pandas.core.groupby.DataFrameGroupBy.median

DataFrameGroupBy.median(numeric_only=False)

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.

See also

Series.groupby

Apply a function groupby to a Series.

DataFrame.groupby

Apply a function groupby to each row or column of a DataFrame.

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