NumPy in Python | Arrays, 1D Arrays, 2D Arrays and Creating Arrays from Lists

Python provides several built-in data structures such as lists, tuples, dictionaries, and sets. While lists are suitable for storing collections of data, they are not the most efficient choice for performing large-scale numerical computations. To overcome this limitation, Python provides the NumPy library.

NumPy (Numerical Python) is one of the most popular Python libraries used for scientific computing, data analysis, machine learning, artificial intelligence, and engineering applications. It provides a powerful array object that is faster and more memory-efficient than Python lists when performing numerical operations.

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Learning Outcomes

After studying this chapter, you will be able to:

• Understand the purpose of the NumPy library.
• Create NumPy arrays.
• Create arrays from Python lists.
• Work with one-dimensional (1D) arrays.
• Work with two-dimensional (2D) arrays.
• Access elements of NumPy arrays.

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What is NumPy?

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Why Do We Need NumPy?

Although Python lists can store multiple values, they are slower when processing large amounts of numerical data. NumPy arrays are optimized for speed and memory usage, making them suitable for scientific and mathematical applications.

Python List	NumPy Array
General-purpose data storage.	Optimized for numerical data.
Consumes more memory.	Consumes less memory.
Slower for calculations.	Faster mathematical operations.
Supports mixed data types.	Generally stores elements of the same data type.

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Installing NumPy

If NumPy is not already installed, it can be installed using the following command:

pip install numpy

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Importing NumPy

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

import numpy as np

The alias np is commonly used to make the code shorter and easier to write.

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What is an Array?

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Advantages of NumPy Arrays

• Fast numerical calculations.
• Less memory consumption.
• Easy mathematical operations.
• Efficient handling of large datasets.
• Foundation for Data Science and Artificial Intelligence.

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Creating an Array from a Python List

The array() function is used to convert a Python list into a NumPy array.

Syntax

np.array(list_name)

Example

import numpy as np

numbers = [10, 20, 30, 40, 50]

arr = np.array(numbers)

print(arr)

Output

[10 20 30 40 50]

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One-Dimensional (1D) Array

Example

import numpy as np

marks = np.array([85, 92, 78, 88, 95])

print(marks)

Output

[85 92 78 88 95]

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Accessing Elements of a 1D Array

import numpy as np

numbers = np.array([5, 10, 15, 20, 25])

print(numbers[0])
print(numbers[3])

Output

5
20

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Negative Indexing in Arrays

import numpy as np

numbers = np.array([5, 10, 15, 20, 25])

print(numbers[-1])
print(numbers[-2])

Output

25
20

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Two-Dimensional (2D) Array

Example

import numpy as np

marks = np.array([
    [85, 90, 88],
    [76, 81, 95],
    [92, 89, 84]
])

print(marks)

Output

[[85 90 88]
 [76 81 95]
 [92 89 84]]

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Structure of a 2D Array

	Column 0	Column 1	Column 2
Row 0	85	90	88
Row 1	76	81	95
Row 2	92	89	84

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Accessing Elements of a 2D Array

Elements in a 2D array are accessed using the row index followed by the column index.

Syntax

array_name[row][column]

Example

import numpy as np

marks = np.array([
    [85, 90, 88],
    [76, 81, 95],
    [92, 89, 84]
])

print(marks[0][1])
print(marks[2][0])

Output

90
92

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Real-Life Applications of NumPy

Application	Use of NumPy
Artificial Intelligence	Training machine learning models.
Data Science	Analyzing large datasets.
Computer Vision	Processing images as arrays.
Scientific Research	Performing numerical computations.
Weather Forecasting	Analyzing climate data.

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Comparison Between Python Lists and NumPy Arrays

Python List	NumPy Array
Built into Python.	Provided by the NumPy library.
Can store different data types.	Generally stores elements of the same data type.
Suitable for general-purpose programming.	Suitable for numerical and scientific computations.
Slower for mathematical calculations.	Faster for mathematical calculations.
Consumes comparatively more memory.	Consumes comparatively less memory.

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One-Dimensional (1D) Array vs Two-Dimensional (2D) Array

1D Array	2D Array
Contains only one row of elements.	Contains multiple rows and columns.
Has one axis.	Has two axes.
Similar to a Python list.	Similar to a table or matrix.
Accessed using one index.	Accessed using row and column indexes.

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Solved Program 1: Create a NumPy Array

import numpy as np

numbers = np.array([5, 10, 15, 20])

print(numbers)

Output

[ 5 10 15 20 ]

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Solved Program 2: Display Individual Elements

import numpy as np

marks = np.array([85, 90, 88, 95])

print(marks[0])
print(marks[2])

Output

85
88

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Solved Program 3: Display All Elements of a 1D Array

import numpy as np

numbers = np.array([12, 24, 36, 48])

for value in numbers:
    print(value)

Output

12
24
36
48

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Solved Program 4: Display All Elements of a 2D Array

import numpy as np

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

for row in matrix:
    print(row)

Output

[10 20]
[30 40]

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Solved Program 5: Access an Element from a 2D Array

import numpy as np

matrix = np.array([
    [5, 10, 15],
    [20, 25, 30],
    [35, 40, 45]
])

print(matrix[1][2])

Output

30

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Solved Program 6: Display a Complete Row

import numpy as np

marks = np.array([
    [85, 90, 88],
    [76, 81, 95],
    [92, 89, 84]
])

print(marks[1])

Output

[76 81 95]

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Solved Program 7: Create an Array from a List of Cities

import numpy as np

cities = ["Jaipur", "Delhi", "Mumbai"]

arr = np.array(cities)

print(arr)

Output

['Jaipur' 'Delhi' 'Mumbai']

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Common Errors

Mistake	Correct Practice
Forgetting to import NumPy.	Always use import numpy as np before creating arrays.
Using array() without np.	Use np.array().
Using an invalid index.	Ensure the index is within the valid range.
Confusing Python lists with NumPy arrays.	Remember that NumPy arrays belong to the NumPy library.
Accessing a wrong row or column in a 2D array.	Use the correct row and column indexes.

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Interview Corner

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Competency-Based Questions

1. A data analyst needs to process thousands of numerical values quickly. Should Python lists or NumPy arrays be used? Give a reason.
2. A teacher stores the marks of students in rows and columns. Which type of NumPy array is more suitable?
3. An image processing application stores pixel values in rows and columns. Which type of array is used?
4. A programmer wants to convert a Python list into a NumPy array. Which function should be used?
5. How are elements accessed in a two-dimensional array?

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Multiple Choice Questions

1. NumPy stands for:
   • (a) Numeric Program
   • (b) Numerical Python
   • (c) Number Processing
   • (d) Numeric Package
2. Which statement imports NumPy using the standard alias?
   • (a) import NumPy
   • (b) import numpy
   • (c) import numpy as np
   • (d) include numpy
3. Which function creates a NumPy array?
   • (a) create()
   • (b) array()
   • (c) list()
   • (d) ndarray()
4. A two-dimensional array consists of:
   • (a) Only rows
   • (b) Only columns
   • (c) Rows and columns
   • (d) Keys and values
5. Which library is widely used for numerical computing in Python?
   • (a) Turtle
   • (b) Pandas
   • (c) NumPy
   • (d) Tkinter

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Quick Revision

Concept	Remember
NumPy	Python library for numerical computing.
Array	Collection of similar data elements.
np.array()	Creates a NumPy array.
1D Array	Single row of elements.
2D Array	Rows and columns.
Import Statement	import numpy as np

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Important Points to Remember

• NumPy stands for Numerical Python.
• NumPy is imported using import numpy as np.
• np.array() converts a Python list into a NumPy array.
• A NumPy array usually stores elements of the same data type.
• A 1D array has one axis, whereas a 2D array has two axes.
• Elements of a 2D array are accessed using row and column indexes.
• NumPy arrays are faster and more memory-efficient than Python lists for numerical operations.

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CBSE Exam Tips

• Remember the syntax for importing NumPy.
• Practice creating arrays from Python lists.
• Understand the difference between 1D and 2D arrays.
• Practice accessing elements using indexes.
• Learn the advantages of NumPy arrays over Python lists.
• Practice output-based questions involving array indexing.

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Summary

NumPy is a powerful Python library used for numerical and scientific computing. It provides the efficient ndarray data structure, which is faster and more memory-efficient than Python lists for handling numerical data. Using the np.array() function, Python lists can easily be converted into NumPy arrays. NumPy supports both one-dimensional (1D) and two-dimensional (2D) arrays, making it suitable for representing vectors, tables, matrices, and datasets. A clear understanding of NumPy arrays forms the foundation for advanced topics such as data science, machine learning, artificial intelligence, and scientific computing.