pandas.core.window.Rolling.cov

Calculate the rolling sample covariance.

param other

Series, DataFrame, or ndarray, optional

If not supplied then will default to self and produce pairwise output.

param pairwise

bool, 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.

param ddof

int, default 1

Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. **kwargs Keyword arguments to be passed into func.

return

Series or DataFrame Return type is determined by the caller.

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 covariance.

import pandas as pd
from numba import njit


@njit
def series_rolling_cov():
    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).cov(other)

    return out_series  # Expect series of NaN, NaN, NaN, 0.166667, 4.333333


print(series_rolling_cov())

$ python ./series/rolling/series_rolling_cov.py
0         NaN
1         NaN
2         NaN
3    0.166667
4    4.333333
dtype: float64

Calculate rolling covariance.

import pandas as pd
from numba import njit


@njit
def df_rolling_cov():
    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).cov(other)

    # Expect DataFrame of
    # {'A': [NaN, NaN, NaN, 0.166667, 4.333333],
    #  'B': [NaN, NaN, NaN, 0.166667, 4.333333]}
    return out_df


print(df_rolling_cov())

$ python ./dataframe/rolling/dataframe_rolling_cov.py
          A         B
0       NaN       NaN
1       NaN       NaN
2       NaN       NaN
3  0.166667  0.166667
4  4.333333  4.333333

See also

Series.rolling

Calling object with a Series.

DataFrame.rolling

Calling object with a DataFrame.

Series.cov

Similar method for Series.

DataFrame.cov

Similar method for DataFrame.