跨两个2D（或3D）numpy数组获取交叉（公共）索引 [英] Get intersecting (common) indexes across two 2D (or 3D) numpy arrays

问题描述

我有两个不同目录的数据，我想使用坐标来匹配这两个目录。数据I是来自目录1的 x1，y1，z1，a1，b1，c1等（约50万个元素）， x2，我想要做的是首先构造一个二维数组，其中包含（x，y）坐标（x，y），其中x，y，z2，a2，e2，m2，n2，etc ，如果必要，我将扩展到（x，y，z），并比较二维数组找到相同的元素。

I have data from two different catalogs, I want to use the coordinates to match those two catalogs. The data I have is x1,y1,z1,a1,b1,c1,etc (about half million elements) from catalog 1, and x2,y2,z2,a2,e2,m2,n2,etc (about million elements) from catalog 2. What I am trying to do is first to construct a 2D array which contains (x,y) coordinates, if necessary I will extend to (x,y,z), and compare the 2D arrays to find the same elements.

co1 = np.vstack((x1,y1)).T
co2 = np.vstack((x2,y2)).T

idx1 = np.in1d(co1,co2)   # not working for 2D arrays
idx2 = np.in1d(co2,co1)

np.savetxt('combined_data.txt',np.c_[x1[idx1],y1[idx1],a1[idx1],e2[idx2],n2[idx2]],fmt='%1.4f   %1.4f   %1.4f   %1.4f   %1.4f')

例如，我有以下数据集：

For example, I have the following dataset:

x1 = np.array([1,2,3,4,5])
y1 = np.array([5,4,3,2,1])
x2 = np.array([1,4,6,2,6,4,8,9,3])
y2 = np.array([5,1,5,3,6,2,8,3,3])

(1,5), (3,3), (4,2) are the common coordinates between the two catalogs. Therefore,

idx1 = [Ture, False, True, True, False], idx2 = [True, False, False, False, False, True, False, False, True]. 

但问题是 np.in1d 是一个1D程序，它不能应用于2D或3D阵列。任何人都知道一些numpy例程来完成这个任务？

But the problem is that np.in1d is a 1D routine, it can not be applied to 2D or 3D arrays. Anyone knows some numpy routines to accomplish this task?

推荐答案

将这两个数组转换为pandas dataframes：

Convert both arrays to pandas dataframes:

df1 = pd.DataFrame({"x" : x1, "y" : y1})).reset_index()

合并它们：

result = pd.merge(df1, df2, left_on=["x","y"], right_on=["x","y"])
#   index_x  x  y  index_y
#0        0  1  5        0
#1        2  3  3        8
#2        3  4  2        5

并获得索引：

result[["index_x","index_y"]]
#   index_x  index_y
#0        0        0
#1        2        8
#2        3        5

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