Python is one of the most versatile and widely used programming languages that developers can use to code almost everything. From web apps to microservices, developing packages and working with machine learning, this general-purpose programming language is used by development teams to perform a variety of tasks.
If truth be told, Python is a complete development framework, if mastered, helps developers learn other programming languages and make programming fun. Python is famous for its one-liners, amazing built-in packages and simple syntax.
Despite its amazing benefits, many developers have speed concerns when they use Python. In this fast-paced digital world, speed matters a lot. If you want to write code faster and improve Python’s performance, here are some interesting tips and tricks to improve your coding speed.
Let’s discover tips to write Python code faster.
1. Make the Most Out of Built-in Functions
Python programming is packed with a myriad number of built-in functions that help developers write code faster. There is a built-in function available to fulfill your every development need. Developers are advised to get acquainted with these functions so that they can easily complete their development tasks. Some commonly used Python functions are:
- abs()
- len()
- max()
- min()
- set()
- sum()
- any()
- filter()
- map()
So, don’t build what is already available, it will simply waste your time and cause your code to be slower. Make the most out of the Python built-in features to speed up your development project.
2. Use the Right Data Structure
Python has a number of built-in data structures, including lists, dictionaries, sets, tuples and much more. When it comes to writing code in Python, it is a worthwhile idea to know the right data structure to make your application development project superfast. For instance, developers can use dictionaries and sets for developing search applications as they are based on hash tables and need o (1) to find any item. Keep in mind finding the proper data structure for your application development project is the key to make your project smooth.
3. Avoid Using Loops
It’s no surprise that programming languages are filled with loops. But loops take up a significant amount of time in execution, which will make the code slower. Therefore, it is advised to minimize the amount of looping the code. You can use different variables for sorting a list instead of looping, or simply use refactoring techniques such as intersection and unions for code optimization. Developers are advised to use list comprehension and recursive functions to use loops more efficiently.
4. Reduce Memory Usage
Memory footprint is the amount of memory the program uses while running. Memory interactions take up a lot of execution time. If your team of app developers wants to make the Python code run faster, all they need is to follow some memory-saving strategies to save up memory time. Python allows you to write simple code with less memory consumption by following these easy tips:
- Use sort () as it requires less memory than sorted ()
- slots can be used to declare variables that take the instance of a variable
- Use generators to use one data at a time, as it will save you a lot of time and occupy less memory
5. Import Modules When Needed
When you are using Python, importing all the modules is a common practice as it makes it simpler to keep track of the dependencies of your program. You can opt for a highly effective approach and load modules only when your development team needs them. It is a really effective way to distribute the loading time for modules and significantly reduce memory consumption.
6. Say No to Global Variables
Did you know Python is more efficient in accessing local variables than global variables? Avoiding global variables is a sensible approach as a global variable, once defined, will stay in a program until the program compilation. It takes memory space during the whole process. In contrast, local variables only take up the memory until the function completes the process. So, avoid global variables as much as possible.
7. Updated with the New Release
The community of Python developers strives hard to make this programming language faster, easier and robust. They come up with a new release from time to time to improve Python performance and better security. It is suggested to check the libraries and functions you want to use in your project are recent and compatible with the latest version of the release.
8. Use Apps
There are a number of applications available that your development team can use to increase the speed of writing Python code. By using these applications, all you need is to rewrite parts of your code to make it more efficient. Here are some applications that you can use to speed up your Python code:
- PyPy
- Numba
- Cython
9. Use Itertools
If you are using Python for building a software application, mobile app or web app, you should learn itertools. It is a standard Python library that allows you to use functions to create the code faster and consume less memory. You will find additional building blocks, and recipes to use Python more efficiently.
10. Linked List
Adding an element to the Python list data type is a time-consuming process, as every element has to be moved forward. Using a linked list is a handy option that is completely different from arrays. Each item is linked to the next item in the list and name accordingly. It allows you to occupy the memory only when you need it because every item is stored in different parts of memory and uses links to join each item.
Final Thoughts
So, here you go. These are a few really effective and workable strategies to speed up your Python code. Hope these tips will help you write fast Python code. Python’s unlimited benefits encourage millions of developers and companies to use this programming language to build a variety of applications. If you want to use this simple, robust and advanced programming language for your upcoming app development project, contact a mobile app development company to discuss your requirements and optimize your codebase.
