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2012-11-02T10:16:00.113
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You can use the <code>to_records</code> method, but have to play around a bit with the dtypes if they are not what you want from the get go. In my case, having copied your DF from a string, the index type is string (represented by an <code>object</code> dtype in pandas): <pre><code>In [102]: df Out[102]: label A B C ID 1 NaN 0.2 NaN 2 NaN NaN 0.5 3 NaN 0.2 0.5 4 0.1 0.2 NaN 5 0.1 0.2 0.5 6 0.1 NaN 0.5 7 0.1 NaN NaN In [103]: df.index.dtype Out[103]: dtype('object') In [104]: df.to_records() Out[104]: rec.array([(1, nan, 0.2, nan), (2, nan, nan, 0.5), (3, nan, 0.2, 0.5), (4, 0.1, 0.2, nan), (5, 0.1, 0.2, 0.5), (6, 0.1, nan, 0.5), (7, 0.1, nan, nan)], dtype=[('index', '|O8'), ('A', '<f8'), ('B', '<f8'), ('C', '<f8')]) In [106]: df.to_records().dtype Out[106]: dtype([('index', '|O8'), ('A', '<f8'), ('B', '<f8'), ('C', '<f8')]) </code></pre> Converting the recarray dtype does not work for me, but one can do this in Pandas already: <pre><code>In [109]: df.index = df.index.astype('i8') In [111]: df.to_records().view([('ID', '<i8'), ('A', '<f8'), ('B', '<f8'), ('C', '<f8')]) Out[111]: rec.array([(1, nan, 0.2, nan), (2, nan, nan, 0.5), (3, nan, 0.2, 0.5), (4, 0.1, 0.2, nan), (5, 0.1, 0.2, 0.5), (6, 0.1, nan, 0.5), (7, 0.1, nan, nan)], dtype=[('ID', '<i8'), ('A', '<f8'), ('B', '<f8'), ('C', '<f8')]) </code></pre> Note that Pandas does not set the name of the index properly (to <code>ID</code>) in the exported record array (a bug?), so we profit from the type conversion to also correct for that. At the moment Pandas has only 8-byte integers, <code>i8</code>, and floats, <code>f8</code> (see this <a href="https://github.com/pydata/pandas/issues/622">issue</a>).
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