pandas.DataFrame.var#

DataFrame.var(axis=0,skipna=True,ddof=1,numeric_only=False,**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), columns (1)}

ForSeries this parameter is unused and defaults to 0.

Warning

The behavior of DataFrame.var withaxis=None is deprecated,in a future version this will reduce over both axes and return a scalarTo retain the old behavior, pass axis=0 (or do not pass axis).

skipnabool, default True

Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.

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 False

Include only float, int, boolean columns. Not implemented for Series.

Returns:

Series or DataFrame (if level specified)

Examples

>>>df=pd.DataFrame({'person_id':[0,1,2,3],...'age':[21,25,62,43],...'height':[1.61,1.87,1.49,2.01]}...).set_index('person_id')>>>df           age  heightperson_id0           21    1.611           25    1.872           62    1.493           43    2.01

>>>df.var()age       352.916667height      0.056367dtype: float64

Alternatively,ddof=0 can be set to normalize by N instead of N-1:

>>>df.var(ddof=0)age       264.687500height      0.042275dtype: float64