Repetition of numeric values ​​and specification of dtype

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I want to generate a numpy array of the form:

0.5*[[0, 0], [1, 1], [2, 2], ...]

I want the final array to have a dtype of numpy.float32.

Here is my attempt:

>>> import numpy as np
>>> N = 5
>>> x = np.array(np.repeat(0.5*np.arange(N), 2), np.float32)
>>> x
array([ 0. ,  0. ,  0.5,  0.5,  1. ,  1. ,  1.5,  1.5,  2. ,  2. ], dtype=float32)

Is this a good way? Can I avoid the copy (if it is indeed copying) just for type conversion?


You only has to reshape your final result to obtain what you want:

x = x.reshape(-1, 2)

But you could also run arange passing the dtype:

x = np.repeat(0.5*np.arange(N, dtype=np.float32), 2).reshape(-1, 2)

You can easily cast the array as another type using the astype method, which accepts an argument copy:

x.astype(np.int8, copy=False)

But, as explained in the documentation, numpy checks for some requirements in order to return the view. If those requirements are not satisfied a copy is returned.

You can check if a given array is a copy or a view from another by checking the OWNDATA attribute accessible through the flags property of the ndarray.


EDIT: more on checking if a given array is a copy...

  • Is there a way to check if numpy arrays share the same data?