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Learn Python by Building Data Science Applications

You're reading from   Learn Python by Building Data Science Applications A fun, project-based guide to learning Python 3 while building real-world apps

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789535365
Length 482 pages
Edition 1st Edition
Languages
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Authors (2):
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Philipp Kats Philipp Kats
Author Profile Icon Philipp Kats
Philipp Kats
David Katz David Katz
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David Katz
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Toc

Table of Contents (26) Chapters Close

Preface 1. Section 1: Getting Started with Python
2. Preparing the Workspace FREE CHAPTER 3. First Steps in Coding - Variables and Data Types 4. Functions 5. Data Structures 6. Loops and Other Compound Statements 7. First Script – Geocoding with Web APIs 8. Scraping Data from the Web with Beautiful Soup 4 9. Simulation with Classes and Inheritance 10. Shell, Git, Conda, and More – at Your Command 11. Section 2: Hands-On with Data
12. Python for Data Applications 13. Data Cleaning and Manipulation 14. Data Exploration and Visualization 15. Training a Machine Learning Model 16. Improving Your Model – Pipelines and Experiments 17. Section 3: Moving to Production
18. Packaging and Testing with Poetry and PyTest 19. Data Pipelines with Luigi 20. Let's Build a Dashboard 21. Serving Models with a RESTful API 22. Serverless API Using Chalice 23. Best Practices and Python Performance 24. Assessments 25. Other Books You May Enjoy

To get the most out of this book

This book is designed for complete beginners and people who have just started to learn to code. It does not require any specific knowledge besides basic computer literacy.

The execution of the code examples provided in this book requires an installation of Python 3.7.3 or later on macOS, Linux, or Microsoft Windows. The code presented throughout the book makes use of many Python libraries. In each chapter, a list of required libraries is given at the beginning. A full list of libraries is stored in the GitHub repository, in the environment.yaml file. The same file can be used to install Python and all of the required libraries in bulk—full instructions are given in Chapter 1, Preparing the Workspace.

The code for this book was developed in and extensively uses two development environments—VS Code editor with its Python bundle, and Jupyter. We recommend using both for better alignment with the book's narrative.

The code for Chapter 6, First Script – Geocoding with Web APIs, Chapter 7, Scraping Data from the Web with Beautiful Soup 4, Chapter 11, Data Cleaning and Manipulation, and Chapter 16, Data Pipelines with Luigi, requires an internet connection.

The first chapter will provide you with step-by-step instructions and some useful tips for setting up your Python environment, the core libraries, and all the necessary tools.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at www.packt.com.
  2. Select the SUPPORT tab.
  3. Click on Code Downloads & Errata.
  4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

  • WinRAR/7-Zip for Windows
  • Zipeg/iZip/UnRarX for Mac
  • 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Learn-Python-by-Building-Data-Science-Applications. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

Code in Action

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "As you can see, pi is a float, name is a string, age is an integer, and sky_is_blue is a Boolean."

A block of code is set as follows:

import pandas as pd

for word in 'Hello Word!'.split():
print(word)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

pi = 3.14159265359    # Decimal
name = 'Philipp' # Text
age = 31 # Integer
sky_is_blue = True # Boolean

Often code will be shown as a print of an interactive console, with both code and the output being mixed. In this case, all input code lines will start with a triple "greater than" sign. Lines with no such sign represent the output:

>>> import pandas as pd
>>> for word in 'Hello Word!'.split():
>>> print(word)

Hello
Word

Any command-line input or output is written as follows:

> conda install <mypackage>
> conda install -c <mychannel> <mypackage>

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Just use the Clone or download button on the right-hand side (1), and select Download ZIP (2)."

Warnings or important notes appear like this.
Tips and tricks appear like this.
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