Data Analytics

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Nelli

Shelve in: Programming Languages/General User level: Intermediate

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Python Data Analytics

Python Data Analytics will help you tackle the world of data acquisition and analysis

using the power of the Python language. At the heart of this book lies the coverage of pandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.

Author Fabio Nelli expertly shows the strength of the Python programming language when applied to processing, managing and retrieving information. Inside, you will see how intuitive and flexible it is to discover and communicate meaningful patterns of data using Python scripts, reporting systems, and data export. This book examines how to go about obtaining, processing, storing, managing and analyzing data using the Python programming language.

You will use Python and other open source tools to wrangle data and tease out interesting and important trends in that data that will allow you to predict future patterns. Whether you are dealing with sales data, investment data (stocks, bonds, etc.), medical data, web page usage, or any other type of data set, Python can be used to interpret, analyze, and glean information from a pile of numbers and statistics.

This book is an invaluable reference with its examples of storing and accessing data in a database; it walks you through the process of report generation; it provides three real world case studies or examples that you can take with you for your everyday analysis needs.

• How to set up your environment and use automation scripts • Where and how to get your data

• How to analyze your data with Python’s data analysis library, called pandas • How to carry out visualization with matplotlib

• How to analyze text data using Python’s natural language processing

9 781484 209592 5 4 9 9 9 ISBN 978-1-4842-0959-2

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Python Data Analytics

Data Analysis and Science Using

Pandas, matplotlib, and the

Python Programming Language

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Python Data Analytics

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ISBN-13 (pbk): 978-1-4842-0959-2 ISBN-13 (electronic): 978-1-4842-0958-5

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Contents at a Glance

About the Author ��

xvii

About the Technical Reviewer ��

xix

Acknowledgments ��

xxi

■

_{Chapter 1: An Introduction to Data Analysis ��}

₁

■

_{Chapter 2: Introduction to the Python’s World ��}

₁₃

■

_{Chapter 3: The NumPy Library ��}

₃₅

■

_{Chapter 4: The pandas Library—An Introduction ��}

₆₃

■

_{Chapter 5: pandas: Reading and Writing Data ��}

₁₀₃

■

_{Chapter 6: pandas in Depth: Data Manipulation ��}

₁₃₁

■

_{Chapter 7: Data Visualization with matplotlib ��}

₁₆₇

■

_{Chapter 8: Machine Learning with scikit-learn ��}

₂₃₇

■

_{Chapter 9: An Example—Meteorological Data ��}

₂₆₅

■

_{Chapter 10: Embedding the JavaScript D3 Library in IPython Notebook ��}

₂₈₉

■

_{Chapter 11: Recognizing Handwritten Digits��}

₃₁₁

■

_{Appendix A: Writing Mathematical Expressions with LaTeX ��}

₃₁₇

■

_{Appendix B: Open Data Sources ��}

₃₂₇

Index ��

331

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About the Author ��

xvii

About the Technical Reviewer ��

xix

Acknowledgments ��

xxi

■

Chapter 1: An Introduction to Data Analysis ��

1 Data Analysis ��

1 Knowledge Domains of the Data Analyst ��

2 Computer Science ��

2 Mathematics and Statistics ��

3 Machine Learning and Artificial Intelligence ��

3 Professional Fields of Application ��

3 Understanding the Nature of the Data ��

4 When the Data Become Information ��

4 When the Information Becomes Knowledge ��

4 Types of Data ��

4 The Data Analysis Process ��

5 Problem Definition ��

5 Data Extraction ��

6 Data Preparation ��

7 Data Exploration/Visualization ��

7 Predictive Modeling ��

8 Model Validation ��

8 Deployment��

8

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■ Contents

Open Data ��

10 Python and Data Analysis ��

11 Conclusions ��

12 ■

Chapter 2: Introduction to the Python’s World ��

13 Python—The Programming Language ��

13 Python—The Interpreter ��

14 Cython��

15 Jython ��

15 PyPy ��

15 Python 2 and Python 3 ��

15 Installing Python ��

16 Python Distributions ��

16 Anaconda ��

16 Enthought Canopy ��

17 Python(x,y) ��

18 Using Python ��

18 Python Shell ��

18 Run an Entire Program Code ��

18 Implement the Code Using an IDE ��

19 Interact with Python ��

19 Writing Python Code ��

19 Make Calculations ��

20 Import New Libraries and Functions ��

20 Functional Programming (Only for Python 3�4) ��

22 Indentation ��

24 IPython ��

24 IPython Shell ��

24

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■ Contents

PyPI—The Python Package Index ��

28 The IDEs for Python ��

28 IDLE (Integrated DeveLopment Environment) ��

29 Spyder ��

29 Eclipse (pyDev) ��

30 Sublime��

30 Liclipse ��

31 NinjaIDE ��

32 Komodo IDE ��

32 SciPy ��

32 NumPy ��

33 Pandas ��

33 matplotlib ��

34 Conclusions ��

34 ■

Chapter 3: The NumPy Library ��

35 NumPy: A Little History ��

35 The NumPy Installation ��

35 Ndarray: The Heart of the Library ��

36 Create an Array ��

37 Types of Data ��

38 The dtype Option ��

39 Intrinsic Creation of an Array ��

39 Basic Operations ��

40 Arithmetic Operators ��

41 The Matrix Product ��

42 Increment and Decrement Operators ��

43 Universal Functions (ufunc) ��

44

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■ Contents

Indexing, Slicing, and Iterating ��

45 Indexing ��

45 Slicing ��

46 Iterating an Array ��

48 Conditions and Boolean Arrays��

50 Shape Manipulation ��

50 Array Manipulation ��

51 Joining Arrays ��

51 Splitting Arrays ��

52 General Concepts ��

54 Copies or Views of Objects ��

54 Vectorization ��

55 Broadcasting ��

55 Structured Arrays ��

58 Reading and Writing Array Data on Files ��

59 Loading and Saving Data in Binary Files ��

59 Reading File with Tabular Data ��

60 Conclusions ��

61 ■

Chapter 4: The pandas Library—An Introduction ��

63 pandas: The Python Data Analysis Library ��

63 Installation ��

64 Installation from Anaconda ��

64 Installation from PyPI ��

65 Installation on Linux ��

65 Installation from Source ��

66 A Module Repository for Windows ��

66 Test Your pandas Installation ��

66

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■ Contents

Introduction to pandas Data Structures ��

67 The Series ��

68 The DataFrame ��

75 The Index Objects ��

81 Other Functionalities on Indexes ��

83 Reindexing ��

83 Dropping ��

85 Arithmetic and Data Alignment ��

86 Operations between Data Structures ��

87 Flexible Arithmetic Methods ��

88 Operations between DataFrame and Series ��

88 Function Application and Mapping ��

89 Functions by Element ��

89 Functions by Row or Column ��

90 Statistics Functions ��

91 Sorting and Ranking ��

91 Correlation and Covariance ��

94 “Not a Number” Data ��

95 Assigning a NaN Value ��

96 Filtering Out NaN Values ��

96 Filling in NaN Occurrences ��

97 Hierarchical Indexing and Leveling ��

97 Reordering and Sorting Levels ��

100 Summary Statistic by Level ��

100

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■ Contents

■

Chapter 5: pandas: Reading and Writing Data ��

103 I/O API Tools ��

103 CSV and Textual Files ��

104 Reading Data in CSV or Text Files ��

104 Using RegExp for Parsing TXT Files ��

106 Reading TXT Files into Parts or Partially ��

108 Writing Data in CSV��

109 Reading and Writing HTML Files ��

111 Writing Data in HTML ��

111 Reading Data from an HTML File ��

113 Reading Data from XML ��

114 Reading and Writing Data on Microsoft Excel Files ��

116 JSON Data ��

118 The Format HDF5 ��

121 Pickle—Python Object Serialization ��

122 Serialize a Python Object with cPickle ��

122 Pickling with pandas ��

� 123

Interacting with Databases ��

124 Loading and Writing Data with SQLite3 ��

124 Loading and Writing Data with PostgreSQL ��

126 Reading and Writing Data with a NoSQL Database: MongoDB ��

128 Conclusions ��

130 ■

Chapter 6: pandas in Depth: Data Manipulation ��

131 Data Preparation ��

131 Merging ��

132 Concatenating ��

136 Combining ��

139 Pivoting ��

140 Removing ��

142

Data Analytics

Nelli

BOOKS FOR PROFESSIONALS BY PROFESSIONALS

®

Python Data Analytics

Python Data Analytics

Data Analysis and Science Using

Pandas, matplotlib, and the

Python Programming Language

Python Data Analytics

Contents at a Glance

xvii

About the Technical Reviewer ��������������������������������������������������������������������������������

xix

xxi

■

Chapter 1: An Introduction to Data Analysis ���������������������������������������������������������

1

■

Chapter 2: Introduction to the Python’s World ����������������������������������������������������

13

■

Chapter 3: The NumPy Library �����������������������������������������������������������������������������

35

■

Chapter 4: The pandas Library—An Introduction ������������������������������������������������

63

■

Chapter 5: pandas: Reading and Writing Data ���������������������������������������������������

103

■

Chapter 6: pandas in Depth: Data Manipulation ������������������������������������������������

131

■

Chapter 7: Data Visualization with matplotlib ���������������������������������������������������

167

■

Chapter 8: Machine Learning with scikit-learn �������������������������������������������������

237

■

Chapter 9: An Example—Meteorological Data ��������������������������������������������������

265

■

Chapter 10: Embedding the JavaScript D3 Library in IPython Notebook ���������

289

■

Chapter 11: Recognizing Handwritten Digits�����������������������������������������������������

311

■

Appendix A: Writing Mathematical Expressions with LaTeX �����������������������������

317

■

Appendix B: Open Data Sources ������������������������������������������������������������������������

327

331

Contents

xvii

About the Technical Reviewer ��������������������������������������������������������������������������������

xix

xxi

■

Chapter 1: An Introduction to Data Analysis ���������������������������������������������������������

1

1

Knowledge Domains of the Data Analyst �������������������������������������������������������������������������

2

2

3

3

3

Understanding the Nature of the Data ������������������������������������������������������������������������������

4

4

4

4

5

5

6

7

7

About the Technical Reviewer ��

_{Chapter 1: An Introduction to Data Analysis ��}

₁

_{Chapter 2: Introduction to the Python’s World ��}

₁₃

_{Chapter 3: The NumPy Library ��}

₃₅

_{Chapter 4: The pandas Library—An Introduction ��}

₆₃

_{Chapter 5: pandas: Reading and Writing Data ��}

₁₀₃

_{Chapter 6: pandas in Depth: Data Manipulation ��}

₁₃₁

_{Chapter 7: Data Visualization with matplotlib ��}

₁₆₇

_{Chapter 8: Machine Learning with scikit-learn ��}

₂₃₇

_{Chapter 9: An Example—Meteorological Data ��}

₂₆₅

_{Chapter 10: Embedding the JavaScript D3 Library in IPython Notebook ��}

₂₈₉

_{Chapter 11: Recognizing Handwritten Digits��}

₃₁₁

_{Appendix A: Writing Mathematical Expressions with LaTeX ��}

₃₁₇

_{Appendix B: Open Data Sources ��}

₃₂₇

About the Technical Reviewer ��

Chapter 1: An Introduction to Data Analysis ��

Knowledge Domains of the Data Analyst ��

Understanding the Nature of the Data ��

Chapter 2: Introduction to the Python’s World ��

Python—The Programming Language ��