如何检查两个数据集的匹配列之间的相关性?[英] How to check correlation between matching columns of two data sets?

本文是小编为大家收集整理的关于如何检查两个数据集的匹配列之间的相关性?的处理方法,想解了如何检查两个数据集的匹配列之间的相关性?的问题怎么解决?如何检查两个数据集的匹配列之间的相关性?问题的解决办法?那么可以参考本文帮助大家快速定位并解决问题。

问题描述

如果我们有数据集:

import pandas as pd
a = pd.DataFrame({"A":[34,12,78,84,26], "B":[54,87,35,25,82], "C":[56,78,0,14,13], "D":[0,23,72,56,14], "E":[78,12,31,0,34]})
b = pd.DataFrame({"A":[45,24,65,65,65], "B":[45,87,65,52,12], "C":[98,52,32,32,12], "D":[0,23,1,365,53], "E":[24,12,65,3,65]})

一个人如何创建一个相关矩阵,其中y轴代表" a",x轴代表" b"?

目的是查看两个数据集的匹配列之间的相关性:

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推荐答案

这完全实现了您想要的:

from scipy.stats import pearsonr

# create a new DataFrame where the values for the indices and columns
# align on the diagonals
c = pd.DataFrame(columns = a.columns, index = a.columns)

# since we know set(a.columns) == set(b.columns), we can just iterate
# through the columns in a (although a more robust way would be to iterate
# through the intersection of the two sets of columns, in the case your actual dataframes' columns don't match up
for col in a.columns:
    correl_signif = pearsonr(a[col], b[col]) # correlation of those two Series
    correl = correl_signif[0] # grab the actual Pearson R value from the tuple from above
    c.loc[col, col] = correl   # locate the diagonal for that column and assign the correlation coefficient   

编辑:嗯,它完全实现了您想要的,直到修改了问题为止.虽然很容易更改:

c = pd.DataFrame(columns = a.columns, index = a.columns)

for col in c.columns:
    for idx in c.index:
        correl_signif = pearsonr(a[col], b[idx])
        correl = correl_signif[0]
        c.loc[idx, col] = correl

c现在是:

Out[16]: 
           A          B         C         D          E
A   0.713185  -0.592371 -0.970444  0.487752 -0.0740101
B  0.0306753 -0.0705457  0.488012   0.34686  -0.339427
C  -0.266264 -0.0198347  0.661107  -0.50872   0.683504
D   0.580956  -0.552312 -0.320539  0.384165  -0.624039
E  0.0165272   0.140005 -0.582389   0.12936   0.286023

其他推荐答案

如果您不介意基于Numpy的矢量化解决方案,请基于 this solution post href =" https://stackoverflow.com/q/330143417/3293881"> Computing the correlation coefficient between two multi-dimensional arrays -

corr2_coeff(a.values.T,b.values.T).T # func from linked solution post.

样本运行 -

In [621]: a
Out[621]: 
    A   B   C   D   E
0  34  54  56   0  78
1  12  87  78  23  12
2  78  35   0  72  31
3  84  25  14  56   0
4  26  82  13  14  34

In [622]: b
Out[622]: 
    A   B   C    D   E
0  45  45  98    0  24
1  24  87  52   23  12
2  65  65  32    1  65
3  65  52  32  365   3
4  65  12  12   53  65

In [623]: corr2_coeff(a.values.T,b.values.T).T
Out[623]: 
array([[ 0.71318502, -0.5923714 , -0.9704441 ,  0.48775228, -0.07401011],
       [ 0.0306753 , -0.0705457 ,  0.48801177,  0.34685977, -0.33942737],
       [-0.26626431, -0.01983468,  0.66110713, -0.50872017,  0.68350413],
       [ 0.58095645, -0.55231196, -0.32053858,  0.38416478, -0.62403866],
       [ 0.01652716,  0.14000468, -0.58238879,  0.12936016,  0.28602349]])

其他推荐答案

我使用此功能将其分解为Numpy

def corr_ab(a, b):

    a_ = a.values
    b_ = b.values
    ab = a_.T.dot(b_)
    n = len(a)

    sums_squared = np.outer(a_.sum(0), b_.sum(0))
    stds_squared = np.outer(a_.std(0), b_.std(0))

    return pd.DataFrame((ab - sums_squared / n) / stds_squared / n,
                        a.columns, b.columns)

demo

corr_ab(a, b)

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