binning data in python with scipy/numpy

Question

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... :)

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 np
from scipy.stats import binned_statistic

data = 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 np

data = np.random.rand(100)
bins = 10
slices = np.linspace(0, 100, bins+1, True).astype(np.int)
counts = np.diff(slices)

mean = np.add.reduceat(data, slices[:-1]) / counts
print mean

The numpy_indexed package (disclaimer: I am its author) contains functionality to efficiently perform operations of this type:

import numpy_indexed as npi
print(npi.group_by(np.digitize(data, bins)).mean(data))

This is essentially the same solution as the one I posted earlier; but now wrapped in a nice interface, with tests and all :)

I would add, and also to answer the question find mean bin values using histogram2d python that the scipy also have a function specially designed to compute a bidimensional binned statistic for one or more sets of data

import numpy as np
from scipy.stats import binned_statistic_2d

x = np.random.rand(100)
y = np.random.rand(100)
values = np.random.rand(100)
bin_means = binned_statistic_2d(x, y, values, bins=10).statistic

the function scipy.stats.binned_statistic_dd is a generalization of this funcion for higher dimensions datasets