cProfile decorator is a Python programming concept that involves using the
cProfile module to profile the performance of a Python function or method.
Profiling helps you understand how much time your code spends on various operations, which can be useful for identifying bottlenecks and optimizing your code.
cProfile Decorator Example
Here’s an example of how you can create a
import cProfile def profiled(func): def wrapper(*args, **kwargs): profiler = cProfile.Profile() profiler.enable() result = func(*args, **kwargs) profiler.disable() profiler.print_stats(sort='cumulative') return result return wrapper def some_function(): # Your code here pass some_function()Code language: Python (python)
In this example:
- We import the
- We define a
profileddecorator function that takes another function (
func) as its argument.
- Inside the
wrapperfunction, we create a
cProfile.Profile()object to start profiling.
- We enable profiling with
- We call the original function
funcwith the provided arguments and keyword arguments and store its result in the
- We disable profiling with
- We print the profiling statistics using
profiler.print_stats(sort='cumulative'). This line prints the cumulative statistics, which can help you identify which functions or parts of your code consume the most time.
- Finally, the
wrapperfunction returns the result of the original function.
To use this
cProfile decorator, you simply decorate the functions or methods you want to profile with
@profiled. When you call the decorated function, it will be profiled, and the profiling statistics will be printed.
Keep in mind that profiling adds some overhead to your code, so you should use it for performance analysis and optimization purposes, and not in production code.
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