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Название MASTERING PYTHON: write powerful and efficient code using the full range of python's capabilities.
Авторы HATTEM RICK VAN.
Выходные сведения [S.l.]: PACKT PUBLISHING LIMITED, 2022
Коллекция Электронные книги зарубежных издательств ; Общая коллекция
Тематика Python (Computer program language) ; EBSCO eBooks
Тип документа Другой
Тип файла PDF
Язык Английский
Права доступа Доступ по паролю из сети Интернет (чтение, печать, копирование)
Ключ записи on1319198748
Дата создания записи 21.05.2022

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  • Preface
  • Getting Started – One Environment per Project
    • Virtual environments
      • Why virtual environments are a good idea
      • Using venv and virtualenv
        • Creating a venv
        • Activating a venv/virtualenv
        • Installing packages
      • Using pyenv
      • Using Anaconda
        • Getting started with Anaconda Navigator
        • Getting started with conda
    • Managing dependencies
      • Using pip and a requirements.txt file
      • Version specifiers
      • Installing through source control repositories
      • Additional dependencies using extras
      • Conditional dependencies using environment markers
      • Automatic project management using poetry
        • Creating a new poetry project
        • Adding dependencies
        • Upgrading dependencies
        • Running commands
      • Automatic dependency tracking using pipenv
        • Updating your packages
        • Deploying to production
        • Running cron commands
    • Exercises
      • Reading the Python Enhancement Proposals (PEPs)
      • Combining pyenv and poetry or pipenv
      • Converting an existing project to a poetry project
    • Summary
  • Interactive Python Interpreters
    • The Python interpreter
      • Modifying the interpreter
        • Enabling and enhancing autocompletion
    • Alternative interpreters
      • bpython
        • Rewinding your session
        • Reloading modules
      • ptpython
      • IPython and Jupyter
        • Basic interpreter usage
        • Saving and loading sessions
        • Regular Python prompt/doctest mode
        • Introspection and help
        • Autocompletion
        • Jupyter
        • Installing Jupyter
        • IPython summary
    • Exercises
    • Summary
  • Pythonic Syntax and Common Pitfalls
    • A brief history of Python
    • Code style – What is Pythonic code?
      • Whitespace instead of braces
      • Formatting strings – printf, str.format, or f-strings?
        • Simple formatting
        • Named variables
        • Arbitrary expressions
      • PEP 20, the Zen of Python
        • Beautiful is better than ugly
        • Explicit is better than implicit
        • Simple is better than complex
        • Flat is better than nested
        • Sparse is better than dense
        • Readability counts
        • Practicality beats purity
        • Errors should never pass silently
        • In the face of ambiguity, refuse the temptation to guess
        • One obvious way to do it
        • Hard to explain, easy to explain
        • Namespaces are one honking great idea
      • Explaining PEP 8
        • Duck typing
        • Differences between value and identity comparisons
        • Loops
        • Maximum line length
      • Verifying code quality, pep8, pyflakes, and more
      • Recent additions to the Python syntax
        • PEP 572: Assignment expressions/the walrus operator
        • PEP 634: Structural pattern matching, the switch statement
    • Common pitfalls
      • Scope matters!
        • Global variables
        • Mutable function default arguments
        • Class properties
      • Overwriting and/or creating extra built-ins
      • Modifying while iterating
      • Catching and storing exceptions
      • Late binding and closures
      • Circular imports
      • Import collisions
    • Summary
  • Pythonic Design Patterns
    • Time complexity – The big O notation
    • Core collections
      • list – A mutable list of items
      • dict – A map of items
      • set – Like a dict without values
      • tuple – The immutable list
    • Pythonic patterns using advanced collections
      • Smart data storage with type hinting using dataclasses
      • Combining multiple scopes with ChainMap
      • Default dictionary values using defaultdict
      • enum – A group of constants
      • Sorting collections using heapq
      • Searching through sorted collections using bisect
      • Global instances using Borg or Singleton patterns
      • No need for getters and setters with properties
      • Dict union operators
    • Exercises
    • Summary
  • Functional Programming – Readability Versus Brevity
    • Functional programming
      • Purely functional
      • Functional programming and Python
      • Advantages of functional programming
    • list, set, and dict comprehensions
      • Basic list comprehensions
      • set comprehensions
      • dict comprehensions
      • Comprehension pitfalls
    • lambda functions
      • The Y combinator
    • functools
      • partial – Prefill function arguments
      • reduce – Combining pairs into a single result
        • Implementing a factorial function
        • Processing trees
        • Reducing in the other direction
    • itertools
      • accumulate – reduce with intermediate results
      • chain – Combining multiple results
      • compress – Selecting items using a list of Booleans
      • dropwhile/takewhile – Selecting items using a function
      • count – Infinite range with decimal steps
      • groupby – Grouping your sorted iterable
    • Exercises
    • Summary
  • Decorators – Enabling Code Reuse by Decorating
    • Decorating functions
      • Generic function decorators
      • The importance of functools.wraps
      • Chaining or nesting decorators
      • Registering functions using decorators
      • Memoization using decorators
      • Decorators with (optional) arguments
      • Creating decorators using classes
    • Decorating class functions
      • Skipping the instance – classmethod and staticmethod
      • Properties – Smart descriptor usage
    • Decorating classes
      • Singletons – Classes with a single instance
      • Total ordering – Making classes sortable
    • Useful decorators
      • Single dispatch – Polymorphism in Python
      • contextmanager — with statements made easy
      • Validation, type checks, and conversions
      • Useless warnings – How to ignore them safely
    • Exercises
    • Summary
  • Generators and Coroutines – Infinity, One Step at a Time
    • Generators
      • Creating generators
      • Creating infinite generators
      • Generators wrapping iterables
      • Generator comprehensions
      • Class-based generators and iterators
    • Generator examples
      • Breaking an iterable up into chunks/groups
      • itertools.islice – Slicing iterables
      • itertools.chain – Concatenating multiple iterables
      • itertools.tee – Using an output multiple times
      • contextlib.contextmanager – Creating context managers
    • Coroutines
      • A basic example
      • Priming
      • Closing and throwing exceptions
      • Mixing generators and coroutines
      • Using the state
    • Exercises
    • Summary
  • Metaclasses – Making Classes (Not Instances) Smarter
    • Dynamically creating classes
    • A basic metaclass
      • Arguments to metaclasses
      • Accessing metaclass attributes through classes
    • Abstract classes using collections.abc
      • Internal workings of the abstract classes
      • Custom type checks
    • Automatically registering plugin systems
      • Importing plugins on-demand
      • Importing plugins through configuration
      • Importing plugins through the filesystem
    • Dataclasses
    • Order of operations when instantiating classes
      • Finding the metaclass
      • Preparing the namespace
      • Executing the class body
      • Creating the class object (not instance)
      • Executing the class decorators
      • Creating the class instance
      • Example
    • Storing class attributes in definition order
      • The classic solution without metaclasses
      • Using metaclasses to get a sorted namespace
    • Exercises
    • Summary
  • Documentation – How to Use Sphinx and reStructuredText
    • Type hinting
      • Basic example
      • Custom types
      • Generics
      • Type checking
      • Python type interface files
      • Type hinting conclusion
    • reStructuredText and Markdown
      • Getting started with reStructuredText
      • Getting started with Markdown
      • Inline markup
      • Headers
        • Headers with reStructuredText
        • Headers with Markdown
      • Lists
        • Enumerated lists
        • Bulleted lists
        • Option lists
        • Definition lists (reST only)
        • Nested lists
      • Links, references, and labels
      • Images
        • Images with reStructuredText
        • Images with Markdown
      • Substitutions
      • Blocks, code, math, comments, and quotes
      • Conclusion
    • The Sphinx documentation generator
      • Getting started with Sphinx
        • Using sphinx-quickstart
        • Using sphinx-apidoc
      • Sphinx directives
      • Sphinx roles
    • Documenting code
      • Documenting a class with the Sphinx style
      • Documenting a class with the Google style
      • Documenting a class with the NumPy style
      • Which style to choose
    • Exercises
    • Summary
  • Testing and Logging – Preparing for Bugs
    • Using documentation as tests with doctest
      • A simple doctest example
      • Writing doctests
      • Testing with documentation
      • The doctest flags
        • True and False versus 1 and 0
        • Normalizing whitespace
        • Ellipsis
      • Doctest quirks
        • Testing dictionaries
        • Testing floating-point numbers
        • Times and durations
    • Testing with py.test
      • The difference between the unittest and py.test output
      • The difference between unittest and py.test tests
        • Simplifying assertions
        • Parameterizing tests
        • Automatic arguments using fixtures
        • Print statements and logging
        • Plugins
    • Mock objects
      • Using unittest.mock
      • Using py.test monkeypatch
    • Testing multiple environments with tox
      • Getting started with tox
      • The tox.ini config file
      • Running tox
    • Logging
      • Configuration
        • Basic logging configuration
        • Dictionary configuration
        • JSON configuration
        • ini file configuration
        • The network configuration
      • Logger
        • Usage
        • Formatting
        • Modern formatting using f-strings and str.format
      • Logging pitfalls
      • Debugging loggers
    • Exercises
    • Summary
  • Debugging – Solving the Bugs
    • Non-interactive debugging
      • Inspecting your script using trace
      • Debugging using logging
      • Showing the call stack without exceptions
      • Handling crashes using faulthandler
    • Interactive debugging
      • Console on demand
      • Debugging using Python debugger (pdb)
        • Breakpoints
        • Catching exceptions
        • Aliases
        • commands
      • Debugging with IPython
      • Debugging with Jupyter
      • Other debuggers
        • Debugging services
    • Exercises
    • Summary
  • Performance – Tracking and Reducing Your Memory and CPU Usage
    • What is performance?
    • Measuring CPU performance and execution time
      • Timeit – comparing code snippet performance
      • cProfile – Finding the slowest components
        • First profiling run
        • Calibrating your profiler
        • Selective profiling using decorators
        • Using profile statistics
      • Line profiler – Tracking performance per line
    • Improving execution time
      • Using the right algorithm
      • Global interpreter lock
      • try versus if
      • Lists versus generators
      • String concatenation
      • Addition versus generators
      • Map versus generators and list comprehensions
      • Caching
      • Lazy imports
      • Using slots
      • Using optimized libraries
      • Just-in-time compiling
      • Converting parts of your code to C
    • Memory usage
      • tracemalloc
      • Memory Profiler
      • Memory leaks
        • Circular references
        • Analyzing memory usage using the garbage collector
        • Weak references
        • Weakref limitations and pitfalls
      • Reducing memory usage
        • Generators versus lists
        • Recreating collections versus removing items
        • Using slots
    • Performance monitoring
    • Exercises
    • Summary
  • asyncio – Multithreading without Threads
    • Introduction to asyncio
      • Backward compatibility and async/await statements
        • Python 3.4
        • Python 3.5
        • Python 3.7
      • A basic example of parallel execution
      • asyncio concepts
        • Coroutines, Futures, and Tasks
        • Event loops
        • Executors
    • Asynchronous examples
      • Processes
      • Interactive processes
      • Echo client and server
      • Asynchronous file operations
      • Creating async generators to support async for
      • Asynchronous constructors and destructors
    • Debugging asyncio
      • Forgetting to await a coroutine
      • Slow blocking functions
      • Forgetting to check the results or exiting early
      • Exiting before all tasks are done
    • Exercises
    • Summary
  • Multiprocessing – When a Single CPU Core Is Not Enough
    • The Global Interpreter Lock (GIL)
      • The use of multiple threads
      • Why do we need the GIL?
      • Why do we still have the GIL?
    • Multiple threads and processes
      • Basic examples
        • concurrent.futures
        • threading
        • multiprocessing
      • Cleanly exiting long-running threads and processes
      • Batch processing using concurrent.futures
      • Batch processing using multiprocessing
    • Sharing data between threads and processes
      • Shared memory between processes
      • Thread safety
      • Deadlocks
      • Thread-local variables
    • Processes, threads, or a single thread?
      • threading versus concurrent.futures
      • multiprocessing versus concurrent.futures
    • Hyper-threading versus physical CPU cores
    • Remote processes
      • Distributed processing using multiprocessing
      • Distributed processing using Dask
        • Installing Dask
        • Basic example
        • Running a single thread
        • Distributed execution across multiple machines
      • Distributed processing using ipyparallel
        • ipython_config.py
        • ipython_kernel_config.py
        • ipcontroller_config.py
        • ipengine_config.py
        • ipcluster_config.py
    • Summary
  • Scientific Python and Plotting
    • Installing the packages
    • Arrays and matrices
      • NumPy – Fast arrays and matrices
      • Numba – Faster Python on CPU or GPU
      • SciPy – Mathematical algorithms and NumPy utilities
        • Sparse matrices
      • Pandas – Real-world data analysis
        • Input and output options
        • Pivoting and grouping
        • Merging
        • Rolling or expanding windows
      • Statsmodels – Statistical models on top of Pandas
      • xarray – Labeled arrays and datasets
      • STUMPY – Finding patterns in time series
    • Mathematics and precise calculations
      • gmpy2 – Fast and precise calculations
      • Sage – An alternative to Mathematica/Maple/MATLAB
      • mpmath – Convenient, precise calculations
      • SymPy – Symbolic mathematics
      • Patsy – Describing statistical models
    • Plotting, graphing, and charting
      • Matplotlib
        • Seaborn
        • Yellowbrick
      • Plotly
      • Bokeh
      • Datashader
    • Exercises
    • Summary
  • Artificial Intelligence
    • Introduction to artificial intelligence
      • Types of AI
    • Installing the packages
    • Image processing
      • scikit-image
        • Installing scikit-image
        • Edge detection
        • Face detection
        • scikit-image overview
      • OpenCV
        • Installing OpenCV for Python
        • Edge detection
        • Object detection
      • OpenCV versus scikit-image
    • Natural language processing
      • NLTK – Natural Language Toolkit
      • spaCy – Natural language processing with Cython
      • Gensim – Topic modeling for humans
    • Machine learning
      • Types of machine learning
        • Supervised learning
        • Reinforcement learning
        • Unsupervised learning
        • Combinations of learning methods
        • Deep learning
      • Artificial neural networks and deep learning
        • Tensors
        • PyTorch – Fast (deep) neural networks
        • PyTorch Lightning and PyTorch Ignite – High-level PyTorch APIs
        • Skorch – Mixing PyTorch and scikit-learn
        • TensorFlow/Keras – Fast (deep) neural networks
        • TensorFlow versus PyTorch
      • Evolutionary algorithms
      • Support-vector machines
      • Bayesian networks
    • Versatile AI libraries and utilities
      • scikit-learn – Machine learning in Python
        • Supervised learning
        • Unsupervised learning
      • auto-sklearn – Automatic scikit-learn
      • mlxtend – Machine learning extensions
      • scikit-lego – scikit-learn utilities
      • XGBoost – eXtreme Gradient Boosting
      • Featuretools – Feature detection and prediction
      • Snorkel – Improving your ML data automatically
      • TPOT – Optimizing ML models using genetic programming
    • Exercises
    • Summary
  • Extensions in C/C++, System Calls, and C/C++ Libraries
    • Setting up tooling
      • Do you need C/C++ modules?
      • Windows
      • OS X
      • Linux/Unix
    • Calling C/C++ with ctypes
      • Platform-specific libraries
        • Windows
        • Linux/Unix
        • OS X
        • Making it easy
      • Calling functions and native types
      • Complex data structures
      • Arrays
      • Gotchas with memory management
    • CFFI
      • Complex data structures
      • Arrays
      • ABI or API?
      • CFFI or ctypes?
    • Native C/C++ extensions
      • A basic example
      • C is not Python – Size matters
      • The example explained
        • static
        • PyObject*
        • Parsing arguments
      • C is not Python – Errors are silent or lethal
      • Calling Python from C – Handling complex types
    • Exercises
    • Summary
  • Packaging – Creating Your Own Libraries or Applications
    • Introduction to packages
      • Types of packages
        • Wheels – The new eggs
        • Source packages
      • Package tools
    • Package versioning
    • Building packages
      • Packaging using pyproject.toml
        • Creating a basic package
        • Installing packages for development
        • Adding code and data
        • Adding executable commands
        • Managing dependencies
        • Building the package
        • Building C/C++ extensions
      • Packaging using setuptools with setup.py or setup.cfg
        • Creating a basic package
        • Installing the package for development
        • Adding packages
        • Adding package data
        • Managing dependencies
        • Adding executable commands
        • Building the package
    • Publishing packages
      • Adding URLs
      • PyPI trove classifiers
      • Uploading to PyPI
    • C/C++ extensions
      • Regular C/C++ extensions
      • Cython extensions
    • Testing
      • unittest
      • py.test
    • Exercises
    • Summary
  • Other Books You May Enjoy
  • Index
  • _gjdgxs
  • _gjdgxs
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