Lesson 9: Grouping, Aggregation & Pivot Tables

1. Grouping & Aggregation

Group by category → compute stats per group (like Excel pivot table).

df.groupby("species")["petal_length"].mean()
# Average petal length per flower species

Multiple aggregations:

df.groupby("city").agg({
    "age": "mean",
    "salary": ["min", "max", "mean"]
})

2. Pivot Tables

Reshape data — summarize with rows/columns.

pd.pivot_table(df, 
    values="salary",
    index="department",
    columns="year",
    aggfunc="mean")

Used in ML: compare features across categories.