numpy array TypeError: only integer scalar arrays can be converted to a scalar index

i=np.arange(1,4,dtype=np.int)
a=np.arange(9).reshape(3,3)

and

a
>>>array([[0, 1, 2],
          [3, 4, 5],
          [6, 7, 8]])
a[:,0:1]
>>>array([[0],
          [3],
          [6]])
a[:,0:2]
>>>array([[0, 1],
          [3, 4],
          [6, 7]])
a[:,0:3]
>>>array([[0, 1, 2],
          [3, 4, 5],
          [6, 7, 8]])

Now I want to vectorize the array to print them all together. I try

a[:,0:i]

or

a[:,0:i[:,None]]

It gives TypeError: only integer scalar arrays can be converted to a scalar index

Short answer:

[a[:,:j] for j in i]

---

What you are trying to do is not a vectorizable operation. Wikipedia defines vectorization as a batch operation on a single array, instead of on individual scalars:

> In computer science, array programming languages (also known as vector or multidimensional languages) generalize operations on scalars to apply transparently to vectors, matrices, and higher-dimensional arrays. > > ... > > ... an operation that operates on entire arrays can be called a vectorized operation...

In terms of CPU-level optimization, the definition of vectorization is:

> "Vectorization" (simplified) is the process of rewriting a loop so that instead of processing a single element of an array N times, it processes (say) 4 elements of the array simultaneously N/4 times.

The problem with your case is that the result of each individual operation has a different shape: (3, 1), (3, 2) and (3, 3). They can not form the output of a single vectorized operation, because the output has to be one contiguous array. Of course, it can contain (3, 1), (3, 2) and (3, 3) arrays inside of it (as views), but that's what your original array a already does.

What you're really looking for is just a single expression that computes all of them:

[a[:,:j] for j in i]

... but it's not vectorized in a sense of performance optimization. Under the hood it's plain old for loop that computes each item one by one.

I ran into the problem when venturing to use numpy.concatenate to emulate a C++ like pushback for 2D-vectors; If A and B are two 2D numpy.arrays, then numpy.concatenate(A,B) yields the error.

The fix was to simply to add the missing brackets: numpy.concatenate( ( A,B ) ), which are required because the arrays to be concatenated constitute to a single argument

This could be unrelated to this specific problem, but I ran into a similar issue where I used NumPy indexing on a Python list and got the same exact error message:

# incorrect
weights = list(range(1, 129)) + list(range(128, 0, -1))
mapped_image = weights[image[:, :, band]] # image.shape = [800, 600, 3]
# TypeError: only integer scalar arrays can be converted to a scalar index

It turns out I needed to turn weights, a 1D Python list, into a NumPy array before I could apply multi-dimensional NumPy indexing. The code below works:

# correct
weights = np.array(list(range(1, 129)) + list(range(128, 0, -1)))
mapped_image = weights[image[:, :, band]] # image.shape = [800, 600, 3]

try the following to change your array to 1D

a.reshape((1, -1))

You can use numpy.ravel to return a flattened array from n-dimensional array:

>>> a
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])
>>> a.ravel()
array([0, 1, 2, 3, 4, 5, 6, 7, 8])

I had a similar problem and solved it using list...not sure if this will help or not

classes = list(unique_labels(y_true, y_pred))

this problem arises when we use vectors in place of scalars for example in a for loop the range should be a scalar, in case you have given a vector in that place you get error. So to avoid the problem use the length of the vector you have used