Aprende Challenge: Automate a Product Report | Automating Product Management Workflows

In product management, automating the creation of weekly reports saves time, reduces manual errors, and ensures stakeholders receive consistent updates. When generating such reports, follow best practices: organize your metrics clearly, use meaningful labels, and format the output for easy reading. Summarizing key data points like Daily Active Users (DAU), churn rate, and feature usage gives your team a quick snapshot of product health and engagement. Python makes this process straightforward by allowing you to calculate summaries and print well-formatted reports with minimal code.


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# Hardcoded weekly metrics
weekly_metrics = {
    "DAU": [120, 135, 140, 125, 150, 160, 155],
    "churn_rate": [0.02, 0.025, 0.018, 0.022, 0.019, 0.021, 0.02],
    "feature_A_usage": [50, 60, 55, 53, 65, 70, 68]
}

# Summarize metrics
average_dau = sum(weekly_metrics["DAU"]) / len(weekly_metrics["DAU"])
average_churn = sum(weekly_metrics["churn_rate"]) / len(weekly_metrics["churn_rate"])
total_feature_a_usage = sum(weekly_metrics["feature_A_usage"])

# Format and print report
report = (
    "Weekly Product Metrics Report\n"
    "----------------------------\n"
    f"Average DAU: {average_dau:.1f}\n"
    f"Average Churn Rate: {average_churn:.2%}\n"
    f"Total Feature A Usage: {total_feature_a_usage}\n"
)

print(report)

Tarea

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Write a function that generates a formatted report summarizing weekly product metrics from the provided dictionary.

Calculate the average value of the "DAU" list.
Calculate the average value of the "churn" list.
Calculate the total sum of the "feature_B_usage" list.
Return a formatted string report with labeled lines for average DAU, average churn rate (as a percent with two decimals), and total Feature B usage.

Solución

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Sección 3. Capítulo 3

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Suggested prompts:

Can you explain what DAU and churn rate mean in this context?

How can I add more metrics to this report?

Can you show me how to customize the report formatting?

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# Hardcoded weekly metrics
weekly_metrics = {
    "DAU": [120, 135, 140, 125, 150, 160, 155],
    "churn_rate": [0.02, 0.025, 0.018, 0.022, 0.019, 0.021, 0.02],
    "feature_A_usage": [50, 60, 55, 53, 65, 70, 68]
}

# Summarize metrics
average_dau = sum(weekly_metrics["DAU"]) / len(weekly_metrics["DAU"])
average_churn = sum(weekly_metrics["churn_rate"]) / len(weekly_metrics["churn_rate"])
total_feature_a_usage = sum(weekly_metrics["feature_A_usage"])

# Format and print report
report = (
    "Weekly Product Metrics Report\n"
    "----------------------------\n"
    f"Average DAU: {average_dau:.1f}\n"
    f"Average Churn Rate: {average_churn:.2%}\n"
    f"Total Feature A Usage: {total_feature_a_usage}\n"
)

print(report)