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: High-Performance Data Engine for AI and Multimodal Workloads

Eventual-Inc/Daft | Trendshift

Daft is a high-performance data engine for AI and multimodal workloads. Process images, audio, video, and structured data at any scale.

  • Native multimodal processing: Process images, audio, video, and embeddings alongside structured data in a single framework

  • Built-in AI operations: Run LLM prompts, generate embeddings, and classify data at scale using OpenAI, Transformers, or custom models

  • Python-native, Rust-powered: Skip the JVM complexity with Python at its core and Rust under the hood for blazing performance

  • Seamless scaling: Start local, scale to distributed clusters on Ray, Kubernetes

  • Universal connectivity: Access data anywhere (S3, GCS, Iceberg, Delta Lake, Hugging Face, Unity Catalog)

  • Out-of-box reliability: Intelligent memory management and sensible defaults eliminate configuration headaches

Getting Started

Installation

Install Daft with pip install daft. Requires Python 3.10 or higher.

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

Get started in minutes with our Quickstart - load a real-world e-commerce dataset, process product images, and run AI inference at scale.

More Resources

  • 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

Benchmarks

AI Benchmarks

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

Contributing

We ❤️ 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: No session IDs or user identifiers are collected

  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 Jul 6, 2026
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 (0.7.23)

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 Jul 6, 2026
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 >=3.10

Provides Extra

all audio aws azure clickhouse deltalake gcp google gravitino hdf5 hudi huggingface iceberg lance numpy openai pandas postgres ray sql transformers turbopuffer unity video 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-0.7.17.tar.gz (3.4 MB view details)

Uploaded Source

Built Distributions

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

daft-0.7.17-cp310-abi3-win_amd64.whl (55.2 MB view details)

Uploaded CPython 3.10+Windows x86-64

daft-0.7.17-cp310-abi3-manylinux_2_24_x86_64.whl (56.0 MB view details)

Uploaded CPython 3.10+manylinux: glibc 2.24+ x86-64

daft-0.7.17-cp310-abi3-manylinux_2_24_aarch64.whl (53.8 MB view details)

Uploaded CPython 3.10+manylinux: glibc 2.24+ ARM64

daft-0.7.17-cp310-abi3-macosx_11_0_arm64.whl (51.5 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

daft-0.7.17-cp310-abi3-macosx_10_12_x86_64.whl (55.6 MB view details)

Uploaded CPython 3.10+macOS 10.12+ x86-64

File details

Details for the file daft-0.7.17.tar.gz.

File metadata

  • Download URL: daft-0.7.17.tar.gz
  • Upload date:
  • Size: 3.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for daft-0.7.17.tar.gz
Algorithm Hash digest
SHA256 7a916be3722f3618a15b3400425efdab94cfedeb549953356572733b45cf0b63
MD5 ab2ab44415622d7d2f473e49a07216aa
BLAKE2b-256 053b17d9f26fba520a4e2effbb7a87ca40cc506fa7f298cd4ff8c43ee0e32bb2

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.7.17.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-0.7.17-cp310-abi3-win_amd64.whl.

File metadata

  • Download URL: daft-0.7.17-cp310-abi3-win_amd64.whl
  • Upload date:
  • Size: 55.2 MB
  • Tags: CPython 3.10+, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for daft-0.7.17-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 c00b1f3cf356edfbde64951bef2d09740ed3d8931e403cbcbd3ccac1e8f239f7
MD5 431b48217f90143199fb075dd8b6cc3c
BLAKE2b-256 eb8c61686fae74c0bed78b4badd9c591db03ccbe30124c69fef4c0172406c8ba

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.7.17-cp310-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-0.7.17-cp310-abi3-manylinux_2_24_x86_64.whl.

File metadata

File hashes

Hashes for daft-0.7.17-cp310-abi3-manylinux_2_24_x86_64.whl
Algorithm Hash digest
SHA256 b235596ba7429db94b5ff647106a9b1228d0eb837f8cae660e46c1f503ed56c5
MD5 70a5af0435c6a86b684de8a7e48b73a8
BLAKE2b-256 d77522ca4363d5cbbe09615c153c18388d25685d32b7beeef2bc2b82446d15d8

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.7.17-cp310-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-0.7.17-cp310-abi3-manylinux_2_24_aarch64.whl.

File metadata

File hashes

Hashes for daft-0.7.17-cp310-abi3-manylinux_2_24_aarch64.whl
Algorithm Hash digest
SHA256 858be5fd51fecbfc2db4cae940a1abef3dd4ccb3ee9dbee9b0c612e82e28b68a
MD5 72a6ea86d0923496f5e4d8770e31fc39
BLAKE2b-256 b0cbbb947e4f4c22b20346b30543c088ad98b866a915586b075fa4d31a51845b

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.7.17-cp310-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-0.7.17-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for daft-0.7.17-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 29aaa63de59923e81b5eeae4a0154337abd1fd1f0af709165a119ec8c716864d
MD5 ed4ba200b0d9e9f4c44c266759e0d150
BLAKE2b-256 c47daf8184c6d3e1d580584dad4ccfdad8324d25c47024621c9c947642f63665

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.7.17-cp310-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-0.7.17-cp310-abi3-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for daft-0.7.17-cp310-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 e688c9c545f4e77ac01507f7e8054979a2ec8ba1baa15a55bfd7e0064e80a6c4
MD5 5b2cd4a683cdbe4db65d1689219aa701
BLAKE2b-256 a7bf15dd083b7fdc34e3a3c3d5c09d3540b78d1df60b32fe2ab0991023523fe3

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.7.17-cp310-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

0.7.23

0.7.22

0.7.21

0.7.20

0.7.19

0.7.18

This release

0.7.17

0.7.16

0.7.15

0.7.14

0.7.13

0.7.12

0.7.11 yanked

Reason this release was yanked:

Breaking change in extensions

0.7.10

0.7.9

0.7.8

0.7.7

0.7.6

0.7.5

0.7.4

0.7.3

0.7.2

0.7.1

0.7.0

0.6.14

0.6.13

0.6.12

0.6.11

0.6.10

0.6.8

0.6.7

0.6.6

0.6.5

0.6.4

0.6.3

0.6.2

0.6.1

0.6.0

0.5.22

0.5.21

0.5.20

0.5.19

0.5.18

0.5.17

0.5.16

0.5.15

0.5.14

0.5.13

0.5.12

0.5.11

0.5.10

0.5.9

0.5.8

0.5.7

0.5.6

0.5.5

0.5.4

0.5.3

0.5.2

0.5.1

0.5.0

0.4.18

0.4.17

0.4.16

0.4.15

0.4.14

0.4.13

0.4.12

0.4.11

0.4.10

0.1.3 yanked

Reason this release was yanked:

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

0.1.2 yanked

Reason this release was yanked:

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

0.1.0 yanked

Reason this release was yanked:

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

0.0.4 yanked

Reason this release was yanked:

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

0.0.3 yanked

Reason this release was yanked:

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

0.0.2 yanked

Reason this release was yanked:

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

0.0.1 yanked

Reason this release was yanked:

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

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page