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Learn Python by Building Data Science Applications

You're reading from  Learn Python by Building Data Science Applications

Product type Book
Published in Aug 2019
Publisher Packt
ISBN-13 9781789535365
Pages 482 pages
Edition 1st Edition
Languages
Authors (2):
Philipp Kats Philipp Kats
Profile icon Philipp Kats
David Katz David Katz
Profile icon David Katz
View More author details

Table of Contents (26) Chapters

Preface Section 1: Getting Started with Python
Preparing the Workspace First Steps in Coding - Variables and Data Types Functions Data Structures Loops and Other Compound Statements First Script – Geocoding with Web APIs Scraping Data from the Web with Beautiful Soup 4 Simulation with Classes and Inheritance Shell, Git, Conda, and More – at Your Command Section 2: Hands-On with Data
Python for Data Applications Data Cleaning and Manipulation Data Exploration and Visualization Training a Machine Learning Model Improving Your Model – Pipelines and Experiments Section 3: Moving to Production
Packaging and Testing with Poetry and PyTest Data Pipelines with Luigi Let's Build a Dashboard Serving Models with a RESTful API Serverless API Using Chalice Best Practices and Python Performance Assessments Other Books You May Enjoy

Summary

In this chapter, we learned about the basic and more advanced data structures in Python. We covered how to create, interact with, and operate on those structures. In addition to structures, we covered generators, which are functions that pretend to be iterable structures, and comprehensions, which are a concise and fast way to create and work with data structures. We also briefly touched on loops and if statements and learned how they work.

In the next chapter, we'll discuss different types of loops and statements, which will allow us to operate on datasets dynamically and embed logic into our code.

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