Скачать или смотреть How to Efficiently Replace the iterrows Function in a Pandas DataFrame

Discover how to replace the `iterrows` function in a Pandas DataFrame for improved performance and cleaner code. Learn through a step-by-step guide with examples.
---
This video is based on the question https://stackoverflow.com/q/63871412/ asked by the user 'hamza chenni' ( https://stackoverflow.com/u/11089078/ ) and on the answer https://stackoverflow.com/a/63871673/ provided by the user 'RichieV' ( https://stackoverflow.com/u/6692898/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: how to replace itrerows function in pandas dataframe

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Efficiently Replace the iterrows Function in a Pandas DataFrame

When working with a Pandas DataFrame in Python, you may often find yourself using the iterrows function to iterate over rows. However, while iterrows is convenient, it can be quite slow, especially with larger datasets. In this guide, we'll discuss how to replace the iterrows function with a more efficient solution.

The Problem with iterrows

Using iterrows can be tempting for those who want to perform operations on each row, but it comes with performance drawbacks, including:

Increased processing time: Since iterrows processes one row at a time, it can be inefficient when handling large DataFrames.

Complexity: Code written with loops tends to be less clean and harder to read compared to vectorized solutions.

Example of Using iterrows

Consider the following code snippet where the iterrows function is used to create a new column 'image' in new_frame based on another column 'Code':

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

The Solution: Vectorized Operations

Instead of using iterrows, you can take advantage of Pandas' ability to handle vectorized operations. This is not only faster but also leads to cleaner and more maintainable code.

Step-by-Step Guide to Replace iterrows

Understand the Requirements
You want to create a new column image in the dataframe that combines image_folder and the value in the Code column, formatted in a specific way.

Use Vectorization
The goal is to apply a transformation across an entire column without the need for an explicit loop. For your case, you can achieve this using string concatenation as follows:

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

Explanation of the Solution:

new_frame['image']: This creates a new column called 'image' in the new_frame DataFrame.

image_folder: Assumes this variable is a string that contains the path or folder related to images.

new_frame['Code'].astype(str): Converts the values in the 'Code' column to strings to ensure proper concatenation.

'_1.jpg': Appends the file extension to the constructed image path.

Benefits of the New Approach

By replacing iterrows with the vectorization method described above, you'll enjoy several advantages:

Improved Performance: Faster execution time, particularly for large datasets.

Cleaner Code: It's more concise and easier to read, making it simpler for others (and yourself) to understand your logic later on.

Less Verbosity: No need to set values by index, eliminating the overhead involved in managing row and index operations.

Conclusion

Using vectorized operations in place of iterrows can lead to significant improvements in both performance and code readability. Next time you find yourself reaching for iterrows, consider whether a vectorized approach might be a better fit for your needs. Happy coding!