Using Numpy to Count Numbers in a range

Question

I've been trying to write some code which will add the numbers which fall into a certain range and add a corresponding number to a list. I also need to pull the range from a cumsum range.

numbers = []
i=0

z = np.random.rand(1000)
arraypmf = np.array(pmf)
summation = np.cumsum(z)

while i < 6:
   index = i-1

    a = np.extract[condition, z] # I can't figure out how to write the condition.
    length = len(a)
    length * numbers.append(i)

Answer 1

I'm not entirely sure what you're trying to do, but the easiest way to do conditions in numpy is to just apply them to the whole array to get a mask:

mask = (z >= 0.3) & (z < 0.6)

Then you can use, e.g., extract or ma if necessary—but in this case, I think you can just rely on the fact that True==1 and False==0 and do this:

zm = z * mask

After all, if all you're doing is summing things up, 0 is the same as not there, and you can just replace len with count_nonzero.

For example:

In [588]: z=np.random.rand(10)
In [589]: z
Out[589]: 
array([ 0.33335522,  0.66155206,  0.60602815,  0.05755882,  0.03596728,
        0.85610536,  0.06657973,  0.43287193,  0.22596789,  0.62220608])
In [590]: mask = (z >= 0.3) & (z < 0.6)
In [591]: mask
Out[591]: array([ True, False, False, False, False, False, False,  True, False, False], dtype=bool)
In [592]: z * mask
Out[592]: 
array([ 0.33335522,  0.        ,  0.        ,  0.        ,  0.        ,
        0.        ,  0.        ,  0.43287193,  0.        ,  0.        ])
In [593]: np.count_nonzero(z * mask)
Out[593]: 2
In [594]: np.extract(mask, z)
Out[594]: array([ 0.33335522,  0.43287193])
In [595]: len(np.extract(mask, z))
Out[595]: 2

Answer 2

Here is another approach to do (what I think) you're trying to do:

import numpy as np
z = np.random.rand(1000)
bins = np.asarray([0, .1, .15, 1.])

# This will give the number of values in each range
counts, _ = np.histogram(z, bins)

# This will give the sum of all values in each range
sums, _ = np.histogram(z, bins, weights=z)