python why use numpy.r_ instead of concatenate

Question

In which case using objects like numpy.r_ or numpy.c_ is better (more efficient, more suitable) than using fonctions like concatenate or vstack for example ?

I am trying to understand a code where the programmer wrote something like:

return np.r_[0.0, 1d_array, 0.0] == 2

where 1d_array is an array whose values can be 0, 1 or 2. Why not using np.concatenate (for example) instead ? Like :

return np.concatenate([[0.0], 1d_array, [0.0]]) == 2

It is more readable and apparently it does the same thing.

Answer 1

np.r_ is implemented in the numpy/lib/index_tricks.py file. This is pure Python code, with no special compiled stuff. So it is not going to be any faster than the equivalent written with concatenate, arange and linspace. It's useful only if the notation fits your way of thinking and your needs.

In your example it just saves converting the scalars to lists or arrays:

In [452]: np.r_[0.0, np.array([1,2,3,4]), 0.0]
Out[452]: array([ 0.,  1.,  2.,  3.,  4.,  0.])

error with the same arguments:

In [453]: np.concatenate([0.0, np.array([1,2,3,4]), 0.0])
...
ValueError: zero-dimensional arrays cannot be concatenated

correct with the added []

In [454]: np.concatenate([[0.0], np.array([1,2,3,4]), [0.0]])
Out[454]: array([ 0.,  1.,  2.,  3.,  4.,  0.])

hstack takes care of that by passing all arguments through [atleast_1d(_m) for _m in tup]:

In [455]: np.hstack([0.0, np.array([1,2,3,4]), 0.0])
Out[455]: array([ 0.,  1.,  2.,  3.,  4.,  0.])

So at least in simple cases it is most similar to hstack.

But the real usefulness of r_ comes when you want to use ranges

np.r_[0.0, 1:5, 0.0]
np.hstack([0.0, np.arange(1,5), 0.0])
np.r_[0.0, slice(1,5), 0.0]

r_ lets you use the : syntax that is used in indexing. That's because it is actually an instance of a class that has a __getitem__ method. index_tricks uses this programming trick several times.

They've thrown in other bells-n-whistles

Using an imaginary step, uses np.linspace to expand the slice rather than np.arange.

np.r_[-1:1:6j, [0]*3, 5, 6]

produces:

array([-1. , -0.6, -0.2,  0.2,  0.6,  1. ,  0. ,  0. ,  0. ,  5. ,  6. ])

There are more details in the documentation.

I did some time tests for many slices in https://stackoverflow.com/a/37625115/901925