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

7.1.2 Approach

This project is based on the initial inspection notebook from Chapter 6, Project 2.1: Data Inspection Notebook. Some of the essential cell content will be reused in this notebook. We’ll add components to the components shown in the earlier chapter – specifically, the samples_iter() function to iterate over samples in an open file. This feature will be central to working with the raw data.

In the previous chapter, we suggested avoiding conversion functions. When starting down the path of inspecting data, it’s best to assume nothing and look at the text values first.

There are some common patterns in the source data values:

  • The values appear to be all numeric values. The int() or float() function works on all of the values. There are two sub-cases here:

    • All of the values seem to be proper counts or measures in some expected range. This is ideal.

    • A few “outlier” values are present. These are values that seem to be outside the expected...