CODEWITHSUNDEEP
pythonarraysnumpyindexingarray-broadcasting
kinder chen
kinder chen

Reputation: 1461

IndexError: shape mismatch: indexing arrays could not be broadcast together with shapes

a=np.arange(240).reshape(3,4,20)
b=np.arange(12).reshape(3,4)
c=np.zeros((3,4),dtype=int)
x=np.arange(3)
y=np.arange(4)

I wanna get a 2d (3,4) shape array by the following step without loop.

for i in x:
    c[i]=a[i,y,b[i]]
c
array([[  0,  21,  42,  63],
       [ 84, 105, 126, 147],
       [168, 189, 210, 231]])

I tried,

c=a[x,y,b]

but it shows

IndexError: shape mismatch: indexing arrays could not be broadcast together with shapes (3,) (4,) (3,4)

and then I also tried to establish newaxis by [:,None], it also doesn't work.

Upvotes: 1

Views: 4324

Answers (2)

John1024
John1024

Reputation: 113864

Try:

>>> a[x[:,None], y[None,:], b]
array([[  0,  21,  42,  63],
       [ 84, 105, 126, 147],
       [168, 189, 210, 231]])

Discussion

You tried a[x,y,b]. Note the error message:

>>> a[x, y, b]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
IndexError: shape mismatch: indexing arrays could not be broadcast
            together with shapes (3,) (4,) (3,4)

The (3,) means that we need to extend x to have 3 as the first dimension and 4 as the second dimension. We do this by specifying x[:,None] (which actually allows x to be broadcast to any size second dimension).

Similarly, the error message shows that we need to map y to shape (3,4) and we do that with y[None,:].

Alternative style

If one prefers, we can replace None with np.newaxis:

>>> a[x[:,np.newaxis], y[np.newaxis,:], b]
array([[  0,  21,  42,  63],
       [ 84, 105, 126, 147],
       [168, 189, 210, 231]])

np.newaxis is None:

>>> np.newaxis is None
True

(If I recall correctly, some earlier versions of numpy used a different capitalization style for newaxis. For all versions, though, None seems to works.)

Upvotes: 5

wwii
wwii

Reputation: 23753

Similar but different, hard coded not generic.

>>> b = np.ravel(a)[np.arange(0,240,21)]
>>> b.reshape((3,4))
array([[  0,  21,  42,  63],
       [ 84, 105, 126, 147],
       [168, 189, 210, 231]])
>>>

Upvotes: 1

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