Скачать или смотреть Understanding the Difference Between append and concat in Python Pandas

Discover how to effectively combine multiple Excel files using Python's Pandas library. Learn why your `append` method may lead to an empty DataFrame and how to correctly use `concat` for a successful result.
---
This video is based on the question https://stackoverflow.com/q/64451723/ asked by the user 'roamingseaside' ( https://stackoverflow.com/u/14459926/ ) and on the answer https://stackoverflow.com/a/64451892/ provided by the user 'Michael Szczesny' ( https://stackoverflow.com/u/14277722/ ) 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: Python append versus concat with multiple files in a "For Loop"

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.
---
Understanding the Difference Between append and concat in Python Pandas

When working with data analyses in Python, particularly with multiple files, a common question arises: how do you properly combine data from multiple sources? In this guide, we will explore the nuances between the append and concat methods in Pandas, especially in the context of loading multiple Excel files. This exploration will help you understand why one approach may work while another yields an unexpected result.

The Problem: Loading Multiple Excel Files

Suppose you are tasked with loading numerous Excel files from a government website into a single DataFrame for analysis. You might think that using a simple loop with the append method will suffice. However, the first attempt may leave you with an empty DataFrame despite what seems like a correct approach.

Initial Code Example

Here’s how the first code may look:

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

As expected, you might end up with an empty DataFrame. Let’s break down why this occurs.

Why the First Attempt Yielded an Empty DataFrame

The main issue with the first code snippet is how the append method was utilized. In the statement:

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

This operation creates a new DataFrame that contains the original df_list with df added, but it does not modify df_list in place. Instead, it returns the new DataFrame and since nothing was assigned back to df_list, the original DataFrame remains unchanged, leading to the output of an empty DataFrame.

Key Takeaway:

The append method does not modify the DataFrame in place; you need to assign the result back to a variable:

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

The Solution: Using concat to Combine DataFrames

In your second attempt, you discovered a more effective way to combine the data using a list to collect the DataFrames and then applying concat:

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

This approach works successfully for several reasons:

Reasons Why concat Works Better

List Storage: By collecting all DataFrames into a list (df_list), each DataFrame can be easily accessed and concatenated later.

Efficient Combination: The pd.concat() function can efficiently combine all DataFrames stored in the list into a single DataFrame.

Simplicity: Applying transformations like renaming columns becomes straightforward when handled before concatenation.

Benefits of Using concat

Performance: For large datasets or numerous files, concat is faster as it's designed for combining DataFrames.

Flexibility: You can choose various axis options and control how to join the DataFrames.

Conclusion: Choosing the Right Method for Data Management

In summary, it's essential to understand the implications of how data is appended or concatenated in Pandas. The append method can be misleading if not assigned properly, leading to empty outputs, while concat provides a robust way to bring multiple DataFrames together seamlessly. For any data analysis workflow involving multiple sources, emphasizing clarity and efficiency in code can greatly enhance your results.

By mastering these techniques, you're on your way to becoming proficient in managing data with Python and Pandas!