Package for calculating and visualising confidence intervals, e.g. for A/B test analysis.
Project description
Spotify Confidence
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 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), 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
Code of Conduct
This project adheres to the Open Code of Conduct By participating, you are expected to honor this code.
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