book

Python 3 - Second Edition

Name: Python 3 - Second Edition
ISBN: 9781808828270

by Rheinwerk Publishing, Inc, Johannes Ernesti, Peter Kaiser

September 2026

1051 pages

35h 4m

English

Packt Publishing

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Notes on Usage
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 Second English Edition (2026)
1.7 Acknowledgments
2 The Python Programming Language
2.1 History and Origin
2.2 Basic Concepts

2.3 Possible Areas of Use and Strengths
2.4 Installing Python
2.4.1 Installing Anaconda on Windows2.4.2 Installing Anaconda on macOS2.4.3 Installing Anaconda on Linux
2.5 Installing Third-Party Modules
2.6 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 Underscore3.6.2 Identifiers
3.7 Logical Expressions
3.8 Functions and Methods
3.8.1 Functions3.8.2 Methods
3.9 Screen Outputs
3.10 Modules
4 The Path to the First Program
4.1 Typing, Compiling, and Testing4.1.1 Windows4.1.2 Linux and macOS4.1.3 Shebang4.1.4 Internal Processes
4.2 Basic Structure of a Python Program
4.2.1 Wrapping Long Lines4.2.2 Joining Multiple Lines
4.3 The First Program
4.3.1 Initialization4.3.2 Loop Header4.3.3 Loop Body4.3.4 Screen Output
4.4 Comments
4.5 In Case of Error
5 Control Structures
5.1 Conditionals5.1.1 The if Statement5.1.2 Conditional Expressions
5.2 Loops
5.2.1 The while Loop5.2.2 Termination of a Loop5.2.3 Detecting a Loop Break5.2.4 Aborting the Current Iteration5.2.5 The for Loop
5.3 The pass Statement
5.4 Assignment Expressions
5.4.1 Motivation5.4.2 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 File6.2.2 The with Statement6.2.3 Reading the File Content
6.3 Writing Data to a File
6.4 Generating the File Object
6.4.1 The Built-In open Function6.4.2 Attributes and Methods of a File Object6.4.3 Changing the Write/Read Position
7 The Data Model
7.1 The Structure of Instances7.1.1 Data Type7.1.2 Value7.1.3 Identity
7.2 Deleting References
7.3 Mutable Versus Immutable Data Types
7.3.1 Mutable Data Types and Side Effects
8 Functions, Methods, and Attributes
8.1 Parameters of Functions and Methods8.1.1 Positional Parameters8.1.2 Keyword Arguments8.1.3 Optional Parameters8.1.4 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 Precedence10.2.2 Evaluation Order10.2.3 Concatenating Comparisons
11 Numeric Data Types
11.1 Arithmetic Operators11.1.1 Augmented Assignments
11.2 Comparison Operators
11.3 Conversion between Numeric Data Types
11.4 Integers: int
11.4.1 Numeral Systems11.4.2 Bit Operations11.4.3 The bit_length Method
11.5 Floats: float
11.5.1 Exponential Notation11.5.2 Precision11.5.3 Infinite and Not a Number
11.6 Boolean Values: bool
11.6.1 Logical Operators11.6.2 Truth Values of Non-Boolean Data Types11.6.3 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 Elements12.2.2 Concatenation12.2.3 Repetition12.2.4 Indexing12.2.5 Slicing12.2.6 Length of a Sequence12.2.7 The Smallest and the Largest Element12.2.8 Searching for an Element12.2.9 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 Instances12.3.5 Sorting Lists: s.sort([key, reverse])12.3.6 Side Effects12.3.7 List Comprehensions
12.4 Immutable Lists: tuple
12.4.1 Packing and Unpacking12.4.2 Immutable Doesn’t Necessarily Mean Unchangeable!
12.5 Strings: str, bytes, bytearray
12.5.1 Control Characters12.5.2 Splitting Strings12.5.3 Searching for Substrings12.5.4 Replacing Substrings12.5.5 Removing Prefixes and Suffixes12.5.6 Aligning Strings12.5.7 String Tests12.5.8 Concatenating Elements in Sequential Data Types12.5.9 Formatting Strings12.5.10 Character Sets and Special Characters12.5.11 Template Strings
13 Mappings and Sets
13.1 Dictionary: dict13.1.1 Creating a Dictionary13.1.2 Keys and Values13.1.3 Iteration13.1.4 Operators13.1.5 Methods13.1.6 Dict Comprehensions
13.2 Sets: set and frozenset
13.2.1 Creating a Set13.2.2 Iteration13.2.3 Operators13.2.4 Methods13.2.5 Mutable Sets: set13.2.6 Immutable Sets: frozenset
14 Collections
14.1 Chained Dictionaries
14.2 Counting Frequencies
14.2.1 d.elements()14.2.2 d.most_common([n])14.2.3 d.subtract([iterable])14.2.4 d.update([iterable])
14.3 Dictionaries with Default Values
14.4 Doubly Linked Lists
14.5 Named Tuples
14.5.1 namedtuple(typename, field_names, {rename})
15 Date and Time
15.1 Elementary Time Functions: time15.1.1 The struct_time Data Type15.1.2 Constants15.1.3 Functions
15.2 Object-Oriented Date Management: datetime
15.2.1 datetime.date15.2.2 datetime.time15.2.3 datetime.datetime15.2.4 datetime.timedelta15.2.5 Operations for datetime.datetime and datetime.date
15.3 Time Zones: zoneinfo
15.3.1 The IANA Time Zone Database15.3.2 Specifying the Time in Local Time Zones15.3.3 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: global17.11.2 Accessing the Global Namespace17.11.3 Local Functions17.11.4 Accessing Parent Namespaces: nonlocal17.11.5 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, strict])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 Conflicts18.2.2 Module-Internal References18.2.3 Executing Modules
18.3 Packages
18.3.1 Importing All Modules of a Package18.3.2 Namespace Packages18.3.3 Relative Import Statements
18.4 The importlib Package
18.5 Planned Language Elements
19 Object-Oriented Programming
19.1 Example: A Non-Object-Oriented Account19.1.1 Creating a New Account19.1.2 Transferring Money19.1.3 Depositing and Withdrawing Money19.1.4 Viewing the Account Balance
19.2 Classes
19.2.1 Defining Methods19.2.2 The Constructor19.2.3 Attributes19.2.4 Example: An Object-Oriented Account
19.3 Inheritance
19.3.1 A Simple Example19.3.2 Overriding Methods19.3.3 Example: Checking Account with Daily Turnover19.3.4 Outlook
19.4 Multiple Inheritance
19.4.1 Potential Pitfalls of Multiple Inheritance
19.5 Property Attributes
19.5.1 Setters and Getters19.5.2 Defining Property Attributes
19.6 Static Methods
19.6.1 Defining 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 Instance19.9.2 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 Methods19.11.2 Overloading Operators19.11.3 Emulating Data Types: Duck Typing
19.12 Data Classes
19.12.1 Tuples and Lists19.12.2 Dictionaries19.12.3 Named Tuples19.12.4 Mutable Data Classes19.12.5 Immutable Data Classes19.12.6 Default Values in Data Classes
20 Exception Handling
20.1 Exceptions20.1.1 Built-In Exceptions20.1.2 Raising an Exception20.1.3 Handling an Exception20.1.4 Custom Exceptions20.1.5 Reraising an Exception20.1.6 Exception Chaining20.1.7 Exception Notes
20.2 Assertions
20.3 Warnings
20.4 Exception Groups
20.4.1 An Exception Group20.4.2 The try/except* Statement
21 Generators and Iterators
21.1 Generators21.1.1 Subgenerators21.1.2 Generator Expressions
21.2 Iterators
21.2.1 The Iterator Protocol21.2.2 Example: The Fibonacci Sequence21.2.3 Example: The Golden Ratio21.2.4 A Generator for the Implementation of __iter__21.2.5 Using Iterators21.2.6 Multiple Iterators for the Same Instance21.2.7 Disadvantages of Iterators Compared to Direct Access via Indexes21.2.8 Alternative Definition for Iterable Objects21.2.9 Function Iterators
21.3 Special Generators: itertools
21.3.1 accumulate(iterable, [func])21.3.2 batched(iterable, n, {strict})21.3.3 chain([*iterables])21.3.4 combinations(iterable, r)21.3.5 combinations_with_replacement(iterable, r)21.3.6 compress(data, selectors)21.3.7 count([start, step])21.3.8 cycle(iterable)21.3.9 dropwhile(predicate, iterable)21.3.10 filterfalse(predicate, iterable)21.3.11 groupby(iterable, [key])21.3.12 islice(iterable, [start], stop, [step])21.3.13 permutations(iterable, [r])21.3.14 product([*iterables], [repeat])21.3.15 repeat(object, [times])21.3.16 starmap(function, iterable)21.3.17 takewhile(predicate, iterable)21.3.18 tee(iterable, [n])21.3.19 zip_longest([*iterables], {fillvalue})
21.4 Generators as Consumers
21.4.1 Triggering Exceptions in a Generator21.4.2 An Example Application for Consuming Generator Functions
22 Context Manager
22.1 The with Statement22.1.1 __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: ExitStack22.2.2 Suppressing Certain Exception Types22.2.3 Redirecting the Standard Output Stream22.2.4 Optional Contexts22.2.5 Simple Functions as Context Manager22.2.6 Temporarily Changing the Working Directory
23 Decorators
23.1 Function Decorators23.1.1 Decorating Functions and Methods23.1.2 Name and Docstring after Applying a Decorator23.1.3 Nested Decorators23.1.4 Example: A Cache Decorator
23.2 Class Decorators
23.3 The functools Module
23.3.1 Simplifying Function Interfaces23.3.2 Simplifying Method Interfaces23.3.3 Caches23.3.4 Completing Orderings of Custom Classes23.3.5 Overloading Functions
24 Annotations for Static Type Checking
24.1 Annotations24.1.1 Annotating Functions and Methods24.1.2 Annotating Variables and Attributes24.1.3 Accessing Annotations at Runtime24.1.4 When Are Annotations Evaluated?
24.2 Type Hints: The typing Module
24.2.1 Valid Type Hints24.2.2 Container Types24.2.3 Abstract Container Types24.2.4 Type Aliases24.2.5 Type Unions and Optional Values24.2.6 Literals24.2.7 Type Variables
24.3 Static Type Checking in Python: mypy
24.3.1 Installation24.3.2 Example
25 Structural Pattern Matching
25.1 The match Statement
25.2 Pattern Types in the case Statement
25.2.1 Literal and Value Patterns25.2.2 OR Pattern25.2.3 Patterns with Type Checking25.2.4 Specifying Conditions for Matches25.2.5 Grouping Subpatterns25.2.6 Capture and Wildcard Patterns25.2.7 Sequence Patterns25.2.8 Mapping Patterns25.2.9 Patterns for Objects and Their Attribute Values
Part IV The Standard Library
26 Mathematics
26.1 Mathematical Functions: math, cmath26.1.1 General Mathematical Functions26.1.2 Exponential and Logarithm Functions26.1.3 Trigonometric and Hyperbolic Functions26.1.4 Distances and Norms26.1.5 Converting Angles26.1.6 Representations of Complex Numbers
26.2 Random Number Generator: random
26.2.1 Saving and Loading the Random State26.2.2 Generating Random Integers26.2.3 Generating Random Floats26.2.4 Random Operations on Sequences26.2.5 SystemRandom([seed])
26.3 Statistical Calculations: statistics
26.4 Intuitive Decimal Numbers: decimal
26.4.1 Using the Data Type26.4.2 Nonnumeric Values26.4.3 The Context Object
26.5 Hash Functions: hashlib
26.5.1 Using the Module26.5.2 Other Hash Algorithms26.5.3 Comparing Large Files26.5.4 Passwords
27 Screen Outputs and Logging
27.1 Formatted Output of Complex Objects: pprint27.1.1 pprint(object, [stream, indent, width, depth], {compact})
27.2 Log Files: logging
27.2.1 Customizing the Message Format27.2.2 Logging Handlers
28 Regular Expressions
28.1 Syntax of Regular Expressions28.1.1 Any Character28.1.2 Character Classes28.1.3 Quantifiers28.1.4 Predefined Character Classes28.1.5 Other Special Characters28.1.6 Nongreedy Quantifiers28.1.7 Groups28.1.8 Alternatives28.1.9 Extensions
28.2 Using the re Module
28.2.1 Searching28.2.2 Matching28.2.3 Splitting a String28.2.4 Replacing Parts of a String28.2.5 Replacing Problem Characters28.2.6 Compiling a Regular Expression28.2.7 Flags28.2.8 The Match Object
28.3 A Simple Sample Program: Searching
28.4 A More Complex Sample Program: Matching
28.5 Comments in Regular Expressions
28.6 Catastrophic Backtracking
29 Interface to Operating System and Runtime Environment
29.1 Operating System Functionality: os29.1.1 environ29.1.2 getpid()29.1.3 cpu_count()29.1.4 system(cmd)29.1.5 popen(command, [mode, buffering])
29.2 Starting Subprocesses: subprocess
29.2.1 Starting a Subprocess29.2.2 The Default Streams: stdin, stdout, and stderr29.2.3 The Return Code29.2.4 Environment variables
29.3 Accessing the Runtime Environment: sys
29.3.1 Command Line Parameters29.3.2 Default Paths29.3.3 Standard Input/Output Streams29.3.4 Exiting the Program29.3.5 Details of the Python Version29.3.6 Operating System Details29.3.7 Hooks
29.4 Command Line Parameters: argparse
29.4.1 Calculator: A Simple Example29.4.2 A More Complex Example
30 File System
30.1 Basics of File Systems and Paths30.1.1 Path Names30.1.2 File Names30.1.3 Absolute and Relative Paths30.1.4 Access Rights
30.2 The Modern Approach: pathlib
30.2.1 The Path Class30.2.2 Combining Paths30.2.3 Attributes of a Path30.2.4 Checking Path Properties30.2.5 Reading and Writing Files30.2.6 Renaming Files30.2.7 Copying and Moving Files30.2.8 Deleting Files30.2.9 Creating and Deleting Directories30.2.10 Links30.2.11 Globbing
30.3 Accessing the File System: os
30.3.1 access(path, mode)30.3.2 chmod(path, mode)30.3.3 listdir([path])30.3.4 mkdir(path, [mode]) and makedirs(path, [mode])30.3.5 remove(path)30.3.6 removedirs(path)30.3.7 rename(src, dst) and renames(old, new)30.3.8 walk(top, [topdown, onerror])
30.4 File Paths: os.path
30.4.1 abspath(path)30.4.2 basename(path)30.4.3 commonprefix(list)30.4.4 dirname(path)30.4.5 join(path, *paths)30.4.6 normcase(path)30.4.7 split(path)30.4.8 splitdrive(path)30.4.9 splitext(path)
30.5 Accessing the File System: shutil
30.5.1 Directory and File Operations30.5.2 Archive Operations
30.6 Temporary Files: tempfile
30.6.1 TemporaryFile([mode, [bufsize, suffix, prefix, dir])30.6.2 TemporaryDirectory([suffix, prefix, dir])
31 Parallel Programming
31.1 Processes, Multitasking, and Threads31.1.1 The Lightweights Among the Processes: Threads31.1.2 The Global Interpreter Lock31.1.3 Threads or Processes?31.1.4 Cooperative Multitasking
31.2 Python's Interfaces for Parallelization
31.3 The Abstract Interface: concurrent.futures
31.3.1 An Example with a futures.ThreadPoolExecutor31.3.2 Executor Instances as Context Managers31.3.3 Using futures.ProcessPoolExecutor31.3.4 Managing the Tasks of an Executor
31.4 The Flexible Interface: threading and multiprocessing
31.4.1 Threads in Python: threading31.4.2 Processes in Python: multiprocessing
31.5 Cooperative Multitasking
31.5.1 Cooperative Functions: Coroutines31.5.2 Awaitable Objects31.5.3 The Cooperation of Coroutines: Tasks31.5.4 A Cooperative Web Crawler31.5.5 Blocking Operations in Coroutines31.5.6 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 The JSON Data Exchange Format: json
32.2 Serializing Instances: pickle
32.2.1 Functional Interface32.2.2 Object-Oriented Interface
32.3 The CSV Table Format: csv
32.3.1 Reading Data from a CSV File with reader Objects32.3.2 Using Custom Dialects: Dialect Objects
32.4 Compressed Files and Archives
32.4.1 gzip.open(filename, [mode, compresslevel])32.4.2 Other Modules for Accessing Compressed Data
32.5 Databases
32.5.1 The Built-In Database in Python: sqlite3
32.6 XML
32.6.1 ElementTree32.6.2 SAX
33 Network Communication
33.1 Socket API33.1.1 Client-Server Systems33.1.2 UDP33.1.3 TCP33.1.4 Blocking and Nonblocking Sockets33.1.5 Creating a Socket33.1.6 The Socket Class33.1.7 Network Byte Order33.1.8 Multiplexing Servers: selectors33.1.9 Object-Oriented Server Development: socketserver
33.2 XML-RPC
33.2.1 The Server33.2.2 The Client33.2.3 Multicall33.2.4 Limitations
34 Accessing Resources on the Internet
34.1 Protocols34.1.1 Hypertext Transfer Protocol34.1.2 File Transfer Protocol
34.2 Solutions
34.2.1 Outdated Solutions for Python 234.2.2 Solutions in the Standard Library34.2.3 Third-Party Solutions
34.3 The Easy Way: requests
34.3.1 Simple Requests via GET and POST34.3.2 Web APIs
34.4 URLs: urllib
34.4.1 Accessing Remote Resources: urllib.request34.4.2 Reading and Processing URLs: urllib.parse
34.5 FTP: ftplib
34.5.1 Connecting to an FTP Server34.5.2 Executing FTP commands34.5.3 Working with Files and Directories34.5.4 Transferring Files
35 Email
35.1 SMTP: smtplib35.1.1 SMTP([host, port, local_hostname, timeout, source_address])35.1.2 Establishing and Terminating a Connection35.1.3 Sending an Email35.1.4 Example
35.2 POP3: poplib
35.2.1 POP3(host, [port, timeout])35.2.2 Establishing and Terminating a Connection35.2.3 Listing Existing Emails35.2.4 Retrieving and Deleting Emails35.2.5 Example
35.3 IMAP4: imaplib
35.3.1 IMAP4([host, port, timeout])35.3.2 Establishing and Terminating a Connection35.3.3 Finding and Selecting a Mailbox35.3.4 Operations with Mailboxes35.3.5 Searching Emails35.3.6 Retrieving Emails35.3.7 Example
35.4 Creating Complex Emails: email
35.4.1 Creating a Simple Email35.4.2 Creating an Email with Attachments35.4.3 Reading an Email
36 Debugging and Quality Assurance
36.1 The Debugger
36.2 Automated Testing
36.2.1 Test Cases in Docstrings: doctest36.2.2 Unit Tests: unittest
36.3 Analyzing the Runtime Performance
36.3.1 Runtime Measurement: timeit36.3.2 Profiling: cProfile36.3.3 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 Python38.1.1 The Classic Approach: distutils38.1.2 The New Standard: setuptools38.1.3 The Package Index: PyPI
38.2 Creating Distributions: setuptools
38.2.1 Installation38.2.2 Writing the Module38.2.3 The Installation Script38.2.4 Creating a Source Distribution38.2.5 Creating a Binary Distribution38.2.6 Installing Distributions
38.3 Creating EXE files: cx_Freeze
38.3.1 Installation38.3.2 Usage
38.4 Package Manager
38.4.1 The Python Package Manager: pip38.4.2 The conda Package Manager
38.5 Localizing Programs: gettext
38.5.1 Example of Using gettext38.5.2 Creating the Language Compilation
39 Virtual Environments
39.1 Using Virtual Environments: venv39.1.1 Activating a Virtual Environment39.1.2 Working in a Virtual Environment39.1.3 Deactivating a Virtual Environment
39.2 Virtual Environments in Anaconda
40 Alternative Interpreters and Compilers
40.1 Just-in-Time Compilation: PyPy40.1.1 Installation and Use40.1.2 Example
40.2 Numba
40.2.1 Installation40.2.2 Example
40.3 Connecting to C and C++: Cython
40.3.1 Installation40.3.2 The Functionality of Cython40.3.3 Compiling a Cython Program40.3.4 A Cython Program with Static Typing40.3.5 Using a C Library
40.4 The Interactive Python Shell: IPython
40.4.1 Installation40.4.2 The Interactive Shell40.4.3 The Jupyter Notebook
41 Graphical User Interfaces
41.1 Toolkits41.1.1 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 Example41.2.2 Control Variables41.2.3 The Packer41.2.4 Events41.2.5 Widgets41.2.6 Drawings: The Canvas Widget41.2.7 Other Modules
41.3 Introduction to PySide6
41.3.1 Installation41.3.2 Basic Concepts of Qt41.3.3 Development Process41.3.4 Signals and Slots41.3.5 Important Widgets41.3.6 Drawing Functionality41.3.7 Model-View Architecture
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 Server42.3.2 Configuring the Project
42.4 Creating an Application
42.4.1 Importing the Application into the Project42.4.2 Defining a Model42.4.3 Relationships between Models42.4.4 Transferring the Model to the Database42.4.5 The Model API42.4.6 The Project Gets a Face42.4.7 Django's Template System42.4.8 Processing Form Data42.4.9 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 matplotlib43.2.2 Vectorization and the numpy.ndarray Data Type43.2.3 Visualizing Data Using matplotlib.pyplot
43.3 Overview of the numpy and scipy Modules
43.3.1 Overview of the numpy.ndarray Data Type43.3.2 Overview of scipy
43.4 An Introduction to Data Analysis with pandas
43.4.1 The DataFrame Object43.4.2 Selective Data Access43.4.3 Deleting Rows and Columns43.4.4 Inserting Rows and Columns43.4.5 Logical Expressions on Data Records43.4.6 Manipulating Data Records43.4.7 Input and Output43.4.8 Visualization
44 Inside Knowledge
44.1 Opening URLs in the Default Browser: webbrowser44.1.1 open(url, [new, autoraise])
44.2 Interpreting Binary Data: struct
44.3 Hidden Password Entry
44.3.1 getpass([prompt, stream], {echo_char})44.3.2 getpass.getuser()
44.4 Command Line Interpreter
44.5 File Interface for Strings: io.StringIO
44.6 Copying Instances: copy
44.6.1 Back to the Initial Example
44.7 Image Processing: Pillow
44.7.1 Installation44.7.2 Loading and Saving Image Files44.7.3 Accessing Individual Pixels44.7.4 Manipulating Images44.7.5 Interoperability
45 A History of Python Versions
45.1 Python’s Version History
45.2 The Leap to Python 3
45.2.1 Input/Output45.2.2 Iterators45.2.3 Strings45.2.4 Integers45.2.5 Exception Handling45.2.6 Standard Library
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 PyCharmA.5.2 Visual Studio CodeA.5.3 Spyder
B The Authors
Index
Service Pages
Legal Notes

Content preview from Python 3 - Second Edition

40.3 Connecting to C and C++: Cython

Cython isn’t an alternative interpreter, but a programming language in its own right that’s essentially compatible with Python and is compiled to C. This creates a convenient mode of interoperability between Python and C programs.

Basic use cases for Cython are, on the one hand, compiling and accelerating the execution of an existing Python program and, on the other hand, easily combining Python code with external code written in C or C++.

Note

Note that a basic knowledge of the C programming language is assumed for understanding this section.

40.3.1 Installation

If you use the Anaconda Python distribution, you can install Cython via the conda package manager:

$ conda install cython

Alternatively, ...

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