DuckDB: The In-Process Analytics Database Every Python Developer Needs in 2026 — DuckDB is the perfect companion to Polars. It brings full SQL power directly inside your Python process — no server required.
1. Installation
uv add duckdb polars
2. Basic Usage
import duckdb
import polars as pl
# Query Parquet/CSV directly
result = duckdb.sql("""
SELECT
category,
COUNT(*) as count,
AVG(value) as avg_value
FROM ''read_parquet('data/*.parquet')''
GROUP BY category
ORDER BY count DESC
""").pl() # Convert to Polars DataFrame
print(result)
3. Integration with Polars
df = pl.read_parquet("large_data.parquet")
duckdb.sql("""
SELECT * FROM df
WHERE date >= ''2026-01-01''
""").pl()
Conclusion
DuckDB + Polars is the ultimate combination for fast local analytics in 2026. Use DuckDB for complex SQL and Polars for Pythonic transformations.