x[0].shape will give the Length of 1st row of an array. x.shape[0] will give the number of rows in an array. In your case it will give output 10. If you will type x.shape[1], it will print out the number of columns i.e 1024. If you would type x.shape[2], it will give an error, since we are working on a 2-d array and we are out of index. Let me explain you all the uses of 'shape' with a simple example by taking a 2-d array of zeros of dimension 3x4.

import numpy as np
#This will create a 2-d array of zeroes of dimensions 3x4
x = np.zeros((3,4))
print(x)
[[ 0.  0.  0.  0.]
[ 0.  0.  0.  0.]
[ 0.  0.  0.  0.]]

#This will print the First Row of the 2-d array
x[0]
array([ 0.,  0.,  0.,  0.])

#This will Give the Length of 1st row
x[0].shape
(4,)

#This will Give the Length of 2nd row, verified that length of row is showing same 
x[1].shape
(4,)

#This will give the dimension of 2-d Array 
x.shape
(3, 4)

# This will give the number of rows is 2-d array 
x.shape[0]
3

# This will give the number of columns is 2-d array 
x.shape[1]
3

# This will give the number of columns is 2-d array 
x.shape[1]
4

# This will give an error as we have a 2-d array and we are asking value for an index 
out of range
x.shape[2]
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-20-4b202d084bc7> in <module>()
----> 1 x.shape[2]

IndexError: tuple index out of range

np.shape is a function; x.shape is a property, that normally returns the same thing.

Make a sample array:

In [119]: x = np.ones((1,2,3),int)
In [120]: x
Out[120]: 
array([[[1, 1, 1],
        [1, 1, 1]]])
In [121]: x.shape
Out[121]: (1, 2, 3)          # a tuple
In [122]: x.shape[1]
Out[122]: 2                  # select the 2nd element of the tuple

The function returns the same tuple:

In [123]: np.shape(x)
Out[123]: (1, 2, 3)

Here x[0] is performed first, returning a new array:

In [124]: np.shape(x[0])
Out[124]: (2, 3)
In [125]: x[0].shape
Out[125]: (2, 3)

np.shape can take a non-array, such as a list of lists

In [126]: np.shape([[1],[2]])
Out[126]: (2, 1)

But if you want the 2nd dimension, you could use size, which returns just one number (depending on the optional axis parameter):

In [127]: np.size(x,1)
Out[127]: 2
In [128]: np.size(x)
Out[128]: 6

shape is a tuple that gives dimensions of the array..

>>> c = arange(20).reshape(5,4)
>>> c
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11],
       [12, 13, 14, 15],
       [16, 17, 18, 19]])

c.shape[0] 
5

Gives the number of rows

c.shape[1] 
4

Gives number of columns