cudf.DataFrame.var#
- DataFrame.var(axis=_NoDefault.no_default, skipna=True, ddof=1, numeric_only=False, **kwargs)[source]#
Return unbiased variance of the DataFrame.
Normalized by N-1 by default. This can be changed using the ddof argument.
- Parameters:
- axis: {index (0), columns(1)}
Axis for the function to be applied on.
- skipna: bool, default True
Exclude NA/null values. If an entire row/column is NA, the result will be NA.
- ddof: int, default 1
Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
- numeric_onlybool, default False
If True, includes only float, int, boolean columns. If False, will raise error in-case there are non-numeric columns.
- Returns:
- scalar
Examples
>>> import cudf >>> df = cudf.DataFrame({'a': [1, 2, 3, 4], 'b': [7, 8, 9, 10]}) >>> df.var() a 1.666667 b 1.666667 dtype: float64
Pandas Compatibility Note
pandas.DataFrame.var(),pandas.Series.var()Parameters currently not supported are level and numeric_only