＆QUOT; IndexError：太多的指数＆QUOT;在numpy的蟒蛇 [英] "IndexError: too many indices" in numpy python

问题描述

我知道很多人问过这个问题，但我无法获得可以解决我的问题一个合适的答案。

I know many people asked this question, but I could not get an appropriate answer that can solve my problem.

我有一个数组X ::

I have an array X::

    X=
    [1. 2. -10.]

现在我试图做一个矩阵Y读该X阵列。我的code是：

Now I am trying to make a matrix Y reading this X array. My code is::

#   make Y matrix

Y=np.matrix(np.zeros((len(X),2)))
i=0

while i < len(load_value):
    if X[i,1] % 2 != 0:
        Y[i,0] = X[i,0]*2-1
    elif X[i,1] % 2 == 0:
        Y[i,0] = X[i,0] * 2
    Y[i,1] = X[i,2]
    i = i + 1
print('Y=')
print(Y)

现在，如果我运行它，它提供了以下错误：

Now if I run this, it gives following error::

    Traceback (most recent call last):
      File "C:\Users\User\Desktop\Code.py", line 251, in <module>
        if X[i,1] % 2 != 0:
    IndexError: too many indices

在这里，我的数组只有1列。如果我让阵列X具有2个或更多的行，它不会给我任何错误。它给我的错误只有当X阵列有1列。现在，在我的情况下，阵列X可以有任何的行数。它可以有1行或5行或100行。我想写一个code能与没有任何错误任意数量的行读取阵列的X.我怎样才能解决这个问题？

here, my array has only 1 row. If I make array X with 2 or more rows, it does not give me any error. It gives me error only when X array has 1 row. Now, in my case, array X can have any number of rows. It can have 1 row or 5 rows or 100 rows. I want to write a code which can read array X with any number of rows without any error. How can I solve this problem?

在此先感谢....

推荐答案

我建议使用 numpy.matrix 而不是 ndarray ，它保持的2维，无论你有多少行：

I suggest using numpy.matrix instead of ndarray, it keeps a dimension of 2 regardless of how many rows you have:

In [17]: x
Out[17]: 
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])

In [18]: m=np.asmatrix(x)

In [19]: m[1]
Out[19]: matrix([[3, 4, 5]])

In [20]: m[1][0, 1]
Out[20]: 4

In [21]: x[1][0, 1]
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-21-bef99eb03402> in <module>()
----> 1 x[1][0, 1]

IndexError: too many indices

THX为@askewchan提的是，如果你想使用numpy的数组运算，使用 np.atleast_2d ：

In [85]: np.atleast_2d(x[1])[0, 1]
Out[85]: 4

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