Book Image

Python for Geeks

By : Muhammad Asif
Book Image

Python for Geeks

By: Muhammad Asif

Overview of this book

Python is a multipurpose language that can be used for multiple use cases. Python for Geeks will teach you how to advance in your career with the help of expert tips and tricks. You'll start by exploring the different ways of using Python optimally, both from the design and implementation point of view. Next, you'll understand the life cycle of a large-scale Python project. As you advance, you'll focus on different ways of creating an elegant design by modularizing a Python project and learn best practices and design patterns for using Python. You'll also discover how to scale out Python beyond a single thread and how to implement multiprocessing and multithreading in Python. In addition to this, you'll understand how you can not only use Python to deploy on a single machine but also use clusters in private as well as in public cloud computing environments. You'll then explore data processing techniques, focus on reusable, scalable data pipelines, and learn how to use these advanced techniques for network automation, serverless functions, and machine learning. Finally, you'll focus on strategizing web development design using the techniques and best practices covered in the book. By the end of this Python book, you'll be able to do some serious Python programming for large-scale complex projects.
Table of Contents (20 chapters)
1
Section 1: Python, beyond the Basics
5
Section 2: Advanced Programming Concepts
9
Section 3: Scaling beyond a Single Thread
13
Section 4: Using Python for Web, Cloud, and Network Use Cases

Learning when not to use OOP in Python

Python has the flexibility to develop programs using either OOP languages such as Java or using declarative programming such as C. OOP is always appealing to developers because it provides powerful tools such as encapsulation, abstraction, inheritance, and polymorphism, but these tools may not fit every scenario and use case. These tools are more beneficial when used to build a large and complex application, especially one that involves user interfaces (UIs) and user interactions.

If your program is more like a script that has to execute certain tasks and there is no need to keep the state of objects, using OOP is overkill. Data science applications and intensive data processing are examples where it is less important to use OOP but more important to define how to execute tasks in a certain order to achieve goals. A real-world example is writing client programs for executing data-intensive jobs on a cluster of nodes, such as Apache Spark for...