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

Question

i np arange 1 4 dtype np int  a np arange 9  reshape 3 3    and  a  gt  gt  gt array   0  1  2              3  4  5              6  7  8    a   0 1   gt  gt  gt array   0              3              6    a   0 2   gt  gt  gt array   0  1              3  4              6  7    a   0 3   gt  gt  gt 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

User · Answer

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

User · Answer

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

User · Answer

You can use numpy ravel to return a flattened array from n-dimensional array    gt  gt  gt  a array   0  1  2           3  4  5           6  7  8     gt  gt  gt  a ravel   array  0  1  2  3  4  5  6  7  8

User · Answer

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

User · Answer

try the following to change your array to 1D  a reshape  1  -1

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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

User · Answer

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

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

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