Python for Data Mining Quick Syntax Reference

✍ Scribed by SpringerLink (Online service); Porcu, Valentina

• Publisher: Apress
• Year: 2018
• Tongue: English
• Leaves: 269
• Category: Library

✦ Synopsis

​Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis.
Python for Data Mining Quick Syntax Referencecovers each concept concisely, with many illustrative examples. You'll be introduced to several data mining packages, with examples of how to use each of them.

The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning.
What You'll Learn
Install Python and choose a development environment


Understand the basic concepts of object-oriented programming


Import, open, and edit files


Review the differences between Python 2.x and 3.x
Who This Book Is For

Programmers new to Python's data mining packages or with experience in other languages, who want a quick guide to Pythonic tools and techniques.

✦ Table of Contents

Table of Contents......Page 4
About the Author......Page 9
About the Technical Reviewer......Page 10
Introduction......Page 11
Installing Python......Page 14
Editor and IDEs......Page 15
Differences between Python2 and Python3......Page 20
Work Directory......Page 21
Using a Terminal......Page 23
Summary......Page 24
Objects in Python......Page 25
Entering Comments in the Code......Page 26
Types of Data......Page 27
Operators......Page 28
Mathematical Operators......Page 29
Comparison and Membership Operators......Page 30
Bitwise Operators......Page 33
Assignment Operators......Page 34
Operator Order......Page 36
Indentation......Page 37
Summary......Page 38
Numbers......Page 39
Container Objects......Page 40
Tuples......Page 41
Lists......Page 44
Dictionaries......Page 49
Sets......Page 54
Strings......Page 56
Files......Page 64
Immutability......Page 65
Converting Formats......Page 68
Summary......Page 69
Some words about functions in Python......Page 70
Some Predefined Built-in Functions......Page 71
Obtain Function Information......Page 73
Create Your Own Functions......Page 76
Save and run Your Own Modules and Files......Page 78
Summary......Page 79
Conditional Instructions......Page 80
if + else......Page 81
elif......Page 82
for......Page 84
while......Page 89
continue and break......Page 91
map() and filter() Functions......Page 95
The lambda Function......Page 97
Scope......Page 98
Summary......Page 99
More on Objects......Page 100
Inheritance......Page 101
Modules......Page 103
Methods......Page 107
List Comprehension......Page 109
Regular Expressions......Page 110
User Input......Page 117
Errors and Exceptions......Page 119
Summary......Page 122
Chapter 7: Importing Files......Page 123
.csv Format......Page 127
From the Web......Page 128
In JSON......Page 129
Summary......Page 130
Libraries for Data Mining......Page 131
pandas: Series......Page 132
pandas: Data Frames......Page 140
pandas: Importing and Exporting Data......Page 157
pandas: Data Manipulation......Page 163
pandas: Missing Values......Page 172
pandas: Merging Two Datasets......Page 179
pandas: Basic Statistics......Page 184
Summary......Page 186
SciPy......Page 187
NumPy......Page 189
NumPy: Generating Random Numbers and Seeds......Page 201
Summary......Page 210
Basic Plots......Page 211
Pie Charts......Page 225
Other Plots and Charts......Page 228
Saving Plots and Charts......Page 238
Selecting Plot and Chart Styles......Page 239
More on Histograms......Page 241
Summary......Page 244
What Is Machine Learning?......Page 245
Import Datasets Included in Scikit-learn......Page 247
Creation of Training and Testing Datasets......Page 249
Regression......Page 250
K-Nearest Neighbors......Page 252
Support Vector Machine......Page 253
KMeans......Page 254
Managing Dates......Page 255
Data Sources......Page 261
Index......Page 264