尝试对数组进行索引时，包含多个元素的数组的真值不明确 [英] The truth value of an array with more than one element is ambigous when trying to index an array

问题描述

如果var(另一个numpy数组)中的元素> = 0且< =.1，我正在尝试将rbs的所有元素放入一个新数组中.但是，当我尝试以下代码时，出现此错误:

I am trying to put all elements of rbs into a new array if the elements in var(another numpy array) is >=0 and <=.1 . However when I try the following code I get this error:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

---

rbs = [ish[4] for ish in realbooks]
for book in realbooks:
    var -= float(str(book[0]).replace(":", ""))
    bidsred = rbs[(var <= .1) and (var >=0)]

关于我在做什么错的任何想法?

any ideas on what I'm doing wrong?

推荐答案

正如我在对先前答案的评论中告诉您的那样，您需要使用以下任一方法:

As I told you in a comment to a previous answer, you need to use either:

c[a & b]

或

c[np.logical_and(a, b)] 

原因是Python使用and关键字在两个布尔之间进行测试.数组如何成为布尔值?如果其项目的75％是True，是True还是False?因此，numpy拒绝比较两者.

The reason is that the and keyword is used by Python to test between two booleans. How can an array be a boolean? If 75% of its items are True, is it True or False? Therefore, numpy refuses to compare the two.

因此，您要么必须使用逻辑函数来逐个元素地比较两个布尔数组(np.logical_and)，要么要使用二进制运算符&.

So, you either have to use the logical function to compare two boolean arrays on an element-by-element basis (np.logical_and) or the binary operator &.

此外，出于索引目的，您确实需要一个布尔数组，其大小与要索引的数组相同.而且它必须是一个数组，您不能使用True/False的列表: 原因是使用布尔数组告诉NumPy要返回哪个元素.如果使用True/False的列表，则NumPy会将其解释为作为整数(即​​索引)的1/0列表，这意味着您将获得数组的第二个或第一个元素.不是你想要的.

Moreover, for indexing purposes, you really need a boolean array with the same size as the array you're indexing. And it has to be an array, you cannot use a list of True/False instead: The reason is that using a boolean array tells NumPy which element to return. If you use a list of True/False, NumPy will interpret that as a list of 1/0 as integers, that is, indices, meaning that you' either get the second or first element of your array. Not what you want.

现在，您可以猜测，如果要使用两个布尔数组a或b进行索引，请选择a或b为True的项目，您将使用

Now, as you can guess, if you want to use two boolean arrays a or b for indexing, choosing the items for which either a or b is True, you'd use

c[np.logical_or(a,b)]

或

c[a | b]

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