python - Fast numpy fancy indexing

Question

My code for slicing a numpy array (via fancy indexing) is very slow. It is currently a bottleneck in program.

a.shape
(3218, 6)

ts = time.time(); a[rows][:, cols]; te = time.time(); print('%.8f' % (te-ts));
0.00200009

What is the correct numpy call to get an array consisting of the subset of rows 'rows' and columns 'col' of the matrix a? (in fact, I need the transpose of this result)

Answer 1

Let my try to summarize the excellent answers by Jaime and TheodrosZelleke and mix in some comments.

1. Advanced (fancy) indexing always returns a copy, never a view.
2. a[rows][:,cols] implies two fancy indexing operations, so an intermediate copy a[rows] is created and discarded. Handy and readable, but not very efficient. Moreover beware that [:,cols] usually generates a Fortran contiguous copy form a C-cont. source.
3. a[rows.reshape(-1,1),cols] is a single advanced indexing expression basing on the fact that rows.reshape(-1,1) and cols are broadcast to the shape of the intended result.

4. A common experience is that indexing in a flattened array can be more efficient than fancy indexing, so another approach is

   indx = rows.reshape(-1,1)*a.shape[1] + cols
   a.take(indx)
   

   or

   a.take(indx.flat).reshape(rows.size,cols.size)
   

5. Efficiency will depend on memory access patterns and whether the starting array is C-countinous or Fortran continuous, so experimentation is needed.

6. Use fancy indexing only if really needed: basic slicing a[rstart:rstop:rstep, cstart:cstop:cstep] returns a view (although not continuous) and should be faster!