两个数据帧的所有列对之间的逐列相关性

Question

大家好，我已经创建了一个函数来检查两个变量之间的相关性，有谁知道我如何从这个函数中创建一个新的数据框？

In [1]:from scipy.stats import pearsonr
for colY in Y.columns:
    for colX in X.columns:
    #print('Pearson Correlation')
        corr, _ = pearsonr(numerical_cols_target[colX], numerical_cols_target[colY])
        alpha = 0.05
        print('Pearson Correlation', (alpha, corr))
        if corr <= alpha:
            print(colX +' and ' +colY+ ' two ariables are not correlated ')
        else:
            print(colX +' and ' +colY+ ' two variables are highly correlated ')
        print('\n')
    print('\n')

下面是相关函数的输出示例：

Out [1]: 
Pearson Correlation (0.05, -0.1620045985125294)
banana and orange are not correlated 

Pearson Correlation (0.05, 0.2267582070839226)
apple and orange are highly correlated

Answer 1

我会避免使用两个for循环。根据数据集的大小，这将非常慢。

Pandas提供了一个关联函数，这里可能会用到：

import pandas as pd

df = pd.DataFrame({'A': range(4), 'B': [2*i for i in range(4)]})

使用corr()将给出成对的关联，然后返回，并返回一个新的数据帧

df.corr()

有关更多信息，请查看手册：https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corr.html

Answer 2

您可以只执行以下操作。

df = pd.DataFrame(index=X.columns, columns=Y.columns)

#In your loop
df[colY][colX] = corr

然后，您的循环将是

for colY in Y.columns:
    for colX in X.columns:
    #print('Pearson Correlation')
        corr, _ = pearsonr(numerical_cols_target[colX], numerical_cols_target[colY])
        alpha = 0.05
        print('Pearson Correlation', (alpha, corr))
        df[colY][colX] = corr
        if corr <= alpha:
            print(colX +' and ' +colY+ ' two ariables are not correlated ')
        else:
            print(colX +' and ' +colY+ ' two variables are highly correlated ')
        print('\n')
    print('\n')

Answer 3

我认为你正在寻找这个:这将获得X和Y数据帧之间每两对列的按列相关，并创建另一个保持所有相关性以及它们是否通过阈值alpha的数据帧:这假设Y的列数少于或等于X。如果不是，只需切换X和Y位置：

import collections
corr_df = pd.DataFrame(columns=['col_X', 'col_Y', 'corr', 'is_correlated'])
d = collections.deque(X.columns)
Y_cols = Y.columns
alpha = 0.05
for i in range(len(d)):
  d.rotate(i)
  X = X[d]
  corr = Y.corrwith(X, axis=0)
  corr_df = corr_df.append(pd.DataFrame({'col_X':list(d)[:len(Y_cols)], 'col_Y':Y.columns, 'corr':corr[:len(Y_cols)], 'is_correlated':corr[:len(Y_cols)]>alpha}))
print(corr_df.reset_index())

示例输入和输出：

X:
   A  B   C
0  2  2  10
1  4  0   2
2  8  0   1
3  0  0   8

Y:
   B   C
0  2  10
1  0   2
2  0   1
3  0   8


correlation(X, Y):

  col_X col_Y  corr is_correlated
0     A     B   1.0          True
1     B     C   1.0          True
2     C     B   1.0          True
3     A     C   1.0          True
4     A     B   1.0          True
5     B     C   1.0          True