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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

By : Kats, Katz
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Learn Python by Building Data Science Applications

Learn Python by Building Data Science Applications

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

Using generators

Generators are not exactly data structures—they are functions. However, while normal functions compute their results and return them at once, generators can be stopped and resumed on the fly, resulting in an iterable-like behavior. In other words, you can loop over a generator, retrieving one value at a time. Unlike classic iterables, however, generators are lazy. They compute values once we ask for them, but not before we do. As a result of that, there are a few significant differences in their behavior as compared to iterables:

  • First, generators use a fixed amount of memory. Even if you ask one to compute zillions of values, a generator will produce and store just one value every time you ask, which is great! In fact, generators can produce an infinite number of values with no memory issues.
  • Second, as generators do not store the values, there is no...
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Learn Python by Building Data Science Applications
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