Lära Challenge: User Segmentation Script

In growth hacking, understanding how users engage with your product is crucial for tailoring marketing strategies. Segmenting users based on their activity helps you identify who is highly engaged, who might need a nudge, and who has stopped interacting altogether. You will now create a script to automate this segmentation process using Python and pandas. The goal is to write a function that categorizes users into three segments based on their activity counts: active for those with 10 or more activities, dormant for those with 1 to 9, and churned for those with zero activity. This approach lets you quickly spot trends and target users with the right messaging.


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import pandas as pd

def segment_users():
    # Hardcoded user activity data
    data = {
        "user_id": ["u1", "u2", "u3", "u4", "u5", "u6"],
        "activity_count": [12, 0, 7, 3, 0, 15]
    }
    df = pd.DataFrame(data)
    
    # Function to assign segment
    def assign_segment(activity):
        if activity >= 10:
            return "active"
        elif activity >= 1:
            return "dormant"
        else:
            return "churned"
    
    df["segment"] = df["activity_count"].apply(assign_segment)
    return df[["user_id", "segment"]]

# Run the function and display the result
segmented_df = segment_users()
print(segmented_df)

This script uses a hardcoded DataFrame to simulate real user data. Each user is assigned a segment based on their activity count. The segmentation logic is handled by the assign_segment function, which is applied to each user's activity value. The resulting DataFrame gives you a clear view of which users are "active", "dormant", or "churned", making it easier to plan your next growth move.

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Write a function called segment_users that:

Creates a pandas DataFrame with two columns: "user_id" and "activity_count".
Hardcodes at least five users with varying activity counts (including at least one with 0, one with 1–9, and one with 10 or more).
Assigns each user to a segment:
- "active" if activity_count is 10 or more.
- "dormant" if activity_count is between 1 and 9 (inclusive).
- "churned" if activity_count is 0.
Returns a new DataFrame with columns "user_id" and "segment".

Your function should not take any arguments.

Test your function by calling it and printing the result.

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Avsnitt 2. Kapitel 5

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              12345678910111213141516171819202122232425
            
import pandas as pd

def segment_users():
    # Hardcoded user activity data
    data = {
        "user_id": ["u1", "u2", "u3", "u4", "u5", "u6"],
        "activity_count": [12, 0, 7, 3, 0, 15]
    }
    df = pd.DataFrame(data)
    
    # Function to assign segment
    def assign_segment(activity):
        if activity >= 10:
            return "active"
        elif activity >= 1:
            return "dormant"
        else:
            return "churned"
    
    df["segment"] = df["activity_count"].apply(assign_segment)
    return df[["user_id", "segment"]]

# Run the function and display the result
segmented_df = segment_users()
print(segmented_df)