Calculate chi-square test in Python Using the Scipy library

To calculate a chi-square test in Python, you can use the scipy.stats library. The chi2_contingency function in this library can be used to perform the test on a contingency table.

The chi-square test is a statistical test used to determine if there is a significant association between two categorical variables. In Python, you can easily perform this test using the scipy.stats library. In this blog post, we will walk you through the step-by-step process of calculating a chi-square test in Python.

Step 1: Import the necessary libraries

Before you can perform a chi-square test, you need to import the scipy.stats library. You can do this by using the following code snippet:

import scipy.stats as stats

Step 2: Create a contingency table

Next, you need to create a contingency table that represents the frequencies of the different categories in your data. This table should be in the form of a 2D array. Here is an example of how you can create a contingency table:

observed_values = [[10, 15, 20], [5, 10, 15]]

Step 3: Perform the chi-square test

Now that you have imported the necessary library and created a contingency table, you can perform the chi-square test using the chi2_contingency function. This function returns four values: the chi-square statistic, the p-value, the degrees of freedom, and the expected frequencies. Here is an example of how you can perform the test:

chi2_stat, p_val, dof, expected = stats.chi2_contingency(observed_values)

we use unpacking to assign the values returned by the chi2_contingency function to their respective variables.

Step 4: Interpret the results

Finally, you can interpret the results of the chi-square test. The p-value will indicate whether there is a significant association between the two categorical variables. If the p-value is less than 0.05, you can reject the null hypothesis and conclude that there is a significant association.

In conclusion, calculating a chi-square test in Python is a simple process that can be done using the scipy.stats library. By following the steps outlined in this blog post, you can easily perform this test on your own data. Happy coding!