pyspark.pandas.groupby.GroupBy.median¶

GroupBy.median(numeric_only: bool = True, accuracy: int = 10000) → FrameLike[source]¶

Compute median of groups, excluding missing values.

For multiple groupings, the result index will be a MultiIndex

Note

Unlike pandas’, the median in pandas-on-Spark is an approximated median based upon approximate percentile computation because computing median across a large dataset is extremely expensive.

Parameters

numeric_onlybool, default True: Include only float, int, boolean columns. False is not supported. This parameter is mainly for pandas compatibility.

Returns

Series or DataFrame: Median of values within each group.

Examples

>>> psdf = ps.DataFrame({'a': [1., 1., 1., 1., 2., 2., 2., 3., 3., 3.],
...                     'b': [2., 3., 1., 4., 6., 9., 8., 10., 7., 5.],
...                     'c': [3., 5., 2., 5., 1., 2., 6., 4., 3., 6.]},
...                    columns=['a', 'b', 'c'],
...                    index=[7, 2, 4, 1, 3, 4, 9, 10, 5, 6])
>>> psdf
      a     b    c
7   1.0   2.0  3.0
2   1.0   3.0  5.0
4   1.0   1.0  2.0
1   1.0   4.0  5.0
3   2.0   6.0  1.0
4   2.0   9.0  2.0
9   2.0   8.0  6.0
10  3.0  10.0  4.0
5   3.0   7.0  3.0
6   3.0   5.0  6.0

DataFrameGroupBy

>>> psdf.groupby('a').median().sort_index()  
       b    c
a
1.0  2.0  3.0
2.0  8.0  2.0
3.0  7.0  4.0

SeriesGroupBy

>>> psdf.groupby('a')['b'].median().sort_index()
a
1.0    2.0
2.0    8.0
3.0    7.0
Name: b, dtype: float64

pyspark.pandas.groupby.GroupBy.mean pyspark.pandas.groupby.GroupBy.min