Python has become one of the most popular programming languages. People like it for its simplicity and wide range of uses. Python is everywhere, from data science and artificial intelligence to web development. However, did you know that there's a smaller version of Python? Yes, you heard it right.

MicroPython (a version of Python) runs on tiny devices like sensors, robots, and smart home gadgets. It brings the familiar ease of Python to the world of microcontrollers. MicroPython plays an important role in how data is collected and processed at the source. For data scientists, this raises an interesting question: should they learn MicroPython today? If you have also come up with the same query, we have got you covered. So, keep reading till the end!

What is MicroPython and Why Is It Special?

MicroPython is a compact version of Python 3, which is designed to run on small computers (microcontrollers). Normal Python is usually used on laptops, servers, or big machines. However, MicroPython brings the power of Python to much smaller hardware. What makes MicroPython special is its simplicity and flexibility. It allows developers to write Python code that directly controls sensors, lights, and motors. It is possible because MicroPython is designed to work with limited memory. But it still offers the friendly and familiar Python programming style.

In fact, many of the basic Python libraries are included. Anyone familiar with Python can quickly get started with MicroPython. MicroPython also works as a bridge between hardware and software. Traditionally, programming microcontrollers required complex languages like C or C++. However, you can achieve the same results using much easier code using MicroPython. It makes coding on small devices accessible and powerful. 

Why Should Data Scientists Know About MicroPython?

Data scientists might wonder why this lightweight language is essential. After all, most data work is performed on powerful computers using tools like Python or SQL. However, many scientists are unaware that MicroPython is gaining importance due to the way data is being collected today. Many modern projects rely on data from devices called IoT devices. Such devices often run on small microcontrollers. These controllers cannot handle heavy languages or big software. That's where MicroPython comes in very handy. It allows these small devices to collect and process data. It also sometimes even cleans data before sending it to larger systems.

For a data scientist, understanding MicroPython means being closer to the source of data. They can help design or control how data is captured, rather than relying on data from spreadsheets or databases. It can improve data quality and open up opportunities. Another reason to be aware of MicroPython is its application in prototyping. With just a few lines of code, you can quickly test how a sensor behaves. It is extremely useful in experimental projects. MicroPython doesn't replace your regular tools as a data scientist. However, it adds an extra skill that helps you understand and work with data.

MicroPython vs. Other Programming Languages for Microcontrollers

Microcontrollers are tiny computers that run inside every day. To make them work, you need programming languages. Traditionally, languages such as C and C++ have been the standard choices. They are powerful and give you complete control over the hardware. However, they can also be challenging and require more lines of code to accomplish simple tasks. It is where MicroPython stands out.

Unlike C or C++, MicroPython uses the familiar and easy-to-read Python style. You can make an LED blink, read data from a sensor, and do a lot more with just a few lines. It makes it great for quick experiments. Another standard option is Arduino’s C-based language. It’s simpler than traditional C, but still not as easy as Python. MicroPython often feels more natural for people who already know Python. However, it is slower than C or C++. It is not the best choice for very demanding applications where speed and memory are important. But for most everyday projects, it is fast enough and much easier to work with.

Advantages of MicroPython

MicroPython offers several advantages that make it a popular choice. Let's discuss the benefits of MicroPython.  

• Easy to Use: It is based on Python, which is why coding is simple and readable. It requires fewer lines compared to languages like C or C++. It makes it much easier to start programming microcontrollers.
• Rich set of Libraries: It features a comprehensive collection of libraries that enable you to connect to sensors, control devices, and more quickly and easily. It saves time and effort during projects.
• Portability: MicroPython can run on different kinds of microcontrollers and boards. It means you don’t have to learn a new language for each device. You can use the same Python-style code on many platforms.

MicroPython makes working with small devices faster, simpler, and more accessible for everyone.

Disadvantages of MicroPython

MicroPython is useful, but it also comes with some disadvantages. Let's discuss them here:

• Performance: MicroPython is an interpreted language, which means it runs slower than other languages, such as C or C++. It can be a problem for projects that require very fast processing or handling large amounts of data.
• Limited Library Support: MicroPython also doesn't support every single Python library. It includes many valuable features, advanced capabilities, and larger libraries commonly used in regular Python, which may not be compatible with small devices.
• Limited Resources: Microcontrollers already have small memory and storage. However, MicroPython itself occupies a significant portion of that space. It leaves less room for complex programs. In some cases, you will need to optimize your code carefully or switch to another language.

Conclusion

MicroPython may not be the main tool for data science. However, it offers a unique advantage. It brings Python's simplicity to the world of small devices. For data scientists, this means having the ability to understand how data is collected and analyzed. MicroPython is not a must-have skill for every data scientist today, but it is a valuable one. Learning it can open new opportunities and make you more versatile in handling data from the ground up.