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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 6
Project 2.1: Data Inspection Notebook

We often need to do an ad hoc inspection of source data. In particular, the very first time we acquire new data, we need to see the file to be sure it meets expectations. Additionally, debugging and problem-solving also benefit from ad hoc data inspections. This chapter will guide you through using a Jupyter notebook to survey data and find the structure and domains of the attributes.

The previous chapters have focused on a simple dataset where the data types look like obvious floating-point values. For such a trivial dataset, the inspection isn’t going to be very complicated.

It can help to start with a trivial dataset and focus on the tools and how they work together. For this reason, we’ll continue using relatively small datasets to let you learn about the tools without having the burden of also trying to understand the data.

This chapter’s projects cover how to create and use a Jupyter notebook for data inspection...

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