Metadata-Version: 2.1
Name: statannotations
Version: 0.3.1
Summary: add statistical significance annotations on seaborn boxplot/barplot. 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)
        ## What is it
        
        Python package to optionally compute statistical test and add statistical
        annotations on boxplots or barplots generated with seaborn.
        
        ## Derived work
        
        This repository is evolving independently from
        [webermarcolivier/statannot](https://github.com/webermarcolivier/statannot)
        by Marc Weber. It is based on 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). *New issues and PRs are welcome and will be 
        looked into.*
        
        The statannot interface, at least until its version 0.2.3, is directly usable in
        statannotations, which provides additional features.
        
        ## Features
        
        - Single function to add statistical annotations on an existing boxplot/barplot
          generated by seaborn boxplot.
        - 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.
        - 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, 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.**
        - And various fixes (see [CHANGELOG.md](https://raw.githubusercontent.com/trevismd/statannotations/master/CHANGELOG.md)).
        ## Installation
        
        From version 0.3.0 on, the package is distributed on PyPi.  
        The latest stable release can be downloaded and installed with:
        ```bash
        pip install statannotations
        ```
        
        or, after cloning the repository,
        ```bash
        pip install -r requirements.txt .
        ```
        
        ## Documentation
        
        See example jupyter notebook [doc/example.ipynb](`https://raw.githubusercontent.com/trevismd/statannotations/master/doc/example.ipynb).
        
        ## Usage
        
        Here is a minimal example:
        
        ```python
        import seaborn as sns
        from statannotations import add_stat_annotation
        
        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)
        add_stat_annotation(
            ax, data=df, x=x, y=y, order=order,
            box_pairs=[("Thur", "Fri"), ("Thur", "Sat"), ("Fri", "Sun")],
            test='Mann-Whitney', text_format='star', loc='outside', verbose=2)
        
        ```
        
        ## Examples
        
        ![Example 1](https://raw.githubusercontent.com/trevismd/statannotations/master/doc/example_non-hue_outside.png)
        
        ![Example 2](https://raw.githubusercontent.com/trevismd/statannotations/master/doc/example_hue_layout.png)
        
        ## 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)
        
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
