If you’ve been working with Python for a little while, there’s a good chance you’ve encountered a situation where your code runs slower than you’d like. Or perhaps, your script eats more memory than you anticipated. This is where Python profiling comes into play and becomes an invaluable tool in your toolkit.
Table of contents
What is Python Profiling?
Python profiling is a process that helps us understand the behavior of our Python code. With it, we can find bottlenecks in the code, spots where the code slows down or uses excessive amounts of memory.
Why Profiling?
Profiling allows us to optimize our code and make it run more efficiently. It provides us with insights on where to focus our optimization efforts. This leads to an overall performance boost in our code and ensures that we deliver a better user experience.
Should I Learn Python Profiling?
The answer is a resounding yes! Every Python programmer, regardless of their experience level, should learn about profiling. Knowing how to profile your code is an essential skill, especially when you work with larger, more complex projects.
What is Profiling Useful For?
Profiling can be used not only in boosting the code’s performance but also in managing resources better, debugging, and even in testing. It’s important in the development of efficient, scalable software.
Now that we’ve outlined the importance and uses of Python profiling, let’s dive into some practical examples in the next parts of this article.
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Getting Started With Python Profiling: A Basic Example
Python standard library has a built-in module for profiling, known as the cProfile module. Let’s first start with a simple example:
import cProfile
def sum_of_numbers():
return sum([i for i in range(1000000)])
cProfile.run('sum_of_numbers()')When this code is run, the output will display a detailed performance of every invoked function, including the number of calls and the time spent on each call.
Python Profiling Using Time Module
The time module is another simple way to measure time taken for a process. Here’s how you can use it:
import time
def sum_of_numbers():
return sum([i for i in range(1000000)])
start_time = time.time()
sum_of_numbers()
end_time = time.time()
print("Time taken: ", end_time - start_time)This code will print the time taken to calculate the sum of numbers from 1 to 1000000.
Python Profiling Using memory-profiler
For memory profiling, we can use memory-profiler, a third-party library. Let’s see how to use it:
from memory_profiler import profile
@profile
def sum_of_numbers():
return sum([i for i in range(1000000)])
sum_of_numbers()This script will print the memory usage in each line of our function. To execute this correctly, make sure to install the library by running pip install memory-profiler
Python Profiling Using line_profiler
To get a line-by-line report of time consumed, we use another third-party library called line_profiler. Here’s a basic example:
from line_profiler import LineProfiler
def do_profile(func):
profiler = LineProfiler()
profiler.add_function(func)
profiler.runcall(func)
profiler.print_stats()
do_profile(sum_of_numbers)Here, do_profile function will print details for each line of function sum_of_numbers. Install the library by running pip install line_profiler.
Python Profiling Using Py-Spy
Py-Spy is a sampling profiler for python programs. It lets us visualize what the python program is doing without changing the code or slowing down the program. Let’s see an example:
# Run pip install py-spy # Then on the command-line, run # py-spy top -- python your_file_name.py
The above command will run and generate a live view of functions running at the top of CPU usage.
Python Profiling Using Py-Instrument
Py-Instrument is another tool providing statistical python profiling. Here is how to use it:
from pyinstrument import Profiler profiler = Profiler() profiler.start() # code you want to profile sum_of_numbers() profiler.stop() print(profiler.output_text(unicode=True, color=False))
This profiler code will profile the execution of the sum_of_numbers function and give us a detailed output with timing metrics.
Python Profiling Using profiling package
The profiling module is another powerful third-party package that provides visualization in a web browser. Here’s an example of how to use:
# Install the package # pip install profiling # Run the command-line profiling # profiling your-script.py
This command will generate a web server running at localhost:8000 displaying a live profiling of your script.
Python Profiling With SnakeViz
SnakeViz is a browser-based graphical viewer for the output of Python’s built-in cProfile module and other profiling format. Here is an example:
# Install using pip # pip install snakeviz # Run your script with cProfile, and save the result in a file # python -m cProfile -o my_script.prof my_script.py # Now run snakeviz with the saved file # snakeviz my_script.prof
SnakeViz will create a visualization for the profile in your default web browser.
Python Profiling With Yappi
Yappi is yet another Python profiler, but this one is multi-thread aware. It’s a low-overhead, robust, and easy-to-use Python profiler. Here’s an example:
import yappi yappi.start() # your code here sum_of_numbers() yappi.get_func_stats().print_all() yappi.get_thread_stats().print_all()
This code snippet will start the Yappi profiler, run your function, and print a summary of both the function statistics and the thread statistics.
Where To Go Next?
As we have just scratched the surface of Python profiling, it is essential to continue learning and practicing to master the art. Profiling Python code is a skill that comes with experience and regular practice.
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Conclusion
Python profiling is an essential aspect of writing optimized and efficient code. Given its importance and wide-ranging utilities, it’s a skill that every Python programmer must possess. With the tools and techniques mentioned in this tutorial, you’re well on your way to master Python profiling.
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