pandas.Series.var#

Series.var(axis=None, skipna=True, 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)}: For Series this parameter is unused and defaults to 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.

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.
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.

Deprecated since version 1.5.0: Specifying numeric_only=None is deprecated. The default value will be False in a future version of pandas.

Returns

scalar or Series (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  height
person_id
0           21    1.61
1           25    1.87
2           62    1.49
3           43    2.01

>>> df.var()
age       352.916667
height      0.056367

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

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