Вивчайте Challenge: Visualize User Growth | Product Metrics and Data Exploration

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.


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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()

Завдання

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Write a script that visualizes monthly active user (MAU) data for a product review presentation.

Plot the mau values against the months list.
Add an x-axis label "Month".
Add a y-axis label "Monthly Active Users".
Add a chart title "Monthly Active Users (MAU) Growth in 2023".

Рішення

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Секція 1. Розділ 7

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Suggested prompts:

Can you explain how to interpret the MAU trend shown in the chart?

What are some best practices for presenting MAU data to stakeholders?

Can you suggest other metrics to track alongside MAU for a product review?

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              12345678910111213
            
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()