DataFrame.corr(method='pearson', min_periods=None, split_every=False)[source]

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.

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_periodsint, optional

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

numeric_onlybool, default True (Not supported in Dask)

Include only float, int or boolean data.

New in version 1.5.0.

Deprecated since version 1.5.0: The default value of `numeric_only` will be `False` in a future version of pandas.

Returns
DataFrame

Correlation matrix.

`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.

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
```