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

When there is no API

As with API services, web pages have their owners, and they may or may not be open to the idea of scraping their data. If there is an API in place, this is always preferred over scraping, for the following reasons:

  • First, it is usually way better and simpler to use, and there are a number of guarantees that API owners will retain its structure, or at least let you know of upcoming changes in advance. With HTML web pages, there is no guarantee whatsoever; the website will often change, and they won't tell you ahead of time, so expect lots of emergency breaking changes!
  • Second, being a good citizen, it is substantially cheaper, computation-wise, to serve raw data than a full-blown HTML page, so the service owners will be thankful.
  • Lastly, some data (for example, historic changes) will not be available via the web page.

However, there are plenty of examples...