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Daft: the distributed Python dataframe for complex data
Daft is a fast, Pythonic and scalable open-source dataframe library built for Python and Machine Learning workloads.
Daft is currently in its Beta release phase - please expect bugs and rapid improvements to the project. We welcome user feedback/feature requests in our Discussions forums
Table of Contents
About Daft
The Daft dataframe is a table of data with rows and columns. Columns can contain any Python objects, which allows Daft to support rich complex data types such as images, audio, video and more.
- Any Data: Beyond the usual strings/numbers/dates, Daft columns can also hold complex multimodal data such as Images, Embeddings and Python objects. Ingestion and basic transformations of complex data is extremely easy and performant in Daft.
- Notebook Computing: Daft is built for the interactive developer experience on a notebook - intelligent caching/query optimizations accelerates your experimentation and data exploration.
- Distributed Computing: Rich complex formats such as images 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 getdaft.
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 10-minute 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)
Benchmarks
To see the full benchmarks, detailed setup, and logs, check out our benchmarking page.
More Resources
- 10-minute tour of Daft - 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
Contributing
To start contributing to Daft, please read CONTRIBUTING.md
Telemetry
To help improve Daft, we collect non-identifiable data.
To disable this behavior, set the following environment variable: DAFT_ANALYTICS_ENABLED=0
The data that we collect is:
- Non-identifiable: events are keyed by a session ID which is generated on import of Daft
- Metadata-only: we do not collect any of our users’ proprietary code or data
- For development only: we do not buy or sell any user data
Please see our documentation for more details.
Related Projects
| Dataframe | Query Optimizer | Complex Types | 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 dataframe comparison page for more details!
License
Daft has an Apache 2.0 license - please see the LICENSE file.