Introduction:
As manufacturing systems become more data-driven, connecting PLCs with higher-level software like Python enables flexible data processing and smarter control.
In this post, I’ll share how we integrate PLCs and Python in practical automation projects.
Section 1: Why Connect PLCs to Python?
PLCs handle real-time control, but they’re not designed for advanced data analysis or visualization.
By linking PLCs to Python, we can extend their capabilities — collecting, analyzing, and visualizing data seamlessly.
Section 2: Typical Communication Methods
We often use industrial protocols such as Modbus TCP or EtherNet/IP to exchange data between PLCs and PCs.
Python libraries like pyModbusTCP or pycomm3 help simplify the communication setup.
Section 3: Use Cases from the Field
At Re:Code Labs, we’ve implemented PLC-to-Python connections for data logging, production monitoring, and predictive maintenance.
These integrations provide real-time insights while reducing manual data handling.
Conclusion:
Python bridges the gap between field control and data intelligence.
In future posts, we’ll explore step-by-step examples for each protocol and library.