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.
Exclude NA/null values. If an entire row/column is NA, the result will be NA.
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.
Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
Return unbiased variance over requested axis.
>>> df = pd.DataFrame({'person_id': [0, 1, 2, 3],This example is valid syntax, but we were not able to check execution
... '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 0.042275See :
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