pandas.Series.var¶
- Series.var(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)[source]¶
Return unbiased variance over requested axis.
Normalized by N-1 by default. This can be changed using the ddof argument
- Parameters
- axis{index (0)}
- skipnabool, default True
Exclude NA/null values. If an entire row/column is NA, the result will be NA.
- levelint or level name, default None
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.
- 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. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
- Returns
- scalar or Series (if level specified)
Notes
To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)