CourtPy

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What is CourtPy?

CourtPy collects court opinions, turns them into data, and analyzes them. It is designed for empirical legal research, where the data has to be explained and the results reproduced, and for researchers who do not write much code.

• Collect opinions from CourtListener, from its free bulk data or its API, or use opinions you downloaded from Lexis-Nexis.
• Parse them into a table, one row for each case, with rules written in spreadsheets (CSV files) that anyone can read and change: the parties and their roles, the court, the judges, the disposition, the issues discussed, the citations, and more.
• Code derived variables, such as whether a case is criminal, whether the decision reversed the court below, and which side won.
• Analyze the table with amos, which compares models, preprocessing, and statistical methods, and records everything needed to reproduce the results.

Why use CourtPy?

• Transparent. What CourtPy finds in an opinion comes from rules in CSV files, not from code, so a study's methods can be read, reported, and changed by anyone who can use a spreadsheet. The rules are part of the study, so others can check them.
• Free data at scale. CourtPy reads CourtListener's bulk data, which covers millions of opinions, needs no account, and has no limits on downloads. Its API client handles CourtListener's rate limits by waiting and resuming.
• Reproducible. A whole study (which cases, how they were parsed and coded, and how they were analyzed) fits in one settings file, and amos records every step and the version of every package.
• Safe with secrets. Your CourtListener API key is kept in your computer's keyring (or a private file in your user folder), never in a project, so it cannot be shared or committed by accident.

CourtPy is not a citation extractor or a legal research tool. For finding and resolving citations in text, see eyecite.

Getting started

Requirements

CourtPy requires Python 3.11 or later. It runs on Linux, macOS, and Windows. It is built on amos, pandas, and requests, which are installed automatically.

Extracting cases from CourtListener's bulk data needs a good connection and room for the bulk files (about 63 GB for the opinions, clusters, and dockets), unless they are streamed. Downloading with the API needs a free CourtListener account.

Installation

To install CourtPy, use pip:

pip install courtpy

To install the latest development version from GitHub instead:

pip install git+https://github.com/WithPrecedent/CourtPy

To work on CourtPy itself, see the contribution guide.

Usage

Collecting opinions from CourtListener

CourtListener offers its opinions in two ways, and CourtPy uses both. Both save each case as a JSON file in a folder named for its court (such as court_listener/ca1/4567890.json), and the parser reads them the same way.

The bulk data is the whole CourtListener database, published as compressed CSV files every three months. It needs no API key and has no limits, so it is the best way to collect many cases. The files are large (the opinions file is more than 50 GB), so extracting cases takes a few hours. The files are kept outside your project so that they can be used again:

import courtpy

courtpy.BulkData().extract(
    courts = "federal_circuits",
    start_date = "2019-01-01",
    end_date = "2019-12-31",
    folder = "court_listener")

The API is best for small or very recent sets of cases. It needs an API key: sign in to CourtListener and open the "API" page of your profile. A free account can make 5 requests a minute, 50 an hour, and 125 a day (as of 2026). CourtPy asks for 20 cases at a time, waits when CourtListener asks it to, and saves its place, so a download stopped by the daily limit resumes where it stopped when it is run again:

courtpy.CourtListener().download("ca1", "2026-09-01", "2026-09-30", "court_listener")

Courts are named by their CourtListener ids (such as ca1, cadc, cafc, and scotus) or by groups: federal_appellate, federal_circuits, and supreme_court.

Keeping your API key secret

Your API key is never written in a project's files or in CourtPy itself. Store it once:

courtpy key set

That asks for the key (without showing it) and stores it in your computer's keyring (Windows Credential Manager, the macOS Keychain, or the Secret Service on Linux), or, if there is none, in a private file in your user folder. From then on, CourtPy finds it whenever it is needed. courtpy key show says where it was found (with most of it hidden), and courtpy key delete removes it.

CourtPy looks for the key in this order: the COURTLISTENER_API_KEY environment variable, the keyring, the private file, and a .env file in the current folder (with a line such as COURTLISTENER_API_KEY=your-key; keep such a file out of version control). In Python, use courtpy.secrets.set_api_key(...) and courtpy.secrets.get_api_key().

Using opinions from Lexis-Nexis

Lexis-Nexis downloads many cases into one text file. Divide such files into one file for each case, and then parse them as the source "lexis_nexis":

courtpy.lexis.split("downloads", folder = "lexis_nexis")
table = courtpy.parse("lexis_nexis", source = "lexis_nexis")

Parsing and coding

courtpy.parse reads a folder of saved cases and returns a pandas.DataFrame, one row for each case, labeled by its id:

table = courtpy.parse("court_listener")
dataset = courtpy.code(table)  # parties, case types, and outcomes
courtpy.save_cases(dataset.data, "cases.csv")

The rules are in the CSV files in CourtPy's instructions folder, with a guide to writing your own. The "federal" rules make more than 250 variables, including:

The coders (which are amos techniques) add type_criminal, each party's role in the appeal and in the case (such as party2_criminal_defendant), and the outcomes: outcome_reversal, outcome_party1_won, outcome_criminal_defendant_won, and others.

Describing a whole study in one file

A settings file can describe the cases and the analysis together. Its "cases" section says which cases to collect and how to parse them, and its other sections are an amos project. See examples/federal_appeals.ini:

[general]
seed = 43
label = outcome_reversal

[cases]
download = bulk
courts = federal_circuits
start_date = 2019-01-01
end_date = 2019-12-31
save = data/cases.csv
reuse = true

[appeals_project]
appeals_workers = wrangler, analyst, critic

[wrangler]
techniques = filter_rows, keep_columns, drop_missing

[analyst]
design = experiment
criterion = roc_auc
steps = split, model
split_techniques = stratified
model_techniques = baseline, logit, random_forest

[critic]
techniques = scorecard

Run it from a terminal with courtpy run federal_appeals.ini --export, or in Python:

project = courtpy.Project.create("federal_appeals.ini")
print(project.result.tables["scorecard"])
project.export()

courtpy.Project is an amos.Project, so everything in the amos documentation works.

Commands

Add --help to any command for its options. See the documentation for a tutorial, the advanced user guide, a guide to writing rules, and recipes.

Contributing

Contributors are always welcome. Feel free to grab an issue to work on or make a suggested improvement. If you wish to contribute, please read the Contribution Guide and Code of Conduct.

Similar Projects

• CourtListener and the Free Law Project's tools, including eyecite (which finds and resolves legal citations in text) and juriscraper (which collects opinions from court websites).
• amos, which CourtPy uses to analyze the cases it parses.

Acknowledgments

CourtPy's opinions come from CourtListener, a project of the Free Law Project, whose bulk data and API make empirical legal research on court opinions possible at no cost.

This project was generated from @WithPrecedent's template.

License

Use of this repository is authorized under the Apache Software License 2.0.