Скачать или смотреть How to Remove Suffix from a Pandas DataFrame or Series in Python

Learn how to efficiently remove unwanted suffixes from your data using Pandas in Python and troubleshoot common errors.
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This video is based on the question https://stackoverflow.com/q/65397315/ asked by the user 'Tickets2Moontown' ( https://stackoverflow.com/u/14443410/ ) and on the answer https://stackoverflow.com/a/65397516/ provided by the user 'Prateek Jain' ( https://stackoverflow.com/u/14849533/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Removing Suffix from a Pandas DataFrame or Series in Python

When working with datasets in Python, particularly using the Pandas library, you may encounter situations where you need to clean or manipulate string data. One common issue is the need to remove unwanted suffixes from the values in a DataFrame or Series. For instance, you might want to strip off the '00' suffix from a list of phone numbers or IDs as shown below.

Example Dataset

Consider the following dataset, which illustrates a series of numbers:

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

The Problem

When attempting to remove the '00' suffix from the values in your DataFrame, you might use code like this:

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

Unfortunately, you may encounter an error:

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

This issue arises because the removesuffix method is not available in earlier versions of Pandas. If you experience this error, don’t worry! There’s a straightforward solution to achieve your goal.

Solution: Remove the Suffix Manually

Instead of using the unavailable removesuffix method, you can apply a more manual approach to achieve the desired results. Below is a step-by-step guide on how to remove the '00' suffix from your DataFrame.

Step-by-Step Code

Create Your Data: Start by creating a list with your initial dataset.

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Initialize an Empty List: Prepare an empty list to store the modified values.

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Loop Through the Data: For each value in your original list, convert it to a string and check if it ends with '00'.

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

View the Result: Your new list b will now hold the modified values without the '00' suffix.

Final Code Example

Here’s the complete code wrapped in a single snippet:

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

Conclusion

While the Pandas library offers a rich set of functionalities for data manipulation, sometimes you may encounter methods that are unavailable or unsupported in your version. By understanding how to loop through your data and manipulate string values directly, you can effectively manage and clean your datasets.

Feel free to adapt this approach for any similar needs in your data processing tasks! Happy coding!