Don't concatenate/append/stack arrays if you can help it, especially big ones. It's very wasteful of memory and slow.
Assign A = np.empty((m, n+2, n+2)) and then fill it with A[i] = np.r_[S1, np.c_[S2, Sc[i], S2], S1]. Or do it vectorized and get rid of the for loops:
A = np.zeros((m, n+2, n+2))
A[:,1:-1,1:-1] = Sc
or even do it in one line:
A = np.pad(Sc, ((0,0),(1,1),(1,1)), 'constant', constant_values = 0)
NumPy
numpy.org › doc › stable › reference › generated › numpy.append.html
numpy.append — NumPy v2.5 Manual
>>> np.append([[1, 2, 3], [4, 5, 6]], [[7, 8, 9]], axis=0) array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
Note.nkmk.me
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NumPy: append() to add values to an array | note.nkmk.me
February 4, 2024 - a_3d_ex = np.arange(12).reshape(2, 3, 2) * 10 print(a_3d_ex) # [[[ 0 10] # [ 20 30] # [ 40 50]] # # [[ 60 70] # [ 80 90] # [100 110]]] print(a_3d_ex.shape) # (2, 3, 2) print(np.append(a_3d, a_3d_ex, axis=0)) # [[[ 0 1] # [ 2 3] # [ 4 5]] # # [[ 6 7] # [ 8 9] # [ 10 11]] # # [[ 0 10] # [ 20 30] # [ 40 50]] # # [[ 60 70] # [ 80 90] # [100 110]]] print(np.append(a_3d, a_3d_ex, axis=0).shape) # (4, 3, 2) print(np.append(a_3d, a_3d_ex, axis=1)) # [[[ 0 1] # [ 2 3] # [ 4 5] # [ 0 10] # [ 20 30] # [ 40 50]] # # [[ 6 7] # [ 8 9] # [ 10 11] # [ 60 70] # [ 80 90] # [100 110]]] print(np.append(a_3d, a_3d
python - Append 2D array to 3D array, extending third dimension - Stack Overflow
I have an array A that has shape (480, 640, 3), and an array B with shape (480, 640). How can I append these two as one array with shape (480, 640, 4)? I tried np.append(A,B) but it doesn't keep... More on stackoverflow.com
Append a "layer" to 3D-Array with Numpy - Stack Overflow
I have a numpy array with dimensions 12 x 12 x 4. Now I'm trying to add an extra layer to this cube resulting in a 12 x 13 x 4 array. This 13th layer should contain the corresponding indices from the More on stackoverflow.com
python - appending 3d arrays in numpy - Stack Overflow
I'm trying to find dominant colors in image and then treshold the most dominant one. However I'm having trouble with data types. My formula gives the most dominant color as: color=[10,10,10] # type= More on stackoverflow.com
python - numpy: Append row to a individual 3D array - Stack Overflow
There's numerous posts and blogs talking about how to manipulate 2D arrays using append, vstack or concatenate, but I couldn't make it work in 3D. Problem Assumptions: --The 3D array will have the More on stackoverflow.com
Top answer 1 of 3
4
Don't concatenate/append/stack arrays if you can help it, especially big ones. It's very wasteful of memory and slow.
Assign A = np.empty((m, n+2, n+2)) and then fill it with A[i] = np.r_[S1, np.c_[S2, Sc[i], S2], S1]. Or do it vectorized and get rid of the for loops:
A = np.zeros((m, n+2, n+2))
A[:,1:-1,1:-1] = Sc
or even do it in one line:
A = np.pad(Sc, ((0,0),(1,1),(1,1)), 'constant', constant_values = 0)
2 of 3
2
You can try this:
A = np.concatenate([A, [Atmp]])
Top answer 1 of 2
55
Use dstack:
>>> np.dstack((A, B)).shape
(480, 640, 4)
This handles the cases where the arrays have different numbers of dimensions and stacks the arrays along the third axis.
Otherwise, to use append or concatenate, you'll have to make B three dimensional yourself and specify the axis you want to join them on:
>>> np.append(A, np.atleast_3d(B), axis=2).shape
(480, 640, 4)
2 of 2
3
using np.stack should work
but the catch is both arrays should be of 2D form.
np.stack([A,B])
DataCamp
datacamp.com › doc › numpy › append
NumPy append()
values: The values to be appended to arr. Note that if values have a different data type than arr, the resulting array will have a data type determined by NumPy’s type promotion rules.
Stack Overflow
stackoverflow.com › questions › 28258497 › appending-3d-arrays-in-numpy
python - appending 3d arrays in numpy - Stack Overflow
February 1, 2015 - You don't want to use atleast_3d here since it reshapes a (N,) array to (1,N,1) (see its docs). np.concatenate joins on the 1st axis, where as np.array adds a 1st dimension and then joins. You probably could get append to work, but it just does a step by step concatenate, which is slower.
Stack Overflow
stackoverflow.com › questions › 28246025 › numpy-append-row-to-a-individual-3d-array
python - numpy: Append row to a individual 3D array - Stack Overflow
There's numerous posts and blogs talking about how to manipulate 2D arrays using append, vstack or concatenate, but I couldn't make it work in 3D. Problem Assumptions: --The 3D array will have the shape (k, m, 2). --k will be a known value · --m could range from 1 to n and is not predetermined · In [1]: import numpy as np In [2]: a = np.empty((3, 1, 2)) Out[2]: array([[[0., 0.]], [[0., 0.]], [[0., 0.]]]) In [3]: a[0] = [[5, 6]] In [4]: a Out[4]: array([[[5., 6.]], [[0., 0.]], [[0., 0.]]]) In [5]: a[0] = np.vstack((a[0], [[10, 15]])) Out[5]: ValueError: could not broadcast input array from shape (2,2) into shape(1,2) In [6]: a[0] = np.append(a[0], [[10, 15]], axis=0) Out[6]: ValueError: could not broadcast input array from shape (2,2) into shape(1,2) The desired output would be.
Stack Overflow
stackoverflow.com › questions › 68263096 › how-to-append-a-new-element-into-3d-numpy-array-in-a-loop
python - How to append a new element into 3D numpy array in a loop? - Stack Overflow
In [290]: arr2=np.zeros((1,4,4),int) In [291]: arr2[:,:,:3]=a ...: arr2 Out[291]: array([[[24, 24, 3, 0], [25, 28, 1, 0], [13, 34, 1, 0], [ 3, 4, 5, 0]]]) In [292]: for i in range(4): ...: arr2[:,i,3]=i ...: In [293]: arr2 Out[293]: array([[[24, 24, 3, 0], [25, 28, 1, 1], [13, 34, 1, 2], [ 3, 4, 5, 3]]]) ... The second solution helped me. Thank you so much. 2021-07-06T09:52:06.733Z+00:00 ... Find the answer to your question by asking.
NumPy
numpy.org › devdocs › reference › generated › numpy.append.html
numpy.append — NumPy v2.6.dev0 Manual
A copy of arr with values appended to axis. Note that append does not occur in-place: a new array is allocated and filled.
Top answer 1 of 2
15
Arrays have a fixed size. You can't append to them. If you know the upper bounds of the array, you can preallocate the array with
np.empty((10, 10, 10))
For a 10x10x10 matrix. You can then keep 3 indices x,y,z to track the actual size you have. Eg, add a new element with:
matrix[x,y,z] = newElement
x += 1
Then when you're done, you can extract the submatrix with
finalMatrix = matrix[:x,:y,:z]
2 of 2
0
You can use np.empty((x,y,z))
You can find a good explanation in this answer: How to create 3 dimensions matrix in numpy , like matlab a(:,:,:)
Reddit
reddit.com › r/learnpython › how to append arrays to make 3d array
r/learnpython on Reddit: How to append arrays to make 3d array
July 19, 2021 -
I have an array of 64 arrays of size 12, and I'd like to append them together to make an array of shape (8,8,12). How would I do this?
Top answer 1 of 2
1
The question isn't really clear, but judging from the fact that 8*8=64 I'm guessing that maybe you want to "chunk" your outer list (Python doesn't really use arrays) of length 64 into 8 lists of size 8 (which happen to contain lists of size 12). Take a look at this stack overflow answer for some ways to chunk a list.
2 of 2
1
Given these 2D arrays ... >>> import numpy as np >>> matrix1 = np.random.rand(8, 8) >>> matrix2 = np.random.rand(8, 8) >>> matrix1.shape, matrix2.shape ((8, 8), (8, 8)) >>> matrix1.ndim, matrix2.ndim (2, 2) >>> matrix1.size, matrix2.size (64, 64) ... you can combine them into a rank 3 tensor using np.array which will effectively concatenate them along an entirely new axis: >>> tensor = np.array([matrix1, matrix2]) >>> tensor.shape (2, 8, 8) >>> tensor.ndim 3 >>> tensor.size 128
Medium
medium.com › @whyamit101 › how-to-append-to-a-ndarray-4d73c600f5e5
How to Append to a ndarray. If you think you need to spend $2,000… | by why amit | Medium
February 9, 2025 - It can be a single value, a list, or even another array. axis (optional): This determines whether you append along a specific axis or flatten the data into one dimension. If you set axis=None (default), the function flattens the result. If you specify an axis (e.g., axis=0), the values will be appended along that dimension. Let’s See It in Action! ... import numpy as np # Original array arr = np.array([10, 20, 30]) # Appending a single value new_arr = np.append(arr, 40) print("After appending 40:", new_arr) # Appending multiple values new_arr = np.append(arr, [50, 60]) print("After appending [50, 60]:", new_arr)
TutorialsPoint
tutorialspoint.com › numpy › numpy_append.htm
Numpy Append() Method
The Numpy Append() method adds values to the end of an input array, allocating a new array for the result rather than modifying the original in place. If no axis is specified then both the array and values are flattened before appending.
TutorialsPoint
tutorialspoint.com › numpy › numpy_append_values_to_an_array.htm
NumPy - Append Values to an Array
Appending to multi-dimensional arrays in NumPy involves adding new elements along specified axes. Unlike 1D and 2D arrays, multi-dimensional arrays (e.g., 3D or higher) require careful alignment of the dimensions and axes along which you want to append data.
GeeksforGeeks
geeksforgeeks.org › python › numpy-append-python
numpy.append() in Python - GeeksforGeeks
April 14, 2025 - From the above output we can see that the numpy.append() function does not modify the original arrays (arr1 and arr2). Instead it creates a new array (arr3) with the combined elements. When working with multi-dimensional arrays you can specify the axis parameter to control how the values are appended.
Reddit
reddit.com › r/learnpython › how to append 3d numpy array to a 4d array
r/learnpython on Reddit: How to append 3d numpy array to a 4d array
December 29, 2020 -
I have images with the shape (3,1920,1080) and i want to save them to an array like so (n,3,1920,1080) where n is image order. This will be done continously in a for loop so i only have access to one image at a time
Top answer 1 of 4
1
Sounds like what you really need is a python list of 3D numpy arrays. Appending to a numpy array is possible with np.append or np.concat, but it's very expensive because it forces the entire array to be remade. Is there any reason you want a 4D array?
2 of 4
1
Is this kind of what you're looking for?
>>> a = np.array([[[1, 1, 1],[1, 1, 1]], [[2, 2, 2],[2, 2, 2]], [[3, 3, 3], [3, 3, 3]]])
>>> b = np.array([[[4, 4, 4],[4, 4, 4]], [[5, 5, 5],[5, 5, 5]], [[6, 6, 6], [6, 6, 6]]])
>>> a.shape
(3, 2, 3)
>>> c = np.array([a, b])
>>> c.shape
(2, 3, 2, 3)NumPy
numpy.org › doc › 2.0 › reference › generated › numpy.append.html
numpy.append — NumPy v2.0 Manual
A copy of arr with values appended to axis. Note that append does not occur in-place: a new array is allocated and filled.
w3resource
w3resource.com › numpy › manipulation › append.php
Numpy: numpy.append() function - w3resource
April 25, 2026 - It can append values to a flattened version of an array or along a specified axis in multi-dimensional arrays. 2. Can numpy.append() handle multi-dimensional arrays?