Python Automation

Introduction

The Python automation package allows you to invoke and execute a Python script wherever necessary within the automation process flow. This functionality is provided by the RunPythonScript component in the Robility , which is part of the Python Automation package.

Moreover, it simplifies the management of Python scripts by centralizing them within RobilityDesigner. As a result, users can efficiently harness the power of Python automation to optimize their tasks and achieve greater productivity.

Pre-requisites

1. Ensure that your Python script is indented correctly to avoid indentation during runtime. When passing a variable name as the value for an argument, the names must match. For example, if the argument name is ‘Sample,’ the variable name should also be ‘Sample.’ Failure to match the names will result in a mismatch error being thrown. 
2. The Python functionality in Robility requires the “Python.exe” file to execute the process, and this file will be automatically installed when you install the feature. Refer the attached sample Python file to view how to parse arguments in the script.

Benefits

1.  Customization: You can create custom Python scripts to handle specific tasks or scenarios that might be challenging with native Robility activities, giving you more control and flexibility.
2.  Integration: Python can easily integrate with external APIs, databases, and web services, enabling you to connect your workflows to a broader array of systems and data sources.
3.  Machine Learning: You can incorporate machine learning models and algorithms to make data-driven decisions and predictions within your workflow.
4.  Performance: Python is known for its performance, making it suitable for handling large datasets and computationally intensive tasks.

Use Cases

1. Data Manipulation and Analysis: Python can be used to clean, transform, and analyze data before or after processing it. This is particularly useful for tasks involving Excel spreadsheets, CSV files, or databases.
2. Image and Video Processing: You can employ Python libraries such as OpenCV to work with images and videos within the workflows. This is valuable for tasks like image recognition, object detection, or video processing.
3. Machine Learning Integration: Integrate machine learning models trained in Python into your automation bot to automate decision making processes, such as fraud detection or recommendation systems.
4. Database Interaction: Python can connect to various databases (SQL, NoSQL) to perform data extraction, transformation, and loading tasks as part of your workflows.
5. Automating Script Execution: Execute Python scripts at specific points in your automation to perform specialized calculations or tasks