![]() Sample_text = "Sample text 123 !!!! Haha. In cases where you want to remove all characters except letters and numbers, you can use a regular expression. Remove all special characters and punctuation # After: Sample text with numbers and words # Before: Sample text with numbers 123455 and words !!! Here's how you use it: sample_text = "Sample text with numbers 123455 and words !!!" isalpha() method of Python strings will come in handy in those cases. Sometimes, you'd like to remove non-alphabetic characters like numbers or punctuation. # After: I want to keep this one: 10/10/20 but not this one # Before: I want to keep this one: 10/10/20 but not this one 222333 isdigit() method of strings: sample_text = "I want to keep this one: 10/10/20 but not this one 222333"Ĭlean_text = " ".join() # Side effect: removes extra spaces Using a regular expression gets a bit trickier. For instance, when you want to remove numbers but not dates. There are cases where you might want to remove digits instead of any number. But don't remove this one H2O"Ĭlean_text = re.sub(r"\b \b\s*", "", sample_text) You can use a regular expression for that: import re In some cases, you might want to remove numbers from text, when you don't feel they're very informative. "Yes, you got it right!\n This one too\n" "This TEXT needs \t\t\tsome cleaning!!!.", Take a look at the example below: import re If you're using pandas, you can apply that function to a specific column using the. Then, you can use that function for pre-processing or tokenizing text. I'd recommend you combine the snippets you need into a function. Then, you can check the snippets on your own and take the ones you need. ![]() In the next section, you can see an example of how to use the code snippets. They're based on a mix of Stack Overflow answers, books, and my experience. I'll continue adding new ones whenever I find something useful. This article contains 20 code snippets you can use to clean and tokenize text using Python. Cleaning and tokenizing text (this article).I'm starting with Natural Language Processing (NLP) because I've been involved in several projects in that area in the last few years.įor now, I'm planning on compiling code snippets and recipes for the following tasks: So, finally, I've decided to compile snippets and small recipes for frequent tasks. At this point, I don't know how many times I've googled for a variant of "remove extra spaces in a string using Python." I end up copying code from old projects, looking for the same questions in Stack Overflow, or reviewing the same Kaggle notebooks for the hundredth time. Remove all special characters and punctuationĮvery time I start a new project, I promise to save the most useful code snippets for the future, but I never do.Remove extra spaces, tabs, and line breaks.Remove cases (useful for caseles matching).Photo by Jasmin Sessler / Unsplash Table of Contents In this article, you'll find 20 code snippets to clean and tokenize text data using Python. If you need to strip extra spaces or other non-printing characters, see the TRIM and CLEAN functions.įinally, the SUBSTITUTE function will let you remove characters with "search and replace" type functionality.The first step in a Machine Learning project is cleaning the data. ![]() You can also use the MID function in more complicated situations. The formulas in C8 and C9 show how to use the LEFT and RIGHT functions to strip non-numeric characters from a text value before it's converted to a number. ![]() If a cell contains non-numeric characters like dashes, punctuation, and so on, you'll need to remove those characters before you can convert to numbers. In the example shown, C7 uses this formula. This has the same functionality as VALUE above. This forces Excel to try and convert the text value to a number to handle the math operation. Add zero insteadĪnother common trick is to simply add zero to the text value with a formula like this: =A1 0 If it doesn't work, you'll get a #VALUE error. In simple cases, it will just work and you'll get a numeric result. The VALUE function will try to "coerce" a number stored as text to a true number. This means if you try to SUM column A, you'll get a result of zero. In this example, the values in column A are "stored as text".
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