dask.dataframe.groupby.DataFrameGroupBy.corr

DataFrameGroupBy.corr(ddof=1, split_every=None, split_out=1)

Compute pairwise correlation of columns, excluding NA/null values.

This docstring was copied from pandas.core.frame.DataFrame.corr.

Some inconsistencies with the Dask version may exist.

Groupby correlation: corr(X, Y) = cov(X, Y) / (std_x * std_y)

Parameters
method{‘pearson’, ‘kendall’, ‘spearman’} or callable (Not supported in Dask)

Method of correlation:

  • pearson : standard correlation coefficient

  • kendall : Kendall Tau correlation coefficient

  • spearman : Spearman rank correlation

  • callable: callable with input two 1d ndarrays

    and returning a float. Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable’s behavior.

min_periodsint, optional (Not supported in Dask)

Minimum number of observations required per pair of columns to have a valid result. Currently only available for Pearson and Spearman correlation.

Returns
DataFrame

Correlation matrix.

See also

DataFrame.corrwith

Compute pairwise correlation with another DataFrame or Series.

Series.corr

Compute the correlation between two Series.

Examples

>>> def histogram_intersection(a, b):  
...     v = np.minimum(a, b).sum().round(decimals=1)
...     return v
>>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],  
...                   columns=['dogs', 'cats'])
>>> df.corr(method=histogram_intersection)  
      dogs  cats
dogs   1.0   0.3
cats   0.3   1.0