pandas 1.4.2

ParametersReturns
var(self, axis=None, skipna=True, level=None, ddof=1, numeric_only=None, **kwargs)

Normalized by N-1 by default. This can be changed using the ddof argument.

Parameters

axis : {index (0)}
skipna : bool, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA.

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.

ddof : int, default 1

Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

numeric_only : bool, 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

scalar or Series (if level specified)

Return unbiased variance over requested axis.

Examples

This example is valid syntax, but we were not able to check execution
>>> 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
This example is valid syntax, but we were not able to check execution
>>> df.var()
age       352.916667
height      0.056367

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

This example is valid syntax, but we were not able to check execution
>>> df.var(ddof=0)
age       264.687500
height      0.042275
See :

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File: /pandas/core/generic.py#10944
type: <class 'function'>
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