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

AI Benchmarks

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

Ray Data

No

Yes

Yes

Yes

Some(PyArrow)

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 Oct 30, 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 Oct 30, 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 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-0.6.8.tar.gz (10.4 MB view details)

Uploaded Source

Built Distributions

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

daft-0.6.8-cp39-abi3-win_amd64.whl (45.8 MB view details)

Uploaded CPython 3.9+Windows x86-64

daft-0.6.8-cp39-abi3-manylinux_2_24_x86_64.whl (48.1 MB view details)

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

daft-0.6.8-cp39-abi3-manylinux_2_24_aarch64.whl (45.2 MB view details)

Uploaded CPython 3.9+manylinux: glibc 2.24+ ARM64

daft-0.6.8-cp39-abi3-macosx_11_0_arm64.whl (43.9 MB view details)

Uploaded CPython 3.9+macOS 11.0+ ARM64

daft-0.6.8-cp39-abi3-macosx_10_12_x86_64.whl (47.8 MB view details)

Uploaded CPython 3.9+macOS 10.12+ x86-64

File details

Details for the file daft-0.6.8.tar.gz.

File metadata

  • Download URL: daft-0.6.8.tar.gz
  • Upload date:
  • Size: 10.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for daft-0.6.8.tar.gz
Algorithm Hash digest
SHA256 692ce1eadbac48c65b1be87cc9f58368e00590f93ce10e5f82bf9d3a511c8c6b
MD5 895b9ce29d53c82bd8c43ecff8b49c39
BLAKE2b-256 5aa5a4c784924fd93bb5636839ac1e69fdd1b3e49fb39bb732964f754388e3e2

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.8.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-0.6.8-cp39-abi3-win_amd64.whl.

File metadata

  • Download URL: daft-0.6.8-cp39-abi3-win_amd64.whl
  • Upload date:
  • Size: 45.8 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-0.6.8-cp39-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 1df735ece1fc41276a001514a039b2abc9b61a0f10bb0d78049068aa7d462586
MD5 477e00ce40640787e16df61f15b0d78a
BLAKE2b-256 9a190ae96bbc09189064efa4034a235b70d5245954bfe2d3385f03285c3ad79f

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.8-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-0.6.8-cp39-abi3-manylinux_2_24_x86_64.whl.

File metadata

  • Download URL: daft-0.6.8-cp39-abi3-manylinux_2_24_x86_64.whl
  • Upload date:
  • Size: 48.1 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-0.6.8-cp39-abi3-manylinux_2_24_x86_64.whl
Algorithm Hash digest
SHA256 6720a5763740ab45bf7ea341ab442e03342a3f6d3c6fd4b03be5fc0dabbb0b98
MD5 c38243efa990ec1a22cf63281cc6e134
BLAKE2b-256 ecda9a9c99a68c9e5a0dbe201b763817dcb55e088c9e5a6933fb0d66485a3ad2

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.8-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-0.6.8-cp39-abi3-manylinux_2_24_aarch64.whl.

File metadata

File hashes

Hashes for daft-0.6.8-cp39-abi3-manylinux_2_24_aarch64.whl
Algorithm Hash digest
SHA256 165fe295d8a5b8015029be7c4c8c6c1a8feb7d14d59074f9778f6d35263231dc
MD5 c9e98bdd0f2ef1cf634b97c6b9d96c0b
BLAKE2b-256 e7e49fa246f3512b94427c1699ba9ec03b6eb0517c3015b1a02ede7d8f0cfc68

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.8-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-0.6.8-cp39-abi3-macosx_11_0_arm64.whl.

File metadata

  • Download URL: daft-0.6.8-cp39-abi3-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 43.9 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-0.6.8-cp39-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ff9e050ec588555ca9b5b50726a320d52f83469a56e8db74c2419848895bb177
MD5 dbbd08582c17c874c990594bf0e816ab
BLAKE2b-256 cc2559ac8a92ea644a66cce67d25539750a958b7ca5458df8a9fd7dcc6cc5c76

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.8-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-0.6.8-cp39-abi3-macosx_10_12_x86_64.whl.

File metadata

  • Download URL: daft-0.6.8-cp39-abi3-macosx_10_12_x86_64.whl
  • Upload date:
  • Size: 47.8 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-0.6.8-cp39-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 c102c4797d91900e6532c10ae1d5fe704b3760e4520ee35ce013e6a9682e07fc
MD5 8643688fafa1ced5f88d6dc8294686a7
BLAKE2b-256 915f82d2ce05a7b27f142daef13478b07099467425a1c7d19bc8502f0026b3f8

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.8-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

This release

0.6.8 This release

6 files

0.6.7

6 files

0.6.6

6 files

0.6.5

6 files

0.6.4

6 files

0.6.3

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