Simulating A/B tests is a practical way to understand how different variants perform in growth experiments. By writing a Python function that models an A/B test, you can quickly analyze which version leads to better user conversion. This approach is essential for making data-driven decisions in growth hacking, as it helps you evaluate experiment outcomes with clear, reproducible calculations.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import io
from contextlib import redirect_stdout

class TestTask(unittest.TestCase):
    def test_conversion_rates_and_comparison(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.ab_test_simulator(1000, 120, 980, 150)
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            "group a: 120/1000 converted (12.00%)" in output,
            "Correct conversion rate for Group A displayed.",
            f"Expected conversion rate for Group A not found. Output: {output}"
        )
        _dynamic_test(
            self,
            "group b: 150/980 converted (15.31%)" in output,
            "Correct conversion rate for Group B displayed.",
            f"Expected conversion rate for Group B not found. Output: {output}"
        )
        _dynamic_test(
            self,
            "group b performed better." in output,
            "Correct group comparison output (Group B better).",
            f"Expected 'Group B performed better.' not found. Output: {output}"
        )

    def test_group_a_better(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.ab_test_simulator(100, 30, 200, 20)
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            "group a performed better." in output,
            "Correct group comparison output (Group A better).",
            f"Expected 'Group A performed better.' not found. Output: {output}"
        )

    def test_groups_equal(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.ab_test_simulator(100, 20, 200, 40)
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            "both groups performed equally." in output,
            "Correct output when both groups are equal.",
            f"Expected 'Both groups performed equally.' not found. Output: {output}"
        )

    def test_zero_users(self):
        import user_code
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            user_code.ab_test_simulator(0, 0, 100, 10)
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            "group a: 0/0 converted (0.00%)" in output,
            "Correctly handles zero users in Group A.",
            f"Expected output for zero users in Group A not found. Output: {output}"
        )
        _dynamic_test(
            self,
            "group b: 10/100 converted (10.00%)" in output,
            "Correctly calculates Group B conversion rate.",
            f"Expected output for Group B not found. Output: {output}"
        )
        _dynamic_test(
            self,
            "group b performed better." in output,
            "Correct group comparison when Group A has zero users.",
            f"Expected 'Group B performed better.' not found. Output: {output}"
        )

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\\s{2,}", " ", text)
    text = re.sub(r"\\s*([,:?])\\s*", r"\\1 ", text)
    return text.strip()

def change_var(code: str, var_name: str, value: str) -> str:
    tree = ast.parse(code)
    lines = code.splitlines()
    changed = False
    # Collect all assignment nodes to modify
    assign_nodes = [
        (i, node)
        for i, node in enumerate(tree.body)
        if isinstance(node, ast.Assign)
        and any(isinstance(target, ast.Name) and target.id == var_name for target in node.targets)
    ]

    # If nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    for i, node in reversed(assign_nodes):
        start_line = node.lineno - 1
        line = lines[start_line]
        indent = ' ' * (len(line) - len(line.lstrip()))
        lines[start_line] = f"{indent}{var_name} = {value}"
        next_line = len(lines)
        for next_node in tree.body[i+1:]:
            if hasattr(next_node, 'lineno'):
                next_line = next_node.lineno - 1
                break
        if next_line > start_line + 1:
            lines[start_line+1:next_line] = []
        changed = True

    return '\\n'.join(lines) if changed else code

if __name__ == "__main__":
    unittest.main()


test_main.py

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Use Python to analyze user engagement, retention, and behavior patterns to inform growth strategies.

Apply Python to design, analyze, and optimize growth experiments such as A/B tests and campaign performance evaluations.

Challenge: A/B Test Simulator

Ratkaisu

Challenge: A/B Test Simulator

Ratkaisu