Book Image

Python Real-World Projects

By : Steven F. Lott
5 (1)
Book Image

Python Real-World Projects

5 (1)
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)
19
Index

7.4 Summary

This chapter expanded on the core features of the inspection notebook. We looked at handling cardinal data (measures and counts), ordinal data (dates and ranks), and nominal data (codes like account numbers).

Our primary objective was to get a complete view of the data, prior to formalizing our analysis pipeline. A secondary objective was to leave notes for ourselves on outliers, anomalies, data formatting problems and other complications. A pleasant consequence of this effort is to be able to write some functions that can be used downstream to clean and normalize the data we’ve found.

Starting in Chapter 9, Project 3.1: Data Cleaning Base Application, we’ll look at refactoring these inspection functions to create a complete and automated data cleaning and normalization application. That application will be based on the lessons learned while creating inspection notebooks.

In the next chapter, we’ll look at one more lesson that’s often learned...