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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 15
Project 5.1: Modeling Base Application

The next step in the pipeline from acquisition to clean-and-convert is the analysis and some preliminary modeling of the data. This may lead us to use the data for a more complex model or perhaps machine learning. This chapter will guide you through creating another application in the three-stage pipeline to acquire, clean, and model a collection of data. This first project will create the application with placeholders for more detailed and application-specific modeling components. This makes it easier to insert small statistical models that can be replaced with more elaborate processing if needed.

In this chapter, we’ll look at two parts of data analysis:

  • CLI architecture and how to design a more complex pipeline of processes for gathering and analyzing data

  • The core concepts of creating a statistical model of the data

Viewed from a distance, all analytical work can be considered to be creating a simplified model of important...

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