Book Image

Learn Python by Building Data Science Applications

By : Philipp Kats, David Katz
Book Image

Learn Python by Building Data Science Applications

By: Philipp Kats, David Katz

Overview of this book

Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards.
Table of Contents (26 chapters)
Free Chapter
1
Section 1: Getting Started with Python
11
Section 2: Hands-On with Data
17
Section 3: Moving to Production

Generating documentation generation with sphinx

Documentation is king when it comes to supporting consumers of your code and convincing newcomers that it actually makes sense to buy in and use your package. For most people, a documentation website is the first place they go to learn about the package. It is, by definition, assumed to be the single source of truth on the code in its current version.

The role of documentation is usually threefold:

  • Explain how to install your package and what the general requirements are (for example, which Python versions are supported)
  • Show how to use the package (preferably with a quick example showing its immediate value)
  • Express the general idea and philosophy of the package

A documentation website does benefit from having tutorials, example cases, and a roadmap. With that being said, the core of any documentation website is, obviously, documentation...