Extracting the First Numerical Value After Any Token in Python Using Regex

Learn how to extract the first numerical value that occurs after a specific token in Python text using regex. Follow our step-by-step guide! --- This video is based on the question https://stackoverflow.com/q/74257178/ asked by the user 'Ruchit' ( https://stackoverflow.com/u/13952588/ ) and on the answer https://stackoverflow.com/a/74257260/ provided by the user 'Nick' ( https://stackoverflow.com/u/9473764/ ) 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: Extracting first numerical value occuring after some token in text in python Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/licensing The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Extracting the First Numerical Value After Any Token in Python Using Regex When you're working with text data in Python, you often need to extract specific pieces of information based on certain patterns. One common task is extracting numerical values that appear after a specific phrase, or token. For instance, you might have sentences like these: "A company must maintain a minimum tangible net worth of $100000000 and leverage ratio of 0.5" "Minimum required tangible net worth the firm needs to maintain is $50000000." In this post, we'll explore how to extract numerical values that occur after the phrase "tangible net worth" using Python's built-in re module, specifically focusing on the use of regular expressions (regex). Understanding the Problem The main challenge is to extract the immediate numerical value that follows a specific keyword or phrase amidst other text. We want to ensure that only the first number encountered after our token of interest is captured, even if there are other numbers present in the sentence. Sample Input Sentences: "A company must maintain a minimum tangible net worth of $100000000 and leverage ratio of 0.5" "Minimum required tangible net worth the firm needs to maintain is $50000000." Desired Output: [[See Video to Reveal this Text or Code Snippet]] Crafting the Regex Pattern To achieve our goal, we can use the following regex pattern: [[See Video to Reveal this Text or Code Snippet]] Breakdown of the Regex: tangible net worth: Matches the exact phrase. **D***: Matches any non-digit characters (like spaces or symbols) that may appear after the phrase. (d+): Captures one or more digits, which is the number we want to extract. This pattern will help us skip any non-numeric characters that may be present before the actual number. Implementing the Solution Below is a step-by-step implementation using the regex we crafted. We will create a list of sentences, apply the regex, and store the result in a dictionary. Python Code: [[See Video to Reveal this Text or Code Snippet]] Expected Output When the above code is executed, you should expect the following output: [[See Video to Reveal this Text or Code Snippet]] This output confirms that the first numerical values after our specified token have been successfully extracted. Conclusion Using regex with Python's re module is a powerful way to manipulate and extract specific patterns from text. In this case, we focused on extracting the monetary value that appears after the phrase "tangible net worth". Remember to always ensure that your regex pattern is tailored to your specific input to avoid unexpected results. By following this guide, you'll be able to efficiently extract valuable information from text data in your Python projects. With this knowledge, you can now apply similar techniques to extract other patterns from your text data as needed. Happy coding!