扁平化numpy数组，还能保留值位置的索引? [英] Flatten numpy array but also keep index of value positions?

问题描述

我有几个2D numpy数组(矩阵)，我想将其转换为包含数组值的向量和包含每个行/列索引的向量.

I have several 2D numpy arrays (matrix) and for each one I would like to convert it to vector containing the values of the array and a vector containing each row/column index.

例如，我可能有一个像这样的数组:

For example I might have an array like this:

x = np.array([[3, 1, 4],
              [1, 5, 9],
              [2, 6, 5]])

我基本上想要这些值

[3, 1, 4, 1, 5, 9, 2, 6, 5]

及其位置

[[0,0], [0,1], [0,2], [1,0], [1,1], [1,2], [2,0], [2,1], [2,2]]

我的最终目标是将它们像这样的列放入pandas DataFrame中:

My end goal is to put these into a pandas DataFrame as columns like this:

V | x | y
--+---+---
3 | 0 | 0
1 | 0 | 1
4 | 0 | 2
1 | 1 | 0
5 | 1 | 1
9 | 1 | 2
6 | 2 | 0
5 | 2 | 1
3 | 2 | 2

其中V是值，x是行位置(索引)，y是列位置(索引).

where V is the value, x is the row position (index), and y is the column position (index).

我认为我可以一起破解一些东西，但我正在尝试找到一种有效的方法来完成此任务，而不是四处摸索.例如，我知道我可以使用类似x.reshape(x.size, 1)的值来获取值，并且可以尝试从x.shape创建索引列，但是似乎应该有更好的方法.

I think I can hack something together but I'm trying to find the efficient way of doing this rather than fumbling around. For example I know I can get the values using something like x.reshape(x.size, 1) and that I could try to create the index columns from x.shape, but there seems like there should be a better way.

推荐答案

我不知道它是否最有效，但是

I don't know if it's most efficient, but numpy.meshgrid is designed for this:

x = np.array([[3, 1, 4],
              [1, 5, 9],
              [2, 6, 5]])
XX,YY = np.meshgrid(np.arange(x.shape[1]),np.arange(x.shape[0]))
table = np.vstack((x.ravel(),XX.ravel(),YY.ravel())).T
print table

这将产生:

[[3 0 0]
 [1 1 0]
 [4 2 0]
 [1 0 1]
 [5 1 1]
 [9 2 1]
 [2 0 2]
 [6 1 2]
 [5 2 2]]

然后我认为df = pandas.DataFrame(table)将为您提供所需的数据框.

Then I think df = pandas.DataFrame(table) will give you your desired data frame.

这篇关于扁平化numpy数组，还能保留值位置的索引?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！