Series.var(axis=None, 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.

axis{index (0)}

For Series this parameter is unused and defaults to 0.


The behavior of DataFrame.var with axis=None is deprecated, in a future version this will reduce over both axes and return a scalar To 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 result will 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.

scalar or Series (if level specified)


>>> 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  height
0           21    1.61
1           25    1.87
2           62    1.49
3           43    2.01
>>> df.var()
age       352.916667
height      0.056367
dtype: float64

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

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