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how to add new elements to a numpy array

I created an array with size (256, 144, 3).
empty_windows = np.empty((256, 144, 3))
Then I want to append new elements into the array with:
for i in range(256):
for j in range(144):
empty_windows[i, j] = np.append(empty_windows[i, j], np.asarray(some_new_array)).reshape(3, )
But it doesnt work as I get the error msg:
ValueError: cannot reshape array of size 6 into shape (3,)
Is there a way of doing it? Thank you.

I hope, it will help you understanding concatenate 3dim array
import numpy as np
empty_windows = np.empty((256, 144, 3))
random_arr = np.random.randint(0, 100, size=(256, 144, 3)) # it's dimension should be same
np.concatenate([empty_windows, random_arr], axis=2) # it can concatenate into an array axis=2 defines 3rd dimension

np.empty and np.append are dangerous functions to use. They are not clones of the the empty list [] and list.append.
empty_windows = np.empty((256, 144, 3))
has made a (256,144,3) shape array with float values - they are unpredictable, but more than likely not what you want. Look at that array, or a smaller example to see for yourself. Also read, and if necessary reread, the np.empty docs. np.zeros is safer.
With scalar i,j,
empty_windows[i, j]
is a (3,) shape array, or slot.
When you np.append it with another (3,) shape, the result is a (6,) shape, with the first 3 value being those "random" values originally in empty_window. The error tells you quite clearly that it can't put a (6,) shape array into a slot that only holds (3,).
Your goal isn't clear, but you can't grow a (n,m,3) shape array to (n,m,6) by doing this kind of "row" by "row" append.
You can set the "row" with new values, as in:
empty_windows[i, j] = np.asarray(some_new_array)).reshape(3, )

Vstack of two arrays with same number of rows gives an error

I have a numpy array of shape (29, 10) and a list of 29 elements and I want to end up with an array of shape (29,11)
I am basically converting the list to a numpy array and trying to vstack, but it complain about dimensions not being the same.
Toy example
a = np.zeros((29,10))
a.shape
(29,10)
b = np.array(['A']*29)
b.shape
(29,)
np.vstack((a, b))
ValueError: all the input array dimensions except for the concatenation axis must match exactly
Dimensions do actually match, why am I getting this error and how can I solve it?

I think you are looking for np.hstack.
np.hstack((a, b.reshape(-1,1)))
Moreover b must be 2-dimensional, that's why I used a reshape.

The problem is that you want to append a 1D array to a 2D array.
Also, for the dimension you've given for b, you are probably looking for hstack.
Try this:
a = np.zeros((29,10))
a.shape
(29,10)
b = np.array(['A']*29)[:,None] #to ensure 2D structure
b.shape
(29,1)
np.hstack((a, b))
If you do want to vertically stack, you'd need this:
a = np.zeros((29,10))
a.shape
(29,10)
b = np.array(['A']*10)[None,:] #to ensure 2D structure
b.shape
(1,10)
np.vstack((a, b))

Flatten matrix in python :

A trick when you want to flatten a matrix X of shape (a,b,c,d) to a matrix X_flatten of shape (b ∗∗ c ∗∗ d, a) is to use:
X_flatten = X.reshape(X.shape[0], -1).T
I read this trick in coursera DL course, how does this work? Where did -1 come from and what does it mean?

X.shape[0] returns the first dimension of your original array:
X = np.random.rand(4, 4, 4, 4)
print(X.shape)
results in
(4, 4, 4, 4)
and therefore
X.shape[0]
returns
4
Using the reshape command, you can omit one of the target matrix dimensions by using -1 as a placeholder,
because one of the dimensions can be inferred by numpy.
I.e. by supplying the 4 from X.shape[0], numpy knows what the remaining first dimension must be for the array to contain all your values.
In the example
new_X = X.reshape(X.shape[0], -1).T
print(new_X.shape)
it is
(64, 4)
which would be equivalent to calling
new_X = X.reshape(X.shape[0], 64).T
print(new_X.shape)
The .T function just transposes the array resulting from the reshape command.

We can do the same in, Using the basic concepts of python
nested_list=[10,20,[30,40,[50]],[80,[10,[20]],90],60]
flat_list=[]
def unpack(list1):
for item in list1:
try:
len(item)
unpack(item)
except:
flat_list.append(item)
unpack(nested_list)
print (flat_list)

Insert A numpy multi dimensional array inside another 1d array

I already have an array with shape (1, 224, 224), a single channel image. I want to change that to (1, 1, 224, 224). I have been trying
newarr.shape
#(1,224,224)
arr = np.array([])
np.append(arr, newarr, 1)
I always get this
IndexError: axis 1 out of bounds [0, 1). If i remove the axis as 0 , then the array gets flattened . What am I doing wrong ?

A dimension of 1 is arbitrary, so it sounds like you want to simply reshape the array. This can accomplished by:
newarr.shape = (1, 1, 244, 244)
or
newarr = newarr[None]

The only way to do an insert into a higher dimensional array is
bigger_arr = np.zeros((1, 1, 224, 224))
bigger_arr[0,...] = arr
In other words, make a target array of the right size, and assign values.
np.append is a booby trap. Avoid it.
Occasionally that's a useful way of thinking of this. But it's simpler, and quicker, to think of this as a reshape problem.
bigger_arr = arr.reshape(1,1,224,224)
bigger_arr = arr[np.newaxis,...]
arr.shape = (1,1,224,224) # a picky inplace change
bigger_arr = np.expand_dims(arr, 0)
This last one does
a.reshape(shape[:axis] + (1,) + a.shape[axis:])
which gives an idea of how to deal with dimensions programmatically.

Python how to convert a value with shape (1000L, 1L) to the value of the shape (1000L,)

I has a variable with a shape of (1000L, 1L), but the structure causes some errors for subsequent analysis. It needs to be converted to the one with the shape (1000L,). Let me be more specific.
import numpy as np
a = np.array([1,2,3])
b = np.array([[1],[2],[3]])
I want to convert b to a. Is there any quick way to do that?

There are a lot of ways you could do that, such as indexing:
a = b[:, 0]
raveling:
a = numpy.ravel(b)
or reshaping:
a = numpy.reshape(b, (-1,))