Leer Challenge: Retention Cohort Analyzer

To deepen your understanding of cohort analysis, you will write a Python script that simulates a simple retention cohort analyzer. Cohort analysis is a powerful tool for growth hackers, as it helps you track user retention over time by grouping users based on shared characteristics—in this case, their signup month. You will use a hardcoded pandas DataFrame to represent user data, including user IDs, their signup months, and retention status (where 1 means retained and 0 means not retained). Your task is to calculate the retention rate for each signup month and display the results, giving you practical experience with both pandas and user retention analytics.

Begin by importing the pandas library and creating a DataFrame with the necessary columns. The DataFrame will contain three columns: user_id, signup_month, and retained. Each row represents a user, their signup month, and whether they were retained.


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import pandas as pd

# Hardcoded user data for cohort analysis
data = {
    "user_id": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    "signup_month": ["2024-01", "2024-01", "2024-01", "2024-02", "2024-02", 
                     "2024-02", "2024-03", "2024-03", "2024-03", "2024-03"],
    "retained": [1, 0, 1, 1, 0, 1, 0, 1, 1, 0]
}

df = pd.DataFrame(data)

# Calculate retention rate for each signup month
cohort = df.groupby("signup_month")["retained"].mean().reset_index()
cohort["retention_rate"] = (cohort["retained"] * 100).round(2)

# Print the results
for _, row in cohort.iterrows():
    print(
        f"Signup Month: {row['signup_month']} - Retention Rate: {row['retention_rate']}%"
    )

This script groups users by their signup_month and calculates the average of the retained column for each group, which represents the retention rate. The retention rate is then multiplied by 100 and rounded to two decimal places for readability. Finally, the script prints the retention rate for each signup month, allowing you to see which cohorts are performing better in terms of user retention. By analyzing these results, you can identify trends and make data-driven decisions to improve user engagement and retention strategies.

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Write a Python script that:

Imports the pandas library.
Creates a DataFrame with the following columns: user_id, signup_month, and retained.
The DataFrame should have at least 8 rows, with at least two unique values for signup_month.
Calculates the retention rate (mean of retained) for each signup month.
Prints each signup month and its retention rate as a percentage, rounded to two decimal places.

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import pandas as pd

# Hardcoded user data for cohort analysis
data = {
    "user_id": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    "signup_month": ["2024-01", "2024-01", "2024-01", "2024-02", "2024-02", 
                     "2024-02", "2024-03", "2024-03", "2024-03", "2024-03"],
    "retained": [1, 0, 1, 1, 0, 1, 0, 1, 1, 0]
}

df = pd.DataFrame(data)

# Calculate retention rate for each signup month
cohort = df.groupby("signup_month")["retained"].mean().reset_index()
cohort["retention_rate"] = (cohort["retained"] * 100).round(2)

# Print the results
for _, row in cohort.iterrows():
    print(
        f"Signup Month: {row['signup_month']} - Retention Rate: {row['retention_rate']}%"
    )