How to Use cbind in Python?

Contingency Table in Python

Last Updated : 21 Mar, 2024

Estimations like mean, median, standard deviation, and variance are very much useful in case of the univariate data analysis. But in the case of bivariate analysis(comparing two variables) correlation comes into play.

Contingency Table is one of the techniques for exploring two or even more variables. It is basically a tally of counts between two or more categorical variables.

Loading Libraries

import numpy as np 
import pandas as pd 
import matplotlib as plt 

Loading Data

data = pd.read_csv("loan_status.csv") 
  
print (data.head(10)) 

Output:

Describe Data

data.describe()

Output:

Data Info

data.info()

Output:

Data Types

# data types of feature/attributes  
# in the data 
data.dtypes 

Output:

Code #1: Contingency Table showing correlation between Grades and loan status.

data_crosstab = pd.crosstab(data['grade'], 
                            data['loan_status'],  
                               margins = False) 
print(data_crosstab) 

Output:

Code #2: Contingency Table showing correlation between Purpose and loan status.

data_crosstab = pd.crosstab(data['purpose'],  
                            data['loan_status'], 
                                margins = False) 
print(data_crosstab) 

Output:

Code #3: Contingency Table showing correlation between Grades+Purpose and loan status.

data_crosstab = pd.crosstab([data.grade, data.purpose],  
                             data.loan_status, margins = False) 
print(data_crosstab) 

Output:

So as in the code, Contingency Tables are giving clear correlation values between two and more variables. Thus making it much more useful to understand the data for further information extraction.
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