Python3 Numpy np.where错误 [英] Python3 Numpy np.where Error

问题描述

我有2个这样的列表

x = [None,[1, 15, 175, 20],
    [150, 175, 18, 20],
    [150, 175, 18],
    [192, 150, 177],...]


y = [None,[12, 43, 55, 231],
    [243, 334, 44, 12],
    [656, 145, 138],
    [12, 150, 177],
    [150, 177, 188],...]

现在，我想擦除小于30的x值和与擦除的x值相对应的y值. (例如，x [1]和y [1]中的(x，y)=(1,12))

Now I want to erase the x values lower than 30 and y values that correspond with the erased x values. (For example, (x,y) = (1,12) in x[1] and y[1])

为此，我尝试在Numpy中使用np.where.

In order to do that, I tried to use np.where in Numpy.

我使用np.array将x和y列表转换为数组，并将其用于x

I converted the x and y lists into arrays by using np.array and got this for x

array([None, list([11]), list([12, 11]), ..., list([12, 13]),list([13, 13]), list([13, 15])], dtype=object)

然后我使用np.where(a< 30)并收到此错误

Then I used np.where(a<30) and got this error

TypeError: '>' not supported between instances of 'NoneType' and 'int'

我认为第一个列表中的None值是问题，所以我实现了

I thought the None values in the first lists were the problem so I implemented

np.where(a[1:]>30)

然后我得到了 TypeError:"list"和"int"的实例之间不支持>"

Then I got TypeError: '>' not supported between instances of 'list' and 'int'

我是一个初学者，想知道是什么导致了此错误.

I'm a beginner and want to know what caused this errors.

推荐答案

使用列表理解:

In [161]: x = [None,[1, 15, 175, 20],
     ...:     [150, 175, 18, 20],
     ...:     [150, 175, 18],
     ...:     [192, 150, 177]]
     ...:     

我们要从x(和y?)中删除的项目的索引.我使用x[1:]跳过了None，这需要额外的测试:

Indices of items that we want to remove from x (and y?). I use x[1:] to skip the None which needs an extra test:

In [163]: [(i,j) for i,v1 in enumerate(x[1:]) for j,v2 in enumerate(v1) if v2<30]
Out[163]: [(0, 0), (0, 1), (0, 3), (1, 2), (1, 3), (2, 2)]

x中的值为>=30:

In [164]: [[v2 for v2 in v1 if v2>=30] for v1 in x[1:]]
Out[164]: [[175], [150, 175], [150, 175], [192, 150, 177]]

我们可以使用Out[163]值从y中删除项目.另外，我们可以一起迭代x和y(zip(x,y)等).

We could use the Out[163] values to remove items from y. Alternatively we could iterate through x and y together (zip(x,y), etc).

如果列表理解太混乱，则可以将其重写为循环，也可以重新编写功能.

If the list comprehensions get too messy they can be rewritten as loops, and possibly functions.

对于像这样的不规则嵌套列表结构，我认为使用numpy没有任何意义.对象dtype数组基本上是列表(但没有有用的列表方法).

With an irregular nested list structure like this I don't see any point to using numpy. Object dtype arrays are basically lists (but without useful list methods).

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