pandas.core.groupby.DataFrameGroupBy.corr

DataFrameGroupBy.corr(method='pearson', min_periods=1, numeric_only=False)[source]

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

Parameters:

method:{‘pearson’, ‘kendall’, ‘spearman’} or callable

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_periods:int, optional

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

numeric_only:bool, default False

Include only float, int or boolean data.

Added in version 1.5.0.

Changed in version 2.0.0: The default value of numeric_only is now False.

Returns:

DataFrame

Correlation matrix.

See also

DataFrame.corrwith

Compute pairwise correlation with another DataFrame or Series.

Series.corr

Compute the correlation between two Series.

Notes

Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.

• Pearson correlation coefficient

• Kendall rank correlation coefficient

• Spearman’s rank correlation coefficient

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

>>> df = pd.DataFrame([(1, 1), (2, np.nan), (np.nan, 3), (4, 4)],
...                   columns=['dogs', 'cats'])
>>> df.corr(min_periods=3)
      dogs  cats
dogs   1.0   NaN
cats   NaN   1.0