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E-raamat: Foundational Python for Data Science

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Data science and machine learning – two of the world’s hottest fields – are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world’s #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning. 


Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once you’ve learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving.


Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more – all created with colab (jupyter compatible) notebooks, so you can execute all coding examples interactively without installing or configuring any software.



Data science and machine learning – two of the world’s hottest fields – are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world’s #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help students with widely diverse backgrounds learn foundational Python so they can use it for data science and machine learning. This book is catered to introductory-level college courses on data science. 


Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once students have learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more – all created with colab (jupyter compatible) notebooks, so students can execute all coding examples interactively without installing or configuring any software.

Preface xiii
I Learning Python in a Notebook Environment
1(82)
1 Introduction to Notebooks
3(10)
Running Python Statements
4(1)
Jupyter Notebooks
4(1)
Google Colab
5(7)
Colab Text Cells
6(3)
Colab Code Cells
9(1)
Colab Files
9(1)
Managing Colab Documents
10(1)
Colab Code Snippets
11(1)
Existing Collections
11(1)
System Aliases
11(1)
Magic Functions
12(1)
Summary
12(1)
Questions
12(1)
2 Fundamentals of Python
13(12)
Basic Types in Python
14(7)
High-Level Versus Low-Level Languages
15(1)
Statements
15(6)
Performing Basic Math Operations
21(1)
Using Classes and Objects with Dot Notation
22(1)
Summary
22(1)
Questions
23(2)
3 Sequences
25(12)
Shared Operations
25(4)
Testing Membership
26(1)
Indexing
26(1)
Slicing
27(1)
Interrogation
27(1)
Math Operations
28(1)
Lists and Tuples
29(3)
Creating Lists and Tuples
29(1)
Adding and Removing List Items
30(1)
Unpacking
31(1)
Sorting Lists
32(1)
Strings
32(2)
Ranges
34(1)
Summary
35(1)
Questions
35(2)
4 Other Data Structures
37(18)
Dictionaries
37(9)
Creating Dictionaries
38(1)
Accessing, Adding, and Updating by Using Keys
38(1)
Removing Items from Dictionaries
39(1)
Dictionary Views
40(3)
Checking to See If a Dictionary Has a Key
43(1)
The get Method
43(1)
Valid Key Types
44(1)
The hash Method
45(1)
Sets
46(7)
Set Operations
48(5)
Frozensets
53(1)
Summary
53(1)
Questions
53(2)
5 Execution Control
55(12)
Compound Statements
55(4)
Compound Statement Structure
56(1)
Evaluating to True or False
56(3)
If Statements
59(3)
While Loops
62(1)
For Loops
63(1)
Break and continue Statements
64(1)
Summary
65(1)
Questions
65(2)
6 Functions
67(16)
Defining Functions
67(8)
Control Statement
68(1)
Docstrings
68(1)
Parameters
69(6)
Return Statements
75(1)
Scope in Functions
75(1)
Decorators
76(4)
Anonymous Functions
80(1)
Summary
81(1)
Questions
81(2)
II Data Science Libraries
83(88)
7 NumPy
85(18)
Installing and Importing NumPy
86(1)
Creating Arrays
86(3)
Indexing and Slicing
89(2)
Element-by-Element Operations
91(1)
Filtering Values
92(2)
Views Versus Copies
94(1)
Some Array Methods
95(3)
Broadcasting
98(2)
NumPy Math
100(2)
Summary
102(1)
Questions
102(1)
8 SciPy
103(10)
SciPy Overview
103(1)
The scipy.misc Submodule
104(1)
The scipy.special Submodule
105(1)
The scipy.stats Submodule
105(6)
Discrete Distributions
105(3)
Continuous Distributions
108(3)
Summary
111(1)
Questions
111(2)
9 Pandas
113(22)
About DataFrames
113(1)
Creating DataFrames
114(3)
Creating a DataFrame from a Dictionary
114(1)
Creating a DataFrame from a List of Lists
115(1)
Creating a DataFrame from a File
116(1)
Interacting with DataFrame Data
117(10)
Heads and Tails
117(1)
Descriptive Statistics
118(2)
Accessing Data
120(1)
Bracket Syntax
121(2)
Optimized Access by Label
123(1)
Optimized Access by Index
124(1)
Masking and Filtering
125(1)
Pandas Boolean Operators
126(1)
Manipulating DataFrames
127(2)
Manipulating Data
129(4)
The replace Method
131(2)
Interactive Display
133(1)
Summary
133(1)
Questions
133(2)
10 Visualization Libraries
135(18)
matplotlib
135(9)
Styling Plots
137(3)
Labeled Data
140(1)
Plotting Multiple Sets of Data
141(2)
Object-Oriented Style
143(1)
Seaborn
144(4)
Seaborn Themes
145(3)
Plotly
148(1)
Bokeh
149(2)
Other Visualization Libraries
151(1)
Summary
151(1)
Questions
151(2)
11 Machine Learning Libraries
153(6)
Popular Machine Learning Libraries
153(1)
How Machine Learning Works
154(3)
Transformations
154(1)
Splitting Test and Training Data
155(1)
Training and Testing
156(1)
Learning More About Scikit-learn
157(1)
Summary
157(1)
Questions
157(2)
12 Natural Language Toolkit
159(12)
NLTK Sample Texts
159(2)
Frequency Distributions
161(4)
Text Objects
165(1)
Classifying Text
166(3)
Summary
169(1)
Exercises
169(2)
III Intermediate Python
171(44)
13 Functional Programming
173(14)
Introduction to Functional Programming
173(6)
Scope and State
174(1)
Depending on Global State
174(1)
Changing State
175(1)
Changing Mutable Data
176(1)
Functional Programming Functions
177(2)
List Comprehensions
179(3)
List Comprehension Basic Syntax
179(1)
Replacing map and filter
180(1)
Multiple Variables
181(1)
Dictionary Comprehensions
181(1)
Generators
182(2)
Generator Expressions
182(1)
Generator Functions
183(1)
Summary
184(1)
Questions
185(2)
14 Object-Oriented Programming
187(14)
Grouping State and Function
187(4)
Classes and Instances
188(2)
Private Methods and Variables
190(1)
Class Variables
190(1)
Special Methods
191(5)
Representation Methods
192(1)
Rich Comparison Methods
192(3)
Math Operator Methods
195(1)
Inheritance
196(3)
Summary
199(1)
Questions
199(2)
15 Other Topics
201(14)
Sorting
201(3)
Lists
201(3)
Reading and Writing Files
204(2)
Context Managers
205(1)
Date time Objects
206(1)
Regular Expressions
207(5)
Character Sets
208(1)
Character Classes
209(1)
Groups
209(1)
Named Groups
210(1)
Find All
210(1)
Find Iterator
211(1)
Substitution
211(1)
Substitution Using Named Groups
211(1)
Compiling Regular Expressions
211(1)
Summary
212(1)
Questions
212(3)
A Answers to End-of-Chapter Questions 215(6)
Index 221
Kennedy Behrman is a veteran software and data engineer. He first used Python writing asset management systems in the Visual Effects industry. He then moved into the startup world, using Python at startups using machine learning to characterize videos and predict the social media power of athletes.
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