Using pandas, calculate Cramér’s coefficient matrix

cramers V seems pretty over optimistic in a few tests that I did. Wikipedia recommends a corrected version.

import scipy.stats as ss

def cramers_corrected_stat(confusion_matrix):
    """ calculate Cramers V statistic for categorial-categorial association.
        uses correction from Bergsma and Wicher, 
        Journal of the Korean Statistical Society 42 (2013): 323-328
    """
    chi2 = ss.chi2_contingency(confusion_matrix)[0]
    n = confusion_matrix.sum()
    phi2 = chi2/n
    r,k = confusion_matrix.shape
    phi2corr = max(0, phi2 - ((k-1)*(r-1))/(n-1))    
    rcorr = r - ((r-1)**2)/(n-1)
    kcorr = k - ((k-1)**2)/(n-1)
    return np.sqrt(phi2corr / min( (kcorr-1), (rcorr-1)))

Also note that the confusion matrix can be calculated via a built-in pandas method for categorical columns via:

import pandas as pd
confusion_matrix = pd.crosstab(df[column1], df[column2])