Metadata-Version: 2.1
Name: spotify-confidence
Version: 2.0.2
Summary: Package for calculating and visualising confidence intervals, e.g. for A/B test analysis.
Home-page: https://github.com/spotify/confidence
Author: Per Sillren
Author-email: pers@spotify.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/spotify/confidence/issues
Description: Spotify Confidence
        ========
        
        ![Status](https://img.shields.io/badge/Status-Beta-blue.svg)
        ![Latest release](https://img.shields.io/badge/release-2.0.2-green.svg "Latest release: 2.0.2")
        ![Python](https://img.shields.io/badge/Python-3.6-blue.svg "Python")
        ![Python](https://img.shields.io/badge/Python-3.7-blue.svg "Python")
        
        Python library for AB test analysis.
        
        Why use Spotify Confidence?
        -----------------
        
        Spotify Confidence provides convinience wrappers around statsmodel's various functions for computing p-values and confidence intervalls. 
        With Spotify Confidence it's easy to compute several p-values and confidence bounds in one go, e.g. one for each country or for each date. 
        Each function comes in two versions: 
         - one that return a pandas dataframe,
         - one that returns a [Chartify](https://github.com/spotify/chartify) chart.
        
        Spotify Confidence has support calculating p-values and confidence intervals using Z-statistics, Student's T-statistics 
        (or more exactly [Welch's T-test](https://en.wikipedia.org/wiki/Welch%27s_t-test)), as well as Chi-squared statistics.
        
        There is also a Bayesian alternative in the BetaBinomial class.
        
        Examples
        --------
        ```
        import spotify_confidence as confidence
        import pandas as pd
        
        data = pd.DataFrame(
            {'variation_name': ['treatment1', 'control', 'treatment2', 'treatment3'],
             'success': [50, 40, 10, 20],
             'total': [100, 100, 50, 60]
            }
        )
        
        test = confidence.ZTest(
            self.data,
            numerator_column='success',
            numerator_sum_squares_column=None,
            denominator_column='total',
            categorical_group_columns='variation_name',
            correction_method='bonferroni')
            
        test.summary()
        test.difference(level_1='control', level_2='treatment1')
        test.multiple_diffence(level='control', level_as_reference=True)
        
        test.summary_plot().show()
        test.difference_plot(level_1='control', level_2='treatment1').show()
        test.multiple_diffence_plot(level='control', level_as_reference=True).show()
        ```
        
        See jupyter notebooks in `examples` folder for more complete examples.
        
        Installation
        ------------
        Spotify Confidence can be installed via pip:
        
        ```pip install spotify-confidence```
        
        [Find the latest release version here](https://github.com/spotify/confidence/releases)
        
        ### Code of Conduct
        
        This project adheres to the [Open Code of Conduct](https://github.com/spotify/code-of-conduct/blob/master/code-of-conduct.md) By participating, you are expected to honor this code.
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
