Debugging a Python script execution error in Power BI requires identifying and extracting errors effectively and using the best practices and tools. It also includes the necessary considerations:
Check Configuration of Python Environment:
Make sure that the correct Python installation has been connected by Power BI. Open Options > Python scripting in Power BI and verify the path to your Python executables. Use a virtual environment that is clearly set up specifically for Power BI to avoid mismatched libraries. Ensure that all necessary libraries are installed in that environment.
Examine Error Messages:
Study the error messages produced by Power BI. Usually; these messages hint at the real problem: missing libraries, wrong paths to files, or unsupported data types. Copy the error and test the script externally in an IDE like Jupyter Notebook or Visual Studio Code to see what went wrong.
Correct Library Conflicts:
Only a limited number of Python libraries are supported in Power BI. Refer to Power BI documentation for details on compatible libraries and install the necessary versions using pip. If the library is not supported, check for an alternative or do preprocessing outside Power BI before importing.
Check Data Types:
Data has to be "Pandas DataFrame or Series" in Python for Power BI. Logic about the data and format through which you have to report it in your script should ensure a DataFrame is returned along with valid column names and data types. Simplify the data if anything tricky comes before a Power BI import.
Use Debugging tools:
Insert print statements or use the logging module in Python to follow variable value movement against the flow of the script. For more detailed advanced debugging, you will run the script in an external debugger to catch problems before bringing it in for use in Power BI.
