Expanding.
corr
Calculate the expanding correlation.
If not supplied then will default to self and produce pairwise output.
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.
For NumPy compatibility and will not have an effect on the result.
Return type is the same as the original object.
See also
cov
Similar method to calculate covariance.
numpy.corrcoef
NumPy Pearson’s correlation calculation.
pandas.Series.expanding
Calling expanding with Series data.
pandas.DataFrame.expanding
Calling expanding with DataFrames.
pandas.Series.corr
Aggregating corr for Series.
pandas.DataFrame.corr
Aggregating corr for DataFrame.
Notes
This function uses Pearson’s definition of correlation (https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).
When other is not specified, the output will be self correlation (e.g. all 1’s), except for DataFrame inputs with pairwise set to True.
DataFrame
Function will return NaN for correlations of equal valued sequences; this is the result of a 0/0 division error.
NaN
When pairwise is set to False, only matching columns between self and other will be used.
When pairwise is set to True, the output will be a MultiIndex DataFrame with the original index on the first level, and the other DataFrame columns on the second level.
In the case of missing elements, only complete pairwise observations will be used.