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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

13.1 Description

In the previous chapters, the sequence of projects created a pipeline to acquire and then clean the raw data. The intent is to build automated data gathering as Python applications.

We noted that ad hoc data inspection is best done with a notebook, not an automated CLI tool. Similarly, creating command-line applications for analysis and presentation can be challenging. Analytical work seems to be essentially exploratory, making it helpful to have immediate feedback from looking at results.

Additionally, analytical work transforms raw data into information, and possibly even insight. Analytical results need to be shared to create significant value. A Jupyter notebook is an exploratory environment that can create readable, helpful presentations.

One of the first things to do with raw data is to create diagrams to illustrate the distribution of univariate data and the relationships among variables in multivariate data. We’ll emphasize the following common kinds of...

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