最新章節

• Summary
• Compiling an extension module in Python
• Using cpdef in a Cython class
• Methods to increase the execution speed of Python code
• Writing extension modules in Cython
• Additional import methods in Cython

• Summary 更新時間：2021-07-02 21:10:27
• Compiling an extension module in Python
• Using cpdef in a Cython class
• Methods to increase the execution speed of Python code
• Writing extension modules in Cython
• Additional import methods in Cython
• Working with Cython
• Interfacing with C code using Cython
• Providing data structure layouts
• Providing a function signature
• Using a pointer as a parameter of a function
• Assigning attributes to a function
• Accessing functions defined in the library
• Locating and linking a dynamic library
• Accessing a dynamic library using ctypes
• The downsides of compiled code
• Advantages and disadvantages of compiled code
• Extension Modules and Compiled Code
• Summary
• Interacting with a microservice by creating another microservice
• Interacting with a microservice manually using the nameko shell
• Interacting with our microservice
• Things to know before using nameko
• Running and connecting a microservice using nameko
• Installing nameko
• Building high-level microservices with nameko
• Test running the microservice
• Running and connecting to our microservice using Flask
• Making Flask handle a request
• Building a microservice to maintain a database
• Creating endpoints for a RESTful API in Flask
• Installing Flask
• Building high-level microservices with Flask
• Applying the microservice architecture to web servers
• Advantages of the microservice architecture
• Microservices and the advantages of process isolation
• Microservices
• Summary
• The where factory method
• Empty return_value and from_iterable factory methods
• The Observable.select_many method
• The Observable.create method
• Miscellaneous observable factory methods
• Combining and processing observable sequences
• Creating an asyncio scheduler
• Explaining the observable sequence of events
• Observable factory methods
• Translating our zoo demo into Rx
• Using the reactive extensions for Python (RxPY)
• Composing an observable sequence
• Illustrating a stream of animal events
• Building the observable sequence
• Emitting events
• Observables
• Observers
• Building a simple reactive programming framework
• The concept of reactive programming
• Reactive Programming
• Summary
• Testing multiple worker processes
• The cover-package option
• Running our tests with nose
• Using nose for unified test discovery and reporting
• Command-line options in unit test discovery
• Unittest's discovery tool
• Using unittest's test discovery
• The unittest.mock patch function
• assert methods of mock objects
• Preconfiguring mock objects
• What is a mock object?
• Using unittest.mock
• Comparing what happens to what should happen in unit tests
• assert methods
• Structuring a test file
• Using the unittest package
• What is a unit test?
• Understanding the principle of unit testing
• Unit Testing
• Summary
• Writing descriptors as classes
• Using @property to create a descriptor
• Descriptors
• Creating an asynchronous-coroutine-based context manager
• Synchronous-coroutine-based context managers
• Adding context manager behavior to a class
• Defining a context manager as a generator
• Context managers
• The __new__ method
• The __prepare__method
• What can we do with a metaclass?
• Metaclasses
• Class definitions
• The factory_constructed function
• The factory function
• Modifying class attributes
• Class decorators
• Inspecting the package signature function
• Keyword arguments
• Annotations as input to function decorators
• The @no_type_check decorator
• Accessing annotation data
• Function annotation syntax
• Function annotations
• The only function
• The @wraps decorator
• Enclosing the function in a wrapper
• Attributes
• Global decorator - @staticmethod
• Using the @ syntax in a function decorator
• Using function decorators
• Metaprogramming
• Summary
• Handling client disconnections
• Creating a simple server in asyncio
• Creating a simple client in asyncio
• Communicating across the network
• Queue types
• The asyncio Queue class
• The gather coroutine
• The wait_for coroutine
• The wait coroutine
• Synchronization primitives
• Synchronizing multiple tasks
• Asynchronous iterations
• asyncio's future objects
• Awaiting data availability
• Closing event_loop
• The run_forever/run_until_complete methods
• ensure_future
• The asyncio scheduler - event_loop
• Creating a coroutine
• Using the asyncio event loop and coroutine scheduler
• The coroutine scheduler
• Python coroutines
• Cooperative coroutine scheduler versus coroutine
• Multithreading is not good for servers
• The difference between asynchronous processing and parallel processing
• Coroutines and Asynchronous I/O
• Summary
• The semaphore object
• The condition object
• The event object
• The lock object
• Manager
• Pipes
• Queues
• Process class in the multiprocessing module
• Using the multiprocessing packages
• The cancel method
• The add done callback function
• The wait and as_completed functions
• The done and result methods
• Using the submit method
• Using the map method
• Calling ProcessPoolExecutor
• The concurrent.futures module
• Using the concurrent.futures package
• Parallel Processing
• Summary
• Creating launches for our program
• Setting up a shell script or batch file to launch the program
• Finishing up our code example
• The wait method
• The PIPE constant
• Using the Popen subprocess
• Subprocess and its variants
• Executing other programs with subprocess
• The Pipeline user interface
• The cmd class
• The pprint package
• The getpass package
• Python's built-in functions - print and input
• Python tools to interact with the user
• nargs
• Setting the name of argument
• Creating an ArgumentParser object
• Handling command-line arguments with argparse
• Pipeline program
• Making a package executable via Python -m
• Making a Command-Line Utility
• Summary
• What it means when a code example fails
• Testing examples using doctest
• Using doctest to test documentation examples
• Turning docstrings into HTML
• Sphinx
• PEP 257 and docutils
• Getting the most out of docstrings
• pip in virtual environments
• Activating a virtual environment
• Creating a virtual environment
• Using venv to create a stable and isolated work area
• The pull command
• The mergetool command
• Merging codes
• Branches
• Undoing the changes
• Committing the changes in Git
• Initializing Git
• Using version control
• Naming conventions
• Formatting recommendations
• Code indentation
• PEP 8 — guidelines for Python code
• PEP 8 and writing readable code
• Basic Best Practices
• Summary
• Adding static data files to the package
• Resolving attribute errors raised due to cyclic dependencies
• Importing a cyclic dependency
• Accessing code from other modules
• The Package structure and interface
• Module loading with namespace packages
• Adding modules to the package
• Importing all package modules
• Turning a regular folder into a package
• Creating an empty package
• Making a Package
• Summary
• Legalities and licenses of the Python Package Index
• Searching the Package Index with pip
• Using Package Index
• Using keywords
• Finding packages in the Python Package Index
• Managing installed packages
• The pip tool for packages
• Installing packages with pip
• Python interactive shell
• Opening a command-line window
• Using the command line and the interactive shell
• Installing Python
• Choosing a suitable version
• Downloading and installing Python
• Setting Up
• Summary
• Other changes in Python packages
• Changes in packages
• The changes in the syntactic
• What's new in modern Python
• Different types of packages
• The standard library
• Attributes
• The defaultdict class
• First-class functions and classes
• Comprehension
• Set
• Tuple
• List
• Dictionaries
• Python's built-in data structures and comprehensions
• Indentation
• Flow control statements
• Classes
• Expressions
• Variables
• Functions
• Basic building blocks
• Python basic syntax and block structure
• Python Primer
• Questions
• Piracy
• Errata
• Downloading the example code
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• Customer Feedback
• Why subscribe?
• www.PacktPub.com
• About the Author
• Credits
• Title Page
• coverpage

• coverpage
• Title Page
• Credits
• About the Author
• www.PacktPub.com
• Why subscribe?
• Customer Feedback
• Preface
• What this book covers
• What you need for this book
• Who this book is for
• Conventions
• Reader feedback
• Customer support
• Downloading the example code
• Errata
• Piracy
• Questions
• Python Primer
• Python basic syntax and block structure
• Basic building blocks
• Functions
• Variables
• Expressions
• Classes
• Flow control statements
• Indentation
• Python's built-in data structures and comprehensions
• Dictionaries
• List
• Tuple
• Set
• Comprehension
• First-class functions and classes
• The defaultdict class
• Attributes
• The standard library
• Different types of packages
• What's new in modern Python
• The changes in the syntactic
• Changes in packages
• Other changes in Python packages
• Summary
• Setting Up
• Downloading and installing Python
• Choosing a suitable version
• Installing Python
• Using the command line and the interactive shell
• Opening a command-line window
• Python interactive shell
• Installing packages with pip
• The pip tool for packages
• Managing installed packages
• Finding packages in the Python Package Index
• Using keywords
• Using Package Index
• Searching the Package Index with pip
• Legalities and licenses of the Python Package Index
• Summary
• Making a Package
• Creating an empty package
• Turning a regular folder into a package
• Importing all package modules
• Adding modules to the package
• Module loading with namespace packages
• The Package structure and interface
• Accessing code from other modules
• Importing a cyclic dependency
• Resolving attribute errors raised due to cyclic dependencies
• Adding static data files to the package
• Summary
• Basic Best Practices
• PEP 8 and writing readable code
• PEP 8 — guidelines for Python code
• Code indentation
• Formatting recommendations
• Naming conventions
• Using version control
• Initializing Git
• Committing the changes in Git
• Undoing the changes
• Branches
• Merging codes
• The mergetool command
• The pull command
• Using venv to create a stable and isolated work area
• Creating a virtual environment
• Activating a virtual environment
• pip in virtual environments
• Getting the most out of docstrings
• PEP 257 and docutils
• Sphinx
• Turning docstrings into HTML
• Using doctest to test documentation examples
• Testing examples using doctest
• What it means when a code example fails
• Summary
• Making a Command-Line Utility
• Making a package executable via Python -m
• Pipeline program
• Handling command-line arguments with argparse
• Creating an ArgumentParser object
• Setting the name of argument
• nargs
• Python tools to interact with the user
• Python's built-in functions - print and input
• The getpass package
• The pprint package
• The cmd class
• The Pipeline user interface
• Executing other programs with subprocess
• Subprocess and its variants
• Using the Popen subprocess
• The PIPE constant
• The wait method
• Finishing up our code example
• Setting up a shell script or batch file to launch the program
• Creating launches for our program
• Summary
• Parallel Processing
• Using the concurrent.futures package
• The concurrent.futures module
• Calling ProcessPoolExecutor
• Using the map method
• Using the submit method
• The done and result methods
• The wait and as_completed functions
• The add done callback function
• The cancel method
• Using the multiprocessing packages
• Process class in the multiprocessing module
• Queues
• Pipes
• Manager
• The lock object
• The event object
• The condition object
• The semaphore object
• Summary
• Coroutines and Asynchronous I/O
• The difference between asynchronous processing and parallel processing
• Multithreading is not good for servers
• Cooperative coroutine scheduler versus coroutine
• Python coroutines
• The coroutine scheduler
• Using the asyncio event loop and coroutine scheduler
• Creating a coroutine
• The asyncio scheduler - event_loop
• ensure_future
• The run_forever/run_until_complete methods
• Closing event_loop
• Awaiting data availability
• asyncio's future objects
• Asynchronous iterations
• Synchronizing multiple tasks
• Synchronization primitives
• The wait coroutine
• The wait_for coroutine
• The gather coroutine
• The asyncio Queue class
• Queue types
• Communicating across the network
• Creating a simple client in asyncio
• Creating a simple server in asyncio
• Handling client disconnections
• Summary
• Metaprogramming
• Using function decorators
• Using the @ syntax in a function decorator
• Global decorator - @staticmethod
• Attributes
• Enclosing the function in a wrapper
• The @wraps decorator
• The only function
• Function annotations
• Function annotation syntax
• Accessing annotation data
• The @no_type_check decorator
• Annotations as input to function decorators
• Keyword arguments
• Inspecting the package signature function
• Class decorators
• Modifying class attributes
• The factory function
• The factory_constructed function
• Class definitions
• Metaclasses
• What can we do with a metaclass?
• The __prepare__method
• The __new__ method
• Context managers
• Defining a context manager as a generator
• Adding context manager behavior to a class
• Synchronous-coroutine-based context managers
• Creating an asynchronous-coroutine-based context manager
• Descriptors
• Using @property to create a descriptor
• Writing descriptors as classes
• Summary
• Unit Testing
• Understanding the principle of unit testing
• What is a unit test?
• Using the unittest package
• Structuring a test file
• assert methods
• Comparing what happens to what should happen in unit tests
• Using unittest.mock
• What is a mock object?
• Preconfiguring mock objects
• assert methods of mock objects
• The unittest.mock patch function
• Using unittest's test discovery
• Unittest's discovery tool
• Command-line options in unit test discovery
• Using nose for unified test discovery and reporting
• Running our tests with nose
• The cover-package option
• Testing multiple worker processes
• Summary
• Reactive Programming
• The concept of reactive programming
• Building a simple reactive programming framework
• Observers
• Observables
• Emitting events
• Building the observable sequence
• Illustrating a stream of animal events
• Composing an observable sequence
• Using the reactive extensions for Python (RxPY)
• Translating our zoo demo into Rx
• Observable factory methods
• Explaining the observable sequence of events
• Creating an asyncio scheduler
• Combining and processing observable sequences
• Miscellaneous observable factory methods
• The Observable.create method
• The Observable.select_many method
• Empty return_value and from_iterable factory methods
• The where factory method
• Summary
• Microservices
• Microservices and the advantages of process isolation
• Advantages of the microservice architecture
• Applying the microservice architecture to web servers
• Building high-level microservices with Flask
• Installing Flask
• Creating endpoints for a RESTful API in Flask
• Building a microservice to maintain a database
• Making Flask handle a request
• Running and connecting to our microservice using Flask
• Test running the microservice
• Building high-level microservices with nameko
• Installing nameko
• Running and connecting a microservice using nameko
• Things to know before using nameko
• Interacting with our microservice
• Interacting with a microservice manually using the nameko shell
• Interacting with a microservice by creating another microservice
• Summary
• Extension Modules and Compiled Code
• Advantages and disadvantages of compiled code
• The downsides of compiled code
• Accessing a dynamic library using ctypes
• Locating and linking a dynamic library
• Accessing functions defined in the library
• Assigning attributes to a function
• Using a pointer as a parameter of a function
• Providing a function signature
• Providing data structure layouts
• Interfacing with C code using Cython
• Working with Cython
• Additional import methods in Cython
• Writing extension modules in Cython
• Methods to increase the execution speed of Python code
• Using cpdef in a Cython class
• Compiling an extension module in Python
• Summary 更新時間：2021-07-02 21:10:27