Python: Accumulating Student Assessments using pd.groupby

In this article, we will delve into a common problem in data analysis involving pandas, where we need to accumulate scores for each student based on their assessment performance. We’ll explore how to use the pd.groupby function to achieve this and provide insights into its usage.

Introduction

The power of pandas lies in its ability to efficiently handle structured data, making it a go-to library for data analysis tasks in Python. One such task is accumulating scores for each student based on their assessment performance. This can be achieved by leveraging the pd.groupby function, which groups similar elements (in this case, students) and performs operations on them.

Dataset Overview

Let’s assume we have a dataset that looks like this:

We want to accumulate extra columns based on the number of assessments completed by each student.

Solution Approach

To solve this problem, we can use the pd.groupby function along with some clever indexing and data manipulation techniques. Here’s a step-by-step guide:

Step 1: Create a New Column for Assessment Count

First, let’s create a new column that counts the number of assessments completed by each student.

import pandas as pd

# Sample dataset
data = {
    'id_student': [1, 2, 3, 4, 5],
    'id_assessment': ['A', 'B', 'A', 'C', 'B'],
    'score': [90, 85, 95, 80, 90]
}

df = pd.DataFrame(data)

# Create a new column for assessment count
df['assessment_count'] = df['id_assessment'].value_counts().add_suffix('_count')

Step 2: Sort and Group by Assessment Count

Next, let’s sort the dataframe by the assessment_count column in ascending order. This will ensure that we group students with fewer assessments together.

# Sort the dataframe by assessment count
df = df.sort_values('assessment_count').reset_index(drop=True)

Step 3: Accumulate Scores using pd.groupby

Now, let’s use pd.groupby to accumulate scores for each student based on their assessment performance.

# Group students by assessment count and calculate total score
grouped_df = df.groupby('assessment_count')['score'].sum().reset_index()

# Rename columns for clarity
grouped_df.columns = ['assessment_count', 'total_score']

Step 4: Insert Scores into Relevant Assessments

Finally, let’s insert scores into the relevant assessments using some clever indexing.

# Create a new column for assessment mapping
grouped_df['id_assessment'] = grouped_df.apply(lambda row: row['assessment_count'] + chr(ord('A') + int(row['assessment_count']) - 1), axis=1)

# Insert scores into the dataframe
df = df.merge(grouped_df, on='id_student')

Example Output

Here’s an example of what our final output might look like:

As you can see, the id_assessment column now contains the assessment count plus the corresponding letter (A, B, or C). The scores are also inserted into their relevant assessments.

Conclusion

In this article, we explored a common problem in data analysis involving pandas and demonstrated how to use pd.groupby to accumulate scores for each student based on their assessment performance. We provided a step-by-step guide on how to achieve this using indexing, grouping, and data manipulation techniques.

By following these steps, you can efficiently handle structured data and extract valuable insights from your datasets.

Last modified on 2024-02-29