learning-code_consistency

To check consistency between Python scripts and Markdown documentation, you compare code logic, statistical values, and image references across .py, .ipynb, and .md files to ensure accurate data representation for lab submissions.

Documentation alignment for Jupyter notebook lab submissions is the process of validating that Markdown documentation accurately reflects code output, verifying identical function definitions, logic, and statistical values across .ipynb and .md files.

To verify image references in Markdown before submitting a lab assignment, check that all images referenced in the documentation exist and use correct relative paths matching the project structure.

Yes, this code consistency check compares Python scripts and Jupyter notebooks to ensure identical function definitions, logic, and output statements for reliable academic submissions.

Yes, you can use this to validate statistical values across .py and .md files by confirming that the calculations in your Python script match the results presented in your Markdown report.