Learn Enough Python to Be Dangerous: Software Development, Flask Web Apps, and Beginning Data Science with Python

✍ Scribed by Michael Hartl

• Publisher: Independently Published
• Year: 2024
• Tongue: English
• Leaves: 445
• Category: Library

✦ Synopsis

All You Need to Know, and Nothing You Don't, to Solve Real Problems with Python

Python is one of the most popular programming languages in the world, used for everything from shell scripts to web development to data science. As a result, Python is a great language to learn, but you don't need to learn "everything" to get started, just how to use it efficiently to solve real problems. In Learn Enough Python to Be Dangerous, renowned instructor Michael Hartl teaches the specific concepts, skills, and approaches you need to be professionally productive.

Even if you've never programmed before, Hartl helps you quickly build technical sophistication and master the lore you need to succeed. Hartl introduces Python both as a general-purpose language and as a specialist tool for web development and data science, presenting focused examples and exercises that help you internalize what matters, without wasting time on details pros don't care about. Soon, it'll be like you were born knowing this stuff—and you'll be suddenly, seriously dangerous.

Learn enough about . . .
• Applying core Python concepts with the interactive interpreter and command line
• Writing object-oriented code with Python's native objects
• Developing and publishing self-contained Python packages
• Using elegant, powerful functional programming techniques, including Python comprehensions
• Building new objects, and extending them via Test-Driven Development (TDD)
• Leveraging Python's exceptional shell scripting capabilities
• Creating and deploying a full web app, using routes, layouts, templates, and forms
• Getting started with data-science tools for numerical computations, data visualization, data analysis, and machine learning
• Mastering concrete and informal skills every developer needs

Michael Hartl's Learn Enough Series includes books and video courses that focus on the most important parts of each subject, so you don't have to learn everything to get started—you just have to learn enough to be dangerous and solve technical problems yourself.

Like this book? Don't miss Michael Hartl's companion video tutorial, Learn Enough Python to Be Dangerous LiveLessons.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Acknowledgments
About the Author
Chapter 1 Hello, World!
1.1 Introduction to Python
1.1.1 System Setup and Installation
1.2 Python in a REPL
1.2.1 Exercises
1.3 Python in a File
1.3.1 Exercise
1.4 Python in a Shell Script
1.4.1 Exercise
1.5 Python in a Web Browser
1.5.1 Deployment
1.5.2 Exercises
Chapter 2 Strings
2.1 String Basics
2.1.1 Exercises
2.2 Concatenation and Interpolation
2.2.1 Formatted Strings
2.2.2 Raw Strings
2.2.3 Exercises
2.3 Printing
2.3.1 Exercises
2.4 Length, Booleans, and Control Flow
2.4.1 Combining and Inverting Booleans
2.4.2 Boolean Context
2.4.3 Exercises
2.5 Methods
2.5.1 Exercises
2.6 String Iteration
2.6.1 Exercises
Chapter 3 Lists
3.1 Splitting
3.1.1 Exercises
3.2 List Access
3.2.1 Exercises
3.3 List Slicing
3.3.1 Exercises
3.4 More List Techniques
3.4.1 Element Inclusion
3.4.2 Sorting and Reversing
3.4.3 Appending and Popping
3.4.4 Undoing a Split
3.4.5 Exercises
3.5 List Iteration
3.5.1 Exercises
3.6 Tuples and Sets
3.6.1 Exercises
Chapter 4 Other Native Objects
4.1 Math
4.1.1 More Advanced Operations
4.1.2 Math to String
4.1.3 Exercises
4.2 Times and Datetimes
4.2.1 Exercises
4.3 Regular Expressions
4.3.1 Splitting on Regexes
4.3.2 Exercises
4.4 Dictionaries
4.4.1 Dictionary Iteration
4.4.2 Merging Dictionaries
4.4.3 Exercises
4.5 Application: Unique Words
4.5.1 Exercises
Chapter 5 Functions and Iterators
5.1 Function Definitions
5.1.1 First-Class Functions
5.1.2 Variable and Keyword Arguments
5.1.3 Exercises
5.2 Functions in a File
5.2.1 Exercise
5.3 Iterators
5.3.1 Generators
5.3.2 Exercises
Chapter 6 Functional Programming
6.1 List Comprehensions
6.1.1 Exercise
6.2 List Comprehensions with Conditions
6.2.1 Exercise
6.3 Dictionary Comprehensions
6.3.1 Exercise
6.4 Generator and Set Comprehensions
6.4.1 Generator Comprehensions
6.4.2 Set Comprehensions
6.4.3 Exercise
6.5 Other Functional Techniques
6.5.1 Functional Programming and TDD
6.5.2 Exercise
Chapter 7 Objects and Classes
7.1 Defining Classes
7.1.1 Exercises
7.2 CustomIterators
7.2.1 Exercise
7.3 Inheritance
7.3.1 Exercise
7.4 Derived Classes
7.4.1 Exercises
Chapter 8 Testing and Test-Driven Development
8.1 Package Setup
8.1.1 Exercise
8.2 Initial Test Coverage
8.2.1 A Useful Passing Test
8.2.2 Pending Tests
8.2.3 Exercises
8.3 Red
8.3.1 Exercise
8.4 Green
8.4.1 Exercise
8.5 Refactor
8.5.1 Publishing the Python Package
8.5.2 Exercises
Chapter 9 Shell Scripts
9.1 Reading from Files
9.1.1 Exercises
9.2 Reading from URLs
9.2.1 Exercises
9.3 DOM Manipulation at the Comm and Line
9.3.1 Exercises
Chapter 10 A Live Web Application
10.1 Setup
10.1.1 Exercise
10.2 Site Pages
10.2.1 Exercises
10.3 Layouts
10.3.1 Exercises
10.4 Template Engine
10.4.1 Variable Titles
10.4.2 Site Navigation
10.4.3 Exercises
10.5 Palindrome Detector
10.5.1 Form Tests
10.5.2 Exercises
10.6 Conclusion
Chapter 11 Data Science
11.1 Data Science Setup
11.2 Numerical Computations with NumPy
11.2.1 Arrays
11.2.2 Multidimensional Arrays
11.2.3 Constants, Functions, and Linear Spacing
11.2.4 Exercises
11.3 Data Visualization with Matplotlib
11.3.1 Plotting
11.3.2 Scatter Plots
11.3.3 Histograms
11.3.4 Exercises
11.4 Introduction to Data Analysis with pandas
11.4.1 Handcrafted Examples
11.4.2 Exercise
11.5 pandas Example: Nobel Laureates
11.5.1 Exercises
11.6 pandasExample:Titanic
11.6.1 Exercises
11.7 MachineLearningwithscikit-learn
11.7.1 LinearRegression
11.7.2 Machine-LearningModels
11.7.3 k-MeansClustering
11.7.4 Exercises
11.8 FurtherResourcesandConclusion
Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
R
S
T
U
V
W
X
Y
Z