed, dtype: float64