How do i convert the entries within an array into arrays in python?

I need to convert a numpy array as follows:

[1,1,1,1] -> [[1],[1],[1],[1]]

Use np.reshape:

import numpy as np

values = np.random.randint(1, 10, 10)
print(values)
values = values.reshape((-1, 1))
print(values)

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# Before reshaping
[5 9 8 1 2 6 2 2 9 8]

# After reshaping
[[5]
 [9]
 [8]
 [1]
 [2]
 [6]
 [2]
 [2]
 [9]
 [8]]

Using -1 tells NumPy that the new shape should be compatible with the original shape (i.e. it should figure out the value for that dimension based on the number of elements in the array).

An alternative answer to the already given .reshape(-1,1) is to add a new axis

x=np.arange(4)
x[:,None]
#array([[0],
#       [1],
#       [2],
#       [3]])

Note that both, (reshape and [:,None]) create just a view of the original array.
So

x=np.arange(4)
x[:,None][0,0] = 12
x.reshape(-1,1)[1,0]=13
x
# [12, 13, 2, 3]

If you wanted a new array, you may need to .copy() the result.

In such case, I wouldn't have any preference between .reshape(-1,1) and [:,None]. But the latter may be more flexible when you have more than 1 axis. For example, let say that you have a 2d array (that I create for this example using a reshape, incidently)

x=np.arange(12).reshape(-1, 4) # here I use reshape, because it is a shape that I want to specify : I want rows of 4.
# array([[ 0,  1,  2,  3],
#       [ 4,  5,  6,  7],
#       [ 8,  9, 10, 11]])

Now you want to convert this to

array([[ [0],  [1], [2],  [3]],
       [ [4],  [5], [6],  [7]],
       [ [8],  [9], [10], [11]]])

reshape is harder to use in such case. You need to know the shape of your x (which isn't a top secret information, sure, but it becomes less natural).

You cannot x.reshape(-1,-1,1). You have to either x.reshape(3,4,1) or having one, no more, -1, like x.reshape(3,-1,1) or x.reshape(-1,4,1). Or, more generically x.reshape(x.shape+(1,))

Where as with newaxis, since we are just adding an axis, not specifying the size of the axes, it is still the same as with 1D.

x[:,:,None]
# aka
x[:,:,np.newaxis]

So, I would let reshape the job, when you want to reshape things (change the sizes), and let newaxis the job when you just want to add a new axis (written like that, it seems obvious).

Although not numpy, you could also do this really easily with list comprehension in plain old Python:

>>> lst = [1,1,1,1]
>>> reshaped = [[el] for el in lst]
>>> print(reshaped)
[[1], [1], [1], [1]]