使用 scipy/numpy 在 python 中分箱数据 [英] binning data in python with scipy/numpy

问题描述

是否有更有效的方法在预先指定的 bin 中取数组的平均值?例如，我有一个数字数组和一个对应于该数组中 bin 开始和结束位置的数组，我只想取这些 bin 中的平均值?我有下面的代码，但我想知道如何减少和改进它.谢谢.

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

推荐答案

使用起来可能更快更容易 numpy.digitize():

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))]

另一种方法是使用 numpy.直方图():

An alternative to this is to use numpy.histogram():

bin_means = (numpy.histogram(data, bins, weights=data)[0] /
             numpy.histogram(data, bins)[0])

自己试试哪个更快... :)

Try for yourself which one is faster... :)

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