Python and Efficiency:
The case is the simpler the programming the faster the execution. As for Python, it has spread across all the domains due to its popularity and functionality. With top-rated computer science schools in the U.S. teaching Python Development, surpassing Java. The main reason behind this is the ease of syntax in Python, that makes it easy to learn and scalable particularly in a cloud-based environment.
How to achieve it?
As good as Python is there is still room for improvement. We have listed down some of the best industry practices that improve Python Code Efficiency
1. Use some of Python’s “speedup” applications
Python has grown up as a fast, tactile and high-performance language. With some application development in this area in recent years, a lot of projects have come up such as the PyPy project which has the main aim to speed up Python as a whole. Another tool called Numba does a great job in this regard by improving the performance of the process by implementing high-performance tools into Python.
2. Using the latest releases of Python
Python shell is maintained by a community of developers committed to keeping the language robust and current. Each language release is faster, updated and is in line with the latest standards, so it is always a good idea to take the move. Make sure that your required libraries are in line with the latest update.
3. Avoid unwanted loops
This will put unnecessary strain on your server and decrease performance. Some simple things, such as using multiple variables instead of a giant array that makes the program go through the entire sequence can optimize your code a lot. Also, try redesigning your code to use intersections and unions. Always avoid expensive lookup functions in inner iterations. This may push you to take actions as far as storing built-in functions in a local variable.
For example, you can use this:
return set(a) ; set(b)
Instead of this:
for x in a:
for y in b:
if x == y:
4. Keep Python code small and light
The rule of thumb is that too much typing is not a good thing. In this age performance is critical and it is especially important to keep your code as compact as possible to reduce latency and speed. Before you add anything to your program, ask yourself, “Do I really need this module?” “Is this framework so essential?” “Is this worth the hype?” or straight away, “Can I do this in a simpler manner?”
5. Using the most suitable data structures. Don’t simply use lists or dicts. The default standard library contains other data types, and there are numerous libraries out there. Consider which one should be used for the most efficient operations to solve your problem and choose the correct data structure.
6. Meta programming must be avoided at all costs. If you want speed, there is no need to trigger 10 method calls with a complex logic behind them for a simple attribute lookup.
7. Always profile your code. It helps in finding the bottleneck and optimizing it. Often our thinking about the performance of some concrete code is entirely wrong.
8. Avoid explicit loops when you already have a function/method that does what you want.
9. Disassemble the code with dis module for bytecode. This gives you a simple way to understand what the interpreter will actually do.
10. Another good idea is to study all of the most used built-in and stdlib data types since each one of them has its own positive and negative properties.
Python is reliable fast and robust in itself and with simple changes in the way in the manner of programming, you can drastically improve its code efficiency while maintaining a consistent flow.