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

14.4 Summary

In this chapter, we have built two important working results of data analysis:

  • Slide decks that can be used as presentations to interested users and stakeholders

  • Reports in PDF format that can be distributed to stakeholders

The line between these two is always hazy. Some presentations have a lot of details and are essentially reports presented in small pages.

Some reports are filled with figures and bullet points; they often seem to be presentations written in portrait mode.

Generally, presentations don’t have the depth of detail reports do. Often, reports are designed for long-term retention and provide background, as well as a bibliography to help readers fill in missing knowledge. Both are first-class parts of a Jupyter notebook and creating these should be part of every analyst’s skills.

This chapter has emphasized the additional tools required to create outstanding results. In the next chapter, we’ll shift gears and look at some of the statistical...