Python Forum

I have a Windows text file with the following info inside it:

25 05 38 26 53 04
07 45 50 33 19 34
55 25 21 30 09 39
26 11 30 12 13 41
32 23 44 11 50 39
45 30 07 44 55 54
21 10 35 46 48 27
52 41 05 53 11 50
40 38 17 43 10 54
45 27 29 12 39 31
24 42 38 02 18 09
13 43 28 06 53 30
45 47 29 30 53 13
38 45 28 48 47 36
25 34 18 06 07 55


How can I code them to break that info apart into six columns and put each column into their own array?

(in case you can't see the image, please click on this link - https://imgur.com/AQ0BAxW

Read the file line by line.
Separate each line into its components using string.split(' ') - this will be a list (do NOT use string or list as variable names)
Add each member of the list to its respective column list.

Try it, show your work, and post again if you get stuck on any part of this.

or you can use pandas and read the file into dataframe. Then you can work with columns

Also posted on Dream.In.Code: https://www.dreamincode.net/forums/topic...n-for-this.

As mentioned by others, you can do this using pandas transpose() method.

>>> import pandas
>>> file_obj = '''25 05 38 26 53 04
... 07 45 50 33 19 34
... 55 25 21 30 09 39
... 26 11 30 12 13 41
... 32 23 44 11 50 39
... 45 30 07 44 55 54
... 21 10 35 46 48 27
... 52 41 05 53 11 50
... 40 38 17 43 10 54
... 45 27 29 12 39 31
... 24 42 38 02 18 09
... 13 43 28 06 53 30
... 45 47 29 30 53 13
... 38 45 28 48 47 36
... 25 34 18 06 07 55'''.split('\n')
>>> data = []
>>> for row in file_obj:
...     data.append(row.split())
...
>>> data
[['25', '05', '38', '26', '53', '04'], ['07', '45', '50', '33', '19', '34'], ['55', '25', '21', '30', '09', '39'], ['26', '11', '30', '12', '13', '41'], ['32', '23', '44', '11', '50', '39'], ['45', '30', '07', '44', '55', '54'], ['21', '10', '35', '46', '48', '27'], ['52', '41', '05', '53', '11', '50'], ['40', '38', '17', '43', '10', '54'], ['45', '27', '29', '12', '39', '31'], ['24', '42', '38', '02', '18', '09'], ['13', '43', '28', '06', '53', '30'], ['45', '47', '29', '30', '53', '13'], ['38', '45', '28', '48', '47', '36'], ['25', '34', '18', '06', '07', '55']]
>>> df = pandas.DataFrame(data)
>>> df
     0   1   2   3   4   5
0   25  05  38  26  53  04
1   07  45  50  33  19  34
2   55  25  21  30  09  39
3   26  11  30  12  13  41
4   32  23  44  11  50  39
5   45  30  07  44  55  54
6   21  10  35  46  48  27
7   52  41  05  53  11  50
8   40  38  17  43  10  54
9   45  27  29  12  39  31
10  24  42  38  02  18  09
11  13  43  28  06  53  30
12  45  47  29  30  53  13
13  38  45  28  48  47  36
14  25  34  18  06  07  55
>>> rotated = df.transpose()
>>> rotated
   0   1   2   3   4   5   6   7   8   9   10  11  12  13  14
0  25  07  55  26  32  45  21  52  40  45  24  13  45  38  25
1  05  45  25  11  23  30  10  41  38  27  42  43  47  45  34
2  38  50  21  30  44  07  35  05  17  29  38  28  29  28  18
3  26  33  30  12  11  44  46  53  43  12  02  06  30  48  06
4  53  19  09  13  50  55  48  11  10  39  18  53  53  47  07
5  04  34  39  41  39  54  27  50  54  31  09  30  13  36  55

If you'd rather do it manually, that's also fairly easy (pandas DataFrame added to the end, to show they're the same).

>>> file_obj = '''25 05 38 26 53 04
... 07 45 50 33 19 34
... 55 25 21 30 09 39
... 26 11 30 12 13 41
... 32 23 44 11 50 39
... 45 30 07 44 55 54
... 21 10 35 46 48 27
... 52 41 05 53 11 50
... 40 38 17 43 10 54
... 45 27 29 12 39 31
... 24 42 38 02 18 09
... 13 43 28 06 53 30
... 45 47 29 30 53 13
... 38 45 28 48 47 36
... 25 34 18 06 07 55'''.split('\n')
>>> columns = []
>>> for row in file_obj:
...     values = row.split()
...     for index, val in enumerate(values):
...         if index >= len(columns):
...             columns.append([])
...         columns[index].append(val)
...
>>> columns
[['25', '07', '55', '26', '32', '45', '21', '52', '40', '45', '24', '13', '45', '38', '25'], ['05', '45', '25', '11', '23', '30', '10', '41', '38', '27', '42', '43', '47', '45', '34'], ['38', '50', '21', '30', '44', '07', '35', '05', '17', '29', '38', '28', '29', '28', '18'], ['26', '33', '30', '12', '11', '44', '46', '53', '43', '12', '02', '06', '30', '48', '06'], ['53', '19', '09', '13', '50', '55', '48', '11', '10', '39', '18', '53', '53', '47', '07'], ['04', '34', '39', '41', '39', '54', '27', '50', '54', '31', '09', '30', '13', '36', '55']]
>>> pandas.DataFrame(columns)
   0   1   2   3   4   5   6   7   8   9   10  11  12  13  14
0  25  07  55  26  32  45  21  52  40  45  24  13  45  38  25
1  05  45  25  11  23  30  10  41  38  27  42  43  47  45  34
2  38  50  21  30  44  07  35  05  17  29  38  28  29  28  18
3  26  33  30  12  11  44  46  53  43  12  02  06  30  48  06
4  53  19  09  13  50  55  48  11  10  39  18  53  53  47  07
5  04  34  39  41  39  54  27  50  54  31  09  30  13  36  55

Just to mention that reading into pandas can be

import pandas as pd 

df = pd.read_csv('test.txt', sep = ' ', header=None)
print(df)
print(df.dtypes)

Output:
     0   1   2   3   4   5
0   25   5  38  26  53   4
1    7  45  50  33  19  34
2   55  25  21  30   9  39
3   26  11  30  12  13  41
4   32  23  44  11  50  39
5   45  30   7  44  55  54
6   21  10  35  46  48  27
7   52  41   5  53  11  50
8   40  38  17  43  10  54
9   45  27  29  12  39  31
10  24  42  38   2  18   9
11  13  43  28   6  53  30
12  45  47  29  30  53  13
13  38  45  28  48  47  36
14  25  34  18   6   7  55
0    int64
1    int64
2    int64
3    int64
4    int64
5    int64
dtype: object