# `lenu - Legal Entity Name Understanding` by GLEIF and Sociovestix Labs | |
# Written in 2022 by Sociovestix Labs | |
# To the extent possible under law, the author(s) have dedicated all copyright | |
# and related and neighboring rights to this software to the public domain | |
# worldwide. This software is distributed without any warranty. | |
# | |
# You should have received a copy of the CC0 Public Domain Dedication along | |
# with this software. | |
# If not, see <http://creativecommons.org/publicdomain/zero/1.0/>. | |
"""This helper script creates the classnames variable for lenu.py""" | |
from lenu import URL | |
import pandas | |
relevant_cols = [ | |
"LEI", | |
"Entity.LegalName", | |
"Entity.LegalForm.EntityLegalFormCode", | |
"Entity.LegalJurisdiction", | |
"Entity.EntityCategory", | |
"Entity.EntityStatus", | |
"Registration.RegistrationStatus", | |
] | |
COL_LEI, COL_NAME, COL_ELF, COL_JUR, COL_CAT, COL_ESTATUS, COL_RSTATUS = relevant_cols | |
if __name__ == "__main__": | |
d = pandas.read_csv( | |
URL, | |
compression="zip", | |
low_memory=True, | |
dtype=str, | |
# the following will prevent pandas from converting words like 'NA' to NaN. We want to work with the LEI data as is. | |
na_values=[""], | |
keep_default_na=False, | |
usecols=relevant_cols, | |
) | |
d_issued = d[(d[COL_ESTATUS] == "ACTIVE") & (d[COL_RSTATUS] == "ISSUED")] | |
classnames = { | |
jur: classes | |
for jur, classes in d_issued.groupby(COL_JUR)[COL_ELF] | |
.unique() | |
.apply(list) | |
.to_dict() | |
.items() | |
if jur | |
in [ | |
"AT", | |
"AU", | |
"CH", | |
"CN", | |
"CZ", | |
"DE", | |
"DK", | |
"EE", | |
"ES", | |
"FI", | |
"GB", | |
"HU", | |
"IE", | |
"JP", | |
"KY", | |
"LI", | |
"LU", | |
"NL", | |
"NO", | |
"PL", | |
"PT", | |
"SE", | |
"US-CA", | |
"US-DE", | |
"US-MA", | |
"US-NY", | |
"VG", | |
"ZA", | |
] # not CA, BE, FR, BG | |
} | |
print("Please copy the following snippet into lenu.py:") | |
print("") | |
print("classnames = ", classnames) | |