Contingency Table in Python (original) (raw)

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

Python3 1== `

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

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Loading Data

Python3 1== `

data = pd.read_csv("loan_status.csv")

print (data.head(10))

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Output: Describe Data

Python3 1== `

data.describe()

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Output: Data Info

Python3 1== `

data.info()

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Output: Data Types

Python3 1== `

data types of feature/attributes

in the data

data.dtypes

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

Python3 1== `

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

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

Python3 1== `

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

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

Python3 1== `

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

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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. .