Calculation of the number of Klamer correlations

I couldn't find a module to calculate the number of Klamer correlations in Python, so I made it myself. For the calculation method, I referred to here.

cramerV.py


# -*- coding: utf-8 -*-

import numpy as np


def cramersV(x, y):
    """
    Calc Cramer's V.

    Parameters
    ----------
    x : {numpy.ndarray, pandas.Series}
    y : {numpy.ndarray, pandas.Series}
    """
    table = np.array(pd.crosstab(x, y)).astype(np.float32)
    n = table.sum()
    colsum = table.sum(axis=0)
    rowsum = table.sum(axis=1)
    expect = np.outer(rowsum, colsum) / n
    chisq = np.sum((table - expect) ** 2 / expect)
    return np.sqrt(chisq / (n * (np.min(table.shape) - 1)))


if __name__ == "__main__":
    import pandas as pd
    data = pd.DataFrame(
        {'science': ['like', 'like', 'like', 'like', 'like', 'like', 'like',
                     'like', 'like', 'like', 'like', 'like', 'like', 'like',
                     'like', 'like', 'like', 'like', 'like', 'like', 'like',
                     'like', 'like', 'like', 'like', 'like', 'like', 'like',
                     'like', 'like', 'like', 'dislike', 'dislike', 'dislike',
                     'dislike', 'dislike', 'dislike', 'dislike', 'dislike',
                     'dislike', 'dislike', 'dislike', 'dislike', 'dislike',
                     'dislike', 'dislike', 'dislike', 'dislike', 'dislike',
                     'dislike'],
         'math': ['like', 'like', 'like', 'like', 'like', 'like', 'like',
                  'like', 'like', 'like', 'like', 'like', 'like', 'like',
                  'like', 'like', 'like', 'like', 'like', 'like', 'like',
                  'like', 'like', 'like', 'dislike', 'dislike', 'dislike',
                  'dislike', 'dislike', 'dislike', 'dislike', 'like', 'like',
                  'like', 'like', 'like', 'like', 'dislike', 'dislike',
                  'dislike', 'dislike', 'dislike', 'dislike', 'dislike',
                  'dislike', 'dislike', 'dislike', 'dislike', 'dislike',
                  'dislike']})
    print cramersV(data['science'], data['math'])

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