Python for Geeks

Muhammad Asif

• Editorial: Packt Publishing
• Año: 2021
• ISBN:
  9781801073356
• Edición: 1
• Categoría: Computer Science
• Categoría: Programming in Python

Take your Python skills to the next level to develop scalable, real-world applications for local as well as cloud deploymentKey Features• All code examples have been tested with Python 3.7 and Python 3.8 and are expected to work with any future 3.x release• Learn how to build modular and object-oriented applications in Python• Discover how to use advanced Python techniques for the cloud and clustersBook DescriptionPython 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.What you will learn• Understand how to design and manage complex Python projects• Strategize test-driven development (TDD) in Python• Explore multithreading and multiprogramming in Python• Use Python for data processing with Apache Spark and Google Cloud Platform (GCP)• Deploy serverless programs on public clouds such as GCP• Use Python to build web applications and application programming interfaces• Apply Python for network automation and serverless functions• Get to grips with Python for data analysis and machine learningWho this book is forThis book is for intermediate-level Python developers in any field who are looking to build their skills to develop and manage large-scale complex projects. Developers who want to create reusable modules and Python libraries and cloud developers building applications for cloud deployment will also find this book useful. Prior experience with Python will help you get the most out of this book.

Section 1: Python, beyond the Basics

We start our journey by exploring different ways of using Python optimally, from both the design and the implementation points of view. We provide a deeper understanding of the life cycle of a large-scale Python project and its phases. Once we have that understanding, we investigate different ways of creating an elegant design by modularizing a Python project. Wherever necessary, we look under the hood to understand the internal workings of Python. This is followed by a deep dive into object-oriented programming in Python.

This section contains the following chapters:

• Chapter 1, Optimal Python Development Life Cycle
• Chapter 2, Using Modularization to Handle Complex Projects
• Chapter 3, Advanced Object-Oriented Python Programming

Chapter 1: Optimal Python Development Life Cycle

Keeping in mind your prior experience with Python, we have skipped the introductory details of the Python language in this chapter. First, we will have a short discussion of the broader open source Python community and its specific culture. That introduction is important, as this culture is reflected in code being written and shared by the Python community. Then, we will present the different phases of a typical Python project. Next, we will look at different ways of strategizing the development of a typical Python project.

Moving on, we will explore different ways of documenting the Python code. Later, we will look into various options of developing an effective naming scheme that can greatly help improve the maintenance of the code. We will also look into various options for using source control for Python projects, including situations where developers are mainly using Jupyter notebooks for development. Finally, we explore the best practices to deploy the code for use, once it is developed and tested.

We will cover the following topics in this chapter:

• Python culture and community
• Different phases of a Python project
• Strategizing the development process
• Effectively documenting Python code
• Developing an effective naming scheme
• Exploring choices for source control
• Understanding strategies for deploying the code
• Python development environments

This chapter will help you understand the life cycle of a typical Python project and its phases so that you can fully utilize the power of Python.

Python culture and community

Python is an interpreted high-level language that was originally developed by Guido van Rossum in 1991. The Python community is special in the sense that it pays close attention to how the code is written. For that, since the early days of Python, the Python community has created and maintained a particular flavor in its design philosophy. Today, Python is used in a wide variety of industries, ranging from education to medicine. But regardless of the industry in which it is used, the particular culture of the vibrant Python community is usually seen to be part and parcel of Python projects.

In particular, the Python community wants us to write simple code and avoid complexity wherever possible. In fact, there is an adjective, Pythonic, which means there are multiple ways to accomplish a certain task but there is a preferred way as per the Python community conventions and as per the founding philosophy of the language. Python nerds try their best to create artifacts that are as Pythonic as possible. Obviously, unpythonic code means that we are not good coders in the eyes of these nerds. In this book, we will try to go as Pythonic as possible as we can in our code and design.

And there is something official about being Pythonic as well. Tim Peters has concisely written the philosophy of Python in a short document, The Zen of Python. We know that Python is said to be one of the easiest languages to read, and The Zen of Python wants to keep it that way. It expects Python to be explicit through good documentation and as clean and clear as possible. We can read The Zen of Python ourselves, as explained next.

In order to read The Zen of Python, open up a Python console and run the import this command, as shown in the following screenshot:

Figure 1.1 – The Zen of Python

The Zen of Python seems to be a cryptic text discovered in an old Egyptian tomb. Although it is deliberately written in this casual cryptic way, there is a deeper meaning to each line of text. Actually, look closer—it can be used as a guideline to code in Python. We will refer to different lines from The Zen of Python throughout the book. Let's first look into some excerpts from it, as follows:

• Beautiful is better than ugly: It is important to write code that is well-written, readable, and self-explanatory. Not only should it work—it should be beautifully written. While coding, we should avoid using shortcuts in favor of a style that is self-explanatory.
• Simple is better than complex: We should not unnecessarily complicate things. Whenever facing a choice, we should prefer the simpler solution. Nerdy, unnecessary, and complicated ways of writing code are discouraged. Even when it adds some more lines to the source code, simpler remains better than the complex alternative.
• There should be one-- and preferably only one --obvious way to do it: In broader terms, for a given problem there should be one possible best solution. We should strive to discover this. As we iterate through the design to improve it, regardless of our approach, our solution is expected to evolve and converge toward that preferable solution.
• Now is better than never: Instead of waiting for perfection, let's start solving the given problem using the information, assumptions, skills, tools, and infrastructure we have. Through the process of iteration, we will keep improving the solution. Let's keep things moving instead of idling. Do not slack while waiting for the perfect time. Chances are that the perfect time will never come.
• Explicit is better than implicit: The code should be as self-explanatory as possible. This should be reflected ...