Python Internals for Developers

Concise Interpretation of every essential element of Python with Use-cases

Key Features

● Numerous examples and solutions to assist beginners in understanding the concept.

● Contains visual representations of data structures.

● Demonstrations of how to use data structures with a Python implementation.

Description

This book will aid you in your learning of the Python 3.x programming language. The chapters in this book will benefit every aspect of a programmer's or developer's life by preparing them to solve problems using Python programming and its key data structures and internals.

This book explains the built-in and user-defined data structures in Python 3.x. The book begins by introducing Python, its fundamental data structures, and asymptotic notations. Once you master the fundamentals of Python, you'll be able to fully comprehend the built-in data structures. The book covers real-world applications to understand user-defined data structures and their actual implementation. Towards the end, it will help you investigate how to solve practical problems by first comprehending the issue at hand.

After reading this book, you will be able to identify data structures and utilize them to solve a specific problem. You will learn about various algorithm implementations in Python and use this knowledge to advance your Python skills.

After reading this book you will be able to plan your application’s migration to containers, prepare for Docker and Kubernetes Certifications, or apply for six digit DevOps jobs.

What you will learn

● Calculate the complexity of time and space using asymptotic notations.

● Discover Python 3.x's built-in and user-defined data structures.

● Create user-defined data structures from the bottom up.

● Make use of libraries to create new user-defined data structures.

● Determine and implement the most appropriate data structure for resolving issues.

Who this book is for

This book caters to those who want to enhance their careers as application developers, machine learning engineers, or researchers. Knowing basic programming concepts will be good, but not mandatory.

Table of Contents

1. Python

2. Data Types

3. Algorithm Analysis

4. Data Structure Introduction

5. List

6. Dictionary

7. Tuple

8. Sets

9. Arrays

10. Stack

11. Queue

12. Trees

13. Linked Lists

14. Graphs

15. HashMaps

16. Practical Problem Solutions