I've got three different numpy arrays
a = array([ 0, 3, 6, 9, 12])
b = array([ 1, 4, 7, 10, 13])
c = array([ 2, 5, 8, 11, 14])
How can I join them using numpy methods that
d = array[(0,1,2,3,4,...,12,13,14)]
I don't want to write a loop like
for i in range(len(a)):
[...]
This is only an example in my project the arrays are not sorted and I want to keep their order.
You can transpose and flatten the arrays:
d = numpy.array([a, b, c]).T.flatten()
An alternative way to combine the arrays is to use numpy.vstack()
:
d = numpy.vstack((a, b, c)).T.flatten()
(I don't know which one is faster, by the way.)
Edit: In response to the answer by Nicolas Barbey, here is how to make do with copying the data only once:
d = numpy.empty((len(a), 3), dtype=a.dtype)
d[:, 0], d[:, 1], d[:, 2] = a, b, c
d = d.ravel()
This code ensures that the data is layed out in a way that ravel()
does not need to make a copy, and indeed it is quite a bit faster than the original code on my machine:
In [1]: a = numpy.arange(0, 30000, 3)
In [2]: b = numpy.arange(1, 30000, 3)
In [3]: c = numpy.arange(2, 30000, 3)
In [4]: def f(a, b, c):
...: d = numpy.empty((len(a), 3), dtype=a.dtype)
...: d[:, 0], d[:, 1], d[:, 2] = a, b, c
...: return d.ravel()
...:
In [5]: def g(a, b, c):
...: return numpy.vstack((a, b, c)).T.ravel()
...:
In [6]: %timeit f(a, b, c)
10000 loops, best of 3: 34.4 us per loop
In [7]: %timeit g(a, b, c)
10000 loops, best of 3: 177 us per loop
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