pandas.core.window.ewm.ExponentialMovingWindow.corr#

ExponentialMovingWindow.corr(other=None, pairwise=None, numeric_only=False)[source]#

Calculate the ewm (exponential weighted moment) sample correlation.

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 MultiIndex DataFrame in the case of DataFrame inputs. In the case of missing elements, only complete pairwise observations will be used.
numeric_onlybool, default False: Include only float, int, boolean columns.

New 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.ewm: Calling ewm with Series data.
pandas.DataFrame.ewm: Calling ewm with DataFrames.
pandas.Series.corr: Aggregating corr for Series.
pandas.DataFrame.corr: Aggregating corr for DataFrame.

Examples

>>> ser1 = pd.Series([1, 2, 3, 4])
>>> ser2 = pd.Series([10, 11, 13, 16])
>>> ser1.ewm(alpha=.2).corr(ser2)
0         NaN
1    1.000000
2    0.982821
3    0.977802
dtype: float64