如何在python中检查连续变量和分类变量之间的相关性？

Question

我有一个数据集,包括分类变量(二进制)和连续变量.我正在尝试应用线性回归模型来预测连续变量.有人可以让我知道如何检查分类变量和连续目标变量之间的相关性.

现行代码:

import pandas as pd
df_hosp = pd.read_csv('C:\Users\LAPPY-2\Desktop\LengthOfStay.csv')

data = df_hosp[['lengthofstay', 'male', 'female', 'dialysisrenalendstage', 'asthma', \
              'irondef', 'pneum', 'substancedependence', \
              'psychologicaldisordermajor', 'depress', 'psychother', \
              'fibrosisandother', 'malnutrition', 'hemo']]
print data.corr()

除了lengthofstay之外的所有变量都是绝对的.这有用吗？

Answer 1

在此处将分类变量转换为虚拟变量,并将变量放在numpy.array中.例如:

data.csv:

age,size,color_head
4,50,black
9,100,blonde
12,120,brown
17,160,black
18,180,brown

提取数据:

import numpy as np
import pandas as pd

df = pd.read_csv('data.csv')

DF:

将分类变量转换color_head为虚拟变量:

df_dummies = pd.get_dummies(df['color_head'])
del df_dummies[df_dummies.columns[-1]]
df_new = pd.concat([df, df_dummies], axis=1)
del df_new['color_head']

df_new:

把它放在numpy数组中:

x = df_new.values

计算相关性:

correlation_matrix = np.corrcoef(x.T)
print(correlation_matrix)

输出:

array([[ 1.        ,  0.99574691, -0.23658011, -0.28975028],
       [ 0.99574691,  1.        , -0.30318496, -0.24026862],
       [-0.23658011, -0.30318496,  1.        , -0.40824829],
       [-0.28975028, -0.24026862, -0.40824829,  1.        ]])

见:

numpy.corrcoef