Data Wrangling

Data cleaning often takes 80% of the time. This skill provides a structured approach to making that process more efficient and reproducible.

Core Patterns

1. Robust Imputation

• Don't just fill with the mean.
• Use group-specific medians for numerical data.
• Use 'unknown' or the mode for categorical data.

2. Memory Optimization

• Downcast numeric types (float64 -> float32).
• Convert low-cardinality strings to category type.

3. Outlier Handling

• Winsorization: Capping extreme values at the 1st and 99th percentiles.
• Z-Score Filter: Removing values beyond 3 standard deviations.