pycvcqv 0.4.1

Coefficient of Variation (CV) and Coefficient of Quartile Variation (CQV) with Confidence Intervals (CI)

pycvcqv

Find homogeneity with confidence.

Python port of cvcqv

Introduction

pycvcqv provides some easy-to-use functions to calculate the Coefficient of Variation (cv) and Coefficient of Quartile Variation (cqv) with confidence intervals provided with all available methods.

Install

pip install pycvcqv

Usage

import pandas as pd
from pycvcqv import coefficient_of_variation, cqv

coefficient_of_variation(
    data=[
        0.2, 0.5, 1.1, 1.4, 1.8, 2.3, 2.5, 2.7, 3.5, 4.4,
        4.6, 5.4, 5.4, 5.7, 5.8, 5.9, 6.0, 6.6, 7.1, 7.9
    ],
    multiplier=100,
    ndigits=2
)
# {'cv': 57.77, 'lower': 41.43, 'upper': 98.38}
cqv(
    data=[0.2, 0.5, 1.1, 1.4, 1.8, 2.3, 2.5, 2.7, 3.5, 4.4, 4.6, 5.4, 5.4],
    multiplier=100,
)
# 51.7241
data = pd.DataFrame(
    {
        "col-1": pd.Series([0.2, 0.5, 1.1, 1.4, 1.8, 2.3, 2.5, 2.7, 3.5]),
        "col-2": pd.Series([5.4, 5.4, 5.7, 5.8, 5.9, 6.0, 6.6, 7.1, 7.9]),
    }
)
coefficient_of_variation(data=data, num_threads=3)
#   columns      cv      lower      upper
# 0   col-1  0.6076     0.3770     1.6667
# 1   col-2  0.1359     0.0913     0.2651
cqv(data=data, num_threads=-1)
#   columns      cqv
# 0   col-1  0.3889
# 1   col-2  0.0732

Confidence-interval methods for cv

coefficient_of_variation accepts a method argument that selects the confidence-interval estimator. The closed-form methods listed below are ported math-for-math from the R cvcqv package.

from pycvcqv import coefficient_of_variation

x = [
    0.2, 0.5, 1.1, 1.4, 1.8, 2.3, 2.5, 2.7, 3.5, 4.4,
    4.6, 5.4, 5.4, 5.7, 5.8, 5.9, 6.0, 6.6, 7.1, 7.9,
]

for method in (
    "kelley", "mckay", "miller", "vangel",
    "mahmoudvand_hassani", "equal_tailed",
    "shortest_length", "normal_approximation",
):
    print(method, coefficient_of_variation(
        data=x, method=method, multiplier=100, ndigits=3,
    ))

The output (95% CI, multiplier=100, ndigits=3):

method	est	lower	upper	description
kelley	57.774	41.303	97.950	cv with Kelley 95% CI
mckay	57.774	41.441	108.483	cv with McKay 95% CI
miller	57.774	34.053	81.495	cv with Miller 95% CI
vangel	57.774	40.955	103.931	cv with Vangel 95% CI
mahmoudvand_hassani	57.774	43.476	82.857	cv with Mahmoudvand-Hassani 95% CI
equal_tailed	57.774	43.937	84.383	cv with Equal-Tailed 95% CI
shortest_length	57.774	42.015	81.013	cv with Shortest-Length 95% CI
normal_approximation	57.774	44.533	85.272	cv with Normal Approximation 95% CI

The bootstrap-based methods (norm, basic, perc, bca) are not yet ported.

Credits

This project was generated with python-package-template