In two years, the dynamics haven’t changed considerably. Python is still the primary matter of interest for new developers. Even those engineers who have significant experience in other languages are transferring to Python.
Let’s take a look at what factors will influence Python’s growth in the future and whether the language will be able to keep a similar growth rate.
Factor #1 – Artificial Intelligence and Machine Learning
Innovative tech corporations, investors, and customers – pretty much everyone who influences software development trends, have understood the revolutionary impact of AI on any industry out there.
Here are just several facts that fully demonstrate how implementing AI became crucial for companies, clients, and investors.
- AI increases employability. In Adobe, the number of job positions with requirements of AI skills has grown by 450% compared to 2013.
- Monster.com defines AI and Machine Learning as primary requested skills.
- 38% of customers think that AI drastically improves their online experience.
Even by browsing tech news, you‘ll see that AI is practically on every headline. It’s combined with other innovations too – like Blockchain or Internet of Things.
Python is considered to be the Best Language for AI
IBM developer community were among the first ones to bring up Python’s popularity in the fields of Machine Learning, Artificial Intelligence, and even Data Science. The situation didn’t change since then. Multiple online courses for Python and AI or Machine Learning were introduced on education platforms like Udemy or Coursera.
The tendency started in 2016, will continue in 2020.
Why is Python so comfortable for AI development?
Python is adapted well to complicated calculations and energy-consuming computing processes. Developers can use multiple libraries for writing various machine learning operations and share ready-to-use code fragments.
Here are some essential AI-development operations that were made much simpler by Python.
- Handling crucial ML-processes like linear regression or classification is done with a library Scikit-learn;
- Scientific calculations are performed by NumPy;
- Deep learning with energy-consuming CPU processes is enabled by Caffe;
- Data management is done in Panda – a smart library for information organization.
No matter what kind of complex operation you want to perform in your AI-project, there are good chances of finding a rich library with ready-to-go pieces of code. It saves a lot of time and lowers the entry barrier to trendy innovation.
Factor #2 – Simple and Understandable Syntax
Today developers have to handle more data than ever before, and this tendency remains steady. Managing massive amounts of information is easy when the programming language is understandable. Unlike complex multi-layered languages (C, C++, Java), Python is similar to the English language.
Learning Python requires a developer to remember patterns and create logical connections between language functions, but there is no need to learn complex terminology. Simple, almost conversational syntax, makes the commands easy to remember. The language can be understood even by school students.
The data is presented in a clear and organized way.
Python development communities and official resources provide rich support on language’s terminology and usage with practical examples. This makes self-learning faster and easier.
Factor #3 – Flexible design
The ability of a language to adapt to different tasks is crucial for a software developer. You want the language to be as universal as possible so you can apply it in multiple projects without having to re-learn. Python is an example of a flexible structure that fits different tasks.
For one thing, code doesn’t have to be recompiled after each change – these results are seen immediately. Also, Python can be integrated into other language-based environments and combined with various technology stacks.
Lastly, Python offers an impressive variety of styles, giving developers a possibility to choose a particular style to a specific task:
- the functional or declarative style manages operations in the form of a mathematical equation, without taking the program state into account;
- the imperative style describes specific computations that allow a program to achieve these commands;
- the object-oriented style allows combining similar objects into a single class. It’s not as well developed as in Golang, for instance, but it’s been widely used to group data in the complex project;
- the procedural style is the simplest one as it classifies the commands one by one, in a kind of a to-do list.
Functional programming in Python.
Such a variety of styles makes development more comfortable because you can adapt the language to your project by picking the most comfortable method. This is why the use of Python is not limited to a particular field. The language has enormous potential for all industries, from financial software to healthcare platforms. Developers can use their skills for multiple projects and companies don’t have to make twice as many investments.
Factor #4 – Python is Popular in Web Development
Because of its simplicity and thought out design, developers prefer Python not just for complicate projects like ML, but for more common projects as well. A great example of this is the use of Python in web development.
Python is gradually taking over PHP in web development.
Some time ago, PHP remained one of the leading languages for web development, and there was no question about its superiority. As Python started gaining traction, developers paid attention to its simplicity and universality and considered replacing PHP with a more accessible language.
Optimized frameworks significantly increase the efficiency of Python development. Some of the most popular Python’s environments are Django and Flask. Just like Python itself, its frameworks are easy to learn with fast implementation and flexible settings.
On top of that, it can be seamlessly integrated with other languages, popular in web development. CPython is a simple combination of C and Python. RubyPython and Jython, two other robust integrations, combine the language with Ruby and Java.
Factor #5 – A Supportive Open Source Community
As an Open Source project itself, Python does not lack the attention of GitHub developers. There are thousands of free frameworks, automated testing and development algorithms, improved compilers, and pre-written templates, that can be edited on GitHub.
There is no need to write the majority of code from scratch if software engineers apply materials from Open Source repositories. Of course, having pre-made pieces of functionality saves a lot of time and money.
Our favorite Open Source projects for Python developers
- Flask, a simple framework for developing a web application. The framework works best with small projects but can be easily scaled.
- Zulip, a team management software that connects developers in a chat and enables exchanging code fragments, and collective editing;
- Rebound, an automated tool for bug search on StackOverflow. The tool is connected to the compiler – as soon as you get an error, the search results from bug threads show up immediately.
The active community also proves useful when it comes to learning. On Youtube, beginning developers can easily find free courses on web development and Python application. On top of that, the language is explored and supported by big companies like Google and IBM. There are plenty of professional resources that educate beginning Python developers, and a lot of them are either free or affordable.
The Bottom Line
Python is regarded both by developers and companies as a long-term investment. It’s a modern language that enables creating recent innovations and benefiting from free Open Source tools. The fact that the language is so popular now will undoubtedly motivate other developers to join the community.
Software development companies and business owners certainly benefit from Python’s popularity as well. With the abundance of free resources information, the number of investments will decrease. Also, looking for talented Python developers is going to become much more manageable. In less populated fields, like AI and ML, where finding a skilled candidate is still a challenge, having available talents is a game-changing improvement.
Roman Zhidkov is CTO at DDI development company. Roman is responsible for DDI’s technology strategy and plays a key role in driving new tech initiatives within the company. He understands the context of the technology in terms of other technical areas, the customer’s needs, the business impact, and the corporate strategy.