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)

15.4 Summary

In this chapter we have created a foundation for building and using a statistical model of source data. We’ve looked at the following topics:

  • Designing and building a more complex pipeline of processes for gathering and analyzing data.

  • Some of the core concepts behind creating a statistical model of some data.

  • Use of the built-in statistics library.

  • Publishing the results of the statistical measures.

This application tends to be relatively small. The actual computations of the various statistical values leverage the built-in statistics library and tend to be very small. It often seems like there’s far more programming involved in parsing the CLI argument values, and creating the required output file, than doing the “real work” of this application.

This is a consequence of the way we’ve been separating the various concerns in data acquisition, cleaning, and analysis. We’ve partitioned the work into several, isolated stages along...