关于numpy的串联，hstack，vstack函数？

Question

See some examples

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(np.concatenate((a,b), axis=0)) # [1,2,3,4,5,6]
print(np.hstack((a,b))) # [1,2,3,4,5,6]

print(np.vstack((a,b))) # [[1,2,3],[4,5,6]]
print(np.concatenate((a,b), axis=1)) # IndexError: axis 1 out of bounds [0, 1)

The result of hstack is the same as concatenate along axis=0, but the api document says hstack=concatenate along axis=1, please look at the https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.hstack.html#numpy.hstack

And concatenating along the axis=1 raise an IndexError, the api document says hstack=concatenate along axis=0, please look at the https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.vstack.html#numpy.vstack

Can anybody explain it?By the way, can anybody explain how to broadcast when the ndarray's dimension is less than 2 and concatenating along axis=1?

Answer 1

Look at the actual code for hstack :

arrs = [atleast_1d(_m) for _m in tup]
# As a special case, dimension 0 of 1-dimensional arrays is "horizontal"
if arrs[0].ndim == 1:
    return _nx.concatenate(arrs, 0)
else:
    return _nx.concatenate(arrs, 1)

I don't see anything in the docs about axis=1 . The term it uses is 'stack them horizontally' .

As I noted a year ago, Concatenation of 2 1D numpy arrays along 2nd axis , earlier versions don't raise an error if the axis is too high. But in 1.12 we get an error.

There is a newish np.stack that can add a dimension where needed:

In [46]: np.stack((np.arange(3), np.arange(4,7)),axis=1)
Out[46]: 
array([[0, 4],
       [1, 5],
       [2, 6]])

The base function is concatenate . The various stack functions adjust array dimensions in one way or other, and then do concatenate . Look at their code to see the details. (I've summarized the differences in earlier posts as well).

Answer 2

np.hstack(tup) and np.concatenate(tup, axis=1) are indeed equivalent but only if tup contains arrays that are at least 2-dimensional. This was in fact spelled out in the documentation for vstack , so it looks like it was just an oversight that it did not also in the documentation for hstack ; it will for future versions though.