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 tr

Answer 1

I was also interested in this question and compared the speed of

numpy.c_[a, a]
numpy.stack([a, a]).T
numpy.vstack([a, a]).T
numpy.column_stack([a, a])
numpy.concatenate([a[:,None], a[:,None]], axis=1)

which all do the same thing for any input vector a. Here's what I found (using perfplot):

For smaller numbers, numpy.concatenate is the winner, for larger (from about 3000) stack/vstack.

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The plot was created with

import numpy
import perfplot

perfplot.show(
    setup=lambda n: numpy.random.rand(n),
    kernels=[
        lambda a: numpy.c_[a, a],
        lambda a: numpy.stack([a, a]).T,
        lambda a: numpy.vstack([a, a]).T,
        lambda a: numpy.column_stack([a, a]),
        lambda a: numpy.concatenate([a[:, None], a[:, None]], axis=1),
    ],
    labels=["c_", "stack", "vstack", "column_stack", "concat"],
    n_range=[2 ** k for k in range(22)],
    xlabel="len(a)",
    logx=True,
    logy=True,
)