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<p>NumPy与同构的dtype数组配合使用效果最好。熊猫是一个很好的选择,如果你有不同的类型。在</p>
<p>但是,对于NumPy <a href="https://docs.scipy.org/doc/numpy/user/basics.rec.html#module-numpy.doc.structured_arrays" rel="nofollow noreferrer">structured arrays</a>,您的要求是可能的:</p>
<pre><code>import numpy as np
x = np.array([['b8:27:eb:d6:e3:10', '0.428s', '198'],
['b8:27:eb:d6:e3:10', '0.428s', '232'],
['b8:27:eb:07:65:ad', '0.796s', '180'],
['b8:27:eb:07:65:ad', '0.796s', '255']],
dtype='<U17')
arr = np.core.records.fromarrays(x.transpose(),
formats='<U17,<U17,i4',
names='col1,col2,col3')
print(arr)
rec.array([('b8:27:eb:d6:e3:10', '0.428s', 198),
('b8:27:eb:d6:e3:10', '0.428s', 232),
('b8:27:eb:07:65:ad', '0.796s', 180),
('b8:27:eb:07:65:ad', '0.796s', 255)],
dtype=[('col1', '<U17'), ('col2', '<U17'), ('col3', '<i4')])
</code></pre>