pandas.DataFrame.corr — pandas 0.25.3 documentation (original) (raw)

DataFrame. corr(self, method='pearson', min_periods=1)[source]¶

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

Parameters:	method : {‘pearson’, ‘kendall’, ‘spearman’} or callable 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 .. versionadded:: 0.24.0 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.
Returns:	DataFrame Correlation matrix.

Parameters:

method : {‘pearson’, ‘kendall’, ‘spearman’} or callable 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 .. versionadded:: 0.24.0 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.

Returns:

DataFrame Correlation matrix.

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