mars.dataframe.DataFrame.corr

DataFrame.corr(method='pearson', min_periods=1)

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.

  Note

  kendall, spearman and callables not supported on multiple chunks yet.

• 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

Correlation matrix.

Return type

DataFrame

See also

DataFrame.corrwith

Compute pairwise correlation with another DataFrame or Series.

Series.corr

Compute the correlation between two Series.

Examples

>>> import mars.dataframe as md
>>> df = md.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
...                   columns=['dogs', 'cats'])
>>> df.corr(method='pearson').execute()
          dogs      cats
dogs  1.000000 -0.851064
cats -0.851064  1.000000