pandas.DataFrame.var

Return unbiased variance over requested axis.

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

param axis

{index (0), columns (1)}

param skipna

bool, default True

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

param level

int or level name, default None

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

param ddof

int, default 1

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

param 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.

return

Series or DataFrame (if level specified)

Limitations

Parameters axis, level and numeric_only are unsupported.

Examples

Return unbiased variance over requested axis.

import pandas as pd
import numpy as np
from numba import njit


@njit
def dataframe_var():
    df = pd.DataFrame({"A": [.2, .0, .6, .2],
                       "B": [2, 0, 6, 2],
                       "C": [-1, np.nan, 1, np.inf]})

    return df.var()


print(dataframe_var())

$ python ./dataframe/dataframe_var.py
A    0.063333
B    6.333333
C         NaN
dtype: float64

See also

Series.std

Returns sample standard deviation over Series.

Series.var

Returns unbiased variance over Series.

DataFrame.std

Returns sample standard deviation over DataFrame.