pandas.DataFrame.var — pandas 3.0.0.dev0+2095.g2e141aaf99 documentation (original) (raw)
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.
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
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.
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