Reading specific columns from a text file in pytho

问题:

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?

回答1:

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.

回答2:

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]

回答3:

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')

回答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']

回答5:

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.])