Daft dataframes can load any data such as PDF documents, images, protobufs, csv, parquet and audio files into a table dataframe structure for easy querying

GitHub Actions tests PyPI latest tag Coverage slack community

Website • Docs • Installation • Daft Quickstart • Community and Support

Daft: Unified Engine for Data Analytics, Engineering & ML/AI

Eventual-Inc/Daft | Trendshift

Daft is a distributed query engine for large-scale data processing using Python or SQL, implemented in Rust.

  • Familiar interactive API: Lazy Python Dataframe for rapid and interactive iteration, or SQL for analytical queries

  • Focus on the what: Powerful Query Optimizer that rewrites queries to be as efficient as possible

  • Data Catalog integrations: Integration with data catalogs (AWS Glue, Unity Catalog) and table formats like Apache Iceberg

  • Rich multimodal type-system: Supports multimodal types such as Images, URLs, Tensors and more

  • Seamless Interchange: Built on the Apache Arrow In-Memory Format

  • Built for the cloud: Record-setting I/O performance for integrations with S3 cloud storage

Table of Contents

About Daft

Daft was designed with the following principles in mind:

  1. Any Data: Beyond the usual strings/numbers/dates, Daft columns can also hold complex or nested multimodal data such as Images, Embeddings and Python objects efficiently with its Arrow based memory representation. Ingestion and basic transformations of multimodal data is extremely easy and performant in Daft.

  2. Interactive Computing: Daft is built for the interactive developer experience through notebooks or REPLs - intelligent caching/query optimizations accelerates your experimentation and data exploration.

  3. Distributed Computing: Some workloads can quickly outgrow your local laptop’s computational resources - Daft integrates natively with Ray for running dataframes on large clusters of machines with thousands of CPUs/GPUs.

Getting Started

Installation

Install Daft with pip install daft.

For more advanced installations (e.g. installing from source or with extra dependencies such as Ray and AWS utilities), please see our Installation Guide

Quickstart

Check out our quickstart!

In this example, we load images from an AWS S3 bucket’s URLs and resize each image in the dataframe:

import daft

# Load a dataframe from filepaths in an S3 bucket
df = daft.from_glob_path("s3://daft-public-data/laion-sample-images/*")

# 1. Download column of image URLs as a column of bytes
# 2. Decode the column of bytes into a column of images
df = df.with_column("image", df["path"].url.download().image.decode())

# Resize each image into 32x32
df = df.with_column("resized", df["image"].image.resize(32, 32))

df.show(3)

Dataframe code to load a folder of images from AWS S3 and create thumbnails

Benchmarks

Benchmarks for SF100 TPCH

To see the full benchmarks, detailed setup, and logs, check out our benchmarking page.

More Resources

  • Daft Quickstart - learn more about Daft’s full range of capabilities including dataloading from URLs, joins, user-defined functions (UDF), groupby, aggregations and more.

  • Examples - see Daft in action with use cases across text, images, audio, and more

  • User Guide - take a deep-dive into each topic within Daft

  • API Reference - API reference for public classes/functions of Daft

  • SQL Reference - Daft SQL reference

Contributing

We <3 developers! To start contributing to Daft, please read CONTRIBUTING.md. This document describes the development lifecycle and toolchain for working on Daft. It also details how to add new functionality to the core engine and expose it through a Python API.

Here’s a list of good first issues to get yourself warmed up with Daft. Comment in the issue to pick it up, and feel free to ask any questions!

Telemetry

To help improve Daft, we collect non-identifiable data via Scarf (https://scarf.sh).

To disable this behavior, set the environment variable DO_NOT_TRACK=true.

The data that we collect is:

  1. Non-identifiable: Events are keyed by a session ID which is generated on import of Daft

  2. Metadata-only: We do not collect any of our users’ proprietary code or data

  3. For development only: We do not buy or sell any user data

Please see our documentation for more details.

https://static.scarf.sh/a.png?x-pxid=31f8d5ba-7e09-4d75-8895-5252bbf06cf6

Related Projects

Engine

Query Optimizer

Multimodal

Distributed

Arrow Backed

Vectorized Execution Engine

Out-of-core

Daft

Yes

Yes

Yes

Yes

Yes

Yes

Pandas

No

Python object

No

optional >= 2.0

Some(Numpy)

No

Polars

Yes

Python object

No

Yes

Yes

Yes

Modin

Yes

Python object

Yes

No

Some(Pandas)

Yes

PySpark

Yes

No

Yes

Pandas UDF/IO

Pandas UDF

Yes

Dask DF

No

Python object

Yes

No

Some(Pandas)

Yes

License

Daft has an Apache 2.0 license - please see the LICENSE file.

Project links

Data verified by PyPI on Sep 25, 2025
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.7.24)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for madvart from gravatar.com madvart Avatar for samster25 from gravatar.com samster25

Credits

Author: Eventual Inc

Maintainer: Sammy Sidhu

GitHub Statistics

Data verified by PyPI on Sep 25, 2025
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

Requires

Python >=3.9

Provides Extra

all aws azure clickhouse deltalake gcp hudi huggingface iceberg lance numpy openai pandas ray sentence-transformers transformers spark sql turbopuffer unity viz

Report project as malware

Download files

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

Source Distribution

daft-0.6.3.tar.gz (9.7 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

daft-0.6.3-cp39-abi3-win_amd64.whl (44.5 MB view details)

Uploaded CPython 3.9+Windows x86-64

daft-0.6.3-cp39-abi3-manylinux_2_24_x86_64.whl (46.8 MB view details)

Uploaded CPython 3.9+manylinux: glibc 2.24+ x86-64

daft-0.6.3-cp39-abi3-manylinux_2_24_aarch64.whl (43.9 MB view details)

Uploaded CPython 3.9+manylinux: glibc 2.24+ ARM64

daft-0.6.3-cp39-abi3-macosx_11_0_arm64.whl (42.7 MB view details)

Uploaded CPython 3.9+macOS 11.0+ ARM64

daft-0.6.3-cp39-abi3-macosx_10_12_x86_64.whl (46.5 MB view details)

Uploaded CPython 3.9+macOS 10.12+ x86-64

File details

Details for the file daft-0.6.3.tar.gz.

File metadata

  • Download URL: daft-0.6.3.tar.gz
  • Upload date:
  • Size: 9.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for daft-0.6.3.tar.gz
Algorithm Hash digest
SHA256 c6cca6b346b551328353e30b03d8e84e2863964ef7c8bf3e49e943be54d6ae47
MD5 21a77a6bc7633b0ae498885a83a8bf2a
BLAKE2b-256 a346771b25e47f537b88edddef0fb0a0f32b656a90dc4a38792fe6383c449232

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.3.tar.gz:

Publisher: publish-pypi.yml on Eventual-Inc/Daft

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file daft-0.6.3-cp39-abi3-win_amd64.whl.

File metadata

  • Download URL: daft-0.6.3-cp39-abi3-win_amd64.whl
  • Upload date:
  • Size: 44.5 MB
  • Tags: CPython 3.9+, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for daft-0.6.3-cp39-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 e8f18ed455f5e933dbfece7c0a2ac0321aa11a4f772fc1cb3eae77b9f8dd6559
MD5 86b68c60b70b07629885fc6ef4657708
BLAKE2b-256 9d2781e76f0c31134aa2d157e7f1f8723aba75f92ea9f7102d2609bca7554912

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.3-cp39-abi3-win_amd64.whl:

Publisher: publish-pypi.yml on Eventual-Inc/Daft

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file daft-0.6.3-cp39-abi3-manylinux_2_24_x86_64.whl.

File metadata

  • Download URL: daft-0.6.3-cp39-abi3-manylinux_2_24_x86_64.whl
  • Upload date:
  • Size: 46.8 MB
  • Tags: CPython 3.9+, manylinux: glibc 2.24+ x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for daft-0.6.3-cp39-abi3-manylinux_2_24_x86_64.whl
Algorithm Hash digest
SHA256 e5588aad1988b5d2630d4864cabb492ebde170387de2b568adf9cb7de15a7325
MD5 564ae57379e09ea704145e89213e8f25
BLAKE2b-256 dff3ca3947a77aa7ecdddc5b71b9776669fd52c1a797db4293485925162fcf27

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.3-cp39-abi3-manylinux_2_24_x86_64.whl:

Publisher: publish-pypi.yml on Eventual-Inc/Daft

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file daft-0.6.3-cp39-abi3-manylinux_2_24_aarch64.whl.

File metadata

File hashes

Hashes for daft-0.6.3-cp39-abi3-manylinux_2_24_aarch64.whl
Algorithm Hash digest
SHA256 76d6fe7a873b73c53b686a5e9e843e4b974900fd27f9b589bd38dbdbe6f0919e
MD5 8933eefa82de0095ecaf2d0ba9a04c1f
BLAKE2b-256 a4c383976e89d4c2e54e162d78fcf20ca998009a0950d0f080835a8a99ca2920

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.3-cp39-abi3-manylinux_2_24_aarch64.whl:

Publisher: publish-pypi.yml on Eventual-Inc/Daft

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file daft-0.6.3-cp39-abi3-macosx_11_0_arm64.whl.

File metadata

  • Download URL: daft-0.6.3-cp39-abi3-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 42.7 MB
  • Tags: CPython 3.9+, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for daft-0.6.3-cp39-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 31fad8ecb673bbb1359d4bfa0525610b43df733c920ed692596c90d4e3068bd4
MD5 89be0a2a3aa1f47a43da05fdc2fd1a1a
BLAKE2b-256 b91123c33663d79a3d6f765414c6fda4aa1392812dffa6219403eb7c17fa05a7

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.3-cp39-abi3-macosx_11_0_arm64.whl:

Publisher: publish-pypi.yml on Eventual-Inc/Daft

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file daft-0.6.3-cp39-abi3-macosx_10_12_x86_64.whl.

File metadata

  • Download URL: daft-0.6.3-cp39-abi3-macosx_10_12_x86_64.whl
  • Upload date:
  • Size: 46.5 MB
  • Tags: CPython 3.9+, macOS 10.12+ x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for daft-0.6.3-cp39-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 57789828aa64bddd2d186e2ec8c3aeae31f4827cb51d2a33088a31880a16b707
MD5 c50ab0932773b0b5f2f67cacd1c3b70f
BLAKE2b-256 39babb2fa9fd6d0b5d9e3298fe33f9f37664fc4873eb38cf84a7bc51b236d7cf

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.3-cp39-abi3-macosx_10_12_x86_64.whl:

Publisher: publish-pypi.yml on Eventual-Inc/Daft

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.7.24

5 files

0.7.23

5 files

0.7.22

5 files

0.7.21

5 files

0.7.20

5 files

0.7.19

5 files

0.7.18

6 files

0.7.17

6 files

0.7.16

6 files

0.7.15

6 files

0.7.14

6 files

0.7.13

6 files

0.7.12

6 files

Yanked

0.7.11

6 files

Yanked reason: Breaking change in extensions

0.7.10

6 files

0.7.9

6 files

0.7.8

6 files

0.7.7

6 files

0.7.6

6 files

0.7.5

6 files

0.7.4

6 files

0.7.3

6 files

0.7.2

6 files

0.7.1

6 files

0.7.0

6 files

0.6.14

6 files

0.6.13

6 files

0.6.12

6 files

0.6.11

6 files

0.6.10

6 files

0.6.8

6 files

0.6.7

6 files

0.6.6

6 files

0.6.5

6 files

0.6.4

6 files

This release

0.6.3 This release

6 files

0.6.2

6 files

0.6.1

6 files

0.6.0

6 files

0.5.22

6 files

0.5.21

6 files

0.5.20

6 files

0.5.19

6 files

0.5.18

6 files

0.5.17

6 files

0.5.16

6 files

0.5.15

6 files

0.5.14

6 files

0.5.13

6 files

0.5.12

6 files

0.5.11

6 files

0.5.10

6 files

0.5.9

6 files

0.5.8

6 files

0.5.7

6 files

0.5.6

6 files

0.5.5

6 files

0.5.4

6 files

0.5.3

6 files

0.5.2

6 files

0.5.1

6 files

0.5.0

6 files

0.4.18

6 files

0.4.17

6 files

0.4.16

6 files

0.4.15

6 files

0.4.14

6 files

0.4.13

6 files

0.4.12

6 files

0.4.11

6 files

0.4.10

6 files

Yanked

0.1.3

2 files

Yanked reason: This project has migrated to `daft-pgm`. Please run `pip install daft-pgm` instead

Yanked

0.1.2

2 files

Yanked reason: This project has migrated to `daft-pgm`. Please run `pip install daft-pgm` instead

Yanked

0.1.0

1 file

Yanked reason: This project has migrated to `daft-pgm`. Please run `pip install daft-pgm` instead

Yanked

0.0.4

1 file

Yanked reason: This project has migrated to `daft-pgm`. Please run `pip install daft-pgm` instead

Yanked

0.0.3

1 file

Yanked reason: This project has migrated to `daft-pgm`. Please run `pip install daft-pgm` instead

Yanked

0.0.2

1 file

Yanked reason: This project has migrated to `daft-pgm`. Please run `pip install daft-pgm` instead

Yanked

0.0.1

1 file

Yanked reason: This project has migrated to `daft-pgm`. Please run `pip install daft-pgm` instead

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page