Automating Weekly Product Reports
Automating weekly product reports can transform your workflow as a Product Manager. Instead of spending hours compiling data, you can use Python to quickly generate summaries of your key metrics such as daily active users (DAU), churn rates, and feature usage. This approach not only saves time but also ensures consistency and accuracy in your reporting process.
123456789101112131415161718192021222324# Sample data for a week's product metrics weekly_data = [ {"day": "Monday", "dau": 1200, "churn": 30, "feature_a": 400, "feature_b": 250}, {"day": "Tuesday", "dau": 1250, "churn": 28, "feature_a": 420, "feature_b": 260}, {"day": "Wednesday", "dau": 1230, "churn": 35, "feature_a": 410, "feature_b": 255}, {"day": "Thursday", "dau": 1280, "churn": 32, "feature_a": 430, "feature_b": 265}, {"day": "Friday", "dau": 1300, "churn": 27, "feature_a": 440, "feature_b": 270}, {"day": "Saturday", "dau": 1150, "churn": 40, "feature_a": 390, "feature_b": 240}, {"day": "Sunday", "dau": 1100, "churn": 45, "feature_a": 380, "feature_b": 230}, ] # Summarize metrics total_dau = sum(day["dau"] for day in weekly_data) avg_dau = total_dau / len(weekly_data) total_churn = sum(day["churn"] for day in weekly_data) avg_churn = total_churn / len(weekly_data) total_feature_a = sum(day["feature_a"] for day in weekly_data) total_feature_b = sum(day["feature_b"] for day in weekly_data) print("Weekly Metrics Summary:") print(f"Average DAU: {avg_dau:.1f}") print(f"Average Churn: {avg_churn:.1f}") print(f"Total Feature A Usage: {total_feature_a}") print(f"Total Feature B Usage: {total_feature_b}")
By automating these calculations, you eliminate the need to manually collect and summarize data each week. Automation reduces the risk of manual errors, such as copying numbers incorrectly or overlooking a day's data. It also ensures that your reports are generated the same way every time, improving reliability and enabling you to focus on analyzing the results rather than assembling them.
123456789101112# Formatting and printing a weekly report summary report = ( "WEEKLY PRODUCT REPORT\n" "---------------------\n" f"Average Daily Active Users: {avg_dau:.1f}\n" f"Average Daily Churn: {avg_churn:.1f}\n" f"Feature A Usage (total): {total_feature_a}\n" f"Feature B Usage (total): {total_feature_b}\n" ) print(report)
1. What are the benefits of automating product reports?
2. Which Python features help automate repetitive tasks?
3. How can formatted output improve report readability?
Merci pour vos commentaires !
Demandez à l'IA
Demandez à l'IA
Posez n'importe quelle question ou essayez l'une des questions suggérées pour commencer notre discussion
Can you explain how to add more metrics to the report?
How can I automate sending this report via email?
Can you show how to visualize these metrics with charts?
Génial!
Completion taux amélioré à 4.76Automating Weekly Product Reports
Glissez pour afficher le menu
Automating weekly product reports can transform your workflow as a Product Manager. Instead of spending hours compiling data, you can use Python to quickly generate summaries of your key metrics such as daily active users (DAU), churn rates, and feature usage. This approach not only saves time but also ensures consistency and accuracy in your reporting process.
123456789101112131415161718192021222324# Sample data for a week's product metrics weekly_data = [ {"day": "Monday", "dau": 1200, "churn": 30, "feature_a": 400, "feature_b": 250}, {"day": "Tuesday", "dau": 1250, "churn": 28, "feature_a": 420, "feature_b": 260}, {"day": "Wednesday", "dau": 1230, "churn": 35, "feature_a": 410, "feature_b": 255}, {"day": "Thursday", "dau": 1280, "churn": 32, "feature_a": 430, "feature_b": 265}, {"day": "Friday", "dau": 1300, "churn": 27, "feature_a": 440, "feature_b": 270}, {"day": "Saturday", "dau": 1150, "churn": 40, "feature_a": 390, "feature_b": 240}, {"day": "Sunday", "dau": 1100, "churn": 45, "feature_a": 380, "feature_b": 230}, ] # Summarize metrics total_dau = sum(day["dau"] for day in weekly_data) avg_dau = total_dau / len(weekly_data) total_churn = sum(day["churn"] for day in weekly_data) avg_churn = total_churn / len(weekly_data) total_feature_a = sum(day["feature_a"] for day in weekly_data) total_feature_b = sum(day["feature_b"] for day in weekly_data) print("Weekly Metrics Summary:") print(f"Average DAU: {avg_dau:.1f}") print(f"Average Churn: {avg_churn:.1f}") print(f"Total Feature A Usage: {total_feature_a}") print(f"Total Feature B Usage: {total_feature_b}")
By automating these calculations, you eliminate the need to manually collect and summarize data each week. Automation reduces the risk of manual errors, such as copying numbers incorrectly or overlooking a day's data. It also ensures that your reports are generated the same way every time, improving reliability and enabling you to focus on analyzing the results rather than assembling them.
123456789101112# Formatting and printing a weekly report summary report = ( "WEEKLY PRODUCT REPORT\n" "---------------------\n" f"Average Daily Active Users: {avg_dau:.1f}\n" f"Average Daily Churn: {avg_churn:.1f}\n" f"Feature A Usage (total): {total_feature_a}\n" f"Feature B Usage (total): {total_feature_b}\n" ) print(report)
1. What are the benefits of automating product reports?
2. Which Python features help automate repetitive tasks?
3. How can formatted output improve report readability?
Merci pour vos commentaires !
