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

Chapter 15

What are the benefits of packaging code?

Packaging code is a great way to do the following:

  • Make certain code available to use from multiple other packages
  • Share code with colleagues or make it easy to install for yourself
  • Set a project to collaborate on with others
  • Add reliability to your code by constantly running tests
  • Structure code better and isolate it from your day-to-day work

What is the main difference between Conda and pip as package managers?

At this moment, the difference is not as great as it was before. Historically, pip didn't support adding non-Python code as a binary for various reasons. This is a problem for data analysis projects since many data-related packages, namely NumPy, SciPy, and sklearn, use C and even Fortran under the hood.

This is where Conda comes into play—it allows you to install any tool in any language, even one that...