String to List question help

[deanhystad]

Please post text, not screenshots. When you post text, others can easily copy/paste to create data for their own testing.

It looks like this would be easy using pandas.

I mocked up some data that looks something like yours. Tab delimited values with some lines at the top that need to be skipped, and an annoying "!" before the column headers.

Output:
# 34589043! FILE NAME
! DATE NOW
! RAW DATA
# More information
!   FREQ	S11M	S21M	S21A
0.11444405920970335	0.22851792934978143	0.16724464908750036	0.054813079444560264
0.11565032195951042	0.23010933442970138	0.3048773751796404	0.11465260170716174
0.20102541854079847	0.27830101316888567	0.3995954975408248	0.12034618280172404
0.41276142607996513	0.31147127777115224	0.46636510024384636	0.2806783250819618
0.495356229079312	0.3495931528852747	0.6389401268977468	0.38079631999232333
0.556074773658631	0.3894689120388013	0.6779475794713076	0.43728550829612134
0.6332585933784733	0.4924761223011904	0.7503465368809027	0.7386632480838207
0.6341059239183447	0.5130383410762516	0.8554371206142789	0.7695854491274398
0.8902822004610119	0.7219019187548961	0.8712110079483135	0.8443884202719537
0.9170577193238573	0.7970927214052331	0.9344766589022325	0.8597198838156557

This code reads the file to extract the column names, then uses pandas to read the file into a dataframe.

import pandas as pd

# Read column names from 4th line in file.  Remove leading "!"
with open("data.csv", "r") as file:
    columns = file.readlines()[4][1:].split()

# Read file into dataframe.  Skip the first 5 rows and use the column
# names from above.
df = pd.read_csv("data.csv", sep="\t", skiprows=5, names=columns)
print(df)

Output:
       FREQ      S11M      S21M      S21A
0  0.114444  0.228518  0.167245  0.054813
1  0.115650  0.230109  0.304877  0.114653
2  0.201025  0.278301  0.399595  0.120346
3  0.412761  0.311471  0.466365  0.280678
4  0.495356  0.349593  0.638940  0.380796
5  0.556075  0.389469  0.677948  0.437286
6  0.633259  0.492476  0.750347  0.738663
7  0.634106  0.513038  0.855437  0.769585
8  0.890282  0.721902  0.871211  0.844388
9  0.917058  0.797093  0.934477  0.859720

Now it is easy to get the columns.

print(df["FREQ"])

Output:
0    0.114444
1    0.115650
2    0.201025
3    0.412761
4    0.495356
5    0.556075
6    0.633259
7    0.634106
8    0.890282
9    0.917058
Name: FREQ, dtype: float64