pandas.Series.var

Series.var(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

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)

pandas.Series.value_counts pandas.Series.view