It also includes a script called kernprof for profiling Python applications and scripts using line_profiler. ![]() There’s a neat 3rd party project called line_profiler that is designed to profile the time each individual line takes to execute. Give it a try on one of your own modules or try it on one of Python’s modules to see how it works. The main difference is that you would pass a Python script to it instead of just passing a snippet. You can call cProfile on the command line in much the same way as we did with the timeit module. filename:lineno(function) provides the respective data of each function.The second percall column is the quotient of cumtime divided by primitive calls.It’s even accurate for recursive functions! cumtime is the cumulative time spent in this and all subfunctions.percall refers to the quotient of tottime divided by ncalls.tottime is a total of the time spent in the given function.According to the documentation, standard name refers to the far right column. The next line tells us how the results are ordered. The first line shows that there were 4 function calls. Regardless, we’re going to write our own custom function timing decorator! Here’s the code:Ī nested function for timing other functions Writing your own timer is a lot of fun too, although it may not be as accurate as just using timeit depending on the use case. ![]() And that’s really all there is to it! Now let’s learn about how to write our own timer decorator. You will note that we pass a call to the function in quotes, then the setup string. If it is, then we import timeit, create a setup string to import the function into timeit’s namespace and then we call timeit.timeit. Here we check to see if the script is being run directly (i.e. Print timeit.timeit("my_function()", setup=setup) We’ll use the same silly script from before and show you how below: Using the timeit module inside your code is also pretty easy. Now we’re ready to learn how to use timeit inside an actual Python script. Note that we separate the import and the function call with semi-colons and that the Python code is in quotes. Here we import the function and then call it. Python -m timeit "import simple_func simple_func.my_function()"ġ000000 loops, best of 3: 1.77 usec per loop To get timeit to run this code on the command line, we will need to import the code into its namespace, so make sure you have changed your current working directory to be in the same folder that this script is in. Let’s write a silly function and see if we can time it from the command line:Īll this function does is cause an error that is promptly ignored. Your output will likely be slightly different as it is dependent on your computer’s specifications. For these silly examples, you won’t see much difference. Then it runs the code for n number of loops 3 times and returns the best average of the 3 runs. The “-s†tells the timeit module to run setup once. What’s going on here? Well, when you call Python on the command line and pass it the “-m†option, you are telling it to look up a module and use it as the main program. ![]() Open up a terminal and try the following examples: We will start out by looking at how to use timeit from the command line. The timeit module has a command line interface, but it can also be imported. The timeit module uses platform-specific time functions so that you will get the most accurate timings possible. You can use it to time small code snippets. Python comes with a module called timeit. It will not go into actually optimizing your code. This chapter will only cover how to profile your code using a variety of tools. When this happens, you will need to figure out what parts of your code are slowing it down. You will run into situations where you need your code to run faster because your business needs have changed. The main reason to do this sort of thing is for optimization. What does it mean to benchmark ones code? The main idea behind benchmarking or profiling is to figure out how fast your code executes and where the bottlenecks are.
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