🐗 pykirill

Documentation

This is my personal Python package, pykirill, which includes a collection of utilities and functions that I frequently use during scientific exploration. This package is especially designed to be portable, making it suitable for environments like Google Colab where setup needs to be minimal.

Installation

There are several ways to install pykirill

PyPI

You can use regualar pip install from PyPI

pip install pykirill

GitHub source

You can use pip to install directly from GitHub. This method ensures you always get the latest version. Also gives access to experimental features in development

pip install git+https://github.com/kirilledition/pykirill.git@main

GitHub release

You also can use link to wheel from github releases

pip install https://github.com/kirilledition/pykirill/releases/download/2024.2.1/pykirill-2024.2.1-py3-none-any.whl

GitHub Container Registry

And finally package is also runnable as docker container from GitHub Container Registry

docker run --rm -it ghcr.io/kirilledition/pykirill:2024.2.1

Usage

You can have a look at showcase jupyter notebooks, that shows primitive examples of how to use pykirill: showcase.ipynb

Transforms

from pykirill import transforms

scaled_data = data.apply(transforms.log_scale)
pca = transforms.principal_component_analysis(
  scaled_data, n_components=3
)

Plotting

from pykirill import plotting
plotting.setup()

axm = plotting.SubplotsManager(pca.n_components)

for pc, score in pca.scores.items():
    ax = axm.nextax()

    ax.set_title(pc)
    ax.set_ylabel("PC score")
    ax.set_xlabel("species")

    sns.boxplot(x=target, y=score, ax=ax)

axm.show()

License

pykirill is open-sourced under the MIT license. The details can be found in the LICENSE.md file.