With list comprehension, I am able to take a 20x20 block of numbers in string format, and convert it to a list of lists of integers. The numbers are seperated by white space and the lines are seperated by a newline.
grid = [[int(x) for x in line.split()] for line in nums.split('\n')]
However, what I want is to use numpy for its speed. I could use np.asarray() with my intermediate list, but I don't think that is efficient use of numpy.
I also tried using np.fromstring()
, but I can't figure out the logic to make it work for a 2D array.
Is there any way to accomplish this task without the use of creating intermediate python lists?
You could use np.fromstring
setting a space as separator and reshape
to the desired shape:
np.fromstring(s, sep=' ').reshape(20, 20)
Or as a more general solution, following @mihammad's solution:
rows = s.count('\n') + 1
np.fromstring(s, sep=' ').reshape(-1, rows)
More general for any 2D grid:
rows = s.count('\n') + 1
np.fromstring(s, sep=' ').reshape(rows, -1)
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