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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 13
Project 4.1: Visual Analysis Techniques

When doing exploratory data analysis (EDA), one common practice is to use graphical techniques to help understand the nature of data distribution. The US National Institute of Standards and Technology (NIST) has an Engineering Statistics Handbook that strongly emphasizes the need for graphic techniques. See https://doi.org/10.18434/M32189.

This chapter will create some additional Jupyter notebooks to present a few techniques for displaying univariate and multivariate distributions.

In this chapter, we’ll focus on some important skills for creating diagrams for the cleaned data:

  • Additional Jupyter Notebook techniques

  • Using PyPlot to present data

  • Unit testing for Jupyter Notebook functions

This chapter has one project, to build the start of a more complete analysis notebook. A notebook can be saved and exported as a PDF file, allowing an analyst to share preliminary results for early conversations. In the next chapter, we...

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