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.

axis{index (0), columns (1)}

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.


Additional keywords passed.

Series or scalaer

Unbiased variance over requested axis.

See also


Equivalent function in NumPy.


Return unbiased variance over Series values.


Return standard deviation over Series values.


Return standard deviation of the values over the requested axis.


>>> 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