pandas.core.window.rolling.Rolling.corr#

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

Calculate the rolling 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 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.

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

cov: Similar method to calculate covariance.
numpy.corrcoef: NumPy Pearson’s correlation calculation.
pandas.Series.rolling: Calling rolling with Series data.
pandas.DataFrame.rolling: Calling rolling 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.

Function will return NaN for correlations of equal valued sequences; this is the result of a 0/0 division error.

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.

Examples

The below example shows a rolling calculation with a window size of four matching the equivalent function call using numpy.corrcoef().

>>> v1 = [3, 3, 3, 5, 8]
>>> v2 = [3, 4, 4, 4, 8]
>>> # numpy returns a 2X2 array, the correlation coefficient
>>> # is the number at entry [0][1]
>>> print(f"{np.corrcoef(v1[:-1], v2[:-1])[0][1]:.6f}")
0.333333
>>> print(f"{np.corrcoef(v1[1:], v2[1:])[0][1]:.6f}")
0.916949
>>> s1 = pd.Series(v1)
>>> s2 = pd.Series(v2)
>>> s1.rolling(4).corr(s2)
0         NaN
1         NaN
2         NaN
3    0.333333
4    0.916949
dtype: float64

The below example shows a similar rolling calculation on a DataFrame using the pairwise option.

>>> matrix = np.array([[51., 35.], [49., 30.], [47., 32.],        [46., 31.], [50., 36.]])
>>> print(np.corrcoef(matrix[:-1,0], matrix[:-1,1]).round(7))
[[1.         0.6263001]
 [0.6263001  1.       ]]
>>> print(np.corrcoef(matrix[1:,0], matrix[1:,1]).round(7))
[[1.         0.5553681]
 [0.5553681  1.        ]]
>>> df = pd.DataFrame(matrix, columns=['X','Y'])
>>> df
      X     Y
0  51.0  35.0
1  49.0  30.0
2  47.0  32.0
3  46.0  31.0
4  50.0  36.0
>>> df.rolling(4).corr(pairwise=True)
            X         Y
0 X       NaN       NaN
  Y       NaN       NaN
1 X       NaN       NaN
  Y       NaN       NaN
2 X       NaN       NaN
  Y       NaN       NaN
3 X  1.000000  0.626300
  Y  0.626300  1.000000
4 X  1.000000  0.555368
  Y  0.555368  1.000000