NumPy stack, vstack and sdtack usage - numpy

Question

I am trying to better understand hstack, vstack, and dstack in NumPy.
a = np.arange(96).reshape(2,4,4,3)
print(a)
print(f"dimensions of a:", np.ndim(a))
print(f"Shape of a:", a.shape)
b = np.arange(201,225).reshape(2,4,3)
print(f"Shape of b:", b)
c = np.arange(101,133).reshape(2,4,4)
print(c)
print(f"dimensions of c:", np.ndim(c))
print(f"Shape of c:", c.shape)
a is:
[[[[ 0 1 2]
[ 3 4 5]
[ 6 7 8]
[ 9 10 11]]
[[12 13 14]
[15 16 17]
[18 19 20]
[21 22 23]]
[[24 25 26]
[27 28 29]
[30 31 32]
[33 34 35]]
[[36 37 38]
[39 40 41]
[42 43 44]
[45 46 47]]]
[[[48 49 50]
[51 52 53]
[54 55 56]
[57 58 59]]
[[60 61 62]
[63 64 65]
[66 67 68]
[69 70 71]]
[[72 73 74]
[75 76 77]
[78 79 80]
[81 82 83]]
[[84 85 86]
[87 88 89]
[90 91 92]
[93 94 95]]]]
and c is:
[[[101 102 103 104]
[105 106 107 108]
[109 110 111 112]
[113 114 115 116]]
[[117 118 119 120]
[121 122 123 124]
[125 126 127 128]
[129 130 131 132]]]
and b is:
[[[201 202 203]
[204 205 206]
[207 208 209]
[210 211 212]]
[[213 214 215]
[216 217 218]
[219 220 221]
[222 223 224]]]
How do I reshape c so that I can use hstack correctly: I wish to add one column for each row in each of the dimensions.
How do I reshape b so that I can use vstack correctly: I wish one row for each column in each of the dimensions.
I would like to come up with a general rule on the dimensions to check for the array that needs to be added to an existing array.

Answer 1

You can concatenate to a (2,4,4,3) a
(1,4,4,3) axis 0
(2,1,4,3) with axis=1
(2,4,1,3) axis 2
(2,4,4,1) axis 3
Read and reread as needed, the np.concatenate docs.
edit
In previous post(s) I've summarized the code of hstack and vstack, though you easily read that via the [source] link in the official docs.
When should I use hstack/vstack vs append vs concatenate vs column_stack?
hstack makes sure all arguments are atleast_1d and does a concatenate on axis 0 or 1. vstack makes sure all are atleast_2d, and does a concatenate on axis 0.
Maybe I should have insisted on seeing your attempts and any errors (and attempts to understand the errors).
For adding c to a:
In [58]: np.hstack((a,c))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [58], in <cell line: 1>()
----> 1 np.hstack((a,c))
File <__array_function__ internals>:5, in hstack(*args, **kwargs)
File ~\anaconda3\lib\site-packages\numpy\core\shape_base.py:345, in hstack(tup)
343 return _nx.concatenate(arrs, 0)
344 else:
--> 345 return _nx.concatenate(arrs, 1)
File <__array_function__ internals>:5, in concatenate(*args, **kwargs)
ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 4 dimension(s) and the array at index 1 has 3 dimension(s)
Notice, the error was raised by concatenate, and focuses on the number of dimensions - 4d and 3d. The hstack wrapper did not change inputs at all.
If I add a trailing dimension to c, I get:
In [62]: c[...,None].shape
Out[62]: (2, 4, 4, 1)
In [63]: np.concatenate((a, c[...,None]),axis=3).shape
Out[63]: (2, 4, 4, 4)
Similarly for b:
In [64]: np.concatenate((a, b[...,None,:]),axis=2).shape
Out[64]: (2, 4, 5, 3)
The hstack/vstack docs specify 2nd and 1st axis concatenate. But you want to use axis 2 or 3. So those 'stack' functions don't apply, do they?