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    <p>By default, <code>np.genfromtxt</code> uses <code>dtype=float</code>: that's why you string columns are converted to NaNs because, after all, they're Not A Number... </p> <p>You can ask <code>np.genfromtxt</code> to try to guess the actual type of your columns by using <code>dtype=None</code>:</p> <pre><code>&gt;&gt;&gt; from StringIO import StringIO &gt;&gt;&gt; test = "a,1,2\nb,3,4" &gt;&gt;&gt; a = np.genfromtxt(StringIO(test), delimiter=",", dtype=None) &gt;&gt;&gt; print a array([('a',1,2),('b',3,4)], dtype=[('f0', '|S1'),('f1', '&lt;i8'),('f2', '&lt;i8')]) </code></pre> <p>You can access the columns by using their name, like <code>a['f0']</code>...</p> <p>Using <code>dtype=None</code> is a good trick if you don't know what your columns should be. If you already know what type they should have, you can give an explicit <code>dtype</code>. For example, in our test, we know that the first column is a string, the second an int, and we want the third to be a float. We would then use</p> <pre><code>&gt;&gt;&gt; np.genfromtxt(StringIO(test), delimiter=",", dtype=("|S10", int, float)) array([('a', 1, 2.0), ('b', 3, 4.0)], dtype=[('f0', '|S10'), ('f1', '&lt;i8'), ('f2', '&lt;f8')]) </code></pre> <p>Using an explicit <code>dtype</code> is much more efficient than using <code>dtype=None</code> and is the recommended way. </p> <p>In both cases (<code>dtype=None</code> or explicit, non-homogeneous <code>dtype</code>), you end up with a structured array. </p> <p>[Note: With <code>dtype=None</code>, the input is parsed a second time and the type of each column is updated to match the larger type possible: first we try a bool, then an int, then a float, then a complex, then we keep a string if all else fails. The implementation is rather clunky, actually. There had been some attempts to make the type guessing more efficient (using regexp), but nothing that stuck so far]</p>
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    1. POHow to use numpy.genfromtxt when first column is string and the remaining columns are numbers?
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    1. USPierre GM
    1. USPierre GM
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    1. POHow to use numpy.genfromtxt when first column is string and the remaining columns are numbers?
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    1. COThis is so much easier than the experiments with `dtype` I was trying it isn't even funny. :^) I was off building a `dict` rather than using `None`.. *sigh*
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    2. COLots of people were arguing for `dtype=None` as the default. It would have broken backwards compatibility, though, so we stayed with `dtype=float`. Yes, it's rather powerful, but it's a performance hit...
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      1. USPierre GM
    3. CO@PierreGM: maybe `Sniffer(**hints).sniff_dtype(sample)` could be an efficient solution: no need to read all input twice or hardcode dtype.
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