dask_expr._collection.DataFrame.corr
dask_expr._collection.DataFrame.corr¶
- DataFrame.corr(method='pearson', min_periods=None, numeric_only=False, 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 False
Include only float, int or boolean data.
New in version 1.5.0.
Changed in version 2.0.0: The default value of
numeric_only
is nowFalse
.
- 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.
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