LEIPY
A python wrapper / client for GLEIF public API. Suggestions, requests, contributions welcome.
(I started this as a hobby / learning project, so it's rough around the edges. If you are looking for a better solution, check out pygleif)
Dependencies
- Python >= 3.4
- requests: For API requests
- dateutil: For correctly parsing dates as datetime objects
- pandas: Optional, for returning output as DataFrame
Usage
Import, instantiate and make the request:
from leipy import GLEIF
gleif = GLEIF(api_version='v1')
raw_output, results, results_df = gleif.request(
['HWUPKR0MPOU8FGXBT394','7ZW8QJWVPR4P1J1KQY45'],
return_dataframe=True
)After that, you can access the raw output from the API, results as a class with easy to access instance members, and results as a pandas DataFrame
Raw output example
[{'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'}}}]Results class example
>>> print(results.legal_name)
['Apple Inc.', 'Google LLC']
>>> print(results.lei_reg_status)
['ISSUED', 'ISSUED']
>>> print(results.date_last_updated)
[datetime.datetime(2017, 12, 12, 21, 19, tzinfo=tzutc()),
datetime.datetime(2018, 3, 28, 17, 0, tzinfo=tzutc())]Results DataFrame example
>>>results_df| country_hq | country_legal | date_initial_reg | date_last_updated | date_next_renewal | legal_name | lei | lei_reg_status | status | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | US | US | 2012-06-06 15:53:00+00:00 | 2017-12-12 21:19:00+00:00 | 2018-12-13 00:31:00+00:00 | Apple Inc. | HWUPKR0MPOU8FGXBT394 | ISSUED | ACTIVE |
| 1 | US | US | 2012-06-06 15:52:00+00:00 | 2018-03-28 17:00:00+00:00 | 2018-08-17 18:10:00+00:00 | Google LLC | 7ZW8QJWVPR4P1J1KQY45 | ISSUED | ACTIVE |
