quack-kernels

library0.6.4pypypi✓ verified 25d ago

quack-kernels provides Jupyter Kernels for DuckDB, enabling users to interact with DuckDB databases directly from Jupyter notebooks using either SQL or Python. It currently stands at version 0.3.9 and is actively maintained with releases tied to DuckDB and Jupyter ecosystem updates.

database data

pip install quack-kernels

package ↗

INSTALL

IMPORT

SIG · QUACK-KERNELS

quack-kernels

databasepythonv0.6.4

Install

74.1s avg

Import

—

Disk

5094MB

Pass rate

4/ 10

Env Coverage4 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.6.4 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✓ 79.8s

py 3.11

✕ build_error

✓ 75.7s

py 3.12

✕ build_error

✓ 72s

py 3.13

✕ build_error

✓ 69s

py 3.9

✕ build_error

5094MB installed

● package 5094MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

DuckDBSQLKernel

✓ from quack_kernels.duckdb_sql import DuckDBSQLKernel

✗ from quack_kernels import DuckDBSQLKernel

Primarily used for programmatic kernel management or customization. End-users typically interact by selecting kernels in Jupyter, not through direct Python imports in their notebooks.

DuckDBPythonKernel

✓ from quack_kernels.duckdb_python import DuckDBPythonKernel

Similar to DuckDBSQLKernel, this is for programmatic interaction; standard usage is via Jupyter kernel selection.

After installing with `pip install quack-kernels`, launch Jupyter Lab or Notebook (`jupyter lab`). Create a new notebook and select either the 'DuckDB SQL' or 'DuckDB Python' kernel. The provided Python code demonstrates interaction with DuckDB using the 'DuckDB Python' kernel, where direct `duckdb` module calls are supported.

import duckdb import pandas as pd # This code runs within a Jupyter notebook using the 'DuckDB Python' kernel. # The 'quack-kernels' library provides the infrastructure for this. # Connect to an in-memory DuckDB database con = duckdb.connect(database=':memory:', read_only=False) # Create a table and insert data using SQL con.execute("CREATE TABLE my_data (id INTEGER, name VARCHAR);") con.execute("INSERT INTO my_data VALUES (1, 'Alice'), (2, 'Bob'), (3, 'Charlie');") # Query the table and fetch results into a pandas DataFrame result_df = con.execute("SELECT * FROM my_data WHERE id > 1;").fetchdf() print("Fetched data from DuckDB (as pandas DataFrame):") print(result_df) # Close the connection (important for file-based DBs, optional for :memory:) con.close()

Debug

Known issues

gotchaDuckDB version compatibility: `quack-kernels` is closely tied to specific `duckdb` versions. Ensure your `duckdb` installation is compatible with the `quack-kernels` version to avoid unexpected behavior or errors.

fix

Always install `quack-kernels` in a fresh environment or ensure your `duckdb` version aligns with the `quack-kernels` requirements. If issues arise, try upgrading/downgrading `duckdb` or `quack-kernels` to a known working pair.

affects: All versions

gotchaKernel not appearing in Jupyter: After installation, if the 'DuckDB SQL' or 'DuckDB Python' kernels do not appear in Jupyter, the kernelspecs might not have been installed correctly.

fix

Run `python -m ipykernel install --user --name duckdb_sql --display-name 'DuckDB SQL'` and `python -m ipykernel install --user --name duckdb_python --display-name 'DuckDB Python'` (though `quack-kernels` handles this, manual intervention might be needed for unusual environments), or reinstall `quack-kernels` and restart Jupyter.

affects: All versions

gotchaIn-memory database persistence: When using `duckdb.connect(':memory:')`, the database and all its data are lost if the kernel is restarted or the notebook session ends. This is a common pitfall for data analysis.

fix

For persistent data, connect to a file-based DuckDB database: `con = duckdb.connect(database='my_database.duckdb')`. The file will persist across kernel restarts and sessions.

affects: All versions

gotchaMisunderstanding kernel purpose (SQL vs. Python): Users sometimes try to run Python code in the 'DuckDB SQL' kernel or raw SQL in the 'DuckDB Python' kernel without magic commands.

fix

Select the 'DuckDB Python' kernel for general Python code that uses `duckdb` module. Select the 'DuckDB SQL' kernel for running raw SQL queries directly in cells. Use Jupyter magic commands (`%%sql` etc.) in Python notebooks if you need to mix languages.

affects: All versions

Upgrade

Version history

0.6.4latest on PyPI · released Aug 7, 2026

Audit

Dependencies

duckdbrequiredCore database engine for the kernels.

jupyterlabrequiredRequired for running Jupyter Notebooks or Lab where kernels are used.

pandasrequiredUsed for data interchange and display within the Python kernel.

pyarrowrequiredUsed for efficient data transfer with DuckDB.

Agent activity

23 hits · last 30 days

node

OpenAI (training)

Resources

packagehttps://pypi.org/project/quack-kernels/ ↗