Verfügbarkeit: Expert Python programming | Technische Universität München

Expert Python programming: become a master in Python by learning coding best practices and advanced programming concepts in Python 3.7

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Bibliographische Detailangaben
Beteiligte Personen:	Jaworski, Michał (VerfasserIn), Ziadé, Tarek (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	Birmingham Packt April 2019
Ausgabe:	third edition
Schlagwörter:	Python > Programmiersprache
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031297641&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	xi, 627 Seiten 24 cm
ISBN:	9781789808896

Internformat

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Datensatz im Suchindex

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adam_text	Table of Contents Preface_i Section 1: Before You Start_ Chapter 1: Current Status of Python g Technical requirements 10 Where are we now and where we are going to? 10 Why and how Python changes 11 Being up-to-date with changes by following PEP documents 11 Python 3 adoption at the time of writing this book 12 The main differences between Python 3 and Python 2 14 Why should I care? 14 The main syntax differences and common pitfalls 14 Syntax changes 15 Changes in the standard library 16 Changes in data types and collections and string literals 17 The popular tools and techniques used for maintaining cross-version compatibility 17 Not only CPython 21 Why should I care? 21 Stackless Python 22 Jython 23 I ran Python 23 PyPy 24 MicroPython 25 Useful resources 26 Summary 27 Chapter 2: Modern Python Development Environments 29 Technical requirements 30 Installing additional Python packages using pip 30 Isolating the runtime environment 32 Application-level isolation versus system-level isolation 33 Python's venv 34 venv versus virtualenv 36 System-level environment isolation 37 Virtual development environments using Vagrant 39 Virtual environments using Docker 41 Containerization versus virtualization 41 Writing your first Dockerfile 42 Table of Contents Running containers Setting up complex environments Useful Docker recipes for Python Reducing the size of containers Addressing services inside of a Compose environment Communicating between multiple Compose environments Popular productivity tools Custom Python shells - ipython, bpython, ptpython, and so on Setting up the PYTHONSTARTUP environment variable IPython bpython ptpython Incorporating shells in your own scripts and programs Interactive debuggers Summary Section 2: Python Craftsmanship_ Chapter 3: Modern Syntax Elements - Below the Class Level Technical requirements Python's built-in types Strings and bytes Implementation details String concatenation Constant folding, the peephole optimizer, and the AST optimizer String formatting with f-strings Containers Lists and tuples Implementation details List comprehensions Other idioms Dictionaries Implementation details Weaknesses and alternatives Sets Implementation details Supplemental data types and containers Specialized data containers from the collections module Symbolic enumeration with the enum module Advanced syntax Iterators Generators and yield statements Decorators General syntax and possible implementations As a function As a class Parametrizing decorators Introspection preserving decorators Usage and useful examples Argument checking 45 46 47 48 49 50 52 52 54 54 54 55 55 56 57 61 61 62 62 65 65 67 67 69 69 70 71 72 75 76 77 79 80 81 81 82 85 85 88 92 92 93 94 94 95 97 97 [ii] Table of Contents Caching gg Proxy 102 Context provider 103 Context managers - the with statement 104 The general syntax and possible implementations 105 As a class 106 As a function - the contextlib module 108 Functional-style features of Python 109 What is functional programming? no Lambda functions ill map(), filter(), and reduce() 112 Partial objects and partial() functions 115 Generator expressions 116 Function and variable annotations 117 The general syntax 117 The possible uses 118 Static type checking with mypy 119 Other syntax elements you may not know of yet 120 The for else statement 120 Keyword-only arguments 121 Summary 123 Chapter 4: Modern Syntax Elements - Above the Class Level 125 Technical requirements 126 The protocols of the Python language - dunder methods and attributes 126 Reducing boilerplate with data classes 128 Subclassing built-in types 131 MRO and accessing methods from superclasses 134 Old-style classes and super in Python 2 136 Understanding Python's Method Resolution Order 137 Super pitfalls 142 Mixing super and explicit class calls 142 Heterogeneous arguments 143 Best practices 145 Advanced attribute access patterns 145 Descriptors 146 Real-life example - lazily evaluated attributes 149 Properties 152 Slots 155 Summary 156 Chapter 5: Elements of Metaprogramming 157 Technical requirements 157 What is metaprogramming? 158 Decorators - a method of metaprogramming 158 Class decorators 159 ----------------------------------- [iii] ------------------------------------- Table of Contents Using new () for overriding the instance creation process Metadiises The general syntax New Python 3 syntax for metaclasses Metaclass usage Metaclass pitfalls Code generation exec, eval, and compile Abstract syntax tree (AST) Import hooks Projects that use code generation patterns Falcon's compiled router Hy Summary Chapter 6: Choosing Good Names Technical requirements PEP 8 and naming best practices Why and when to follow PEP 8? Beyond PEP 8 - Team-specific style guidelines Naming styles Variables Constants Naming and usage Public and private variables Functions and methods The private controversy Special methods Arguments Properties Classes Modules and packages The naming guide Using the has/is prefixes for Boolean elements Using plurals for variables that are collections Using explicit names for dictionaries Avoid generic names and redundancy Avoiding existing names Best practices for arguments Building arguments by iterative design Trusting the arguments and your tests Using args and *kwargs magic arguments carefully Class names Module and package names Useful tools Pylint pycodestyle and flake8 161 164 165 168 171 171 172 173 174 176 176 177 178 179 181 182 182 182 183 184 184 184 186 187 189 190 191 192 192 192 193 194 194 194 194 195 196 196 197 197 198 200 201 201 202 203 [iv] Table of Contents Summary 204 Chapter 7: Writing a Package 205 Technical requirements 206 Creating a package 206 The confusing state of Python packaging tools 207 The current landscape of Python packaging thanks to PyPA 207 Tool recommendations 208 Project configuration 209 setup py 209 setup, cfg 210 MANIFEST in 211 Most important metadata 212 Trove classifiers 212 Common patterns 214 Automated inclusion of version string from package 215 README file 217 Managing dependencies 218 The custom setup command 219 Working with packages during development 220 setup py install 220 Uninstalling packages 220 setup py develop or pip -e 221 Namespace packages 221 Why is it useful? 222 PEP 420 - implicit namespace packages 224 Namespace packages in previous Python versions 225 Uploading a package 226 PyPI - Python Package Index 226 Uploading to PyPI - or other package index 226 pypirc 228 Source packages versus built packages 229 sdist 229 bdist and wheels 230 Standalone executables 233 When standalone executables useful? 234 Popular tools 235 Pylnstaller 235 cx_Freeze 239 py2exe and py2app 241 Security of Python code in executable packages 242 Making decompilation harder 242 Summary 243 Chapter 8: Deploying the Code 245 Technical requirements 246 The Twelve-Factor App 246 Various approaches to deployment automation 248 ----------------------------------- [v] --------------------------------------- i icinn Fabric for deployment automation Your own package index or index mirror PvPI mirroring Bundling additional resources with your Python package Common conventions and practices The filesystem hierarchy Isolation Using process supervision tools Application code running in user space Using reverse HTTP proxies Reloading processes gracefully Code instrumentation and monitoring Logging errors - Sentry/Raven Monitoring system and application metrics Dealing with application logs Basic low-level log practices Tools for log processing Summary 249 253 254 255 264 264 265 265 267 268 269 271 271 274 277 278 280 282 Chapter 9: Python Extensions in Other Languages Technical requirements Differentiating between the C and C++ languages Loading extensions in C or C++ The need to use extensions Improving the performance in critical code sections Integrating existing code written in different languages Integrating third-party dynamic libraries Creating custom datatypes Writing extensions Pure C extensions A closer look at Python/C API Calling and binding conventions Exception handling Releasing GIL Reference counting Writing extensions with Cython Cython as a source-to-source compiler Cython as a language Challenges with using extensions Additional complexity Debugging Interfacing with dynamic libraries without extensions The ctypes module Loading libraries Calling C functions using ctypes Passing Python functions as C callbacks 283 284 285 285 287 288 289 290 290 290 292 295 300 302 304 306 308 309 311 314 314 315 316 316 316 318 320 - [vi] Table of Contents CFFI 323 Summary 325 Section 3: Quality over Quantity_ Chapter 10: Managing Code 329 Technical requirements 329 Working with a version control system 330 Centralized systems 330 Distributed systems 333 Distributed strategies 334 Centralized or distributed? 335 Use Git if you can 336 GitFlow and GitHub Flow 337 Setting up continuous development processes 341 Continuous integration 342 Testing every commit 343 Merge testing through Cl 344 Matrix testing 345 Continuous delivery 346 Continuous deployment 347 Popular tools for continuous integration 348 Jenkins 348 Buildbot 352 Travis Cl 355 GitLab Cl 357 Choosing the right tool and common pitfalls 357 Problem 1 - Complex build strategies 358 Problem 2 - Long building time 358 Problem 3 - External job definitions 359 Problem 4 - Lack of isolation 360 Summary 36i Chapter 11: Documenting Your Project 363 Technical requirements 363 The seven rules of technical writing 364 Write in two steps 364 Target the readership 365 Use a simple style 366 Limit the scope of information 367 Use realistic code examples 367 Use a light but sufficient approach 368 Use templates 369 Documentation as code 369 Using Python docstrings 370 Popular markup languages and styles for documentation 372 Popular documentation generators for Python libraries 373 [ vii ] Tahte of Contents _ SplWorKing with the index pages Registering module helpers Adding index markers Cross-references Docurnentat'on building and continuous integration Documenting web APIs Documentation as API prototype with API Blueprint Self-documenting APIs with Swagger/OpenAPI Building a well-organized documentation system Building documentation portfolio Design Usage Recipe Tutorial Module helper Operations Your very own documentation portfolio Building a documentation landscape Producer's layout Consumer's layout Summary 373 376 376 377 377 378 378 379 379 381 382 382 383 384 385 387 387 388 389 390 390 391 392 Chapter 12: Test-Driven Development Technical requirements I don't test Three simple steps of test-driven development Preventing software regression Improving code quality Providing the best developer documentation Producing robust code faster What kind of tests? Unit tests Acceptance tests Functional tests Integration tests Load and performance testing Code quality testing Python standard test tools unittest doctest I do test unittest pitfalls unittest alternatives nose Test runner Writing tests Writing test fixtures 393 394 394 394 397 398 398 399 399 399 400 400 401 401 402 402 403 406 408 408 409 409 410 410 411 [ viii] Table of Contents Integration with setuptools and plugin system 411 Wrap-up 412 py test 412 Writing test fixtures 413 Disabling test functions and classes 415 Automated distributed tests 416 Wrap-up 417 Testing coverage 417 Fakes and mocks 420 Building a fake 420 Using mocks 425 Testing environment and dependency compatibility 427 Dependency matrix testing 427 Document-driven development 431 Writing a story 431 Summary 433 Section 4: Need for Speed_ Chapter 13: Optimization - Principles and Profiling Techniques 437 Technical requirements 437 The three rules of optimization 438 Making it work first 438 Working from the user's point of view 440 Keeping the code readable and maintainable 440 Optimization strategy 441 Looking for another culprit 441 Scaling the hardware 442 Writing a speed test 443 Finding bottlenecks 444 Profiling CPU usage 444 Macro-profiling 445 Micro-profiling 449 Profiling memory usage 453 How Python deals with memory 454 Profiling memory 455 objgraph 457 C code memory leaks 464 Profiling network usage 466 Tracing network transactions 467 Summary 469 Chapter 14: Optimization - Some Powerful Techniques 471 Technical requirements 473 Defining complexity 473 Cyclomatic complexity 474 The big O notation 475 Reducing complexity by choosing proper data structures 478 ------------------------------------ [ix] -------------------------------------- _ Searching in a list Using sets Using collections deque defaultdict namedtuple Using architectural trade-offs Using heuristics and approximation algorithms Using task queues and delayed processing Using probabilistic data structures Caching Deterministic caching Non-deterministic caching Cache services Memcached Summary Chapter 15: Concurrency Technical requirements Why concurrency? Multithreading What is multithreading? How Python deals with threads When should we use threading? Building responsive interfaces Delegating work Multiuser applications An example of a threaded application Using one thread per item Using a thread pool Using two-way queues Dealing with errors and rate limiting Multiprocessing The built-in multiprocessing module Using process pools Using multiprocessing dummy as the multithreading interface Asynchronous programming Cooperative multitasking and asynchronous I/O Python async and await keywords asyncio in older versions of Python A practical example of asynchronous programming Integrating non-asynchronous code with async using futures Executors and futures Using executors in an event loop Summary 478 479 480 480 482 483 485 485 486 490 491 492 495 496 498 500 501 502 502 504 504 505 507 507 507 508 509 512 513 516 518 523 525 529 531 532 532 534 538 539 541 543 544 545 [X] Table of Contents Section 5: Technical Architecture Chapter 16: Event-Driven and Signal Programming 549 Technical requirements 550 What exactly is event-driven programming? 550 Event-driven != asynchronous 551 Event-driven programming in GUIs 552 Event-driven communication 554 Various styles of event-driven programming 556 Callback-based style 557 Subject-based style 558 Topic-based style 561 Event-driven architectures 563 Event and message queues 565 Summary 567 Chapter 17: Useful Design Patterns 569 Technical requirements 570 Creational patterns 570 Singleton 570 Structural patterns 573 Adapter 574 Interfaces 576 Using zope interface 577 Using function annotations and abstract base classes 581 Using collections abc 589 Proxy 590 Facade 591 Behavioral patterns 592 Observer 592 Visitor 595 Template 597 Summary 599 Appendix A: reStructuredText Primer_601 reStructuredText 601 Section structure 603 Lists 605 Inline markup 606 Literal block 607 Links 608 Other Books You May Enjoy_611
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title_fullStr	Expert Python programming become a master in Python by learning coding best practices and advanced programming concepts in Python 3.7 Michał Jaworski, Tarek Ziadé
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