Python - Read lines from txt that are space and | delimited to add values to list

Question

I have a text file that appears to be both space and pipe delimited.

                                                                   Test Codes

 ABCBBA        3       -1189.59   |   ABCCHOICE     1          22.56    |    ABCELECT     31       13516.72   |    ABCFED       14        9070.74
 ABCHMOBLUE   38       13183.27   |   DCMCDNY       1           8.86    |    ABCMEDHMO     7        6189.83   |    ABCMEDPPO    17        6730.53

What I need to pull out is any code that starts with D and the corresponding value. So using the example above, my desired output would be:

Code     Total
DCMCDNY  8.86

When I use:

for index, line in enumerate(lines):
if "Test Codes" in line:
    print(re.split(r'\s{2,}',lines[index+2].lstrip()))
if "Test Codes" in line:
    print(re.split(r'\s{2,}',lines[index+3].lstrip()))

I get the below output:

['ABCBBA', '3', '-1189.59', '|', 'ABCCHOICE', '1', '22.56', '|', 'ABCELECT', '31', '13516.72', '|', 'ABCFED', '14', '9070.74']
['ABCHMOBLUE', '38', '13183.27', '|', 'DCMCDNY', '1', '8.86', '|', 'ABCMEDHMO', '7', '6189.83', '|', 'ABCMEDPPO', '17', '6730.53']

However, I'm not sure if this is the most scalable approach or how I can pull the code and value from the list.

Answer 1

I would begin with splitting on the "|"character.

 candidates = {}  # To store results
 lines = data_file.readlines()
 for line in lines:
    strip_pipe = line.strip("|")

    # Process the list from split for whitespace delimiters
    for candidate in strip_pipe:
        stripped = candidate.strip()  # Removes begin and end whitespace

        # Check if the first item has the letter "D"
        if stripped[0] == "D":
           split_space = stripped.split(" ")
           candidates.update({"Code": split_space[0], "Total": split_space[-1]})

    

From your example data above, this code will give you an output of

{'DCMCDNY': '8.86'}

Now, while this at least gives you the desired output, it may not be the most scalable for large data. Hopefully it sparks some ideas for you to improve it and make it meet your needs!