pyspark.pandas.Series.median

Series.median(axis: Union[int, str, None] = None, skipna: bool = True, numeric_only: bool = None, accuracy: int = 10000) → Union[int, float, bool, str, bytes, decimal.Decimal, datetime.date, datetime.datetime, None, Series]

Return the median of the values for the requested axis.

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

axis: {index (0), columns (1)}

Axis for the function to be applied on.

skipna: bool, default True

Exclude NA/null values when computing the result.

Changed in version 3.4.0: Supported including NA/null values.

numeric_only: bool, default None

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

accuracy: int, optional

Default accuracy of approximation. Larger value means better accuracy. The relative error can be deduced by 1.0 / accuracy.

Returns

median: scalar or Series

Examples

>>> df = ps.DataFrame({
...     'a': [24., 21., 25., 33., 26.], 'b': [1, 2, 3, 4, 5]}, columns=['a', 'b'])
>>> df
      a  b
0  24.0  1
1  21.0  2
2  25.0  3
3  33.0  4
4  26.0  5

On a DataFrame:

>>> df.median()
a    25.0
b     3.0
dtype: float64

On a Series:

>>> df['a'].median()
25.0
>>> (df['b'] + 100).median()
103.0

For multi-index columns,

>>> df.columns = pd.MultiIndex.from_tuples([('x', 'a'), ('y', 'b')])
>>> df
      x  y
      a  b
0  24.0  1
1  21.0  2
2  25.0  3
3  33.0  4
4  26.0  5

On a DataFrame:

>>> df.median()
x  a    25.0
y  b     3.0
dtype: float64

>>> df.median(axis=1)
0    12.5
1    11.5
2    14.0
3    18.5
4    15.5
dtype: float64

On a Series:

>>> df[('x', 'a')].median()
25.0
>>> (df[('y', 'b')] + 100).median()
103.0

pyspark.pandas.Series.sum pyspark.pandas.Series.var