The Public Utility Data Liberation Project

PUDL is a data processing pipeline created by Catalyst Cooperative that cleans, integrates, and standardizes some of the most widely used public energy datasets in the US. The data serve researchers, activists, journalists, and policy makers that might not have the technical expertise to access it in its raw form, the time to clean and prepare the data for bulk analysis, or the means to purchase it from existing commercial providers.

Available Data

We focus primarily on poorly curated data published by the US government in semi-structured but machine readable formats. For details on exactly what data is available from these data sources and what state it is in, see the the individual pages for each source:

PUDL’s clean and complete versions of these data sources are stored in the pudl.sqlite database. Larger datasets like EPA CEMS are stored in parquet files. To get started using PUDL data, visit our Data Access page, or continue reading to learn more about the PUDL data processing pipeline.

We also publish SQLite databases containing relatively pristine versions of our more difficult to parse inputs, especially the old Visual FoxPro (DBF, pre-2021) and new XBRL data (2021+) published by FERC:

Raw Data Archives

PUDL depends on “raw” data inputs from sources that are known to occasionally update their data or alter the published format. These changes may be incompatible with the way the data are read and interpreted by PUDL, so, to ensure the integrity of our data processing, we periodically create archives of the raw inputs on Zenodo. Each of the data inputs may have several different versions archived, and all are assigned a unique DOI and made available through the REST API. Each release of the PUDL Python package is embedded with a set of of DOIs to indicate which version of the raw inputs it is meant to process. This process helps ensure that our outputs are replicable.

To enable programmatic access to individual partitions of the data (by year, state, etc.), we archive the raw inputs as Frictionless Data Packages. The data packages contain both the raw data in their originally published format (CSVs, Excel spreadsheets, and Visual FoxPro database (DBF) files) and metadata that describes how each the dataset is partitioned.

The PUDL software will download a copy of the appropriate raw inputs automatically as needed and organize them in a local datastore.

See also

The software that creates and archives the raw inputs can be found in our PUDL Archiver repository on GitHub.

The ETL Process

PUDL’s ETL produces a data warehouse that can be used for analytics. The processing happens within Dagster assets that are persisted to storage, typically pickle, parquet or SQLite files. The raw data moves through three layers of processing.

Raw Layer

Assets in the Raw layer read the raw data from the original heterogeneous formats into a collection of pandas.DataFrame with uniform column names across all years so that it can be easily processed in bulk. Data distributed as binary database files, such as the DBF files from FERC Form 1, may be converted into a unified SQLite database before individual dataframes are created. Raw data assets are not written to pudl.sqlite. Instead they are persisted to pickle files and not distributed to users.

See also

Module documentation within the pudl.extract subpackage.

Core Layer

The Core layer contains well-modeled assets that serve as building blocks for downstream wide tables and analyses. Well-modeled means tables in the database have logical primary keys, foreign keys, datatypes and generally follow Tidy Data standards. The assets are loaded to a SQLite database or Parquet file.

These outputs can be accessed via Python, R, and many other tools. See the PUDL Data Dictionary page for a list of the normalized database tables and their contents.

Data processing in the Core layer is generally broken down into two phases. Phase one focuses on cleaning and organizing data within individual tables while phase two focuses on the integration and deduplication of data between tables. These tasks can be tedious data wrangling toil that impose a huge amount of overhead on anyone trying to do analysis based on the publicly available data. PUDL implements common data cleaning operations in the hopes that we can all work on more interesting problems most of the time. These operations include:

  • Standardization of units (e.g. dollars not thousands of dollars)

  • Standardization of N/A values

  • Standardization of freeform names and IDs

  • Use of controlled vocabularies for categorical values like fuel type

  • Use of more readable codes and column names

  • Imposition of well defined, rich data types for each column

  • Converting local timestamps to UTC

  • Reshaping of data into well normalized tables which minimize data duplication

  • Inferring Plant IDs which link records across many years of FERC Form 1 data

  • Inferring linkages between FERC and EIA Plants and Utilities.

  • Inferring more complete associations between EIA boilers and generators

See also

The module and per-table transform functions in the pudl.transform sub-package have more details on the specific transformations applied to each table.

Many of the original datasets contain large amounts of duplicated data. For instance, the EIA reports the name of each power plant in every table that refers to otherwise unique plant-related data. Similarly, many attributes like plant latitude and longitude are reported separately every year. Often, these reported values are not self-consistent. There may be several different spellings of a plant’s name, or an incorrectly reported latitude in one year.

Assets in the Core layer attempt to eliminate this kind of inconsistent and duplicate information when normalizing the tables by choosing only the most consistently reported value for inclusion in the final database. If a value which should be static is not consistently reported, it may also be set to N/A.

Output Layer

Assets in the Core layer normalize the data to make storage more efficient and avoid data integrity issues, but you may want to combine information from more than one of the tables to make the data more readable and readily interpretable. For example, PUDL stores the name that EIA uses to refer to a power plant in the core_eia__entity_plants table in association with the plant’s unique numeric ID. If you are working with data from the core_eia923__monthly_fuel_receipts_costs table, which records monthly per-plant fuel deliveries, you may want to have the name of the plant alongside the fuel delivery information since it’s more recognizable than the plant ID.

Rather than requiring everyone to write their own SQL SELECT and JOIN statements or do a bunch of pandas.merge() operations to bring together data, PUDL provides a variety of output tables that contain all of the useful information in one place. In some cases, like with EIA, the output tables are composed to closely resemble the raw spreadsheet tables you’re familiar with.

The Output layer also contains tables produced by analytical routines for calculating derived values like the heat rate by generation unit (hr_by_unit) or the capacity factor by generator (capacity_factor). We intend to integrate more analytical outputs into the library over time.

See also

  • The PUDL Examples GitHub repo to see how to access the PUDL Database directly, use the output functions, or work with the EPA CEMS data using Dask.

  • How to Learn Dask in 2021 is a great collection of self-guided resources if you are already familiar with Python, Pandas, and NumPy.

Data Validation

We have a growing collection of data validation test cases that we run before publishing a data release to try and avoid publishing data with known issues. Most of these validations are described in the pudl.validate module. They check things like:

  • The heat content of various fuel types are within expected bounds.

  • Coal ash, moisture, mercury, sulfur etc. content are within expected bounds

  • Generator heat rates and capacity factors are realistic for the type of prime mover being reported.

Some data validations are currently only specified within our test suite, including:

  • The expected number of records within each table

  • The fact that there are no entirely N/A columns

A variety of database integrity checks are also run either during the data processing or when the data is loaded into SQLite.

See our Testing PUDL documentation for more information.