pandas.core.groupby.SeriesGroupBy.median#

SeriesGroupBy.median(numeric_only=False)[source] #

Compute median of groups, excluding missing values.

For multiple groupings, the result index will be a MultiIndex

Parameters:

numeric_onlybool, default False

Include only float, int, boolean columns.

Changed in version 2.0.0: numeric_only no longer accepts None and defaults to False.

Returns:

Series or DataFrame

Median of values within each group.

Examples

For SeriesGroupBy:

>>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
>>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst)
>>> ser
a 7
a 2
a 8
b 4
b 3
b 3
dtype: int64
>>> ser.groupby(level=0).median()
a 7.0
b 3.0
dtype: float64

For DataFrameGroupBy:

>>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
>>> df = pd.DataFrame(data, index=['dog', 'dog', 'dog',
...  'mouse', 'mouse', 'mouse', 'mouse'])
>>> df
 a b
 dog 1 1
 dog 3 4
 dog 5 8
mouse 7 4
mouse 7 4
mouse 8 2
mouse 3 1
>>> df.groupby(level=0).median()
 a b
dog 3.0 4.0
mouse 7.0 3.0

For Resampler:

>>> ser = pd.Series([1, 2, 3, 3, 4, 5],
...  index=pd.DatetimeIndex(['2023年01月01日',
...  '2023年01月10日',
...  '2023年01月15日',
...  '2023年02月01日',
...  '2023年02月10日',
...  '2023年02月15日']))
>>> ser.resample('MS').median()
2023年01月01日 2.0
2023年02月01日 4.0
Freq: MS, dtype: float64