The Chi-square independence test is use to determine if two categorical variables are related or not. This is a useful procedure to go through when preprocessing categorical data for Data Science tasks. We found the below link a good resource to develop an understanding of Chi-square independence test
SPSS Tutorials Chi-Square Independence Test
The Python code provided here takes a data frame as an input expecting at least two columns of categorical variables in it and produces two data frames, one of P-values (to indicate to the significance) and the other of Cramers V values (to indicate to the strength of a relationship).
The code is given below or downloadable from:
#*****************************************************************## Chi-square independence test: p-value and Carmers V calculation# Version 1# Copyrights: SirnaTechSolutions# Date: 17 March 2019#*****************************************************************#def chi2ind(df): import numpy as np from scipy import stats import pandas as pd import math dataframe = df.dropna() #Drop missing values from the data frame dataframeCatOnly = dataframe.select_dtypes(include =['object']) #Pick out only Categorical parts of the dataframe if(dataframeCatOnly.shape[1]<2): #If the dataframe does not have atleast two categorical variables print("The given dataframe does not have enough categorical variables") dataP = 0 dataCramersV = 0 else: #Calculate the chi square independence test p values and cramers V so we can assess independence catColumnsList = dataframeCatOnly.columns; #Extract the list of column names from the data frame dataP = pd.DataFrame(columns = catColumnsList, index = catColumnsList) #Create a blank dataframe for the p values dataP.fillna('x', inplace = True) dataCramersV = pd.DataFrame(columns = catColumnsList, index = catColumnsList) #Create a blank dataframe for Cramers V values dataCramersV.fillna('x', inplace = True) catColumnsList = amesHousingCatOnly.columns for i in range(len(catColumnsList)-1): for j in range(i+1,len(catColumnsList)): table = pd.crosstab(dataframeCatOnly[catColumnsList[i]], dataframeCatOnly[catColumnsList[j]], margins = True) tableSize = table.shape tableRowInd = table.index[list(range(tableSize[0]-1))] tableColInd = table.columns[list(range(tableSize[1]-1))] f_obs = table.loc[tableRowInd,tableColInd].values chiTest = stats.chi2_contingency(f_obs)[0:3] dataP.iloc[i][j] = chiTest[1] dataCramersV.iloc[i][j] = math.sqrt(chiTest[0]/(dataframeCatOnly.shape[0]*(min(tableSize)-1))) return dataP, dataCramersV #Return the data frame of p values and Cramers v