Скачать или смотреть Filtering Rows in Pandas DataFrame Using regex to Exclude Client Codes

Discover how to effectively filter rows in a Pandas DataFrame based on regex criteria, ensuring you understand how to exclude specific ClientCodes from FileNames.
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How to Filter Rows in a Pandas DataFrame Using Regex

When working with data in Python, Pandas is an invaluable tool that allows you to manipulate and filter data efficiently. Sometimes you may encounter a situation where you need to filter rows based on certain conditions, specifically using regular expressions (regex). In this guide, we will address a common problem: how to filter rows in a DataFrame where a specific column’s values should not appear in the response of another column.

The Problem

Imagine you have a DataFrame with two columns, ClientCode and FileName. Your task is to filter out rows where the ClientCode is not included in the FileName field. For example, let’s consider the following DataFrame:

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

Upon inspection, the goal is to return rows where the ClientCode is not found in the FileName, producing the following resulting DataFrame:

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

Solution Overview

The good news is, filtering these rows in Pandas is straightforward using built-in functions. Below, we’ll detail the steps necessary to achieve this filtering with a straightforward expression.

Step-by-Step Solution

Step 1: Import the Necessary Libraries

Before beginning, ensure you have Pandas imported. You can do this with the following line of code:

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Step 2: Create Your DataFrame

Create the DataFrame using the provided sample data:

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Step 3: Apply the Filtering Criteria

To filter rows where the ClientCode is not found in the FileName, utilize the following code:

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

Breaking Down the Code:

df['FileName'].str.split('--', expand=True)[0]: This splits the FileName at the '--' and retrieves the first part, which is the code.

.astype(int): Converts the split string values into integers for comparison.

df['ClientCode'].eq(...): Compares the ClientCode column with the extracted values from FileName.

~: This operator negates the condition, returning rows where the ClientCode is not equal to the extracted values.

Step 4: Result

When you print filtered_df, you will get:

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

These are the rows that meet the criteria of having ClientCode not present in the FileName.

Conclusion

Filtering rows based on conditions in a Pandas DataFrame can significantly enhance your data analysis capabilities. Utilizing the power of regex and the various functions provided by the Pandas library allows you to refine your datasets efficiently. The method shared in this post is just one way to achieve this filtering; the versatility of Pandas means there are often multiple approaches to solve a problem.

Now, you are equipped to filter rows based on your desired conditions. Happy coding!