distfit's documentation! ======================== |python| |pypi| |docs| |stars| |LOC| |downloads_month| |downloads_total| |license| |forks| |open issues| |project status| |medium| |colab| |DOI| |repo-size| |donate| .. note:: `Medium Blog: How to Find the Best Theoretical Distribution for Your Data. `_ .. |logo| image:: ../figs/logo.png .. table:: :align: center +----------+ | |logo| | +----------+ ----------------------------------- ``distfit`` is a python package for probability density fitting of univariate distributions for random variables. With the random variable as an input, distfit can find the best fit for parametric, non-parametric, and discrete distributions. * For the parametric approach, the distfit library can determine the best fit across 89 theoretical distributions. To score the fit, one of the scoring statistics for the good-of-fitness test can be used used, such as RSS/SSE, Wasserstein, Kolmogorov-Smirnov (KS), or Energy. After finding the best-fitted theoretical distribution, the loc, scale, and arg parameters are returned, such as mean and standard deviation for normal distribution. * For the non-parametric approach, the distfit library contains two methods, the quantile and percentile method. Both methods assume that the data does not follow a specific probability distribution. In the case of the quantile method, the quantiles of the data are modeled whereas for the percentile method, the percentiles are modeled. * In case the dataset contains discrete values, the distift library contains the option for discrete fitting. The best fit is then derived using the binomial distribution. .. |fig1| image:: ../figs/distfit.png .. table:: :align: center +----------+ | |fig1| | +----------+ ----------------------------------- .. note:: **Your ❤️ is important to keep maintaining this package.** You can `support `_ in various ways, have a look at the `sponser page `_. Report bugs, issues and feature extensions at `github `_ page. .. code-block:: console pip install distfit ----------------------------------- Contents ======== .. toctree:: :maxdepth: 1 :caption: Background Abstract .. toctree:: :maxdepth: 1 :caption: Installation Installation .. toctree:: :maxdepth: 3 :caption: Methods Parametric Quantile Percentile Discrete .. toctree:: :maxdepth: 3 :caption: Generate samples Generate .. toctree:: :maxdepth: 3 :caption: Performance Performance .. toctree:: :maxdepth: 3 :caption: Plots Plots .. toctree:: :maxdepth: 3 :caption: Save and Load Save and Load .. toctree:: :maxdepth: 1 :caption: Examples Examples .. toctree:: :maxdepth: 2 :caption: Functions Functions .. toctree:: :maxdepth: 2 :caption: Documentation Documentation Coding quality distfit.distfit Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` .. |medium| image:: https://img.shields.io/badge/Medium-Blog-green.svg :alt: Medium Blog :target: https://erdogant.github.io/distfit/pages/html/Documentation.html#medium-blog .. |repo-size| image:: https://img.shields.io/github/repo-size/erdogant/distfit :alt: repo-size :target: https://img.shields.io/github/repo-size/erdogant/distfit .. |stars| image:: https://img.shields.io/github/stars/erdogant/distfit :alt: Stars :target: https://img.shields.io/github/stars/erdogant/distfit .. |python| image:: https://img.shields.io/pypi/pyversions/distfit.svg :alt: |Python :target: https://erdogant.github.io/distfit/ .. |pypi| image:: https://img.shields.io/pypi/v/distfit.svg :alt: |Python Version :target: https://pypi.org/project/distfit/ .. |docs| image:: https://img.shields.io/badge/Sphinx-Docs-blue.svg :alt: Sphinx documentation :target: https://erdogant.github.io/distfit/ .. |LOC| image:: https://sloc.xyz/github/erdogant/distfit/?category=code :alt: lines of code :target: https://github.com/erdogant/distfit .. |downloads_month| image:: https://static.pepy.tech/personalized-badge/distfit?period=month&units=international_system&left_color=grey&right_color=brightgreen&left_text=PyPI%20downloads/month :alt: Downloads per month :target: https://pepy.tech/project/distfit .. |downloads_total| image:: https://static.pepy.tech/personalized-badge/distfit?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=Downloads :alt: Downloads in total :target: https://pepy.tech/project/distfit .. |license| image:: https://img.shields.io/badge/license-MIT-green.svg :alt: License :target: https://github.com/erdogant/distfit/blob/master/LICENSE .. |forks| image:: https://img.shields.io/github/forks/erdogant/distfit.svg :alt: Github Forks :target: https://github.com/erdogant/distfit/network .. |open issues| image:: https://img.shields.io/github/issues/erdogant/distfit.svg :alt: Open Issues :target: https://github.com/erdogant/distfit/issues .. |project status| image:: http://www.repostatus.org/badges/latest/active.svg :alt: Project Status :target: http://www.repostatus.org/#active .. |donate| image:: https://img.shields.io/badge/Support%20this%20project-grey.svg?logo=github%20sponsors :alt: donate :target: https://erdogant.github.io/distfit/pages/html/Documentation.html# .. |colab| image:: https://colab.research.google.com/assets/colab-badge.svg :alt: Colab example :target: https://erdogant.github.io/distfit/pages/html/Documentation.html#colab-notebook .. |DOI| image:: https://zenodo.org/badge/231843440.svg :alt: Cite :target: https://zenodo.org/badge/latestdoi/231843440 .. include:: add_bottom.add