pyspark.pandas.Series.var#
- Series.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