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Featuretools

Featuretools is an open-source Python library that automates feature engineering for machine-learning workflows. Its Deep Feature Synthesis workflow can generate a feature matrix from related, timestamped tables such as customers, sessions and transactions.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Featuretools — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when feature preparation involves related tables and repeated transformations such as aggregations, date extraction or statistical summaries. It is also relevant when a team wants a reusable automated feature-generation stage before model training.

Who it is for

  • Python data scientists preparing features for machine-learning models.
  • Teams building reusable feature-engineering layers.
  • Practitioners working with related multi-table datasets.

Documented uses

  • Generate engineered features from related tables.
  • Create a single feature matrix for a selected target dataframe.
  • Add automated feature generation to Python and scikit-learn-oriented workflows.
  • Extend feature generation with documented premium, NLP or Dask add-ons.
Before you choose

Requirements & limits

The repository is public and describes Featuretools as open source. The documented installation paths are pip and Conda.

  • Python is the documented implementation language and installation environment.
  • The central workflow expects an entity set and a target dataframe.
  • Dask support is provided through a documented add-on for running Deep Feature Synthesis with njobs greater than one.
  • The BSD-3-Clause license is identified in the repository metadata.
  • The library can generate a broad set of derived aggregations and transformations from related data, which may reduce manually authored feature definitions.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Featuretools in a Python environment

Set up the Featuretools library and select documented add-ons for a data-science workflow.

Before you start

  • A Python environment.
  • pip or Conda.
  • A need for automated feature engineering, with Dask support requiring the documented Dask add-on.

Steps

  1. Install the base library with pip or from the Conda-forge channel.

  2. Install the complete add-on set, or select the documented premium, NLP or Dask extra as needed.

  3. Import Featuretools in Python and prepare the entity-set inputs required by the feature-engineering workflow.

Expected outcome

Featuretools is installed with the selected extension set and ready for feature-engineering work.

Guide limitations

  • The installation instructions do not by themselves describe a complete production pipeline or model-training procedure.
  • The documented example requires an entity set and a target dataframe.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
2 October 2026
Original website title
alteryx/featuretools · Official homepage title · retained in its source language
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