Скачать или смотреть How to Append to a List of DataFrames with Correct Object Type in Pandas

Learn how to effectively handle dataframes in pandas by avoiding dynamic variable naming and using lists or dictionaries for successful concatenation.
---
This video is based on the question https://stackoverflow.com/q/62257926/ asked by the user 'zip' ( https://stackoverflow.com/u/13704775/ ) and on the answer https://stackoverflow.com/a/62258620/ provided by the user 'Serge Ballesta' ( https://stackoverflow.com/u/3545273/ ) 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: Append to a list of dataframes with correct object type

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 Append to a List of DataFrames with Correct Object Type in Pandas

When working with multiple Excel files in Python using the pandas library, you may come across a common situation: needing to read each file into a DataFrame, rename them for identifying purposes, and then concatenate them into a single DataFrame. This use case can sometimes lead to frustration, especially when an inappropriate method is used. In this post, we’ll explore the pitfalls of dynamic variable names and provide the correct approach to effectively store and concatenate dataframes.

The Problem

Imagine you have several Excel files in a folder, and each file contains valuable data. You want to load these files into separate DataFrames, rename them slightly (perhaps adding a date stamp based on the filename), and then merge them all into one comprehensive DataFrame.

However, if you attempt to assign dynamic variable names using exec and then manage them in a list, you may encounter errors, such as:

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

This error indicates that what you thought was a collection of DataFrames is actually just a list of strings.

Understanding the Solution

Instead of using dynamic variable names—which can lead to hard-to-debug issues—there are two effective solutions:

Using a Dictionary

Using a Simple List

Let's break down both methods.

Method 1: Using a Dictionary

Using a dictionary allows you to store your DataFrames with meaningful keys, making it easier to reference them later. Here’s how you can do this:

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

Advantages of This Method:

Easy to manage and access DataFrames using their date keys.

Avoids the complexity and risks associated with dynamic variable names.

Method 2: Using a Simple List

If you don’t need to reference the DataFrames by name afterwards, you can simply use a list to accumulate them. Here’s how:

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

You could also streamline this using a list comprehension:

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

Advantages of This Method:

Simplifies the code significantly.

Directly collects batch DataFrames in a single line.

Conclusion

Both methods outlined above avoid the pitfalls of dynamic variable naming and instead promote clarity and efficiency in your code. Whether you use a dictionary or a simple list, you can easily load and concatenate multiple DataFrames, leading to a more robust data processing workflow.

When dealing with data in pandas, emphasizing proper data structures not only streamlines your code but also minimizes potential bugs and confusion. Embrace these strategies for better data handling!



With these techniques in hand, you can efficiently append to a list of DataFrames with the correct object types and avoid common pitfalls in the data loading process.