Скачать или смотреть How to Group Pandas Columns for Mapping and Creating New Columns Based on Values

This guide explains how to group pandas columns and create a new column based on mapping values, using a student test score example.
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How to Group Pandas Columns for Mapping and Creating New Columns Based on Values

When working with data in Python, particularly with a library like Pandas, you might find yourself needing to group data based on certain columns and derive new columns from that grouping. In this post, we will explore a practical example that demonstrates how to group student test data by their IDs, create a mapping of subjects to the number of tests, and generate a new column for grades based on preset conditions.

The Problem

Imagine that you have a dataset of students that includes their IDs along with the subjects they’ve studied and the number of tests they’ve taken in each subject. Your goal is to transform this dataset into a more structured format where:

Each student's ID is listed once.

The subjects and their corresponding number of tests are represented as a dictionary.

A new column will show the grade (A or B) based on the number of tests taken in specific subjects.

What You Have:

Here’s an example of the dataset you might start with:

[[See Video to Reveal this Text or Code Snippet]]

What You Want:

The desired output would look something like this:

[[See Video to Reveal this Text or Code Snippet]]

The Solution

Let’s break down the solution into clear and organized steps.

Step 1: Grouping the Data

To start, we need to group the data by the student ID and aggregate their subjects and the number of tests taken. This can be achieved using the groupby method in Pandas.

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Creating the Grade Column

Next, we need to create a new column called Grade. The criteria for determining the grade is as follows:

If the number of tests in Maths, Science, and History is greater than 5, assign the grade "A".

Otherwise, assign the grade "B".

This can be implemented with a list comprehension:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Mapping Subjects to Tests

Now, we will combine the subjects and their corresponding number of tests into a single dictionary column named Tests. We can achieve this with:

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Cleaning Up the DataFrame

Finally, we can drop the unnecessary columns that we no longer need (i.e., "Subject" and "Number_Of_Tests"):

[[See Video to Reveal this Text or Code Snippet]]

Final Output

After executing all the steps above, the final DataFrame will appear as follows:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By following these steps, you can efficiently group your Pandas DataFrame to map and create new columns based on specific values. This method is not only applicable to student data but can also be adapted for various datasets where similar transformations are required.

Feel free to experiment with this approach on your datasets to gain familiarity with grouping and aggregating data in Pandas!