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    <p>Ray's solution is good. However, on my machine it is about 2.5x faster to use <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html" rel="noreferrer"><code>numpy.sum</code></a> in place of <code>numpy.min</code>:</p> <pre><code>In [13]: %timeit np.isnan(np.min(x)) 1000 loops, best of 3: 244 us per loop In [14]: %timeit np.isnan(np.sum(x)) 10000 loops, best of 3: 97.3 us per loop </code></pre> <p>Unlike <code>min</code>, <code>sum</code> doesn't require branching, which on modern hardware tends to be pretty expensive. This is probably the reason why <code>sum</code> is faster.</p> <p><strong>edit</strong> The above test was performed with a single NaN right in the middle of the array.</p> <p>It is interesting to note that <code>min</code> is slower in the presence of NaNs than in their absence. It also seems to get slower as NaNs get closer to the start of the array. On the other hand, <code>sum</code>'s throughput seems constant regardless of whether there are NaNs and where they're located:</p> <pre><code>In [40]: x = np.random.rand(100000) In [41]: %timeit np.isnan(np.min(x)) 10000 loops, best of 3: 153 us per loop In [42]: %timeit np.isnan(np.sum(x)) 10000 loops, best of 3: 95.9 us per loop In [43]: x[50000] = np.nan In [44]: %timeit np.isnan(np.min(x)) 1000 loops, best of 3: 239 us per loop In [45]: %timeit np.isnan(np.sum(x)) 10000 loops, best of 3: 95.8 us per loop In [46]: x[0] = np.nan In [47]: %timeit np.isnan(np.min(x)) 1000 loops, best of 3: 326 us per loop In [48]: %timeit np.isnan(np.sum(x)) 10000 loops, best of 3: 95.9 us per loop </code></pre>
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    1. CO`np.min` is faster when the array contains no NaNs, which is my expected input. But I've decided to accept this one anyway, because it catches `inf` and `neginf` as well.
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    2. COThis only catches `inf` or `-inf` if the input contains both, and it has problems if the input contains large but finite values that overflow when added together.
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