Monthly Active Users (`MAU`) is a foundational product metric that reflects how many unique users engage with your product within a given month. Tracking `MAU` trends helps you understand product adoption, user retention, and the overall health of your product. When preparing for a product review presentation, it is crucial not only to analyze the `MAU` data but also to visualize it clearly. Effective charts make it easier for stakeholders to see growth patterns, spot anomalies, and make informed decisions. Adding clear labels and a descriptive title ensures your `MAU` trend chart communicates insights quickly and professionally.

import matplotlib.pyplot as plt

months = [
    "Jan", "Feb", "Mar", "Apr", "May", "Jun",
    "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"
]
mau = [1200, 1350, 1500, 1700, 1800, 2000, 2200, 2400, 2600, 2800, 3000, 3200]

plt.plot(months, mau, marker='o')
plt.xlabel("Month")
plt.ylabel("Monthly Active Users")
plt.title("MAU Trend for 2023")
plt.show()


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import types

class TestTask(unittest.TestCase):
    def setUp(self):
        # Patch plt.show to prevent blocking
        self._original_show = plt.show
        plt.show = lambda *args, **kwargs: None
        self.user_code = importlib.import_module("user_code")
        importlib.reload(self.user_code)

    def tearDown(self):
        plt.show = self._original_show

    def test_plot_data_and_labels(self):
        # Clear current figure
        plt.clf()
        # Re-run user code in a fresh global namespace
        code = ""
        with open("user_code.py", "r") as f:
            code = f.read()
        ns = {}
        exec(code, ns)
        # After running, check the current axes
        ax = plt.gca()
        # Test x and y data
        lines = ax.get_lines()
        _dynamic_test(
            self,
            len(lines) > 0,
            "A line is plotted on the chart.",
            "No line was plotted on the chart.",
        )
        line = lines[0]
        # Check data
        expected_months = [
            "Jan", "Feb", "Mar", "Apr", "May", "Jun",
            "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"
        ]
        expected_mau = [950, 1100, 1250, 1400, 1600, 1800, 2000, 2200, 2350, 2500, 2700, 2900]
        xdata = list(line.get_xdata())
        ydata = list(line.get_ydata())
        # Matplotlib will convert categorical xdata to indices, so we check ydata and x label ticks
        # Check ydata
        _dynamic_test(
            self,
            ydata == expected_mau,
            "MAU values are plotted in correct order.",
            f"Expected ydata {expected_mau}, got {ydata}",
        )
        # Check x tick labels
        actual_xticklabels = [tick.get_text() for tick in ax.get_xticklabels() if tick.get_text()]
        # Must include all expected months in order
        _dynamic_test(
            self,
            actual_xticklabels[:len(expected_months)] == expected_months,
            "X-axis labels are correct months in order.",
            f"Expected x tick labels {expected_months}, got {actual_xticklabels}",
        )
        # Check x and y axis labels
        _dynamic_test(
            self,
            ax.get_xlabel() == "Month",
            "X-axis is labeled 'Month'.",
            f"Expected x-axis label 'Month', got '{ax.get_xlabel()}'",
        )
        _dynamic_test(
            self,
            ax.get_ylabel() == "Monthly Active Users",
            "Y-axis is labeled 'Monthly Active Users'.",
            f"Expected y-axis label 'Monthly Active Users', got '{ax.get_ylabel()}'",
        )
        # Check title
        _dynamic_test(
            self,
            ax.get_title() == "Monthly Active Users (MAU) Growth in 2023",
            "Chart title is correct.",
            f"Expected chart title 'Monthly Active Users (MAU) Growth in 2023', got '{ax.get_title()}'",
        )

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

A hands-on course designed for Product Managers to leverage Python for data-driven decision making, product analytics, and business insights. Learn how to use Python to analyze product metrics, visualize trends, and automate reporting, all without prior data science experience.

Learn how to use Python to explore, summarize, and interpret key product metrics. This section focuses on understanding user data, product usage, and basic analytics relevant to product management.

Explore how Python can be used to design, analyze, and interpret product experiments, including A/B testing and hypothesis validation.

Master Python techniques to automate repetitive product management tasks, generate reports, and streamline product operations.

Challenge: Visualize User Growth

Ratkaisu

Challenge: Visualize User Growth

Ratkaisu