I’ve decided to combine into a tuple to more easily keepīecause of this data structure, we need to break the list of tuples into two separate lists.ĭef generalize ( ser, match_name, default = None, regex = False, case = False ): """ Search a series for text matches. One of the big challenge when working withĬonditions and values mismatched. contains ( 'Kwik', case = False, regex = False ), 'Kwik Shop' ) ] contains ( "Bucky's", case = False, regex = False ), "Bucky's Express" ), ( df. contains ( 'Hometown Foods', regex = False, case = False ), 'Hometown Foods' ), ( df. contains ( 'Circle K', regex = False, case = False ), 'Circle K' ), ( df. contains ( 'Quik Trip', regex = False, case = False ), 'Quik Trip' ), ( df. contains ( 'Target Store', regex = False, case = False ), 'Target' ), ( df. contains ( 'Yesway', regex = False, case = False ), 'Yesway Store' ), ( df. contains ( 'Walgreens', regex = False, case = False ), 'Walgreens' ), ( df. contains ( 'CVS', regex = False, case = False ), 'CVS Pharmacy' ), ( df. contains ( 'Kum & Go', regex = False, case = False ), 'Kum & Go' ), ( df. contains ( "Sam's Club", case = False, regex = False ), "Sam's Club" ), ( df. contains ( "Casey's", case = False, regex = False ), "Casey's General Store" ), ( df. contains ( 'Fareway Stores', case = False, regex = False ), 'Fareway Stores' ), ( df. contains ( 'Walmart|Wal-Mart', case = False ), 'Wal-Mart' ), ( df. contains ( "Smokin' Joe's", case = False, regex = False ), "Smokin' Joe's" ), ( df. contains ( 'Central City', case = False, regex = False ), 'Central City' ), ( df. contains ( 'Hy-Vee', case = False, regex = False ), 'Hy-Vee' ), ( df. To highlight how useful it can be for these data exploration scenarios. It’s not required for the cleaning but I wanted Let’s get started by importing our modules and reading the data. Due to the size, youĬan download it from the state site for a different time period. That some of the pandas approaches will be relatively slow on your laptop.įor this article, I’ll be using data that includes all of 2019 sales. This is not bigĭata by any means but it is big enough that it can make Excel crawl. Theĭata set for this case is a 565MB CSV file with 24 columns and 2.3M rows. With that data, you can plan your sales process for each of the accounts.Įxcited about the opportunity, you download the data and realize it’s pretty large. You to use your analysis skills to see who the biggest accounts are in the state. That shows all of the liquor sales in the state. Your territory includes Iowa and there just happens to be an open data set Related Work Generic text cleaning packagesįull-blown NLP libraries with some text cleaningīuilt upon the work by Burton DeWilde for Textacy.For the sake of this article, let’s say you have a brand new craft whiskey that you would If you don't like the output of clean-text, consider adding a test with your specific input and desired output. Pull requests are especially welcomed when they fix bugs or improve the code quality. If you have a question, found a bug or want to propose a new feature, have a look at the issues page. Pip install clean-text from cleantext.sklearn import CleanTransformer cleaner = CleanTransformer ( no_punct = False, lower = False ) cleaner. There is also scikit-learn compatible API to use in your pipelines.Īll of the parameters above work here as well. If you need some special handling for your language, feel free to contribute. It should work for the majority of western languages. So far, only English and German are fully supported. For this, take a look at the source code. You may also only use specific functions for cleaning. "you are right ", replace_with_email = "", replace_with_phone_number = "", replace_with_number = "", replace_with_digit = "0", replace_with_currency_symbol = "", lang = "en" # set to 'de' for German special handling )Ĭarefully choose the arguments that fit your task. Into this clean output: A bunch of 'new' references, including (). For instance, turn this corrupted input: A bunch of ‘new’ references, including (). Preprocess your scraped data with clean-text to create a normalized text representation. User-generated content on the Web and in social media is often dirty.
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