Manipulating array shapes

In this section, our main focus is on array manipulation. Let's learn some new Python functions of NumPy, such as reshape(), flatten(), ravel(), transpose(), and resize():

• reshape() will change the shape of the array:

# Create an array
arr = np.arange(12)
print(arr)

Output: [ 0  1  2  3  4  5  6  7  8  9 10 11]

# Reshape the array dimension
new_arr=arr.reshape(4,3)

print(new_arr)

Output: [[ 0,  1,  2],
               [ 3,  4,  5],
               [ 6,  7,  8],
               [ 9, 10, 11]]

# Reshape the array dimension
new_arr2=arr.reshape(3,4)

print(new_arr2)

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

• Another operation that can be applied to arrays is flatten(). flatten() transforms an n-dimensional array into a one-dimensional array:

# Create an array
arr=np.arange(1,10).reshape(3,3)
print(arr)

Output: 
[[1 2 3]
 [4 5 6]
 [7 8 9]]

print(arr.flatten())

Output: 
[1 2 3 4 5 6 7 8 9]

• The ravel() function is similar to the flatten() function...