pyspark.pandas.Series.var

Series.var(axis: Union[int, str, None] = None, ddof: int = 1, numeric_only: bool = None) → Union[int, float, bool, str, bytes, decimal.Decimal, datetime.date, datetime.datetime, None, pyspark.pandas.series.Series]

Return unbiased variance.

Parameters

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

Axis for the function to be applied on.

ddofint, default 1

Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

numeric_onlybool, default None

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

Returns

varscalar for a Series, and a Series for a DataFrame.

Examples

>>> df = ps.DataFrame({'a': [1, 2, 3, np.nan], 'b': [0.1, 0.2, 0.3, np.nan]},
...                   columns=['a', 'b'])

On a DataFrame:

>>> df.var()
a    1.00
b    0.01
dtype: float64

>>> df.var(axis=1)
0    0.405
1    1.620
2    3.645
3      NaN
dtype: float64

>>> df.var(ddof=0)
a    0.666667
b    0.006667
dtype: float64

On a Series:

>>> df['a'].var()
1.0

>>> df['a'].var(ddof=0)
0.6666666666666666

pyspark.pandas.Series.median pyspark.pandas.Series.kurtosis