pycvcqv 0.6.0

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 a variety of 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.

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",
    "norm", "basic", "perc", "bca",
):
    print(method, coefficient_of_variation(
        data=x,
        method=method,
        multiplier=100,
        ndigits=3,
        num_replicates=10000,
        random_state=42,
    ))

Output (95% CI, multiplier=100, ndigits=3, bootstrap methods use num_replicates=10000, random_state=42):

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
norm	57.774	38.850	78.379	cv with Normal Approximation Bootstrap 95% CI
basic	57.774	37.716	77.166	cv with Basic Bootstrap 95% CI
perc	57.774	38.382	77.832	cv with Bootstrap Percentile 95% CI
bca	57.774	41.556	83.032	cv with Adjusted Bootstrap Percentile (BCa) 95% CI

Confidence-interval methods for cqv

cqv accepts a method argument that selects the confidence-interval estimator. When method is omitted only the point estimate is returned (the legacy behavior).

from pycvcqv import cqv

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 ("bonett", "norm", "basic", "perc", "bca"):
    print(method, cqv(
        data=x,
        method=method,
        multiplier=100,
        ndigits=3,
        num_replicates=10000,
        random_state=42,
    ))

Output (95% CI, multiplier=100, ndigits=3, bootstrap methods use num_replicates=10000, random_state=42):

method	est	lower	upper	description
bonett	45.625	24.785	77.329	cqv with Bonett 95% CI
norm	45.625	19.937	70.403	cqv with Normal Approximation Bootstrap 95% CI
basic	45.625	21.081	73.923	cqv with Basic Bootstrap 95% CI
perc	45.625	17.327	70.169	cqv with Bootstrap Percentile 95% CI
bca	45.625	22.006	76.331	cqv with Adjusted Bootstrap Percentile (BCa) 95% CI

Credits

This project was generated with python-package-template