[Numpy-discussion] can int and float exists in one array?(about difference in indexing Matlab matrix and Numpy array)

Tim Hochberg tim.hochberg at ieee.org
Mon Nov 27 20:33:34 CST 2006

Zhang Sam wrote:
> Thanks for so many replies.
> In fact, I want to use several arrays to store the original data from 
> a practical project. In every arrays, two or three column will be 
> store the index. The main computation is still on matrices(float type) 
> which is built from the original data. When building the main 
> matrix, I need the repeated use of the index stored in the original 
> data. So I hope both int and float can exist in one array with numpy, 
> just for the original data.
> Before Python, I used matlab and fortran. In matlab, it is just I have 
> said. In fortran, a module can used for storing different data type. 
> I used Python just now, so I don't know which one in python is best 
> for my case. What's the suggestion?
Here are slightly more fleshed out suggestions:

1. Break the indices out into a separate matrices. That is in instead of 
one matrix 'x' containing both the indices and and data, have two 
matrices: 'x_indices' of type int and 'x_data' of type float. This is 
probably what I would do, at least given my limited knowledge of the 
problem, since with suitable names for the two indices this is likeliest 
to be the clearest. For example:

    x_indices = np.array([2, 1]), dtype=int)
    x_data = np.array([[2.5, 3.5], [2.6, 3.5]], dtype=float)

2. Use record arrays. This allows you to pack different types into a 
single matrix, but you then need to refer to the different fields 
(formerly columns) by name:

        import numpy as np
        my_dtype = np.dtype([('indices',int), ('data_1', float),
    ('data_2', float)])
        x = np.array([(2, 2.5, 3.5), (1, 2.6, 3.5)], dtype=my_dtype)

        print x['indices']
        print x['data_1']
        print x['data_2']

Whether this makes any sense will depend on your actual use case.

3. Use object arrays as already suggested.

There are several other approaches I can think of (for example, if you 
are willing to swap rows and columns, you could create a tuple of 
arrays). However, in the absence of some compelling reason to do 
otherwise, I'd use #1.



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