Скачать или смотреть How to Add Rows and Columns to a Pandas DataFrame Efficiently

Learn how to add rows and columns to a Pandas DataFrame by extracting portions of strings from a column. This guide provides two solutions to help you manage your data effectively.
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How to Add Rows and Columns to a Pandas DataFrame Efficiently

When working with data in Python, particularly with the Pandas library, you might encounter a situation where you need to modify your DataFrame by adding new rows or columns. This might involve breaking down strings from one column into various parts.

In this post, we will delve into a specific example where we need to take reviews from a DataFrame and split them into two new columns based on certain parts of the text. Let's explore how you can achieve this effectively!

The Problem

Imagine you have a Pandas DataFrame, correct_X_test, containing a column named review. Each review is a string comprised of multiple words. The goal is to create two new columns:

sub_review_1_i containing portions of the review from the beginning up to some index i.

sub_review_i_n containing the remaining part of the review starting from index i.

For example, if you have a review like this:

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

You would want to extract parts like:

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

The Solutions

Option 1: Store the Sub-Reviews as Lists

In this first option, we will create lists for sub_str_1_i and sub_str_i_n, and each of those lists will be stored in their respective new columns of the DataFrame.

Here's how to implement it:

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

Output:

This code will print the modified DataFrame that includes two lists of extracted sub-reviews.

Option 2: Create New Rows for Every Combination

If your goal is to have a more granular representation, you might want to create new rows for each combination of sub_str_1_i and sub_str_i_n.

To do this, you can utilize the pd.DataFrame.explode method to convert the lists into separate rows:

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

Result:

The exploded DataFrame will provide a row for each combination of sub-reviews, making it easier to analyze or visualize.

Conclusion

Adding rows and columns to a Pandas DataFrame by extracting parts of strings can greatly enhance your data manipulation capabilities. In this guide, we outlined two distinct approaches to achieve this: storing them as lists and exploding them into new rows. Depending on your needs, you can choose the method that best suits your project.



The Pandas library is incredibly powerful, and learning to manipulate DataFrames effectively can lead to significant improvements in your data processing tasks. Happy coding!