Understanding the attributes of NumPy arrays and their versatility.

Numpy is at the backbone of numerical computing in Python.
Below are the attributes of a Numpy array.

• ndarray.ndim
  Gives the number of dimensions of the array as an integer value

array = np.array([1, 2, 3])
array.ndim # Returns 1

array2 = np.array([[2, 3, 4], [2, 3, 4]])
array2.ndim # Returns 2

• ndarray.shape
  Gives the sequence of integers indicating the size of the array for each dimension

array = np.array([1, 2, 3])
array.shape # Returns (3,)

array2 = np.array([[3, 4, 5], [6, 7, 8]])

array2.shape # Returns (2, 3)

• ndarray.size
  It returns the total number of elements in the array

arr = np.array([3, 4, 6])

np.size # Returns 3

arr2 = np.array([[3, 5, 7], [8, 90, 10]])
arr2.size # Returns 6

• ndarray.dtype
  Returns the data type of the elements in the array. All elements in the array are of same data type. Common data types are int32, int64 float32, float64, U32

array = np.array([10, 20, 30])
array.dtype # Returns int64

array2 = np.array(["Python", "Pytorch", "Maths", "Statistics", "AI"])

array2.dtype # Returns <U10>

• ndarray.itemsize
  It specifies the size in bytes of each element in the array. Data type int32 and float32 means each element of the array occupies 32 bits in memory. 8 bits form a byte. Thus, an array of elements of type int32 has itemsize 32/8 = 4 bytes.

arr = np.array([3, 4, 5])
arr.itemsize # Returns 8 - the dtype of array is int64, thus each item has 64 / 8 = 8 bytes