binning data in python with scipy/numpy
It's probably faster and easier to use numpy.digitize():
import numpydata = 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... :)
The Scipy (>=0.11) function scipy.stats.binned_statistic specifically addresses the above question.
For the same example as in the previous answers, the Scipy solution would be
import numpy as npfrom scipy.stats import binned_statisticdata = np.random.rand(100)bin_means = binned_statistic(data, data, bins=10, range=(0, 1))[0]
Not sure why this thread got necroed; but here is a 2014 approved answer, which should be far faster:
import numpy as npdata = np.random.rand(100)bins = 10slices = np.linspace(0, 100, bins+1, True).astype(np.int)counts = np.diff(slices)mean = np.add.reduceat(data, slices[:-1]) / countsprint mean