is there a more efficient way to take an average of an array in prespecified bins? for example, i have an array of numbers and an array corresponding to bin start and end positions in that array, and I want to just take the mean in those bins? I have code that does it below but i am wondering how it can be cut down and improved. thanks.
from scipy import *
from numpy import *
def get_bin_mean(a, b_start, b_end):
ind_upper = nonzero(a >= b_start)[0]
a_upper = a[ind_upper]
a_range = a_upper[nonzero(a_upper < b_end)[0]]
mean_val = mean(a_range)
return mean_val
data = rand(100)
bins = linspace(0, 1, 10)
binned_data = []
n = 0
for n in range(0, len(bins)-1):
b_start = bins[n]
b_end = bins[n+1]
binned_data.append(get_bin_mean(data, b_start, b_end))
print binned_data
It's probably faster and easier to use numpy.digitize():
import numpy
data = numpy.random.random(100)
bins = numpy.linspace(0, 1, 10)
digitized = numpy.digitize(data, bins)
bin_means = [data[digitized == i].mean() for i in range(1, len(bins))]
An alternative to this is to use numpy.histogram():
bin_means = (numpy.histogram(data, bins, weights=data)[0] /
numpy.histogram(data, bins)[0])
Try for yourself which one is faster... :)