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

First Script – Geocoding with Web APIs

Now that we know how to write functions, let's apply that knowledge to a practical task. In this chapter, we will build a function that will communicate with a web service via a REST API in order to get the latitude and longitude of a given address. Furthermore, we'll discuss how to use built-in Python libraries to read and write data from and to files. Finally, we will wrap this functionality into a standalone script, so that it can be used from the command line, with no Jupyter Notebook attached.

In this chapter, we will learn how to do the following:

  • Work generally with Python's built-in libraries and requests in particular
  • Communicate with web services via APIs
  • Read and write data using the CSV file format
  • Wrap code into a standalone script with the command-line interface, using the built-in sys.argv library, and...