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        "author": "emredjan",
        "author_email": "emredjan@gmail.com",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Office/Business :: Financial"
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        "description": "# LEIPY\n\nA python wrapper / client for GLEIF public API. Suggestions, requests, contributions welcome.\n\n(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](https://github.com/ggravlingen/pygleif))\n\n## Dependencies\n\n1. Python >= 3.4\n2. [requests](http://docs.python-requests.org/en/master/): For API requests\n3. [dateutil](https://dateutil.readthedocs.io/en/stable/): For correctly parsing dates as datetime objects\n4. [pandas](https://pandas.pydata.org/): Optional, for returning output as DataFrame \n\n## Usage\n\nImport, instantiate and make the request:\n\n```python\nfrom leipy import GLEIF\n\ngleif = GLEIF(api_version='v1')\n\nraw_output, results, results_df = gleif.request(\n    ['HWUPKR0MPOU8FGXBT394','7ZW8QJWVPR4P1J1KQY45'], \n    return_dataframe=True\n)\n```\n\nAfter 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\n\n#### Raw output example\n\n```python\n[{'LEI': {'$': 'HWUPKR0MPOU8FGXBT394'},\n  'Entity': {'LegalName': {'$': 'Apple Inc.'},\n   'LegalAddress': {'Line1': {'$': 'C/O C T Corporation System'},\n    'Line2': {'$': '818 West 7th Street'},\n    'Line3': {'$': 'Suite 930'},\n    'City': {'$': 'Los Angeles'},\n    'Region': {'$': 'US-CA'},\n    'Country': {'$': 'US'},\n    'PostalCode': {'$': '90017'}},\n   'HeadquartersAddress': {'Line1': {'$': '1 Infinite Loop'},\n    'City': {'$': 'Cupertino'},\n    'Region': {'$': 'US-CA'},\n    'Country': {'$': 'US'},\n    'PostalCode': {'$': '95014'}},\n   'BusinessRegisterEntityID': {'@register': 'RA000598', '$': 'C0806592'},\n   'LegalJurisdiction': {'$': 'US'},\n   'LegalForm': {'$': 'INCORPORATED'},\n   'EntityStatus': {'$': 'ACTIVE'}},\n  'Registration': {'InitialRegistrationDate': {'$': '2012-06-06T15:53:00.000Z'},\n   'LastUpdateDate': {'$': '2017-12-12T21:19:00.000Z'},\n   'RegistrationStatus': {'$': 'ISSUED'},\n   'NextRenewalDate': {'$': '2018-12-13T00:31:00.000Z'},\n   'ManagingLOU': {'$': 'EVK05KS7XY1DEII3R011'},\n   'ValidationSources': {'$': 'FULLY_CORROBORATED'}}},\n {'LEI': {'$': '7ZW8QJWVPR4P1J1KQY45'},\n  'Entity': {'LegalName': {'$': 'Google LLC'},\n   'LegalAddress': {'Line1': {'$': 'C/O Corporation Service Company'},\n    'Line2': {'$': '251 Little Falls Drive'},\n    'City': {'$': 'Wilmington'},\n    'Region': {'$': 'US-DE'},\n    'Country': {'$': 'US'},\n    'PostalCode': {'$': '19808'}},\n   'HeadquartersAddress': {'Line1': {'$': '1600 Amphitheatre Parkway'},\n    'City': {'$': 'Mountain View'},\n    'Region': {'$': 'US-CA'},\n    'Country': {'$': 'US'},\n    'PostalCode': {'$': '94043'}},\n   'BusinessRegisterEntityID': {'@register': 'RA000602', '$': '3582691'},\n   'LegalJurisdiction': {'$': 'US'},\n   'LegalForm': {'$': 'LIMITED LIABILITY COMPANY'},\n   'EntityStatus': {'$': 'ACTIVE'}},\n  'Registration': {'InitialRegistrationDate': {'$': '2012-06-06T15:52:00.000Z'},\n   'LastUpdateDate': {'$': '2018-03-28T17:00:00.000Z'},\n   'RegistrationStatus': {'$': 'ISSUED'},\n   'NextRenewalDate': {'$': '2018-08-17T18:10:00.000Z'},\n   'ManagingLOU': {'$': 'EVK05KS7XY1DEII3R011'},\n   'ValidationSources': {'$': 'FULLY_CORROBORATED'}}}]\n```\n\n#### Results class example\n\n```python\n>>> print(results.legal_name)\n['Apple Inc.', 'Google LLC']\n\n>>> print(results.lei_reg_status)\n['ISSUED', 'ISSUED']\n\n>>> print(results.date_last_updated)\n[datetime.datetime(2017, 12, 12, 21, 19, tzinfo=tzutc()),\n datetime.datetime(2018, 3, 28, 17, 0, tzinfo=tzutc())]\n```\n\n#### Results DataFrame example\n\n```python\n>>>results_df\n```\n\n| |country_hq|country_legal|date_initial_reg|date_last_updated|date_next_renewal|legal_name|lei|lei_reg_status|status|\n|-|----------|-------------|----------------|-----------------|-----------------|----------|---|--------------|------|\n|0\t|US\t|US\t|2012-06-06 15:53:00+00:00\t|2017-12-12 21:19:00+00:00\t|2018-12-13 00:31:00+00:00\t|Apple Inc.\t|HWUPKR0MPOU8FGXBT394\t|ISSUED\t|ACTIVE|\n|1\t|US\t|US\t|2012-06-06 15:52:00+00:00\t|2018-03-28 17:00:00+00:00\t|2018-08-17 18:10:00+00:00\t|Google LLC\t|7ZW8QJWVPR4P1J1KQY45\t|ISSUED\t|ACTIVE|\n\n",
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