矩阵x [i，j]和x [i] [j]两种形式之间的python差异 [英] python difference between the two form of matrix x[i,j] and x[i][j]

问题描述

我想了解x[i,j]和x[i][j]的区别，其中x是矩阵

i want to understand the difference between x[i,j] and x[i][j] where x is a matrix

x = np.zeros((N,M))

我在进行研究时发现的答案始终是关于二维尺寸的数组，但是在我的情况下，我有一个带有两个索引的矩阵以与i和j一起工作，我需要使用for循环根据索引来操纵矩阵

The answer that i found while doing the research is always about array with 2D dimension but in my case i have a matrix with two index to work with i and j and i need to manipulate the matrix according the index with a for loop.

    for i in range(1,N+1):
        for j in range(1,M+1):
            x[i-1][j-1]=random.uniform(5,10)

所以您能帮助我理解x[i,j]和x[i][j]之间的区别吗?为了更清楚地说明每个i(基站)有j个(用户)

so can you help me understand the difference between x[i,j] and x[i][j]and to be more clear for each i(base station) there is a number of j (users)

推荐答案

对于2d数组的简单索引编制，两种形式都可以工作:

For simple indexing of a 2d array both forms work:

In [28]: x = np.arange(6).reshape(2,3)
In [29]: x
Out[29]: 
array([[0, 1, 2],
       [3, 4, 5]])
In [30]: x[1,2]
Out[30]: 5
In [31]: x[1][2]
Out[31]: 5

对于np.matrix(无论如何您可能都不应该使用它们)不是:

For np.matrix (which you probably shouldn't be using anyways) they aren't:

In [32]: X = np.matrix(x)
In [33]: X
Out[33]: 
matrix([[0, 1, 2],
        [3, 4, 5]])
In [34]: X[1,2]
Out[34]: 5
In [35]: X[1][2]
...
IndexError: index 2 is out of bounds for axis 0 with size 1

这两种形式在语法上并不相同. [1][2]首先使用1进行索引，然后使用2进行结果索引.这与同时使用两个参数进行索引不同.

The two forms are not syntactically the same. [1][2] first indexes with 1, and then indexes the result with 2. That's not the same as indexing once with both parameters.

In [36]: x[1]
Out[36]: array([3, 4, 5])      # (3,) shape
In [37]: X[1]
Out[37]: matrix([[3, 4, 5]])   # (1,3) shape

出现错误是因为np.matrix返回另一个np.matrix.因此，下一个[2]索引将再次索引第一个维度.

The error arises because np.matrix returns another np.matrix. So the next [2] indexing will again be indexing the first dimension.

x[1]实际上是x[1,:]的缩写，也就是说，索引第一个维度，然后切片其余所有维度(或X[1,...]允许3d或更高).所以x[1][2]确实是

x[1] is really short for x[1,:], that is, index the first dimension, and slice all the rest (or X[1,...] to allow for 3d and higher). So x[1][2] is really

temp = x[1,:]
temp[2]

或对于矩阵情况:

temp = X[1,:]
temp[2,:]

换句话说，它是2个索引操作.这是一个Python表达式，而不是特定的numpy用法.

In other words, it is 2 indexing operations. It's a Python expression, not a specific numpy usage.

当我们用列表或切片建立索引时，两种形式之间的差异变得尤为重要，尤其是在设置值时.

When we index with lists or slices, the difference between the two forms becomes more significant, especially when setting values.

我鼓励初学者使用x[i,j]表格.除非您真的了解发生了什么，否则不要使用x[1][2].

I encourage beginners to use the x[i,j] form. Don't use x[1][2] unless you really understand what is going on.

如果需要，我可以研究如何将索引转换为对__setitem__和__getitem__的调用.

If needed I could get into how indexing is translated into calls to __setitem__ and __getitem__.

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