Python Performance Monitoring

There are plenty of reasons to back up the growing popularity of python as a programming language. The language is easy to learn compared to other programming languages since it has an easy syntax. Additionally, it has been known to scale up easily, especially when used in cloud-based environments.

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As much as python is a powerful programming language, there is always going to be some room for improvement. The need for faster apps, better UIs and even more efficient designs is insatiable. The question is, how exactly should you improve the performance of your python apps?

Here are five ways to improve performance:

Use the Latest Updated Versions

Python is run by a group of developers who are always on their toes looking for areas of improvement. This statement means that every new version produced will always be faster and more efficient than its predecessor. The trick is determining whether your favorite libraries have already started supporting the new version.

Instead of wondering about whether to proceed with the move or not, the critical issue should be whether the support that the new version of python has can suffice to make a move viable. To be on the safe path, use the new libraries you obtain on your application and be on the lookout for any loopholes. You will only notice a change in performance once you can solve the loopholes of how your app runs, according to

Use Multiple Coding Approaches

It is typical for developers to use a similar code throughout their system. However, this situation can easily lead to the applications running slower and less efficiently than expected. Among the best approaches is to experiment with different codes to determine which one is superior to the other. Regardless of the language you are working with, this approach will help to keep you, as a programmer, sharp and on your toes. On the other hand, new codes have the potential to result in increasing the overall performance of your application.

Perform Cloud-Based Application Monitoring

The performance of your python based applications will always be as good as the infrastructure that you use. A loophole in the infrastructure could mean an inefficient application, not to mention the likelihood of losing your business. This further makes application performance monitoring essential.

All it takes is having an in-house team tasked with the duty of observing and analyzing the application’s metrics. In case they find a loophole within the system, changes should be made with immediate effect. On the other hand, if you might lack the workforce to perform effective infrastructure monitoring, consider outsourcing the task.

Keep the Code Light and Small

As a rule of thumb, the simplest code is the fastest. It is necessary to keep the code you would like to use as simple as possible both to reduce latency and improve the performance of the application at large. Among the best ways to reduce the size of your code is to ask a few questions as you code for specific tasks.

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Do you really need to use that specific module, and can you do the task in a simpler way? Why would you use a specific framework instead of another? Is it worth the overhead it comes bundled with? Such questions will help you determine the extent of coding work needed for specific tasks, so you can choose the most efficient option.

Optimize Loops

Excessive looping in any programing language can act as a bottleneck to the application achieving optimal performance. Additionally, it can lead to a strain on your server. Luckily, there are plenty of ways to avoid this through loop optimization. For instance, you can consider list comprehensions to achieve better speed benefits from the loops, according to Programiz.

Other slight changes such as using a different variable to store the length of an entire array can go a long way into increasing the expected efficiencies of your program. In some situations, you can also choose to include unions and intersections in order to refactor your code.


While there is no magic pill for making your application perform even faster, the above tips can take your app to the next step performance wise. However, not all of them will work for your specific app, and their efficiency will depend on your version of python or the platform that you use. To be safe, profile your application to pinpoint the slow areas before trying to see if the above tips are the solutions you need.