Data science is rapidly expanding beyond traditional software applications and venturing into the realm of hardware, particularly with the rise of the Internet of Things (IoT) and edge computing. This raises an important question: Should data scientists learn MicroPython? This report examines MicroPython, its applications, and its relevance to data scientists.
What is MicroPython?
MicroPython is a streamlined and compact version of Python 3 designed for microcontrollers and embedded systems with limited resources. Unlike standard Python, which targets powerful devices like PCs and servers, MicroPython is tailored for devices with kilobytes of RAM. It features a reduced standard library but includes specialized modules for direct interaction with hardware components like GPIO pins, sensors, and LEDs. Microcontrollers, the target devices for MicroPython, are essentially tiny computers on a chip used in IoT sensors, smart home devices, and robots.
Python vs. MicroPython: Key Differences
Understanding the distinction between Python and MicroPython is crucial for determining when each language is most appropriate.
| Feature | Python | MicroPython |
|---|---|---|
| Target Devices | Powerful devices (PCs, servers) | Low-resource devices (microcontrollers) |
| Memory Footprint | Larger | Smaller |
| Libraries | Extensive (pandas, numpy, scikit-learn, tensorflow) | Reduced standard library; hardware modules (‘utime’, ‘machine’) |
| Use Cases | Data-intensive tasks, general software applications | Direct hardware access, embedded systems, IoT projects |
Python excels in data analysis, machine learning, and web development due to its rich ecosystem of libraries. MicroPython, on the other hand, prioritizes direct hardware control, making it suitable for applications where interaction with sensors, actuators, and other physical components is essential.
Why Should Data Scientists Care About MicroPython?
The increasing prominence of IoT and edge computing in data science makes MicroPython a valuable asset. MicroPython bridges the gap between hardware and software, enabling data scientists to:
- Collect Sensor Data: Interface directly with sensors to gather data for data science pipelines and machine learning models. For instance, a MicroPython-enabled sensor can measure air quality and transmit the data to a machine learning pipeline.
- Edge Computing: Run simple AI models directly on IoT devices, reducing reliance on cloud infrastructure. This is particularly relevant in scenarios where latency and bandwidth are concerns.
- Smart City Projects: Contribute to smart city initiatives by developing and deploying sensor networks for environmental monitoring, traffic management, and other applications.
MicroPython allows Python programmers to program hardware without needing to learn languages like C++ or assembly. It’s an ideal starting point for creating prototypes with relatively little effort
Applications of MicroPython
MicroPython is employed in diverse applications, including:
- Internet of Things (IoT): Controlling smart home devices, collecting sensor data for dashboards.
- Edge Computing: Running machine learning models on IoT sensors, smartphones, and other edge devices.
- Prototyping: Quickly setting up hardware prototypes.
- Robotics: Controlling motors and sensors in robotic systems.
Getting Started with MicroPython
A MicroPython simulator is available online at https://micropython.org/unicorn/, allowing experimentation without physical hardware.
To run the simulator:
- Open the link.
- Import
time, then define the function and call the function at the end. Type in each code snippet in the web terminal separately, and then click ‘Enter’.
For example, a flashing LED program involves importing time, defining a blink_led function that prints “LED ist jetzt: ON” and “LED ist jetzt: OFF” with 0.5-second delays, and then calling the function blink_led(). To run the example with real hardware, use additional libraries such as machine or utime.
Further Reading
- MicroPython Official Website: https://micropython.org/
- MicroPython for Raspberry Pi: https://www.raspberrypi.com/documentation/microcontrollers/micropython.html
- MicroPython Wiki: https://github.com/micropython/micropython/wiki
- Hello IoT YouTube Playlist: https://www.youtube.com/playlist?list=PLmsFUfdnGr3xRts0TIwyaHyQuHaNQcb6-
Conclusion
MicroPython bridges the gap between data science, AI, and hardware. Knowing its purpose and differences from Python is beneficial, especially for those interested in smart home devices or IoT. If you know Python, you can use MicroPython and apply it in smart home projects. For data scientists venturing into IoT and edge computing, learning MicroPython is not just beneficial but increasingly essential. It offers a pathway to directly interact with hardware, enabling innovative solutions for data collection, edge intelligence, and smart environments.