NumPy Array Objects

NumPy Array Objects

• The NumPy array object is a core data structure provided by the NumPy library. It is also known as ndarray which stands for N‒dimensional array.

Structure of NumPy array object(ndarray) is

Component       :          Purpose

data ‒ The actual values stored. It can be numbers, strings.

shape ‒ The dimension of array(rows * columns)

dtype ‒ It is basically the data type of elements in the memory.

strides ‒ Steps to move through elements in memory.

flags ‒ Internal information about memory and storage

Python program for creation of NumPy array object

import numpy as np

arr = np.array([10,20,30,40,50])

print(arr)

print(type(arr))

Output.

 [10 20 30 40 50]

<class 'numpy.ndarray'>

Code explanation: In above code,

We have created an array object arr. It stores 5 integers. The values are stored in continuous memory allowing fast mathematical operations. Note that in the output we get the type of array as <class 'numpy.ndarray"> which indicates that it is an object,

Python program for creating 2D array object

import numpy as np

arr = np.array([[10,20,30],[40,50,60]])

print(arr)

print(type(arr))

Output

[[10 20 30]

 [40 50 60]]

<class 'numpy.ndarray'>

Code explanation: In above code,

The 2D Numpy array object is created in variable arr. It has 2 rows and 3 columns. The type returns the datatype of variable arr and it is ndarray. This is nothing but the array object.

Features of NumPy array object

1) Homogeneous: All the elements are of same type. For instance ‒ All the elements in array can be integers.

2) Efficient storage: The NumPy arrays use less memory than Python lists.

3) Vectorized operations: It allows element ‒ wise computations such as arr1+arr2 or arrl‒arr2.

4) Multidimensional: We can create 1D, 2D, 3D arrays

5) Attribute support: The array objects can use various attributes such as shape, size, type and so on.

Review Question

1. What is the class name of a NumPy array object?