""" Cannabis Licenses | Get Nevada Licenses Copyright (c) 2022 Cannlytics Authors: Keegan Skeate Candace O'Sullivan-Sutherland Created: 9/29/2022 Updated: 9/29/2022 License: Description: Collect Nevada cannabis license data. Data Source: - Nevada URL: """ # Standard imports. from datetime import datetime import os from typing import Optional # External imports. from bs4 import BeautifulSoup from cannlytics.data.gis import geocode_addresses from cannlytics.utils.constants import DEFAULT_HEADERS from dotenv import dotenv_values import pandas as pd import requests # Specify where your data lives. DATA_DIR = '../data/nv' ENV_FILE = '../.env' # Specify state-specific constants. STATE = 'NV' NEVADA = { 'licensing_authority_id': 'NVCCB', 'licensing_authority': 'Nevada Cannabis Compliance Board', 'licenses': { 'key': 'Active-License-List', 'columns': { 'LicenseNumber': 'license_number', 'LicenseName': 'business_dba_name', 'CE ID': 'id', 'LicenseType': 'license_type', 'County': 'premise_county', }, 'url': 'https://ccb.nv.gov/list-of-licensees/', } } def get_licenses_nv( data_dir: Optional[str] = None, env_file: Optional[str] = '.env', ): """Get Nevada cannabis license data.""" # Create the necessary directories. file_dir = f'{data_dir}/.datasets' if not os.path.exists(data_dir): os.makedirs(data_dir) if not os.path.exists(file_dir): os.makedirs(file_dir) #-------------------------------------------------------------------------- # Get all license data. #-------------------------------------------------------------------------- # Find the latest licenses workbook. licenses_url = '' retailer_key = NEVADA['licenses']['key'] url = NEVADA['licenses']['url'] response = requests.get(url, headers=DEFAULT_HEADERS) soup = BeautifulSoup(response.content, 'html.parser') links = soup.find_all('a') for link in links: href = link['href'] if retailer_key in href: licenses_url = href break # Download the workbook. filename = licenses_url.split('/')[-1] licenses_source_file = os.path.join(file_dir, filename) response = requests.get(licenses_url, headers=DEFAULT_HEADERS) with open(licenses_source_file, 'wb') as doc: doc.write(response.content) # Extract and standardize the data from the workbook. licenses = pd.read_excel(licenses_source_file, skiprows=1) licenses.rename(columns=NEVADA['licenses']['columns'], inplace=True) licenses['id'] = licenses['license_number'] licenses['licensing_authority_id'] = NEVADA['licensing_authority_id'] licenses['licensing_authority'] = NEVADA['licensing_authority'] licenses['license_designation'] = 'Adult-Use' licenses['premise_state'] = STATE licenses['license_status_date'] = None licenses['license_term'] = None licenses['issue_date'] = None licenses['expiration_date'] = None licenses['business_legal_name'] = licenses['business_dba_name'] licenses['business_owner_name'] = None licenses['business_structure'] = None licenses['business_email'] = None licenses['activity'] = None licenses['parcel_number'] = None licenses['business_image_url'] = None licenses['business_phone'] = None licenses['business_website'] = None # Convert certain columns from upper case title case. cols = ['business_dba_name', 'premise_county'] for col in cols: licenses[col] = licenses[col].apply(lambda x: x.title().strip()) # Get the refreshed date. date = filename.split('.')[0].replace(retailer_key, '').lstrip('-') date = '-'.join([date[:2], date[2:4], date[4:]]) licenses['data_refreshed_date'] = pd.to_datetime(date) # Wish: Geocode licenses to get `premise_latitude` and `premise_longitude`. # Save the licenses if data_dir is not None: timestamp = datetime.now().isoformat()[:19].replace(':', '-') licenses.to_csv(f'{data_dir}/licenses-{STATE.lower()}-{timestamp}.csv', index=False) #-------------------------------------------------------------------------- # Get retailer data #-------------------------------------------------------------------------- # Get the retailer data. retailers = [] tables = soup.find_all('table', attrs={'class': 'customTable'}) for table in tables: try: city = table.find('span').text except AttributeError: continue rows = table.find_all('td') for row in rows: cells = row.text.split(' – ') name = cells[0] street = cells[1] designation = cells[-1] retailers.append({ 'business_legal_name': name, 'business_dba_name': name, 'license_designation': designation, 'premise_city': city, 'premise_street_address': street, }) # Standardize the retailers. retailers = pd.DataFrame(retailers) retailers['licensing_authority_id'] = NEVADA['licensing_authority_id'] retailers['licensing_authority'] = NEVADA['licensing_authority'] retailers['license_type'] = 'Commercial - Retailer' retailers['license_status'] = 'Active' retailers['license_designation'] = 'Adult-Use' retailers['premise_state'] = STATE retailers['license_status_date'] = None retailers['license_term'] = None retailers['issue_date'] = None retailers['expiration_date'] = None retailers['business_owner_name'] = None retailers['business_structure'] = None retailers['business_email'] = None retailers['activity'] = None retailers['parcel_number'] = None retailers['business_website'] = None retailers['business_image_url'] = None retailers['business_phone'] = None # FIXME: Merge `license_number`, `premise_county`, `data_refreshed_date` # from licenses. retailers['license_number'] = None retailers['id'] = None retailers['data_refreshed_date'] = datetime.now().isoformat() # Geocode the retailers. config = dotenv_values(env_file) google_maps_api_key = config['GOOGLE_MAPS_API_KEY'] cols = ['premise_street_address', 'premise_city', 'premise_state'] retailers['address'] = retailers[cols].apply( lambda row: ', '.join(row.values.astype(str)), axis=1, ) retailers = geocode_addresses( retailers, api_key=google_maps_api_key, address_field='address', ) drop_cols = ['state', 'state_name', 'address', 'formatted_address'] gis_cols = { 'county': 'premise_county', 'latitude': 'premise_latitude', 'longitude': 'premise_longitude' } licenses['premise_zip_code'] = licenses['formatted_address'].apply( lambda x: x.split(', ')[2].split(',')[0].split(' ')[-1] if STATE in str(x) else x ) retailers.drop(columns=drop_cols, inplace=True) retailers.rename(columns=gis_cols, inplace=True) # Save the retailers if data_dir is not None: timestamp = datetime.now().isoformat()[:19].replace(':', '-') retailers.to_csv(f'{data_dir}/retailers-{STATE.lower()}-{timestamp}.csv', index=False) # Return all of the data. return pd.concat([licenses, retailers]) if __name__ == '__main__': # Support command line usage. import argparse try: arg_parser = argparse.ArgumentParser() arg_parser.add_argument('--d', dest='data_dir', type=str) arg_parser.add_argument('--data_dir', dest='data_dir', type=str) arg_parser.add_argument('--env', dest='env_file', type=str) args = arg_parser.parse_args() except SystemExit: args = {'d': DATA_DIR, 'env_file': ENV_FILE} # Get licenses, saving them to the specified directory. data_dir = args.get('d', args.get('data_dir')) env_file = args.get('env_file') data = get_licenses_nv(data_dir, env_file=env_file)