Скачать или смотреть Resolving TypeError When Running Function on Pandas DataFrame

Learn how to fix the `TypeError` encountered when applying custom functions in Pandas DataFrames, specifically addressing issues with list and float comparisons.
---
This video is based on the question https://stackoverflow.com/q/68738462/ asked by the user 'Nandula' ( https://stackoverflow.com/u/11800667/ ) and on the answer https://stackoverflow.com/a/68738566/ provided by the user 'Andrej Kesely' ( https://stackoverflow.com/u/10035985/ ) 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: TypeError when trying to run function on 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.
---
Solving TypeError When Running Functions on Pandas DataFrames

If you've ever worked with Pandas, the powerful Python data analysis library, you might have encountered an error message that left you scratching your head. One common issue is receiving a TypeError when applying a function to a DataFrame. This post dives into one such scenario where a user attempts to classify data based on throughput values but runs into problems. Let's break down the issue and how to resolve it effectively.

The Problem

You have a Pandas DataFrame named month_values that holds throughput values for various areas. Here’s an example of how your DataFrame looks:

AreaThrpt_AThrpt_BThrpt_CThrpt_DGalle9581.536790.668340.495940.89Colombo5948.014245.627084.933915.93You want to classify each area based on the throughput values using a custom function. However, when you apply this function using the apply() method, you encounter the dreaded TypeError, which indicates that there is an unsupported operation between a list and a float.

Understanding the Error

The error message you received is:

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

This indicates that somewhere in your function, a comparison is being made incorrectly, specifically between a list and a float, which isn’t allowed in Python. The error points to how you are trying to access DataFrame columns in your defined function.

The Solution

To solve this problem, let's carefully examine the custom function you've created. The function is structured to classify areas as 'A', 'B', 'C', or 'D' based on their throughput values. However, there is a small mistake in how you're accessing the DataFrame columns.

Incorrect Code Snippet

You wrote this part of the condition:

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

Notice the brackets around ['Thrpt_A'], which erroneously treats it as a list instead of a proper DataFrame column. This leads to the TypeError when making comparisons.

Corrected Code Snippet

Here’s the corrected part of your code where you should NOT use square brackets around column names:

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

Full Corrected Function

Integrating this change, your function should look like this:

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

Conclusion

With this correction made, applying your function to the DataFrame should work without throwing any errors. In essence, always ensure that you access DataFrame columns without using list brackets when performing conditional checks. Once you’ve made the necessary changes, run your command again:

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

Final Thoughts

Using Pandas can sometimes lead to frustrating errors, but understanding the underlying DataFrame structure and how to manipulate it correctly can save you time and trouble. Happy coding!