Python is a powerful programming language. But no matter how robust the language is, there is always room for improvement. As a Python development company with 7 years of experience, we have identified some of those Python performance optimization tricks.
So, here are 5 tips to ensure great performance with your Python applications.
Prior to getting started, you need to have a clear idea regarding your own expectations from the Python app. Ask yourself questions like ‘Do I need a complex, full-fledged solution?’, ‘Will a simple application work for now?’, ‘What is the magnitude of work expected from the app?’, ‘Who is going to maintain the application once it’s been deployed?’, etc.
Once these questions have been answered, check whether application performance optimization is still a critical factor for you. You might identify some other priorities on the way. Another factor to consider is the time to market of the application. Once you are clear regarding the application live date, then formulate a plan to progress on this.
This is the most important step. A good team helps you accomplish the application goals. With the help of platforms like Clutch and GoodFirms, you can build a list of the top Python development companies within the selected location.
To shortlist further, gather details regarding the companies’ background, technology expertise, team size, application portfolio, engagement model, and so on. Here, check whether the chosen company has a un-flinched focus on Python app development. Unlike other application development companies with distributed attention towards ‘n’ number of technologies, a Python-focused company would have more dedication and expertise in the domain.
Python is a continuously evolving language. Since its inception in the ’80s, developers have seen multiple version releases. The Python community is committed to keeping the programming language up-to-date to maintain its robustness.
Before beginning your application development, understand from the team regarding the Python release that will be used. Prompt them to use any of the latest releases which have proven their functionality in the past. Also, form an idea regarding its maintenance activities required in the future.
Today, there are various speedup apps in the market which are used to fasten the performance of Python apps. Relying on these external packages helps to complete time-critical Python tasks without losing much time.
PyPy and Numba are some examples of apps used to accelerate Python performance. PyPy is known to be the fastest implementation of Python. The Just-In-Time (JIT) compiler in PyPy enables the Python code to run faster and consume less memory.
We have been talking a lot about code optimization. But what if I tell you that optimization of the code is only a secondary factor. Yes, this is the truth. Because writing simple code is the first one. Keeping the code simple and readable is a key towards achieving less latency and improving app speed.
The tips given above are the fruits of our experience in developing and managing Python apps. However, there are no silver bullets. What works for one might not work for another. It varies depending upon the application environment and other factors. Despite all these, the golden rule to accelerate Python performance is to identify the bottlenecks. Once you or your team realizes what’s slowing the code down, dig deeper and eradicate it straight away!
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