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Chapter 30: Advanced Python Programming – Different Python Libraries

Prof.(Dr.) D G Mahto, Founder & CEO -
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Chapter Overview Python’s true power lies in its rich ecosystem of libraries that extend its capabilities across data science, web development, machine learning, automation, and more. This chapter presents a comprehensive overview of popular and powerful Python libraries across different domains. It explores their core features, real-world use cases, and how they enable advanced Python programming. 30.1 Introduction to Python Libraries A Python library is a collection of modules that provide functions and tools for performing specific tasks. Libraries help developers avoid reinventing the wheel and accelerate application development. Benefits of Using Libraries: Reduce development time Improve code readability and reliability Enable complex functionality with minimal code Promote standard practices and code reuse 30.2 Standard Python Libraries 30.2.1 math and cmath Used for basic and complex mathematical operations. import math print(math.sqrt(25))...
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Chapter 29: Advanced Python Programming – GitHub for Python Programmers

Prof.(Dr.) D G Mahto, Founder & CEO -
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Chapter Overview In modern software development, version control is a fundamental requirement, especially for collaborative and open-source projects. GitHub, built on Git, is a powerful platform that enables Python developers to manage their code efficiently, collaborate with others, track issues, and automate workflows. This chapter explores the integration of Python programming with GitHub, offering a comprehensive guide to leveraging GitHub features to boost productivity, maintain cleaner repositories, and contribute to the wider developer community. 29.1 Introduction to Git and GitHub Git is a distributed version control system that allows multiple developers to work on a project without overwriting each other's changes. GitHub is a cloud-based hosting service for Git repositories. It adds features like pull requests, issue tracking, GitHub Actions, and project wikis. Benefits for Python Developers: Version control of Python scripts and notebooks....
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Chapter 28: Advanced Python Programming – Unit Testing

Prof.(Dr.) D G Mahto, Founder & CEO -
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28.1 Introduction Software development is incomplete without rigorous testing. Unit testing ensures that individual parts of your program—typically functions and methods—work as intended. It helps catch bugs early, facilitates code changes, and improves design. Python offers built-in tools and third-party frameworks to support test-driven development (TDD). This chapter delves into the concepts, methodologies, and best practices of unit testing in Python, with practical examples. 28.2 What is Unit Testing? Unit Testing is the process of testing individual components or "units" of a program in isolation to ensure that they perform as expected. Each test case typically targets a specific function or method and checks its behavior against expected outputs. Benefits of Unit Testing Detects bugs early Facilitates refactoring Promotes cleaner design Supports continuous integration Enhances code reliability 28.3 The unittest Module in Python Py...
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Chapter 27: Advanced Python Programming – Jupyter Notebook

Prof.(Dr.) D G Mahto, Founder & CEO -
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Abstract : Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations, and narrative text. These documents, known as notebooks, are valuable for data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and more.   Advanced Features Interactive Coding: Jupyter Notebooks provide a live, interactive environment for Python coding. This allows for real-time testing and debugging of code snippets. Rich Output Media: Beyond basic text output, Jupyter Notebooks can display images, render SVG images, play audio and video, and even embed interactive webpages. It also supports LaTeX rendering for mathematical equations. Interactive Plots and Visualizations: Libraries like Matplotlib can be used to create interactive plots and visualizations directly within the notebook. Multi-Language Support: While commonly used with Pyt...
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Chapter 26: Advanced Python Programming – Iterators and Generators

Prof.(Dr.) D G Mahto, Founder & CEO -
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Abstract : In Python, iterators and generators are  both used for creating sequences of values , but they differ in their implementation and how they generate these values. Iterators are objects that provide sequential access to elements of an iterable, while generators are functions that yield values one at a time.   Iterators: Definition: An iterator is an object that implements the  __iter__  and  __next__  methods.   Creation: You can create iterators from iterables (like lists, tuples, strings) using the  iter()  function.   State: Iterators maintain internal state, allowing them to track their position within the sequence.   Implementation: Iterators can be implemented as custom classes that define  __iter__  and  __next__  methods.   Generators: Definition:  Generators are special functions that use the  yield  keyword to produce values.   Creation:  Generator...
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Chapter 25: Advanced Python Programming - Understanding sys Module

Prof.(Dr.) D G Mahto, Founder & CEO -
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25.1 Introduction In advanced Python programming, system-level operations often become essential, especially when interacting with the interpreter or manipulating the execution environment. The sys module provides access to variables and functions closely associated with the Python interpreter, allowing developers to perform a variety of system-specific tasks such as command-line argument parsing, input/output redirection, program termination, and more. This chapter provides an in-depth understanding of the sys module, covering its core components, use cases, and best practices. 25.2 What is the sys Module? The sys module is a built-in Python module that provides functions and variables used to manipulate different parts of the Python runtime environment. It is especially useful for interacting with the operating system via command-line interfaces or scripting environments. Importing sys : import sys 25.3 Commonly Used sys Module Attributes and Methods 25.3....
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Chapter 24: Advanced Python Programming – Understanding JSON Data

Prof.(Dr.) D G Mahto, Founder & CEO -
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24.1 Introduction to JSON JSON (JavaScript Object Notation) is a lightweight data-interchange format that is easy for humans to read and write and easy for machines to parse and generate. Though it originated from JavaScript, it is language-independent and is widely supported in modern programming languages, including Python. JSON is commonly used for: Exchanging data between web clients and servers. Storing configuration files. Data serialization and deserialization. Example of a JSON Object: { "name": "John Doe", "age": 30, "is_employee": true, "skills": ["Python", "Data Analysis", "Machine Learning"], "address": { "city": "New York", "zipcode": "10001" } } 24.2 The json Module in Python Python provides a built-in module called json to work with JSON data. The json module can be used to: Convert Pyth...
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Chapter 23: Advanced Python Programming – Mastering Python Regular Expressions

Prof.(Dr.) D G Mahto, Founder & CEO -
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23.1 Introduction to Regular Expressions (Regex) Regular Expressions (Regex) are powerful tools used to perform pattern matching and text manipulation in strings. Python provides a built-in library called re for working with regular expressions. Understanding and mastering Regex can dramatically increase efficiency in text processing, data extraction, and input validation tasks. 23.2 The re Module in Python The re module offers several functions: re.match() : Determines if the RE matches at the beginning of the string. re.search() : Scans through a string looking for any location where the RE matches. re.findall() : Returns all non-overlapping matches of RE in the string. re.finditer() : Returns an iterator yielding match objects over all matches. re.sub() : Replaces matched substrings with a new string. re.split() : Splits the string by occurrences of the pattern. Example: import re text = "Python is powerful. Python is easy to learn....
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Chapter 22: Advanced Python Programming - Using the Pillow Library to Edit Images

Prof.(Dr.) D G Mahto, Founder & CEO -
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22.1 Introduction Image processing is a common requirement in many Python applications, including automation, web development, and data science. The Pillow library—an easy-to-use fork of the Python Imaging Library (PIL)—offers powerful tools for image manipulation such as resizing, cropping, filtering, and format conversion. This chapter provides a practical introduction to Pillow, explaining its installation, basic operations, and advanced image editing techniques with code examples. 22.2 Installing Pillow To install Pillow, use the Python package installer: pip install Pillow You can now import the library using: from PIL import Image 22.3 Opening and Displaying Images Opening an Image from PIL import Image img = Image.open("example.jpg") img.show() Getting Image Information print(img.format) # JPEG, PNG, etc. print(img.size) # (width, height) print(img.mode) # RGB, L (grayscale), etc. 22.4 Saving Images You can save an i...
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