Announcing Bootstrapped A Python library to generate confidence intervals

bootstrapped - confidence intervals made easy

bootstrappedis a python library that allows you to build confidence intervals from data. This is useful in a variety of contexts - including during ad-hoc a/b test analysis.

bootstrapped - Benefits Efficient computation of percentile based confidence intervals Functions to handle single populations and a/b test scenarios Functions to understand statistical power Example Usage import numpy as np import bootstrapped.bootstrap as bs import bootstrapped.stats_functions as bs_stats mean = 100 stdev = 10 population = np.random.normal(loc=mean, scale=stdev, size=50000) # take 1k 'samples' from the larger population samples = population[:1000] print bs.bootstrap(samples, stat_func=bs_stats.mean) >> 100.08 (99.46, 100.69) print bs.bootstrap(samples, stat_func=bs_stats.std) >> 9.49 (9.92, 10.36) Extended Examples Bootstrap Intro Bootstrap A/B Testing More notebooks can be found in the examples/ directory Requirements

bootstrappedrequires numpy and pandas. The power analysis plotting function requires matplotlib. statsmodels is used in some of the examples.

Installation # clone bootstrapped cd bootstrapped pip install -r requirements.txt python setup.py install How bootstrapped works

tldr - Percentile based confidence intervals based on bootstrap re-sampling with replacement.

Bootstrapped generates confidence intervals given input data by:

Generating a large number of samples from the input (re-sampling) For each re-sample, calculate the mean (or whatever statistic you care about) Of these results, calculate the 2.5th and 97.5 percentiles (default range) Use this as the 95% confidence interval

For more information please see:

Bootstrap confidence intervals (good intro) An introduction to Bootstrap Methods When the bootstrap dosen't work (book) An Introduction to the Bootstrap (book) Bootstrap Methods and their Application

See the CONTRIBUTING file for how to help out.

Contributors

Spencer Beecher, Don van der Drift, David Martin, Lindsay Vass, Sergey Goder, Benedict Lim, and Matt Langner.

Special thanks to Eytan Bakshy.

License

bootstrappedis BSD-licensed. We also provide an additional patent grant.