pandas.core.resample.Resampler.median#
- final Resampler.median(numeric_only=False, *args, **kwargs)[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(['2023-01-01', ... '2023-01-10', ... '2023-01-15', ... '2023-02-01', ... '2023-02-10', ... '2023-02-15'])) >>> ser.resample('MS').median() 2023-01-01 2.0 2023-02-01 4.0 Freq: MS, dtype: float64