Duckdb Python 1 5 4

duckdb-python 1.5.4

DuckDB Python client providing in-process analytical SQL database with zero-config deployment. Runs entirely in-process (no server), supports pandas, PyArrow, Polars, and NumPy natively.

Overview

DuckDB is a columnar OLAP engine that runs inside the Python process. Two main API styles:

• Connection/DB-API 2.0 — duckdb.connect(), conn.execute(), fetchall() — standard cursor interface
• Relational API — duckdb.sql(), .filter(), .project(), .join() — lazy, chainable, returns DuckDBPyRelation

Key strengths:

• Reads CSV/Parquet/JSON directly from paths or buffers without loading into memory first
• Seamless pandas DataFrame and PyArrow Table interop via from_df(), fetchdf(), to_arrow_table()
• Python scalar UDFs registered with create_function() (native or arrow-backed)
• fsspec filesystem integration for S3, GCS, Azure, and in-memory storage
• ADBC driver included (adbc_driver_duckdb)

Usage

Quick start — top-level convenience functions

import duckdb

# Query returns DuckDBPyRelation (lazy)
rel = duckdb.sql("SELECT 42 as x, 'hello' as y")
rel.show()

# Read files directly
rel = duckdb.read_parquet("data/*.parquet")
rel = duckdb.read_csv("data.csv", header=True)
rel = duckdb.read_json("data.json")

# Convert to pandas / PyArrow
df = rel.df()                          # pandas DataFrame
table = rel.to_arrow_table()           # pyarrow.Table
reader = rel.to_arrow_reader()         # streaming RecordBatchReader

Connection-based workflow

import duckdb

conn = duckdb.connect("my_db.duckdb")  # persistent; ":memory:" is default

# Register a pandas DataFrame as a virtual table
conn.register("sales", sales_df)

# Execute SQL, get Relation back
rel = conn.sql("SELECT region, SUM(amount) FROM sales GROUP BY region")
rel.show()

# Standard DB-API 2.0 cursor interface
cursor = conn.cursor()
cursor.execute("SELECT * FROM sales WHERE amount > ?", [1000])
rows = cursor.fetchall()

conn.close()

Relational API chaining (lazy evaluation)

rel = (duckdb.from_df(df)
       .filter("amount > 100")
       .project("region, customer_id, amount * 1.1 as taxed_amount")
       .order("taxed_amount DESC")
       .limit(10))

rel.show()

Python UDFs

import duckdb

def double_it(x):
    return x * 2

duckdb.create_function("double_it", double_it, ["integer"], "integer")
duckdb.sql("SELECT double_it(i) FROM range(5)").show()

Gotchas

• Default connection is :memory: — data disappears when the process exits. Pass a file path to connect() for persistence.
• fetch_arrow_table() and fetch_record_batch() are deprecated — use to_arrow_table() and to_arrow_reader() instead (same on both Connection and Relation).
• Relations are lazy — .filter(), .project(), .join() etc. build a query plan. Results materialize only on .show(), .fetchall(), .df(), .execute(), or .create().
• conn.register() creates a virtual table reference — it holds a Python object alive as long as the view/table exists in DuckDB catalog. Unregister with conn.unregister("name") to release.
• UDF parameter types must match exactly — create_function("fn", fn_impl, ["bigint"], "varchar") requires input columns to be castable to BIGINT. Mismatched types raise InvalidInputException.
• Arrow UDFs receive ChunkedArrays — use @duckdb.udf.vectorized decorator or annotate parameters with pa.ChunkedArray for arrow-mode UDFs.
• conn.pl() returns Polars DataFrame — requires polars installed. Use lazy=True for LazyFrame.
• Free-threaded Python (3.13t, 3.14t) is not supported — the production client does not work with free-threaded builds.
• conn.duplicate() clones a connection sharing the same database but with independent transaction state. Useful for concurrent queries on the same DB.

References

• [01-connection-api](references/01-connection-api.md) — connect, execute, cursor, transactions, config
• [02-relational-api](references/02-relational-api.md) — DuckDBPyRelation: lazy chaining, joins, aggregations, exports
• [03-data-io](references/03-data-io.md) — CSV, Parquet, JSON read/write with options
• [04-udfs](references/04-udfs.md) — Python scalar UDFs (native and arrow), type annotations, null handling
• [05-types-values](references/05-types-values.md) — DuckDBPyType, sqltypes constants, Value classes, DB-API type objects
• [06-integrations](references/06-integrations.md) — pandas, PyArrow, Polars, NumPy, fsspec, ADBC, Spark compat

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: tangledgroup
• Source: tangledgroup/tangled-skills
• License: MIT

Install and usage instructions live in the source repository linked above.