The Best Python Profiling Tools Tutorial For Beginners

For software companies, the performance of their application is crucial to their company’s success. When an application executes code quickly, customers perceive it as a time saver. On the other hand, they have a very different reaction when code goes into unnecessary loops, calls extraneous functions, or trips over itself in some other way. They may find the app slow or unresponsive and conclude that using it is a waste of time. If left unchecked, this leads to disgruntled users, a tarnished corporate reputation, and millions of dollars in revenue lost. For any real-world use, Python profiling tools are necessary and why software companies spend millions on good Python profiling tools tutorials.

What do Python profiling tools do?

Code profiling tools allow you to assess the performance of your code by calculating the time it takes for your methods to run as well as the amount of CPU and memory they use. Datadog code profiling tools allow you to profile your code in any environment, including production, without affecting the performance of your application.

Why use Python profiling?

Here are some of the benefits of a good Python profiler:

• A good Python profiler decreases the number of software development cycles and makes them more agile.
• Moreover, it keeps the application stable and allows it to perform reliably under different circumstances.
• In Furthermore, good Python profilers improve the overall end-user experience. They allow developers to fix anomalies in real-time, thus reducing overall effort by developers and decreasing development time.

Additionally, you can detect high resource-consuming methods and optimize the application performance of your Python code using Python profiling tools. Moreover, you can see how much CPU and memory are being used and how much time is spent on locks, I/O, and garbage collection, right down to the line of code. Therefore, by optimizing your slowest and resource-intensive lines of code, these tools can also help you reduce latency, improve end-user experience, and save money on cloud provider costs.

Furthermore, profiling can even reduce MTTR by identifying issues in production code that are invisible to other tools and difficult to replicate in other environments. Exploring various profile types (CPU, memory, lock, I/O) to find the source of code issues is also an option.

Which IDE should you use while code profiling in Python?

PyScripter is always developers’ first choice when coding or profiling in Python.

PyScripter is a lightweight Python IDE with all the features you’d expect from a modern Python IDE. This includes a Python interpreter, which can be very useful. Furthermore, this interpreter lets users run Python programs without having to save them, and it also offers code completion and call hints. PyScripter also allows the user to see the entire command history.

In addition, Brace highlighting, code folding, code completion, and syntax checking as you type are just a few of PyScripter’s features.

The best Python IDE also allows you to import existing directories and multiple run configurations as a project manager. Moreover, integrated unit testing boosts productivity by allowing automated test generation. This software’s unit testing GUI is extremely sophisticated. In addition, this can also be useful when Python profiling.

Most importantly, this fantastic IDE allows for simple and quick integration with Python tools. As a result, developers will find it convenient to use built-in or additional Python profiling tools. You can learn more about PyScripter in its official documentation.

How to use the cProfile library?

The cProfile library is a built-in Python module part of the Python standard library. It is used as a complete whole-program analysis profiler that many software developers prefer due to its simplicity and convenience. When appropriately used, cProfile tracks every function in the program and generates a list of which functions are being called most often, and also logs the time that calls took on average.

cProfile has three great strengths: 

1. Since cProfile is shipped with the Python standard library by default, it is available for use right after Python installation.
2. Moreover, cProfile profiles many different statistics about function call behavior. cProfile separates the time taken by each function call and the time spent by the functions in a function call to help you determine which function runs slow in a program.
3. You can constrain cProfile freely. With cProfile, developers can toggle whether to sample a whole program’s run or when a select function runs. It’s better to focus on what that function is doing and what it calls.

As cProfile is a built-in Python library, no further installation is needed.

To see a basic example, let’s profile a function that takes a single argument:

import cProfile
import re
cProfile.run('re.compile("foo|bar")')

Moreover, users even have the option to save the results to a file by specifying a filename to the run() function as a second argument, instead of printing the output at the end of the profile run:

import cProfile
import re
cProfile.run('re.compile("foo|bar")', 'data')

What other tools are available for profiling Python code?

Many functions and tools are available for Python profiling; however, only two stopwatch functions are included by default in the Python standard library. The perf_counter function generates an arbitrary time stamp using the operating system’s high-resolution timer. To find the difference in time, we simply call time.perf_counter twice, once before and once after an action.

The Timeit module in the Python Time library module tries to simulate real-world benchmarking on Python code. The timeit.timeit function takes a code snippet and runs it many times (the default is 1 million passes) to calculate the required time.

Another Python profiling tool is FunctionTrace. In terms of functionality, it’s similar to cProfile. That is, you give it the name of the script you want to profile, and it generates a detailed graph of function calls and memory usage over time without adding instrumentation to the code. Moreover, FunctionTrace can also handle multithreaded/multiprocess applications without any additional effort on your part.

Pyinstrument, a Python profiling library, works similarly to cProfile in that it traces your program and generates reports about the code that takes up the most time. We recommend checking out this article to learn more about built-in Python profiling tools.

What is the Snakeviz library?

Another standard library module, pstats, is the most common way to visualize data from a cProfile trace. On the other hand, pstats generates plain-text reports that lack the data visualization required for profile statistics.

Snakeviz takes the data from cProfile and turns it into easy-to-read, interactive graphics in HTML. There are two types of plots: “icicle” and “sunburst,” both show where your program spends most of its time at a glance. Each segment of the graph represents the call time for a function. Simply click a segment to zoom in on a function, and you’ll be able to see how much time everything in the stack below it takes.

In addition, Snakeviz also creates a searchable and sortable HTML table view of the trace data, similar to the traces created by pstats but more interactive. Furthermore, even if you don’t use the charts, the table view of the trace is a powerful way to understand cProfile data on its own.

To install the latest snakeviz, simply use the following pip command:

pip install snakeviz

Do you know enough about Python profiling tools? 

Every programming language has two types of speed: development speed and execution speed. Python always has prioritized writing speed over running speed. In addition, even though Python code is almost always fast enough for the job, it isn’t always the case. In those cases, you’ll need to figure out where and why it’s lagging and take action.

Fortunately, Python comes with several packages that can help you profile your applications and identify the bottlenecks. We’ve gone over a few of them in this post to give you an overview of Python profiling. If you wish to learn more, check out this article on adding profiling tools in machine learning code.

At this point, you’re well on your way to benchmarking your code. Give it a shot and see if you can identify any bottlenecks in your process. For the best experience, use the best Python IDE!