- Advantages of using Python
- Simple syntax
- Asynchronous development
- Rich libraries
- Extensive list of third-party integrations
- It is great for data science apps
- It is ideal for large projects
- It’s open source technology with large community
- Disadvantages of using Python
- Not suitable for mobile development and games
- Slow execution speed
- High memory consumption
- Get expertise for your project
Python is widely praised. In 2019 its popularity increased by 4.2%. By stack overflow 2020 survey Python gets the 3rd most loved language after Rust and TypeScript. What makes this programming language successful and adored by engineers worldwide? In the article there are described Python key benefits to answer this question. In contrast to its pluses, we also outline some of disadvantages of using Python.
Advantages of using Python
Python has remarkably easier syntax compared with other programming languages. With less punctuation and symbols in Python the code is shorter and clearer and completely understandable even for those who are beginners in the technology. Simple syntax also makes the language easy-to-learn.
If you compare Python with Java, Python is a dynamic language. In contrast with static Java, engineers do not have to provide a type while declaring the array and can put whatever they want in it.
Asynchronous development can be more difficult than linear programming, but it improves application performance and enhances responsiveness. Asynchronous means parallel programming at which several tasks can be processed simultaneously.
Python multiprocessing allows solving CPU-bound tasks. If there is a need to make a system execute several things at a given time, Python will manage that perfectly. Imagine the app has to connect to 6 databases and perform matrix transformations. Multiprocessing will help each task run on its own CPU and at the same time reduce time of executing making it efficient.
Python offers a comprehensive standard library which is a collection of more than 200 core modules. There, python developers can find and manage documentation, databases, web browsers, unit testing. It is really huge and provides many facilities for engineers to reuse the code and include it into their projects. Engineers can also install useful packages from the Python Package Index (PyPI).
A list of additional Python libraries is enormous. In general, there are 137 thousand ready libraries that help engineers a lot and save them from writing a code from scratch. Majority of the libraries in Python deal with data analytics, data mining, automation and design solutions. The most popular are:
Extensive list of third-party integrations
Python allows numerous third party integrations. If you want to integrate your app with such solutions as Twilio for messaging and voice services or Stripe for payments. To implement necessary API functionality Python provides API frameworks:
- Django Rest Framework
- API Star
- Flask RESTful
It is great for data science apps
Python is easy to use for quantitative and analytical programming. There are great opportunities in using python for machine learning and artificial intelligence. Python is rich in ready-made functional libraries and sustainable frameworks – Flask, Django as the most popular ones. With extensive lists of possibilities and clear code Python allows building large web applications for diverse spheres of application. The technology is also great for building AI and ML applications.
Artificial intelligence is critical in car building and implementing cruise controls for example. It is also critical for sales predicting and analytics, fintech applications with voice payments system. Majority of companies which deliver their product with integrated AI systems rely on Python. Machine Learning scientists use Python for sentiment analysis systems and NLP (natural language processing tools). You can add image or text analytics, language detection into the project and completely rely on python capabilities. Some of the most popular libraries in Python are Seaborn, Pytorch, TensorFlow, Scikit Learn. Python is a highly scalable and fast language which offers a lot of flexibility in solving data issues.
The first step of data analytics is data structuring with which Python copes very well. Imagine having an excel file with data in numerous rows and columns. To derive insights on data type it would take you pretty much time. However with libraries like Pandas and Numpy it is possible to conduct computational tasks quickly due to parallel processing. The next case when you have to extract more data. For this, you can install Scrapy and BeautifulSoup to automate data collection from the Internet.
Then, comes data visualization. Representing data in graphics, charts and other formats can as well be made with Python code. Python is well-equipped to perform machine learning which involves complex mathematical computations like probability and matrix functions.
It is ideal for large projects
Python is an object oriented programming language and supports structured and functional programming styles. Which means Python can find its application almost anywhere.
Taking into account its rich library and ease, Python also wins with its scalability.
Most data mining, automation and big data platforms rely on python. With Python you can build web frameworks and web apps, GUI based desktop applications, enterprise applications.
It’s open source technology with large community
Python is open source. It has a large community which has been evolving all these years. Python developers share and contribute to the development of technology. There are around 7 million developers who code in Python.
Engineers love Python for code readability, fast troubleshooting and many possibilities it offers for engineers, possibility of integration with other libraries particularly in handling large data issues.
Disadvantages of using Python
Not suitable for mobile development and games
Python is excellent for desktop and web server-side applications. Unfortunately, Python does not suit mobile and game development because of memory consumption and speed. For mobile applications there are such winning technologies as React Native and Flutter for iOS and Android development from a single code base.
Slow execution speed
Python development is a little bit slower if compared with Java, C# or C/C++. Interpreted code is a reason for slower code. Languages with compilation to native code take less time for running. However, the difference in speed between Python and other named languages is not that critical. Python is fast enough for software development. It’s a high-level and general purpose language with advantages definitely outweighing Python cons if it concerns choosing python for a web project or not.
High memory consumption
Python consumes a lot of memory and memory issues may arise when there is a large amount of objects active in RAM. In large projects engineers have to deal with memory leaks and memory usage issues. However, if done wisely everything can be fixed by reducing the size of objects and diagnosing memory leaks.
Get some info on the comparison of .net and python here!
Get expertise for your project
If you are interested in developing web applications which are definitely going to be long-term projects, you can once again review Python development. It’s an ideal choice for you if your project deals with data and has to be scalable. Despite disadvantages, which indeed each and every programming language has, Python is one the world’s best languages. Companies like Netflix, Facebook, Spotify, Dropbox, Google use Python for their websites. Consult our experts to learn whether Python suits your project.