Python for Data Science #4: Advanced Data Cleaning (Handling Outliers, Duplicates, and Inconsistencies)
Python for Data Science #4: Advanced Data Cleaning (Handling Outliers, Duplicates, and Inconsistencies) Python for Data Science #4: Advanced Data Cleaning (Handling Outliers, Duplicates, and Inconsistencies) Welcome to the final installment of our Python for Data Science series. The most critical step in any data science workflow is **data cleaning**. Without it, all your sophisticated models and visualizations are meaningless. As the old adage warns: Garbage In, Garbage Out . This deep dive explores three advanced cleaning problems in Pandas: **Duplicates, Outliers, and Inconsistencies**. Why Advanced Cleaning? Garbage In, Garbage Out If you feed **Raw, Untrustworthy Data** into your analysis pipeline, the result will be **Misleading Insights & Wrong Decisions**. For example, duplicated rows will inflate your counts, and unhandled outliers will skew your averages. Advanced cleaning ensur...