pyspark.pandas.DataFrame.var

DataFrame.var(axis=None, ddof=1, numeric_only=None)

Return unbiased variance.

New in version 3.3.0.

Parameters

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

Axis for the function to be applied on.

ddof: int, default 1

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

Changed in version 3.4.0: Supported including arbitary integers.

numeric_only: bool, default None

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

Returns

var: scalar 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(ddof=2)
a    2.00
b    0.02
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

>>> df['a'].var(ddof=-2)
0.4