Pairwise association measures of statistical variable types

# PyPair

PyPair is a statistical library to compute pairwise association between any two variables. In general, statistical variable types are viewed as categorical or continuous. Categorical variables have no inherit order to their values, while continuous variables do. This API has over 130 association measures implemented for any combination of categorical and/or continuous variables.

Here's a short and sweet snippet for using the API against a dataframe that stores strictly binary data.

from pypair.association import binary_binary

jaccard = lambda a, b: binary_binary(a, b, measure='jaccard')
tanimoto = lambda a, b: binary_binary(a, b, measure='tanimoto_i')

df = get_a_pandas_binary_dataframe()

jaccard_corr = df.corr(method=jaccard)
tanimoto_corr = df.corr(method=tanimoto)

print(jaccard_corr)
print('-' * 15)
print(tanimoto_corr)


Another way to get started with PyPair is to use the convenience methods whose names indicate the variable pair types.

from pypair.association import binary_binary, categorical_categorical, \
binary_continuous, concordance, categorical_continuous, continuous_continuous, confusion, agreement

# assume a and b are the appropriate iterables of values for 2 variables
jaccard = binary_binary(a, b, measure='jaccard')
acc = confusion(a, b, measure='acc')
phi = categorical_categorical(a, b, measure='phi')
kappa = agreement(a, b, measure='cohen_k')
biserial = binary_continuous(a, b, measure='biserial')
tau = concordance(a, b, measure='kendall_tau')
eta = categorical_continuous(a, b, measure='eta')
pearson = continuous_continuous(a, b, measure='pearson')


Copyright 2020 One-Off Coder

you may not use this file except in compliance with the License.
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# Citation

@misc{oneoffcoder_pypair_2020,
title={PyPair, A Statistical API for Bivariate Association Measures},
url={https://github.com/oneoffcoder/py-pair},
author={Jee Vang},
year={2020},
month={Nov}}


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