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][:,         


        

Answer 1

To my surprise this, kind of lenghty expression, which calculates first linear 1D-indices, is more than 50% faster than the consecutive array indexing presented in the question:

(a.ravel()[(
   cols + (rows * a.shape[1]).reshape((-1,1))
   ).ravel()]).reshape(rows.size, cols.size)

UPDATE: OP updated the description of the shape of the initial array. With the updated size the speedup is now above 99%:

In [93]: a = np.random.randn(3218, 1415)

In [94]: rows = np.random.randint(a.shape[0], size=2000)

In [95]: cols = np.random.randint(a.shape[1], size=6)

In [96]: timeit a[rows][:, cols]
10 loops, best of 3: 186 ms per loop

In [97]: timeit (a.ravel()[(cols + (rows * a.shape[1]).reshape((-1,1))).ravel()]).reshape(rows.size, cols.size)
1000 loops, best of 3: 1.56 ms per loop

INITAL ANSWER: Here is the transcript:

In [79]: a = np.random.randn(3218, 6)
In [80]: a.shape
Out[80]: (3218, 6)

In [81]: rows = np.random.randint(a.shape[0], size=2000)
In [82]: cols = np.array([1,3,4,5])

Time method 1:

In [83]: timeit a[rows][:, cols]
1000 loops, best of 3: 1.26 ms per loop

Time method 2:

In [84]: timeit (a.ravel()[(cols + (rows * a.shape[1]).reshape((-1,1))).ravel()]).reshape(rows.size, cols.size)
1000 loops, best of 3: 568 us per loop

Check that results are actually the same:

In [85]: result1 = a[rows][:, cols]
In [86]: result2 = (a.ravel()[(cols + (rows * a.shape[1]).reshape((-1,1))).ravel()]).reshape(rows.size, cols.size)

In [87]: np.sum(result1 - result2)
Out[87]: 0.0