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Learn how to efficiently process multiple CSV files and calculate student averages using `Pandas` in Python. This guide will walk you through each step!
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Streamline Your Data Analysis: Process Multiple CSV Files with Pandas

In today's data-driven world, being able to process and analyze data efficiently is paramount. A common task in data analysis is handling CSV files, especially when dealing with results or records for multiple subjects or periods. If you have multiple CSV files containing grades for students across different assignments, and you want to calculate their average grades, you're in the right place.

In this guide, we'll walk through the steps of processing three CSV files containing student grades using Pandas, a powerful data manipulation library in Python.

The Problem

Suppose you have three CSV files, each containing student grades for different assignments structured as follows:

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

Your goal is to read these files separately and then compute the average grade for each student, combining all the data into a single output file. Let's break down the solution step by step.

Step-by-Step Solution

1. Reading the CSV Files

First, you will need to load the data from your CSV files using Pandas. Here's how you can do that:

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

2. Combining the Data

Once the data is loaded, you can combine the three DataFrames into one. The best way to do this is by using the append method or using pd.concat. Here’s how you can do this with append:

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

Alternatively, you can achieve this using pd.concat:

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

3. Calculating the Average Marks

Now that you have a single DataFrame (combined_df), you can calculate the average marks for each student by grouping the data by "Student id" and using the mean() function.

Here’s how you can do it:

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

4. Storing the Result

Finally, you can store the resulting DataFrame containing the average grades back to a CSV file:

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

Summary

By following the above steps, you can effectively read, combine, and process data from multiple CSV files using Pandas. This method not only streamlines your data analysis but also allows you to handle larger datasets with ease.

Key Takeaways:

Load your CSV files using pandas.read_csv().

Combine multiple DataFrames using append() or pd.concat().

Group your data by a key (in this case, "Student id") to calculate statistics like the average.

Save your processed data back to a CSV file for further use.

Feel free to adapt the provided code snippets for your specific needs and watch your data processing tasks become more manageable!



With these steps, you should now have a solid understanding of how to handle multiple CSV files with Pandas in Python. Happy coding!