binning data in python with scipy/numpy

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

Answers:

Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve the issue immediately. So please treat them as advisements. If you found the post helpful (or not), leave a comment & I’ll get back to you as soon as possible.

Method 1

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… 🙂

Method 2

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]

Method 3

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

Method 4

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 🙂

Method 5

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

Method 6

Another alternative is to use the ufunc.at. 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.

np.random.seed(1)
data = np.random.random(100) * 100
bins = np.linspace(0, 100, 10)

histogram = np.zeros_like(bins)

bin_indexes = np.searchsorted(bins, data)
np.add.at(histogram, bin_indexes, 1)

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0