Скачать или смотреть How to Use apply to Integrate Values Back into Your Original DataFrame in Pandas

Learn how to efficiently integrate values calculated with `apply` back into your original Pandas DataFrame while maintaining clean and readable code.
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How to Use apply to Integrate Values Back into Your Original DataFrame in Pandas

Working with Pandas in Python can be a bit tricky, especially when you're trying to manipulate and aggregate data across different columns. If you’ve ever found yourself in a position where you create a new DataFrame, but want to bring those computed values back into your original DataFrame, you’re not alone!

In this guide, we’ll explore a common scenario where you might want to create a new column based on the values in existing columns and then merge that new data back into your main DataFrame. We'll break it down step by step, using a simple data example to highlight the process.

Problem Statement

Let's say you have the following dataset:

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

Your goal is to create a column c that calculates an average based on the values of column a. You might initially think of applying custom functions to subsets of your DataFrame. Here's how you might start:

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

While this method gives you the desired results in a temporary DataFrame (dataSetToProcess), it does not reflect those values in your original DataFrame (df).

The Solution: Merging DataFrames

Instead of processing the values separately, a more efficient way is to compute the necessary values and then merge them back into the original DataFrame. Here’s how to do it:

Step 1: Create Your Main DataFrame

First, let’s define our main DataFrame:

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

Your DataFrame df1 now looks like this:

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

Step 2: Calculate Your Values

Next, let’s assume we derive the average for column b based on unique values in column a:

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

This will create a new DataFrame (dataSetToProcess):

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

Step 3: Merge the DataFrames

Now, we can merge this calculated DataFrame back into the original, allowing us to retain all original values while adding the computed average:

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

Your final DataFrame now showcases the new computed column:

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

Final Thoughts

This method allows you to create new calculated columns based on existing ones, while keeping your original DataFrame intact and organized. The merge function is a powerful tool that facilitates combining related data easily.

In summary:

Use groupby to perform calculations on subsets of your DataFrame.

Use reset_index() to format your results as a new DataFrame.

Finally, apply merge() to integrate your computed values back into your original DataFrame.

By following these steps, you'll enhance your data manipulation skills in Pandas and ensure your DataFrames remain robust and comprehensive!

Remember, mastering Pandas can greatly improve your data analysis and manipulation capabilities in Python. Happy coding!