how to check for correlation among continuous and categorical variables in python?

Question

I have a dataset including categorical variables(binary) and continuous variables. I'm trying to apply a linear regression model for predicting a continuous variable. Can someone please let me know how to check for correlation among the categorical variables and the continuous target variable.

Current Code:

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()

All of the variables apart from lengthofstay are categorical. Should this work?

Answer 1

Convert your categorical variable into dummy variables here and put your variable in numpy.array. For example:

data.csv:

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

Extract data:

import numpy as np
import pandas as pd

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

df:

Convert categorical variable color_head into dummy variables:

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:

Put that in numpy array:

x = df_new.values

Compute the correlation:

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

Output:

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.        ]])

See :

numpy.corrcoef