pby.SeriesGroupBy.rolling ](pandas.core.groupby.SeriesGroupBy.rolling.html)
    * [ pandas.core.groupby.SeriesGroupBy.sample ](pandas.core.groupby.SeriesGroupBy.sample.html)
    * [ pandas.core.groupby.SeriesGroupBy.sem ](pandas.core.groupby.SeriesGroupBy.sem.html)
    * [ pandas.core.groupby.SeriesGroupBy.shift ](pandas.core.groupby.SeriesGroupBy.shift.html)
    * [ pandas.core.groupby.SeriesGroupBy.size ](pandas.core.groupby.SeriesGroupBy.size.html)
    * [ pandas.core.groupby.SeriesGroupBy.skew ](pandas.core.groupby.SeriesGroupBy.skew.html)
    * [ pandas.core.groupby.SeriesGroupBy.std ](pandas.core.groupby.SeriesGroupBy.std.html)
    * [ pandas.core.groupby.SeriesGroupBy.sum ](pandas.core.groupby.SeriesGroupBy.sum.html)
    * [ pandas.core.groupby.SeriesGroupBy.var ](pandas.core.groupby.SeriesGroupBy.var.html)
    * [ pandas.core.groupby.SeriesGroupBy.tail ](pandas.core.groupby.SeriesGroupBy.tail.html)
    * [ pandas.core.groupby.SeriesGroupBy.take ](pandas.core.groupby.SeriesGroupBy.take.html)
    * [ pandas.core.groupby.SeriesGroupBy.value_counts ](pandas.core.groupby.SeriesGroupBy.value_counts.html)
    * [ pandas.core.groupby.DataFrameGroupBy.boxplot ](pandas.core.groupby.DataFrameGroupBy.boxplot.html)
    * [ pandas.core.groupby.DataFrameGroupBy.hist ](pandas.core.groupby.DataFrameGroupBy.hist.html)
    * [ pandas.core.groupby.SeriesGroupBy.hist ](pandas.core.groupby.SeriesGroupBy.hist.html)
    * [ pandas.core.groupby.DataFrameGroupBy.plot ](pandas.core.groupby.DataFrameGroupBy.plot.html)
    * [ pandas.core.groupby.SeriesGroupBy.plot ](pandas.core.groupby.SeriesGroupBy.plot.html)
  * [ Resampling ](../resampling.html)
  * [ Style ](../style.html)
  * [ Plotting ](../plotting.html)
  * [ Options and settings ](../options.html)
  * [ Extensions ](../extensions.html)
  * [ Testing ](../testing.html)
  * [ Missing values ](../missing_value.html)

  * [ __ ](../../index.html)
  * [ API reference ](../index.html)
  * [ GroupBy ](../groupby.html)
  * pandas.core.... 

#  pandas.core.groupby.DataFrameGroupBy.median  #

DataFrameGroupBy.  median  (  _ numeric_only  =  False  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L2459-L2537) #

    

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.

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: float6