The Basics of Debugging Python Problems - KDnuggets

This comprehensive tutorial by Nate Rosidi from StrataScratch demonstrates essential Python debugging techniques specifically for data science projects using a real DoorDash delivery duration prediction dataset.

The article walks through common debugging scenarios that plague data scientists, from basic file loading errors to complex modeling failures. Rosidi systematically addresses FileNotFoundError handling, dtype misinterpretation issues, and date parsing problems that often occur during data ingestion. The tutorial then progresses to more sophisticated challenges like handling missing values, KeyError debugging, and feature engineering glitches that can silently sabotage machine learning pipelines.

Key debugging strategies include:

• Proactive error handling - Using try-except blocks with informative diagnostics rather than generic error catching • Data validation techniques - Checking for hidden null values, mixed data types, and logical inconsistencies before they break models
• Shape mismatch resolution - Ensuring proper alignment between features and targets in machine learning workflows • Type conversion debugging - Converting datetime objects to numeric formats compatible with scikit-learn models

The tutorial emphasizes shifting from reactive debugging to preventive data validation, showing how simple checks like .info(), .isna().sum(), and .dtypes can expose hidden issues before they crash your models.