Python 3.7 is the latest version of Python programming language. It was released in June 2018 and brings a lot of exciting new features and improvements.
In this article, we will take a closer look at some of the most significant changes in Python 3.7.to Python 3.7
Python is one of the most popular programming languages in the world, and it continues to grow in popularity year after year. Python is easy to learn, and it has a wide range of applications, including web development, data analysis, and machine learning.
Python 3.7 is the latest version of Python, and it comes with many new features and improvements that make it even more powerful and efficient than previous versions. Some of the primary keywords that you need to keep in mind when talking about Python 3.7 are Python 3.7, new features, and improvements.
Overview of New Features in Python 3.7
Python 3.7 brings a lot of new features and improvements that make it a better programming language. One of the most significant changes in Python 3.7 is the optimized _ _bool_ _ implementation.
This optimization improves the performance of boolean operations, making them faster and more efficient. Another feature that makes Python 3.7 stand out is the introduction of context variables.
Context variables are a new way of managing variable scopes, and they allow for more flexible and dynamic control over variables in functions. Python 3.7 also introduces data classes, which allow developers to create classes that are used primarily for holding data.
Data classes are similar to named tuples, but they include additional features such as automatic __init__ and __repr__ methods.
The Breakpoint() Built-In
The breakpoint() built-in is a new function in Python 3.7 that helps developers debug their code more efficiently. The breakpoint() function allows developers to set a breakpoint in their code, which will pause the execution of the program and start a debugging session.
The primary keyword when discussing the breakpoint() built-in is breakpoint() and debugger. It is a great debugging tool that helps find and fix errors in your code.
The primary advantage of breakpoint() over other debugging tools is its customizability.
Advantages and Customizability of Breakpoint()
When using the breakpoint() built-in, the PYTHONBREAKPOINT environment variable can be set to any callable, allowing the developer to customize the debugging behavior of breakpoint(). One of the most significant advantages of breakpoint() is its ability to work with any debugger that supports the PEP 553 interface.
This interface defines a standard way for debuggers to communicate with the Python debugger APIs, making it easier for developers to use their preferred debugger with Python. Another advantage is that breakpoint() respects the current Python debug mode.
If the Python interpreter is already running in a debug mode, then breakpoint() will use that same debug mode. This behavior makes the debugging process much smoother and less prone to errors.
Conclusion
Python 3.7 brings many exciting new features and improvements that make it an even more powerful and efficient programming language. The introduction of context variables, data classes, and the optimized _ _bool_ _ implementation make Python 3.7 a more elegant and sophisticated language than previous versions.
The breakpoint() built-in is also an invaluable tool for debugging your Python code. Its customizability and flexibility make it a tool that every developer should have in their arsenal.
Whether you’re a seasoned Python developer or just starting with Python, Python 3.7 and its new features are an excellent choice for your next project.
3) Data Classes
In Python 3.7, the @dataclass decorator was introduced, which provides a concise way to create classes that are primarily used for holding data. Data classes are similar to named tuples, but they include additional features such as automatic __init__ and __repr__ methods.
The primary keyword(s) associated with data classes are data classes and @dataclass decorator.to Data Classes and Their Benefits over Regular Classes
Data classes provide a way to create classes that are primarily used for holding data. The @dataclass decorator generates several special methods for us automatically, including the __init__ and __repr__ methods.
Using a data class can make your code much more concise and easier to read. With a regular class, you would need to define the __init__ and __repr__ methods manually, which can be time-consuming and error-prone.
Data classes also provide a way to enforce types and defaults for the attributes of the class. This ensures that the code is correct and produces fewer bugs.
Example of Using Data Classes to Create a Country Class
Here is an example of using data classes to create a Country class:
“`
from dataclasses import dataclass
@dataclass
class Country:
name: str
population: int
capital: str
“`
This data class represents a country and includes its name, population, and capital city. We can create an instance of this class like any other class:
“`
us = Country(“United States”, 328200000, “Washington D.C.”)
“`
We can then access the attributes of the class using dot notation:
“`
print(us.name) # “United States”
print(us.population) # 328200000
print(us.capital) # “Washington D.C.”
“`
Data classes also include a __eq__ method by default, which compares the attribute values of two instances of the class.
4) Customization of Module Attributes
Python has a lot of different attributes that can be used to manipulate and customize the behavior of modules. Attributes are accessed using dot notation and can be set at runtime.
The primary keyword(s) associated with customization of module attributes are attributes and dot notation.
Overview of Attributes in Python and Their Use Cases
There are many attributes in Python that can be used to customize the behavior of modules. Some of the most common ones include:
– __name__: the name of the module
– __doc__: the docstring of the module
– __file__: the path to the module’s source file
– __all__: a list of the names of all the public objects defined in the module
– __path__: a list of directories containing other packages to be found during import
– __loader__: the object responsible for loading the module
Attributes can be accessed using dot notation, like so:
“`
import datetime
print(datetime.__name__) # “datetime”
“`to the New __getattr__() and __dir__() Functions in Modules
In Python 3.7, two new functions were added to modules that allow for more customization: __getattr__() and __dir__(). The __getattr__() function is called when an attribute is accessed that does not exist in the module.
This allows for dynamic attribute creation and can be used to implement plugins or other code that is only loaded at runtime. The __dir__() function is used to customize the list of attributes that are returned when the dir() function is called on the module.
This allows for more fine-grained control over what is visible in the module namespace.
Example of a Simple Plugin System Using __getattr__() and __dir__()
Here is an example of a simple plugin system using __getattr__() and __dir__():
“`
class Plugin:
def greet(self):
print(“Hello from plugin!”)
def __getattr__(name):
if name == “plugin”:
return Plugin()
else:
raise AttributeError(f”module has no attribute ‘{name}'”)
def __dir__():
return [‘plugin’]
“`
In this example, we define a Plugin class and then use __getattr__() to create a plugin instance when the “plugin” attribute is accessed. We also use __dir__() to ensure that only the “plugin” attribute is visible in the module namespace.
We can then use this plugin in other parts of our code:
“`
import mymodule
mymodule.plugin.greet() # “Hello from plugin!”
“`
Conclusion
Python 3.7 includes many new features and improvements that make it a better programming language. Data classes provide a more concise and elegant way to create classes that are used primarily for holding data, while the customization of module attributes allows for more fine-grained control over module behavior.
These updates to Python 3.7 make it a more powerful and efficient language and are sure to benefit Python developers everywhere.
5) Typing Enhancements
Python’s typing system is an optional feature that allows developers to add type annotations to their code. Type annotations help with code clarity and improve code quality by providing better documentation and error checking.
With Python 3.7, the typing system has become more stable with improved performance and core support. The primary keywords associated with typing enhancements are typing system, stability, performance, core support, and forward references.
Overview of Python’s Typing System and its Stability in Python 3.7
Python’s typing system is a way to add type hints to Python code to improve readability and check for errors. Type hints help with code understanding and maintainability by providing clear documentation on the data types involved in the code.
Python 3.7 includes several enhancements to the typing system, including improved performance and core support. These enhancements improve the stability of the typing system and make it more suitable for larger codebases.
Enhancements to Python’s Typing System in Python 3.7
Python 3.7 includes several enhancements to the typing system that improve its performance and core support. Some of the most significant changes include:
– Better performance: Python 3.7 includes several improvements to type checking, including faster annotation parsing, improved attribute access, and better type inference.
These changes improve the performance of the typing system and make it more suitable for larger codebases. – Core support: Python 3.7 includes better support for core data types in the typing system, including Tuple, List, Set, and Dict.
These changes make it easier to use the typing system with built-in data types. – Forward references: Python 3.7 includes support for forward references in type annotations.
This allows developers to define a class or function before its use in a type hint, which makes it easier to use the typing system with complex code. Overall, these enhancements make Python’s typing system more stable and suitable for complex and large codebases.
6) Timing Precision
Timing precision is an essential feature for measuring the performance of code and optimizing it for better performance. Python 3.7 includes higher precision timing functions that provide better accuracy when measuring the performance of code.
The primary keywords associated with timing precision are timing precision, time.perf_counter_ns, and performance benchmarking.to Higher Precision Timing Functions in Python 3.7
Python 3.7 includes the time.perf_counter_ns function, which provides nanosecond-level precision when measuring time. This function is ideal for benchmarking code and measuring its performance accurately.
Benefits and Practical Use Cases of Higher Precision Timing Functions
Higher precision timing functions are essential for benchmarking code and measuring its performance. With Python 3.7, developers can use the time.perf_counter_ns function to measure the performance of their code accurately.
Here is an example of using time.perf_counter_ns to measure the performance of a function:
“`
import time
def
my_function():
# some code to measure
start_time = time.perf_counter_ns()
my_function()
end_time = time.perf_counter_ns()
time_elapsed = end_time – start_time
print(f”Time elapsed: {time_elapsed}ns”)
“`
In this example, we use time.perf_counter_ns to measure the performance of the my_function function. We measure the time elapsed by subtracting the start time from the end time and print the result.
Higher precision timing functions can also be used for profiling code and identifying performance bottlenecks. By measuring the performance of different parts of the code, developers can identify areas that need optimization and improve the overall performance of their code.
Conclusion
Python 3.7 includes several enhancements that improve the performance and stability of its typing system and provide higher precision timing functions for measuring the performance of code. These updates make Python 3.7 a more powerful and efficient language and are sure to benefit Python developers everywhere.
7) Other Pretty Cool Features
Python 3.7 introduced several other exciting features that make it a worthwhile upgrade for developers. These features include guaranteed order of dictionaries, the “async” and “await” keywords, changes to the asyncio module, context variables, importing data files with “importlib.resources,” and developer tricks.
The primary keywords associated with Other Pretty Cool Features are dictionaries, order, “async,” “await,” asyncio, Face Lift, context variables, data import, importlib.resources, developer tools, and Python optimizations.
The Guaranteed Order of Dictionaries
In Python 3.7 and later versions, dictionaries maintain the order in which the items are inserted. Prior to Python 3.7, the order of the items in dictionaries was not guaranteed.
This change makes the behavior of dictionaries more predictable and reliable. Here is an example of how the order is preserved in dictionaries:
“`
d = {‘banana’: 2, ‘apple’: 4, ‘orange’: 3}
print(list(d.keys())) # [‘banana’, ‘apple’, ‘orange’]
“`
The “async” and “await” Keywords
The “async” and “await” keywords allow for the creation of asynchronous code in Python.
Asynchronous code allows for non-blocking execution of code, which can improve the performance of certain applications. The “async” keyword is used to define a coroutine, while the “await” keyword is used to suspend the execution of a coroutine until a certain condition is met.
Here is an example of using “async” and “await” keywords:
“`
async def my_coroutine():
print(“Starting coroutine”)
await asyncio.sleep(1)
print(“Coroutine completed”)
loop = asyncio.get_event_loop()
loop.run_until_complete(my_coroutine())
“`
This code defines a coroutine using the “async” keyword and suspends its execution using the “await” keyword until one second has passed.
Changes to the Asyncio Module
Python 3.7 includes significant changes to the asyncio module, including a major revamp of the core event loop and improvements to the API. The primary changes include a new AbstractEventLoop class, which replaces the previous BaseEventLoop class.
This class makes it easier to create custom event loops and reduces the complexity of the asyncio module. The asyncio module also received a “Face Lift,” including a new high-level API, improvements to SSL support, and better error handling.
Context Variables
Context variables are a new way of managing variable scopes in Python. They allow for more flexible and dynamic control over variables in functions and are easier to use than closures or decorators.
Here is an example of how to use context variables:
“`
from contextvars import ContextVar
greeting = ContextVar(“greeting”, default=”Hello”)
def greet():
print(greeting.get())
with greeting.set(“Hi there!”):
greet() # “Hi there!”
“`
In this example, we define a context variable called “greeting” and set its default value to “Hello.” We then define a function called greet, which uses the “greeting” context variable. Finally, we temporarily change the value of the context variable using the “set” method and call the greet function, which prints the new value.
Importing Data Files with “importlib.resources”
In Python 3.7, the “importlib.resources” module was introduced, which provides a way to import data files from within Python packages. Here is an example of how to use “importlib.resources” to import a data file:
“`
import importlib.resources
with importlib.resources.open_text(“my_package”, “data_file.txt”) as file:
data = file.read()
print(data)
“`
This code opens a text file called “data_file.txt” from a package called “my_package” and reads its contents.
Developer Tricks
Python 3.7 introduces