Metadata-Version: 2.1
Name: abito
Version: 0.0.6
Summary: Package for hypothesis testing in A/B-experiments
Home-page: https://github.com/avito-tech/abito
Author: Danila Lenkov
Author-email: dlenkoff@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.14.2)
Requires-Dist: scipy (>=1.0.0)
Requires-Dist: cached-property (>=1.4.0)

# abito
[![Build Status](https://travis-ci.com/avito-tech/abito.svg?branch=master)](https://travis-ci.com/avito-tech/abito)
[![Coverage Status](https://coveralls.io/repos/github/avito-tech/abito/badge.svg?branch=master)](https://coveralls.io/github/avito-tech/abito?branch=master)

Python package for hypothesis testing. Suitable for using in A/B-testing software.
Tested for Python >= 3.5

##### Features
1. Based on statistical tests from scipy.stats: t-test, Mann-Whitney U, Shapiro-Wilk, Levene, Mood, Median
2. Works with weighted samples
3. Can trim sample tails
4. Works with Ratio samples

## Installation
```
pip install abito
```

## Usage examples
```python
>>> from abito import RatioSample
>>> sample = RatioSample(num=[1, 2, 3], den=[4, 5, 6])
>>> sample.t_test_1samp(0.5)
TTestResult(statistic=-1.4433756729740654, p_value=0.2857142857142853, mean_diff=-0.10000000000000003, mean_diff_std=0.06928203230275506)
>>> sample_control = RatioSample(num=[1, 2, 8], den=[4, 5, 10])
>>> sample.t_test(sample_control)
TTestResult(statistic=-0.9481011064982815, p_value=0.42240549320152565, mean_diff=-0.1789473684210527, mean_diff_std=0.18874291696797746)
```


