上篇介绍了Python装饰器及相关学习资源：Python高阶函数与装饰器，这次整了网上一些简单实用的Python装饰器示例。
1 @timer:测量执行时间
优化代码性能是非常重要的。@timer装饰器可以帮助我们跟踪特定函数的执行时间。通过用这个装饰器包装函数，可以快速识别瓶颈并优化代码的关键部分。下面是它的工作原理:
import time
def timer(func):
def wrapper(*args, **kwargs):
start_time = time.time()
result = func(*args, **kwargs)
end_time = time.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 here将@timer与其他装饰器结合使用，可以全面地分析代码的性能。
2 @memoize:缓存结果
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)3 @validate_input:数据验证
数据完整性至关重要，@validate_input装饰器可以验证函数参数，确保它们在继续计算之前符合特定的标准:
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 here可以方便的使用@validate_input在数据科学项目中一致地实现数据验证。
4 @log_results:日志输出
def log_results(func):
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
with open("results.log", "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 here将@log_results与日志库结合使用，以获得更高级的日志功能。5 @suppress_errors:优雅的错误处理
数据科学项目经常会遇到意想不到的错误，可能会破坏整个计算流程。@suppress_errors装饰器可以优雅地处理异常并继续执行: 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 here6 @validate_output:确保质量结果
确保数据分析的质量至关重要。@validate_output装饰器可以帮助我们验证函数的输出，确保它在进一步处理之前符合特定的标准:
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 here7 @retry:重试执行
@retry装饰器帮助我在遇到异常时重试函数执行，确保更大的弹性:
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
time.sleep(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 here8 @visualize_results:漂亮的可视化
@visualize_results装饰器数据分析中自动生成漂亮的可视化结果：
import matplotlib.pyplot as plt def visualize_results(func): def wrapper(*args, **kwargs): result = func(*args, **kwargs) plt.figure() # Your visualization code here plt.show() return result return wrapper @visualize_results def analyze_and_visualize(data): # Your combined analysis and visualization code here
9 @debug:调试变得更容易
def debug(func):
def wrapper(*args, **kwargs):
print(f"Debugging {func.__name__} start，- args: {args}, kwargs: {kwargs}")
result = func(*args, **kwargs)
print(f"Debugging {func.__name__} end，- return: {result}")
return result
return wrapper
@debug
def complex_data_processing(data, threshold=0.5):
# Your complex data processing code here10 @deprecated:处理废弃的函数
随着我们的项目更新迭代，一些函数可能会过时。@deprecated装饰器可以在一个函数不再被推荐时通知用户: import warnings
def deprecated(func):
def wrapper(*args, **kwargs):
warnings.warn(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 here作者：Gabe A, M.Sc
