Question

I have an array (2000 * 2000) with floats and I want to classify the numbers.
So all numbers between 10 and 20 should be replaced with 15 and numbers between 20 – 60 should be replaced with 40 and so on.

I wrote something looping over all the rows and columns with a couple of if statements… but it takes forever to run over large arrays. Does anybody know how to speed things up?

for a in range(grid.shape[0]): #grid is an array
    for b in range(grid.shape[1]):    
        for c in range(len(z)):
            if z[c][0] <= grid[a][b] < z[c][1]: # z is a list containing [lower,upper,replace_value]
                grid[a][b]=z[c][2]

Answer 1

Would something like this work for you?

>>> import numpy as np
>>> grid = np.random.random((5,5)) * 100
>>> z = np.array([0, 10, 20, 60, 100.])
>>> replace_value = np.array([np.nan, 5., 15., 40., 80.])

>>> grid = replace_value[z.searchsorted(grid)]
>>> print grid
[[ 15.  40.  80.  80.  15.]
[ 80.  40.  15.  80.  80.]
[ 15.  80.   5.  15.  40.]
[ 40.  80.   5.   5.  80.]
[ 40.   5.  80.   5.  40.]]