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Python Real-World Projects

Python Real-World Projects

By : Steven F. Lott
4.4 (5)
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Python Real-World Projects

Python Real-World Projects

4.4 (5)
By: Steven F. Lott

Overview of this book

In today's competitive job market, a project portfolio often outshines a traditional resume. Python Real-World Projects empowers you to get to grips with crucial Python concepts while building complete modules and applications. With two dozen meticulously designed projects to explore, this book will help you showcase your Python mastery and refine your skills. Tailored for beginners with a foundational understanding of class definitions, module creation, and Python's inherent data structures, this book is your gateway to programming excellence. You’ll learn how to harness the potential of the standard library and key external projects like JupyterLab, Pydantic, pytest, and requests. You’ll also gain experience with enterprise-oriented methodologies, including unit and acceptance testing, and an agile development approach. Additionally, you’ll dive into the software development lifecycle, starting with a minimum viable product and seamlessly expanding it to add innovative features. By the end of this book, you’ll be armed with a myriad of practical Python projects and all set to accelerate your career as a Python programmer.
Table of Contents (20 chapters)
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19
Index

Chapter 10
Data Cleaning Features

There are a number of techniques for validating and converting data to native Python objects for subsequent analysis. This chapter guides you through three of these techniques, each appropriate for different kinds of data. The chapter moves on to the idea of standardization to transform unusual or atypical values into a more useful form. The chapter concludes with the integration of acquisition and cleansing into a composite pipeline.

This chapter will expand on the project in Chapter 9, Project 3.1: Data Cleaning Base Application. The following additional skills will be emphasized:

  • CLI application extension and refactoring to add features.

  • Pythonic approaches to validation and conversion.

  • Techniques for uncovering key relationships.

  • Pipeline architectures. This can be seen as a first step toward a processing DAG (Directed Acyclic Graph) in which various stages are connected.

We’ll start with a description of the first project to expand...

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