Notebook and DataFrame integration#

The DuckDB demo registers Pandas DataFrames directly into SQL queries. DuckPlus keeps the workflow minimal by exposing the managed connection while the :class:~duckplus.relation.Relation wrapper protects downstream transformations.

import pandas as pd

from duckplus import DuckCon, Relation
from duckplus.typed import ducktype

sales_frame = pd.DataFrame(
    {
        "region": ["north", "north", "south"],
        "amount": [1000, 1200, 800],
    }
)

manager = DuckCon()
with manager as connection:
    connection.register("sales", sales_frame)
    base = Relation.from_sql(manager, "SELECT * FROM sales")

    summary = (
        base.aggregate()
        .start_agg()
        .agg(ducktype.Numeric("amount").sum(), alias="total")
        .agg(ducktype.Numeric("amount").avg(), alias="average")
        .by("region")
    )
    print(summary.relation.fetchall())

Because relations remain immutable, notebooks can keep the original registered DataFrame untouched while layering multiple projections, filters, and export calls. This mirrors the DuckDB demo’s emphasis on interactivity while preserving type safety through DuckPlus’ typed expressions.

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