Decorators are a powerful and flexible feature in Python for modifying or enhancing the behavior of a function or class. A decorator is essentially a function that takes another function or class as an argument and returns a new function or class. They are often used to add additional functionality or features without modifying the original code.
Syntactic use of decorators
@
symbol to apply the decorator to the target function or class. Below we will present 10 very simple but useful custom decorators.
1、@timer:Measure execution time
Optimizing code performance is very important. The @timer decorator helps us track the execution time of a specific function. By wrapping functions with this decorator, I can quickly identify bottlenecks and optimize key parts of my code. Here's how it works.
import time
def timer(func):
def wrapper(*args, **kwargs):
start_time = ()
result = func(*args, **kwargs)
end_time = ()
print(f"{func.__name__} took {end_time - start_time:.2f} seconds to execute.")
return result
return wrapper
@timer
def my_data_processing_function():
# Your data processing code hereUsing @timer in conjunction with other decorators allows you to fully analyze the performance of your code.
2、@memoize:cache result
In data science, we often use computationally expensive functions. The @memoize decorator helps me cache function results, avoiding redundant computations on the same input and significantly speeding up the workflow:.
def memoize(func):
cache = {}
def wrapper(*args):
if args in cache:
return cache[args]
result = func(*args)
cache[args] = result
return result
return wrapper
@memoize
def fibonacci(n):
if n <= 1:
return n
return fibonacci(n - 1) + fibonacci(n - 2)You can also use @memoize in recursive functions to optimize repeated calculations.
3、@validate_input:data validation
Data integrity is critical, and the @validate_input decorator validates function arguments to ensure that they meet specific criteria before proceeding with computation:.
def validate_input(func):
def wrapper(*args, **kwargs):
# Your data validation logic here
if valid_data:
return func(*args, **kwargs)
else:
raise ValueError("Invalid data. Please check your inputs.")
return wrapper
@validate_input
def analyze_data(data):
# Your data analysis code hereIt is easy to use @validate_input to consistently implement data validation in data science projects.
4、@log_results:log_results:log output
When running complex data analysis, it becomes critical to track the output of each function. The @log_results decorator helps us log the results of our functions for debugging and monitoring purposes:.
def log_results(func):
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
with open("", "a") as log_file:
log_file.write(f"{func.__name__} - Result: {result}\n")
return result
return wrapper
@log_results
def calculate_metrics(data):
# Your metric calculation code hereUse @log_results in conjunction with the logging library for more advanced logging capabilities.
5、@suppress_errors:Elegant Error Handling
Data science projects often encounter unexpected errors that can disrupt the entire computational process. The @suppress_errors decorator gracefully handles exceptions and continues execution:.
def suppress_errors(func):
def wrapper(*args, **kwargs):
try:
return func(*args, **kwargs)
except Exception as e:
print(f"Error in {func.__name__}: {e}")
return None
return wrapper
@suppress_errors
def preprocess_data(data):
# Your data preprocessing code here@suppress_errors avoids hiding serious errors and also allows for detailed error output for debugging purposes.
6. @validate_output: ensure quality results
Ensuring the quality of data analysis is critical. The @validate_output decorator helps us validate the output of a function to ensure that it meets specific criteria before further processing:.
def validate_output(func):
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
if valid_output(result):
return result
else:
raise ValueError("Invalid output. Please check your function logic.")
return wrapper
@validate_output
def clean_data(data):
# Your data cleaning code hereThis will always define clear criteria for validating function output.
7、@retry:Retry execution
The @retry decorator helps me retry function execution when an exception is encountered, ensuring greater resilience: the
import time
def retry(max_attempts, delay):
def decorator(func):
def wrapper(*args, **kwargs):
attempts = 0
while attempts < max_attempts:
try:
return func(*args, **kwargs)
except Exception as e:
print(f"Attempt {attempts + 1} failed. Retrying in {delay} seconds.")
attempts += 1
(delay)
raise Exception("Max retry attempts exceeded.")
return wrapper
return decorator
@retry(max_attempts=3, delay=2)
def fetch_data_from_api(api_url):
# Your API data fetching code hereExcessive retries should be avoided when using @retry.
8、@visualize_results:beautiful visualization
@visualize_results Decorator data analysis to automatically generate beautiful visualization results
import as plt
def visualize_results(func):
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
()
# Your visualization code here
()
return result
return wrapper
@visualize_results
def analyze_and_visualize(data):
# Your combined analysis and visualization code here9, @debug: debugging becomes easier
Debugging complex code can be very time consuming. The @debug decorator prints the input parameters of a function and their values for debugging purposes: the
def debug(func):
def wrapper(*args, **kwargs):
print(f"Debugging {func.__name__} - args: {args}, kwargs: {kwargs}")
return func(*args, **kwargs)
return wrapper
@debug
def complex_data_processing(data, threshold=0.5):
# Your complex data processing code here10、@deprecated:Processing deprecated functions
As our project iterates, some functions may become deprecated. The @deprecated decorator can notify the user when a function is no longer recommended: the
import warnings
def deprecated(func):
def wrapper(*args, **kwargs):
(f"{func.__name__} is deprecated and will be removed in future versions.", DeprecationWarning)
return func(*args, **kwargs)
return wrapper
@deprecated
def old_data_processing(data):
# Your old data processing code heresummarize
Decorators are a very powerful and commonly used feature in Python, which can be used in many different situations such as caching, logging, permission control, etc. By using these Python decorators that we have introduced in our projects, we can simplify our development process or make our code more robust.
To this article on the 10 simple but very useful Python decorator to share the article is introduced to this, more related Python decorator content, please search for my previous posts or continue to browse the following related articles I hope you will support me in the future more!