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Aprenda Challenge: Automate a Product Report | Automating Product Management Workflows
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Python for Product Managers

bookChallenge: Automate a Product Report

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)
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Tarefa

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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.

Solução

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Seção 3. Capítulo 3
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bookChallenge: Automate a Product Report

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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)
copy
Tarefa

Swipe to start coding

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.

Solução

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Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 3. Capítulo 3
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single

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