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Data Mining with Python (Working draft)

Finn

Arup Nielsen

November 29, 2017

Contentsi

List of Figuresvii

List of Tablesix

1 Introduction1

1.1 Other introductions to Python?

1.2 Why Python for data mining?

1.3 Why not Python for data mining?

1.4 Components of the Python language and software

1.5 Developing and running Python

1.5.1 Python, pypy, IPython ...

1.5.2 Jupyter Notebook

1.5.3 Python 2 vs. Python 3

1.5.4 Editing

1.5.5 Python in the cloud

1.5.6 Running Python in the browser

2 Python9

2.1 Basics

2.2 Datatypes

2.2.1 Booleans (bool). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2.2 Numbers (int,float,complexandDecimal). . . . . . . . . . . . . . . . . . . . . . . 10

2.2.3 Strings (str). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2.4 Dictionaries (dict). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2.5 Dates and times

2.2.6 Enumeration

2.2.7 Other containers classes

2.3 Functions and arguments

2.3.1 Anonymous functions withlambdas. . . . . . . . . . . . . . . . . . . . . . . . . . . .14

2.3.2 Optional function arguments

2.4 Object-oriented programming

2.4.1 Objects as functions

2.5 Modules and import

2.5.1 Submodules

2.5.2 Globbing import

2.5.3 Coping with Python 2/3 incompatibility

2.6 Persistency

2.6.1 Pickle and JSON

2.6.2 SQL

21
i

2.6.3 NoSQL. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.7 Documentation

2.8 Testing

2.8.1 Testing for type

2.8.2 Zero-one-some testing

2.8.3 Test layout and test discovery

2.8.4 Test coverage

2.8.5 Testing in dierent environments

2.9 Proling

2.10 Coding style

2.10.1 Where isprivateandpublic?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.11 Command-line interface scripting

2.11.1 Distinguishing between module and script

2.11.2 Argument parsing

2.11.3 Exit status

2.12 Debugging

2.12.1 Logging

2.13 Advices

3 Python for data mining

3.1 Numpy

3.2 Plotting

3.2.1 3D plotting

3.2.2 Real-time plotting

3.2.3 Plotting for the Web

3.3 Pandas

3.3.1 Pandas data types

3.3.2 Pandas indexing

3.3.3 Pandas joining, merging and concatenations

3.3.4 Simple statistics

3.4 SciPy

3.4.1scipy.linalg. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .44

3.4.2 Fourier transform withscipy.fftpack. . . . . . . . . . . . . . . . . . . . . . . . . .44

3.5 Statsmodels

3.6 Sympy

3.7 Machine learning

3.7.1 Scikit-learn

3.8 Text mining

3.8.1 Regular expressions

3.8.2 Extracting from webpages

3.8.3 NLTK

3.8.4 Tokenization and part-of-speech tagging

3.8.5 Language detection

3.8.6 Sentiment analysis

3.9 Network mining

3.10 Miscellaneous issues

3.10.1 Lazy computation

3.11 Testing data mining code

4 Case: Pure Python matrix library

4.1 Code listing

59
ii

5 Case: Pima data set65

5.1 Problem description and objectives

5.2 Descriptive statistics and plotting

5.3 Statistical tests

5.4 Predicting diabetes type

6 Case: Data mining a database

6.1 Problem description and objectives

6.2 Reading the data

6.3 Graphical overview on the connections between the tables

6.4 Statistics on the number of tracks sold

7 Case: Twitter information diusion

7.1 Problem description and objectives

7.2 Building a news classier

8 Case: Big data77

8.1 Problem description and objectives

8.2 Stream processing of JSON

8.2.1 Stream processing of JSON Lines

Bibliography81

Index85

iii iv

Preface

Python has grown to become one of the central languages in data mining oering both a general programming

language and libraries specically targeted numerical computations. This book is continuously being written and grew out of course given at the Technical University of

Denmark.

v vi

List of Figures

1.1 The Python hierarchy.

2.1 Overview of methods and attributes in the common Python 2 built-in data types plotted as a

formal concept analysis lattice graph. Only a small subset of methods and attributes is shown. 16

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