Lära Challenge: Signup Trend Analyzer | Analyzing User Behavior

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.


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import 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)")

Note

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.

Uppgift

Swipe to start coding

Write a script that:

Creates a pandas DataFrame 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.

Lösning

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import 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)")