pandas.DataFrame.var

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

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)}

For Series this parameter is unused and defaults to 0.

Warning

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.

**kwargs

Additional keywords passed.

Returns:

Series or scalaer

Unbiased variance over requested axis.

See also

numpy.var

Equivalent function in NumPy.

Series.var

Return unbiased variance over Series values.

Series.std

Return standard deviation over Series values.

DataFrame.std

Return standard deviation of the values over the requested axis.

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
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