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: Full integration with data catalogs such as 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.

  • 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

Check out our engine comparison page for more details!

License

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

Project links

Data verified by PyPI on Sep 10, 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 Sep 10, 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-0.6.1.tar.gz (7.8 MB view details)

Uploaded Source

Built Distributions

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

daft-0.6.1-cp39-abi3-win_amd64.whl (45.1 MB view details)

Uploaded CPython 3.9+Windows x86-64

daft-0.6.1-cp39-abi3-manylinux_2_24_x86_64.whl (47.4 MB view details)

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

daft-0.6.1-cp39-abi3-manylinux_2_24_aarch64.whl (44.6 MB view details)

Uploaded CPython 3.9+manylinux: glibc 2.24+ ARM64

daft-0.6.1-cp39-abi3-macosx_11_0_arm64.whl (43.3 MB view details)

Uploaded CPython 3.9+macOS 11.0+ ARM64

daft-0.6.1-cp39-abi3-macosx_10_12_x86_64.whl (47.1 MB view details)

Uploaded CPython 3.9+macOS 10.12+ x86-64

File details

Details for the file daft-0.6.1.tar.gz.

File metadata

  • Download URL: daft-0.6.1.tar.gz
  • Upload date:
  • Size: 7.8 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for daft-0.6.1.tar.gz
Algorithm Hash digest
SHA256 5759e8eeda525b2f86a59e411f31afe9b06b35b431e6a820a7acd35d1d4c920f
MD5 098417ba2abc9a9dbba7ee6b8dc457ab
BLAKE2b-256 acaf5093c3b1d8c66fe6840eaa288250aaf1e49792a36822b75b71a6cd310df3

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.1.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-0.6.1-cp39-abi3-win_amd64.whl.

File metadata

  • Download URL: daft-0.6.1-cp39-abi3-win_amd64.whl
  • Upload date:
  • Size: 45.1 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-0.6.1-cp39-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 50c7cccba56e5640ddc151a488d44a4716906cdc6c3fa0489a0a1f871713692d
MD5 6d40574cc4d2f34a74d9e6c87b9a231f
BLAKE2b-256 5eac35dd4bfa58ad9cd759e17f9c58899e297b7e0f21394557d23d5dff82f932

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.1-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-0.6.1-cp39-abi3-manylinux_2_24_x86_64.whl.

File metadata

  • Download URL: daft-0.6.1-cp39-abi3-manylinux_2_24_x86_64.whl
  • Upload date:
  • Size: 47.4 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-0.6.1-cp39-abi3-manylinux_2_24_x86_64.whl
Algorithm Hash digest
SHA256 7baceb0817492e79cc451d2e36545faaa1011c5dd82b3c9c42ede624f565ec3b
MD5 c3171086bf779d694f75bddee0e0bb54
BLAKE2b-256 785c836f5da3689e4df430b68062f9ded21440db7f834cc2b0863e717d944fe6

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.1-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-0.6.1-cp39-abi3-manylinux_2_24_aarch64.whl.

File metadata

File hashes

Hashes for daft-0.6.1-cp39-abi3-manylinux_2_24_aarch64.whl
Algorithm Hash digest
SHA256 51d90f3f5e2714f15416797cf7d68d2db21dee8b03bd93eef22ebca096ecc669
MD5 83a85a121ea85a56afa3566a6cdf56e8
BLAKE2b-256 cd4ad06fa16e8774b76a35290d74d06a1f2cafb4398e92d42d149e359b19582a

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.1-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-0.6.1-cp39-abi3-macosx_11_0_arm64.whl.

File metadata

  • Download URL: daft-0.6.1-cp39-abi3-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 43.3 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-0.6.1-cp39-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 31cf0d2eadde4ebc5d50de9e20462fbda550f1735372f09ee2f88a82b395958d
MD5 0c14de0a96ff0a39469a9e9b13c6ffd7
BLAKE2b-256 89a530a9b6e8671e3fed93f045b4118c9c21bf3a2bdb67e5b7e095921cfc0f4b

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.1-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-0.6.1-cp39-abi3-macosx_10_12_x86_64.whl.

File metadata

  • Download URL: daft-0.6.1-cp39-abi3-macosx_10_12_x86_64.whl
  • Upload date:
  • Size: 47.1 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-0.6.1-cp39-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 f7e47f433b7d5e6902b6310f6953b3edc60267bbdb3bb7a89a46f7122d3612c8
MD5 a0ad4cc121905870ee552a0fc99d10be
BLAKE2b-256 948ef43fd3db85a801c4f700381c30b63b81ef9b959dc55cf65425f43893266f

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.6.1-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

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

This release

0.6.1 This release

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