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Cprofile and profile provide deterministic profiling of python programs You nearly always want to use cprofile, as it’s faster and doesn’t skew measurements as much. A profile is a set of statistics that describes how often and for how long various parts of the program executed

These statistics can be formatted into reports via the pstats module. They have the same api, but cprofile is a c extension, while profile is implemented in python Learn how to expertly use cprofile in python to help identify bottlenecks and optimize program code performance in order to reduce execution time.

To check time metrics given by each function and to better understand how much time is spent on functions, you can use the inbuilt cprofile in python

Going into more detailed metrics like performance, time is not the only metric. This article will walk you through the process of using cprofile module for extracting profiling data, using the pstats module to report it and snakeviz for visualization. The cprofiler module provides all information about how long the program is executing and how many times the function gets called in a program. You’re generally advised to use the much more performant cprofile, which has significantly less overhead.

Cprofile is a deterministic profiler implemented in c for python It works by inserting probes at the entry and exit points of each function in the python interpreter When a function is called, the profiler records the start time, and when it returns, it records the end time. In this article we explored how to profile python code with cprofile module

Code profiling helps identify bottlenecks in the code and helps understand which parts of the code should be optimized for better overall performance.

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