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Python Programming A Stepbystep Guide To Learning The Language C K Dhaliwal

Python Programming A Stepbystep Guide To Learning The Language C K Dhaliwal Python Programming A Stepbystep Guide To Learning The Language C K Dhaliwal Python Programming A Stepbystep Guide To Learning The Language C K Dhaliwal

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Python Programming A Step-by-Step Guide to Learning the Language
Python Programming A Step-by-Step Guide to Learning the Language Dr. C. K. Dhaliwal Assistant Professor Chandigarh Business School of Administrations Mohali, Punjab *** Poonam Rana Assistant Professor Chandigarh Business School of Administrations Mohali, Punjab *** Dr. T. P. S. Brar Professor & Head of Department Chandigarh Group of Colleges Mohali, Punjab
First published 2025 by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN and by CRC Press 2385 NW Executive Center Drive, Suite 320, Boca Raton FL 33431 CRC Press is an imprint of Informa UK Limited © 2025 Manakin Press Pvt. Ltd The right of of C. K. Dhaliwal, Poonam Rana and Dr. T. P. S. Brar to be identified as authors of this work has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. For permission to photocopy or use material electronically from this work, access www. copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact mpkbookspermissions@tandf.co.uk Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Print edition not for sale in South Asia (India, Sri Lanka, Nepal, Bangladesh, Pakistan or Bhutan). British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 9781032646558 (hbk) ISBN: 9781032669571 (pbk) ISBN: 9781032691053 (ebk) DOI: 10.4324/9781032691053 Typeset in Times New Roman by Manakin Press, Delhi
Organization of the Book Chapter 1 This is an introductory chapter that provides an overview of Python, covering its history, features, applications, and installation process. It highlights Python's dynamic, high-level, and object-oriented language features and cross- platform compatibility. The chapter emphasizes the use of Python in web development, data science, and machine learning. It also explains Python interactive help and demonstrates how to install and execute Python on different platforms. Additionally, the chapter covers how Python differs from other programming languages. Chapter 2 This chapter introduces the basics of Python programming language. It covers keywords and identifiers, explaining their differences and how to use them properly. The chapter then moves on to Python statements and demonstrates how to use them to create simple programs. It highlights the importance of documentation and indentation in Python programming. The chapter covers variables and their declaration in Python, including the rules for naming variables. It also covers the different data types in Python, such as numbers, strings, lists, and tuples, along with examples of how to use them. Chapter 3 This chapter covers Python operators, including arithmetic, relational, logical, bitwise, assignment, and identity operators. It also explains the precedence and associativity of operators, which determine the order in which they are evaluated. The chapter demonstrates how to use expressions, which are combinations of operands and operators, to perform calculations and manipulate data. Chapter 4 This chapter covers conditional statements in Python, including if, if- else, and if-elif-if statements. It also covers loops in Python, including while, for, and infinite loops, along with examples of how to use them. Additionally, the chapter covers the use of the break, continue, and pass statements in Python loops, which are used to change the flow of control in a program. Chapter 5 This chapter covers the native data types in Python, including numbers, lists, tuples, sets, dictionaries, and strings. It provides examples and use cases for each data type. The chapter highlights the differences between mutable and immutable data types and how to work with them. Additionally, it covers how to manipulate and operate on data types, including slicing and indexing. Chapter 6 This chapter covers Python functions, including the types of functions in Python, such as built-in functions, user-defined functions, and anonymous functions. It discusses the advantages of using functions, such as code reusability, modularity, and easier debugging. The chapter also covers the differences between pass by value and pass by reference and demonstrates recursion, which is the ability of a function to call itself.
vi Python Programming: A Step-by-Step Guide to Learning the Language Chapter 7 This chapter covers Python modules, which are files that contain Python definitions and statements. It demonstrates how to create a module and how to import it into another Python program. Additionally, the chapter covers standard modules, which are built-in modules that come with Python, and Python packages, which are directories containing modules. It highlights how to use and install standard modules and how to create and install Python packages. Chapter 8 This chapter covers Python exceptions, which are errors that occur during program execution. It explains the different types of built-in exceptions in Python, such as ZeroDivisionError and TypeError. The chapter demonstrates how to handle exceptions using the try-except block and how to raise and catch user-defined exceptions. It also provides examples of exception handling and how to use the else and finally clauses with the try-except block. Chapter 9 This chapter covers file operations in Python, including how to create, open, read, write, and close files using file methods such as read() and write(). The chapter also covers renaming and deleting files, as well as creating and navigating directories in Python using the os module. It provides examples of how to use file methods and how to handle file exceptions. Chapter 10 This chapter covers designing classes in Python, which are templates for creating objects that have similar properties and behaviors. It explains how to create objects from a class, how to access object attributes, and how to use built-in class attributes, such as name and doc. The chapter also covers garbage collection in Python, which is the process of freeing up memory that is no longer being used by a program. It provides examples of how to design and use classes in Python. Chapter 11 This chapter covers inheritance in Python, which is the ability to create a new class from an existing class. It explains the different types of inheritance in Python, including single inheritance, multiple inheritance, and multilevel inheritance. The chapter also covers method overriding in Python, which is the ability to redefine a method in a subclass. Additionally, the chapter discusses special functions in Python, which are predefined methods that are called under certain circumstances, such as init and str. It provides examples of how to use inheritance and special functions in Python. Chapter 12 This chapter covers operator overloading in Python, which is the ability to redefine the behavior of an operator in a class. It explains how to overload the + and - operators in Python, as well as bitwise and relational operators. The chapter provides examples of how to use operator overloading to customize the behavior of operators in Python. The Appendix- I is given which provides the list of Python Standard Modules with the description of each. The bibliography is given at the end for reference of readers. Authors
1. Introduction to Python Language 1–24 1.1 Programming Language 2 1.2 History of Python Language 3 1.3 Origin of Python Programming Language 5 1.4 Features of Python 5 1.5 Limitations of Python 6 1.6 Major Applications of Python 7 1.7 Getting Python 8 1.8 Installing Python 8 1.8.1 Unix and Linux Installation 9 1.8.2 Windows Installation 9 1.8.3 Macintosh Installation 10 1.9 Setting up Path 10 1.9.1 Setting up Path at Unix/Linux 11 1.9.2 Setting up the Path at windows 11 1.10 Python Environment Variables 11 1.11 Running Python 12 1.11.1 Interactive Interpreter 13 1.11.2 Script from the Command-Line 13 1.11.3 Integrated Development Environment 14 1.12 First Python Program 14 1.12.1 Interactive Mode Programming 15 1.12.2 The Script Mode Programming 16 1.13 Python’s Interactive Help 16 1.13.1 Python Help Through a Web Browser 17 1.14 Python Differences From Other Languages 17 1.14.1 Difference Between C and Python 18 1.14.2 Difference Between C++ and Python 19 1.14.3 Difference Between Java and Python 21 1.15 Summary 22 Review Questions 22 Detailed Contents
2. Python Data Types and Input Output 25–52 2.1 Keywords 25 2.2 Identifiers 27 2.3 Python Statements 28 2.4 Indentation 29 2.5 Python Documentation 30 2.5.1 Single Line Comment 30 2.5.2 Multi Line Comments 30 2.6 Docstrings 31 2.7 Variables 32 2.7.1 Variable Assignment 33 2.7.2 Variable Types in Python 33 2.8 Multiple Assignment 34 2.9 Python Data Types 36 2.9.1 Numeric Datatype 37 2.9.1.1 Integers 37 2.9.1.2 Floating-point Numbers 38 2.9.1.3 Complex Numbers 39 2.9.2 Strings 40 2.9.2.1 Indexing of a String: 40 2.9.2.2 Negative Indexing: 41 2.9.2.3 Slicing 41 2.9.3 Booleans 42 2.9.4 Lists 42 2.9.5 Tuples 43 2.9.6 Sets 44 2.7.8 Dictionaries 46 2.10 Data Type Conversion 48 2.10.1 Implicit Type Conversion in Python 49 2.10.2 Explicit Type Conversion in Python 49 2.11 Input and Output 50 2.12 Import 51 2.13 Summary 51 Review Questions 52 3. Operators and Expressions 53–70 3.1 Operator 53 3.1.1 Arithmetic Operators 54 viii Python Programming: A Step-by-Step Guide to Learning the Language
3.1.2 Comparison Operators 56 3.1.3 Assignment Operator 58 3.1.4 Logical Operators 59 3.1.5 Bitwise Operators 61 3.1.6 Special Operators 63 3.1.6.1 Identity Operators 63 3.1.6.2 Membership Operators 64 3.2 Expressions 65 3.2.1 Python Operator Precedence 66 3.2.2 Associativity 67 3.2.3 Non-Associative Operators 68 3.3 Summary 68 Review Questions 68 4. Control Structures 71–92 4.1 Decision Making Statements 72 4.1.1 Python if Statement 72 4.1.2 Python if-else Statement 73 4.1.3 Python if-elif-else 75 4.1.4 Python Nested if Statements 77 4.2 Python Loops 78 4.2.1 Types of Loops 79 4.2.2 Python While Loop 79 4.2.3 The Infinite Loop 81 4.2.4 Using else with While Loop 81 4.2.5 Python for Loop 82 4.2.6 The range() Function 84 4.2.7 For Loop with else 85 4.2.8 Nested Loops 86 4.3 Python Control Statements 87 4.3.1 Python Break Statement 87 4.3.2 Python Continue Statement 89 4.3.3 Python Pass Statement 90 4.4 Summary 90 Review Questions 91 5. Python Native Data Types 93–142 5.1 Numbers 94 5.1.1 Number Type Conversion 94 Detailed Contents ix
5.1.2 Python Mathematical Functions 95 5.1.3 Python Trigonometric Functions 97 5.1.4 Python Random Number Functions 99 5.1.5 Python Mathematical Constants 100 5.2 Python Lists 101 5.2.1 Creating a List 101 5.2.2 Traversing a List 101 5.2.2.1 Indexing 102 5.2.2.2 Traversing Nested Lists 103 5.2.2.3 Negative Indexing 104 5.2.2.4 Slicing 105 5.2.3 Changing or Adding Elements to a List 105 5.2.4 List Methods 106 5.2.5 List Functions 107 5.2.6 List Comprehension 108 5.2.7 List Membership Test 108 5.3 Python Tuples 109 5.3.1 Creating a Tuple 110 5.3.2 Unpacking Tuple 111 5.3.3 Traversing Elements in a Tuple 111 5.3.3.1 Indexing 112 5.3.3.2 Negative Indexing 113 5.3.3.3 Tuple Slicing 113 5.3.3.4 Changing/Updating a Tuple 114 5.3.3.5 Deleting a Tuple 115 5.3.3.6 Python Tuple Methods 115 5.3.3.7 Python Tuple Functions 116 5.3.3.8 Advantages of Tuple 116 5.4 Python Sets 117 5.4.1 Creating a Set 118 5.4.2 Changing/Adding Elements to a Set 118 5.4.3 Removing Elements from a Set 119 5.4.4 Python Set Operations 119 5.4.4.1 Set Union 120 x Python Programming: A Step-by-Step Guide to Learning the Language
5.4.4.2 Set Intersection 120 5.4.4.3 Set Difference 120 5.4.4.4 Set Symmetric Difference 121 5.4.5 Python Set Methods 122 5.4.6 The in Operator 123 5.4.7 Python Set Functions 123 5.4.8 Frozen Sets 124 5.5 Python Dictionary 125 5.5.1 Creating a Dictionary 125 5.5.2 Accessing a Dictionary 125 5.5.3 Updating a Dictionary 126 5.5.4 Removing or Deleting Elements of a Dictionary 127 5.5.5 Python Dictionary Methods 127 5.5.6 Python Dictionary Membership Test 128 5.5.7 Python Dictionary Functions 129 5.6 Python Strings 130 5.6.1 Creating a String in Python 131 5.6.2 Accessing String Characters 132 5.6.3 Changing or Deleting String Characters 133 5.6.4 Python String Operations 135 5.6.4.1 Concatenation 136 5.6.4.2 Iteration and Membership Test 137 5.6.5 String Formatting 138 5.6.6 Python String Built-in Methods 139 5.7 Summary 140 Review Questions 140 6. Python Functions 143–160 6.1 Python Functions 143 6.2 Advantages of Python 144 6.3 Types of Functions 145 6.4 Built-in Functions 145 6.5 Python User Defined Functions 146 6.5.1 Function Definition 147 6.5.2 Function Call 147 Detailed Contents xi
6.5.3 Types of Function Arguments (Parameters) 148 6.5.3.1 Function with No Arguments 149 6.5.3.2 Function with Required Arguments 150 6.5.3.3 Function with Arbitrary Length Arguments 150 6.5.3.4 Function with Keyword Based Arguments 151 6.5.3.5 Function with Default Arguments 152 6.6 Python Anonymous Functions 153 6.6.1 Characteristics of Lambda Form 154 6.7 Pass by Value vs. Pass by Reference 154 6.7.1 Pass by Value 155 6.7.2 Pass by Object Reference 156 6.8 Recursion 156 6.8.1 Advantages of Recursion 157 6.8.2 Disadvantages of Recursion 158 6.9 Scope and Lifetime of Variables 158 6.10 Summary 159 Review Questions 159 7. Python Modules 161–172 7.1 Need of Module 162 7.2 Module Definition 163 7.3 Creating a Module 163 7.4 Importing Module in The Interpreter 164 7.5 Importing Module in The Another Script 165 7.6 Importing Modules 165 7.7 Search Path of Module 166 7.8 Module Reloading 167 7.9 The dir() Function 168 7.10 Standard Modules 168 7.11 Python Packages 169 7.12 Summary 170 Review Questions 171 8. Exception Handling 173–182 8.1 Exception 173 8.2 Python Built-in Exceptions 174 xii Python Programming: A Step-by-Step Guide to Learning the Language
8.3 Exception Handling 175 8.3.1 Try, Except, Else and Finally 176 8.3.2 Catching Specific Exceptions in Python 178 8.3.3 try….finally 179 8.4 Python User Defined Exceptions 179 8.5 Summary 181 Review Questions 181 9. File Management in Python 183–196 9.1 Operations on Files 183 9.1.1 Opening a File 184 9.1.2 File Modes 185 9.1.3 File Object Attributes 186 9.1.4 File Encoding 186 9.1.5 Closing a File 187 9.2 write() and read() Methods 188 9.2.1 Writing to a File 188 9.2.2 Reading from a File 188 9.3 Python File Methods 189 9.4 tell() and seek() Methods 190 9.5 Renaming and Deleting Files 190 9.5.1 Rename() Method 191 9.5.2 Remove() Method 191 9.6 Directories in Python 192 9.6.1 mkdir() Method 192 9.6.2 chdir() Method 193 9.6.3 getcwd() Method 193 9.6.4 rmdir() Method 194 9.6.5 listdir() Method 194 9.7 Python Directory Methods 194 9.8 Summary 195 Review Questions 195 10. Classes and Objects 197–212 10.1 Designing Classes 199 10.2 Creating Objects 200 Detailed Contents xiii
10.2.1 Class Variable 201 10.2.2 Instance Variable 202 10.3 Types of Methods 203 10.4 Access Specifiers in Python 204 10.5 Accessing Attributes 205 10.6 The Class Program 206 10.6.1 Using a Class with Input 207 10.6.2 A Class Program with Computations 208 10.7 Editing Class Attributes 208 10.8 Built-in Class Attributes 209 10.9 Garbage Collection/Destroying Objects 211 10.10 Summary 211 Review Questions 212 11. Inheritance 213–220 11.1 Python Single Inheritance 214 11.2 Python Multiple Inheritance 215 11.3 Python Multilevel Inheritance 216 11.4 Method Overriding in Python 217 11.5 Special Functions in Python 218 11.6 Summary 219 Review Questions 220 12. Python Operator Overloading 221–226 12.1 Overloading ‘+’ Operator in Python 221 12.2 Overloading ‘-’ Operator in Python 222 12.3 Overloading Bitwise Operators 223 12.4 Overloading Relational Operators 224 12.5 Summary 225 Review Questions 225 Appendix 227-232 Bibliography 233 xiv Python Programming: A Step-by-Step Guide to Learning the Language
1 Introduction to Python Language Highlights l Introduction and History of Python Language l Features of Python l Applications of Python l Python Interactive Help l Installing and Executing Python l How Python Differs from Other Languages We can see that computers have a wide range of real-world problem-solving abilities. The issues could be as straightforward as multiplying two numbers or as complex as designing and launching a space shuttle. Assuming that a machine can complete all jobs on its own would be wrong. Any problem whose solution is not defined cannot be resolved by a computer. A computer cannot solve any issue whose answer is not known. The computer merely executes the set of instructions that a programmer has provided to it. There may be mistakes and no resolution if the computer cannot comprehend the instructions. As a result, it is the programmer’s grave responsibility to come up with a solution by giving the machine the right commands. As a result, it is the programmer’s grave responsibility to come up with a solution by giving the machine the right commands.
2 Python Programming: A Step-by-Step Guide to Learning the Language 1.1 Programming Language A programming language is a formal language used to instruct a computer to perform a specific task or set of tasks. It provides a set of rules and syntax for creating and manipulating code, allowing developers to write programs and applications that can run on a computer. Programming languages can be classified into different types based on their purpose and structure. Some common types of programming languages include: 1. Procedural languages: These languages use a series of steps to solve a problem or accomplish a task. Examples include C, Fortran, and Pascal. 2. Object-oriented languages: These languages model the problem as a set of objects that interact with each other to accomplish a task. Examples include Java, Python, and C++. 3. Functional languages: These languages focus on the evaluation of expressions and functions, treating them as mathematical equations. Examples include Haskell, Lisp, and ML. 4. Scripting languages: These languages are used to automate tasks, such as web development, and are often interpreted rather than compiled. Examples include JavaScript, PHP, and Python. There are many programming languages in use today, each with its own strengths and weaknesses, and new languages are continually being developed to meet the evolving needs of the technology industry. Some of the most common programming languages used today include: 1. Java: Java is an object-oriented programming language that is widely used for developing enterprise-level applications, mobile applications, and web applications. 2. Python: Python is a high-level programming language that is popular for its readability, ease of use, and versatility. It is commonly used for web development, data analysis, and artificial intelligence. 3. JavaScript: JavaScript is a scripting language that is used for developing web applications and interactive front-end interfaces. 4. C#: C# is an object-oriented programming language that is commonly used for developing Windows desktop applications, video games, and web applications.
Introduction to Python Language 3 5. C++: C++ is a high-performance language that is used for developing operating systems, video games, and other resource-intensive applications. 6. PHP: PHP is a server-side scripting language that is used for developing dynamic web applications and websites. 7. Ruby: Ruby is a high-level scripting language that is known for its simplicity and ease of use. It is commonly used for web development and building web applications. It is worth noting that the popularity of programming languages can vary depending on factors such as industry trends, the rise of new technologies, and the emergence of new use cases. 1.2 History of Python Language Python is a high-level, interpreted programming language that was first released in 1991 by its creator, Guido van Rossum. It was designed to be easy to read and write, and to emphasize code readability and simplicity. The origin of the name “Python” comes from a TV show from the 1970s called “Monty Python’s Flying Circus”. Guido van Rossum is a big fan of the show, and he named the language after it. Python was initially developed as a hobby project, and its first version was released in February 1991. The language was designed with a clear and concise syntax that allowed developers to write code quickly and efficiently. Python’s creators were also focused on making the language easily readable, which has helped to make it popular with beginners and experts alike. Following are the illustrations of different versions of Python along with the timeline. In 2000, Python 2.0 was released, which included many new features such as garbage collection, Unicode support, and list comprehensions. This version of the language became the dominant version of Python for many years, and it is still widely used today, despite being officially deprecated as of 2020. In 2008, Python 3.0 was released, which was a major overhaul of the language that introduced many changes and new features. One of the most significant changes was the removal of backward compatibility with Python 2.x, which made it more difficult for developers to transition to the new version. However, Python 3.0 brought many improvements and new features, including better Unicode support, improved I/O, and more efficient handling of exceptions.
4 Python Programming: A Step-by-Step Guide to Learning the Language Fig. 1.1: Versions of Python Programming Language. Today, Python is one of the most popular programming languages in the world, and is used in a wide variety of applications, including web development, data analysis, artificial intelligence, and scientific computing. It is a powerful and flexible language that is well-suited to many different tasks, and it has a large and vibrant community of developers who continue to work on improving the language and developing new libraries and tools.
Introduction to Python Language 5 1.3 Origin of Python Programming Language Python programming language was created by Guido van Rossum in the late 1980s, while he was working at the Netherlands-based research institute called the National Research Institute for Mathematics and Computer Science (CWI). Guido was tasked with creating a successor to the ABC programming language that was easy to learn and use. He aimed to design a language with an easy-to-understand syntax, which would allow developers to write and maintain code more efficiently. 1.4 Features of Python Python is a high-level, interpreted programming language that is known for its simplicity, readability, and ease of use. Here are some of the key features of Python: 1. Simple and easy-to-learn syntax: Python has a simple and concise syntax, which makes it easy to read and write. Its code is easy to understand, even for beginners, and this simplicity is one of the reasons why Python has become so popular. 2. Interpreted language: Python is an interpreted language, which means that you don’t need to compile your code before running it. This makes it faster to develop and test code, as you can run it immediately and see the results. 3. Cross-platform compatibility: Python code can run on many different platforms, including Windows, Linux, and macOS. This is because Python code is interpreted, and the interpreter is available on all of these platforms. 4. Large standard library: Python comes with a large and comprehensive standard library that provides many useful functions and modules for developers. This makes it easy to perform common tasks, such as reading and writing files, working with databases, and performing network operations. 5. Third-party modules and libraries: Python has a huge and active community of developers who create and maintain many useful third-party modules and libraries. These libraries provide additional functionality, such as scientific computing, data analysis, web development, and artificial intelligence.
6 Python Programming: A Step-by-Step Guide to Learning the Language 6. Object-oriented programming support: Python supports object- oriented programming, which allows developers to write modular, reusable, and maintainable code. 7. Dynamic typing: Python is a dynamically-typed language, which means that the type of a variable is determined at runtime, rather than at compile time. This makes it more flexible and allows developers to write code more quickly. 8. High-level abstractions: Python provides many high-level abstractions, such as list comprehensions, lambda functions, and decorators, which make it easier to write code that is concise and expressive. Overall, Python is a powerful and flexible programming language that is well-suited to many different tasks, and its features make it easy to learn and use, even for beginners 1.5 Limitations of Python While Python is a popular and powerful programming language, it has some limitations that developers should be aware of. Here are some of the main limitations of Python: 1. Performance: Python is an interpreted language, which means that it is generally slower than compiled languages like C++ or Java. This can be a limitation when developing applications that require high performance or low latency, such as real-time systems or high-transaction web applications. 2. Global Interpreter Lock (GIL): The GIL is a mechanism that ensures that only one thread executes Python bytecode at a time. This can limit the ability of developers to take advantage of multiple processors or cores, which can impact performance. 3. Weak typing: While dynamic typing is a key feature of Python, it can also be a limitation in some cases. Without type checking, errors can go undetected until runtime, and the lack of strong typing can make it more difficult to reason about code and catch errors early. 6. Mobile development: While Python can be used to develop mobile applications, it is not as well-suited to mobile development as other languages, such as Java or Kotlin. This is because the Android and iOS ecosystems are primarily based on those languages, and they offer more robust support for mobile development.
Introduction to Python Language 7 5. Memory consumption: Python is known for its high memory consumption, which can be a limitation when developing applications that need to run on low-memory devices or in constrained environments. 6. Dependencies and version compatibility: Python has a large and active community of developers who create and maintain many useful third-party modules and libraries. However, this can lead to version compatibility issues and dependency management challenges. It’s worth noting that many of these limitations can be mitigated by using best practices, such as optimizing code, using appropriate libraries, and following good design principles. Despite these limitations, Python remains a popular and versatile language that is well-suited to many different applications 1.6 Major Applications of Python Python is a versatile language that can be used for many different applications, from web development to scientific computing. Here are some of the major applications of Python: 1. Web development: Python is widely used for web development, both on the server-side and the client-side. Popular web frameworks include Django, Flask, Pyramid, and Bottle. 2. Data analysis and scientific computing: Python has become a popular language for data analysis and scientific computing, thanks to libraries such as NumPy, Pandas, SciPy, and Matplotlib. These libraries provide support for numerical operations, data manipulation, statistical analysis, and data visualization. 3. Machine learning and artificial intelligence: Python is widely used in the field of machine learning and artificial intelligence, thanks to libraries such as TensorFlow, Keras, PyTorch, and Scikit-learn. These libraries provide support for deep learning, neural networks, natural language processing, and other AI applications. 4. Desktop applications: Python can be used to develop desktop applications with graphical user interfaces (GUIs), thanks to libraries such as PyQt, PyGTK, and wxPython. 5. Game development: Python is increasingly being used in the field of game development, thanks to libraries such as Pygame and Panda3D. 6. Automation and scripting: Python is a popular language for automation and scripting tasks, thanks to its simple syntax and extensive library support.
8 Python Programming: A Step-by-Step Guide to Learning the Language 7. DevOps: Python is used in DevOps for automation, testing, and deployment, thanks to libraries such as Fabric and Ansible. 8. Education: Python is a popular language for teaching programming, thanks to its simple syntax and ease of use. Overall, Python is a versatile language that can be used for many different applications, and its popularity and extensive library support make it a valuable tool for developers in many fields. 1.7 Getting Python Python can be downloaded for free from the official Python website, which provides installers for Windows, macOS, and Linux. Here are the steps to get Python: 1. Go to the Python website: Visit the official Python website at https:// www.python.org/ and click on the “Downloads” link at the top of the page. 2. Select your operating system: Choose your operating system from the list of available options. You can choose between Windows, macOS, and a variety of Linux distributions. 3. Choose your Python version: Python is available in two major versions, Python 2 and Python 3. While Python 2 is still in use, it is no longer actively developed and new users are advised to use Python 3. Choose the version of Python that you want to install. 4. Download the installer: Once you have chosen your operating system and Python version, download the installer for your system. 5. Run the installer: Run the installer on your computer and follow the on-screen instructions to complete the installation process. After the installation process is complete, you should have access to the Python interpreter and the Python standard library. You can also use an integrated development environment (IDE) such as PyCharm, Spyder, or Jupyter Notebook to write and run Python code. 1.8 Installing Python Python is a cross-platform programming language, with distributions available for multiple operating systems. To install Python, you can download the binary code for your platform from the official website, and run the installation process. In the event that the binary code is not available for
Introduction to Python Language 9 your platform, you can compile the source code manually using a C compiler. The installation process for Python may differ slightly depending on the platform, and specific instructions can be found on the official website for each platform, such as Unix or Linux. 1.8.1 Unix and Linux Installation Here are the general steps to install Python on Unix or Linux: 1. Open a terminal window. 2. Check if Python is already installed on your system by typing “python” or “python3” into the terminal. If Python is already installed, the version number will be displayed. If Python is not installed, you will see an error message. 3. If Python is not installed, you can install it using your system’s package manager. The command may differ depending on your distribution, but some examples are: • Ubuntu/Debian: sudo apt-get install python3 • Red Hat/Fedora: sudo yum install python3 • Arch Linux: sudo pacman -S python 4. Once the installation is complete, you can verify that Python is installed by typing “python” or “python3” into the terminal. 5. Optionally, you can install a Python IDE or code editor to make it easier to write and run Python code. Popular options include PyCharm, Spyder, Visual Studio Code, and Jupyter Notebook. 1.8.2 Windows Installation Here are the general steps to install Python on Windows: 1. Go to the official Python website at https://www.python.org/downloads/ and download the latest version of Python 3.x for Windows. 2. Once the installer is downloaded, run the executable file to begin the installation process. 3. In the installation wizard, select “Add Python 3.x to PATH” so that Python can be accessed from the command line and other applications. 4. Choose the “Customize installation” option and make sure that “pip” is selected. This is a package manager that allows you to easily install third-party Python packages.
10 Python Programming: A Step-by-Step Guide to Learning the Language 5. Continue with the installation process by following the on-screen prompts, such as selecting the installation location and accepting the license agreement. 6. Once the installation is complete, you can verify that Python is installed by opening the command prompt and typing “python” followed by the Enter key. This should launch the Python interpreter. 7. Optionally, you can install a Python IDE or code editor to make it easier to write and run Python code. Popular options include PyCharm, Spyder, Visual Studio Code, and Jupyter Notebook. 1.8.3 Macintosh Installation Here are the general steps to install Python on a Macintosh: 1. Go to the official Python website at https://www.python.org/downloads/ and download the latest version of Python 3.x for Mac. 2. Once the installer is downloaded, open it by double-clicking the .dmg file. 3. Double-click the “Python.mpkg” file to begin the installation process. 4. Follow the on-screen prompts to customize your installation settings, such as the installation directory and any additional components you want to include. The default settings are usually fine for most users. 5. Once the installation is complete, you can verify that Python is installed by opening a terminal window and typing “python3” followed by the Enter key. This should launch the Python interpreter. 6. Optionally, you can install a Python IDE or code editor to make it easier to write and run Python code. Popular options include PyCharm, Spyder, Visual Studio Code, and Jupyter Notebook. 1.9 Setting up Path Directories can contain various programs and executable files, which means that Windows, Unix/Linux, or MAC operating systems must have a way of finding these files. To locate executable files, the operating system provides a search path that includes directories. This search path is stored in an environment variable, which is a named string that contains information that can be accessed by the command shell and other programs.
Introduction to Python Language 11 1.9.1 Setting up Path at Unix/Linux To set up the PATH for Python on Unix/Linux, follow these steps: 1. Open a terminal window. 2. Type “nano ~/.bashrc” to open your Bash profile file in the nano text editor. 3. Add the following line to the end of the file, replacing “3.9” with your version of Python: export PATH=”/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games:/usr/ local/python3.9/bin” 4. Save the file by pressing Ctrl + X, then Y, then Enter. 5. Type “source ~/.bashrc” to apply the changes to your current terminal session. 6. You can now run the “python” command from any directory in the terminal. 1.9.2 Setting up the Path at Windows To set up the PATH for Python on Windows, follow these steps: 1. Right-click on “This PC” or “My Computer” and select “Properties”. 2. Click on “Advanced system settings”. 3. Click on the “Environment Variables” button at the bottom. 4. Under “System Variables”, find the “Path” variable and click “Edit”. 5. Click “New” and enter the path to your Python installation directory (e.g. C:Python39). 6. Click “OK” to close all windows. 7. Open a new command prompt window and type “python” to verify that the PATH has been set up correctly. 1.10 Python Environment Variables Python uses environment variables to store configuration settings and other system information that can be accessed by your code. Here are some common Python environment variables:
12 Python Programming: A Step-by-Step Guide to Learning the Language 1. Path: This variable contains a list of directories where the operating system searches for executable files, including the Python interpreter. When you type “python” in the command prompt, the operating system looks for the “python.exe” file in these directories. 2. Pythonpath: This variable contains a list of directories where Python looks for modules and packages. You can add your own directories to this list to make your own modules and packages available to your Python code. 3. Pythonhome: This variable points to the root directory of your Python installation. If you move your Python installation to a different directory, you can update this variable to point to the new location. 4. Pythonstartup: This variable points to a Python script that is executed every time you start the Python interpreter. You can use this script to define your own Python environment, such as importing modules, defining functions, or setting default values. 5. Pythonioencoding: This variable sets the default encoding for input and output streams, such as stdin, stdout, and stderr. By default, Python uses the system’s default encoding, but you can change it to a different encoding if needed. These are just a few examples of Python environment variables. You can access and modify them using the os module in Python. 1.11 Running Python To run Python code, you first need to install Python on your computer. You can download and install the latest version of Python from the official website: https://www.python.org/downloads/. Once you have installed Python, you can run it in several ways: 1. Using the Python shell: The Python shell is an interactive environment that allows you to run Python code line by line. To open the Python shell, simply type “python” in your terminal or command prompt. 2. Using a text editor: You can write your Python code in a text editor, save it with a .py file extension, and then run it from the command prompt by typing “python filename.py”. 3. Using an Integrated Development Environment (IDE): An IDE provides a more advanced environment for writing, debugging, and
Introduction to Python Language 13 running Python code. Some popular IDEs for Python include PyCharm, Visual Studio Code, and Spyder. Once you have Python installed and set up, you can start writing and running Python code to perform various tasks and solve problems. 1.11.1 Interactive Interpreter The interactive interpreter in Python is a command-line interface that allows you to enter Python commands and immediately see the results of those commands. It’s a great way to experiment with Python and test out code snippets before incorporating them into a larger program. To launch the Python interpreter, open a terminal or command prompt and type python followed by the Enter key. This will launch the interactive interpreter and display the Python version number and a command prompt (>>>). You can then enter Python commands at the prompt and see the output immediately. For example, you could type print(“Hello, World!”) and press Enter, and the interpreter would immediately display the output Hello, World!. You can exit the interactive interpreter by typing exit() or quit() at the command prompt and pressing Enter. This will return you to the terminal or command prompt. 1.11.2 Script from the Command-Line You can run a Python script from the command-line by typing python followed by the name of the script file. Here’s an example: Suppose you have a script called hello.py that contains the following code: print(“Hello, World!”) To run this script, open a terminal or command prompt and navigate to the directory where hello.py is saved. Then, type the following command and press Enter: python hello.py This will execute the script, and you should see the output Hello, World! in the terminal or command prompt. You can also pass command-line arguments to a Python script by including them after the script name. For example, if you have a script called add.py
14 Python Programming: A Step-by-Step Guide to Learning the Language that takes two numbers as arguments and adds them together, you could run it with the following command: python add.py 2 3 This would execute the script with the arguments 2 and 3, and the output would be 5. Note that in order to run a Python script from the command-line, you must have Python installed on your computer and the Python executable must be in your system’s PATH variable. 1.11.3 Integrated Development Environment An Integrated Development Environment (IDE) is a software application that provides a comprehensive environment for writing, testing, and debugging software code. Python has many popular IDEs that provide features like syntax highlighting, code completion, debugging tools, and more. Some popular Python IDEs include: 1. PyCharm: PyCharm is a powerful and full-featured IDE for Python. It includes advanced code completion, debugging tools, and support for web development frameworks like Django and Flask. 2. Visual Studio Code: Visual Studio Code is a lightweight and versatile IDE that supports a wide range of programming languages, including Python. It includes features like syntax highlighting, code completion, and debugging tools. 3. Spyder: Spyder is an IDE designed specifically for scientific computing and data analysis in Python. It includes features like variable explorer, data viewer, and plotting tools. 4. IDLE: IDLE is a simple and lightweight IDE that comes bundled with Python. It includes basic features like syntax highlighting and debugging tools. There are many other IDEs available for Python, and the best one for you will depend on your specific needs and preferences. You can download and install most Python IDEs for free, and they are available for all major operating systems. 1.12 First Python Program Python is a popular high-level programming language that is known for its simplicity, readability, and versatility. It is widely used for a variety of
Introduction to Python Language 15 purposes, including web development, data analysis, machine learning, and more. One of the reasons that Python is so popular is its syntax, which is designed to be easy to read and write. For example, instead of using curly braces and semicolons to denote code blocks and statements, Python uses whitespace and indentation. Here is an example of a simple Python program that prints a message to the console: print(“Hello, World!”) This program simply uses the print() function to display the message “Hello, World!” on the console. You can save this code to a file with a .py extension, such as hello.py, and then execute it from the command line by typing python hello.py. 1.12.1 Interactive Mode Programming In Python, you can also run programs in interactive mode, which allows you to enter code directly into the Python interpreter and immediately see the output. This is a great way to experiment with Python and test out code snippets before incorporating them into a larger program. To launch the interactive interpreter in Python, open a terminal or command prompt and type python followed by the Enter key. This will launch the interpreter and display the Python version number and a command prompt (>>>). You can then enter Python commands at the prompt and see the output immediately. For example, you could type print(“Hello, World!”) and press Enter, and the interpreter would immediately display the output Hello, World!. In interactive mode, you can also define variables, create functions, and import modules, just as you would in a regular Python program. For example, you could define a variable like this: x = 5 And then use it in a calculation like this: y = x * 2 print(y) This would define a variable x with the value 5, and then define a variable y with the value 10 (which is the result of multiplying x by 2). Finally, it would print the value of y to the console.
16 Python Programming: A Step-by-Step Guide to Learning the Language You can exit the interactive interpreter by typing exit() or quit() at the command prompt and pressing Enter. This will return you to the terminal or command prompt. 1.12.2 The Script Mode Programming In addition to running Python code in interactive mode, you can also write code in a file and run it as a script. This is a common way to write larger Python programs that can be executed from the command line or scheduled to run automatically. To create a Python script, simply create a new file with a .py extension and write your code in it using a text editor or integrated development environment (IDE). For example, you could create a file called hello.py and write the following code: print(“Hello, World!”) To run this script, you can execute it from the command line by typing python hello.py and pressing Enter. This will run the script and print the message “Hello, World!” to the console. In a Python script, you can include any valid Python code, including importing modules, defining functions, and using control structures like loops and conditionals. For example, you could write a more complex script that asks the user for their name and then greets them: name = input(“What is your name? “) print(“Hello, “ + name + “!”) This script would prompt the user to enter their name using the input() function, and then use string concatenation to print a personalized greeting. Overall, writing Python scripts is a powerful way to build complex programs that can be used in a variety of contexts. With a little practice, you can use Python to automate repetitive tasks, process data, and build sophisticated applications. 1.13 Python’s Interactive Help Python comes with a built-in help utility, which is one of the major features and support of Python language. The prerequisite of using the built-in help of Python, you must have a little knowledge of programming. For a new
Introduction to Python Language 17 programmer, it could be a bit off-putting. Once a programmer becomes familiar with programming terminology then he can make great use of the built-in help provided by Python. Python programming help can be obtained in the following ways: • Interactive mode help • Getting help online through a web browser 1.13.1 Python Help Through a Web Browser Python has an extensive standard library, as well as a large and active community of developers who have created many third-party modules and libraries. As a result, there are many resources available for getting help with Python online. One popular resource is the Python documentation, which is available online in a web browser at the official Python website (https://www.python.org/). The documentation includes a detailed language reference, as well as tutorials and guides for getting started with Python, building web applications, working with data, and more. In addition to the official Python documentation, there are many online forums and communities where you can ask questions and get help with Python. For example, the Python subreddit (https://www.reddit.com/r/Python/) is a popular forum where Python users can ask questions, share code snippets, and get advice from other developers. There are also many third-party websites and services that offer Python tutorials, courses, and other learning resources. For example, Codecademy (https://www.codecademy.com/learn/learn-python) offers a comprehensive Python course for beginners, while Udemy (https://www.udemy.com/topic/ python/) has a wide range of Python courses for learners of all levels. Overall, there are many resources available for getting help with Python online, and the best approach will depend on your specific needs and learning style. Whether you’re a beginner or an experienced developer, there are many ways to learn and grow your skills with Python. 1.14 Python Differences From Other Languages While the Python language shares some similarities with C, C++, and Java, there are also distinct differences that set it apart from these languages.
18 Python Programming: A Step-by-Step Guide to Learning the Language 1.14.1 Difference Between C and Python C and Python are both popular programming languages, but they differ in several ways. Here are some of the main differences between C and Python: 1. Syntax: C uses a more complex syntax than Python. C requires more code to accomplish the same tasks as Python, and it also has stricter rules for formatting and organization. 2. Compiled vs. Interpreted: C is a compiled language, meaning that the code is translated into machine code by a compiler before it can be executed. Python, on the other hand, is an interpreted language, meaning that the code is executed directly by an interpreter. 3. Typing: C is a statically typed language, meaning that the type of each variable is declared explicitly in the code. Python, on the other hand, is a dynamically typed language, meaning that the type of each variable is determined at runtime. 4. Memory Management: C requires manual memory management, meaning that the programmer must explicitly allocate and deallocate memory for variables and data structures. Python, on the other hand, has automatic memory management, meaning that the interpreter takes care of memory allocation and deallocation. 5. Application: C is often used for low-level systems programming, such as operating systems, device drivers, and embedded systems, as well as for high-performance computing and graphics programming. Python is often used for web development, data analysis, scientific computing, and automation. 6. Object-Oriented Programming: While C supports object-oriented programming (OOP), it is not a pure OOP language like Python. Python is designed to support OOP concepts such as encapsulation, inheritance, and polymorphism, making it easier to write and organize code for larger projects. 7. Platform Independence: Python is more platform-independent than C, meaning that Python code can run on a variety of operating systems and hardware without modification. C code, on the other hand, may need to be recompiled for different platforms or architectures. 8. Libraries and Packages: Python has a vast library of built-in modules and third-party packages that make it easy to perform a wide range of tasks, from web scraping to machine learning. C has a smaller standard
Introduction to Python Language 19 library and fewer third-party packages, making it more difficult to find pre-built solutions for common programming problems. 9. Debugging: Debugging C code can be more challenging than debugging Python code, due to C’s lower-level nature and manual memory management. Python has built-in debugging tools and a more forgiving syntax, making it easier to locate and fix errors in code. 10. Learning Curve: Python is generally considered to be easier to learn and use than C, due to its simpler syntax, automatic memory management, and built-in data structures. C requires a deeper understanding of computer architecture and low-level programming concepts, making it more difficult to learn and master. These are just a few of the many differences between C and Python. While they share some similarities, they are distinct languages with their own strengths and weaknesses, and the choice of language will depend on the specific needs of the project and the preferences of the programmer. 1.14.2 Difference Between C++ and Python C++ and Python are both popular programming languages, but they differ in several ways. Here are some of the main differences between C++ and Python: 1. Syntax: C++ uses a more complex syntax than Python. C++ requires more code to accomplish the same tasks as Python, and it also has stricter rules for formatting and organization. 2. Compiled vs. Interpreted: C++ is a compiled language, meaning that the code is translated into machine code by a compiler before it can be executed. Python, on the other hand, is an interpreted language, meaning that the code is executed directly by an interpreter. 3. Typing: C++ is a statically typed language, meaning that the type of each variable is declared explicitly in the code. Python, on the other hand, is a dynamically typed language, meaning that the type of each variable is determined at runtime. 4. Memory Management: C++ requires manual memory management, meaning that the programmer must explicitly allocate and deallocate memory for variables and data structures. Python, on the other hand, has automatic memory management, meaning that the interpreter takes care of memory allocation and deallocation.
20 Python Programming: A Step-by-Step Guide to Learning the Language 5. Object-Oriented Programming: Both C++ and Python support object- oriented programming (OOP), but C++ is often considered a “pure” OOP language, meaning that all code is organized into objects and classes. Python, on the other hand, allows for more procedural and functional programming styles as well. 6. Application: C++ is often used for systems programming, such as operating systems and device drivers, as well as for high-performance computing, gaming, and graphics programming. Python is often used for web development, data analysis, scientific computing, and automation. 7. Libraries and Packages: Python has a vast library of built-in modules and third-party packages that make it easy to perform a wide range of tasks, from web scraping to machine learning. C++ has a smaller standard library and fewer third-party packages, making it more difficult to find pre-built solutions for common programming problems. 8. Speed: C++ is generally faster than Python because it is a compiled language, meaning that the code is translated into machine code before it is executed. Python, on the other hand, is an interpreted language, meaning that the code is executed directly by an interpreter, which can be slower. However, Python has some libraries that are implemented in C or C++ that can boost its performance. 9. Learning Curve: C++ is generally considered more difficult to learn and use than Python. C++ has a steep learning curve because it is a more complex language with a larger number of features and more stringent syntax rules. Python, on the other hand, has a simpler syntax and a smaller set of features, making it easier to learn and use. 10. Type Safety: C++ is a type-safe language, meaning that the compiler checks for type errors at compile time. Python is not type-safe, meaning that type errors can occur at runtime. 11. Multithreading: C++ has built-in support for multithreading, allowing programs to execute multiple threads of code concurrently. Python also supports multithreading, but it has a global interpreter lock (GIL) that can limit the performance gains of multithreading in some cases. 12. Portability: Python is more portable than C++ because it is a high-level language that can be interpreted on any platform. C++ code must be compiled on the specific platform it will run on, which can make it less portable.
Introduction to Python Language 21 13. Memory Safety: C++ is a language that provides the programmer with the ability to manually manage memory. While this gives the programmer more control over how memory is used, it also means that the program can be susceptible to memory-related bugs, such as buffer overflows and memory leaks. Python has built-in garbage collection, meaning that it automatically manages memory, which makes it less susceptible to memory-related bugs. These are just a few of the many differences between C++ and Python. While both languages are widely used and powerful, they have different strengths and weaknesses, and the choice of language will depend on the specific needs of the project and the preferences of the programmer. 1.14.3 Difference between Java and Python Java and Python are both popular programming languages, but they differ in several ways. Here are some of the main differences between Java and Python: 1. Syntax: Java has a more complex syntax than Python. Java requires more code to accomplish the same tasks as Python, and it also has stricter rules for formatting and organization 2. Compiled vs. Interpreted: Java is a compiled language, meaning that the code is translated into bytecode by a compiler before it can be executed. Python, on the other hand, is an interpreted language, meaning that the code is executed directly by an interpreter. 3. Typing: Java is a statically typed language, meaning that the type of each variable is declared explicitly in the code. Python, on the other hand, is a dynamically typed language, meaning that the type of each variable is determined at runtime. 4. Memory Management: Java has automatic memory management, meaning that the JVM (Java Virtual Machine) takes care of memory allocation and deallocation. Python also has automatic memory management, meaning that the interpreter takes care of memory allocation and deallocation. 5. Object-Oriented Programming: Both Java and Python support object- oriented programming (OOP), and they have similar concepts such as inheritance, polymorphism, and encapsulation. However, Java is often considered a “pure” OOP language, meaning that all code is organized
22 Python Programming: A Step-by-Step Guide to Learning the Language into objects and classes. Python, on the other hand, allows for more procedural and functional programming styles as well. 6. Application: Java is often used for web development, desktop application development, and mobile app development. Python is often used for web development, scientific computing, data analysis, machine learning, and automation. 7. Performance: Java is generally faster than Python because it is a compiled language, and the JVM can optimize the bytecode for performance. Python, on the other hand, is an interpreted language, meaning that the code is executed directly by an interpreter, which can be slower. However, Python has some libraries that are implemented in C or C++ that can boost its performance. 8. Garbage Collection: Java has a more advanced garbage collector than Python, meaning that it can handle memory more efficiently. 9. Learning Curve: Java is generally considered more difficult to learn and use than Python. Java has a steep learning curve because it is a more complex language with a larger number of features and more stringent syntax rules. Python, on the other hand, has a simpler syntax and a smaller set of features, making it easier to learn and use. These are just a few of the many differences between Java and Python. While both languages are widely used and powerful, they have different strengths and weaknesses, and the choice of language will depend on the specific needs of the project and the preferences of the programmer. 1.15 Summary In this chapter, we have learned about the programming language and its needs. Then we gave a brief look at the origin and history of the Python language along with its features and limitations. We have explored in detail how Python language differs from other existing and prominent programming languages such as C,C++, and Java. The setup and installation of Python language along with a simple first program are also discussed in detail. Review Questions 1. What is Python and what makes it a popular programming language? 2. How does Python differ from other programming languages?
Introduction to Python Language 23 3. What is the history of the Python programming language and who developed it? 4. What are the steps to install Python on different operating systems? 5. What are the benefits of using Python for software development? 6. What are the key features of Python, and how do they contribute to its popularity? 7. How does the Python community support and contribute to the language’s development? 8. What are some popular applications built using Python? 9. How does Python compare with other programming languages like Java, C++, and Ruby? 10. What are the most important considerations when choosing a programming language, and how does Python measure up? 11. What is Python and how is it used? 12. Python is a high-level programming language used for a variety of applications, including web development, data analysis, artificial intelligence, and more. 13. Python is a type of reptile found in tropical regions around the world. 14. What are some key features of the Python programming language? a. Python has a simple and easy-to-learn syntax. b. Python is an interpreted language, meaning that code is executed directly by an interpreter without the need for compilation.
2 Python Data Types and Input Output Highlights l Keywords and identifiers l Python statements l Documentation and indentation l Python Variables l Python data types l Input and output l Import Python has several built-in data types including strings, integers, and lists. These data types can be used to store and manipulate different kinds of information in a program. In addition to these basic data types, Python also has advanced data types such as dictionaries and sets. Python also has a built-in module for input/output operations, which allows a program to read from and write to external sources, such as files and streams. The module, called io, provides several functions to perform these operations, such as open(), read(), and write(). 2.1 Keywords In Python, a keyword is a word that has a special meaning in the Python language. Keywords are used to define the syntax and structure of the
26 Python Programming: A Step-by-Step Guide to Learning the Language Python language, and they cannot be used as identifiers (i.e., variable names, function names, etc.) in Python code. Keyword Description and Logical operator returns True if both operands are True, otherwise, it returns False. as Used to create an alias for a module or variable when importing or renaming. assert Used to check if a given condition is True, and raises an exception if it is False. async Used to define an asynchronous function or context manager. await Used inside an async function to wait for an asynchronous operation to complete. break Used to exit a loop early, before the loop condition is met. class Used to define a new class. continue Used to skip the current iteration of a loop and continue with the next iteration. def Used to define a new function. del Used to delete an object or an item from a collection. elif Short for “else if”, used in a conditional statement to check for additional conditions. else Used in a conditional statement as a catch-all option if no other conditions are met. except Used to handle exceptions that are raised in a try block. False Boolean value that represents the absence of truth. finally Used in a try-except block to specify a block of code that will always be executed, regardless of whether an exception was raised or not. for Used to iterate over a sequence of items, such as a list or a tuple. from Used in an import statement to import specific items from a module. global Used to indicate that a variable is a global variable, accessible from anywhere in the code. if Used to start a conditional statement. import Used to import a module or a specific item from a module.
Python Data Types and Input Output 27 Keyword Description in Used to check if an item is in a sequence, such as a list or a tuple. is Used to check if two variables refer to the same object. lambda Used to create small anonymous functions. None Special value that represents the absence of a value or a null value. nonlocal Used to indicate that a variable is nonlocal to the current function, meaning it is defined in an outer function. not Logical operator that negates a boolean value. or Logical operator that returns True if at least one of the operands is True, otherwise it returns False. pass Used as a placeholder for a block of code that does nothing. raise Used to raise an exception. return Used to exit a function and return a value to the calling code. True Boolean value that represents the presence of truth. try Used to specify a block of code that might raise an exception. while Used to start a loop that will continue to execute as long as the loop condition is True. with Used to create a context manager, which is used to automatically set up and tear down resources. yield Used in a function 2.2 Identifiers In Python, an identifier is a name used to identify a variable, function, class, module, or other objects. There are a few rules and conventions for naming identifiers in Python: • Identifiers must start with a letter or an underscore (_). • Identifiers cannot start with a number. • Identifiers can only contain letters, numbers, and underscores. • Identifiers are case-sensitive, so myVariable and myvariable are considered to be different identifiers. • Python reserves a set of keywords that cannot be used as identifiers. Examples include if, else, for, class, etc.
28 Python Programming: A Step-by-Step Guide to Learning the Language • Identifiers should be descriptive and meaningful, using camelCase or snake_case, depending on the project’s style guide. Examples of valid identifiers in Python: • myVariable • _privateVariable • counter • calculate_average • MyClass Examples of invalid identifiers in Python: • 1stVariable (starts with a number) • my-variable (contains a hyphen) • if (reserved keyword) • True (reserved keyword) • class (reserved keyword) 2.3 Python Statements In Python, a statement is a single line of code that performs a specific action or instruction. There are several types of statements in Python, including: • Expressions: These are statements that evaluate to a value, such as mathematical operations or function calls. Examples include “2 + 2” or “print(‘Hello, world!’)”. • Assignment statements: These are statements that assign a value to a variable. Examples include “x = 2” or “name = ‘John’”. • Control flow statements: These are statements that control the flow of execution of a program, such as conditional statements (if/else) and loops (for/while). • Function and class definitions: These are statements that define a function or class, respectively. Examples include “def my_function():” and “class MyClass:”. • Import statements: These statements are used to import modules or packages in python. Examples include “import os” or “from math import sqrt”
Python Data Types and Input Output 29 • Pass statements: A pass statement is a null operation. Nothing happens when it executes. It is useful as a placeholder when a statement is required syntactically, but no code needs to be executed. 2.4 Indentation Indentation is used in Python to indicate blocks of code. The standard indentation is four spaces, and most Python code follows this convention. For example Code 2.1 Illustration of indentation in Python def foo(): # This line is indented by four spaces x = 5 if x > 0: # This line is also indented by four spaces print(“x is positive”) # This line is not indented, so it’s not part of the if block It is important to be consistent with your indentation, as the meaning of the code can change based on the indentation level. For example Code 2.2 Illustration of indentation in Python x = 5 if x > 0: print(“x is positive”) print(“This line is not indented, so it’s not part of the if block”) This code will print both messages because the second print statement is not indented, so it is not part of the if block. Code 2.3 Illustration of indentation in Python x = 5 if x > 0: print(“x is positive”) print(“This line is indented, so it is part of the if block”) print(“This line is not indented, so it’s not part of the if block”)
30 Python Programming: A Step-by-Step Guide to Learning the Language This code will only print the first message, because the second print statement is indented, so it is part of the if block. 2.5 Python Documentation In Python, documentation is typically included in the form of comments in the source code. These comments start with a # symbol, and everything following the # on that line is considered a comment. For example: Code 2.4 Illustration of documentation in Python using # # This is a comment x = 5 # This is also a comment 2.5.1 Single Line Comment In Python, a single line comment is created by placing the “#” symbol at the beginning of the line, followed by the text of the comment. For example: Code 2.5 Illustration of single line documentation in Python # This is a single line comment in Python x = 10 # This is also a single line comment The text following the “#” symbol is ignored by the Python interpreter and is only there for the benefit of the person reading the code. Single line comments are often used to add brief explanations or clarifications to the code. Code 2.6 Illustration of single line documentation in Python # Calculate the area of a rectangle width = 3 height = 12 area = width * height In this example, the single line comments provide some context for the code that follows, explaining what the variables are used for and what the final line of code is doing. 2.5.2 Multi Line Comments In Python, multi-line comments can be created using triple quotes, either single quotes (‘’’) or double quotes (“””). The comments can span multiple
Python Data Types and Input Output 31 lines and are commonly used for longer explanations and docstrings. Here is an example: Code 2.7 Illustration of double quotes in Python “”” This is a multiline comment. It can span multiple lines. “”” You can also use triple single quotes to create a multiline comment: Code 2.8 Illustration of single quotes in Python ‘’’ This is also a multiline comment. It can also span multiple lines. ‘’’ Both triple quotes (single or double) can be used to create a multiline comment in Python. The advantage of using triple quotes is that you can create a multiline comment even if it contains multiple lines of the same type of quote character. 2.6 Docstrings In Python, a docstring is a string literal that appears as the first statement in a module, function, class, or method definition. It is used to provide documentation for the code, and can be accessed using the built-in help() function or the __doc__ attribute. Docstrings are enclosed in triple quotes (either single or double) and are typically written in plain text, but can also include markdown formatting. It is a good practice to include a docstring in any function or class you write, as it makes your code more readable and user-friendly. Here is an example of a simple Python function with a docstring:
32 Python Programming: A Step-by-Step Guide to Learning the Language Code 2.9 Illustration of docstring in Python def add(a, b): “”” This function takes two numbers as input and returns their sum. Parameters: a (int): The first number b (int): The second number Returns: int: The sum of a and b “”” return a + b In this example, the function add takes two numbers as input, adds them together, and returns the result. The docstring provides a brief description of what the function does, and explains the parameters and return value. To access the docstring of this function, you can use the help() function like this: help(add) You can also access the docstring programmatically using the __doc__ attribute: print(add.__doc__) This will output the same string as the docstring. By providing clear and concise documentation, it’s easy for other developers to understand how the function works, what the parameters are and what to expect when calling the function. This can save a lot of time and effort when working on large projects with multiple contributors. 2.7 Variables A Python variable is a reserved memory location to store values. In other words, a variable in a python program gives data to the computer for
300 300 Python Data Types and Input Output 33 processing. Every value in Python has a datatype. Different data types in Python are Numbers, List, Tuple, Strings, Dictionary, etc. Variables can be declared by any name or even alphabets like a, aa, abc, etc. 2.7.1 Variable Assignment Think of a variable as a name attached to a particular object. In Python, variables need not be declared or defined in advance, as is the case in many other programming languages. To create a variable, you just assign it a value and then start using it. The assignment is done with a single equals sign (=): n = 300 This is read or interpreted as “n is assigned the value 300.” Once this is done, n can be used in a statement or expression, and its value will be substituted: print(n) Just as a literal value can be displayed directly from the interpreter prompt in a REPL session without the need for print(), so can a variable: n Later, if you change the value of n and use it again, the new value will be substituted instead: n = 1000 print(n) 1000 Python also allows chained assignment, which makes it possible to assign the same value to several variables simultaneously: Code 2.10 Illustration of variable in Python a = b = c = 300 print(a, b, c) 300 300 300 2.7.2 Variable Types in Python In many programming languages, variables are statically typed. That means a variable is initially declared to have a specific data type, and any value
34 Python Programming: A Step-by-Step Guide to Learning the Language assigned to it during its lifetime must always have that type. Variables in Python are not subject to this restriction. In Python, a variable may be assigned a value of one type and then later re-assigned a value of a different type: Code 2.11 Illustration of variable in Python var = 21.09 print(var) 21.09 Let’s see another example: Code 2.12 Illustration of variable in Python >>> var = “Welcome to Python” >>> print(var) Welcome to Python 2.8 Multiple Assignment Multiple assignment allows you to assign multiple variables at the same time in one line of code. This feature often seems simple after you’ve learned about it, but it can be tricky to recall multiple assignment when you need it most. In this we’ll see what multiple assignment is, we’ll take a look at common uses of multiple assignment, and then we’ll look at a few uses for multiple assignment that are often overlooked. Python’s multiple assignment looks like this: >>> x, y = 10, 20 Here we’re setting x to 10 and y to 20. What’s happening at a lower level is that we’re creating a tuple of 10, 20 and then looping over that tuple and taking each of the two items we get from looping and assigning them to x and y in order. This syntax might make that a bit more clear: >>> (x, y) = (10, 20) Parenthesis are optional around tuples in Python and they’re also optional in multiple assignment (which uses a tuple-like syntax). All of these are equivalent:
Python Data Types and Input Output 35 Code 2.13 Illustration of variable in Python >>> x, y = 10, 20 >>> x, y = (10, 20) >>> (x, y) = 10, 20 >>> (x, y) = (10, 20) Multiple assignments are often called “tuple unpacking” because it’s frequently used with tuples. But we can use multiple assignments with any iterable, not just tuples. Here we’re using it with a list: Code 2.13 Illustration of variable in Python >>> x, y = [10, 20] >>> x 10 >>> y 20 And with a string: Code 2.13 Illustration of a variable with a string in Python >>> x, y = ‘hi’ >>> x ‘h’ >>> y ‘i’ Here’s another example to demonstrate that multiple assignments works with any number of items and that it works with variables as well as objects we’ve just created: Code 2.14 Illustration of a variable in Python >>> point = 10, 20, 30 >>> x, y, z = point >>> print(x, y, z) 10 20 30 >>> (x, y, z) = (z, y, x) >>> print(x, y, z) 30 20 10
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  • 6.
    Python Programming A Step-by-StepGuide to Learning the Language
  • 8.
    Python Programming A Step-by-StepGuide to Learning the Language Dr. C. K. Dhaliwal Assistant Professor Chandigarh Business School of Administrations Mohali, Punjab *** Poonam Rana Assistant Professor Chandigarh Business School of Administrations Mohali, Punjab *** Dr. T. P. S. Brar Professor & Head of Department Chandigarh Group of Colleges Mohali, Punjab
  • 9.
    First published 2025 byCRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN and by CRC Press 2385 NW Executive Center Drive, Suite 320, Boca Raton FL 33431 CRC Press is an imprint of Informa UK Limited © 2025 Manakin Press Pvt. Ltd The right of of C. K. Dhaliwal, Poonam Rana and Dr. T. P. S. Brar to be identified as authors of this work has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. For permission to photocopy or use material electronically from this work, access www. copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact mpkbookspermissions@tandf.co.uk Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Print edition not for sale in South Asia (India, Sri Lanka, Nepal, Bangladesh, Pakistan or Bhutan). British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 9781032646558 (hbk) ISBN: 9781032669571 (pbk) ISBN: 9781032691053 (ebk) DOI: 10.4324/9781032691053 Typeset in Times New Roman by Manakin Press, Delhi
  • 10.
    Organization of theBook Chapter 1 This is an introductory chapter that provides an overview of Python, covering its history, features, applications, and installation process. It highlights Python's dynamic, high-level, and object-oriented language features and cross- platform compatibility. The chapter emphasizes the use of Python in web development, data science, and machine learning. It also explains Python interactive help and demonstrates how to install and execute Python on different platforms. Additionally, the chapter covers how Python differs from other programming languages. Chapter 2 This chapter introduces the basics of Python programming language. It covers keywords and identifiers, explaining their differences and how to use them properly. The chapter then moves on to Python statements and demonstrates how to use them to create simple programs. It highlights the importance of documentation and indentation in Python programming. The chapter covers variables and their declaration in Python, including the rules for naming variables. It also covers the different data types in Python, such as numbers, strings, lists, and tuples, along with examples of how to use them. Chapter 3 This chapter covers Python operators, including arithmetic, relational, logical, bitwise, assignment, and identity operators. It also explains the precedence and associativity of operators, which determine the order in which they are evaluated. The chapter demonstrates how to use expressions, which are combinations of operands and operators, to perform calculations and manipulate data. Chapter 4 This chapter covers conditional statements in Python, including if, if- else, and if-elif-if statements. It also covers loops in Python, including while, for, and infinite loops, along with examples of how to use them. Additionally, the chapter covers the use of the break, continue, and pass statements in Python loops, which are used to change the flow of control in a program. Chapter 5 This chapter covers the native data types in Python, including numbers, lists, tuples, sets, dictionaries, and strings. It provides examples and use cases for each data type. The chapter highlights the differences between mutable and immutable data types and how to work with them. Additionally, it covers how to manipulate and operate on data types, including slicing and indexing. Chapter 6 This chapter covers Python functions, including the types of functions in Python, such as built-in functions, user-defined functions, and anonymous functions. It discusses the advantages of using functions, such as code reusability, modularity, and easier debugging. The chapter also covers the differences between pass by value and pass by reference and demonstrates recursion, which is the ability of a function to call itself.
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    vi Python Programming:A Step-by-Step Guide to Learning the Language Chapter 7 This chapter covers Python modules, which are files that contain Python definitions and statements. It demonstrates how to create a module and how to import it into another Python program. Additionally, the chapter covers standard modules, which are built-in modules that come with Python, and Python packages, which are directories containing modules. It highlights how to use and install standard modules and how to create and install Python packages. Chapter 8 This chapter covers Python exceptions, which are errors that occur during program execution. It explains the different types of built-in exceptions in Python, such as ZeroDivisionError and TypeError. The chapter demonstrates how to handle exceptions using the try-except block and how to raise and catch user-defined exceptions. It also provides examples of exception handling and how to use the else and finally clauses with the try-except block. Chapter 9 This chapter covers file operations in Python, including how to create, open, read, write, and close files using file methods such as read() and write(). The chapter also covers renaming and deleting files, as well as creating and navigating directories in Python using the os module. It provides examples of how to use file methods and how to handle file exceptions. Chapter 10 This chapter covers designing classes in Python, which are templates for creating objects that have similar properties and behaviors. It explains how to create objects from a class, how to access object attributes, and how to use built-in class attributes, such as name and doc. The chapter also covers garbage collection in Python, which is the process of freeing up memory that is no longer being used by a program. It provides examples of how to design and use classes in Python. Chapter 11 This chapter covers inheritance in Python, which is the ability to create a new class from an existing class. It explains the different types of inheritance in Python, including single inheritance, multiple inheritance, and multilevel inheritance. The chapter also covers method overriding in Python, which is the ability to redefine a method in a subclass. Additionally, the chapter discusses special functions in Python, which are predefined methods that are called under certain circumstances, such as init and str. It provides examples of how to use inheritance and special functions in Python. Chapter 12 This chapter covers operator overloading in Python, which is the ability to redefine the behavior of an operator in a class. It explains how to overload the + and - operators in Python, as well as bitwise and relational operators. The chapter provides examples of how to use operator overloading to customize the behavior of operators in Python. The Appendix- I is given which provides the list of Python Standard Modules with the description of each. The bibliography is given at the end for reference of readers. Authors
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    1. Introduction toPython Language 1–24 1.1 Programming Language 2 1.2 History of Python Language 3 1.3 Origin of Python Programming Language 5 1.4 Features of Python 5 1.5 Limitations of Python 6 1.6 Major Applications of Python 7 1.7 Getting Python 8 1.8 Installing Python 8 1.8.1 Unix and Linux Installation 9 1.8.2 Windows Installation 9 1.8.3 Macintosh Installation 10 1.9 Setting up Path 10 1.9.1 Setting up Path at Unix/Linux 11 1.9.2 Setting up the Path at windows 11 1.10 Python Environment Variables 11 1.11 Running Python 12 1.11.1 Interactive Interpreter 13 1.11.2 Script from the Command-Line 13 1.11.3 Integrated Development Environment 14 1.12 First Python Program 14 1.12.1 Interactive Mode Programming 15 1.12.2 The Script Mode Programming 16 1.13 Python’s Interactive Help 16 1.13.1 Python Help Through a Web Browser 17 1.14 Python Differences From Other Languages 17 1.14.1 Difference Between C and Python 18 1.14.2 Difference Between C++ and Python 19 1.14.3 Difference Between Java and Python 21 1.15 Summary 22 Review Questions 22 Detailed Contents
  • 13.
    2. Python DataTypes and Input Output 25–52 2.1 Keywords 25 2.2 Identifiers 27 2.3 Python Statements 28 2.4 Indentation 29 2.5 Python Documentation 30 2.5.1 Single Line Comment 30 2.5.2 Multi Line Comments 30 2.6 Docstrings 31 2.7 Variables 32 2.7.1 Variable Assignment 33 2.7.2 Variable Types in Python 33 2.8 Multiple Assignment 34 2.9 Python Data Types 36 2.9.1 Numeric Datatype 37 2.9.1.1 Integers 37 2.9.1.2 Floating-point Numbers 38 2.9.1.3 Complex Numbers 39 2.9.2 Strings 40 2.9.2.1 Indexing of a String: 40 2.9.2.2 Negative Indexing: 41 2.9.2.3 Slicing 41 2.9.3 Booleans 42 2.9.4 Lists 42 2.9.5 Tuples 43 2.9.6 Sets 44 2.7.8 Dictionaries 46 2.10 Data Type Conversion 48 2.10.1 Implicit Type Conversion in Python 49 2.10.2 Explicit Type Conversion in Python 49 2.11 Input and Output 50 2.12 Import 51 2.13 Summary 51 Review Questions 52 3. Operators and Expressions 53–70 3.1 Operator 53 3.1.1 Arithmetic Operators 54 viii Python Programming: A Step-by-Step Guide to Learning the Language
  • 14.
    3.1.2 Comparison Operators56 3.1.3 Assignment Operator 58 3.1.4 Logical Operators 59 3.1.5 Bitwise Operators 61 3.1.6 Special Operators 63 3.1.6.1 Identity Operators 63 3.1.6.2 Membership Operators 64 3.2 Expressions 65 3.2.1 Python Operator Precedence 66 3.2.2 Associativity 67 3.2.3 Non-Associative Operators 68 3.3 Summary 68 Review Questions 68 4. Control Structures 71–92 4.1 Decision Making Statements 72 4.1.1 Python if Statement 72 4.1.2 Python if-else Statement 73 4.1.3 Python if-elif-else 75 4.1.4 Python Nested if Statements 77 4.2 Python Loops 78 4.2.1 Types of Loops 79 4.2.2 Python While Loop 79 4.2.3 The Infinite Loop 81 4.2.4 Using else with While Loop 81 4.2.5 Python for Loop 82 4.2.6 The range() Function 84 4.2.7 For Loop with else 85 4.2.8 Nested Loops 86 4.3 Python Control Statements 87 4.3.1 Python Break Statement 87 4.3.2 Python Continue Statement 89 4.3.3 Python Pass Statement 90 4.4 Summary 90 Review Questions 91 5. Python Native Data Types 93–142 5.1 Numbers 94 5.1.1 Number Type Conversion 94 Detailed Contents ix
  • 15.
    5.1.2 Python MathematicalFunctions 95 5.1.3 Python Trigonometric Functions 97 5.1.4 Python Random Number Functions 99 5.1.5 Python Mathematical Constants 100 5.2 Python Lists 101 5.2.1 Creating a List 101 5.2.2 Traversing a List 101 5.2.2.1 Indexing 102 5.2.2.2 Traversing Nested Lists 103 5.2.2.3 Negative Indexing 104 5.2.2.4 Slicing 105 5.2.3 Changing or Adding Elements to a List 105 5.2.4 List Methods 106 5.2.5 List Functions 107 5.2.6 List Comprehension 108 5.2.7 List Membership Test 108 5.3 Python Tuples 109 5.3.1 Creating a Tuple 110 5.3.2 Unpacking Tuple 111 5.3.3 Traversing Elements in a Tuple 111 5.3.3.1 Indexing 112 5.3.3.2 Negative Indexing 113 5.3.3.3 Tuple Slicing 113 5.3.3.4 Changing/Updating a Tuple 114 5.3.3.5 Deleting a Tuple 115 5.3.3.6 Python Tuple Methods 115 5.3.3.7 Python Tuple Functions 116 5.3.3.8 Advantages of Tuple 116 5.4 Python Sets 117 5.4.1 Creating a Set 118 5.4.2 Changing/Adding Elements to a Set 118 5.4.3 Removing Elements from a Set 119 5.4.4 Python Set Operations 119 5.4.4.1 Set Union 120 x Python Programming: A Step-by-Step Guide to Learning the Language
  • 16.
    5.4.4.2 Set Intersection120 5.4.4.3 Set Difference 120 5.4.4.4 Set Symmetric Difference 121 5.4.5 Python Set Methods 122 5.4.6 The in Operator 123 5.4.7 Python Set Functions 123 5.4.8 Frozen Sets 124 5.5 Python Dictionary 125 5.5.1 Creating a Dictionary 125 5.5.2 Accessing a Dictionary 125 5.5.3 Updating a Dictionary 126 5.5.4 Removing or Deleting Elements of a Dictionary 127 5.5.5 Python Dictionary Methods 127 5.5.6 Python Dictionary Membership Test 128 5.5.7 Python Dictionary Functions 129 5.6 Python Strings 130 5.6.1 Creating a String in Python 131 5.6.2 Accessing String Characters 132 5.6.3 Changing or Deleting String Characters 133 5.6.4 Python String Operations 135 5.6.4.1 Concatenation 136 5.6.4.2 Iteration and Membership Test 137 5.6.5 String Formatting 138 5.6.6 Python String Built-in Methods 139 5.7 Summary 140 Review Questions 140 6. Python Functions 143–160 6.1 Python Functions 143 6.2 Advantages of Python 144 6.3 Types of Functions 145 6.4 Built-in Functions 145 6.5 Python User Defined Functions 146 6.5.1 Function Definition 147 6.5.2 Function Call 147 Detailed Contents xi
  • 17.
    6.5.3 Types ofFunction Arguments (Parameters) 148 6.5.3.1 Function with No Arguments 149 6.5.3.2 Function with Required Arguments 150 6.5.3.3 Function with Arbitrary Length Arguments 150 6.5.3.4 Function with Keyword Based Arguments 151 6.5.3.5 Function with Default Arguments 152 6.6 Python Anonymous Functions 153 6.6.1 Characteristics of Lambda Form 154 6.7 Pass by Value vs. Pass by Reference 154 6.7.1 Pass by Value 155 6.7.2 Pass by Object Reference 156 6.8 Recursion 156 6.8.1 Advantages of Recursion 157 6.8.2 Disadvantages of Recursion 158 6.9 Scope and Lifetime of Variables 158 6.10 Summary 159 Review Questions 159 7. Python Modules 161–172 7.1 Need of Module 162 7.2 Module Definition 163 7.3 Creating a Module 163 7.4 Importing Module in The Interpreter 164 7.5 Importing Module in The Another Script 165 7.6 Importing Modules 165 7.7 Search Path of Module 166 7.8 Module Reloading 167 7.9 The dir() Function 168 7.10 Standard Modules 168 7.11 Python Packages 169 7.12 Summary 170 Review Questions 171 8. Exception Handling 173–182 8.1 Exception 173 8.2 Python Built-in Exceptions 174 xii Python Programming: A Step-by-Step Guide to Learning the Language
  • 18.
    8.3 Exception Handling175 8.3.1 Try, Except, Else and Finally 176 8.3.2 Catching Specific Exceptions in Python 178 8.3.3 try….finally 179 8.4 Python User Defined Exceptions 179 8.5 Summary 181 Review Questions 181 9. File Management in Python 183–196 9.1 Operations on Files 183 9.1.1 Opening a File 184 9.1.2 File Modes 185 9.1.3 File Object Attributes 186 9.1.4 File Encoding 186 9.1.5 Closing a File 187 9.2 write() and read() Methods 188 9.2.1 Writing to a File 188 9.2.2 Reading from a File 188 9.3 Python File Methods 189 9.4 tell() and seek() Methods 190 9.5 Renaming and Deleting Files 190 9.5.1 Rename() Method 191 9.5.2 Remove() Method 191 9.6 Directories in Python 192 9.6.1 mkdir() Method 192 9.6.2 chdir() Method 193 9.6.3 getcwd() Method 193 9.6.4 rmdir() Method 194 9.6.5 listdir() Method 194 9.7 Python Directory Methods 194 9.8 Summary 195 Review Questions 195 10. Classes and Objects 197–212 10.1 Designing Classes 199 10.2 Creating Objects 200 Detailed Contents xiii
  • 19.
    10.2.1 Class Variable201 10.2.2 Instance Variable 202 10.3 Types of Methods 203 10.4 Access Specifiers in Python 204 10.5 Accessing Attributes 205 10.6 The Class Program 206 10.6.1 Using a Class with Input 207 10.6.2 A Class Program with Computations 208 10.7 Editing Class Attributes 208 10.8 Built-in Class Attributes 209 10.9 Garbage Collection/Destroying Objects 211 10.10 Summary 211 Review Questions 212 11. Inheritance 213–220 11.1 Python Single Inheritance 214 11.2 Python Multiple Inheritance 215 11.3 Python Multilevel Inheritance 216 11.4 Method Overriding in Python 217 11.5 Special Functions in Python 218 11.6 Summary 219 Review Questions 220 12. Python Operator Overloading 221–226 12.1 Overloading ‘+’ Operator in Python 221 12.2 Overloading ‘-’ Operator in Python 222 12.3 Overloading Bitwise Operators 223 12.4 Overloading Relational Operators 224 12.5 Summary 225 Review Questions 225 Appendix 227-232 Bibliography 233 xiv Python Programming: A Step-by-Step Guide to Learning the Language
  • 20.
    1 Introduction to Python Language Highlights lIntroduction and History of Python Language l Features of Python l Applications of Python l Python Interactive Help l Installing and Executing Python l How Python Differs from Other Languages We can see that computers have a wide range of real-world problem-solving abilities. The issues could be as straightforward as multiplying two numbers or as complex as designing and launching a space shuttle. Assuming that a machine can complete all jobs on its own would be wrong. Any problem whose solution is not defined cannot be resolved by a computer. A computer cannot solve any issue whose answer is not known. The computer merely executes the set of instructions that a programmer has provided to it. There may be mistakes and no resolution if the computer cannot comprehend the instructions. As a result, it is the programmer’s grave responsibility to come up with a solution by giving the machine the right commands. As a result, it is the programmer’s grave responsibility to come up with a solution by giving the machine the right commands.
  • 21.
    2 Python Programming:A Step-by-Step Guide to Learning the Language 1.1 Programming Language A programming language is a formal language used to instruct a computer to perform a specific task or set of tasks. It provides a set of rules and syntax for creating and manipulating code, allowing developers to write programs and applications that can run on a computer. Programming languages can be classified into different types based on their purpose and structure. Some common types of programming languages include: 1. Procedural languages: These languages use a series of steps to solve a problem or accomplish a task. Examples include C, Fortran, and Pascal. 2. Object-oriented languages: These languages model the problem as a set of objects that interact with each other to accomplish a task. Examples include Java, Python, and C++. 3. Functional languages: These languages focus on the evaluation of expressions and functions, treating them as mathematical equations. Examples include Haskell, Lisp, and ML. 4. Scripting languages: These languages are used to automate tasks, such as web development, and are often interpreted rather than compiled. Examples include JavaScript, PHP, and Python. There are many programming languages in use today, each with its own strengths and weaknesses, and new languages are continually being developed to meet the evolving needs of the technology industry. Some of the most common programming languages used today include: 1. Java: Java is an object-oriented programming language that is widely used for developing enterprise-level applications, mobile applications, and web applications. 2. Python: Python is a high-level programming language that is popular for its readability, ease of use, and versatility. It is commonly used for web development, data analysis, and artificial intelligence. 3. JavaScript: JavaScript is a scripting language that is used for developing web applications and interactive front-end interfaces. 4. C#: C# is an object-oriented programming language that is commonly used for developing Windows desktop applications, video games, and web applications.
  • 22.
    Introduction to PythonLanguage 3 5. C++: C++ is a high-performance language that is used for developing operating systems, video games, and other resource-intensive applications. 6. PHP: PHP is a server-side scripting language that is used for developing dynamic web applications and websites. 7. Ruby: Ruby is a high-level scripting language that is known for its simplicity and ease of use. It is commonly used for web development and building web applications. It is worth noting that the popularity of programming languages can vary depending on factors such as industry trends, the rise of new technologies, and the emergence of new use cases. 1.2 History of Python Language Python is a high-level, interpreted programming language that was first released in 1991 by its creator, Guido van Rossum. It was designed to be easy to read and write, and to emphasize code readability and simplicity. The origin of the name “Python” comes from a TV show from the 1970s called “Monty Python’s Flying Circus”. Guido van Rossum is a big fan of the show, and he named the language after it. Python was initially developed as a hobby project, and its first version was released in February 1991. The language was designed with a clear and concise syntax that allowed developers to write code quickly and efficiently. Python’s creators were also focused on making the language easily readable, which has helped to make it popular with beginners and experts alike. Following are the illustrations of different versions of Python along with the timeline. In 2000, Python 2.0 was released, which included many new features such as garbage collection, Unicode support, and list comprehensions. This version of the language became the dominant version of Python for many years, and it is still widely used today, despite being officially deprecated as of 2020. In 2008, Python 3.0 was released, which was a major overhaul of the language that introduced many changes and new features. One of the most significant changes was the removal of backward compatibility with Python 2.x, which made it more difficult for developers to transition to the new version. However, Python 3.0 brought many improvements and new features, including better Unicode support, improved I/O, and more efficient handling of exceptions.
  • 23.
    4 Python Programming:A Step-by-Step Guide to Learning the Language Fig. 1.1: Versions of Python Programming Language. Today, Python is one of the most popular programming languages in the world, and is used in a wide variety of applications, including web development, data analysis, artificial intelligence, and scientific computing. It is a powerful and flexible language that is well-suited to many different tasks, and it has a large and vibrant community of developers who continue to work on improving the language and developing new libraries and tools.
  • 24.
    Introduction to PythonLanguage 5 1.3 Origin of Python Programming Language Python programming language was created by Guido van Rossum in the late 1980s, while he was working at the Netherlands-based research institute called the National Research Institute for Mathematics and Computer Science (CWI). Guido was tasked with creating a successor to the ABC programming language that was easy to learn and use. He aimed to design a language with an easy-to-understand syntax, which would allow developers to write and maintain code more efficiently. 1.4 Features of Python Python is a high-level, interpreted programming language that is known for its simplicity, readability, and ease of use. Here are some of the key features of Python: 1. Simple and easy-to-learn syntax: Python has a simple and concise syntax, which makes it easy to read and write. Its code is easy to understand, even for beginners, and this simplicity is one of the reasons why Python has become so popular. 2. Interpreted language: Python is an interpreted language, which means that you don’t need to compile your code before running it. This makes it faster to develop and test code, as you can run it immediately and see the results. 3. Cross-platform compatibility: Python code can run on many different platforms, including Windows, Linux, and macOS. This is because Python code is interpreted, and the interpreter is available on all of these platforms. 4. Large standard library: Python comes with a large and comprehensive standard library that provides many useful functions and modules for developers. This makes it easy to perform common tasks, such as reading and writing files, working with databases, and performing network operations. 5. Third-party modules and libraries: Python has a huge and active community of developers who create and maintain many useful third-party modules and libraries. These libraries provide additional functionality, such as scientific computing, data analysis, web development, and artificial intelligence.
  • 25.
    6 Python Programming:A Step-by-Step Guide to Learning the Language 6. Object-oriented programming support: Python supports object- oriented programming, which allows developers to write modular, reusable, and maintainable code. 7. Dynamic typing: Python is a dynamically-typed language, which means that the type of a variable is determined at runtime, rather than at compile time. This makes it more flexible and allows developers to write code more quickly. 8. High-level abstractions: Python provides many high-level abstractions, such as list comprehensions, lambda functions, and decorators, which make it easier to write code that is concise and expressive. Overall, Python is a powerful and flexible programming language that is well-suited to many different tasks, and its features make it easy to learn and use, even for beginners 1.5 Limitations of Python While Python is a popular and powerful programming language, it has some limitations that developers should be aware of. Here are some of the main limitations of Python: 1. Performance: Python is an interpreted language, which means that it is generally slower than compiled languages like C++ or Java. This can be a limitation when developing applications that require high performance or low latency, such as real-time systems or high-transaction web applications. 2. Global Interpreter Lock (GIL): The GIL is a mechanism that ensures that only one thread executes Python bytecode at a time. This can limit the ability of developers to take advantage of multiple processors or cores, which can impact performance. 3. Weak typing: While dynamic typing is a key feature of Python, it can also be a limitation in some cases. Without type checking, errors can go undetected until runtime, and the lack of strong typing can make it more difficult to reason about code and catch errors early. 6. Mobile development: While Python can be used to develop mobile applications, it is not as well-suited to mobile development as other languages, such as Java or Kotlin. This is because the Android and iOS ecosystems are primarily based on those languages, and they offer more robust support for mobile development.
  • 26.
    Introduction to PythonLanguage 7 5. Memory consumption: Python is known for its high memory consumption, which can be a limitation when developing applications that need to run on low-memory devices or in constrained environments. 6. Dependencies and version compatibility: Python has a large and active community of developers who create and maintain many useful third-party modules and libraries. However, this can lead to version compatibility issues and dependency management challenges. It’s worth noting that many of these limitations can be mitigated by using best practices, such as optimizing code, using appropriate libraries, and following good design principles. Despite these limitations, Python remains a popular and versatile language that is well-suited to many different applications 1.6 Major Applications of Python Python is a versatile language that can be used for many different applications, from web development to scientific computing. Here are some of the major applications of Python: 1. Web development: Python is widely used for web development, both on the server-side and the client-side. Popular web frameworks include Django, Flask, Pyramid, and Bottle. 2. Data analysis and scientific computing: Python has become a popular language for data analysis and scientific computing, thanks to libraries such as NumPy, Pandas, SciPy, and Matplotlib. These libraries provide support for numerical operations, data manipulation, statistical analysis, and data visualization. 3. Machine learning and artificial intelligence: Python is widely used in the field of machine learning and artificial intelligence, thanks to libraries such as TensorFlow, Keras, PyTorch, and Scikit-learn. These libraries provide support for deep learning, neural networks, natural language processing, and other AI applications. 4. Desktop applications: Python can be used to develop desktop applications with graphical user interfaces (GUIs), thanks to libraries such as PyQt, PyGTK, and wxPython. 5. Game development: Python is increasingly being used in the field of game development, thanks to libraries such as Pygame and Panda3D. 6. Automation and scripting: Python is a popular language for automation and scripting tasks, thanks to its simple syntax and extensive library support.
  • 27.
    8 Python Programming:A Step-by-Step Guide to Learning the Language 7. DevOps: Python is used in DevOps for automation, testing, and deployment, thanks to libraries such as Fabric and Ansible. 8. Education: Python is a popular language for teaching programming, thanks to its simple syntax and ease of use. Overall, Python is a versatile language that can be used for many different applications, and its popularity and extensive library support make it a valuable tool for developers in many fields. 1.7 Getting Python Python can be downloaded for free from the official Python website, which provides installers for Windows, macOS, and Linux. Here are the steps to get Python: 1. Go to the Python website: Visit the official Python website at https:// www.python.org/ and click on the “Downloads” link at the top of the page. 2. Select your operating system: Choose your operating system from the list of available options. You can choose between Windows, macOS, and a variety of Linux distributions. 3. Choose your Python version: Python is available in two major versions, Python 2 and Python 3. While Python 2 is still in use, it is no longer actively developed and new users are advised to use Python 3. Choose the version of Python that you want to install. 4. Download the installer: Once you have chosen your operating system and Python version, download the installer for your system. 5. Run the installer: Run the installer on your computer and follow the on-screen instructions to complete the installation process. After the installation process is complete, you should have access to the Python interpreter and the Python standard library. You can also use an integrated development environment (IDE) such as PyCharm, Spyder, or Jupyter Notebook to write and run Python code. 1.8 Installing Python Python is a cross-platform programming language, with distributions available for multiple operating systems. To install Python, you can download the binary code for your platform from the official website, and run the installation process. In the event that the binary code is not available for
  • 28.
    Introduction to PythonLanguage 9 your platform, you can compile the source code manually using a C compiler. The installation process for Python may differ slightly depending on the platform, and specific instructions can be found on the official website for each platform, such as Unix or Linux. 1.8.1 Unix and Linux Installation Here are the general steps to install Python on Unix or Linux: 1. Open a terminal window. 2. Check if Python is already installed on your system by typing “python” or “python3” into the terminal. If Python is already installed, the version number will be displayed. If Python is not installed, you will see an error message. 3. If Python is not installed, you can install it using your system’s package manager. The command may differ depending on your distribution, but some examples are: • Ubuntu/Debian: sudo apt-get install python3 • Red Hat/Fedora: sudo yum install python3 • Arch Linux: sudo pacman -S python 4. Once the installation is complete, you can verify that Python is installed by typing “python” or “python3” into the terminal. 5. Optionally, you can install a Python IDE or code editor to make it easier to write and run Python code. Popular options include PyCharm, Spyder, Visual Studio Code, and Jupyter Notebook. 1.8.2 Windows Installation Here are the general steps to install Python on Windows: 1. Go to the official Python website at https://www.python.org/downloads/ and download the latest version of Python 3.x for Windows. 2. Once the installer is downloaded, run the executable file to begin the installation process. 3. In the installation wizard, select “Add Python 3.x to PATH” so that Python can be accessed from the command line and other applications. 4. Choose the “Customize installation” option and make sure that “pip” is selected. This is a package manager that allows you to easily install third-party Python packages.
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    10 Python Programming:A Step-by-Step Guide to Learning the Language 5. Continue with the installation process by following the on-screen prompts, such as selecting the installation location and accepting the license agreement. 6. Once the installation is complete, you can verify that Python is installed by opening the command prompt and typing “python” followed by the Enter key. This should launch the Python interpreter. 7. Optionally, you can install a Python IDE or code editor to make it easier to write and run Python code. Popular options include PyCharm, Spyder, Visual Studio Code, and Jupyter Notebook. 1.8.3 Macintosh Installation Here are the general steps to install Python on a Macintosh: 1. Go to the official Python website at https://www.python.org/downloads/ and download the latest version of Python 3.x for Mac. 2. Once the installer is downloaded, open it by double-clicking the .dmg file. 3. Double-click the “Python.mpkg” file to begin the installation process. 4. Follow the on-screen prompts to customize your installation settings, such as the installation directory and any additional components you want to include. The default settings are usually fine for most users. 5. Once the installation is complete, you can verify that Python is installed by opening a terminal window and typing “python3” followed by the Enter key. This should launch the Python interpreter. 6. Optionally, you can install a Python IDE or code editor to make it easier to write and run Python code. Popular options include PyCharm, Spyder, Visual Studio Code, and Jupyter Notebook. 1.9 Setting up Path Directories can contain various programs and executable files, which means that Windows, Unix/Linux, or MAC operating systems must have a way of finding these files. To locate executable files, the operating system provides a search path that includes directories. This search path is stored in an environment variable, which is a named string that contains information that can be accessed by the command shell and other programs.
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    Introduction to PythonLanguage 11 1.9.1 Setting up Path at Unix/Linux To set up the PATH for Python on Unix/Linux, follow these steps: 1. Open a terminal window. 2. Type “nano ~/.bashrc” to open your Bash profile file in the nano text editor. 3. Add the following line to the end of the file, replacing “3.9” with your version of Python: export PATH=”/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games:/usr/ local/python3.9/bin” 4. Save the file by pressing Ctrl + X, then Y, then Enter. 5. Type “source ~/.bashrc” to apply the changes to your current terminal session. 6. You can now run the “python” command from any directory in the terminal. 1.9.2 Setting up the Path at Windows To set up the PATH for Python on Windows, follow these steps: 1. Right-click on “This PC” or “My Computer” and select “Properties”. 2. Click on “Advanced system settings”. 3. Click on the “Environment Variables” button at the bottom. 4. Under “System Variables”, find the “Path” variable and click “Edit”. 5. Click “New” and enter the path to your Python installation directory (e.g. C:Python39). 6. Click “OK” to close all windows. 7. Open a new command prompt window and type “python” to verify that the PATH has been set up correctly. 1.10 Python Environment Variables Python uses environment variables to store configuration settings and other system information that can be accessed by your code. Here are some common Python environment variables:
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    12 Python Programming:A Step-by-Step Guide to Learning the Language 1. Path: This variable contains a list of directories where the operating system searches for executable files, including the Python interpreter. When you type “python” in the command prompt, the operating system looks for the “python.exe” file in these directories. 2. Pythonpath: This variable contains a list of directories where Python looks for modules and packages. You can add your own directories to this list to make your own modules and packages available to your Python code. 3. Pythonhome: This variable points to the root directory of your Python installation. If you move your Python installation to a different directory, you can update this variable to point to the new location. 4. Pythonstartup: This variable points to a Python script that is executed every time you start the Python interpreter. You can use this script to define your own Python environment, such as importing modules, defining functions, or setting default values. 5. Pythonioencoding: This variable sets the default encoding for input and output streams, such as stdin, stdout, and stderr. By default, Python uses the system’s default encoding, but you can change it to a different encoding if needed. These are just a few examples of Python environment variables. You can access and modify them using the os module in Python. 1.11 Running Python To run Python code, you first need to install Python on your computer. You can download and install the latest version of Python from the official website: https://www.python.org/downloads/. Once you have installed Python, you can run it in several ways: 1. Using the Python shell: The Python shell is an interactive environment that allows you to run Python code line by line. To open the Python shell, simply type “python” in your terminal or command prompt. 2. Using a text editor: You can write your Python code in a text editor, save it with a .py file extension, and then run it from the command prompt by typing “python filename.py”. 3. Using an Integrated Development Environment (IDE): An IDE provides a more advanced environment for writing, debugging, and
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    Introduction to PythonLanguage 13 running Python code. Some popular IDEs for Python include PyCharm, Visual Studio Code, and Spyder. Once you have Python installed and set up, you can start writing and running Python code to perform various tasks and solve problems. 1.11.1 Interactive Interpreter The interactive interpreter in Python is a command-line interface that allows you to enter Python commands and immediately see the results of those commands. It’s a great way to experiment with Python and test out code snippets before incorporating them into a larger program. To launch the Python interpreter, open a terminal or command prompt and type python followed by the Enter key. This will launch the interactive interpreter and display the Python version number and a command prompt (>>>). You can then enter Python commands at the prompt and see the output immediately. For example, you could type print(“Hello, World!”) and press Enter, and the interpreter would immediately display the output Hello, World!. You can exit the interactive interpreter by typing exit() or quit() at the command prompt and pressing Enter. This will return you to the terminal or command prompt. 1.11.2 Script from the Command-Line You can run a Python script from the command-line by typing python followed by the name of the script file. Here’s an example: Suppose you have a script called hello.py that contains the following code: print(“Hello, World!”) To run this script, open a terminal or command prompt and navigate to the directory where hello.py is saved. Then, type the following command and press Enter: python hello.py This will execute the script, and you should see the output Hello, World! in the terminal or command prompt. You can also pass command-line arguments to a Python script by including them after the script name. For example, if you have a script called add.py
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    14 Python Programming:A Step-by-Step Guide to Learning the Language that takes two numbers as arguments and adds them together, you could run it with the following command: python add.py 2 3 This would execute the script with the arguments 2 and 3, and the output would be 5. Note that in order to run a Python script from the command-line, you must have Python installed on your computer and the Python executable must be in your system’s PATH variable. 1.11.3 Integrated Development Environment An Integrated Development Environment (IDE) is a software application that provides a comprehensive environment for writing, testing, and debugging software code. Python has many popular IDEs that provide features like syntax highlighting, code completion, debugging tools, and more. Some popular Python IDEs include: 1. PyCharm: PyCharm is a powerful and full-featured IDE for Python. It includes advanced code completion, debugging tools, and support for web development frameworks like Django and Flask. 2. Visual Studio Code: Visual Studio Code is a lightweight and versatile IDE that supports a wide range of programming languages, including Python. It includes features like syntax highlighting, code completion, and debugging tools. 3. Spyder: Spyder is an IDE designed specifically for scientific computing and data analysis in Python. It includes features like variable explorer, data viewer, and plotting tools. 4. IDLE: IDLE is a simple and lightweight IDE that comes bundled with Python. It includes basic features like syntax highlighting and debugging tools. There are many other IDEs available for Python, and the best one for you will depend on your specific needs and preferences. You can download and install most Python IDEs for free, and they are available for all major operating systems. 1.12 First Python Program Python is a popular high-level programming language that is known for its simplicity, readability, and versatility. It is widely used for a variety of
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    Introduction to PythonLanguage 15 purposes, including web development, data analysis, machine learning, and more. One of the reasons that Python is so popular is its syntax, which is designed to be easy to read and write. For example, instead of using curly braces and semicolons to denote code blocks and statements, Python uses whitespace and indentation. Here is an example of a simple Python program that prints a message to the console: print(“Hello, World!”) This program simply uses the print() function to display the message “Hello, World!” on the console. You can save this code to a file with a .py extension, such as hello.py, and then execute it from the command line by typing python hello.py. 1.12.1 Interactive Mode Programming In Python, you can also run programs in interactive mode, which allows you to enter code directly into the Python interpreter and immediately see the output. This is a great way to experiment with Python and test out code snippets before incorporating them into a larger program. To launch the interactive interpreter in Python, open a terminal or command prompt and type python followed by the Enter key. This will launch the interpreter and display the Python version number and a command prompt (>>>). You can then enter Python commands at the prompt and see the output immediately. For example, you could type print(“Hello, World!”) and press Enter, and the interpreter would immediately display the output Hello, World!. In interactive mode, you can also define variables, create functions, and import modules, just as you would in a regular Python program. For example, you could define a variable like this: x = 5 And then use it in a calculation like this: y = x * 2 print(y) This would define a variable x with the value 5, and then define a variable y with the value 10 (which is the result of multiplying x by 2). Finally, it would print the value of y to the console.
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    16 Python Programming:A Step-by-Step Guide to Learning the Language You can exit the interactive interpreter by typing exit() or quit() at the command prompt and pressing Enter. This will return you to the terminal or command prompt. 1.12.2 The Script Mode Programming In addition to running Python code in interactive mode, you can also write code in a file and run it as a script. This is a common way to write larger Python programs that can be executed from the command line or scheduled to run automatically. To create a Python script, simply create a new file with a .py extension and write your code in it using a text editor or integrated development environment (IDE). For example, you could create a file called hello.py and write the following code: print(“Hello, World!”) To run this script, you can execute it from the command line by typing python hello.py and pressing Enter. This will run the script and print the message “Hello, World!” to the console. In a Python script, you can include any valid Python code, including importing modules, defining functions, and using control structures like loops and conditionals. For example, you could write a more complex script that asks the user for their name and then greets them: name = input(“What is your name? “) print(“Hello, “ + name + “!”) This script would prompt the user to enter their name using the input() function, and then use string concatenation to print a personalized greeting. Overall, writing Python scripts is a powerful way to build complex programs that can be used in a variety of contexts. With a little practice, you can use Python to automate repetitive tasks, process data, and build sophisticated applications. 1.13 Python’s Interactive Help Python comes with a built-in help utility, which is one of the major features and support of Python language. The prerequisite of using the built-in help of Python, you must have a little knowledge of programming. For a new
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    Introduction to PythonLanguage 17 programmer, it could be a bit off-putting. Once a programmer becomes familiar with programming terminology then he can make great use of the built-in help provided by Python. Python programming help can be obtained in the following ways: • Interactive mode help • Getting help online through a web browser 1.13.1 Python Help Through a Web Browser Python has an extensive standard library, as well as a large and active community of developers who have created many third-party modules and libraries. As a result, there are many resources available for getting help with Python online. One popular resource is the Python documentation, which is available online in a web browser at the official Python website (https://www.python.org/). The documentation includes a detailed language reference, as well as tutorials and guides for getting started with Python, building web applications, working with data, and more. In addition to the official Python documentation, there are many online forums and communities where you can ask questions and get help with Python. For example, the Python subreddit (https://www.reddit.com/r/Python/) is a popular forum where Python users can ask questions, share code snippets, and get advice from other developers. There are also many third-party websites and services that offer Python tutorials, courses, and other learning resources. For example, Codecademy (https://www.codecademy.com/learn/learn-python) offers a comprehensive Python course for beginners, while Udemy (https://www.udemy.com/topic/ python/) has a wide range of Python courses for learners of all levels. Overall, there are many resources available for getting help with Python online, and the best approach will depend on your specific needs and learning style. Whether you’re a beginner or an experienced developer, there are many ways to learn and grow your skills with Python. 1.14 Python Differences From Other Languages While the Python language shares some similarities with C, C++, and Java, there are also distinct differences that set it apart from these languages.
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    18 Python Programming:A Step-by-Step Guide to Learning the Language 1.14.1 Difference Between C and Python C and Python are both popular programming languages, but they differ in several ways. Here are some of the main differences between C and Python: 1. Syntax: C uses a more complex syntax than Python. C requires more code to accomplish the same tasks as Python, and it also has stricter rules for formatting and organization. 2. Compiled vs. Interpreted: C is a compiled language, meaning that the code is translated into machine code by a compiler before it can be executed. Python, on the other hand, is an interpreted language, meaning that the code is executed directly by an interpreter. 3. Typing: C is a statically typed language, meaning that the type of each variable is declared explicitly in the code. Python, on the other hand, is a dynamically typed language, meaning that the type of each variable is determined at runtime. 4. Memory Management: C requires manual memory management, meaning that the programmer must explicitly allocate and deallocate memory for variables and data structures. Python, on the other hand, has automatic memory management, meaning that the interpreter takes care of memory allocation and deallocation. 5. Application: C is often used for low-level systems programming, such as operating systems, device drivers, and embedded systems, as well as for high-performance computing and graphics programming. Python is often used for web development, data analysis, scientific computing, and automation. 6. Object-Oriented Programming: While C supports object-oriented programming (OOP), it is not a pure OOP language like Python. Python is designed to support OOP concepts such as encapsulation, inheritance, and polymorphism, making it easier to write and organize code for larger projects. 7. Platform Independence: Python is more platform-independent than C, meaning that Python code can run on a variety of operating systems and hardware without modification. C code, on the other hand, may need to be recompiled for different platforms or architectures. 8. Libraries and Packages: Python has a vast library of built-in modules and third-party packages that make it easy to perform a wide range of tasks, from web scraping to machine learning. C has a smaller standard
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    Introduction to PythonLanguage 19 library and fewer third-party packages, making it more difficult to find pre-built solutions for common programming problems. 9. Debugging: Debugging C code can be more challenging than debugging Python code, due to C’s lower-level nature and manual memory management. Python has built-in debugging tools and a more forgiving syntax, making it easier to locate and fix errors in code. 10. Learning Curve: Python is generally considered to be easier to learn and use than C, due to its simpler syntax, automatic memory management, and built-in data structures. C requires a deeper understanding of computer architecture and low-level programming concepts, making it more difficult to learn and master. These are just a few of the many differences between C and Python. While they share some similarities, they are distinct languages with their own strengths and weaknesses, and the choice of language will depend on the specific needs of the project and the preferences of the programmer. 1.14.2 Difference Between C++ and Python C++ and Python are both popular programming languages, but they differ in several ways. Here are some of the main differences between C++ and Python: 1. Syntax: C++ uses a more complex syntax than Python. C++ requires more code to accomplish the same tasks as Python, and it also has stricter rules for formatting and organization. 2. Compiled vs. Interpreted: C++ is a compiled language, meaning that the code is translated into machine code by a compiler before it can be executed. Python, on the other hand, is an interpreted language, meaning that the code is executed directly by an interpreter. 3. Typing: C++ is a statically typed language, meaning that the type of each variable is declared explicitly in the code. Python, on the other hand, is a dynamically typed language, meaning that the type of each variable is determined at runtime. 4. Memory Management: C++ requires manual memory management, meaning that the programmer must explicitly allocate and deallocate memory for variables and data structures. Python, on the other hand, has automatic memory management, meaning that the interpreter takes care of memory allocation and deallocation.
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    20 Python Programming:A Step-by-Step Guide to Learning the Language 5. Object-Oriented Programming: Both C++ and Python support object- oriented programming (OOP), but C++ is often considered a “pure” OOP language, meaning that all code is organized into objects and classes. Python, on the other hand, allows for more procedural and functional programming styles as well. 6. Application: C++ is often used for systems programming, such as operating systems and device drivers, as well as for high-performance computing, gaming, and graphics programming. Python is often used for web development, data analysis, scientific computing, and automation. 7. Libraries and Packages: Python has a vast library of built-in modules and third-party packages that make it easy to perform a wide range of tasks, from web scraping to machine learning. C++ has a smaller standard library and fewer third-party packages, making it more difficult to find pre-built solutions for common programming problems. 8. Speed: C++ is generally faster than Python because it is a compiled language, meaning that the code is translated into machine code before it is executed. Python, on the other hand, is an interpreted language, meaning that the code is executed directly by an interpreter, which can be slower. However, Python has some libraries that are implemented in C or C++ that can boost its performance. 9. Learning Curve: C++ is generally considered more difficult to learn and use than Python. C++ has a steep learning curve because it is a more complex language with a larger number of features and more stringent syntax rules. Python, on the other hand, has a simpler syntax and a smaller set of features, making it easier to learn and use. 10. Type Safety: C++ is a type-safe language, meaning that the compiler checks for type errors at compile time. Python is not type-safe, meaning that type errors can occur at runtime. 11. Multithreading: C++ has built-in support for multithreading, allowing programs to execute multiple threads of code concurrently. Python also supports multithreading, but it has a global interpreter lock (GIL) that can limit the performance gains of multithreading in some cases. 12. Portability: Python is more portable than C++ because it is a high-level language that can be interpreted on any platform. C++ code must be compiled on the specific platform it will run on, which can make it less portable.
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    Introduction to PythonLanguage 21 13. Memory Safety: C++ is a language that provides the programmer with the ability to manually manage memory. While this gives the programmer more control over how memory is used, it also means that the program can be susceptible to memory-related bugs, such as buffer overflows and memory leaks. Python has built-in garbage collection, meaning that it automatically manages memory, which makes it less susceptible to memory-related bugs. These are just a few of the many differences between C++ and Python. While both languages are widely used and powerful, they have different strengths and weaknesses, and the choice of language will depend on the specific needs of the project and the preferences of the programmer. 1.14.3 Difference between Java and Python Java and Python are both popular programming languages, but they differ in several ways. Here are some of the main differences between Java and Python: 1. Syntax: Java has a more complex syntax than Python. Java requires more code to accomplish the same tasks as Python, and it also has stricter rules for formatting and organization 2. Compiled vs. Interpreted: Java is a compiled language, meaning that the code is translated into bytecode by a compiler before it can be executed. Python, on the other hand, is an interpreted language, meaning that the code is executed directly by an interpreter. 3. Typing: Java is a statically typed language, meaning that the type of each variable is declared explicitly in the code. Python, on the other hand, is a dynamically typed language, meaning that the type of each variable is determined at runtime. 4. Memory Management: Java has automatic memory management, meaning that the JVM (Java Virtual Machine) takes care of memory allocation and deallocation. Python also has automatic memory management, meaning that the interpreter takes care of memory allocation and deallocation. 5. Object-Oriented Programming: Both Java and Python support object- oriented programming (OOP), and they have similar concepts such as inheritance, polymorphism, and encapsulation. However, Java is often considered a “pure” OOP language, meaning that all code is organized
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    22 Python Programming:A Step-by-Step Guide to Learning the Language into objects and classes. Python, on the other hand, allows for more procedural and functional programming styles as well. 6. Application: Java is often used for web development, desktop application development, and mobile app development. Python is often used for web development, scientific computing, data analysis, machine learning, and automation. 7. Performance: Java is generally faster than Python because it is a compiled language, and the JVM can optimize the bytecode for performance. Python, on the other hand, is an interpreted language, meaning that the code is executed directly by an interpreter, which can be slower. However, Python has some libraries that are implemented in C or C++ that can boost its performance. 8. Garbage Collection: Java has a more advanced garbage collector than Python, meaning that it can handle memory more efficiently. 9. Learning Curve: Java is generally considered more difficult to learn and use than Python. Java has a steep learning curve because it is a more complex language with a larger number of features and more stringent syntax rules. Python, on the other hand, has a simpler syntax and a smaller set of features, making it easier to learn and use. These are just a few of the many differences between Java and Python. While both languages are widely used and powerful, they have different strengths and weaknesses, and the choice of language will depend on the specific needs of the project and the preferences of the programmer. 1.15 Summary In this chapter, we have learned about the programming language and its needs. Then we gave a brief look at the origin and history of the Python language along with its features and limitations. We have explored in detail how Python language differs from other existing and prominent programming languages such as C,C++, and Java. The setup and installation of Python language along with a simple first program are also discussed in detail. Review Questions 1. What is Python and what makes it a popular programming language? 2. How does Python differ from other programming languages?
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    Introduction to PythonLanguage 23 3. What is the history of the Python programming language and who developed it? 4. What are the steps to install Python on different operating systems? 5. What are the benefits of using Python for software development? 6. What are the key features of Python, and how do they contribute to its popularity? 7. How does the Python community support and contribute to the language’s development? 8. What are some popular applications built using Python? 9. How does Python compare with other programming languages like Java, C++, and Ruby? 10. What are the most important considerations when choosing a programming language, and how does Python measure up? 11. What is Python and how is it used? 12. Python is a high-level programming language used for a variety of applications, including web development, data analysis, artificial intelligence, and more. 13. Python is a type of reptile found in tropical regions around the world. 14. What are some key features of the Python programming language? a. Python has a simple and easy-to-learn syntax. b. Python is an interpreted language, meaning that code is executed directly by an interpreter without the need for compilation.
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    2 Python Data Typesand Input Output Highlights l Keywords and identifiers l Python statements l Documentation and indentation l Python Variables l Python data types l Input and output l Import Python has several built-in data types including strings, integers, and lists. These data types can be used to store and manipulate different kinds of information in a program. In addition to these basic data types, Python also has advanced data types such as dictionaries and sets. Python also has a built-in module for input/output operations, which allows a program to read from and write to external sources, such as files and streams. The module, called io, provides several functions to perform these operations, such as open(), read(), and write(). 2.1 Keywords In Python, a keyword is a word that has a special meaning in the Python language. Keywords are used to define the syntax and structure of the
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    26 Python Programming:A Step-by-Step Guide to Learning the Language Python language, and they cannot be used as identifiers (i.e., variable names, function names, etc.) in Python code. Keyword Description and Logical operator returns True if both operands are True, otherwise, it returns False. as Used to create an alias for a module or variable when importing or renaming. assert Used to check if a given condition is True, and raises an exception if it is False. async Used to define an asynchronous function or context manager. await Used inside an async function to wait for an asynchronous operation to complete. break Used to exit a loop early, before the loop condition is met. class Used to define a new class. continue Used to skip the current iteration of a loop and continue with the next iteration. def Used to define a new function. del Used to delete an object or an item from a collection. elif Short for “else if”, used in a conditional statement to check for additional conditions. else Used in a conditional statement as a catch-all option if no other conditions are met. except Used to handle exceptions that are raised in a try block. False Boolean value that represents the absence of truth. finally Used in a try-except block to specify a block of code that will always be executed, regardless of whether an exception was raised or not. for Used to iterate over a sequence of items, such as a list or a tuple. from Used in an import statement to import specific items from a module. global Used to indicate that a variable is a global variable, accessible from anywhere in the code. if Used to start a conditional statement. import Used to import a module or a specific item from a module.
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    Python Data Typesand Input Output 27 Keyword Description in Used to check if an item is in a sequence, such as a list or a tuple. is Used to check if two variables refer to the same object. lambda Used to create small anonymous functions. None Special value that represents the absence of a value or a null value. nonlocal Used to indicate that a variable is nonlocal to the current function, meaning it is defined in an outer function. not Logical operator that negates a boolean value. or Logical operator that returns True if at least one of the operands is True, otherwise it returns False. pass Used as a placeholder for a block of code that does nothing. raise Used to raise an exception. return Used to exit a function and return a value to the calling code. True Boolean value that represents the presence of truth. try Used to specify a block of code that might raise an exception. while Used to start a loop that will continue to execute as long as the loop condition is True. with Used to create a context manager, which is used to automatically set up and tear down resources. yield Used in a function 2.2 Identifiers In Python, an identifier is a name used to identify a variable, function, class, module, or other objects. There are a few rules and conventions for naming identifiers in Python: • Identifiers must start with a letter or an underscore (_). • Identifiers cannot start with a number. • Identifiers can only contain letters, numbers, and underscores. • Identifiers are case-sensitive, so myVariable and myvariable are considered to be different identifiers. • Python reserves a set of keywords that cannot be used as identifiers. Examples include if, else, for, class, etc.
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    28 Python Programming:A Step-by-Step Guide to Learning the Language • Identifiers should be descriptive and meaningful, using camelCase or snake_case, depending on the project’s style guide. Examples of valid identifiers in Python: • myVariable • _privateVariable • counter • calculate_average • MyClass Examples of invalid identifiers in Python: • 1stVariable (starts with a number) • my-variable (contains a hyphen) • if (reserved keyword) • True (reserved keyword) • class (reserved keyword) 2.3 Python Statements In Python, a statement is a single line of code that performs a specific action or instruction. There are several types of statements in Python, including: • Expressions: These are statements that evaluate to a value, such as mathematical operations or function calls. Examples include “2 + 2” or “print(‘Hello, world!’)”. • Assignment statements: These are statements that assign a value to a variable. Examples include “x = 2” or “name = ‘John’”. • Control flow statements: These are statements that control the flow of execution of a program, such as conditional statements (if/else) and loops (for/while). • Function and class definitions: These are statements that define a function or class, respectively. Examples include “def my_function():” and “class MyClass:”. • Import statements: These statements are used to import modules or packages in python. Examples include “import os” or “from math import sqrt”
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    Python Data Typesand Input Output 29 • Pass statements: A pass statement is a null operation. Nothing happens when it executes. It is useful as a placeholder when a statement is required syntactically, but no code needs to be executed. 2.4 Indentation Indentation is used in Python to indicate blocks of code. The standard indentation is four spaces, and most Python code follows this convention. For example Code 2.1 Illustration of indentation in Python def foo(): # This line is indented by four spaces x = 5 if x > 0: # This line is also indented by four spaces print(“x is positive”) # This line is not indented, so it’s not part of the if block It is important to be consistent with your indentation, as the meaning of the code can change based on the indentation level. For example Code 2.2 Illustration of indentation in Python x = 5 if x > 0: print(“x is positive”) print(“This line is not indented, so it’s not part of the if block”) This code will print both messages because the second print statement is not indented, so it is not part of the if block. Code 2.3 Illustration of indentation in Python x = 5 if x > 0: print(“x is positive”) print(“This line is indented, so it is part of the if block”) print(“This line is not indented, so it’s not part of the if block”)
  • 49.
    30 Python Programming:A Step-by-Step Guide to Learning the Language This code will only print the first message, because the second print statement is indented, so it is part of the if block. 2.5 Python Documentation In Python, documentation is typically included in the form of comments in the source code. These comments start with a # symbol, and everything following the # on that line is considered a comment. For example: Code 2.4 Illustration of documentation in Python using # # This is a comment x = 5 # This is also a comment 2.5.1 Single Line Comment In Python, a single line comment is created by placing the “#” symbol at the beginning of the line, followed by the text of the comment. For example: Code 2.5 Illustration of single line documentation in Python # This is a single line comment in Python x = 10 # This is also a single line comment The text following the “#” symbol is ignored by the Python interpreter and is only there for the benefit of the person reading the code. Single line comments are often used to add brief explanations or clarifications to the code. Code 2.6 Illustration of single line documentation in Python # Calculate the area of a rectangle width = 3 height = 12 area = width * height In this example, the single line comments provide some context for the code that follows, explaining what the variables are used for and what the final line of code is doing. 2.5.2 Multi Line Comments In Python, multi-line comments can be created using triple quotes, either single quotes (‘’’) or double quotes (“””). The comments can span multiple
  • 50.
    Python Data Typesand Input Output 31 lines and are commonly used for longer explanations and docstrings. Here is an example: Code 2.7 Illustration of double quotes in Python “”” This is a multiline comment. It can span multiple lines. “”” You can also use triple single quotes to create a multiline comment: Code 2.8 Illustration of single quotes in Python ‘’’ This is also a multiline comment. It can also span multiple lines. ‘’’ Both triple quotes (single or double) can be used to create a multiline comment in Python. The advantage of using triple quotes is that you can create a multiline comment even if it contains multiple lines of the same type of quote character. 2.6 Docstrings In Python, a docstring is a string literal that appears as the first statement in a module, function, class, or method definition. It is used to provide documentation for the code, and can be accessed using the built-in help() function or the __doc__ attribute. Docstrings are enclosed in triple quotes (either single or double) and are typically written in plain text, but can also include markdown formatting. It is a good practice to include a docstring in any function or class you write, as it makes your code more readable and user-friendly. Here is an example of a simple Python function with a docstring:
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    32 Python Programming:A Step-by-Step Guide to Learning the Language Code 2.9 Illustration of docstring in Python def add(a, b): “”” This function takes two numbers as input and returns their sum. Parameters: a (int): The first number b (int): The second number Returns: int: The sum of a and b “”” return a + b In this example, the function add takes two numbers as input, adds them together, and returns the result. The docstring provides a brief description of what the function does, and explains the parameters and return value. To access the docstring of this function, you can use the help() function like this: help(add) You can also access the docstring programmatically using the __doc__ attribute: print(add.__doc__) This will output the same string as the docstring. By providing clear and concise documentation, it’s easy for other developers to understand how the function works, what the parameters are and what to expect when calling the function. This can save a lot of time and effort when working on large projects with multiple contributors. 2.7 Variables A Python variable is a reserved memory location to store values. In other words, a variable in a python program gives data to the computer for
  • 52.
    300 300 Python Data Typesand Input Output 33 processing. Every value in Python has a datatype. Different data types in Python are Numbers, List, Tuple, Strings, Dictionary, etc. Variables can be declared by any name or even alphabets like a, aa, abc, etc. 2.7.1 Variable Assignment Think of a variable as a name attached to a particular object. In Python, variables need not be declared or defined in advance, as is the case in many other programming languages. To create a variable, you just assign it a value and then start using it. The assignment is done with a single equals sign (=): n = 300 This is read or interpreted as “n is assigned the value 300.” Once this is done, n can be used in a statement or expression, and its value will be substituted: print(n) Just as a literal value can be displayed directly from the interpreter prompt in a REPL session without the need for print(), so can a variable: n Later, if you change the value of n and use it again, the new value will be substituted instead: n = 1000 print(n) 1000 Python also allows chained assignment, which makes it possible to assign the same value to several variables simultaneously: Code 2.10 Illustration of variable in Python a = b = c = 300 print(a, b, c) 300 300 300 2.7.2 Variable Types in Python In many programming languages, variables are statically typed. That means a variable is initially declared to have a specific data type, and any value
  • 53.
    34 Python Programming:A Step-by-Step Guide to Learning the Language assigned to it during its lifetime must always have that type. Variables in Python are not subject to this restriction. In Python, a variable may be assigned a value of one type and then later re-assigned a value of a different type: Code 2.11 Illustration of variable in Python var = 21.09 print(var) 21.09 Let’s see another example: Code 2.12 Illustration of variable in Python >>> var = “Welcome to Python” >>> print(var) Welcome to Python 2.8 Multiple Assignment Multiple assignment allows you to assign multiple variables at the same time in one line of code. This feature often seems simple after you’ve learned about it, but it can be tricky to recall multiple assignment when you need it most. In this we’ll see what multiple assignment is, we’ll take a look at common uses of multiple assignment, and then we’ll look at a few uses for multiple assignment that are often overlooked. Python’s multiple assignment looks like this: >>> x, y = 10, 20 Here we’re setting x to 10 and y to 20. What’s happening at a lower level is that we’re creating a tuple of 10, 20 and then looping over that tuple and taking each of the two items we get from looping and assigning them to x and y in order. This syntax might make that a bit more clear: >>> (x, y) = (10, 20) Parenthesis are optional around tuples in Python and they’re also optional in multiple assignment (which uses a tuple-like syntax). All of these are equivalent:
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    Python Data Typesand Input Output 35 Code 2.13 Illustration of variable in Python >>> x, y = 10, 20 >>> x, y = (10, 20) >>> (x, y) = 10, 20 >>> (x, y) = (10, 20) Multiple assignments are often called “tuple unpacking” because it’s frequently used with tuples. But we can use multiple assignments with any iterable, not just tuples. Here we’re using it with a list: Code 2.13 Illustration of variable in Python >>> x, y = [10, 20] >>> x 10 >>> y 20 And with a string: Code 2.13 Illustration of a variable with a string in Python >>> x, y = ‘hi’ >>> x ‘h’ >>> y ‘i’ Here’s another example to demonstrate that multiple assignments works with any number of items and that it works with variables as well as objects we’ve just created: Code 2.14 Illustration of a variable in Python >>> point = 10, 20, 30 >>> x, y, z = point >>> print(x, y, z) 10 20 30 >>> (x, y, z) = (z, y, x) >>> print(x, y, z) 30 20 10
  • 55.
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