pandas.DataFrame.var¶

DataFrame.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), columns (1)}

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 Series.

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

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  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