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<code>numpy</code> arrays don't play so well with heterogeneous types. When you made <code>data_new</code>, you were storing different kinds of Python objects in it, and so that forced the <code>dtype</code> to be <code>object</code> to be broad enough to handle everything. <pre><code>>>> data array([[ 2.01202031e+13, 2.01202041e+13], [ 5.20000000e+01, 5.30000000e+01], [ -2.20000000e+01, -2.30000000e+01]]) >>> data.dtype dtype('float64') >>> data_new array([[datetime.datetime(2012, 2, 3, 12, 30, 54), datetime.datetime(2012, 2, 4, 12, 30, 54)], [52.0, 53.0], [-22.0, -23.0]], dtype=object) >>> data_new.dtype dtype('O') </code></pre> Since taking the cosine of an arbitrary object doesn't make sense, this isn't implemented: <pre><code>>>> np.cos(data[1]) array([-0.16299078, -0.91828279]) >>> np.cos(data_new[1]) Traceback (most recent call last): File "<ipython-input-87-00101e4be00a>", line 1, in <module> np.cos(data_new[1]) AttributeError: cos </code></pre> You can return to <code>float</code> type (unfortunately it doesn't look like you can use a <code>view</code> with object arrays): <pre><code>>>> np.cos(data_new[1].astype(float)) array([-0.16299078, -0.91828279]) </code></pre> FWIW I prefer <a href="http://pandas.pydata.org" rel="nofollow"><code>pandas</code></a> DataFrames when working with this kind of mixed-type data, but people's preferences vary. I don't know enough about numpy structured arrays to know if you can mix object dtypes and more primitive dtypes, although I didn't think so.
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