pandas.core.window.rolling.Rolling.corr¶
- Rolling.corr(other=None, pairwise=None, ddof=1, **kwargs)[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
, whereN
represents the number of elements.- **kwargs
For NumPy compatibility and will not have an effect on the result.
- 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