![]() What I have works, I just want to make this cleaner and less time consuming for scaling this as it would be applied in a similar way to the data I have scraped too. 'Company Entry Description', 'Descriptive Date', 'Debit/Credit'] 'Recipient Name', 'Effective Date', 'Account Type', 'Description', The over all desired output should look like this: ['Recipient ID', 'Time', 'Entry Class', 'Amount', 'Reason Code', #this would need to repeat across the second and third line. Print("This is the desired output of Line 1") 'Company Entry Description Descriptive Date Debit/Credit'] Here are the originals: original 1 original 2 original 3 original 4 After processing them with this code: img cv2.imread ('original1.jpg', 0) ret,thresh cv2.threshold (img,55,255,cv2.THRESHBINARY) opening cv2.morphologyEx (thresh, cv2.MORPHOPEN, cv2.getStructuringElement (cv2.MORPHRECT, (2,2))) cv2.imwrite ('result1. 'Recipient Name Effective Date Account Type Description', List_data = ['Recipient ID Time Entry Class Amount Reason Code', # scraped data for column headers initially in a pdf. I just see this being tedious and wondering if there is a better way to go about this. Source code for the library can be found here. Here is a snippet of my code so far to transform the first line. cleantext is a an open-source python package to clean raw text data. I'm having trouble splitting it the way I need to store the column names though. import string text 'Hello i2tutorials provides the best Python and Machine Learning Course' textclean ''.join(i.lower() for i in text if i not in string. Let’s take a tweet for example: I enjoyd the event which took place yesteday & I luvd it The link to the show is It's awesome you'll luv it HadFun Enjoyed BFN GN We will be performing data cleaning on this tweet step-wise. So, we’re about to clean them now using the nltk. In this article, we will be learning various text data cleaning techniques using python. ![]() I'm looking for a more systematic/clean way to transform some text gathered from a pdf that I'm working to convert to a pandas dataframe. textcleaner.remove(text, processors): text: str or bytes (unicode or str for Python 2). In text-data, mostly it contains insignificant words that are not used for the analysis process because they could mess up the analysis score.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |