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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 9
Project 3.1: Data Cleaning Base Application

Data validation, cleaning, converting, and standardizing are steps required to transform raw data acquired from source applications into something that can be used for analytical purposes. Since we started using a small data set of very clean data, we may need to improvise a bit to create some ”dirty” raw data. A good alternative is to search for more complicated, raw data.

This chapter will guide you through the design of a data cleaning application, separate from the raw data acquisition. Many details of cleaning, converting, and standardizing will be left for subsequent projects. This initial project creates a foundation that will be extended by adding features. The idea is to prepare for the goal of a complete data pipeline that starts with acquisition and passes the data through a separate cleaning stage. We want to exploit the Linux principle of having applications connected by a shared buffer, often referred...

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