[Numpy-discussion] np.take versus fancy indexing

[Wes McKinney]

Any clue why I'm seeing this behavior? np.take's documentation says it
does the "same thing" as fancy indexing, but from this example I'm not
so sure:

import numpy as np

mat = np.random.randn(5000, 1000)
selector = np.array(np.arange(5000)[::2])

In [95]: timeit submat = mat[selector]
10 loops, best of 3: 68.4 ms per loop

In [96]: timeit submat = np.take(mat, selector, axis=0)
10 loops, best of 3: 21.5 ms per loop

indeed the result is the same:

In [97]: (mat[selector] == np.take(mat, selector, axis=0)).all()
Out[97]: True

In [98]: mat[selector].flags
Out[98]:
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False

In [99]: np.take(mat, selector, axis=0).flags
Out[99]:
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False

What's going on here / am I doing something wrong?

Thanks,
Wes