How to organize values in a numpy array into bins that contain a certain range of values?

Question

I am trying to sort values in an numpy array so that I can store all of the values that are in a certain range (That could probably be phrased better). Anyway ill give an example of what I am trying to do. I have an array called bins that looks like this:

bins = array([11,11.5,12,12.5,13,13.5,14])

I also have another array called avgs:

avgs = array([11.02, 13.67, 11.78, 12.34, 13.24, 12.98, 11.3, 12.56, 13.95, 13.56,
              11.64, 12.45, 13.23, 13.64, 12.46, 11.01, 11.87, 12.34, 13,87, 13.04,
              12.49, 12.5])

What I am trying to do is to find the index values of the avgs array that are in the ranges between the values of the bins array. For example I was trying to make a while loop that would create new variables for each bin. The first bin would be everything that is between bins[0] and bins[1] and would look like:

bin1 = array([0, 6, 15])

Those index values would correspond to the values 11.02, 11.3, and 11.01 in the avgs and would be the values of avgs that were between index values 0 and 1 in bins. I also need the other bins so another example would be:

bin2 = array([2, 10, 16])

However the challenging part of this for me was that the size of bins and avgs changes based on other parameters so I was trying to build something that would be able to be expanded to larger or smaller bins and avgs arrays.

Answer 1

Numpy has some pretty powerful bin counting functions.

>>> binplace = np.digitize(avgs, bins) #Returns which bin an average belongs
>>> binplace
array([1, 6, 2, 3, 5, 4, 1, 4, 6, 6, 2, 3, 5, 6, 3, 1, 2, 3, 5, 7, 5, 3, 4])

>>> np.where(binplace == 1)
(array([ 0,  6, 15]),)
>>> np.where(binplace == 2)
(array([ 2, 10, 16]),)

>>> avgs[np.where(binplace == 1)]
array([ 11.02,  11.3 ,  11.01])