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copied!<p>I would suggest the <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.ravel.html" rel="nofollow"><code>ravel</code></a> or <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.flatten.html#numpy.ndarray.flatten" rel="nofollow"><code>flatten</code></a> method of <code>ndarray</code>.</p> <pre><code>>>> a = numpy.arange(9).reshape(3, 3) >>> a.ravel() array([0, 1, 2, 3, 4, 5, 6, 7, 8]) </code></pre> <p><code>ravel</code> is faster than <code>concatenate</code> and <code>flatten</code> because it doesn't return a copy unless it has to:</p> <pre><code>>>> a.ravel()[5] = 99 >>> a array([[ 0, 1, 2], [ 3, 4, 99], [ 6, 7, 8]]) >>> a.flatten()[5] = 77 >>> a array([[ 0, 1, 2], [ 3, 4, 99], [ 6, 7, 8]]) </code></pre> <p>But if you need a copy to avoid the memory sharing illustrated above, you're better off using <code>flatten</code> than <code>concatenate</code>, as you can see from these timings: </p> <pre><code>>>> %timeit a.ravel() 1000000 loops, best of 3: 468 ns per loop >>> %timeit a.flatten() 1000000 loops, best of 3: 1.42 us per loop >>> %timeit numpy.concatenate(a) 100000 loops, best of 3: 2.26 us per loop </code></pre> <p>Note also that you can achieve the <em>exact</em> result that your output illustrates (a one-row 2-d array) with <code>reshape</code> (thanks Pierre GM!):</p> <pre><code>>>> a = numpy.arange(9).reshape(3, 3) >>> a.reshape(1, -1) array([[0, 1, 2, 3, 4, 5, 6, 7, 8]]) >>> %timeit a.reshape(1, -1) 1000000 loops, best of 3: 736 ns per loop </code></pre>

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