Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value

218 Pages·2016·6.13 MB·English

by  Dmitry Zinoviev

Early praise for Data Science Essentials in Python
This book does a fantastic job at summarizing the various activities when wrangling
data with Python. Each exercise serves an interesting challenge that is fun to
pursue. This book should no doubt be on the reading list of every aspiring data
scientist.
➤ Peter Hampton
Ulster University
Data Science Essentials in Python gets you up to speed with the most common
tasks and tools in the data science field. It’s a quick introduction to many different
techniques for fetching, cleaning, analyzing, and storing your data. This book
helps you stay productive so you can spend less time on technology research and
more on your intended research.
➤ Jason Montojo
Coauthor of Practical Programming: An Introduction to Computer Science Using
Python 3
For those who are highly curious and passionate about problem solving and
making data discoveries, Data Science Essentials in Python provides deep insights
and the right set of tools and techniques to start with. Well-drafted examples and
exercises make it practical and highly readable.
➤ Lokesh Kumar Makani
CASB expert, Skyhigh Networks
We've left this page blank to
make the page numbers the
same in the electronic and
paper books.
We tried just leaving it out,
but then people wrote us to
ask about the missing pages.
Anyway, Eddy the Gerbil
wanted to say “hello.”
Python Companion to Data Science
Collect → Organize → Explore → Predict → Value
Dmitry Zinoviev
The Pragmatic Bookshelf
Raleigh, North Carolina
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Copyright © 2016 The Pragmatic Programmers, LLC.
All rights reserved.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted,
in any form, or by any means, electronic, mechanical, photocopying, recording, or otherwise,
without the prior consent of the publisher.
Printed in the United States of America.
ISBN-13: 978-1-68050-184-1
Encoded using the finest acid-free high-entropy binary digits.
Book version: P1.0—August 2016
To my beautiful and most intelligent wife
Anna; to our children: graceful ballerina
Eugenia and romantic gamer Roman; and to
my first data science class of summer 2015.

Contents
Acknowledgments . . . . . . . . . . . xi
Preface . . . . . . . . . . . . . . xiii
1. What Is Data Science? . . . . . . . . . . 1
Unit 1. Data Analysis Sequence 3
Unit 2. Data Acquisition Pipeline 5
Unit 3. Report Structure 7
Your Turn 8
2. Core Python for Data Science . . . . . . . . . 9
Unit 4. Understanding Basic String Functions 10
Unit 5. Choosing the Right Data Structure 13
Unit 6. Comprehending Lists Through List
Comprehension 15
Unit 7. Counting with Counters 17
Unit 8. Working with Files 18
Unit 9. Reaching the Web 19
Unit 10. Pattern Matching with Regular Expressions 21
Unit 11. Globbing File Names and Other Strings 26
Unit 12. Pickling and Unpickling Data 27
Your Turn 28
3. Working with Text Data . . . . . . . . . . 29
Unit 13. Processing HTML Files 30
Unit 14. Handling CSV Files 34
Unit 15. Reading JSON Files 36
Unit 16. Processing Texts in Natural Languages 38
Your Turn 44
4. Working with Databases . . . . . . . . . . 47
Unit 17. Setting Up a MySQL Database 48
Contents • viii
Unit 18. Using a MySQL Database: Command Line 51
Unit 19. Using a MySQL Database: pymysql 55
Unit 20. Taming Document Stores: MongoDB 57
Your Turn 61
5. Working with Tabular Numeric Data . . . . . . . 63
Unit 21. Creating Arrays 64
Unit 22. Transposing and Reshaping 67
Unit 23. Indexing and Slicing 69
Unit 24. Broadcasting 71
Unit 25. Demystifying Universal Functions 73
Unit 26. Understanding Conditional Functions 75
Unit 27. Aggregating and Ordering Arrays 76
Unit 28. Treating Arrays as Sets 78
Unit 29. Saving and Reading Arrays 79
Unit 30. Generating a Synthetic Sine Wave 80
Your Turn 82
6. Working with Data Series and Frames . . . . . . 83
Unit 31. Getting Used to Pandas Data Structures 85
Unit 32. Reshaping Data 92
Unit 33. Handling Missing Data 98
Unit 34. Combining Data 101
Unit 35. Ordering and Describing Data 105
Unit 36. Transforming Data 109
Unit 37. Taming Pandas File I/O 116
Your Turn 119
7. Working with Network Data. . . . . . . . . 121
Unit 38. Dissecting Graphs 122
Unit 39. Network Analysis Sequence 126
Unit 40. Harnessing Networkx 127
Your Turn 134
8. Plotting . . . . . . . . . . . . . 135
Unit 41. Basic Plotting with PyPlot 136
Unit 42. Getting to Know Other Plot Types 139
Unit 43. Mastering Embellishments 140
Unit 44. Plotting with Pandas 143
Your Turn 146