Concatenating two one-dimensional NumPy arrays

Use:

np.concatenate([a, b])

The arrays you want to concatenate need to be passed in as a sequence, not as separate arguments.

From the NumPy documentation:

numpy.concatenate((a1, a2, ...), axis=0)

Join a sequence of arrays together.

It was trying to interpret your b as the axis parameter, which is why it complained it couldn't convert it into a scalar.

There are several possibilities for concatenating 1D arrays, e.g.,

import numpy as np

np.r_[a, a]
np.stack([a, a]).reshape(-1)
np.hstack([a, a])
np.concatenate([a, a])

All those options are equally fast for large arrays; for small ones, concatenate has a slight edge:

The plot was created with perfplot:

import numpy
import perfplot

perfplot.show(
    setup=lambda n: numpy.random.rand(n),
    kernels=[
        lambda a: numpy.r_[a, a],
        lambda a: numpy.stack([a, a]).reshape(-1),
        lambda a: numpy.hstack([a, a]),
        lambda a: numpy.concatenate([a, a]),
    ],
    labels=["r_", "stack+reshape", "hstack", "concatenate"],
    n_range=[2 ** k for k in range(19)],
    xlabel="len(a)",
)

The first parameter to concatenate should itself be a sequence of arrays to concatenate:

numpy.concatenate((a,b)) # Note the extra parentheses.

An alternative ist to use the short form of "concatenate" which is either "r_[...]" or "c_[...]" as shown in the example code beneath (see Link for additional information):

%pylab
vector_a = r_[0.:10.] #short form of "arange"
vector_b = array([1,1,1,1])
vector_c = r_[vector_a,vector_b]
print vector_a
print vector_b
print vector_c, '\n\n'

a = ones((3,4))*4
print a, '\n'
c = array([1,1,1])
b = c_[a,c]
print b, '\n\n'

a = ones((4,3))*4
print a, '\n'
c = array([[1,1,1]])
b = r_[a,c]
print b

print type(vector_b)

Which results in:

[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]
[1 1 1 1]
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.  1.  1.  1.  1.] 


[[ 4.  4.  4.  4.]
 [ 4.  4.  4.  4.]
 [ 4.  4.  4.  4.]] 

[[ 4.  4.  4.  4.  1.]
 [ 4.  4.  4.  4.  1.]
 [ 4.  4.  4.  4.  1.]] 


[[ 4.  4.  4.]
 [ 4.  4.  4.]
 [ 4.  4.  4.]
 [ 4.  4.  4.]] 

[[ 4.  4.  4.]
 [ 4.  4.  4.]
 [ 4.  4.  4.]
 [ 4.  4.  4.]
 [ 1.  1.  1.]]