A/B testing & experimentation
User bucketing, experiment metrics, statistical comparison, and rollout guardrails.
How to Compute Mann-Whitney U Test in Python
Compute the Mann-Whitney U statistic and p-value manually in Python with tie correction and a normal approximation for independent samples.
import numpy as np
from scipy import stats
def mann_whitney_u_mock(sample_a, sample_b):
"""Compute Mann-Whitney U and p-value manually."""
# Combine and rank
combined = sample_a + sample_b
n_a, n_b = len(sample_a), len(sample_b)
n_total = n_a + n_b
# Rank with ties handling (average ranks…How to Mock Sequential Calls in Python with unittest.mock
Use Mock.side_effect to return a different result for each sequential call and verify the call order with assert_has_calls.
import unittest
from unittest.mock import Mock
class Service:
def fetch(self, item_id):
raise NotImplementedError
def process_items(service, ids):
results = []
for item_id in ids:
result = service.fetch(item_id)
results.append(result)
return results
if __name__ == "__main__":…Browse by section
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A/B testing & experimentation — Python code examples
What you will find here
This page collects a/b testing & experimentation snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.