Skip to main content

No project description provided

Project description

quak logo
quak /kwæk/

an anywidget for data that talks like a duck

quak is a scalable data profiler for quickly scanning large tables, capturing interactions as executable SQL queries.

  • interactive 🖱️ mouse over column summaries, cross-filter, sort, and slice rows.
  • fast ⚡ built with Mosaic; views are expressed as SQL queries lazily executed by DuckDB.
  • flexible 🔄 supports many data types and formats via Apache Arrow, the dataframe interchange protocol, and the Arrow PyCapsule Interface.
  • reproducible 📓 a UI for building complex SQL queries; materialize views in the kernel for further analysis.

install

pip install quak

usage

The easiest way to get started with quak is using the IPython cell magic.

%load_ext quak
import polars as pl

df = pl.read_parquet("https://github.com/uwdata/mosaic/raw/main/data/athletes.parquet")
df
olympic athletes table

quak hooks into Jupyter's display mechanism to automatically render any dataframe-like object (implementing the Python dataframe interchange protocol or Arrow PyCapsule Interface) using quak.Widget instead of the default display.

Alternatively, you can use quak.Widget directly:

import polars as pl
import quak

df = pl.read_parquet("https://github.com/uwdata/mosaic/raw/main/data/athletes.parquet")
widget = quak.Widget(df)
widget

interacting with the data

quak captures all user interactions as queries.

At any point, table state can be accessed as SQL,

widget.sql # SELECT * FROM df WHERE ...

which for convenience can be executed in the kernel to materialize the view for further analysis:

widget.data() # returns duckdb.DuckDBPyRelation object

By representing UI state as SQL, quak makes it easy to generate complex queries via interactions that would be challenging to write manually, while keeping them reproducible.

using quak in marimo

quak can also be used in marimo notebooks, which provide out-of-the-box support for anywidget:

import marimo as mo
import polars as pl
import quak

df = pl.read_parquet("https://github.com/uwdata/mosaic/raw/main/data/athletes.parquet")
widget = mo.ui.anywidget(quak.Widget(df))
widget

contributing

Contributors welcome! Check the Contributors Guide to get started. Note: I'm wrapping up my PhD, so I might be slow to respond. Please open an issue before contributing a new feature.

references

quak pieces together many important ideas from the web and Python data science ecosystems. It serves as an example of what you can achieve by embracing these platforms for their strengths.

  • Observable's data table: Inspiration for the UI design and user interactions.
  • Mosaic: The foundation for linking databases and interactive table views.
  • Apache Arrow: Support for various data types and efficient data interchange between JS/Python.
  • DuckDB: An amazingly engineered piece of software that makes SQL go vroom.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

quak-0.2.1.tar.gz (180.5 kB view details)

Uploaded Source

Built Distribution

quak-0.2.1-py3-none-any.whl (181.3 kB view details)

Uploaded Python 3

File details

Details for the file quak-0.2.1.tar.gz.

File metadata

  • Download URL: quak-0.2.1.tar.gz
  • Upload date:
  • Size: 180.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for quak-0.2.1.tar.gz
Algorithm Hash digest
SHA256 7c23def318870c24f2c5e7b79d786ec901843e8d6da54130092dff40bb5d717d
MD5 03871939dad853981621dcdf76184521
BLAKE2b-256 2bac875b318a8f6d4048d3fa3ed44dccb00109e5983b48c64a851e47ffe81189

See more details on using hashes here.

Provenance

The following attestation bundles were made for quak-0.2.1.tar.gz:

Publisher: GitHub
  • Repository: manzt/quak
  • Workflow: release.yml
Attestations:
  • Statement type: https://in-toto.io/Statement/v1
    • Predicate type: https://docs.pypi.org/attestations/publish/v1
    • Subject name: quak-0.2.1.tar.gz
    • Subject digest: 7c23def318870c24f2c5e7b79d786ec901843e8d6da54130092dff40bb5d717d
    • Transparency log index: 145610588
    • Transparency log integration time:

File details

Details for the file quak-0.2.1-py3-none-any.whl.

File metadata

  • Download URL: quak-0.2.1-py3-none-any.whl
  • Upload date:
  • Size: 181.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for quak-0.2.1-py3-none-any.whl
Algorithm Hash digest
SHA256 a51d247a9d685c3d8ba32a6df5bd8ff2c4a39e468708c21aed3dc5c58e71c98f
MD5 fae7180bea1abaf6b74f20cf75ca6bda
BLAKE2b-256 997d28be942160dd8a3ec692e2a751d7283ba2ff1cd52b1224981432fa3bdf72

See more details on using hashes here.

Provenance

The following attestation bundles were made for quak-0.2.1-py3-none-any.whl:

Publisher: GitHub
  • Repository: manzt/quak
  • Workflow: release.yml
Attestations:
  • Statement type: https://in-toto.io/Statement/v1
    • Predicate type: https://docs.pypi.org/attestations/publish/v1
    • Subject name: quak-0.2.1-py3-none-any.whl
    • Subject digest: a51d247a9d685c3d8ba32a6df5bd8ff2c4a39e468708c21aed3dc5c58e71c98f
    • Transparency log index: 145610591
    • Transparency log integration time:

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page