发布删除特定行，在Python中使用制表符分隔的列将.txt转换为.csv

Question

What I Have: Huge text data (.txt) with text separated by tabs.

What I want: Convert text (.txt) to CSV (.csv) placing each word separated by tabs in different columns using Python.

// Start Time: 10
// Update Rate: 2
// Scenario: 367.3
// Firmware Version: 1.1.1
Count   Temp    V_X V_Y V_Z
25  0   0.28    0.43    -0.07
23  4   0.34    0.33    -0.03
22  3   0.34    0.23    -0.04
21  2   0.35    0.43    -0.03
27  3   0.33    0.33    -0.12

The first problem is that I want to remove all the lines from the text file. Second problem is that I want to get all the tab separated text data into csv columns.

Here is what I am doing at this moment,

infile = open('/Users/parth_To_File/myData.txt','r').readlines()
with open('/Users/parth_To_File/out_myData.txt','w') as outfile:
    for index,line in enumerate(infile):
        if index != 0:
            outfile.write(line)

I am running the above code 4 times to get red of the redundant information in data. Then, I use the below code to convert the data in csv file.

save_path = "/Users/parth_To_File/"
in_filename = os.path.join(save_path,'myData.txt')
out_filename = os.path.join(save_path,'out_myData.csv')
df = pd.read_csv(in_filename, sep=";")
df.to_csv(out_filename, index=False)

The problem with the methods I am using is, - The code is not optimised to delete specific lines from txt data - The code does not provide proper tabular data with individual columns

I would appreciate if someone can help me understand the correct method to perform txt to csv conversion as per above mentioned need.

Answer 1

A file seperated by tabs is a TSV format ( https://en.wikipedia.org/wiki/Tab-separated_values ). Pandas supports this. You can do:

df = pd.read_csv('input.tsv', sep='\t', skiprows=4)
df.to_csv('input.csv', index=False, sep=",")

Answer 2

Everything is provided by pandas, no need to read the file line by line by yourself. You can use read_csv and set the separator to '\\t'. Lines starting with the character given as comment are skipped:

df = pd.read_csv('myData.txt', sep = '\t', comment = '/')

Output:

   Count  Temp   V_X   V_Y   V_Z
0     25     0  0.28  0.43 -0.07
1     23     4  0.34  0.33 -0.03
2     22     3  0.34  0.23 -0.04
3     21     2  0.35  0.43 -0.03
4     27     3  0.33  0.33 -0.12

---

If all you need is just to convert the TSV file to a CSV, you can also do it without any programming by just:

sed '/\//d; s/\t/,/g' myData.txt > myData.csv

or

 sed '/\\//d; s/\\t/,/g' myData.txt > myData.csv 

The former converts any tabs to commas starting from line #5 whereas the latter converts all lines not starting with a / .
If your file is huge as you said, this might be faster than first converting it into a pandas dataframe.