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I have a text file which contains a table comprised of numbers e.g:

5 10 6

6 20 1

7 30 4

8 40 3

9 23 1

4 13 6

if for example I want the numbers contained only in the second column, how do i extract that column into a list?

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Jethro
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6 Answers6

38
f=open(file,"r")
lines=f.readlines()
result=[]
for x in lines:
    result.append(x.split(' ')[1])
f.close()

You can do the same using a list comprehension

print([x.split(' ')[1] for x in open(file).readlines()])

Docs on split()

string.split(s[, sep[, maxsplit]])

Return a list of the words of the string s. If the optional second argument sep is absent or None, the words are separated by arbitrary strings of whitespace characters (space, tab, newline, return, formfeed). If the second argument sep is present and not None, it specifies a string to be used as the word separator. The returned list will then have one more item than the number of non-overlapping occurrences of the separator in the string.

So, you can omit the space I used and do just x.split() but this will also remove tabs and newlines, be aware of that.

user3804598
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ForceBru
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    @StefanPochmann, they're dealing with large files there. Here it's used just for clarity. Honestly, I wouldn't trust a site called _stupid_pythonideas :) – ForceBru May 13 '15 at 14:08
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    It's *not* just about large files. And of course your `x` is very good for clarity. – Stefan Pochmann May 13 '15 at 14:11
  • Agreed with both commenters. `file.readlines` should generally be avoided because there's rarely a good reason to build a list from an iterable unless you need it more than once (which you don't in this case). However it's worth mentioning that *my* answer does effectively the same thing, and isn't drawing criticism. Ultimately @StefanPochmann 's comment is knee-jerk and unhelpful. Most times there will be negligible difference between `for line in f` and `for line in f.readlines()`. – Adam Smith May 13 '15 at 14:42
  • In this particular case, of course, we're not even using that list. Simple removing the line `lines = f.readlines()` and iterating `for x in f` works exactly the same way (barring some trailing whitespace since you should be doing `x.split()` not `x.split(' ')`). It's a negligible difference, but there's no benefit whatsoever. – Adam Smith May 13 '15 at 14:46
  • @AdamSmith Well there's the advantage of not doing something generally bad for no good reason whatsoever and advocating it to others who then do the same thing. Also, I think there's definitely a clarity benefit in `for line in file` (does it get any more natural?) compared to `for x in file.readlines()`. And I don't see how your answer is comparable. Because of the efficiency issue? That's not why I complained. But even if it were - you're doing it as necessary part of your approach. Here, on the other hand, it serves absolutely no purpose. – Stefan Pochmann May 13 '15 at 14:55
  • @StefanPochmann even so, taking the similar approach to my solution would use `[row[1] for row in zip(*reader)]` – Adam Smith May 13 '15 at 15:10
  • I guess with minimum memory requirement, it will be something like the following: import sys import re fp = open(sys.argv[1], "r") second_col_list = [] for line in fp: d_loc = [m.start() for m in re.finditer("\t", line)] # for 2nd column second = line[d_loc[0] + 1: d_loc[1]] second_col_list.append (int(second)) fp.close() – satyanarayan rao Jun 25 '20 at 13:16
13

You have a space delimited file, so use the module designed for reading delimited values files, csv.

import csv

with open('path/to/file.txt') as inf:
    reader = csv.reader(inf, delimiter=" ")
    second_col = list(zip(*reader))[1]
    # In Python2, you can omit the `list(...)` cast

The zip(*iterable) pattern is useful for converting rows to columns or vice versa. If you're reading a file row-wise...

>>> testdata = [[1, 2, 3],
                [4, 5, 6],
                [7, 8, 9]]

>>> for line in testdata:
...     print(line)

[1, 2, 3]
[4, 5, 6]
[7, 8, 9]

...but need columns, you can pass each row to the zip function

>>> testdata_columns = zip(*testdata)
# this is equivalent to zip([1,2,3], [4,5,6], [7,8,9])

>>> for line in testdata_columns:
...     print(line)

[1, 4, 7]
[2, 5, 8]
[3, 6, 9]
Adam Smith
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13

I know this is an old question, but nobody mentioned that when your data looks like an array, numpy's loadtxt comes in handy:

>>> import numpy as np
>>> np.loadtxt("myfile.txt")[:, 1]
array([10., 20., 30., 40., 23., 13.])
aerobiomat
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6

You can use a zip function with a list comprehension :

with open('ex.txt') as f:
    print zip(*[line.split() for line in f])[1]

result :

('10', '20', '30', '40', '23', '13')
Mazdak
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4

First of all we open the file and as datafile then we apply .read() method reads the file contents and then we split the data which returns something like: ['5', '10', '6', '6', '20', '1', '7', '30', '4', '8', '40', '3', '9', '23', '1', '4', '13', '6'] and the we applied list slicing on this list to start from the element at index position 1 and skip next 3 elements untill it hits the end of the loop.

with open("sample.txt", "r") as datafile:
    print datafile.read().split()[1::3]

Output:

['10', '20', '30', '40', '23', '13']
ZdaR
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0

It may help:

import csv
with open('csv_file','r') as f:
    # Printing Specific Part of CSV_file
    # Printing last line of second column
    lines = list(csv.reader(f, delimiter = ' ', skipinitialspace = True))
    print(lines[-1][1])
    # For printing a range of rows except 10 last rows of second column
    for i in range(len(lines)-10):
        print(lines[i][1])
StephanSchrodinger
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