Please don't forget the email when you enter !!!!!' > docx = nt.TextFrame(mytext) > mytext = 'This is the mail ,our WEBSITE is 😊. Alternatively you can also remove all known punctuations from a text.You remove the most common punctuations such as fullstop,comma,exclamation marks and question marks by setting most_common=True which is the default.You can also remove the other non-needed items accordingly.' > clean_text(mytext,puncts= False,stopwords= False) 'mail website ' > clean_text(mytext,puncts= False) You can choose to remove or not remove punctuations by setting to True/False respectively You can remove punctuations,stopwords,urls,emojis,multiple_whitespaces,etc by setting them to True. > mytext = 'This is the mail ,our WEBSITE is 😊.' > clean_text(mytext) > from neattext.functions import clean_text A simplified method for cleaning text by specifying as True/False what to clean from a text.Clean text by removing emails,numbers,stopwords,emojis,etc.'This is the mail ,our WEBSITE is and it will cost $100 to subscribe.' Clean Text > result = docx.remove_emails().remove_urls().remove_emojis() > docx_df = nt.TextFrame().read_txt( 'file.txt')Ĭhaining Methods on TextFrame > t1 = 'This is the mail ,our WEBSITE is 😊 and it will cost $100 to subscribe.' > docx = TextFrame(t1) Alternatively you can instantiate a TextFrame and read a text file into it.'this is the mail examplegmailcom our website is httpsexamplecom ' > docx.remove_puncts() 'this is the mail ,our website is 😊.' > docx.normalize(level= 'deep') > mytext = 'This is the mail ,our WEBSITE is 😊.' > docx = nt.TextFrame(text=mytext)ħ3 > # Scan Percentage of Noise(Unclean data) in text > d.noise_scan()īasic NLP Task (Tokenization,Ngram,Text Generation) > docx.word_tokens()īasic Text Preprocessing > docx.normalize()

This is the simplest way for text preprocessing with this library alternatively you can utilize the other classes too.It inherits the benefits of the TextCleaner and the TextMetrics out of the box with some additional features for handling text data.TextPipeline: combine multiple functions in a pipeline.TextExtractor: extract unwanted text data.TextCleaner: remove or replace specifics.TextFrame : a frame-like object for cleaning text.NeatText offers 5 main classes for working with text data.convert the already known solution for cleaning text into a reuseable package.Reducing repetition of using the same code for text preprocessing.NeatText:a simple NLP package for cleaning textual data and text preprocessing.

Better solution than textify