Many scientific Python users are surprised when I tell them that `ndarray.take`

is faster than __getitem__-based (a.k.a. "fancy" as I call it) indexing.

import numpy as np import random arr = np.random.randn(10000, 5) indexer = np.arange(10000) random.shuffle(indexer) In [26]: timeit arr[indexer] 1000 loops, best of 3: 1.25 ms per loop In [27]: timeit arr.take(indexer, axis=0) 10000 loops, best of 3: 127 us per loop

It's actually kind of unbelievable when you think about it. What's going on here that `take`

is almost **10x faster**? I really should take a closer at the internals of what __getitem__ does because this has always struck me as pretty bad. Maybe I shouldn't be complaining? I mean, R 2.13's indexing falls somewhere in the middle:

mat <- matrix(rnorm(50000), nrow=10000, ncol=5) set.seed(12345) indexer <- sample(1:10000) > system.time(for (i in 1:1000) mat[indexer,]) user system elapsed 0.460 0.197 0.656

So 656 microseconds per iteration. (In an earlier version of this post I used rpy2 to do the benchmark and got 1.05 ms, but there was apparently some overhead from rpy2)

Another peculiarity that I noticed with **out** argument, which tells the function to use
an array you pass in to write out the result:

out = np.empty_like(arr) In [50]: timeit np.take(arr, indexer, axis=0, out=out) 10000 loops, best of 3: 200 us per loop

**EDIT:** I've been informed that using `mode='clip'`

or `mode='wrap'`

makes this run as fast as without the out argument.

Weird! I was dissatisfied by this, so I got curious how fast a hand-coded little Cython function can do this:

@cython.wraparound(False) @cython.boundscheck(False) def take_axis0(ndarray[float64_t, ndim=2] values, ndarray[int32_t] indexer, out=None): cdef: Py_ssize_t i, j, k, n, idx ndarray[float64_t, ndim=2] outbuf if out is None: outbuf = np.empty_like(values) else: outbuf = out n = len(indexer) k = values.shape[1] for i from 0 <= i < n: idx = indexer[i] if idx == -1: for j from 0 <= j < k: outbuf[i, j] = NaN else: for j from 0 <= j < k: outbuf[i, j] = values[idx, j]

Don't worry about the -1 thingâ€” that's a specialization that I'm using inside pandas. Curiously, this function is a lot faster than `take`

using **out** but faster than the regular `take`

by a handful of microseconds.

In [53]: timeit lib.take_axis0(arr, indexer) 10000 loops, best of 3: 115 us per loop In [54]: timeit lib.take_axis0(arr, indexer, out) 10000 loops, best of 3: 109 us per loop

Very interesting.

### TL;DR

- Use
`take`

not`[]`

-based indexing to get best performance - Cython is just as fast for my specific application and a lot faster if you're passing an
**out**array (which I will be for the application that I needed this for) - R's
`matrix`

indexing performance is better than NumPy's fancy indexing, but about 5-6x slower than`ndarray.take`

. This can probably be improved.