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The Quick Python Book, Fourth Edition

Name: The Quick Python Book, Fourth Edition
Author: Naomi Ceder
ISBN: 9781633436336

by Naomi Ceder

March 2025

Intermediate to advanced

584 pages

15h 31m

English

Manning Publications

Audiobook available

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Praise for the Third Edition
The Quick Python Book
Copyright
dedication
contents
front matter
forewordprefaceacknowledgmentsabout this bookWho should read this bookHow this book is organized: A road mapAbout the codeliveBook discussion forumabout the authorabout the cover illustration
Part 1. Starting out
1 About Python
1.1 Why should I use Python?1.2 What Python does well1.2.1 Python is easy to use1.2.2 Python is expressive1.2.3 Python is readable1.2.4 Python is complete: “Batteries included”1.2.5 Python has a rich ecosystem of third-party libraries1.2.6 Python is cross-platform1.2.7 Python is free1.3 What Python is improving1.3.1 Python is getting faster1.3.2 Python doesn’t enforce variable types at compile time1.3.3 Python is improving mobile support1.3.4 Python is improving support for multiple processorsSummary
2 Getting started
2.1 Paradox of choice: Which Python?2.1.1 Python versions2.1.2 Sources of Python2.1.3 Devices, platforms, and operating systems2.1.4 Environments and tools2.2 Colaboratory: Jupyter notebooks in the cloud2.2.1 Getting the source notebooks2.2.2 Getting started with Colaboratory2.2.3 Colaboratory’s Python version2.3 Writing and running code in Colaboratory2.3.1 Hello, World2.3.2 Dealing with errors in Jupyter2.4 Using help and dir to explore Python2.5 Using AI tools to write Python code2.5.1 Benefits of AI tools2.5.2 Negatives of using AI tools2.5.3 AI optionsSummary
3 The quick Python overview
3.1 Python synopsis3.2 Built-in data types3.2.1 Numbers3.2.2 Lists3.2.3 Tuples3.2.4 Strings3.2.5 Dictionaries3.2.6 Sets, frozensets3.2.7 File objects3.3 Type hints in Python3.4 Control flow structures3.4.1 Boolean values and expressions3.4.2 The if-elif-else statement3.4.3 Structural pattern matching with match3.4.4 The while loop3.4.5 The for loop3.4.6 Function definition3.4.7 Exceptions3.4.8 Context handling using the with keyword3.5 Module creation3.6 Object-oriented programmingSummary

Part 2. The essentials
4 The absolute basics
4.1 Indentation and block structuring4.2 Differentiating comments4.3 Variables and assignments4.4 Optional type hints in Python4.4.1 Why use type hints?4.4.2 Why not use type hints?4.4.3 Progressive typing4.5 Expressions4.6 Strings4.7 Numbers4.7.1 Built-in numeric functions4.7.2 Advanced numeric functions4.7.3 Numeric computation4.7.4 Complex numbers4.7.5 Advanced complex-number functions4.8 The None value4.9 Getting input from the user4.10 Built-in operators4.11 Basic Python styleSummary
5 Lists, tuples, and sets
5.1 Lists are like arrays5.2 List indices5.3 Modifying lists5.4 Sorting lists5.4.1 Custom sorting5.4.2 The sorted () function5.5 Other common list operations5.5.1 List membership with the in operator5.5.2 List concatenation with the + operator5.5.3 List initialization with the * operator5.5.4 List minimum or maximum with min and max5.5.5 List search with index5.5.6 List matches with count5.5.7 Summary of list operations5.6 Nested lists and deep copies5.7 Tuples5.7.1 Tuple basics5.7.2 One-element tuples need a comma5.7.3 Packing and unpacking tuples5.7.4 Converting between lists and tuples5.8 Sets5.8.1 Set operations5.8.2 Frozen sets5.9 Lab: Examining a list5.9.1 Why solve it the old-fashioned way?5.9.2 Solving the problem with AI code generation5.9.3 Solutions and discussionSummary
6 Strings
6.1 Strings as sequences of characters6.2 Basic string operations6.3 Special characters and escape sequences6.3.1 Basic escape sequences6.3.2 Numeric (octal and hexadecimal) and Unicode escape sequences6.3.3 Printing vs. evaluating strings with special characters6.4 String methods6.4.1 The split and join string methods6.4.2 Converting strings to numbers6.4.3 Getting rid of extra whitespace6.4.4 String searching6.4.5 Modifying strings6.4.6 Modifying strings with list manipulations6.4.7 Useful methods and constants6.5 Converting objects to strings6.6 Using the format method6.6.1 The format method and positional parameters6.6.2 The format method and named parameters6.6.3 Format specifiers6.7 String interpolation with f-strings6.8 Formatting strings with %6.8.1 Using formatting sequences6.8.2 Named parameters and formatting sequences6.9 Bytes6.10 Preprocessing text6.10.1 Solving the problem with AI-generated code6.10.2 Solutions and discussionSummary
7 Dictionaries
7.1 What is a dictionary?7.2 Other dictionary operations7.3 Word counting7.4 What can be used as a key?7.5 Sparse matrices7.6 Dictionaries as caches7.7 Efficiency of dictionaries7.8 Word counting7.8.1 Solving the problem with AI-generated code7.8.2 Solutions and discussionSummary
8 Control flow
8.1 The if-elif-else statement8.2 Structural pattern matching with match8.3 The while loop8.4 The for loop8.4.1 The range function8.5 Controlling range with starting and stepping values8.6 The for loop and tuple unpacking8.7 The enumerate function8.8 The zip function8.9 List, set, and dictionary comprehensions8.9.1 Generator expressions8.10 Statements, blocks, and indentation8.11 Boolean values and expressions8.11.1 Most Python objects can be used as Booleans8.11.2 Comparison and Boolean operators8.12 Writing a simple program to analyze a text file8.13 Refactoring word_count8.13.1 Solving the problem with AI-generated code8.13.2 Solutions and discussionSummary
9 Functions
9.1 Basic function definitions9.2 Function parameter options9.2.1 Positional parameters9.2.2 Passing arguments by parameter name9.2.3 Variable numbers of arguments9.2.4 Mixing argument-passing techniques9.3 Mutable objects as arguments9.3.1 Mutable objects as default values9.4 Local, nonlocal, and global variables9.5 Assigning functions to variables9.6 lambda expressions9.7 Generator functions9.8 Decorators9.9 Useful functions9.9.1 Solving the problem with AI-generated code9.9.2 Solutions and discussionSummary
10 Modules and scoping rules
10.1 What is a module?10.2 A first module10.3 The import statement10.4 The module search path10.4.1 Where to place your own modules10.5 Private names in modules10.6 Library and third-party modules10.7 Python scoping rules and namespaces10.7.1 The built-in namespace10.8 Creating a module10.8.1 Solving the problem with AI-generated code10.8.2 Solutions and discussionSummary
11 Python programs
11.1 Creating a very basic program11.1.1 Starting a script from a command line11.1.2 Command-line arguments11.1.3 Executing code only as main script11.1.4 Redirecting the input and output of a script11.1.5 The argparse module11.1.6 Using the fileinput module11.2 Running scripts in different operating systems11.2.1 Making a script directly executable on UNIX11.2.2 Scripts on macOS11.2.3 Script execution options in Windows11.3 Programs and modules11.4 Distributing Python applications11.4.1 Wheels packages11.4.2 zipapp and pex11.4.3 py2exe and py2app11.4.4 Creating executable programs with freeze11.5 Creating a program11.5.1 Solving the problem with AI-generated code11.5.2 Solutions and discussionSummary
12 Using the filesystem
12.1 os and os.path vs. pathlib12.2 Paths and pathnames12.2.1 Absolute and relative paths12.2.2 The current working directory12.2.3 Accessing directories with pathlib12.2.4 Manipulating pathnames12.2.5 Manipulating pathnames with pathlib12.2.6 Useful constants and functions12.3 Getting information about files12.3.1 Getting information about files with scandir12.4 More filesystem operations12.4.1 More filesystem operations with pathlib12.5 Processing all files in a directory subtree12.6 More file operations12.6.1 Solving the problem with AI-generated code12.6.2 Solutions and discussionSummary
13 Reading and writing files
13.1 Opening files and file objects13.2 Closing files13.3 Opening files in write or other modes13.4 Functions to read and write text or binary data13.4.1 Using binary mode13.5 Reading and writing with pathlib13.6 Terminal input/output and redirection13.7 Handling structured binary data with the struct module13.8 Pickling objects to files13.8.1 Reasons not to pickle13.9 Shelving objects13.10 Final fixes to wc13.10.1 Solving the problem with AI-generated code13.10.2 Solutions and discussionSummary
14 Exceptions
14.1 Introduction to exceptions14.1.1 General philosophy of errors and exception handling14.1.2 A more formal definition of exceptions14.1.3 Handling different types of exceptions14.2 Exceptions in Python14.2.1 Types of Python exceptions14.2.2 Raising exceptions14.2.3 Catching and handling exceptions14.2.4 Defining new exceptions14.2.5 Exception groups14.2.6 Debugging programs with the assert statement14.2.7 The exception inheritance hierarchy14.2.8 Example: A disk-writing program in Python14.2.9 Example: Exceptions in normal evaluation14.2.10 Where to use exceptions14.3 Context managers using the with keyword14.4 Adding exceptions14.4.1 Solving the problem with AI-generated code14.4.2 Solutions and discussionSummary
Part 3. Advanced language features
15 Classes and object-oriented programming
15.1 Defining classes15.1.1 Using a class instance as a structure or record15.2 Instance variables15.3 Methods15.4 Class variables15.4.1 An oddity with class variables15.5 Static methods and class methods15.5.1 Static methods15.5.2 Class methods15.6 Inheritance15.7 Inheritance with class and instance variables15.8 Recap: Basics of Python classes15.9 Private variables and private methods15.10 Using @property for more flexible instance variables15.11 Scoping rules and namespaces for class instances15.12 Destructors and memory management15.13 Multiple inheritance15.14 HTML classes15.14.1 Solving the problem with AI-generated code15.14.2 Solutions and discussionSummary
16 Regular expressions
16.1 What is a regular expression?16.2 Regular expressions with special characters16.3 Regular expressions and raw strings16.3.1 Raw strings to the rescue16.4 Extracting matched text from strings16.5 Substituting text with regular expressions16.5.1 Using a function with sub16.6 Phone number normalizer16.6.1 Solving the problem with AI-generated code16.6.2 Solutions and discussionSummary
17 Data types as objects
17.1 Types are objects too17.2 Using types17.3 Types and user-defined classes17.4 Duck typing17.5 What is a special method attribute?17.6 Making an object behave like a list17.7 The __getitem__ special method attribute17.7.1 it works17.7.2 Implementing full list functionality17.8 Giving an object full list capability17.9 Subclassing from built-in types17.9.1 Subclassing list17.9.2 Subclassing UserList17.10 When to use special method attributes17.11 Creating a string-only key-value dictionary17.11.1 Solving the problem with AI-generated code17.11.2 Solutions and discussionSummary
18 Packages
18.1 What is a package?18.2 A first example: mathproj18.3 Implementing the mathproj package18.3.1 __init__.py files in packages18.3.2 Basic use of the mathproj package18.3.3 Loading subpackages and submodules18.3.4 import statements within packages18.4 The __all__ attribute18.5 Proper use of packages18.6 Creating a package18.6.1 Solving the problem with AI-generated code18.6.2 Solutions and discussionSummary
19 Using Python libraries
19.1 “Batteries included”: The standard library19.1.1 Managing various data types19.1.2 Manipulating files and storage19.1.3 Accessing operating system services19.1.4 Using internet protocols and formats19.1.5 Development and debugging tools and runtime services19.2 Moving beyond the standard library19.3 Adding more Python libraries19.4 The Python Package Index19.5 Installing Python libraries using pip and venv19.5.1 Installing with the --user flag19.5.2 Virtual environments19.5.3 Other optionsSummary
Part 4. Working with data
20 Basic file wrangling
20.1 The problem: The never-ending flow of data files20.2 Scenario: The product feed from hell20.3 More organization20.4 Saving storage space: Compression and grooming20.4.1 Compressing files20.4.2 Grooming filesSummary
21 Processing data files
21.1 Welcome to ETL21.2 Reading text files21.2.1 Text encoding: ASCII, Unicode, and others21.2.2 Unstructured text21.2.3 Delimited flat files21.2.4 The csv module21.2.5 Reading a csv file as a list of dictionaries21.3 Excel files21.4 Data cleaning21.4.1 Cleaning21.4.2 Sorting21.4.3 Data cleaning problems and pitfalls21.5 Writing data files21.5.1 CSV and other delimited files21.5.2 Writing Excel files21.5.3 Packaging data files21.6 Weather observations21.6.1 Solving the problem with AI-generated code21.6.2 Solutions and discussionSummary
22 Data over the network
22.1 Fetching files22.1.1 Using Python to fetch files from an FTP server22.1.2 Fetching files with SFTP22.1.3 Retrieving files over HTTP/HTTPS22.2 Fetching data via an API22.3 Structured data formats22.3.1 JSON data22.3.2 XML data22.4 Scraping web data22.5 Tracking the weather22.5.1 Solving the problem with AI-generated code22.5.2 Solutions and discussionSummary
23 Saving data
23.1 Relational databases23.1.1 The Python database API23.2 SQLite: Using the sqlite3 database23.3 Using MySQL, PostgreSQL, and other relational databases23.4 Making database handling easier with an object relational mapper23.4.1 SQLAlchemy23.4.2 Using Alembic for database schema changes23.5 NoSQL databases23.6 Key-value stores with Redis23.7 Documents in MongoDB23.8 Creating a databaseSummary
24 Exploring data
24.1 Python tools for data exploration24.1.1 Python’s advantages for exploring data24.1.2 Python can be better than a spreadsheet24.2 Python and pandas24.2.1 Why you might want to use pandas24.2.2 Installing pandas24.2.3 Data frames24.3 Data cleaning24.3.1 Loading and saving data with pandas24.3.2 Data cleaning with a data frame24.4 Data aggregation and manipulation24.4.1 Merging data frames24.4.2 Selecting data24.4.3 Grouping and aggregation24.5 Plotting data24.6 Why you might not want to use pandasSummary
Case study
Downloading the dataParsing the inventory dataSelecting a station based on latitude and longitudeSelecting a station and getting the station metadataFetching and parsing the actual weather dataSaving the weather data in a database (optional)Selecting and graphing dataUsing pandas to graph your data
Appendix. A guide to Python’s documentation
A.1 Accessing Python documentation on the webA.2 Best practices: How to become a PythonistaA.2.1 Ten tips for becoming a PythonistaA.3 PEP 8: Style guide for Python codeA.3.1 IntroductionA.3.2 Code layoutA.4 CommentsA.4.1 Naming conventionsA.4.2 Programming recommendationsA.4.3 Other guides for Python styleA.5 The Zen of Python
index

Content preview from The Quick Python Book, Fourth Edition

8 Control flow

This chapter covers

Making decisions: the if-elif-else statement
Structural pattern matching
Repeating code with a while loop
Iterating over a list with a for loop
Using list and dictionary comprehensions
Delimiting statements and blocks with indentation
Evaluating Boolean values and expressions

Python provides a complete set of control flow elements, with loops and conditionals. This chapter examines each element in detail.

8.1 The if-elif-else statement

One of the most important control flow features in most programming languages is the one for branching—that is, for making decisions on whether or not to execute or skip a piece of code based on some conditional. In Python, as in many languages, there are three parts to this: ...

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The Quick Python Book, Fourth Edition

by Naomi Ceder

8 Control flow

8.1 The if-elif-else statement

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