绑定一个numpy数组 [英] Binning a numpy array

问题描述

我有一个numpy数组，其中包含时间序列数据.我想将该数组分类为给定长度的相等分区(如果大小不相同，则可以删除最后一个分区)，然后计算每个分类的均值.

I have a numpy array which contains time series data. I want to bin that array into equal partitions of a given length (it is fine to drop the last partition if it is not the same size) and then calculate the mean of each of those bins.

我怀疑有numpy，scipy或pandas功能可以做到这一点.

I suspect there is numpy, scipy, or pandas functionality to do this.

示例:

data = [4,2,5,6,7,5,4,3,5,7]

bin大小为2:

bin_data = [(4,2),(5,6),(7,5),(4,3),(5,7)]
bin_data_mean = [3,5.5,6,3.5,6]

对于3的垃圾箱大小

bin_data = [(4,2,5),(6,7,5),(4,3,5)]
bin_data_mean = [7.67,6,4]

推荐答案

只需使用reshape，然后使用mean(axis=1).

作为最简单的示例:

import numpy as np

data = np.array([4,2,5,6,7,5,4,3,5,7])

print data.reshape(-1, 2).mean(axis=1)

更一般而言，我们需要执行以下操作来删除最后一个没有偶数的bin:

More generally, we'd need to do something like this to drop the last bin when it's not an even multiple:

import numpy as np

width=3
data = np.array([4,2,5,6,7,5,4,3,5,7])

result = data[:(data.size // width) * width].reshape(-1, width).mean(axis=1)

print result

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