The Public Utility Data Liberation Project

Project Status: Active – The project has reached a stable, usable state and is being actively developed. Travis CI Build Status Read the Docs Build Status Codecov Test Coverage Codacy Grade PyPI Version conda-forge Version Zenodo DOI

PUDL makes US energy data easier to access and work with. Hundreds of gigabytes of public information is published by government agencies, but in many different formats that make it hard to work with and combine. PUDL takes these spreadsheets, CSV files, and databases and turns them into easy use tabular data packages that can populate a database, or be used directly with Python, R, Microsoft Access, and many other tools.

The project currently integrates data from:

The project is especially meant to serve researchers, activists, journalists, and policy makers that might not otherwise be able to afford access to this data from existing commercial data providers.

Getting Started

Just want to play with some example data? Install Anaconda (or miniconda) with at least Python 3.7. Then work through the following commands.

First, we create and activate conda environment named pudl. All the required packages are available from the community maintained conda-forge channel, and that channel is given priority, to simplify satisfying dependencies. Note that PUDL currently requires Python 3.7, the most recently available major version of Python. In addition to the catalystcoop.pudl package, we’ll also install JupyterLab so we can work with the PUDL data interactively.

$ conda create -y -n pudl -c conda-forge --strict-channel-priority python=3.7 catalystcoop.pudl jupyter jupyterlab pip
$ conda activate pudl

Now we create a data management workspace called pudl-work and download some data. The workspace is a well defined directory structure that PUDL uses to organize the data it downloads, processes, and outputs. You can run pudl_setup --help and pudl_data --help for more information.

$ mkdir pudl-work
$ pudl_setup pudl-work
$ pudl_data --sources eia923 eia860 ferc1 epacems epaipm --years 2017 --states id

Now that we have the original data as published by the federal agencies, we can run the ETL (Extract, Transform, Load) pipeline, that turns the raw data into an well organized, standardized bundle of data packages. This involves a couple of steps: cloning the FERC Form 1 into an SQLite database, extracting data from that database and all the other sources and cleaning it up, outputting that data into well organized CSV/JSON based data packages, and finally loading those data packages into a local database.

PUDL provides a script to clone the FERC Form 1 database, controlled by a YAML file which you can find in the settings folder. Run it like this:

$ ferc1_to_sqlite pudl-work/settings/ferc1_to_sqlite_example.yml

The main ETL process is controlled by the YAML file etl_example.yml which defines what data will be processed. It is well commented – if you want to understand what the options are, open it in a text editor, and create your own version.

The data packages will be generated in a sub-directory in pudl-work/datapackage named pudl-example (you can change this by changing the value of pkg_bundle_name in the ETL settings file you’re using. Run the ETL pipeline with this command:

$ pudl_etl pudl-work/settings/etl_example.yml

The generated data packages are made up of CSV and JSON files. They’re both easy to parse programmatically, and readable by humans. They are also well suited to archiving, citation, and bulk distribution. However, to make the data easier to query and work with interactively, we typically load it into a local SQLite database using this script, which combines several data packages from the same bundle into a single unified structure:

$ datapkg_to_sqlite --pkg_bundle_name pudl-example

Now that we have a live database, we can easily work with it using a variety of tools, including Python, pandas dataframes, and Jupyter notebooks. This command will start up a local Jupyter notebook server, and open a notebook of PUDL usage examples:

$ jupyter lab pudl-work/notebook/pudl_intro.ipynb

For more details, see the full PUDL documentation on Read The Docs.

Contributing to PUDL

Find PUDL useful? Want to help make it better? There are lots of ways to contribute!


The PUDL software is released under the MIT License. The PUDL documentation and the data packages we distribute are released under the CC-BY-4.0 license.

Contact Us

For help with initial setup, usage questions, bug reports, suggestions to make PUDL better and anything else that could conceivably be of use or interest to the broader community of users, use the PUDL issue tracker. on Github. For private communication about the project, you can email the team:

About Catalyst Cooperative

Catalyst Cooperative is a small group of data scientists and policy wonks. We’re organized as a worker-owned cooperative consultancy. Our goal is a more just, livable, and sustainable world. We integrate public data and perform custom analyses to inform public policy making. Our focus is primarily on mitigating climate change and improving electric utility regulation in the United States.

Do you work on renewable energy or climate policy? Have you found yourself scraping data from government PDFs, spreadsheets, websites, and databases, without getting something reusable? We build tools to pull this kind of information together reliably and automatically so you can focus on your real work instead — whether that’s political advocacy, energy journalism, academic research, or public policy making.