I have a text file that needs to be

CodePudding user response：

First, create the dictionary containing these headers. Then read each line in this text file using commands like readline([n]). If your characters are special characters such as commas or spaces. Put these values ​​in the keys in the dictionary. Then you can create a data frame by converting the dictionary to csv file easily with the pandas library of python. You can read the documentation of Pandas.

CodePudding user response：

import re

with open('/Users/archive/combined_data_1.txt') as f:  
  for line in f:
    result = re.search(r"^(\d ),(\d ),(\d{4}-\d{2}-\d{2})"/gm, line)
    result2 = {
      if re.search(r"(^\d ):", line) is not None:
        movie_id = re.search(r"(^\d ):", line).group(1)
      elif result:
        customer_id = result.group(1)
        rating = result.group(2)
        date = result.group(3) 
     
    
        data_list = [customer_id, rating, date, movie_id]    #data that you want. you can store it as csv file
        df1 = pd.DataFrame(data_list)
        df1.to_csv('combineddata1.csv')

      else:
        continue }

Im getting the following syntax error:

Error

CodePudding user response：

import re

with open('text.txt') as f:  #replace text.txt with your text file path
  for line in f:
    result = re.search(r"^(\d ),(\d ),(\d{4}-\d{2}-\d{2})"gm, line)
    if re.search(r"(^\d ):", line) is not None:
      movie_id = re.search(r"(^\d ):", line).group(1)
    elif result:
      costomer_id = result.group(1)
      rating = result.group(2)
      date = result.group(3)

      data_list = [costomer_id, rating, date, movie_id]    #data that you want. you can store it as csv file
      # YOUR CODE

    else:
      continue