6-star library for profiling Python code

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Today I would like to share with you a new thing which is called 6-star library for profiling so let’s started Each programming language has two speeds: development speed and execution speed. Python has always prioritized fast typing over fast execution.

Python code is almost always fast enough for the task, but sometimes it isn’t. In such cases, you need to find out where and why you are late and do something about it.

The most respected and common saying in software development and engineering is “measure, not guess.” With software, it’s easy to accept a problem, but doing so is by no means a good idea.

Actual application performance statistics are always the best first aid to speed up your application.

Time and Timeit

Sometimes all you need is a stopwatch. If you just want to profile the time between two code snippets that take seconds or minutes to run, a timer is sufficient.

The downside of time is that it’s just a stopwatch. The downside of Time is that the most important use cases are individual rows or blocks of code in the micrometer.

These modules only work if you process the code individually. None is enough to analyze the entire program. It’s about figuring out the thousands of lines of code that your program spends most of its time on.


The Python standard library also comes with a complete application analysis profile. When executed, cProfile keeps track of each feature call in your application and creates a list of the most calls and the average time taken. cProfile has three main advantages.

One is included in the standard library, so you can use it with a standard Python installation. The second is a review of various statistics on-call behavior. For example, the time spent teaching one attribute call is separated from the time spent on all other attributes created by that attribute.

You can determine if the function itself is slow or if you want to call another slow function. Three, and perhaps best of all, you are free to limit your cProfile.

You can test and run the entire application, or you can enable profiling only when the selected feature is running and focus on what that feature is doing and what you are calling.

This approach works the best right after you’ve reduced things a bit, but saves you the hassle of going through the noise of full profile tracking.

Read More: How to recover from messed-up Python installation on a Mac?

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