![]() ![]() ![]() Someone who knows the application would need to provide field names. Note how I could get some fields very easily from the structure, but I have no field names - they are just bytes to the database. If you are making your own DDF's, then you must understand the data structures of the application. You don't have the metadata, so you must go through the process of making the DDF's (or contact the original developer to obtain DDF's if they have them). The data dictionary files, indicated in the paper and by Mirtheil, contain the metadata that make this "easy", and you can export directly from there. Once you run the code in Python (adjusted to your paths), you’ll get the CSV file at your specified location.You are seeing it as easy as it gets. ![]() Read_file.to_csv (r'C:\Users\Ron\Desktop\Test\New_Products.csv', index=None) Read_file = pd.read_csv (r'C:\Users\Ron\Desktop\Test\Product_List.txt') So this is the complete code to convert the text file to CSV for our example (note that you’ll need to modify the paths to reflect the location where the files are stored on your computer): import pandas as pd Where the new file name to be created is New_Products and the file extension is csv.The path where the CSV will be saved is: C:\Users\Ron\Desktop\Test\New_Products.csv.Where the file name is Product_List and the file extension is txt.The path where the text file is stored is: C:\Users\Ron\Desktop\Test\Product_List.txt.Read_file.to_csv (r'Path where the CSV will be saved\File name.csv', index=None) Read_file = pd.read_csv (r'Path where the Text file is stored\File name.txt') C:\Users\Ron\Desktop\Test\New_Products.csv Step 4: Convert the text file to CSV using Pythonįinally, you may use the template below in order to facilitate the conversion of your text file to CSV: import pandas as pd ![]()
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