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

9.5 Extras

Here are some ideas for you to add to this project.

9.5.1 Create an output file with rejected samples

In Error reports we suggested there are times when it’s appropriate to create a file of rejected samples. For the examples in this book — many of which are drawn from well-curated, carefully managed data sets — it can feel a bit odd to design an application that will reject data.

For enterprise applications, data rejection is a common need.

It can help to look at a data set like this: https://datahub.io/core/co2-ppm. This contains data same with measurements of CO2 levels measures with units of ppm, parts per million.

This has some samples with an invalid number of days in the month. It has some samples where a monthly CO2 level wasn’t recorded.

It can be insightful to use a rejection file to divide this data set into clearly usable records, and records that are not as clearly usable.

The output will not reflect the analysis model. These objects...