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Data Processing — Polars + DuckDB

packages/omo-codex/plugin/skills/programming/references/python/data-processing.md

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Data Processing — Polars + DuckDB

The rule

NEVER pandas. Polars (with numpy) plus DuckDB. Pandas is 10-50x slower, has weaker types, and the modern Python data ecosystem has moved on.

Quick decision tree

OperationUseWhy
.csv / .parquet / .json direct queryDuckDBZero memory load, SQL ergonomics
.duckdb fileDuckDBNative format
Filter (any size)Polars128x faster than DuckDB for filtering
SortPolars12x faster
Multi-table joinDuckDB3x faster, more join types
Heavy GROUP BY aggregationDuckDB4x faster on large datasets
Window functionPolars3-5x faster
Pivot / melt / string opsPolars2x faster
Larger than RAMPolars streaming or DuckDB out-of-coreBoth handle OOM
Mixed pipelineHybrid (zero-copy via Arrow)Use each tool's strengths

For the deep version (per-operation benchmarks, OOM strategies, full execution templates), load the data-scientist skill - it lives in this same skill set and is the source of truth for performance numbers.

Standard imports

python
import numpy as np
import polars as pl
import duckdb

DuckDB direct file query (zero memory load)

python
result = duckdb.sql("""
    SELECT category, SUM(amount) AS total
    FROM 'data.csv'
    WHERE date >= '2026-01-01'
    GROUP BY category
    ORDER BY total DESC
""").pl()  # zero-copy → Polars DataFrame

.pl() returns Polars; .df() would return pandas - never use .df().

Polars lazy pipeline

python
result = (
    pl.scan_csv("data.csv")              # lazy, no read yet
    .filter(pl.col("amount") > 1000)
    .filter(pl.col("status") == "active")
    .sort("amount", descending=True)
    .head(100)
    .collect()                            # execute optimised plan
)

scan_* over read_* for files; lazy() then collect() for in-memory frames. Polars optimises the entire plan before execution (predicate pushdown, projection pushdown, common subexpression elimination).

Streaming for OOM data

python
result = (
    pl.scan_csv("huge.csv")
    .filter(pl.col("active"))
    .group_by("category")
    .agg([
        pl.len().alias("count"),
        pl.sum("amount").alias("total"),
    ])
    .collect(streaming=True)
)

Hybrid pipeline (most realistic shape)

python
# Phase 1: DuckDB for the join (3x faster)
joined = duckdb.sql("""
    SELECT o.*, c.region, p.category
    FROM 'orders.parquet' o
    JOIN 'customers.parquet' c ON o.customer_id = c.id
    JOIN 'products.parquet'  p ON o.product_id  = p.id
""").pl()

# Phase 2: Polars for filtering and transformation (128x + 2x faster)
processed = (
    joined
    .filter(pl.col("amount") > 100)
    .with_columns([
        (pl.col("amount") * 1.1).alias("amount_with_tax"),
    ])
)

# Phase 3: DuckDB for final aggregation (4x faster) - register Polars frame by name
duckdb.register("processed", processed)
final = duckdb.sql("""
    SELECT region, category, SUM(amount_with_tax) AS revenue
    FROM processed
    GROUP BY region, category
    ORDER BY revenue DESC
""").pl()

Type safety with Polars

Polars supports schema overrides at read time, and .cast() for explicit conversion. Avoid implicit coercion in hot paths.

python
schema = {"id": pl.Int64, "amount": pl.Float64, "date": pl.Date}
df = pl.read_csv("data.csv", schema_overrides=schema)

basedpyright understands polars-stubs, which ship with polars itself. No extra type stubs to install.

Things you might miss from pandas (and how to do them in Polars)

pandaspolars
df.iloc[5]df.row(5) (named tuple) or df[5] (single-row frame)
df.loc[df["x"] > 5]df.filter(pl.col("x") > 5)
df["x"].apply(fn)df["x"].map_elements(fn) (slow path) or use native expressions
df.merge(...)df.join(other, on="key")
df.groupby(...).agg(...)df.group_by(...).agg(...)
pd.read_csv(...).dtypespl.read_csv(...).schema
df.to_dict("records")df.to_dicts()

Sources