Скачать или смотреть How to Loop Through Multiple DataFrames to Modify Them in Python Pandas

Learn how to effectively loop through a list of DataFrames in Python to create dummy variables and concatenate them seamlessly for better data analysis.
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This video is based on the question https://stackoverflow.com/q/67295227/ asked by the user 'Arihant Jain' ( https://stackoverflow.com/u/15782149/ ) and on the answer https://stackoverflow.com/a/67295340/ provided by the user 'Mustafa Aydın' ( https://stackoverflow.com/u/9332187/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Looping to Modify Multiple DataFrames in a List

When working with data in Python, especially with DataFrames, you may find yourself needing to modify multiple DataFrames at once. A common scenario is creating dummy variables from a categorical column. In this post, we will address how to loop through multiple DataFrames and successfully modify them, ensuring that the changes reflect in your DataFrames.

The Problem

Imagine that you have five separate DataFrames, each containing a column called Fuel_Type. Your goal is to transform this categorical variable into multiple dummy variables for analysis purposes. Here is the snippet of code you may have tried:

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

While this code doesn’t produce any errors, the original DataFrames remain unchanged after the loop. You are left without the expected dummy variables in your DataFrames, which can be frustrating when you’re trying to prepare your data for analysis.

The Solution

To effectively modify the DataFrames, we need to ensure that the updates apply to the original DataFrames. This can be accomplished by wrapping the operations in a function and then reassigning the results back to the original DataFrames. Here’s how to do it:

Step 1: Create a Function

We start by creating a function that takes a DataFrame and returns a modified version of that DataFrame with the dummy variables added. Here’s an example:

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

Step 2: Apply the Function to a List of DataFrames

Next, we will create a list of the DataFrames and apply our function to each one. Here’s how you can do that:

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

By wrapping the operations in the add_dummies function, we ensure that we do not lose the reference to the original DataFrames when we modify them.

Why did the Original Code Fail?

The original attempt to modify the DataFrames failed because of the way Python handles variable assignments. When you reassign k inside the loop, you are effectively pointing k to a new object. Therefore, the changes made to k do not reflect back on the list of DataFrames [df1, df2, df3, df4, df5]. Using a function not only encapsulates the process but also allows us to return the modified DataFrame back to the original variables.

Conclusion

By following these steps, you can easily modify multiple DataFrames in Python using Pandas to include dummy variable columns from a specified categorical column. This approach not only simplifies your code but also ensures that your data preparation is efficient and effective.

Now, when you run your code, you can expect all five DataFrames to be updated with the new dummy variable columns created from the Fuel_Type column. Happy coding!