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Metaprogramming with Python

Metaprogramming with Python

By : Sulekha Aloorravi
4.2 (6)
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Metaprogramming with Python

Metaprogramming with Python

4.2 (6)
By: Sulekha Aloorravi

Overview of this book

Effective and reusable code makes your application development process seamless and easily maintainable. With Python, you will have access to advanced metaprogramming features that you can use to build high-performing applications. The book starts by introducing you to the need and applications of metaprogramming, before navigating the fundamentals of object-oriented programming. Next, you will learn about simple decorators, work with metaclasses, and later focus on introspection and reflection. You’ll also delve into generics and typing before defining templates for algorithms. As you progress, you will understand your code using abstract syntax trees and explore method resolution order. This Python book also shows you how to create your own dynamic objects before structuring the objects through design patterns. Finally, you will learn simple code-generation techniques along with discovering best practices and eventually building your own applications. By the end of this learning journey, you’ll have acquired the skills and confidence you need to design and build reusable high-performing applications that can solve real-world problems.
Table of Contents (21 chapters)
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1
Part 1: Fundamentals – Introduction to Object-Oriented Python and Metaprogramming
4
Part 2: Deep Dive – Building Blocks of Metaprogramming I
11
Part 3: Deep Dive – Building Blocks of Metaprogramming II

Generating code

In this section, we are going to make use of codegenerator.py to generate the base classes and its corresponding subclasses, which maintain and print various catalogs for the ABC Car Agency, as follows:

  1. To begin with, let’s start using the automobile data to generate the base classes required for this application. For the base data preparation, let’s import the pandas library, which helps with processing data:
    import pandas as pd
  2. Let’s load the data and make a copy of it. For this application, we need a unique set of car brands and another unique set of car body styles:
    auto = pd.read_csv("automobile.csv")
    auto_truncated = auto.copy(deep=True)
    auto_truncated.drop_duplicates(subset = ['make','body-style'], inplace = True)
    auto_truncated.reset_index(inplace = True, drop = True)
    auto_truncated['make'] = auto_truncated['make'].apply(lambda x: x.title().replace('-',''))...
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Metaprogramming with Python
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