Python library for AB test analysis.

Spotify Confidence provides convenience 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. It also supports a variance reduction technique based on using pre-exposure data to fit a linear model.

There is also a Bayesian alternative in the BetaBinomial class.

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(
    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_difference(level='control', level_as_reference=True)

test.summary_plot().show()
test.difference_plot(level_1='control', level_2='treatment1').show()
test.multiple_difference_plot(level='control', level_as_reference=True).show()

There's a lot more you can do:

• Segment results by one or more dimensions
• Use non-inferiority margins
• Group sequential tests
• Sample size and power calculations
• etc

See jupyter notebooks in examples folder for more complete examples.

Spotify Confidence can be installed via pip:

pip install spotify-confidence

Find the latest release version here

This project adheres to the Open Code of Conduct By participating, you are expected to honor this code.