Grouping & Aggregation

GroupBy

Group rows by column values:

const byCategory = df.groupby("category");

Aggregations

// Count per group (rows)
const counts = df.groupby("category").count();

// Quick helpers
const totals = df.groupby("region").sum("revenue");
const averages = df.groupby("department").mean("salary");

// Flexible: mix aggregations per column
const summary = df.groupby("category").agg({
  revenue: "sum",
  quantity: "mean",
  price: "max",
  first_seen: "first",
  last_seen: "last",
});

Supported aggregation ops: sum, mean, min, max, count, first, last.

Multiple Groups

const nested = df.groupby(["year", "month"]).sum("sales");

Describe

Get summary statistics:

df.describe().print();

Output:

┌───────┬───────────┬───────────┬───────────┐
│       │ age       │ salary    │ score     │
├───────┼───────────┼───────────┼───────────┤
│ count │ 100       │ 100       │ 100       │
│ mean  │ 34.5      │ 65000     │ 78.2      │
│ std   │ 8.2       │ 15000     │ 12.4      │
│ min   │ 22        │ 35000     │ 45        │
│ max   │ 58        │ 120000    │ 99        │
└───────┴───────────┴───────────┴───────────┘

Unique Values

// Remove duplicate rows
const unique = df.unique();

// Unique values in a column
const categories = df.col("category").unique();