# dask.dataframe.tseries.resample.Resampler.median

Resampler.median()

  Compute median of groups, excluding missing values.

  This docstring was copied from pandas.core.resample.Resampler.median.

  Some inconsistencies with the Dask version may exist.

  For multiple groupings, the result index will be a MultiIndex

  Parameters:
    numeric_only bool, default False (Not supported in Dask)

      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