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    <p>To take full advantage of numpy's speed, you want to create <a href="http://docs.scipy.org/doc/numpy/reference/ufuncs.html" rel="nofollow noreferrer">ufuncs</a> whenever possible. Applying <code>vectorize</code> to a function as <a href="https://stackoverflow.com/a/9793070/577088">mgibsonbr</a> suggests is one way to do that, but a better way, if possible, is simply to construct a function that takes advantage of numpy's built-in ufuncs. So something like this:</p> <pre><code>&gt;&gt;&gt; import numpy &gt;&gt;&gt; a = numpy.random.random(10) &gt;&gt;&gt; a + 1 array([ 1.29738145, 1.33004628, 1.45825441, 1.46171177, 1.56863326, 1.58502855, 1.06693054, 1.93304272, 1.66056379, 1.91418473]) &gt;&gt;&gt; (a + 1) * 0.25 / 4 array([ 0.08108634, 0.08312789, 0.0911409 , 0.09135699, 0.09803958, 0.09906428, 0.06668316, 0.12081517, 0.10378524, 0.11963655]) </code></pre> <p>What is the nature of the function you want to apply across the numpy array? If you tell us, perhaps we can help you come up with a version that uses only numpy ufuncs.</p> <p>It's also possible to generate an array of indices without using <code>enumerate</code>. Numpy provides <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndenumerate.html" rel="nofollow noreferrer"><code>ndenumerate</code></a>, which is an iterator, and probably slower, but it also provides <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.indices.html" rel="nofollow noreferrer"><code>indices</code></a>, which is a very quick way to generate the indices corresponding to the values in an array. So...</p> <pre><code>&gt;&gt;&gt; numpy.indices(a.shape) array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]]) </code></pre> <p>So to be more explicit, you can use the above and combine them using <code>numpy.rec.fromarrays</code>:</p> <pre><code>&gt;&gt;&gt; a = numpy.random.random(10) &gt;&gt;&gt; ind = numpy.indices(a.shape) &gt;&gt;&gt; numpy.rec.fromarrays([ind[0], a]) rec.array([(0, 0.092473494150913438), (1, 0.20853257641948986), (2, 0.35141455604686067), (3, 0.12212258656960817), (4, 0.50986868372639049), (5, 0.0011439325711705139), (6, 0.50412473457942508), (7, 0.28973489788728601), (8, 0.20078799423168536), (9, 0.34527678271856999)], dtype=[('f0', '&lt;i8'), ('f1', '&lt;f8')]) </code></pre> <p>It's starting to sound like your main concern is performing the operation in-place. That's harder to do using <code>vectorize</code> but it's easy with the ufunc approach:</p> <pre><code>&gt;&gt;&gt; def somefunc(a): ... a += 1 ... a /= 15 ... &gt;&gt;&gt; a = numpy.random.random(10) &gt;&gt;&gt; b = a &gt;&gt;&gt; somefunc(a) &gt;&gt;&gt; a array([ 0.07158446, 0.07052393, 0.07276768, 0.09813235, 0.09429439, 0.08561703, 0.11204622, 0.10773558, 0.11878885, 0.10969279]) &gt;&gt;&gt; b array([ 0.07158446, 0.07052393, 0.07276768, 0.09813235, 0.09429439, 0.08561703, 0.11204622, 0.10773558, 0.11878885, 0.10969279]) </code></pre> <p>As you can see, numpy performs these operations in-place.</p>
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    1. COI need both index and value. your `numpy.indices()` solution will only give me the indices.
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    2. COSure, it provides the indices, and `a` provides the values.
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    3. CO@memyself, you need to say more about the function that you want to map over the variables. When you say "I need both the index and the value of the current element," do you mean that `somefunc` uses both, or that you need the index of the current element to assign to the array? (Because the latter is invalid for ufuncs.)
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