pycvcqv


Namepycvcqv JSON
Version 0.1.17 PyPI version JSON
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home_pagehttps://github.com/MaaniBeigy/pycvcqv
SummaryCoefficient of Variation (CV) and Coefficient of Quartile Variation (CQV) with Confidence Intervals (CI)
upload_time2024-08-15 07:22:41
maintainerNone
docs_urlNone
authorMaaniBeigy
requires_python<4.0,>=3.9
licenseMIT
keywords coefficient of variation coefficient of quartile variation data science
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requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage 
            # pycvcqv

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pycvcqv, find homogeneity with confidence

Python port of [cvcqv](https://github.com/MaaniBeigy/cvcqv)

</div>

## 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

```bash
pip install pycvcqv
```

## Usage

```python
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],
    multiplier=100,
)
# 64.6467
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
# 0   col-1  0.6076
# 1   col-2  0.1359
cqv(data=data, num_threads=-1)
#   columns      cqv
# 0   col-1  0.3889
# 1   col-2  0.0732
```

## Credits

[![🚀 Your next Python package needs a bleeding-edge project structure.](https://img.shields.io/badge/python--package--template-%F0%9F%9A%80-brightgreen)](https://github.com/TezRomacH/python-package-template)
This project was generated with [`python-package-template`](https://github.com/TezRomacH/python-package-template)

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bandit](https://img.shields.io/badge/security-bandit-green.svg)](https://raw.githubusercontent.com/MaaniBeigy/pycvcqv/main/.logs/bandit.txt)\n[![Pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/MaaniBeigy/pycvcqv/blob/master/.pre-commit-config.yaml)\n[![License](https://img.shields.io/github/license/MaaniBeigy/pycvcqv)](https://github.com/MaaniBeigy/pycvcqv/blob/master/LICENSE)\n\npycvcqv, find homogeneity with confidence\n\nPython port of [cvcqv](https://github.com/MaaniBeigy/cvcqv)\n\n</div>\n\n## Introduction\n\n`pycvcqv` provides some easy-to-use functions to calculate the\nCoefficient of Variation (`cv`) and Coefficient of Quartile Variation (`cqv`)\nwith confidence intervals provided with all available methods.\n\n## Install\n\n```bash\npip install pycvcqv\n```\n\n## Usage\n\n```python\nimport pandas as pd\nfrom pycvcqv import coefficient_of_variation, cqv\n\ncoefficient_of_variation(\n    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],\n    multiplier=100,\n)\n# 64.6467\ncqv(\n    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],\n    multiplier=100,\n)\n# 51.7241\ndata = pd.DataFrame(\n    {\n        \"col-1\": pd.Series([0.2, 0.5, 1.1, 1.4, 1.8, 2.3, 2.5, 2.7, 3.5]),\n        \"col-2\": pd.Series([5.4, 5.4, 5.7, 5.8, 5.9, 6.0, 6.6, 7.1, 7.9]),\n    }\n)\ncoefficient_of_variation(data=data, num_threads=3)\n#   columns      cv\n# 0   col-1  0.6076\n# 1   col-2  0.1359\ncqv(data=data, num_threads=-1)\n#   columns      cqv\n# 0   col-1  0.3889\n# 1   col-2  0.0732\n```\n\n## Credits\n\n[![\ud83d\ude80 Your next Python package needs a bleeding-edge project structure.](https://img.shields.io/badge/python--package--template-%F0%9F%9A%80-brightgreen)](https://github.com/TezRomacH/python-package-template)\nThis project was generated with [`python-package-template`](https://github.com/TezRomacH/python-package-template)\n\n",
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