There are several methods, choose one that is most suitable for your application.
If you are working with numpy, it may be a good idea to use the numpy's load, loadtxt, fromfile or genfromtxt functions, because your file will be loaded into a suitable structure, after the preprocessing.
But if you are not about to work with numpy (or any other big library which has some file loading functionalities), it would be an overkill using it just for loading a file ... Consider using built-in python functions, or the csv module from the standard library instead ... It will be much more flexible, and way smoother.
Here is how, with examples using file.txt (values of each rows are separated with tabs):
1 2 3 4
7 8 9 10 11 12
13 14 15
python built-in
No module to import, pretty easy, flexible, a good option for most situations, imho.
Loading the file in binary mode for reading (rb flags) in a table (list of lists of values, separated in the file with tabs) with only built-in functions:
>>> file = open('file.txt', 'rb')
>>> table = [row.strip().split('') for row in file]
csv
The csv module from the standard library is pretty straightforward as well.
Note that altough CSV means Comma Separated Values, there is actually no standard and you can choose any delimiter you want. Therefore CSV stands for all cells-oriented or table-like files.
Loading the file in binary mode for reading (rb flags) in a table (list of lists of values, separated in the file with tabs) with the csv reader:
>>> import csv
>>> file = open('file.txt', 'rb')
>>> data = csv.reader(file, delimiter='')
>>> table = [row for row in data]
Accessing the cells
The table has been loaded similarly with the two previous examples, and the data of the table can be accessed like table[row][col]:
>>> table
[['1', '2', '3', '4'], ['7', '8', '9', '10', '11', '12'], ['13', '14', '15']]
>>> table[0]
['1', '2', '3', '4']
>>> table[1][2]
9
