bigframes.pandas.api.typing.DataFrameGroupBy.cov#

DataFrameGroupBy.cov(*, numeric_only: bool = False) → DataFrame[source]#

Compute pairwise covariance of columns, excluding NA/null values.

Examples:

>>> df = bpd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600],
...                    'C': [0.8, 0.4, 0.9]})
>>> df.cov(numeric_only=True)
       A        B     C
A    1.0    100.0  0.05
B  100.0  10000.0   5.0
C   0.05      5.0  0.07

[3 rows x 3 columns]

Parameters:

numeric_only (bool, default False) Include only float, int, boolean, decimal data.

Returns:

The covariance matrix of the series of the DataFrame.

Return type:

bigframes.pandas.DataFrame