dask.dataframe.tseries.resample.Resampler.median

dask.dataframe.tseries.resample.Resampler.median¶

Resampler.median()[source]¶

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_onlybool, 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

dask.dataframe.tseries.resample.Resampler.mean

dask.dataframe.tseries.resample.Resampler.min