Python 3.7: A Comprehensive Guide to New Features and Enhancements
Python 3.7 is the latest version of the Python programming language. Released in June 2018, it brings a plethora of exciting new features and improvements that make it even more powerful and efficient than its predecessors. This article will delve into some of the most significant changes in Python 3.7, exploring their benefits and practical use cases.
1) Overview of New Features in Python 3.7
Python 3.7 introduces several new features and improvements that enhance the language’s capabilities and user experience. These features address key areas such as performance optimization, debugging, and type hinting.
2) The Breakpoint() Built-In
Python 3.7 introduces the breakpoint() built-in function, a powerful tool for debugging your code more efficiently. The breakpoint() function allows developers to set a breakpoint in their code, pausing the program’s execution and initiating a debugging session.
Advantages and Customizability of Breakpoint()
The breakpoint() function offers several advantages over traditional debugging methods. It’s highly customizable, allowing developers to tailor the debugging behavior to their specific needs. The PYTHONBREAKPOINT environment variable can be set to any callable, enabling customization of the breakpoint’s functionality.
Furthermore, breakpoint() seamlessly integrates with various debuggers that support the PEP 553 interface, ensuring compatibility across different debugging tools. It also respects the current Python debug mode, making the debugging process smoother and less error-prone.
3) Data Classes
Python 3.7 introduces the @dataclass decorator, providing a concise way to create classes primarily for holding data. Data classes are similar to named tuples but offer additional features like automatic __init__ and __repr__ methods.
Data Classes and Their Benefits over Regular Classes
Data classes offer several advantages over regular classes: they make your code more concise and readable by automatically generating essential methods like __init__ and __repr__. This eliminates the need for manual definition, saving time and reducing errors. Data classes also allow you to enforce types and defaults for attributes, enhancing code correctness and minimizing bugs.
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, including its name, population, and capital city. You can create an instance of this class like any other class:
us = Country("United States", 328200000, "Washington D.C.")
You 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 offers a wide range of attributes that can be used to manipulate and customize the behavior of modules. These attributes are accessed using dot notation and can be set at runtime.
Overview of Attributes in Python and Their Use Cases
Several attributes in Python allow you to customize module behavior. 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, as shown in the following example:
import datetime
print(datetime.__name__) # "datetime"
The New __getattr__() and __dir__() Functions in Modules
Python 3.7 introduces two new functions for modules, __getattr__() and __dir__(), allowing for more customization. The __getattr__() function is called when an attribute is accessed that doesn’t exist in the module. This allows for dynamic attribute creation and can be used to implement plugins or other code loaded at runtime.
The __dir__() function customizes the list of attributes returned when the dir() function is called on the module. This gives you more control over what is visible in the module namespace.
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 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.
You can then use this plugin in other parts of your code:
import mymodule
mymodule.plugin.greet() # "Hello from plugin!"
5) Typing Enhancements
Python’s typing system is an optional feature that allows developers to add type annotations to their code. Type annotations enhance code clarity and quality by providing better documentation and error checking.
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, improving readability and error checking. Type hints provide clear documentation on data types involved in the code, making it easier to understand and maintain.
Python 3.7 introduces several enhancements to the typing system, improving its performance and core support, making it more stable and suitable for larger codebases.
Enhancements to Python’s Typing System in Python 3.7
Python 3.7 includes several key improvements to the typing system:
- Better Performance: Python 3.7 features optimizations to type checking, including faster annotation parsing, improved attribute access, and better type inference. These enhancements improve the typing system’s performance, making it 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. This makes it easier to use the typing system with built-in data types.
- Forward References: Python 3.7 adds support for forward references in type annotations. This allows developers to define a class or function before its use in a type hint, simplifying the use of the typing system with complex code.
6) Timing Precision
Timing precision is crucial for measuring the performance of code and optimizing it. Python 3.7 introduces higher-precision timing functions that provide greater accuracy when measuring code performance.
Higher Precision Timing Functions in Python 3.7
Python 3.7 includes the time.perf_counter_ns() function, offering nanosecond-level precision for 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 time.perf_counter_ns() to measure the performance of their code with accuracy.
Here’s 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, time.perf_counter_ns() is used to measure the performance of the my_function() function. The time elapsed is calculated by subtracting the start time from the end time and printed as a result.
Higher-precision timing functions are also useful for profiling code and identifying performance bottlenecks. By measuring the performance of different parts of the code, developers can pinpoint areas that require optimization and improve the overall performance of their code.
7) Other Pretty Cool Features
Python 3.7 introduces 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 Guaranteed Order of Dictionaries
In Python 3.7 and later versions, dictionaries maintain the order in which items are inserted. Prior to Python 3.7, the order of items in dictionaries was not guaranteed. This change makes the behavior of dictionaries more predictable and reliable. Here’s an example of how 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 enable the creation of asynchronous code in Python. Asynchronous code allows for non-blocking execution, potentially improving the performance of certain applications. The “async” keyword defines a coroutine, while the “await” keyword suspends the coroutine’s execution until a specific condition is met.
Here’s 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 “await” 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. Key changes include a new AbstractEventLoop class, which replaces the previous BaseEventLoop class. This class simplifies the creation of 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 to manage 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’s an example of using 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 named “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”
Python 3.7 introduces the “importlib.resources” module, which provides a way to import data files from within Python packages. Here’s an example of using “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 several optimizations and improvements that streamline development workflows and enhance the developer experience. These include:
- Improved Performance: Python 3.7 includes various performance optimizations, such as faster garbage collection and improved bytecode compilation, leading to faster execution speeds for your Python code.
- Enhanced Error Handling: Python 3.7 enhances error handling with improvements to the traceback module, providing more informative and helpful error messages, making debugging easier.
- Improved Documentation: Python 3.7 includes improvements to the documentation, providing clearer and more comprehensive information on various language features and modules.
Conclusion
Python 3.7 introduces a wide range of new features and enhancements that make it a more powerful, efficient, and developer-friendly language. From improved debugging tools and typing enhancements to advanced asynchronous programming capabilities and data class support, Python 3.7 offers valuable tools and features for developers across various domains. Whether you’re a seasoned Python developer or just starting, Python 3.7 is a compelling choice for your next project.