Introduction

In statistics, cross-tabulation (also known as contingency table analysis) is a technique used to summarize and analyze categorical data. It involves creating a cross-tabulation or a contingency table that shows the distribution of one or more variables against another variable.

Statsmodels is a powerful Python library that provides a wide range of statistical models and tools for data analysis. It also includes functionality for performing cross-tabulation analysis.

In this blog post, we will explore how to use the cross_tab function in statsmodels to perform cross-tabulation analysis in Python.

Installation

Before we begin, make sure you have statsmodels installed. You can install it using pip:

pip install statsmodels

Example Usage

Let’s consider a simple example where we have survey data collected from 100 individuals. Each individual was asked to choose their favorite color among three options: red, blue, and green. Additionally, we have information about their gender: male or female.

We want to perform cross-tabulation analysis to understand the relationship between favorite color and gender.

Here’s how you can perform cross-tabulation analysis using statsmodels:

import pandas as pd
import statsmodels.api as sm

# Create a DataFrame with the survey data
data = {'Favorite Color': ['red', 'green', 'green', 'blue', 'red', 'blue', 'red', 'red', 'green', 'blue'],
        'Gender': ['female', 'female', 'male', 'female', 'female', 'male', 'male', 'female', 'male', 'male']}
df = pd.DataFrame(data)

# Perform cross-tabulation analysis
cross_tab = sm.stats.Table(pd.crosstab(df['Favorite Color'], df['Gender']))

# Print the cross-tabulation table
print(cross_tab.table_orig)

# Perform chi-square test
print(cross_tab.test_nominal_association())

In this example, we first create a DataFrame with the survey data. We then use the crosstab function from pandas to create a cross-tabulation table. Finally, we pass the cross-tabulation table to the Table function from statsmodels and perform chi-square test using the test_nominal_association method.

Conclusion

Cross-tabulation analysis is a useful technique for exploring the relationship between categorical variables. With statsmodels library in Python, performing cross-tabulation analysis becomes effortless. In this blog post, we learned how to use the cross_tab function from statsmodels to perform cross-tabulation analysis.

Statsmodels provides various statistical tests and tools, making it a powerful library for data analysis and modeling.