xorbits.pandas.DataFrame.corr

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

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

参数

• 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.

  1.5.0(pandas) 新版功能.

  在 2.0.0(pandas) 版更改: The default value of numeric_only is now False.

返回

Correlation matrix.

返回类型

DataFrame

参见

DataFrame.corrwith

Compute pairwise correlation with another DataFrame or Series.

Series.corr

Compute the correlation between two Series.

提示

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

• Pearson correlation coefficient

• Kendall rank correlation coefficient

• Spearman’s rank correlation coefficient

实际案例

>>> 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

This docstring was copied from pandas.core.frame.DataFrame.