pandas.core.window.expanding.Expanding.cov#

Expanding.cov(other=None, pairwise=None, ddof=1, numeric_only=False)[source]#

Calculate the expanding sample covariance.

Parameters:

otherSeries or DataFrame, optional: If not supplied then will default to self and produce pairwise output.
pairwisebool, default None: If False then only matching columns between self and other will be used and the output will be a DataFrame. If True then all pairwise combinations will be calculated and the output will be a MultiIndexed DataFrame in the case of DataFrame inputs. In the case of missing elements, only complete pairwise observations will be used.
ddofint, default 1: Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
numeric_onlybool, default False: Include only float, int, boolean columns.

Added in version 1.5.0.

Returns:

Series or DataFrame: Return type is the same as the original object with np.float64 dtype.

See also

pandas.Series.expanding: Calling expanding with Series data.
pandas.DataFrame.expanding: Calling expanding with DataFrames.
pandas.Series.cov: Aggregating cov for Series.
pandas.DataFrame.cov: Aggregating cov for DataFrame.

Examples

>>> ser1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
>>> ser2 = pd.Series([10, 11, 13, 16], index=['a', 'b', 'c', 'd'])
>>> ser1.expanding().cov(ser2)
a         NaN
b    0.500000
c    1.500000
d    3.333333
dtype: float64