Python 3: The Comprehensive Guide to Hands-On Python Programming

Dear Reader
Notes on Usage
Table of Contents
1 Introduction
	1.1 Why Did We Write This Book?
	1.2 What Does This Book Provide?
	1.3 Structure of the Book
	1.4 How Should You Read This Book?
	1.5 Sample Programs
	1.6 Preface To the First English Edition (2022)
	1.7 Acknowledgments
2 The Python Programming Language
	2.1 History, Concepts, and Areas of Application
		2.1.1 History and Origin
		2.1.2 Basic Concepts
		2.1.3 Possible Areas of Use and Strengths
		2.1.4 Examples of Use
	2.2 Installing Python
		2.2.1 Installing Anaconda on Windows
		2.2.2 Installing Anaconda on Linux
		2.2.3 Installing Anaconda on macOS
	2.3 Installing Third-Party Modules
	2.4 Using Python
Part I Getting Started with Python
3 Getting Started with the Interactive Mode
	3.1 Integers
	3.2 Floats
	3.3 Character Strings
	3.4 Lists
	3.5 Dictionaries
	3.6 Variables
		3.6.1 The Special Meaning of the Underscore
		3.6.2 Identifiers
	3.7 Logical Expressions
	3.8 Functions and Methods
		3.8.1 Functions
		3.8.2 Methods
	3.9 Screen Outputs
	3.10 Modules
4 The Path to the First Program
	4.1 Typing, Compiling, and Testing
		4.1.1 Windows
		4.1.2 Linux and macOS
		4.1.3 Shebang
		4.1.4 Internal Processes
	4.2 Basic Structure of a Python Program
		4.2.1 Wrapping Long Lines
		4.2.2 Joining Multiple Lines
	4.3 The First Program
		4.3.1 Initialization
		4.3.2 Loop Header
		4.3.3 Loop Body
		4.3.4 Screen Output
	4.4 Comments
	4.5 In Case of Error
5 Control Structures
	5.1 Conditionals
		5.1.1 The if Statement
		5.1.2 Conditional Expressions
	5.2 Loops
		5.2.1 The while Loop
		5.2.2 Termination of a Loop
		5.2.3 Detecting a Loop Break
		5.2.4 Aborting the Current Iteration
		5.2.5 The for Loop
		5.2.6 The for Loop as a Counting Loop
	5.3 The pass Statement
	5.4 Assignment Expressions
		5.4.1 The Guessing Numbers Game with Assignment Expressions
6 Files
	6.1 Data Streams
	6.2 Reading Data from a File
		6.2.1 Opening and Closing a File
		6.2.2 The with Statement
		6.2.3 Reading the File Content
	6.3 Writing Data to a File
	6.4 Generating the File Object
		6.4.1 The Built-In open Function
		6.4.2 Attributes and Methods of a File Object
		6.4.3 Changing the Write/Read Position
7 The Data Model
	7.1 The Structure of Instances
		7.1.1 Data Type
		7.1.2 Value
		7.1.3 Identity
	7.2 Deleting References
	7.3 Mutable versus Immutable Data Types
8 Functions, Methods, and Attributes
	8.1 Parameters of Functions and Methods
		8.1.1 Positional Parameters
		8.1.2 Keyword Arguments
		8.1.3 Optional Parameters
		8.1.4 Keyword-Only Parameters
	8.2 Attributes
9 Sources of Information on Python
	9.1 The Built-In Help Function
	9.2 The Online Documentation
	9.3 PEPs
Part II Data Types
10 Basic Data Types: An Overview
	10.1 Nothingness: NoneType
	10.2 Operators
		10.2.1 Operator Precedence
		10.2.2 Evaluation Order
		10.2.3 Concatenating Comparisons
11 Numeric Data Types
	11.1 Arithmetic Operators
	11.2 Comparison Operators
	11.3 Conversion between Numeric Data Types
	11.4 Integers: int
		11.4.1 Numeral Systems
		11.4.2 Bit Operations
		11.4.3 Methods
	11.5 Floats: float
		11.5.1 Exponential Notation
		11.5.2 Precision
		11.5.3 Infinite and Not a Number
	11.6 Boolean Values: bool
		11.6.1 Logical Operators
		11.6.2 Truth Values of Non-Boolean Data Types
		11.6.3 Evaluating Logical Operators
	11.7 Complex Numbers: complex
12 Sequential Data Types
	12.1 The Difference between Text and Binary Data
	12.2 Operations on Instances of Sequential Data Types
		12.2.1 Checking for Elements
		12.2.2 Concatenation
		12.2.3 Repetition
		12.2.4 Indexing
		12.2.5 Slicing
		12.2.6 Length of a Sequence
		12.2.7 The Smallest and the Largest Element
		12.2.8 Searching for an Element
		12.2.9 Counting Elements
	12.3 The list Data Type
		12.3.1 Changing a Value within the List: Assignment via []
		12.3.2 Replacing Sublists and Inserting New Elements: Assignment via []
		12.3.3 Deleting Elements and Sublists: del in Combination with []
		12.3.4 Methods of list Instances
		12.3.5 Sorting Lists: s.sort([key, reverse])
		12.3.6 Side Effects
		12.3.7 List Comprehensions
	12.4 Immutable Lists: tuple
		12.4.1 Packing and Unpacking
		12.4.2 Immutable Doesn’t Necessarily Mean Unchangeable!
	12.5 Strings: str, bytes, bytearray
		12.5.1 Control Characters
		12.5.2 String Methods
		12.5.3 Formatting Strings
		12.5.4 Character Sets and Special Characters
13 Mappings and Sets
	13.1 Dictionary: dict
		13.1.1 Creating a Dictionary
		13.1.2 Keys and Values
		13.1.3 Iteration
		13.1.4 Operators
		13.1.5 Methods
		13.1.6 Dict Comprehensions
	13.2 Sets: set and frozenset
		13.2.1 Creating a Set
		13.2.2 Iteration
		13.2.3 Operators
		13.2.4 Methods
		13.2.5 Mutable Sets: set
		13.2.6 Immutable Sets: frozenset
14 Collections
	14.1 Chained Dictionaries
	14.2 Counting Frequencies
	14.3 Dictionaries with Default Values
	14.4 Doubly Linked Lists
	14.5 Named Tuples
15 Date and Time
	15.1 Elementary Time Functions—time
		15.1.1 The struct_time Data Type
		15.1.2 Constants
		15.1.3 Functions
	15.2 Object-Oriented Date Management: datetime
		15.2.1 datetime.date
		15.2.2 datetime.time
		15.2.3 datetime.datetime
		15.2.4 datetime.timedelta
		15.2.5 Operations for datetime.datetime and datetime.date
	15.3 Time Zones: zoneinfo
		15.3.1 The IANA Time Zone Database
		15.3.2 Specifying the Time in Local Time Zones
		15.3.3 Calculating with Time Indications in Local Time Zones
16 Enumerations and Flags
	16.1 Enumeration Types: enum
	16.2 Enumeration Types for Bit Patterns: flag
	16.3 Integer Enumeration Types: IntEnum
Part III Advanced Programming Techniques
17 Functions
	17.1 Defining a Function
	17.2 Return Values
	17.3 Function Objects
	17.4 Optional Parameters
	17.5 Keyword Arguments
	17.6 Arbitrarily Many Parameters
	17.7 Keyword-Only Parameters
	17.8 Positional-Only Parameters
	17.9 Unpacking When Calling a Function
	17.10 Side Effects
	17.11 Namespaces
		17.11.1 Accessing Global Variables: global
		17.11.2 Accessing the Global Namespace
		17.11.3 Local Functions
		17.11.4 Accessing Parent Namespaces: nonlocal
		17.11.5 Unbound Local Variables: A Stumbling Block
	17.12 Anonymous Functions
	17.13 Recursion
	17.14 Built-In Functions
		17.14.1 abs(x)
		17.14.2 all(iterable)
		17.14.3 any(iterable)
		17.14.4 ascii(object)
		17.14.5 bin(x)
		17.14.6 bool([x])
		17.14.7 bytearray([source, encoding, errors])
		17.14.8 bytes([source, encoding, errors])
		17.14.9 chr(i)
		17.14.10 complex([real, imag])
		17.14.11 dict([source])
		17.14.12 divmod(a, b)
		17.14.13 enumerate(iterable, [start])
		17.14.14 eval(expression, [globals, locals])
		17.14.15 exec(object, [globals, locals])
		17.14.16 filter(function, iterable)
		17.14.17 float([x])
		17.14.18 format(value, [format_spec])
		17.14.19 frozenset([iterable])
		17.14.20 globals()
		17.14.21 hash(object)
		17.14.22 help([object])
		17.14.23 hex(x)
		17.14.24 id(object)
		17.14.25 input([prompt])
		17.14.26 int([x, base])
		17.14.27 len(s)
		17.14.28 list([sequence])
		17.14.29 locals()
		17.14.30 map(function, [*iterable])
		17.14.31 max(iterable, {default, key}), max(arg1, arg2, [*args], {key})
		17.14.32 min(iterable, {default, key}), min(arg1, arg2, [*args], {key})
		17.14.33 oct(x)
		17.14.34 ord(c)
		17.14.35 pow(x, y, [z])
		17.14.36 print([*objects], {sep, end, file, flush})
		17.14.37 range([start], stop, [step])
		17.14.38 repr(object)
		17.14.39 reversed(sequence)
		17.14.40 round(x, [n])
		17.14.41 set([iterable])
		17.14.42 sorted(iterable, [key, reverse])
		17.14.43 str([object, encoding, errors])
		17.14.44 sum(iterable, [start])
		17.14.45 tuple([iterable])
		17.14.46 type(object)
		17.14.47 zip([*iterables], {strict})
18 Modules and Packages
	18.1 Importing Global Modules
	18.2 Local Modules
		18.2.1 Name Conflicts
		18.2.2 Module-Internal References
		18.2.3 Executing Modules
	18.3 Packages
		18.3.1 Importing All Modules of a Package
		18.3.2 Namespace Packages
		18.3.3 Relative Import Statements
	18.4 The importlib Package
		18.4.1 Importing Modules and Packages
		18.4.2 Changing the Import Behavior
	18.5 Planned Language Elements
19 Object-Oriented Programming
	19.1 Example: A Non-Object-Oriented Account
		19.1.1 Creating a New Account
		19.1.2 Transferring Money
		19.1.3 Depositing and Withdrawing Money
		19.1.4 Viewing the Account Balance
	19.2 Classes
		19.2.1 Defining Methods
		19.2.2 The Constructor
		19.2.3 Attributes
		19.2.4 Example: An Object-Oriented Account
	19.3 Inheritance
		19.3.1 A Simple Example
		19.3.2 Overriding Methods
		19.3.3 Example: Checking Account with Daily Turnover
		19.3.4 Outlook
	19.4 Multiple Inheritance
	19.5 Property Attributes
		19.5.1 Setters and Getters
		19.5.2 Defining Property Attributes
	19.6 Static Methods
	19.7 Class Methods
	19.8 Class Attributes
	19.9 Built-in Functions for Object-Oriented Programming
		19.9.1 Functions for Managing the Attributes of an Instance
		19.9.2 Functions for Information about the Class Hierarchy
	19.10 Inheriting Built-In Data Types
	19.11 Magic Methods and Magic Attributes
		19.11.1 General Magic Methods
		19.11.2 Overloading Operators
		19.11.3 Emulating Data Types: Duck Typing
	19.12 Data Classes
		19.12.1 Tuples and Lists
		19.12.2 Dictionaries
		19.12.3 Named Tuples
		19.12.4 Mutable Data Classes
		19.12.5 Immutable Data Classes
		19.12.6 Default Values in Data Classes
20 Exception Handling
	20.1 Exceptions
		20.1.1 Built-In Exceptions
		20.1.2 Raising an Exception
		20.1.3 Handling an Exception
		20.1.4 Custom Exceptions
		20.1.5 Re-Raising an Exception
		20.1.6 Exception Chaining
	20.2 Assertions
	20.3 Warnings
21 Generators and Iterators
	21.1 Generators
		21.1.1 Subgenerators
		21.1.2 Generator Expressions
	21.2 Iterators
		21.2.1 The Iterator Protocol
		21.2.2 Example: The Fibonacci Sequence
		21.2.3 Example: The Golden Ratio
		21.2.4 A Generator for the Implementation of __iter__
		21.2.5 Using Iterators
		21.2.6 Multiple Iterators for the Same Instance
		21.2.7 Disadvantages of Iterators Compared to Direct Access via Indexes
		21.2.8 Alternative Definition for Iterable Objects
		21.2.9 Function Iterators
	21.3 Special Generators: itertools
		21.3.1 accumulate(iterable, [func])
		21.3.2 chain([*iterables])
		21.3.3 combinations(iterable, r)
		21.3.4 combinations_with_replacement(iterable, r)
		21.3.5 compress(data, selectors)
		21.3.6 count([start, step])
		21.3.7 cycle(iterable)
		21.3.8 dropwhile(predicate, iterable)
		21.3.9 filterfalse(predicate, iterable)
		21.3.10 groupby(iterable, [key])
		21.3.11 islice(iterable, [start], stop, [step])
		21.3.12 permutations(iterable, [r])
		21.3.13 product([*iterables], [repeat])
		21.3.14 repeat(object, [times])
		21.3.15 starmap(function, iterable)
		21.3.16 takewhile(predicate, iterable)
		21.3.17 tee(iterable, [n])
		21.3.18 zip_longest([*iterables], {fillvalue})
22 Context Manager
	22.1 The with Statement
		22.1.1 __enter__(self)
		22.1.2 __exit__(self, exc_type, exc_value, traceback)
	22.2 Helper Functions for with Contexts: contextlib
		22.2.1 Dynamically Assembled Context Combinations - ExitStack
		22.2.2 Suppressing Certain Exception Types
		22.2.3 Redirecting the Standard Output Stream
		22.2.4 Optional Contexts
		22.2.5 Simple Functions as Context Manager
23 Decorators
	23.1 Function Decorators
		23.1.1 Decorating Functions and Methods
		23.1.2 Name and Docstring after Applying a Decorator
		23.1.3 Nested Decorators
		23.1.4 Example: A Cache Decorator
	23.2 Class Decorators
	23.3 The functools Module
		23.3.1 Simplifying Function Interfaces
		23.3.2 Simplifying Method Interfaces
		23.3.3 Caches
		23.3.4 Completing Orderings of Custom Classes
		23.3.5 Overloading Functions
24 Annotations for Static Type Checking
	24.1 Annotations
		24.1.1 Annotating Functions and Methods
		24.1.2 Annotating Variables and Attributes
		24.1.3 Accessing Annotations at Runtime
		24.1.4 When are Annotations Evaluated?
	24.2 Type Hints: The typing Module
		24.2.1 Valid Type Hints
		24.2.2 Container Types
		24.2.3 Abstract Container Types
		24.2.4 Type Aliases
		24.2.5 Type Unions and Optional Values
		24.2.6 Type Variables
	24.3 Static Type Checking in Python: mypy
		24.3.1 Installation
		24.3.2 Example
25 Structural Pattern Matching
	25.1 The match Statement
	25.2 Pattern Types in the case Statement
		25.2.1 Literal and Value Patterns
		25.2.2 OR Pattern
		25.2.3 Patterns with Type Checking
		25.2.4 Specifying Conditions for Matches
		25.2.5 Grouping Subpatterns
		25.2.6 Capture and Wildcard Patterns
		25.2.7 Sequence Patterns
		25.2.8 Mapping Patterns
		25.2.9 Patterns for Objects and Their Attribute Values
Part IV The Standard Library
26 Mathematics
	26.1 Mathematical Functions: math, cmath
		26.1.1 General Mathematical Functions
		26.1.2 Exponential and Logarithm Functions
		26.1.3 Trigonometric and Hyperbolic Functions
		26.1.4 Distances and Norms
		26.1.5 Converting Angles
		26.1.6 Representations of Complex Numbers
	26.2 Random Number Generator: random
		26.2.1 Saving and Loading the Random State
		26.2.2 Generating Random Integers
		26.2.3 Generating Random Floats
		26.2.4 Random Operations on Sequences
		26.2.5 SystemRandom([seed])
	26.3 Statistical Calculations: statistics
	26.4 Intuitive Decimal Numbers: decimal
		26.4.1 Using the Data Type
		26.4.2 Nonnumeric Values
		26.4.3 The Context Object
	26.5 Hash Functions: hashlib
		26.5.1 Using the Module
		26.5.2 Other Hash Algorithms
		26.5.3 Comparing Large Files
		26.5.4 Passwords
27 Screen Outputs and Logging
	27.1 Formatted Output of Complex Objects: pprint
	27.2 Log Files: logging
		27.2.1 Customizing the Message Format
		27.2.2 Logging Handlers
28 Regular Expressions
	28.1 Syntax of Regular Expressions
		28.1.1 Any Character
		28.1.2 Character Classes
		28.1.3 Quantifiers
		28.1.4 Predefined Character Classes
		28.1.5 Other Special Characters
		28.1.6 Nongreedy Quantifiers
		28.1.7 Groups
		28.1.8 Alternatives
		28.1.9 Extensions
	28.2 Using the re Module
		28.2.1 Searching
		28.2.2 Matching
		28.2.3 Splitting a String
		28.2.4 Replacing Parts of a String
		28.2.5 Replacing Problem Characters
		28.2.6 Compiling a Regular Expression
		28.2.7 Flags
		28.2.8 The Match Object
	28.3 A Simple Sample Program: Searching
	28.4 A More Complex Sample Program: Matching
	28.5 Comments in Regular Expressions
29 Interface to Operating System and Runtime Environment
	29.1 Operating System Functionality: os
		29.1.1 environ
		29.1.2 getpid()
		29.1.3 cpu_count()
		29.1.4 system(cmd)
		29.1.5 popen(command, [mode, buffering])
	29.2 Accessing the Runtime Environment: sys
		29.2.1 Command Line Parameters
		29.2.2 Default Paths
		29.2.3 Standard Input/Output Streams
		29.2.4 Exiting the Program
		29.2.5 Details of the Python Version
		29.2.6 Operating System Details
		29.2.7 Hooks
	29.3 Command Line Parameters: argparse
		29.3.1 Calculator: A Simple Example
		29.3.2 A More Complex Example
30 File System
	30.1 Accessing the File System: os
		30.1.1 access(path, mode)
		30.1.2 chmod(path, mode)
		30.1.3 listdir([path])
		30.1.4 mkdir(path, [mode]) and makedirs(path, [mode])
		30.1.5 remove(path)
		30.1.6 removedirs(path)
		30.1.7 rename(src, dst) and renames(old, new)
		30.1.8 walk(top, [topdown, onerror])
	30.2 File Paths: os.path
		30.2.1 abspath(path)
		30.2.2 basename(path)
		30.2.3 commonprefix(list)
		30.2.4 dirname(path)
		30.2.5 join(path, *paths)
		30.2.6 normcase(path)
		30.2.7 split(path)
		30.2.8 splitdrive(path)
		30.2.9 splitext(path)
	30.3 Accessing the File System: shutil
		30.3.1 Directory and File Operations
		30.3.2 Archive Operations
	30.4 Temporary Files: tempfile
		30.4.1 TemporaryFile([mode, [bufsize, suffix, prefix, dir])
		30.4.2 tempfile.TemporaryDirectory([suffix, prefix, dir])
31 Parallel Programming
	31.1 Processes, Multitasking, and Threads
		31.1.1 The Lightweights among the Processes: Threads
		31.1.2 Threads or Processes?
		31.1.3 Cooperative Multitasking: A Third Way
	31.2 Python's Interfaces for Parallelization
	31.3 The Abstract Interface: concurrent.futures
		31.3.1 An Example with a futures.ThreadPoolExecutor
		31.3.2 Executor Instances as Context Managers
		31.3.3 Using futures.ProcessPoolExecutor
		31.3.4 Managing the Tasks of an Executor
	31.4 The Flexible Interface: threading and multiprocessing
		31.4.1 Threads in Python: threading
		31.4.2 Processes in Python: multiprocessing
	31.5 Cooperative Multitasking
		31.5.1 Cooperative Functions: Coroutines
		31.5.2 Awaitable Objects
		31.5.3 The Cooperation of Coroutines: Tasks
		31.5.4 A Cooperative Web Crawler
		31.5.5 Blocking Operations in Coroutines
		31.5.6 Other Asynchronous Language Features
	31.6 Conclusion: Which Interface Is the Right One?
		31.6.1 Is Cooperative Multitasking an Option?
		31.6.2 Abstraction or Flexibility?
		31.6.3 Threads or Processes?
32 Data Storage
	32.1 XML
		32.1.1 ElementTree
		32.1.2 Simple API for XML
	32.2 Databases
		32.2.1 The Built-In Database in Python: sqlite3
	32.3 Compressed Files and Archives
		32.3.1 gzip.open(filename, [mode, compresslevel])
		32.3.2 Other Modules for Accessing Compressed Data
	32.4 Serializing Instances: pickle
		32.4.1 Functional Interface
		32.4.2 Object-Oriented Interface
	32.5 The JSON Data Exchange Format: json
	32.6 The CSV Table Format: csv
		32.6.1 Reading Data from a CSV File with reader Objects
		32.6.2 Using Custom Dialects: Dialect Objects
33 Network Communication
	33.1 Socket API
		33.1.1 Client-Server Systems
		33.1.2 UDP
		33.1.3 TCP
		33.1.4 Blocking and Nonblocking Sockets
		33.1.5 Creating a Socket
		33.1.6 The Socket Class
		33.1.7 Network Byte Order
		33.1.8 Multiplexing Servers: selectors
		33.1.9 Object-Oriented Server Development: socketserver
	33.2 XML-RPC
		33.2.1 The Server
		33.2.2 The Client
		33.2.3 Multicall
		33.2.4 Limitations
34 Accessing Resources on the Internet
	34.1 Protocols
		34.1.1 Hypertext Transfer Protocol
		34.1.2 File Transfer Protocol
	34.2 Solutions
		34.2.1 Outdated Solutions for Python 2
		34.2.2 Solutions in the Standard Library
		34.2.3 Third-Party Solutions
	34.3 The Easy Way: requests
		34.3.1 Simple Requests via GET and POST
		34.3.2 Web APIs
	34.4 URLs: urllib
		34.4.1 Accessing Remote Resources: urllib.request
		34.4.2 Reading and Processing URLs: urllib.parse
	34.5 FTP: ftplib
		34.5.1 Connecting to an FTP Server
		34.5.2 Executing FTP commands
		34.5.3 Working with Files and Directories
		34.5.4 Transferring Files
35 Email
	35.1 SMTP: smtplib
		35.1.1 SMTP([host, port, local_hostname, timeout, source_address])
		35.1.2 Establishing and Terminating a Connection
		35.1.3 Sending an Email
		35.1.4 Example
	35.2 POP3: poplib
		35.2.1 POP3(host, [port, timeout])
		35.2.2 Establishing and Terminating a Connection
		35.2.3 Listing Existing Emails
		35.2.4 Retrieving and Deleting Emails
		35.2.5 Example
	35.3 IMAP4: imaplib
		35.3.1 IMAP4([host, port, timeout])
		35.3.2 Establishing and Terminating a Connection
		35.3.3 Finding and Selecting a Mailbox
		35.3.4 Operations with Mailboxes
		35.3.5 Searching Emails
		35.3.6 Retrieving Emails
		35.3.7 Example
	35.4 Creating Complex Emails: email
		35.4.1 Creating a Simple Email
		35.4.2 Creating an Email with Attachments
		35.4.3 Reading an Email
36 Debugging and Quality Assurance
	36.1 The Debugger
	36.2 Automated Testing
		36.2.1 Test Cases in Docstrings: doctest
		36.2.2 Unit Tests: unittest
	36.3 Analyzing the Runtime Performance
		36.3.1 Runtime Measurement: timeit
		36.3.2 Profiling: cProfile
		36.3.3 Tracing: trace
37 Documentation
	37.1 Docstrings
	37.2 Automatically Generated Documentation: pydoc
Part V Advanced Topics
38 Distributing Python Projects
	38.1 A History of Distributions in Python
		38.1.1 The Classic Approach: distutils
		38.1.2 The New Standard: setuptools
		38.1.3 The Package Index: PyPI
	38.2 Creating Distributions: setuptools
		38.2.1 Installation
		38.2.2 Writing the Module
		38.2.3 The Installation Script
		38.2.4 Creating a Source Distribution
		38.2.5 Creating a Binary Distribution
		38.2.6 Installing Distributions
	38.3 Creating EXE files: cx_Freeze
		38.3.1 Installation
		38.3.2 Usage
	38.4 Package Manager
		38.4.1 The Python Package Manager: pip
		38.4.2 The conda Package Manager
	38.5 Localizing Programs: gettext
		38.5.1 Example of Using gettext
		38.5.2 Creating the Language Compilation
39 Virtual Environments
	39.1 Using Virtual Environments: venv
		39.1.1 Activating a Virtual Environment
		39.1.2 Working in a Virtual Environment
		39.1.3 Deactivating a Virtual Environment
	39.2 Virtual Environments in Anaconda
40 Alternative Interpreters and Compilers
	40.1 Just-in-Time Compilation: PyPy
		40.1.1 Installation and Use
		40.1.2 Example
	40.2 Numba
		40.2.1 Installation
		40.2.2 Example
	40.3 Connecting to C and C++: Cython
		40.3.1 Installation
		40.3.2 The Functionality of Cython
		40.3.3 Compiling a Cython Program
		40.3.4 A Cython Program with Static Typing
		40.3.5 Using a C Library
	40.4 The Interactive Python Shell: IPython
		40.4.1 Installation
		40.4.2 The Interactive Shell
		40.4.3 The Jupyter Notebook
41 Graphical User Interfaces
	41.1 Toolkits
		41.1.1 Tkinter (Tk)
		41.1.2 PyGObject (GTK)
		41.1.3 Qt for Python (Qt)
		41.1.4 wxPython (wxWidgets)
	41.2 Introduction to tkinter
		41.2.1 A Simple Example
		41.2.2 Control Variables
		41.2.3 The Packer
		41.2.4 Events
		41.2.5 Widgets
		41.2.6 Drawings: The Canvas Widget
		41.2.7 Other Modules
	41.3 Introduction to PySide6
		41.3.1 Installation
		41.3.2 Basic Concepts of Qt
		41.3.3 Development Process
	41.4 Signals and Slots
	41.5 Important Widgets
		41.5.1 QCheckBox
		41.5.2 QComboBox
		41.5.3 QDateEdit, QTimeEdit, and QDateTimeEdit
		41.5.4 QDialog
		41.5.5 QLineEdit
		41.5.6 QListWidget and QListView
		41.5.7 QProgressBar
		41.5.8 QPushButton
		41.5.9 QRadioButton
		41.5.10 QSlider and QDial
		41.5.11 QTextEdit
		41.5.12 QWidget
	41.6 Drawing Functionality
		41.6.1 Tools
		41.6.2 Coordinate System
		41.6.3 Simple Shapes
		41.6.4 Images
		41.6.5 Text
		41.6.6 Eye Candy
	41.7 Model-View Architecture
		41.7.1 Sample Project: An Address Book
		41.7.2 Selecting Entries
		41.7.3 Editing Entries
42 Python as a Server-Side Programming Language on the Web: An Introduction to Django
	42.1 Concepts and Features of Django
	42.2 Installing Django
	42.3 Creating a New Django Project
		42.3.1 The Development Web Server
		42.3.2 Configuring the Project
	42.4 Creating an Application
		42.4.1 Importing the Application into the Project
		42.4.2 Defining a Model
		42.4.3 Relationships between Models
		42.4.4 Transferring the Model to the Database
		42.4.5 The Model API
		42.4.6 The Project Gets a Face
		42.4.7 Django's Template System
		42.4.8 Processing Form Data
		42.4.9 Django’s Admin Control Panel
43 Scientific Computing and Data Science
	43.1 Installation
	43.2 The Model Program
		43.2.1 Importing numpy, scipy, and matplotlib
		43.2.2 Vectorization and the numpy.ndarray Data Type
		43.2.3 Visualizing Data Using matplotlib.pyplot
	43.3 Overview of the numpy and scipy Modules
		43.3.1 Overview of the numpy.ndarray Data Type
		43.3.2 Overview of scipy
	43.4 An Introduction to Data Analysis with pandas
		43.4.1 The DataFrame Object
		43.4.2 Selective Data Access
		43.4.3 Deleting Rows and Columns
		43.4.4 Inserting Rows and Columns
		43.4.5 Logical Expressions on Data Records
		43.4.6 Manipulating Data Records
		43.4.7 Input and Output
		43.4.8 Visualization
44 Inside Knowledge
	44.1 Opening URLs in the Default Browser: webbrowser
	44.2 Interpreting Binary Data: struct
	44.3 Hidden Password Entry
		44.3.1 getpass([prompt, stream])
		44.3.2 getpass.getuser()
	44.4 Command Line Interpreter
	44.5 File Interface for Strings: io.StringIO
	44.6 Generators as Consumers
		44.6.1 A Decorator for Consuming Generator Functions
		44.6.2 Triggering Exceptions in a Generator
		44.6.3 A Pipeline as a Chain of Consuming Generator Functions
	44.7 Copying Instances: copy
	44.8 Image Processing: Pillow
		44.8.1 Installation
		44.8.2 Loading and Saving Image Files
		44.8.3 Accessing Individual Pixels
		44.8.4 Manipulating Images
		44.8.5 Interoperability
45 From Python 2 to Python 3
	45.1 The Main Differences
		45.1.1 Input/Output
		45.1.2 Iterators
		45.1.3 Strings
		45.1.4 Integers
		45.1.5 Exception Handling
		45.1.6 Standard Library
	45.2 Automatic Conversion
A Appendix
	A.1 Reserved Words
	A.2 Operator Precedence
	A.3 Built-In Functions
	A.4 Built-In Exceptions
	A.5 Python IDEs
		A.5.1 PyCharm
		A.5.2 Visual Studio Code
		A.5.3 PyDev
		A.5.4 Spyder
B The Authors
Index
Service Pages
Legal Notes