![]() ![]() findall ( regex, str ( text )) string = 'The Proclaimers would walk 500 miles and then walk 500 more.' numbers = extract_numbers ( string ) numbers Extracting telephone numbers Import re def extract_numbers ( text ): regex = "+" return re. All this does is tells re to look for continuous strings that start with a # and are followed by a unicode string (\w+), which can contain letters from A-Z, numbers from 0-9 or an underscore. It’s that tiny expression which does the magic. To make it a bit easier to see the regex itself I’ve assigned the regex #(\w+) to a variable and passed it as an argument. In the below example we’ve loaded the re module and written a simple function called extract_hashtags() which takes a text string as its argument and returns a Python list containing any hashtags it finds using the findall() function of re. Practical examples of commonly used Python regexes Extracting hashtags Let’s look at some practical examples and turn them into functions you can re-use in your own projects. Return a Python list split at each match point Return a Match object if the match is found in the string The most commonly used functions are: Function From identifying whether a string contains a particular value to returning a list of the matches found and much more. You can pass a regex to a number of different re functions (and Pandas functions) to allow you to achieve a range of specific data wrangling goals. For example, the regex + will find any numbers with one or more continuous digits from 0 to 9. The regular expression itself consists of a specially constructed list of characters that tell re what pattern to find, or match, in the text. Regular expressions, or regexes, are part of Python itself but to use them you need to specify the re module by typing import re at the top of your Python file. As you’re not expected to memorise these, do expect to go and look up the precise way to write the ones you may need for any future projects. To avoid blowing your mind and trying to cover everything, let’s just look at some common uses for regular expressions in data science. “Some people, when confronted with a problem, think, “I know, I’ll use regular expressions.” Now they have two problems. Few of the developers I’ve worked with have ever been fluent in them, with most happily resorting to using a cheat sheet instead of memorising their complex nuances. They’re very powerful and extremely useful to understand, but they’re also rather confusing and can be one of the most baffling things to learn in data science. Regular expressions are used for pattern matching in programming, allowing you to identify or extract very specific pieces of text from a string or document.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |