Recap of NumPy Library

Before building Artificial Intelligence (AI) and Machine Learning (ML) models, it is important to understand how numerical data is stored and processed efficiently. In Class XI, you learned the basics of the NumPy library. In this section, we will revise those concepts and understand why NumPy is one of the most important Python libraries used in AI.

NumPy provides a powerful multidimensional array object and a collection of mathematical functions that allow developers to perform numerical computations quickly and efficiently. Almost every Machine Learning and Data Science library is built on top of NumPy.

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Learning Objectives

• Understand the purpose of the NumPy library.
• Learn about NumPy arrays and their dimensions.
• Create one-dimensional and multidimensional arrays.
• Use common NumPy functions.
• Understand the role of NumPy in Artificial Intelligence.

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What is NumPy?

NumPy stands for Numerical Python. It is an open-source Python library specially designed for numerical computing and scientific calculations.

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Why Do We Need NumPy?

Python lists are useful for storing data, but they become slow when performing calculations on large datasets. Artificial Intelligence applications often deal with thousands or millions of data records. NumPy makes these calculations much faster by storing data efficiently and providing optimized mathematical operations.

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Advantages of NumPy

• Faster than Python lists.
• Consumes less memory.
• Supports multidimensional arrays.
• Provides hundreds of mathematical functions.
• Performs statistical calculations easily.
• Supports matrix operations.
• Forms the foundation of Machine Learning libraries.

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Applications of NumPy in Artificial Intelligence

Application	Use of NumPy
Machine Learning	Stores and processes training data.
Computer Vision	Represents images as matrices.
Natural Language Processing	Processes numerical text representations.
Robotics	Performs mathematical computations.
Data Analytics	Calculates statistical measures.

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Importing the NumPy Library

Before using NumPy, it must be imported into the Python program.

import numpy as np

Here, np is an alias (short name) for the NumPy library.

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Understanding NumPy Arrays

The primary data structure provided by NumPy is the ndarray (N-dimensional array). Unlike Python lists, NumPy arrays store elements of the same data type and provide faster mathematical operations.

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Types of Arrays

Type	Description	Example
One-Dimensional Array	Contains a single row of elements.	[10, 20, 30]
Two-Dimensional Array	Contains rows and columns.	[[10,20],[30,40]]
Three-Dimensional Array	Contains multiple matrices.	3D Dataset

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Creating a One-Dimensional Array

import numpy as np

marks = np.array([80,85,90,95])

print(marks)

Output

[80 85 90 95]

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Creating a Two-Dimensional Array

import numpy as np

marks = np.array([[80,85],

                  [90,95]])

print(marks)

Output

[[80 85]

 [90 95]]

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Rank (Dimension) of an Array

According to the CBSE AI handbook, the number of dimensions of an array is called its Rank.

Array	Rank
[1,2,3]	1
[[1,2],[3,4]]	2
Three-dimensional array	3

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Finding the Dimension of an Array

import numpy as np

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

print(arr.ndim)

Output

2

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Common NumPy Functions

Function	Purpose
sum()	Returns the sum of elements.
mean()	Calculates the average.
median()	Returns the median value.
std()	Calculates standard deviation.
min()	Returns the smallest value.
max()	Returns the largest value.
shape	Returns rows and columns.
ndim	Returns the dimension (rank).

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Example Program

import numpy as np

marks = np.array([75,82,91,88,95])

print("Marks =", marks)

print("Sum =", np.sum(marks))

print("Average =", np.mean(marks))

print("Highest =", np.max(marks))

print("Lowest =", np.min(marks))

Output

Marks = [75 82 91 88 95]

Sum = 431

Average = 86.2

Highest = 95

Lowest = 75

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Dry Run

Step	Statement	Result
1	Create array	[75,82,91,88,95]
2	np.sum()	431
3	np.mean()	86.2
4	np.max()	95
5	np.min()	75

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Real-Life Example

A school wants to calculate the average marks of 5,000 students. Using NumPy arrays, all marks can be stored efficiently, and functions like mean(), max(), and min() can calculate statistics within seconds.

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Think Like an AI Engineer

An autonomous vehicle continuously receives millions of sensor readings every minute. Which Python library should be used to perform high-speed numerical calculations on this data?

Click to View Answer

NumPy, because it is optimized for fast numerical computation and efficient array processing.

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Competency-Based Question

A weather department collects temperature readings from thousands of sensors every hour. Explain why NumPy is more suitable than Python lists for storing and analyzing this data.

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Common Beginner Mistakes

• Forgetting to import NumPy before using its functions.
• Using Python lists instead of NumPy arrays for mathematical computations.
• Confusing the shape attribute with ndim.
• Using different data types within the same NumPy array.

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Quick Revision

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Memory Trick

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Exam Tips

• Remember that the number of dimensions of an array is called its Rank.
• Practice creating one-dimensional and two-dimensional arrays.
• Know the purpose of commonly used NumPy functions.
• Understand why NumPy is preferred over Python lists for numerical computation.

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Summary

• NumPy is a powerful Python library for numerical computing.
• It provides fast multidimensional arrays and optimized mathematical functions.
• NumPy arrays consume less memory and execute faster than Python lists.
• It is widely used in Artificial Intelligence, Machine Learning, Data Science, and Scientific Computing.
• NumPy serves as the foundation for many advanced Python libraries used in AI.