# pandas.core.window.Rolling.corr¶

Calculate rolling correlation.

param other
Series, DataFrame, or ndarray, optional

If not supplied then will default to self.

param pairwise
bool, default None

Calculate pairwise combinations of columns within a DataFrame. If other is not specified, defaults to True, otherwise defaults to False. Not relevant for Series. **kwargs Unused.

return

Series or DataFrame Returned object type is determined by the caller of the rolling calculation.

## Limitations¶

DataFrame/Series elements cannot be max/min float/integer. Otherwise SDC and Pandas results are different. Resulting DataFrame/Series has default index and name.

## Examples¶

Calculate rolling correlation.
import pandas as pd
from numba import njit

@njit
def series_rolling_corr():
series = pd.Series([3, 3, 3, 5, 8])  # Series of 3, 3, 3, 5, 8
other = pd.Series([3, 4, 4, 4, 8])  # Series of 3, 4, 4, 4, 8
out_series = series.rolling(4).corr(other)

return out_series  # Expect series of NaN, NaN, NaN, 0.333333, 0.916949

print(series_rolling_corr())

$python ./series/rolling/series_rolling_corr.py 0 NaN 1 NaN 2 NaN 3 0.333333 4 0.916949 dtype: float64  Calculate rolling correlation. import pandas as pd from numba import njit @njit def df_rolling_corr(): df = pd.DataFrame({'A': [3, 3, 3, 5, 8], 'B': [-3, -3, -3, -5, -8]}) other = pd.DataFrame({'A': [3, 4, 4, 4, 8], 'B': [-3, -4, -4, -4, -8]}) out_df = df.rolling(4).corr(other) # Expect DataFrame of # {'A': [NaN, NaN, NaN, 0.333333, 0.916949], # 'B': [NaN, NaN, NaN, 0.333333, 0.916949]} return out_df print(df_rolling_corr())  $ python ./dataframe/rolling/dataframe_rolling_corr.py
A         B
0       NaN       NaN
1       NaN       NaN
2       NaN       NaN
3  0.333333  0.333333
4  0.916949  0.916949


Series.rolling

Calling object with a Series.

DataFrame.rolling

Calling object with a DataFrame.

Series.corr

Similar method for Series.

DataFrame.corr

Similar method for DataFrame.