binning data in python with scipy/numpy

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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… 🙂

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(
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

Another alternative is to use the This method applies in-place a desired operation at specified indices.
We can get the bin position for each datapoint using the searchsorted method.
Then we can use at to increment by 1 the position of histogram at the index given by bin_indexes, every time we encounter an index at bin_indexes.

data = np.random.random(100) * 100
bins = np.linspace(0, 100, 10)

histogram = np.zeros_like(bins)

bin_indexes = np.searchsorted(bins, data), bin_indexes, 1)

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