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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

By : Kats, Katz
2.8 (4)
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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

2.8 (4)
By: Kats, 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)
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1
Section 1: Getting Started with Python
11
Section 2: Hands-On with Data
17
Section 3: Moving to Production

Beyond Beautiful Soup

In this example, we used the BS4 library to parse static HTML for us. Beautiful Soup is an invaluable library for dealing with occasionally messy HTML, but when it comes to large scales and dynamic pages, it simply won't suffice. For production scraping in large quantities, perhaps on a regular basis, it is a good idea to utilize the Scrapy (https://scrapy.org/) package. Scrapy is an entire framework for downloading HTML, parsing data, pulling data, and then storing it. One of its killer features is that it can run asynchronously – for example, while it is waiting for one page to load, it can switch to processing another, automatically. Because of that, Scrapy's scrapers are significantly faster on large lists of websites. At the same time, its interface is more expressive for a developer, as it is explicitly designed for scraping.

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