bigframes.pandas.DataFrame.var#

DataFrame.var(axis: str | int = 0, *, numeric_only: bool = False) → Series[source]#

Return unbiased variance over requested axis.

Normalized by N-1 by default.

Examples:

>>> df = bpd.DataFrame({"A": [1, 3], "B": [2, 4]})
>>> df
    A       B
0   1       2
1   3       4

[2 rows x 2 columns]

Calculating the variance of each column (the default behavior without an explicit axis parameter).

>>> df.var()
A    2.0
B    2.0
dtype: Float64

Calculating the variance of each row.

>>> df.var(axis=1)
0    0.5
1    0.5
dtype: Float64

Parameters:

axis ({index (0), columns (1)}) – Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
numeric_only (bool. default False) – Default False. Include only float, int, boolean columns.

Returns:

Series with unbiased variance over requested axis.

Return type:

bigframes.pandas.Series