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Learn why your Pandas DataFrame displays incorrect lists and discover the proper method to update multiple indexes using `df.at`.
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How to Properly Use df.at for Updating Multiple Indexes in Pandas DataFrame

Pandas is a powerful Python library for data manipulation and analysis. One common task you may encounter is updating values in a DataFrame using the df.at method. However, issues can arise when working with lists in iterations. In this guide, we’ll explore a scenario where you might be confused about how your DataFrame is being updated, and we’ll guide you through the solution.

The Problem Statement

Imagine you've written the following code to create a DataFrame and populate a column with lists in iterations. Here’s a simplified version of what you're working with:

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

Expected Output vs. Actual Output

You are expecting the output of the 'test' column in your DataFrame to contain the following format:

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

Instead, you see:

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Why This Happens

The reason for this unexpected behavior is that you're continuously referencing the same list lista. When you update a value in df.at, it updates the reference rather than creating a new entry. As a result, every row ends up showing the last list generated in your iteration.

The Solution

To achieve your desired output, you need to store a copy of the list in each iteration rather than the reference to the list itself. This can be accomplished with the copy() method.

Fixed Code Example

Here’s how you can modify your code to see the intended results:

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

What Changes?

Renaming the variable: We maintain the name lista, but crucially use lista.copy() when assigning it to the DataFrame. This ensures that each row in 'test' receives a distinct list that reflects its state at the time of assignment.

Expected Output After Fixing

After implementing the change, running the fixed code will yield:

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

Conclusion

By understanding how references work in Python, particularly with lists in Pandas DataFrames, you can avoid common pitfalls when updating your data structures. Always ensure that you create copies when needed to maintain data integrity! Happy coding!