Can we generate contingency table for chisquare test using python?

Question

I am using scipy.stats.chi2_contingency method to get chi square statistics. We need to pass frequency table i.e. contingency table as parameter. But I have a feature vector and want to automatically generate the frequency table. Do we have any such function available? I am doing it like this currently:

def contigency_matrix_categorical(data_series,target_series,target_val,indicator_val):
  observed_freq={}
  for targets in target_val:
      observed_freq[targets]={}
      for indicators in indicator_val:
          observed_freq[targets][indicators['val']]=data_series[((target_series==targets)&(data_series==indicators['val']))].count()
  f_obs=[]
  var1=0
  var2=0
  for i in observed_freq:
      var1=var1+1
      var2=0
      for j in observed_freq[i]:
          f_obs.append(observed_freq[i][j]+5)
          var2=var2+1
  arr=np.array(f_obs).reshape(var1,var2)
  c,p,dof,expected=chi2_contingency(arr)
  return {'score':c,'pval':p,'dof':dof}

Where data series and target series are the columns values and the other two are the name of the indicator. Can anyone help? thanks

Hortatory

Now I just have two csv cols as input i.e. data series and target series. – Hortatory

Answer 1

You can use pandas.crosstab to generate a contingency table from a DataFrame. From the documentation:

Compute a simple cross-tabulation of two (or more) factors. By default computes a frequency table of the factors unless an array of values and an aggregation function are passed.

Below is an usage example:

import numpy as np
import pandas as pd
from scipy.stats import chi2_contingency

# Some fake data.
n = 5  # Number of samples.
d = 3  # Dimensionality.
c = 2  # Number of categories.
data = np.random.randint(c, size=(n, d))
data = pd.DataFrame(data, columns=['CAT1', 'CAT2', 'CAT3'])

# Contingency table.
contingency = pd.crosstab(data['CAT1'], data['CAT2'])

# Chi-square test of independence.
c, p, dof, expected = chi2_contingency(contingency)

The following data table

generates the following contingency table

Then, scipy.stats.chi2_contingency(contingency) returns (0.052, 0.819, 1, array([[1.6, 0.4],[2.4, 0.6]])).

Paring