Extracting Substrings with Regex in Python Pandas Multiline Strings

Learn how to effectively extract substrings from multiline texts using regex with `Python Pandas`. Discover step-by-step solutions to common challenges involving new line characters.
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Extracting Substrings with Regex in Python Pandas Multiline Strings

Dealing with multiline text data in Python Pandas can often feel overwhelming, especially when you're trying to extract specific information using regex. In today's post, we are going to tackle a common issue: how to match a substring and extract it along with everything up until a newline character (\n) or carriage return (\r).

The Challenge

Suppose you have a DataFrame column containing multiline strings, similar to the following example:

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

In this scenario, you want to extract the substring WHATIWANT: - this is cool ! = . a1^--% but are facing a roadblock due to the presence of both \n and \r characters at the end of your match. You might have tried using a regex pattern like this one:

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

However, this approach may still leave unwanted newline characters in the output, producing an undesired result:

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

The Solution

Step 1: Import Required Libraries

Make sure you have Pandas and re (regular expression library) imported in your script.

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Step 2: Create Your DataFrame

For the purpose of this demonstration, let's create a DataFrame that mimics your data:

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

Now, you can use the following regex pattern to match the desired substring and extract it correctly without additional newline characters:

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

Understanding the Regex

^ indicates the start of the line.

(WHATIWANT:.*?) captures everything starting with WHATIWANT: followed by any characters (non-greedy), which means it will stop as soon as it can.

\s*$ looks for any whitespace followed by the end of the line.

Step 4: Check the Result

To see your results in action, print out the DataFrame:

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You will successfully have extracted:

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

Without any trailing newline or carriage return characters.

Conclusion

Now you're well-equipped to handle multiline string extraction with ease in Python Pandas! Happy coding!