Metadata-Version: 2.1
Name: statannotations
Version: 0.4.3
Summary: add statistical significance or custom annotations on seaborn plots. Based on statannot 0.2.3
Home-page: https://github.com/trevismd/statannotations
Maintainer: Florian Charlier
Maintainer-email: trevis@cascliniques.be
License: MIT License
Description: [![Active Development](https://img.shields.io/badge/Maintenance%20Level-Actively%20Developed-brightgreen.svg)](https://gist.github.com/cheerfulstoic/d107229326a01ff0f333a1d3476e068d) ![coverage](https://raw.githubusercontent.com/trevismd/statannotations/master/coverage.svg)  ![Python](https://img.shields.io/badge/Python-3.6%2B-blue)[![Documentation Status](https://readthedocs.org/projects/statannotations/badge/?version=latest)](https://statannotations.readthedocs.io/en/master/?badge=latest)
        
        ## What is it
        
        Python package to optionally compute statistical test and add statistical
        annotations on plots generated with seaborn.
        
        ## Derived work
        
        This repository is based on
        [webermarcolivier/statannot](https://github.com/webermarcolivier/statannot)
         (commit 1835078 of Feb 21, 2020, tagged "v0.2.3").
        
        Additions/modifications since that version are below represented **in bold**
        (previous fixes are not listed).
        
        **! From version 0.4.0 onwards (introduction of `Annotator`), `statannot`'s API 
        is no longer usable in `statannotations`**. 
        Please use the latest v0.3.2 release if you must keep `statannot`'s API in your 
        code, but are looking for bug fixes we have covered.
        
        Indeed, the statannot interface, at least until its version 0.2.3, is usable in
        statannotations until v.0.3.x, which already provides additional features (see
        corresponding branch).
        
        ## Features
        
        - Single function to add statistical annotations on plots
          generated by seaborn:
            - Box plots
            - Bar plots
            - **Swarm plots**
            - **Strip plots**
            - **Violin plots** 
            - Supporting `FacetGrid`
        - Integrated statistical tests (binding to `scipy.stats` methods):
            - Mann-Whitney
            - t-test (independent and paired)
            - Welch's t-test
            - Levene test
            - Wilcoxon test
            - Kruskal-Wallis test
        - **Interface to use any other function from any source with minimal extra
          code**
        - Smart layout of multiple annotations with correct y offsets.
        - **Support for vertical and horizontal orientation**
        - Annotations can be located inside or outside the plot.
        - **Corrections for multiple testing can be applied
          (binding to `statsmodels.stats.multitest.multipletests` methods):**
            - Bonferroni
            - Holm-Bonferroni
            - Benjamini-Hochberg
            - Benjamini-Yekutieli
        - **And any other function from any source with minimal extra code**
        - Format of the statistical test annotation can be customized:
              star annotation, simplified p-value format, or explicit p-value.
        - Optionally, custom p-values can be given as input.
              In this case, no statistical test is performed, but **corrections for
              multiple testing can be applied.**
        - Any text can be used as annotation
        - And various fixes (see
          [CHANGELOG.md](https://github.com/trevismd/statannotations/blob/master/CHANGELOG.md)).
        
        ## Installation
        
        From version 0.3.0 on, the package is distributed on PyPi.
        The latest stable release (v0.4.3) can be downloaded and installed with:
        ```bash
        pip install statannotations
        ```
        
        or, after cloning the repository,
        ```bash
        pip install .
        
        # OR, to have optional dependencies too (multiple comparisons & testing)
        pip install -r requirements.txt .
        ```
        
        ## Usage
        
        Here is a minimal example:
        
        ```python
        import seaborn as sns
        
        from statannotations.Annotator import Annotator
        
        df = sns.load_dataset("tips")
        x = "day"
        y = "total_bill"
        order = ['Sun', 'Thur', 'Fri', 'Sat']
        
        ax = sns.boxplot(data=df, x=x, y=y, order=order)
        
        pairs=[("Thur", "Fri"), ("Thur", "Sat"), ("Fri", "Sun")]
        
        annotator = Annotator(ax, pairs, data=df, x=x, y=y, order=order)
        annotator.configure(test='Mann-Whitney', text_format='star', loc='outside')
        annotator.apply_and_annotate()
        ```
        
        ## Examples
        
        ![Example 2](https://raw.githubusercontent.com/trevismd/statannotations/master/usage/example_hue_layout.png)
        
        ![Example 3](https://raw.githubusercontent.com/trevismd/statannotations/master/usage/flu_dataset_log_scale_in_axes.svg)
        
        ![Example 4](https://raw.githubusercontent.com/trevismd/statannotations/master/usage/HorizontalBarplotOutside.png)
        
        ![Example 5](https://raw.githubusercontent.com/trevismd/statannotations/master/usage/example_2facets.png)
        
        ## Documentation
        
        - Usage examples in a jupyter notebook [usage/example.ipynb](https://github.com/trevismd/statannotations/master/usage/example.ipynb),
        - A multipart step-by-step tutorial in a separate [repository](https://github.com/trevismd/statannotations-tutorials) 
          &mdash; [First part here](https://github.com/trevismd/statannotations-tutorials/blob/main/Tutorial_1/Statannotations-Tutorial-1.ipynb),
          also as a blog post on [Medium](https://levelup.gitconnected.com/statistics-on-seaborn-plots-with-statannotations-2bfce0394c00). 
        - *In-progress* sphinx documentation in `/docs`, available on https://statannotations.readthedocs.io/en/latest/index.html
        
        ## Requirements
        
        + Python >= 3.6
        + numpy >= 1.12.1
        + seaborn >= 0.9
        + matplotlib >= 2.2.2
        + pandas >= 0.23.0
        + scipy >= 1.1.0
        + statsmodels (optional, for multiple testing corrections)
        
        ## Contributing
        
        **Opening issues and PRs are very much welcome!** (preferably in that order).  
        In addition to git's history, contributions to statannotations are logged in
        the changelog.  
        If you don't know where to start, there may be a few ideas in opened issues or
        discussion, or something to work for the documentation.
        NB: More on [CONTRIBUTING.md](CONTRIBUTING.md)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
