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

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 it’s 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

To start contributing to Daft, please read CONTRIBUTING.md

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 our own analytics as well as Scarf (https://scarf.sh).

To disable this behavior, set the following environment variables: - DAFT_ANALYTICS_ENABLED=0 - SCARF_NO_ANALYTICS=true or 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

Eagar

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 Jun 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 Jun 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 deltalake gcp hudi iceberg lance numpy pandas ray spark sql 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-0.5.4.tar.gz (4.8 MB view details)

Uploaded Source

Built Distributions

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

daft-0.5.4-cp39-abi3-win_amd64.whl (40.9 MB view details)

Uploaded CPython 3.9+Windows x86-64

daft-0.5.4-cp39-abi3-manylinux_2_24_x86_64.whl (44.0 MB view details)

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

daft-0.5.4-cp39-abi3-manylinux_2_24_aarch64.whl (41.8 MB view details)

Uploaded CPython 3.9+manylinux: glibc 2.24+ ARM64

daft-0.5.4-cp39-abi3-macosx_11_0_arm64.whl (38.3 MB view details)

Uploaded CPython 3.9+macOS 11.0+ ARM64

daft-0.5.4-cp39-abi3-macosx_10_12_x86_64.whl (41.6 MB view details)

Uploaded CPython 3.9+macOS 10.12+ x86-64

File details

Details for the file daft-0.5.4.tar.gz.

File metadata

  • Download URL: daft-0.5.4.tar.gz
  • Upload date:
  • Size: 4.8 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for daft-0.5.4.tar.gz
Algorithm Hash digest
SHA256 fc80e58ec10cf4f01304603471e3a40086ac7b2a283780f67d31e52342743429
MD5 0b4b8bc7f2337a98e499e2b66b96fd71
BLAKE2b-256 3e1cce6e5400ab90a9126ad03fbc6831088cd0cd1d8d6bf7c8e5c897b714850b

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.5.4.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-0.5.4-cp39-abi3-win_amd64.whl.

File metadata

  • Download URL: daft-0.5.4-cp39-abi3-win_amd64.whl
  • Upload date:
  • Size: 40.9 MB
  • Tags: CPython 3.9+, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for daft-0.5.4-cp39-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 db60058b47311aa709739b3ee0ff29288c1f4f3259504758deee95c9ec647097
MD5 d8c0d437d1836aa2ae8a76b955fc054f
BLAKE2b-256 a0334f61376772a35fccebb626c6f8df5e206d8ba5dcebe5911e56d18ad0756c

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.5.4-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-0.5.4-cp39-abi3-manylinux_2_24_x86_64.whl.

File metadata

  • Download URL: daft-0.5.4-cp39-abi3-manylinux_2_24_x86_64.whl
  • Upload date:
  • Size: 44.0 MB
  • Tags: CPython 3.9+, manylinux: glibc 2.24+ x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for daft-0.5.4-cp39-abi3-manylinux_2_24_x86_64.whl
Algorithm Hash digest
SHA256 703f722f6d969ce4df52b3376b0ae0b7eb1076b8d976cef491b7acb09b91dfb9
MD5 5d663e1d03222311a9ee300ad5fb2f7e
BLAKE2b-256 2dc5c6e17a4a10073954b9db580dda4066f2de53006120173da501299f57c996

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.5.4-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-0.5.4-cp39-abi3-manylinux_2_24_aarch64.whl.

File metadata

File hashes

Hashes for daft-0.5.4-cp39-abi3-manylinux_2_24_aarch64.whl
Algorithm Hash digest
SHA256 97bbfd6284fabb77c87cad02816966dce626427ee023cca84f4391cf58b41fe5
MD5 4a921417fcc6bc8cc22b10a85f2e3c9f
BLAKE2b-256 d1979f04442c4011483043c12c145805f1b6b3cabbe41f7a5979f0cda97036b6

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.5.4-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-0.5.4-cp39-abi3-macosx_11_0_arm64.whl.

File metadata

  • Download URL: daft-0.5.4-cp39-abi3-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 38.3 MB
  • Tags: CPython 3.9+, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for daft-0.5.4-cp39-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 5eae0a6945129efe39148e13d7d1941633a596126067223fd43e85b4e9658859
MD5 a837b592fd7f127b85282226d94ad8a4
BLAKE2b-256 3403a9b6938ddb06dfee5adea9fb9476590c1a48d4ecb28f047cb79628d80884

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.5.4-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-0.5.4-cp39-abi3-macosx_10_12_x86_64.whl.

File metadata

  • Download URL: daft-0.5.4-cp39-abi3-macosx_10_12_x86_64.whl
  • Upload date:
  • Size: 41.6 MB
  • Tags: CPython 3.9+, macOS 10.12+ x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for daft-0.5.4-cp39-abi3-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 5f88a339276394463fe36ecde6dea30d02018f2a9644eb53293f3f4de3a43606
MD5 3d9116d8ac8097f3b03d855f818cf308
BLAKE2b-256 5c88ab116bd1a3f2c1bbfdf010f01e1aef0a6c4801ffa66a00ef2ae675c0da71

See more details on using hashes here.

Provenance

The following attestation bundles were made for daft-0.5.4-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

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

This release

0.5.4 This release

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