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A database of courts, tests and other experiments
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Courts-DB is an open source repository to organize a db of all courts current and historical. It was built for use in

Its main goal is to interface with CL to identify historical and current courts by string. It incldues mechanisms to filter results based on dates and/or whether it is a bankruptcy court.

Further development is intended and all contributors, corrections and additions are welcome.


Free Law Project built this database using the metadata (case names, dates etc.) of over 16 millions data points. This data represents hundreds of hours of research and testing. We believe to be the most extensive open dataset of its kind.


You can feed in a courtlistener Court_ID or string to find a court.

from courts_db import find_court, find_court_by_id


    "regex": [
        "${sjc} ${ma}?",
        "${ma} ${sjc}",
        "Supreme Court Of ${ma}",
        "State Of ${ma} Supreme Court"
    "name_abbreviation": "Mass. Sup. Jud. Ct.",
    "dates": [
            "start": "1692-01-01",
            "end": null
    "name": "Massachusetts Supreme Judicial Court",
    "level": "colr",
    "case_types": ["All"],
    "system": "state",
    "examples": [
        "Supreme Court Of Massachusetts",
        "Supreme Judicial Court Of Massachusetts",
        "Massachusetts Supreme Judicial Court"
    "court_url": "",
    "type": "appellate",
    "id": "mass",
    "location": "Massachusetts"
from courts_db import find_court

mass_sjc = find_court(u"Massachusetts Supreme Judicial Court")

returns: ["mass"]

Filtering on less unique strings is built in.

Feed a date string or bankruptcy flag to filter on those parameters For example District of Massachusetts is non unique and returns both the Federal District Court of Massachusetts and its Bankruptcy Court

from datetime import datetime as dt

    u"District of Massachusetts",

returns ==> ["mad", "mab"]

    u"District of Massachusetts",

returns ==> ["mab"]

    u"District of Massachusetts",
    date_found=dt.strptime("10/02/1975", "%m/%d/%Y"),

returns ==> ["mad"]

Some Notes on the Data

Somethings to keep in mind as you are reviewing the data.

  1. The data is devided into two files courts.json and variables.json
  2. Courts.json holds the bulk of the information
  3. Variables.json holds templates for large numbers of regexes.


  1. id ==> string; Courtlistener Court Identifier
  2. court_url ==> string; url for court website
  3. regex ==> array; regexes patterns to find courts
  4. examples ==> array; regexes patterns to find courts
  5. name ==> string; full name of the court
  6. name_abbreviation ==> string; court name abbreviations
  7. dates ==> Array; Contains start date, end date and notes on date range
  8. system ==> string; Defines main jurisdiction, ex. State, Federal, Tribal
  9. level ==> string; code defining where court is in system structure, ex. COLR (Court of Last Resort), IAC (Intermediate Appellate Court), GJC (General Jurisdiction Court), LJC (Limited Jurisdiction Court)
  10. location ==> string; refers to the physical location of the main court
  11. type ==> string; Identifies kind of cases handled (Trial, Appellate, Bankruptcy, AG)


Installing courts-db is easy.

pip install courts_db

Or install the latest dev version from github

pip install git+


  1. Continue to improve and expand the dataset.
  2. Add filtering mechanisms by state, reporters, citation(s), judges, counties and cities.


If you wish to create a new version manually, the process is:

  1. Update version info in

  2. Install the requirements in requirements_dev.txt

  3. Set up a config file at ~/.pypirc

  4. Generate a universal distribution that worksin py2 and py3 (see setup.cfg)

    python sdist bdist_wheel
  5. Upload the distributions

    twine upload dist/* -r pypi (or pypitest)


This repository is available under the permissive BSD license, making it easy and safe to incorporate in your own libraries.

Pull and feature requests welcome. Online editing in Github is possible (and easy!)

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