H = pd.read_csv('../data/vegas/restaurant_establishments.csv', delimiter=';', header=None, skiprows=1)
H.columns = ['permit_number', 'facility_id', 'PE', 'restaurant_name', 'location_name',
'address', 'latitude', 'longitude', 'city_id',
'city_name', 'zip_code', 'nciaa', 'plan_review', 'record_status',
'current_grade', 'current_demerits', 'date_current', 'previous_grade',
'date_previous', 'misc','empty']
H['id_'] = H.facility_id
H['name_'] = H.restaurant_name.apply(lib.standard_name)
H['city_'] = H.city_name.apply(lambda x: '_'.join(x.lower().strip().split()))
H['zip'] = H.zip_code.apply(lambda x: np.nan if type(x)==float else x[:5])
H = pd.concat([H, H.address.apply(split_address)], axis=1)
return H
if __name__ == '__main__':
B_NV = lib.preprocess_yelp(lib.get_yelp_businesses(), 'NV')
NV = preprocess_NV()
merge_level1 = lib.merge_exact_match(NV, B_NV)
merge_level2 = lib.merge_partial_match(NV, B_NV, merge_level1, merge_level=2, dump_tag='vegas_32')
# merge_level3 = lib.merge_partial_match(NV, B_NV, merge_level2, merge_level=3, dump_tag='vegas_33')
# merge_level4 = lib.merge_partial_match(NV, B_NV, merge_level3, merge_level=4, dump_tag='vegas_34')
merge_level2.to_csv('../data/vegas/merge_dump_22.csv', encoding='utf-8')
#merge_level3.to_csv('../data/vegas/merge_dump_23.csv', encoding='utf-8')
lib.save_to_pickle(merge_level1, '../data/vegas/vegas_yelp_merge.pkl')
def preprocess_WI():
H = lib.open_pickle('../data/mad/mad_health_FINAL.pkl')
H['name_'] = H.name.apply(lib.standard_name)
H['id_'] = H.index
H_address = H.address.apply(lambda x: lib.parse_address(x,ampersand=True))
col = H_address.columns.values
col[0] = 'city_'
H_address.columns = col
H = pd.concat([H, H_address], axis=1)
return H
if __name__ == '__main__':
B_WI = lib.preprocess_yelp(lib.get_yelp_businesses(), 'WI', ampersand=True)
WI = preprocess_WI()
merge_level1 = lib.merge_exact_match(WI, B_WI)
merge_level2 = lib.merge_partial_match2(WI, B_WI, merge_level1, merge_level=2, dump_tag='mad_32')
merge_level3 = lib.merge_partial_match2(WI, B_WI, merge_level2, merge_level=3, dump_tag='mad_33')
merge_level4 = lib.merge_partial_match2(WI, B_WI, merge_level3, merge_level=4, dump_tag='mad_34')
# merge_level2.to_csv('../data/mad/merge_dump_22.csv', encoding='utf-8')
# merge_level3.to_csv('../data/mad/merge_dump_23.csv', encoding='utf-8')
lib.save_to_pickle(merge_level4, '../data/mad/madison_yelp_merge.pkl')
import numpy as np
import pickle
import re
import string
import merge_main as lib
pd.options.mode.chained_assignment = None # default='warn'
def preprocess_AZ():
AZ = lib.open_pickle('../data/phx/phoenix_R_full.pkl')
H_address = AZ.address.apply(lib.parse_address)
H_address.set_index(AZ.index, inplace=True)
col = H_address.columns.values
col[0] = 'city_'
H_address.columns = col
H_AZ = pd.concat([AZ, H_address], axis=1)
H_AZ['name_'] = H_AZ.name.apply(lib.standard_name)
H_AZ['id_'] = H_AZ.permit_id
return H_AZ
if __name__ == '__main__':
B_AZ = lib.preprocess_yelp(lib.get_yelp_businesses(), 'AZ')
AZ = preprocess_AZ()
merge_level1 = lib.merge_exact_match(AZ, B_AZ)
merge_level2 = lib.merge_partial_match2(AZ, B_AZ, merge_level1, merge_level=2, dump_tag='phx_32')
merge_level3 = lib.merge_partial_match2(AZ, B_AZ, merge_level2, merge_level=3, dump_tag='phx_33')
merge_level4 = lib.merge_partial_match2(AZ, B_AZ, merge_level3, merge_level=4, dump_tag='phx_34')
lib.save_to_pickle(merge_level4, '../data/phx/phoenix_yelp_merge.pkl')
address[key] = re.sub(r"\s+", " ", value).strip()
return pd.Series(address)
def preprocess_NC():
H = lib.open_pickle("../data/char/char_FULL_04.pkl")
H = pd.DataFrame.from_records(H).T
H["city_"] = H.city.apply(lambda x: x.lower().strip())
H["name_"] = H.name.apply(lib.standard_name)
H["id_"] = H.index
H = pd.concat([H, H.address.apply(split_address)], axis=1)
return H
if __name__ == "__main__":
B_NC = lib.preprocess_yelp(lib.get_yelp_businesses(), "NC")
NC = preprocess_NC()
merge_level1 = lib.merge_exact_match(NC, B_NC)
merge_level2 = lib.merge_partial_match2(NC, B_NC, merge_level1, merge_level=2, dump_tag="char_32")
merge_level3 = lib.merge_partial_match2(NC, B_NC, merge_level2, merge_level=3, dump_tag="char_33")
merge_level4 = lib.merge_partial_match2(NC, B_NC, merge_level3, merge_level=4, dump_tag="char_34")
merge_level2.to_csv("../data/char/merge_dump_22.csv", encoding="utf-8")
merge_level3.to_csv("../data/char/merge_dump_23.csv", encoding="utf-8")
lib.save_to_pickle(merge_level4, "../data/char/charlotte_yelp_merge.pkl")
return pd.Series(address)
def preprocess_PA():
PA = lib.open_pickle('../data/pitt/pitt_inspections_FINAL.pkl')
PA['name_'] = PA.name.apply(lib.standard_name)
PA['city_'] = PA.city.apply(lambda x: x.lower())
PA['id_'] = PA.client_id
PA = pd.concat([PA, PA.address.apply(split_address)], axis=1)
return PA
if __name__ == '__main__':
B_PA = lib.preprocess_yelp(lib.get_yelp_businesses(), 'PA')
PA = preprocess_PA()
merge_level1 = lib.merge_exact_match(PA, B_PA)
merge_level2 = lib.merge_partial_match2(PA, B_PA, merge_level1, merge_level=2, dump_tag='pitt_32')
merge_level3 = lib.merge_partial_match2(PA, B_PA, merge_level2, merge_level=3, dump_tag='pitt_33')
merge_level4 = lib.merge_partial_match2(PA, B_PA, merge_level3, merge_level=4, dump_tag='pitt_34')
# merge_level2.to_csv('../data/pitt/merge_dump_22.csv', encoding='utf-8')
# merge_level3.to_csv('../data/pitt/merge_dump_23.csv', encoding='utf-8')
# merge_level4.to_csv('../data/pitt/merge_dump_24.csv', encoding='utf-8')
lib.save_to_pickle(merge_level4, '../data/pitt/pittsburgh_yelp_merge.pkl')