Challenge: Signup Trend Analyzer
When you want to identify peak signup periods and analyze user growth, you often need to look at the number of signups occurring each day. By doing this, you can spot trends, such as which days see the most activity, and use that insight to optimize your marketing efforts. In this challenge, you will write a Python script using pandas to create a DataFrame with hardcoded user signup dates, then calculate the number of signups per day and find the day with the highest number of signups.
123456789101112131415161718192021import pandas as pd # Create a DataFrame with hardcoded signup dates data = { "user_id": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "signup_date": [ "2024-06-01", "2024-06-01", "2024-06-02", "2024-06-02", "2024-06-02", "2024-06-03", "2024-06-03", "2024-06-03", "2024-06-03", "2024-06-04" ] } df = pd.DataFrame(data) # Count signups per day signups_per_day = df["signup_date"].value_counts().sort_index() print("Signups per day:") print(signups_per_day) # Find the day with the highest number of signups peak_day = signups_per_day.idxmax() peak_count = signups_per_day.max() print(f"\nDay with the highest signups: {peak_day} ({peak_count} signups)")
You can use this approach with real signup data by loading it from a CSV or database instead of hardcoding values. This lets you analyze trends over longer periods and across larger user bases.
Swipe to start coding
Write a script that:
- Creates a
pandasDataFrame with at least 12 user signups and their signup dates (use at least 4 different dates). - Calculates and prints the number of signups per day.
- Identifies and prints the day with the most signups.
Oplossing
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When you want to identify peak signup periods and analyze user growth, you often need to look at the number of signups occurring each day. By doing this, you can spot trends, such as which days see the most activity, and use that insight to optimize your marketing efforts. In this challenge, you will write a Python script using pandas to create a DataFrame with hardcoded user signup dates, then calculate the number of signups per day and find the day with the highest number of signups.
123456789101112131415161718192021import pandas as pd # Create a DataFrame with hardcoded signup dates data = { "user_id": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "signup_date": [ "2024-06-01", "2024-06-01", "2024-06-02", "2024-06-02", "2024-06-02", "2024-06-03", "2024-06-03", "2024-06-03", "2024-06-03", "2024-06-04" ] } df = pd.DataFrame(data) # Count signups per day signups_per_day = df["signup_date"].value_counts().sort_index() print("Signups per day:") print(signups_per_day) # Find the day with the highest number of signups peak_day = signups_per_day.idxmax() peak_count = signups_per_day.max() print(f"\nDay with the highest signups: {peak_day} ({peak_count} signups)")
You can use this approach with real signup data by loading it from a CSV or database instead of hardcoding values. This lets you analyze trends over longer periods and across larger user bases.
Swipe to start coding
Write a script that:
- Creates a
pandasDataFrame with at least 12 user signups and their signup dates (use at least 4 different dates). - Calculates and prints the number of signups per day.
- Identifies and prints the day with the most signups.
Oplossing
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