Python Wrapper for GLEIF Public API

This is a small thing that I started as a necessity for work: We needed to get LEI (Legal Entity Identifier) registration information for our customers, and using GLEIF’s (Global LEI Foundation) public API to query things with Python was convenient at that time. Thinking of reusability, I structured it as a module, covering most of our use cases. Code is now in github, maybe it helps others that need an easier way to query LEI numbers.

To access, go to and download the repository (in fact you only need the file leipy/ You will need requests and dateutil libraries, and optionally pandas if you want DataFrame output of results, which should all be included with a standard anaconda installation.

You can use it as:

from leipy import GLEIF

gleif = GLEIF(api_version='v1')

raw_output, results, results_df = gleif.request(

GLEIF API has a limit of 200 LEIs per request, my wrapper handles that for you, you can provide more than 200 LEIs. It returns the raw json output from the API, a results class and optionally a pandas DataFrame.

Raw json (as a list of dicts), which you can further process as you please, looks like this:

[{'LEI': {'$': 'HWUPKR0MPOU8FGXBT394'},
  'Entity': {'LegalName': {'$': 'Apple Inc.'},
   'LegalAddress': {'Line1': {'$': 'C/O C T Corporation System'},
    'Line2': {'$': '818 West 7th Street'},
    'Line3': {'$': 'Suite 930'},
    'City': {'$': 'Los Angeles'},
    'Region': {'$': 'US-CA'},
    'Country': {'$': 'US'},
    'PostalCode': {'$': '90017'}},
   'HeadquartersAddress': {'Line1': {'$': '1 Infinite Loop'},
    'City': {'$': 'Cupertino'},
    'Region': {'$': 'US-CA'},
    'Country': {'$': 'US'},
    'PostalCode': {'$': '95014'}},
   'BusinessRegisterEntityID': {'@register': 'RA000598', '$': 'C0806592'},
   'LegalJurisdiction': {'$': 'US'},
   'LegalForm': {'$': 'INCORPORATED'},
   'EntityStatus': {'$': 'ACTIVE'}},
  'Registration': {'InitialRegistrationDate': {'$': '2012-06-06T15:53:00.000Z'},
   'LastUpdateDate': {'$': '2017-12-12T21:19:00.000Z'},
   'RegistrationStatus': {'$': 'ISSUED'},
   'NextRenewalDate': {'$': '2018-12-13T00:31:00.000Z'},
   'ManagingLOU': {'$': 'EVK05KS7XY1DEII3R011'},
   'ValidationSources': {'$': 'FULLY_CORROBORATED'}}},
 {'LEI': {'$': '7ZW8QJWVPR4P1J1KQY45'},
  'Entity': {'LegalName': {'$': 'Google LLC'},
   'LegalAddress': {'Line1': {'$': 'C/O Corporation Service Company'},
    'Line2': {'$': '251 Little Falls Drive'},
    'City': {'$': 'Wilmington'},
    'Region': {'$': 'US-DE'},
    'Country': {'$': 'US'},
    'PostalCode': {'$': '19808'}},
   'HeadquartersAddress': {'Line1': {'$': '1600 Amphitheatre Parkway'},
    'City': {'$': 'Mountain View'},
    'Region': {'$': 'US-CA'},
    'Country': {'$': 'US'},
    'PostalCode': {'$': '94043'}},
   'BusinessRegisterEntityID': {'@register': 'RA000602', '$': '3582691'},
   'LegalJurisdiction': {'$': 'US'},
   'LegalForm': {'$': 'LIMITED LIABILITY COMPANY'},
   'EntityStatus': {'$': 'ACTIVE'}},
  'Registration': {'InitialRegistrationDate': {'$': '2012-06-06T15:52:00.000Z'},
   'LastUpdateDate': {'$': '2018-03-28T17:00:00.000Z'},
   'RegistrationStatus': {'$': 'ISSUED'},
   'NextRenewalDate': {'$': '2018-08-17T18:10:00.000Z'},
   'ManagingLOU': {'$': 'EVK05KS7XY1DEII3R011'},
   'ValidationSources': {'$': 'FULLY_CORROBORATED'}}}]

The results class, with easily accesible members for most commonly used LEI information can be accessed as this:

>>> print(results.legal_name)
['Apple Inc.', 'Google LLC']

>>> print(results.lei_reg_status)

>>> print(results.date_last_updated)
[datetime.datetime(2017, 12, 12, 21, 19, tzinfo=tzutc()),
 datetime.datetime(2018, 3, 28, 17, 0, tzinfo=tzutc())]

And if you opt for a DataFrame, the results will be conveniently flattened and ready for further processing for you:



As I said, this is more suited to our use case, and it’s like a hobby project for me. If you need a more generic solution for python check out pygleif, or you can always go for the GLEIF REST API itself.

Next steps for me is to add a solution for automatically downloading and parsing XML and CSV files from GLEIF, and putting everything in a package for easier use (pip and/or conda).

Until then, enjoy!