relazioni 0.0.1

A lightweight package for strength of the relationship between two variables analysis.

Relazioni

Relazioni is a lightweight package for strength of the relationship between variables analysis.

Documentation: https://chicodelarosa.com/relazioni
Source code: https://github.com/chicodelarosa/relazioni
Bug reports: https://github.com/chicodelarosa/relazioni/issues

It provides easy to use functions for measuring the relationship between variables of the following natures:

Two Continuous

A variable that can reasonably take on any value within a range. Examples of continuous variables include height, weight, exam scores, income, salary, etc.

Two Categorical

A variable that is a category without a natural order. Examples of categorical variables are eye color, city of residence, type of dog, etc.

At least One Ordinal

A variable with categories that have an inherent order. For instance, education level (GDE/Bachelors/Masters/PhD), income level (if grouped into high/medium/low) etc.

One Binary and One Continuous

A variable that is a category with only two possible values. Examples of binary variables include gender (male/female) or any True/False or Yes/No variable.

Relazioni currently supports 8 different association functions for investigating the relationship between variables in the following cases:

1. Two Continuous and Covariates
   • Partial Correlation (R)
2. Two Continuous and No Covariates
   • Pearson Correlation
3. Two Categorical and Two Values per Variable
   • Phi Coefficient
4. Two Categorical and More than Two Values per Variable
   • Cramer’s V
   • Theil's U
5. At Least One Ordinal
   • Kendall’s Tau
   • Spearman’s Rho
6. One Continuous and One Binary
   • Point-biserial Correlation

Requirements

scipy
numpy
pandas
scikit-learn

Installation

Installing via pip

pip install .

Installing via setup.py

python setup.py install

Installing via Git

python -m pip install git+https://github.com/chicodelarosa/relazioni.git

Example

import numpy as np
from relationships import associations

v1, v2 = np.array([1, 1, 2]), np.array([1, 1, 2])

matth_corr = associations.matthews_corr(v1, v2)
print(matth_corr) # 1.0

v1, v2 = np.array([1, 1, 2]), np.array([2, 1, 2])

matth_corr = associations.matthews_corr(v1, v2)
print(matth_corr) # 0.5

Call for Contributions

The relationships package welcomes your expertise and enthusiasm!