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Python Data Analytics, Second Edition

Python Data Analytics, Second Edition

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With Pandas, NumPy, and Matplotlib

Fabio Nelli


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Book Details
 Price
 3.00
 Pages
 576 p
 File Size 
 14,283 KB
 File Type
 PDF format
 ISBN
 978-1-4842-3912-4 (pbk)
 978-1-4842-3913-1 (electronic) 
 Copyright©   
 2018 by Fabio Nelli

About the Author
Fabio Nelli is a data scientist and Python consultant, designing and developing Python
applications for data analysis and visualization. He has experience with the scientific
world, having performed various data analysis roles in pharmaceutical chemistry for
private research companies and universities. He has been a computer consultant for
many years at IBM, EDS, and Hewlett-Packard, along with several banks and insurance
companies. He has an organic chemistry master’s degree and a bachelor’s degree in
information technologies and automation systems, with many years of experience in
life sciences (as as Tech Specialist at Beckman Coulter, Tecan, Sciex).
For further info and other examples, 

About the Technical Reviewer
Raul Samayoa is a senior software developer and machine
learning specialist with many years of experience in the
financial industry. An MSc graduate from the Georgia
Institute of Technology, he's never met a neural network or
dataset he did not like. He's fond of evangelizing the use of
DevOps tools for data science and software development.
Raul enjoys the energy of his hometown of Toronto,
Canada, where he runs marathons, volunteers as a
technology instructor with the University of Toronto
coders, and likes to work with data in Python and R.

Table of Contents
About the Author ...................................................................................................xvii
About the Technical Reviewer ................................................................................xix
Table of Contents
Chapter 1: An Introduction to Data Analysis ........................................................... 1
Data Analysis ................................................................................................................................ 1
Knowledge Domains of the Data Analyst ...................................................................................... 3
Computer Science ................................................................................................................... 3
Mathematics and Statistics ..................................................................................................... 4
Machine Learning and Artificial Intelligence ........................................................................... 5
Professional Fields of Application ........................................................................................... 5
Understanding the Nature of the Data .......................................................................................... 5
When the Data Become Information ........................................................................................ 6
When the Information Becomes Knowledge ........................................................................... 6
Types of Data ........................................................................................................................... 6
The Data Analysis Process ............................................................................................................ 6
Problem Definition ................................................................................................................... 8
Data Extraction ........................................................................................................................ 9
Data Preparation .................................................................................................................... 10
Data Exploration/Visualization ............................................................................................... 10
Predictive Modeling ............................................................................................................... 12
Model Validation .................................................................................................................... 13
Deployment ........................................................................................................................... 13
Quantitative and Qualitative Data Analysis ................................................................................. 14
Open Data .................................................................................................................................. 15
Python and Data Analysis ............................................................................................................ 17
Conclusions ................................................................................................................................. 17
Chapter 2: Introduction to the Python World ......................................................... 19
Python—The Programming Language ........................................................................................ 19
Python—The Interpreter ....................................................................................................... 21
Python 2 and Python 3 ................................................................................................................ 23
Installing Python .................................................................................................................... 23
Python Distributions .............................................................................................................. 24
Using Python .......................................................................................................................... 26
Writing Python Code .............................................................................................................. 28
IPython ................................................................................................................................... 35
PyPI—The Python Package Index ............................................................................................... 39
The IDEs for Python ............................................................................................................... 40
SciPy ........................................................................................................................................... 46
NumPy ................................................................................................................................... 47
Pandas ................................................................................................................................... 47
matplotlib .............................................................................................................................. 48
Conclusions ................................................................................................................................. 48
Chapter 3: The NumPy Library ................................................................................ 49
NumPy: A Little History ............................................................................................................... 49
The NumPy Installation ............................................................................................................... 50
Ndarray: The Heart of the Library ................................................................................................ 50
Create an Array ...................................................................................................................... 52
Types of Data ......................................................................................................................... 53
The dtype Option ................................................................................................................... 54
Intrinsic Creation of an Array ................................................................................................. 55
Basic Operations ......................................................................................................................... 57
Arithmetic Operators ............................................................................................................. 57
The Matrix Product ................................................................................................................ 59
Increment and Decrement Operators .................................................................................... 60
Universal Functions (ufunc) ................................................................................................... 61
Aggregate Functions ............................................................................................................. 62
Indexing, Slicing, and Iterating .................................................................................................... 62
Indexing ................................................................................................................................. 63
Slicing .................................................................................................................................... 65
Iterating an Array ................................................................................................................... 67
Conditions and Boolean Arrays ................................................................................................... 69
Shape Manipulation .................................................................................................................... 70
Array Manipulation ...................................................................................................................... 71
Joining Arrays ........................................................................................................................ 71
Splitting Arrays ...................................................................................................................... 72
General Concepts ........................................................................................................................ 74
Copies or Views of Objects .................................................................................................... 75
Vectorization .......................................................................................................................... 76
Broadcasting ......................................................................................................................... 76
Structured Arrays ........................................................................................................................ 79
Reading and Writing Array Data on Files ..................................................................................... 82
Loading and Saving Data in Binary Files ............................................................................... 82
Reading Files with Tabular Data ............................................................................................ 83
Conclusions ................................................................................................................................. 84
Chapter 4: The pandas Library—An Introduction ................................................... 87
pandas: The Python Data Analysis Library .................................................................................. 87
Installation of pandas .................................................................................................................. 88
Installation from Anaconda .................................................................................................... 88
Installation from PyPI ............................................................................................................ 89
Installation on Linux .............................................................................................................. 90
Installation from Source ........................................................................................................ 90
A Module Repository for Windows ......................................................................................... 90
Testing Your pandas Installation ................................................................................................. 91
Getting Started with pandas ....................................................................................................... 92
Introduction to pandas Data Structures ...................................................................................... 92
The Series .............................................................................................................................. 93
The DataFrame .................................................................................................................... 102
The Index Objects ................................................................................................................ 112
Other Functionalities on Indexes ............................................................................................... 114
Reindexing ........................................................................................................................... 114
Dropping ............................................................................................................................. 117
Arithmetic and Data Alignment ............................................................................................ 118
Operations Between Data Structures ........................................................................................ 120
Flexible Arithmetic Methods ................................................................................................ 120
Operations Between DataFrame and Series ........................................................................ 121
Function Application and Mapping ............................................................................................ 122
Functions by Element .......................................................................................................... 123
Functions by Row or Column ............................................................................................... 123
Statistics Functions ............................................................................................................. 125
Sorting and Ranking ................................................................................................................. 126
Correlation and Covariance ....................................................................................................... 129
“Not a Number” Data ................................................................................................................ 131
Assigning a NaN Value ......................................................................................................... 131
Filtering Out NaN Values ...................................................................................................... 132
Filling in NaN Occurrences .................................................................................................. 133
Hierarchical Indexing and Leveling ........................................................................................... 134
Reordering and Sorting Levels ............................................................................................ 137
Summary Statistic by Level ................................................................................................. 138
Conclusions ............................................................................................................................... 139
Chapter 5: pandas: Reading and Writing Data ...................................................... 141
I/O API Tools .............................................................................................................................. 141
CSV and Textual Files ................................................................................................................ 142
Reading Data in CSV or Text Files ............................................................................................. 143
Using RegExp to Parse TXT Files ......................................................................................... 146
Reading TXT Files Into Parts ................................................................................................ 148
Writing Data in CSV ............................................................................................................. 150
Reading and Writing HTML Files ............................................................................................... 152
Writing Data in HTML ........................................................................................................... 153
Reading Data from an HTML File ......................................................................................... 155
Reading Data from XML ............................................................................................................ 157
Reading and Writing Data on Microsoft Excel Files .................................................................. 159
JSON Data ................................................................................................................................. 162
The Format HDF5 ...................................................................................................................... 166
Pickle—Python Object Serialization ......................................................................................... 168
Serialize a Python Object with cPickle ................................................................................ 168
Pickling with pandas ........................................................................................................... 169
Interacting with Databases ....................................................................................................... 170
Loading and Writing Data with SQLite3 ............................................................................... 171
Loading and Writing Data with PostgreSQL ......................................................................... 174
Reading and Writing Data with a NoSQL Database: MongoDB .................................................. 178
Conclusions ............................................................................................................................... 180
Chapter 6: pandas in Depth: Data Manipulation ................................................... 181
Data Preparation ....................................................................................................................... 181
Merging ............................................................................................................................... 182
Concatenating ........................................................................................................................... 188
Combining ........................................................................................................................... 191
Pivoting ................................................................................................................................ 193
Removing ............................................................................................................................. 196
Data Transformation .................................................................................................................. 197
Removing Duplicates ........................................................................................................... 198
Mapping ............................................................................................................................... 199
Discretization and Binning ........................................................................................................ 204
Detecting and Filtering Outliers ........................................................................................... 209
Permutation .............................................................................................................................. 210
Random Sampling ............................................................................................................... 211
String Manipulation ................................................................................................................... 212
Built-in Methods for String Manipulation ............................................................................ 212
Regular Expressions ............................................................................................................ 214
Data Aggregation ...................................................................................................................... 217
GroupBy ............................................................................................................................... 218
A Practical Example ............................................................................................................. 219
Hierarchical Grouping .......................................................................................................... 220
Group Iteration .......................................................................................................................... 222
Chain of Transformations ..................................................................................................... 222
Functions on Groups ............................................................................................................ 224
Advanced Data Aggregation ...................................................................................................... 225
Conclusions ............................................................................................................................... 229
Chapter 7: Data Visualization with matplotlib ...................................................... 231
The matplotlib Library ............................................................................................................... 231
Installation ................................................................................................................................ 233
The IPython and IPython QtConsole .......................................................................................... 233
The matplotlib Architecture ....................................................................................................... 235
Backend Layer ..................................................................................................................... 236
Artist Layer .......................................................................................................................... 236
Scripting Layer (pyplot) ....................................................................................................... 238
pylab and pyplot .................................................................................................................. 238
pyplot ........................................................................................................................................ 239
A Simple Interactive Chart ................................................................................................... 239
The Plotting Window ................................................................................................................. 241
Set the Properties of the Plot .............................................................................................. 243
matplotlib and NumPy ......................................................................................................... 246
Using the kwargs ...................................................................................................................... 248
Working with Multiple Figures and Axes ............................................................................. 249
Adding Elements to the Chart ................................................................................................... 251
Adding Text .......................................................................................................................... 251
Adding a Grid ....................................................................................................................... 256
Adding a Legend .................................................................................................................. 257
Saving Your Charts .................................................................................................................... 260
Saving the Code ................................................................................................................... 260
Converting Your Session to an HTML File ............................................................................ 262
Saving Your Chart Directly as an Image ............................................................................... 264
Handling Date Values ............................................................................................................... 264
Chart Typology ........................................................................................................................... 267
Line Charts ................................................................................................................................ 267
Line Charts with pandas ...................................................................................................... 276
Histograms ................................................................................................................................ 277
Bar Charts ................................................................................................................................. 278
Horizontal Bar Charts .......................................................................................................... 281
Multiserial Bar Charts .......................................................................................................... 282
Multiseries Bar Charts with pandas Dataframe ................................................................... 285
Multiseries Stacked Bar Charts ........................................................................................... 286
Stacked Bar Charts with a pandas Dataframe .................................................................... 290
Other Bar Chart Representations ......................................................................................... 291
Pie Charts .................................................................................................................................. 292
Pie Charts with a pandas Dataframe ................................................................................... 296
Advanced Charts ....................................................................................................................... 297
Contour Plots ....................................................................................................................... 297
Polar Charts ......................................................................................................................... 299
The mplot3d Toolkit ................................................................................................................... 302
3D Surfaces ......................................................................................................................... 302
Scatter Plots in 3D ............................................................................................................... 304
Bar Charts in 3D .................................................................................................................. 306
Multi-Panel Plots ....................................................................................................................... 307
Display Subplots Within Other Subplots .............................................................................. 307
Grids of Subplots ................................................................................................................. 309
Conclusions ............................................................................................................................... 312
Chapter 8: Machine Learning with scikit-learn .................................................... 313
The scikit-learn Library ............................................................................................................. 313
Machine Learning ..................................................................................................................... 313
Supervised and Unsupervised Learning .............................................................................. 314
Training Set and Testing Set ................................................................................................ 315
Supervised Learning with scikit-learn ...................................................................................... 315
The Iris Flower Dataset ............................................................................................................. 316
The PCA Decomposition ...................................................................................................... 320
K-Nearest Neighbors Classifier ................................................................................................. 322
Diabetes Dataset ....................................................................................................................... 327
Linear Regression: The Least Square Regression ..................................................................... 328
Support Vector Machines (SVMs) .............................................................................................. 334
Support Vector Classification (SVC) ..................................................................................... 334
Nonlinear SVC ...................................................................................................................... 339
Plotting Different SVM Classifiers Using the Iris Dataset .................................................... 342
Support Vector Regression (SVR) ........................................................................................ 345
Conclusions ............................................................................................................................... 347
Chapter 9: Deep Learning with TensorFlow .......................................................... 349
Artificial Intelligence, Machine Learning, and Deep Learning ................................................... 349
Artificial intelligence ............................................................................................................ 350
Machine Learning Is a Branch of Artificial Intelligence ....................................................... 351
Deep Learning Is a Branch of Machine Learning ................................................................. 351
The Relationship Between Artificial Intelligence, Machine Learning, and Deep Learning ... 351
Deep Learning ........................................................................................................................... 352
Neural Networks and GPUs ................................................................................................. 352
Data Availability: Open Data Source, Internet of Things, and Big Data ................................ 353
Python ................................................................................................................................. 354
Deep Learning Python Frameworks .................................................................................... 354
Artificial Neural Networks ......................................................................................................... 355
How Artificial Neural Networks Are Structured ................................................................... 355
Single Layer Perceptron (SLP) ............................................................................................. 357
Multi Layer Perceptron (MLP) .............................................................................................. 360
Correspondence Between Artificial and Biological Neural Networks .................................. 361
TensorFlow ................................................................................................................................ 362
TensorFlow: Google’s Framework ........................................................................................ 362
TensorFlow: Data Flow Graph .............................................................................................. 362
Start Programming with TensorFlow ......................................................................................... 363
Installing TensorFlow ........................................................................................................... 363
Programming with the IPython QtConsole ........................................................................... 364
The Model and Sessions in TensorFlow ............................................................................... 364
Tensors ................................................................................................................................ 366
Operation on Tensors ........................................................................................................... 370
Single Layer Perceptron with TensorFlow ................................................................................. 371
Before Starting .................................................................................................................... 372
Data To Be Analyzed ............................................................................................................ 372
The SLP Model Definition .................................................................................................... 374
Learning Phase .................................................................................................................... 378
Test Phase and Accuracy Calculation .................................................................................. 383
Multi Layer Perceptron (with One Hidden Layer) with TensorFlow ........................................... 386
The MLP Model Definition .................................................................................................... 387
Learning Phase .................................................................................................................... 389
Test Phase and Accuracy Calculation .................................................................................. 395
Multi Layer Perceptron (with Two Hidden Layers) with TensorFlow .......................................... 397
Test Phase and Accuracy Calculation .................................................................................. 402
Evaluation of Experimental Data ......................................................................................... 404
Conclusions ............................................................................................................................... 407
Chapter 10: An Example— Meteorological Data .................................................. 409
A Hypothesis to Be Tested: The Influence of the Proximity of the Sea ...................................... 409
The System in the Study: The Adriatic Sea and the Po Valley ............................................. 410
Finding the Data Source ............................................................................................................ 414
Data Analysis on Jupyter Notebook .......................................................................................... 415
Analysis of Processed Meteorological Data .............................................................................. 421
The RoseWind ........................................................................................................................... 436
Calculating the Mean Distribution of the Wind Speed ......................................................... 441
Conclusions ............................................................................................................................... 443
Chapter 11: Embedding the JavaScript D3 Library in the IPython Notebook ....... 445
The Open Data Source for Demographics ................................................................................. 445
The JavaScript D3 Library ......................................................................................................... 449
Drawing a Clustered Bar Chart ................................................................................................. 454
The Choropleth Maps ................................................................................................................ 459
The Choropleth Map of the U.S. Population in 2014 .................................................................. 464
Conclusions ............................................................................................................................... 471
Chapter 12: Recognizing Handwritten Digits ........................................................ 473
Handwriting Recognition ........................................................................................................... 473
Recognizing Handwritten Digits with scikit-learn ..................................................................... 474
The Digits Dataset ..................................................................................................................... 475
Learning and Predicting ............................................................................................................ 478
Recognizing Handwritten Digits with TensorFlow ..................................................................... 480
Learning and Predicting ............................................................................................................ 482
Conclusions ............................................................................................................................... 486
Chapter 13: Textual Data Analysis with NLTK ....................................................... 487
Text Analysis Techniques .......................................................................................................... 487
The Natural Language Toolkit (NLTK) ................................................................................... 488
Import the NLTK Library and the NLTK Downloader Tool ..................................................... 489
Search for a Word with NLTK ............................................................................................... 493
Analyze the Frequency of Words ......................................................................................... 494
Selection of Words from Text ............................................................................................... 497
Bigrams and Collocations .................................................................................................... 498
Use Text on the Network ........................................................................................................... 500
Extract the Text from the HTML Pages ................................................................................ 501
Sentimental Analysis ........................................................................................................... 502
Conclusions ............................................................................................................................... 506
Chapter 14: Image Analysis and Computer Vision with OpenCV .......................... 507
Image Analysis and Computer Vision ........................................................................................ 507
OpenCV and Python ................................................................................................................... 508
OpenCV and Deep Learning ...................................................................................................... 509
Installing OpenCV ...................................................................................................................... 509
First Approaches to Image Processing and Analysis ................................................................ 509
Before Starting .................................................................................................................... 510
Load and Display an Image ................................................................................................. 510
Working with Images ........................................................................................................... 512
Save the New Image ........................................................................................................... 514
Elementary Operations on Images ...................................................................................... 514
Image Blending .................................................................................................................... 520
Image Analysis .......................................................................................................................... 521
Edge Detection and Image Gradient Analysis ........................................................................... 522
Edge Detection .................................................................................................................... 522
The Image Gradient Theory ................................................................................................. 523
A Practical Example of Edge Detection with the Image Gradient Analysis .......................... 525
A Deep Learning Example: The Face Detection ......................................................................... 532
Conclusions ............................................................................................................................... 535
Appendix A: Writing Mathematical Expressions with LaTeX ................................ 537
With matplotlib .......................................................................................................................... 537
With IPython Notebook in a Markdown Cell .............................................................................. 537
With IPython Notebook in a Python 2 Cell ................................................................................. 538
Subscripts and Superscripts ..................................................................................................... 538
Fractions, Binomials, and Stacked Numbers ............................................................................ 538
Radicals .................................................................................................................................... 539
Fonts ......................................................................................................................................... 539
Accents ..................................................................................................................................... 540
Appendix B: Open Data Sources ........................................................................... 549
Political and Government Data .................................................................................................. 549
Health Data ............................................................................................................................... 550
Social Data ................................................................................................................................ 550
Miscellaneous and Public Data Sets ......................................................................................... 551
Financial Data ........................................................................................................................... 552
Climatic Data ............................................................................................................................. 552
Sports Data ............................................................................................................................... 553
Publications, Newspapers, and Books ...................................................................................... 553
Musical Data ............................................................................................................................. 553
Index ..................................................................................................................... 555


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Acquisitions Editor: Todd Green
Development Editor: James Markham
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