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 14, 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 (0.7.25)

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 14, 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-0.6.6.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-0.6.6-cp39-abi3-win_amd64.whl (45.5 MB view details)

Uploaded CPython 3.9+Windows x86-64

daft-0.6.6-cp39-abi3-manylinux_2_24_x86_64.whl (47.9 MB view details)

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

daft-0.6.6-cp39-abi3-manylinux_2_24_aarch64.whl (45.0 MB view details)

Uploaded CPython 3.9+manylinux: glibc 2.24+ ARM64

daft-0.6.6-cp39-abi3-macosx_11_0_arm64.whl (43.7 MB view details)

Uploaded CPython 3.9+macOS 11.0+ ARM64

daft-0.6.6-cp39-abi3-macosx_10_12_x86_64.whl (47.6 MB view details)

Uploaded CPython 3.9+macOS 10.12+ x86-64

File details

Details for the file daft-0.6.6.tar.gz.

File metadata

  • Download URL: daft-0.6.6.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-0.6.6.tar.gz
Algorithm Hash digest
SHA256 e5e5a28f5f34c0113d2938a323221e67d8261d0a113f9d059aab906f25cc0468
MD5 369ee8c802d80d9694f94dbb1ec17916
BLAKE2b-256 0afe0adcd54ff6f68c398ec89af82c9968a7dd2ee362844058110e7f0178e9b3

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.6.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-0.6.6-cp39-abi3-win_amd64.whl.

File metadata

  • Download URL: daft-0.6.6-cp39-abi3-win_amd64.whl
  • Upload date:
  • Size: 45.5 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-0.6.6-cp39-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 bbd1320545abadacecd75c121486efe2c74d17e5903edb0cc409200d11a98ad0
MD5 698bdeedfdbda83a31ef1b940eb74b4d
BLAKE2b-256 5bb45580d2db902e4b1a00cba2296484875bc29ff8808de0546c0c4bea5ea563

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.6-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-0.6.6-cp39-abi3-manylinux_2_24_x86_64.whl.

File metadata

  • Download URL: daft-0.6.6-cp39-abi3-manylinux_2_24_x86_64.whl
  • Upload date:
  • Size: 47.9 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-0.6.6-cp39-abi3-manylinux_2_24_x86_64.whl
Algorithm Hash digest
SHA256 aa9d7fbd53b942bead727f8e5f6624d0537047d5f6bc473d38e106b16fd3286f
MD5 3365b99dc54c538044b36092a038a902
BLAKE2b-256 6f9fcda1bbf823046e087e9381b6bae802227170157a95aa8c5e7c2d41f2ba4b

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.6-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-0.6.6-cp39-abi3-manylinux_2_24_aarch64.whl.

File metadata

File hashes

Hashes for daft-0.6.6-cp39-abi3-manylinux_2_24_aarch64.whl
Algorithm Hash digest
SHA256 d70271f9f98101c56b091e16526283f7d23da8b6afcf2014b858b10fed480664
MD5 4abb92cdfa146a37043836da30708830
BLAKE2b-256 ac0d28262d05b4803d7484780ffca03847536b7ea4950858511525a8d12f12d3

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.6-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-0.6.6-cp39-abi3-macosx_11_0_arm64.whl.

File metadata

  • Download URL: daft-0.6.6-cp39-abi3-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 43.7 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-0.6.6-cp39-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 6f2e5076103da330c11c81f89c5555cbe658976c11c2867030405c10ff3ded6b
MD5 ea6a8cc10f5b7118905a9ab5d0cc3135
BLAKE2b-256 5655bfa1fb58947b6ff7c4b9e732f99598b6ac0d16d3e4da2a23d86da98fe877

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.6-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-0.6.6-cp39-abi3-macosx_10_12_x86_64.whl.

File metadata

  • Download URL: daft-0.6.6-cp39-abi3-macosx_10_12_x86_64.whl
  • Upload date:
  • Size: 47.6 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-0.6.6-cp39-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 8e54345462a39003124c909e9b94be1b8618a1eceb4ddd9b7cbd79d1d6430b47
MD5 d5150a326434910322801ddae38c1d75
BLAKE2b-256 6735c2985796d36be47780e39471e26dfc032a96bfb038cd6a05db675df61e75

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.6-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

0.7.25

5 files

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

This release

0.6.6 This release

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