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

Building a web page

While FastAPI is focused on the APIs, it is still entirely possible to serve HTML pages as well. The code will be almost identical to the preceding code—except that our functions need to return this HTML code.

The most common approach to generate HTML in Python is to use the Jinja2 templating engine—that way, you write the template as an HTML code with some injections of Python and later render them by feeding it with the variables; Jinja will execute and hide the injections, returning the resultant page.

For the sake of building a simple example, however, we will use another package: VDOM, which allows us to generate VDOMs (short for Virtual Document Object Models) in Python and then convert them into HTML. Flask is great for smaller projects, but not for large and complex applications.

To separate this page from the main API, let's create...