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        "author": "Danila Lenkov",
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            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering"
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        "description": "# abito\n[![Build Status](https://travis-ci.com/avito-tech/abito.svg?branch=master)](https://travis-ci.com/avito-tech/abito)\n[![Coverage Status](https://coveralls.io/repos/github/avito-tech/abito/badge.svg?branch=master)](https://coveralls.io/github/avito-tech/abito?branch=master)\n\nPython package for hypothesis testing. Suitable for using in A/B-testing software.\nTested for Python >= 3.5\n\n##### Features\n1. Based on statistical tests from scipy.stats: t-test, Mann-Whitney U, Shapiro-Wilk, Levene, Mood, Median\n2. Works with weighted samples\n3. Can trim sample tails\n4. Works with Ratio samples\n\n## Installation\n```\npip install abito\n```\n\n## Usage examples\n```python\n>>> from abito import RatioSample\n>>> sample = RatioSample(num=[1, 2, 3], den=[4, 5, 6])\n>>> sample.t_test_1samp(0.5)\nTTestResult(statistic=-1.4433756729740654, p_value=0.2857142857142853, mean_diff=-0.10000000000000003, mean_diff_std=0.06928203230275506)\n>>> sample_control = RatioSample(num=[1, 2, 8], den=[4, 5, 10])\n>>> sample.t_test(sample_control)\nTTestResult(statistic=-0.9481011064982815, p_value=0.42240549320152565, mean_diff=-0.1789473684210527, mean_diff_std=0.18874291696797746)\n```\n\n\n",
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