Data Transformation Techniques
Pandas enables powerful data transformations.
Apply Function
df.apply(func) applies function to rows or columns. lambda functions enable inline transformations.
applymap applies element-wise to DataFrame. map applies to Series.
GroupBy Operations
groupby creates grouped DataFrames. .agg({'col': ['mean', 'sum']}) applies multiple aggregations.
transform applies functions within groups. filter keeps groups meeting condition.
Pivot and Melt
pivot creates wide format: df.pivot(index, columns, values). pivot_table adds aggregation.
melt converts wide to long: pd.melt(df, id_vars). This is useful for tidy data.
Key Takeaways
- Apply enables custom transformations
- GroupBy provides powerful grouped operations
- Pivot/melt convert between data formats