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# pandas.api.typing.SeriesGroupBy.median

SeriesGroupBy.median(numeric_only=False, skipna=True)

  Compute median of groups, excluding missing values.

  For multiple groupings, the result index will be a MultiIndex

  Parameters:
    numeric_only bool, default False

      Include only float, int, boolean columns.

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

    skipna bool, default True

      Exclude NA/null values. If an entire group is NA, the result will be NA.

      Added in version 3.0.0.

  Returns:
    Series or DataFrame

      Median of values within each group.

  See also

  `Series.median`

    Apply function median to a Series.

  `DataFrame.median`

    Apply function median 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