snowflake.snowpark.modin.plugin.extensions.window_overrides.Rolling.corr

Rolling.corr(other: Optional[DataFrame] = None, pairwise: Optional[bool] = None, ddof: int = 1, numeric_only: bool = False, **kwargs: Any)[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.26.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/window_overrides.py#L334-L350)

Calculate the rolling correlation.

Parameters:

  • other (Series or DataFrame, optional) – If not supplied then will default to self and produce pairwise output.

  • 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.

  • ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

  • numeric_only (bool, default False) – Include only float, int, boolean columns.

Returns:

Return type is the same as the original object with np.float64 dtype.

Return type:

Series or DataFrame

Examples

>>> df1 = pd.DataFrame({"col1": [1, 4, 3]})
>>> df2 = pd.DataFrame({"col1": [1, 6, 3]})
>>> df1.rolling(window=3, min_periods=3).corr(other=df2,pairwise=None, numeric_only=True)
       col1
0       NaN
1       NaN
2  0.953821
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