pandas.Series.groupby#
- Series.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, observed=<no_default>, 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, 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 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
by
andlevel
.- 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=False
with an ordered categorical grouper will no longer 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 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_keys
will no longer be ignored when the result fromapply
is a like-indexed Series or DataFrame. Specifygroup_keys
explicitly to include the group keys or not.Changed in version 2.0.0:
group_keys
now 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.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