Inhaltsverzeichnis

Alle Kapitel aufklappen
Alle Kapitel zuklappen
1 Introduction
33
1.1 Why Did We Write This Book?
33
1.2 What Does This Book Provide?
34
1.3 Structure of the Book
34
1.4 How Should You Read This Book?
35
1.5 Sample Programs
36
1.6 Preface To the First English Edition (2022)
36
1.7 Acknowledgments
37
2 The Python Programming Language
39
2.1 History, Concepts, and Areas of Application
39
2.1.1 History and Origin
39
2.1.2 Basic Concepts
40
2.1.3 Possible Areas of Use and Strengths
41
2.1.4 Examples of Use
42
2.2 Installing Python
42
2.2.1 Installing Anaconda on Windows
43
2.2.2 Installing Anaconda on Linux
43
2.2.3 Installing Anaconda on macOS
44
2.3 Installing Third-Party Modules
45
2.4 Using Python
45
PART I Getting Started with Python
47
3 Getting Started with the Interactive Mode
49
3.1 Integers
49
3.2 Floats
51
3.3 Character Strings
51
3.4 Lists
52
3.5 Dictionaries
53
3.6 Variables
54
3.6.1 The Special Meaning of the Underscore
54
3.6.2 Identifiers
55
3.7 Logical Expressions
55
3.8 Functions and Methods
57
3.8.1 Functions
57
3.8.2 Methods
58
3.9 Screen Outputs
59
3.10 Modules
60
4 The Path to the First Program
63
4.1 Typing, Compiling, and Testing
63
4.1.1 Windows
63
4.1.2 Linux and macOS
64
4.1.3 Shebang
65
4.1.4 Internal Processes
65
4.2 Basic Structure of a Python Program
66
4.2.1 Wrapping Long Lines
68
4.2.2 Joining Multiple Lines
69
4.3 The First Program
70
4.3.1 Initialization
71
4.3.2 Loop Header
71
4.3.3 Loop Body
71
4.3.4 Screen Output
72
4.4 Comments
72
4.5 In Case of Error
72
5 Control Structures
75
5.1 Conditionals
75
5.1.1 The if Statement
75
5.1.2 Conditional Expressions
78
5.2 Loops
79
5.2.1 The while Loop
79
5.2.2 Termination of a Loop
80
5.2.3 Detecting a Loop Break
81
5.2.4 Aborting the Current Iteration
82
5.2.5 The for Loop
84
5.2.6 The for Loop as a Counting Loop
85
5.3 The pass Statement
87
5.4 Assignment Expressions
87
5.4.1 The Guessing Numbers Game with Assignment Expressions
89
6 Files
91
6.1 Data Streams
91
6.2 Reading Data from a File
92
6.2.1 Opening and Closing a File
92
6.2.2 The with Statement
93
6.2.3 Reading the File Content
94
6.3 Writing Data to a File
96
6.4 Generating the File Object
97
6.4.1 The Built-In open Function
97
6.4.2 Attributes and Methods of a File Object
99
6.4.3 Changing the Write/Read Position
100
7 The Data Model
103
7.1 The Structure of Instances
105
7.1.1 Data Type
106
7.1.2 Value
107
7.1.3 Identity
108
7.2 Deleting References
109
7.3 Mutable versus Immutable Data Types
111
8 Functions, Methods, and Attributes
115
8.1 Parameters of Functions and Methods
115
8.1.1 Positional Parameters
116
8.1.2 Keyword Arguments
116
8.1.3 Optional Parameters
117
8.1.4 Keyword-Only Parameters
117
8.2 Attributes
118
9 Sources of Information on Python
119
9.1 The Built-In Help Function
119
9.2 The Online Documentation
120
9.3 PEPs
120
PART II Data Types
123
10 Basic Data Types: An Overview
125
10.1 Nothingness: NoneType
126
10.2 Operators
127
10.2.1 Operator Precedence
127
10.2.2 Evaluation Order
129
10.2.3 Concatenating Comparisons
129
11 Numeric Data Types
131
11.1 Arithmetic Operators
131
Augmented Assignments
132
11.2 Comparison Operators
133
11.3 Conversion between Numeric Data Types
134
11.4 Integers: int
135
11.4.1 Numeral Systems
135
11.4.2 Bit Operations
137
11.4.3 Methods
141
11.5 Floats: float
141
11.5.1 Exponential Notation
142
11.5.2 Precision
142
11.5.3 Infinite and Not a Number
143
11.6 Boolean Values: bool
144
11.6.1 Logical Operators
144
11.6.2 Truth Values of Non-Boolean Data Types
146
11.6.3 Evaluating Logical Operators
148
11.7 Complex Numbers: complex
149
12 Sequential Data Types
153
12.1 The Difference between Text and Binary Data
153
12.2 Operations on Instances of Sequential Data Types
154
12.2.1 Checking for Elements
155
12.2.2 Concatenation
157
12.2.3 Repetition
158
12.2.4 Indexing
159
12.2.5 Slicing
160
12.2.6 Length of a Sequence
164
12.2.7 The Smallest and the Largest Element
164
12.2.8 Searching for an Element
165
12.2.9 Counting Elements
166
12.3 The list Data Type
166
12.3.1 Changing a Value within the List: Assignment via []
167
12.3.2 Replacing Sublists and Inserting New Elements: Assignment via []
167
12.3.3 Deleting Elements and Sublists: del in Combination with []
168
12.3.4 Methods of list Instances
169
12.3.5 Sorting Lists: s.sort([key, reverse])
171
12.3.6 Side Effects
174
12.3.7 List Comprehensions
177
12.4 Immutable Lists: tuple
179
12.4.1 Packing and Unpacking
179
12.4.2 Immutable Doesn’t Necessarily Mean Unchangeable!
181
12.5 Strings: str, bytes, bytearray
182
12.5.1 Control Characters
185
12.5.2 String Methods
187
12.5.3 Formatting Strings
196
12.5.4 Character Sets and Special Characters
207
13 Mappings and Sets
215
13.1 Dictionary: dict
215
13.1.1 Creating a Dictionary
215
13.1.2 Keys and Values
216
13.1.3 Iteration
218
13.1.4 Operators
219
13.1.5 Methods
221
13.1.6 Dict Comprehensions
227
13.2 Sets: set and frozenset
227
13.2.1 Creating a Set
228
13.2.2 Iteration
229
13.2.3 Operators
230
13.2.4 Methods
235
13.2.5 Mutable Sets: set
236
13.2.6 Immutable Sets: frozenset
237
14 Collections
239
14.1 Chained Dictionaries
239
14.2 Counting Frequencies
240
14.3 Dictionaries with Default Values
242
14.4 Doubly Linked Lists
243
14.5 Named Tuples
245
15 Date and Time
247
15.1 Elementary Time Functions—time
247
15.1.1 The struct_time Data Type
248
15.1.2 Constants
249
15.1.3 Functions
250
15.2 Object-Oriented Date Management: datetime
254
15.2.1 datetime.date
255
15.2.2 datetime.time
256
15.2.3 datetime.datetime
257
15.2.4 datetime.timedelta
259
15.2.5 Operations for datetime.datetime and datetime.date
262
15.3 Time Zones: zoneinfo
263
15.3.1 The IANA Time Zone Database
263
15.3.2 Specifying the Time in Local Time Zones
265
15.3.3 Calculating with Time Indications in Local Time Zones
265
16 Enumerations and Flags
269
16.1 Enumeration Types: enum
269
16.2 Enumeration Types for Bit Patterns: flag
271
16.3 Integer Enumeration Types: IntEnum
272
PART III Advanced Programming Techniques
275
17 Functions
277
17.1 Defining a Function
278
17.2 Return Values
280
17.3 Function Objects
282
17.4 Optional Parameters
282
17.5 Keyword Arguments
283
17.6 Arbitrarily Many Parameters
284
17.7 Keyword-Only Parameters
286
17.8 Positional-Only Parameters
287
17.9 Unpacking When Calling a Function
288
17.10 Side Effects
290
17.11 Namespaces
293
17.11.1 Accessing Global Variables: global
293
17.11.2 Accessing the Global Namespace
294
17.11.3 Local Functions
295
17.11.4 Accessing Parent Namespaces: nonlocal
296
17.11.5 Unbound Local Variables: A Stumbling Block
297
17.12 Anonymous Functions
299
17.13 Recursion
300
17.14 Built-In Functions
300
17.14.1 abs(x)
304
17.14.2 all(iterable)
304
17.14.3 any(iterable)
305
17.14.4 ascii(object)
305
17.14.5 bin(x)
305
17.14.6 bool([x])
306
17.14.7 bytearray([source, encoding, errors])
306
17.14.8 bytes([source, encoding, errors])
307
17.14.9 chr(i)
307
17.14.10 complex([real, imag])
307
17.14.11 dict([source])
308
17.14.12 divmod(a, b)
309
17.14.13 enumerate(iterable, [start])
309
17.14.14 eval(expression, [globals, locals])
309
17.14.15 exec(object, [globals, locals])
310
17.14.16 filter(function, iterable)
310
17.14.17 float([x])
311
17.14.18 format(value, [format_spec])
311
17.14.19 frozenset([iterable])
311
17.14.20 globals()
312
17.14.21 hash(object)
312
17.14.22 help([object])
313
17.14.23 hex(x)
313
17.14.24 id(object)
313
17.14.25 input([prompt])
314
17.14.26 int([x, base])
314
17.14.27 len(s)
315
17.14.28 list([sequence])
315
17.14.29 locals()
315
17.14.30 map(function, [*iterable])
316
17.14.31 max(iterable, {default, key}), max(arg1, arg2, [*args], {key})
317
17.14.32 min(iterable, {default, key}), min(arg1, arg2, [*args], {key})
318
17.14.33 oct(x)
318
17.14.34 ord(c)
318
17.14.35 pow(x, y, [z])
318
17.14.36 print([*objects], {sep, end, file, flush})
318
17.14.37 range([start], stop, [step])
319
17.14.38 repr(object)
320
17.14.39 reversed(sequence)
320
17.14.40 round(x, [n])
321
17.14.41 set([iterable])
321
17.14.42 sorted(iterable, [key, reverse])
321
17.14.43 str([object, encoding, errors])
322
17.14.44 sum(iterable, [start])
323
17.14.45 tuple([iterable])
323
17.14.46 type(object)
323
17.14.47 zip([*iterables], {strict})
324
18 Modules and Packages
325
18.1 Importing Global Modules
326
18.2 Local Modules
328
18.2.1 Name Conflicts
329
18.2.2 Module-Internal References
330
18.2.3 Executing Modules
330
18.3 Packages
331
18.3.1 Importing All Modules of a Package
333
18.3.2 Namespace Packages
333
18.3.3 Relative Import Statements
334
18.4 The importlib Package
335
18.4.1 Importing Modules and Packages
335
18.4.2 Changing the Import Behavior
335
18.5 Planned Language Elements
338
19 Object-Oriented Programming
341
19.1 Example: A Non-Object-Oriented Account
341
19.1.1 Creating a New Account
342
19.1.2 Transferring Money
342
19.1.3 Depositing and Withdrawing Money
343
19.1.4 Viewing the Account Balance
344
19.2 Classes
346
19.2.1 Defining Methods
347
19.2.2 The Constructor
348
19.2.3 Attributes
349
19.2.4 Example: An Object-Oriented Account
349
19.3 Inheritance
351
19.3.1 A Simple Example
352
19.3.2 Overriding Methods
353
19.3.3 Example: Checking Account with Daily Turnover
355
19.3.4 Outlook
363
19.4 Multiple Inheritance
363
19.5 Property Attributes
365
19.5.1 Setters and Getters
365
19.5.2 Defining Property Attributes
366
19.6 Static Methods
367
19.7 Class Methods
369
19.8 Class Attributes
370
19.9 Built-in Functions for Object-Oriented Programming
370
19.9.1 Functions for Managing the Attributes of an Instance
371
19.9.2 Functions for Information about the Class Hierarchy
372
19.10 Inheriting Built-In Data Types
373
19.11 Magic Methods and Magic Attributes
375
19.11.1 General Magic Methods
375
19.11.2 Overloading Operators
382
19.11.3 Emulating Data Types: Duck Typing
388
19.12 Data Classes
393
19.12.1 Tuples and Lists
393
19.12.2 Dictionaries
394
19.12.3 Named Tuples
394
19.12.4 Mutable Data Classes
395
19.12.5 Immutable Data Classes
396
19.12.6 Default Values in Data Classes
396
20 Exception Handling
399
20.1 Exceptions
399
20.1.1 Built-In Exceptions
400
20.1.2 Raising an Exception
401
20.1.3 Handling an Exception
401
20.1.4 Custom Exceptions
406
20.1.5 Re-Raising an Exception
408
20.1.6 Exception Chaining
410
20.2 Assertions
411
20.3 Warnings
412
21 Generators and Iterators
415
21.1 Generators
415
21.1.1 Subgenerators
418
21.1.2 Generator Expressions
421
21.2 Iterators
422
21.2.1 The Iterator Protocol
422
21.2.2 Example: The Fibonacci Sequence
423
21.2.3 Example: The Golden Ratio
424
21.2.4 A Generator for the Implementation of __iter__
424
21.2.5 Using Iterators
425
21.2.6 Multiple Iterators for the Same Instance
428
21.2.7 Disadvantages of Iterators Compared to Direct Access via Indexes
430
21.2.8 Alternative Definition for Iterable Objects
430
21.2.9 Function Iterators
431
21.3 Special Generators: itertools
432
21.3.1 accumulate(iterable, [func])
433
21.3.2 chain([*iterables])
433
21.3.3 combinations(iterable, r)
434
21.3.4 combinations_with_replacement(iterable, r)
434
21.3.5 compress(data, selectors)
435
21.3.6 count([start, step])
435
21.3.7 cycle(iterable)
436
21.3.8 dropwhile(predicate, iterable)
436
21.3.9 filterfalse(predicate, iterable)
436
21.3.10 groupby(iterable, [key])
437
21.3.11 islice(iterable, [start], stop, [step])
437
21.3.12 permutations(iterable, [r])
438
21.3.13 product([*iterables], [repeat])
438
21.3.14 repeat(object, [times])
439
21.3.15 starmap(function, iterable)
439
21.3.16 takewhile(predicate, iterable)
439
21.3.17 tee(iterable, [n])
439
21.3.18 zip_longest([*iterables], {fillvalue})
440
22 Context Manager
441
22.1 The with Statement
441
22.1.1 __enter__(self)
443
22.1.2 __exit__(self, exc_type, exc_value, traceback)
444
22.2 Helper Functions for with Contexts: contextlib
444
22.2.1 Dynamically Assembled Context Combinations - ExitStack
444
22.2.2 Suppressing Certain Exception Types
445
22.2.3 Redirecting the Standard Output Stream
445
22.2.4 Optional Contexts
446
22.2.5 Simple Functions as Context Manager
447
23 Decorators
449
23.1 Function Decorators
449
23.1.1 Decorating Functions and Methods
451
23.1.2 Name and Docstring after Applying a Decorator
451
23.1.3 Nested Decorators
452
23.1.4 Example: A Cache Decorator
452
23.2 Class Decorators
454
23.3 The functools Module
455
23.3.1 Simplifying Function Interfaces
455
23.3.2 Simplifying Method Interfaces
457
23.3.3 Caches
457
23.3.4 Completing Orderings of Custom Classes
459
23.3.5 Overloading Functions
459
24 Annotations for Static Type Checking
463
24.1 Annotations
464
24.1.1 Annotating Functions and Methods
465
24.1.2 Annotating Variables and Attributes
466
24.1.3 Accessing Annotations at Runtime
468
24.1.4 When are Annotations Evaluated?
470
24.2 Type Hints: The typing Module
471
24.2.1 Valid Type Hints
472
24.2.2 Container Types
472
24.2.3 Abstract Container Types
473
24.2.4 Type Aliases
474
24.2.5 Type Unions and Optional Values
474
24.2.6 Type Variables
475
24.3 Static Type Checking in Python: mypy
476
24.3.1 Installation
476
24.3.2 Example
477
25 Structural Pattern Matching
479
25.1 The match Statement
479
25.2 Pattern Types in the case Statement
480
25.2.1 Literal and Value Patterns
481
25.2.2 OR Pattern
481
25.2.3 Patterns with Type Checking
482
25.2.4 Specifying Conditions for Matches
483
25.2.5 Grouping Subpatterns
483
25.2.6 Capture and Wildcard Patterns
484
25.2.7 Sequence Patterns
486
25.2.8 Mapping Patterns
488
25.2.9 Patterns for Objects and Their Attribute Values
490
PART IV The Standard Library
495
26 Mathematics
497
26.1 Mathematical Functions: math, cmath
497
26.1.1 General Mathematical Functions
498
26.1.2 Exponential and Logarithm Functions
501
26.1.3 Trigonometric and Hyperbolic Functions
501
26.1.4 Distances and Norms
502
26.1.5 Converting Angles
502
26.1.6 Representations of Complex Numbers
502
26.2 Random Number Generator: random
503
26.2.1 Saving and Loading the Random State
504
26.2.2 Generating Random Integers
504
26.2.3 Generating Random Floats
505
26.2.4 Random Operations on Sequences
505
26.2.5 SystemRandom([seed])
507
26.3 Statistical Calculations: statistics
507
26.4 Intuitive Decimal Numbers: decimal
509
26.4.1 Using the Data Type
509
26.4.2 Nonnumeric Values
512
26.4.3 The Context Object
513
26.5 Hash Functions: hashlib
514
26.5.1 Using the Module
516
26.5.2 Other Hash Algorithms
517
26.5.3 Comparing Large Files
517
26.5.4 Passwords
518
27 Screen Outputs and Logging
521
27.1 Formatted Output of Complex Objects: pprint
521
pprint(object, [stream, indent, width, depth], {compact})
521
27.2 Log Files: logging
523
27.2.1 Customizing the Message Format
525
27.2.2 Logging Handlers
527
28 Regular Expressions
529
28.1 Syntax of Regular Expressions
529
28.1.1 Any Character
530
28.1.2 Character Classes
530
28.1.3 Quantifiers
531
28.1.4 Predefined Character Classes
533
28.1.5 Other Special Characters
534
28.1.6 Nongreedy Quantifiers
535
28.1.7 Groups
536
28.1.8 Alternatives
536
28.1.9 Extensions
537
28.2 Using the re Module
539
28.2.1 Searching
540
28.2.2 Matching
540
28.2.3 Splitting a String
541
28.2.4 Replacing Parts of a String
541
28.2.5 Replacing Problem Characters
542
28.2.6 Compiling a Regular Expression
542
28.2.7 Flags
543
28.2.8 The Match Object
544
28.3 A Simple Sample Program: Searching
546
28.4 A More Complex Sample Program: Matching
547
28.5 Comments in Regular Expressions
550
29 Interface to Operating System and Runtime Environment
553
29.1 Operating System Functionality: os
553
29.1.1 environ
554
29.1.2 getpid()
554
29.1.3 cpu_count()
554
29.1.4 system(cmd)
554
29.1.5 popen(command, [mode, buffering])
555
29.2 Accessing the Runtime Environment: sys
555
29.2.1 Command Line Parameters
556
29.2.2 Default Paths
556
29.2.3 Standard Input/Output Streams
556
29.2.4 Exiting the Program
556
29.2.5 Details of the Python Version
557
29.2.6 Operating System Details
557
29.2.7 Hooks
559
29.3 Command Line Parameters: argparse
561
29.3.1 Calculator: A Simple Example
562
29.3.2 A More Complex Example
566
30 File System
569
30.1 Accessing the File System: os
569
30.1.1 access(path, mode)
570
30.1.2 chmod(path, mode)
571
30.1.3 listdir([path])
571
30.1.4 mkdir(path, [mode]) and makedirs(path, [mode])
572
30.1.5 remove(path)
572
30.1.6 removedirs(path)
572
30.1.7 rename(src, dst) and renames(old, new)
573
30.1.8 walk(top, [topdown, onerror])
573
30.2 File Paths: os.path
575
30.2.1 abspath(path)
576
30.2.2 basename(path)
577
30.2.3 commonprefix(list)
577
30.2.4 dirname(path)
577
30.2.5 join(path, *paths)
578
30.2.6 normcase(path)
578
30.2.7 split(path)
578
30.2.8 splitdrive(path)
579
30.2.9 splitext(path)
579
30.3 Accessing the File System: shutil
579
30.3.1 Directory and File Operations
581
30.3.2 Archive Operations
582
30.4 Temporary Files: tempfile
585
30.4.1 TemporaryFile([mode, [bufsize, suffix, prefix, dir])
585
30.4.2 tempfile.TemporaryDirectory([suffix, prefix, dir])
586
31 Parallel Programming
587
31.1 Processes, Multitasking, and Threads
587
31.1.1 The Lightweights among the Processes: Threads
588
31.1.2 Threads or Processes?
590
31.1.3 Cooperative Multitasking: A Third Way
590
31.2 Python's Interfaces for Parallelization
591
31.3 The Abstract Interface: concurrent.futures
592
31.3.1 An Example with a futures.ThreadPoolExecutor
593
31.3.2 Executor Instances as Context Managers
595
31.3.3 Using futures.ProcessPoolExecutor
595
31.3.4 Managing the Tasks of an Executor
596
31.4 The Flexible Interface: threading and multiprocessing
602
31.4.1 Threads in Python: threading
603
31.4.2 Processes in Python: multiprocessing
611
31.5 Cooperative Multitasking
613
31.5.1 Cooperative Functions: Coroutines
613
31.5.2 Awaitable Objects
614
31.5.3 The Cooperation of Coroutines: Tasks
615
31.5.4 A Cooperative Web Crawler
618
31.5.5 Blocking Operations in Coroutines
625
31.5.6 Other Asynchronous Language Features
627
31.6 Conclusion: Which Interface Is the Right One?
629
31.6.1 Is Cooperative Multitasking an Option?
629
31.6.2 Abstraction or Flexibility?
630
31.6.3 Threads or Processes?
630
32 Data Storage
631
32.1 XML
631
32.1.1 ElementTree
633
32.1.2 Simple API for XML
640
32.2 Databases
643
32.2.1 The Built-In Database in Python: sqlite3
646
32.3 Compressed Files and Archives
661
32.3.1 gzip.open(filename, [mode, compresslevel])
661
32.3.2 Other Modules for Accessing Compressed Data
662
32.4 Serializing Instances: pickle
662
32.4.1 Functional Interface
663
32.4.2 Object-Oriented Interface
665
32.5 The JSON Data Exchange Format: json
665
32.6 The CSV Table Format: csv
667
32.6.1 Reading Data from a CSV File with reader Objects
668
32.6.2 Using Custom Dialects: Dialect Objects
670
33 Network Communication
673
33.1 Socket API
674
33.1.1 Client-Server Systems
675
33.1.2 UDP
677
33.1.3 TCP
678
33.1.4 Blocking and Nonblocking Sockets
680
33.1.5 Creating a Socket
681
33.1.6 The Socket Class
682
33.1.7 Network Byte Order
685
33.1.8 Multiplexing Servers: selectors
686
33.1.9 Object-Oriented Server Development: socketserver
688
33.2 XML-RPC
690
33.2.1 The Server
691
33.2.2 The Client
694
33.2.3 Multicall
696
33.2.4 Limitations
697
34 Accessing Resources on the Internet
701
34.1 Protocols
701
34.1.1 Hypertext Transfer Protocol
701
34.1.2 File Transfer Protocol
701
34.2 Solutions
702
34.2.1 Outdated Solutions for Python 2
702
34.2.2 Solutions in the Standard Library
702
34.2.3 Third-Party Solutions
702
34.3 The Easy Way: requests
703
34.3.1 Simple Requests via GET and POST
703
34.3.2 Web APIs
704
34.4 URLs: urllib
705
34.4.1 Accessing Remote Resources: urllib.request
706
34.4.2 Reading and Processing URLs: urllib.parse
710
34.5 FTP: ftplib
713
34.5.1 Connecting to an FTP Server
714
34.5.2 Executing FTP commands
715
34.5.3 Working with Files and Directories
715
34.5.4 Transferring Files
716
35 Email
721
35.1 SMTP: smtplib
721
35.1.1 SMTP([host, port, local_hostname, timeout, source_address])
722
35.1.2 Establishing and Terminating a Connection
722
35.1.3 Sending an Email
723
35.1.4 Example
724
35.2 POP3: poplib
724
35.2.1 POP3(host, [port, timeout])
725
35.2.2 Establishing and Terminating a Connection
725
35.2.3 Listing Existing Emails
726
35.2.4 Retrieving and Deleting Emails
727
35.2.5 Example
727
35.3 IMAP4: imaplib
728
35.3.1 IMAP4([host, port, timeout])
729
35.3.2 Establishing and Terminating a Connection
730
35.3.3 Finding and Selecting a Mailbox
730
35.3.4 Operations with Mailboxes
731
35.3.5 Searching Emails
731
35.3.6 Retrieving Emails
732
35.3.7 Example
733
35.4 Creating Complex Emails: email
734
35.4.1 Creating a Simple Email
734
35.4.2 Creating an Email with Attachments
735
35.4.3 Reading an Email
737
36 Debugging and Quality Assurance
739
36.1 The Debugger
739
36.2 Automated Testing
741
36.2.1 Test Cases in Docstrings: doctest
742
36.2.2 Unit Tests: unittest
746
36.3 Analyzing the Runtime Performance
749
36.3.1 Runtime Measurement: timeit
749
36.3.2 Profiling: cProfile
752
36.3.3 Tracing: trace
756
37 Documentation
759
37.1 Docstrings
759
37.2 Automatically Generated Documentation: pydoc
761
PART V Advanced Topics
763
38 Distributing Python Projects
765
38.1 A History of Distributions in Python
765
38.1.1 The Classic Approach: distutils
766
38.1.2 The New Standard: setuptools
766
38.1.3 The Package Index: PyPI
766
38.2 Creating Distributions: setuptools
767
38.2.1 Installation
767
38.2.2 Writing the Module
767
38.2.3 The Installation Script
768
38.2.4 Creating a Source Distribution
773
38.2.5 Creating a Binary Distribution
773
38.2.6 Installing Distributions
774
38.3 Creating EXE files: cx_Freeze
775
38.3.1 Installation
775
38.3.2 Usage
775
38.4 Package Manager
776
38.4.1 The Python Package Manager: pip
777
38.4.2 The conda Package Manager
778
38.5 Localizing Programs: gettext
781
38.5.1 Example of Using gettext
781
38.5.2 Creating the Language Compilation
783
39 Virtual Environments
785
39.1 Using Virtual Environments: venv
786
39.1.1 Activating a Virtual Environment
786
39.1.2 Working in a Virtual Environment
786
39.1.3 Deactivating a Virtual Environment
787
39.2 Virtual Environments in Anaconda
787
40 Alternative Interpreters and Compilers
789
40.1 Just-in-Time Compilation: PyPy
789
40.1.1 Installation and Use
789
40.1.2 Example
790
40.2 Numba
790
40.2.1 Installation
791
40.2.2 Example
791
40.3 Connecting to C and C++: Cython
793
40.3.1 Installation
793
40.3.2 The Functionality of Cython
794
40.3.3 Compiling a Cython Program
794
40.3.4 A Cython Program with Static Typing
796
40.3.5 Using a C Library
797
40.4 The Interactive Python Shell: IPython
799
40.4.1 Installation
799
40.4.2 The Interactive Shell
799
40.4.3 The Jupyter Notebook
802
41 Graphical User Interfaces
805
41.1 Toolkits
805
41.1.1 Tkinter (Tk)
805
41.1.2 PyGObject (GTK)
806
41.1.3 Qt for Python (Qt)
806
41.1.4 wxPython (wxWidgets)
807
41.2 Introduction to tkinter
807
41.2.1 A Simple Example
807
41.2.2 Control Variables
810
41.2.3 The Packer
811
41.2.4 Events
815
41.2.5 Widgets
821
41.2.6 Drawings: The Canvas Widget
839
41.2.7 Other Modules
846
41.3 Introduction to PySide6
850
41.3.1 Installation
850
41.3.2 Basic Concepts of Qt
850
41.3.3 Development Process
852
41.4 Signals and Slots
859
41.5 Important Widgets
861
41.5.1 QCheckBox
862
41.5.2 QComboBox
862
41.5.3 QDateEdit, QTimeEdit, and QDateTimeEdit
863
41.5.4 QDialog
863
41.5.5 QLineEdit
864
41.5.6 QListWidget and QListView
864
41.5.7 QProgressBar
865
41.5.8 QPushButton
865
41.5.9 QRadioButton
865
41.5.10 QSlider and QDial
866
41.5.11 QTextEdit
866
41.5.12 QWidget
867
41.6 Drawing Functionality
868
41.6.1 Tools
868
41.6.2 Coordinate System
870
41.6.3 Simple Shapes
871
41.6.4 Images
873
41.6.5 Text
874
41.6.6 Eye Candy
876
41.7 Model-View Architecture
879
41.7.1 Sample Project: An Address Book
880
41.7.2 Selecting Entries
888
41.7.3 Editing Entries
890
42 Python as a Server-Side Programming Language on the Web: An Introduction to Django
893
42.1 Concepts and Features of Django
894
42.2 Installing Django
895
42.3 Creating a New Django Project
896
42.3.1 The Development Web Server
897
42.3.2 Configuring the Project
898
42.4 Creating an Application
900
42.4.1 Importing the Application into the Project
901
42.4.2 Defining a Model
901
42.4.3 Relationships between Models
902
42.4.4 Transferring the Model to the Database
903
42.4.5 The Model API
904
42.4.6 The Project Gets a Face
909
42.4.7 Django's Template System
915
42.4.8 Processing Form Data
926
42.4.9 Django’s Admin Control Panel
930
43 Scientific Computing and Data Science
935
43.1 Installation
936
43.2 The Model Program
936
43.2.1 Importing numpy, scipy, and matplotlib
937
43.2.2 Vectorization and the numpy.ndarray Data Type
938
43.2.3 Visualizing Data Using matplotlib.pyplot
942
43.3 Overview of the numpy and scipy Modules
944
43.3.1 Overview of the numpy.ndarray Data Type
944
43.3.2 Overview of scipy
952
43.4 An Introduction to Data Analysis with pandas
953
43.4.1 The DataFrame Object
954
43.4.2 Selective Data Access
955
43.4.3 Deleting Rows and Columns
961
43.4.4 Inserting Rows and Columns
961
43.4.5 Logical Expressions on Data Records
962
43.4.6 Manipulating Data Records
963
43.4.7 Input and Output
965
43.4.8 Visualization
966
44 Inside Knowledge
969
44.1 Opening URLs in the Default Browser: webbrowser
969
44.2 Interpreting Binary Data: struct
969
44.3 Hidden Password Entry
971
44.3.1 getpass([prompt, stream])
971
44.3.2 getpass.getuser()
972
44.4 Command Line Interpreter
972
44.5 File Interface for Strings: io.StringIO
975
44.6 Generators as Consumers
976
44.6.1 A Decorator for Consuming Generator Functions
978
44.6.2 Triggering Exceptions in a Generator
978
44.6.3 A Pipeline as a Chain of Consuming Generator Functions
979
44.7 Copying Instances: copy
981
44.8 Image Processing: Pillow
984
44.8.1 Installation
984
44.8.2 Loading and Saving Image Files
984
44.8.3 Accessing Individual Pixels
985
44.8.4 Manipulating Images
986
44.8.5 Interoperability
992
45 From Python 2 to Python 3
993
45.1 The Main Differences
996
45.1.1 Input/Output
996
45.1.2 Iterators
997
45.1.3 Strings
998
45.1.4 Integers
999
45.1.5 Exception Handling
999
45.1.6 Standard Library
1000
45.2 Automatic Conversion
1001
Appendices
1005
A Appendix
1005
A.1 Reserved Words
1005
A.2 Operator Precedence
1005
A.3 Built-In Functions
1007
A.4 Built-In Exceptions
1011
A.5 Python IDEs
1014
B The Authors
1017
Index
1019