""" Cannabis Licenses | Get Oregon Licenses Copyright (c) 2022 Cannlytics Authors: Keegan Skeate Candace O'Sullivan-Sutherland Created: 9/28/2022 Updated: 9/28/2022 License: Description: Collect Oregon cannabis license data. Data Source: - Oregon Liquor and Cannabis Commission URL: """ # Standard imports. from datetime import datetime import os from typing import Optional # External imports. from dotenv import dotenv_values import pandas as pd import requests from cannlytics.data.gis import geocode_addresses # Specify where your data lives. DATA_DIR = '../data/or' ENV_FILE = '../.env' # Specify state-specific constants. OREGON = { 'licensing_authority_id': 'OLCC', 'licensing_authority': 'Oregon Liquor and Cannabis Commission', 'licenses': { 'url': 'https://www.oregon.gov/olcc/marijuana/Documents/MarijuanaLicenses_Approved.xlsx', }, 'retailers': { 'url': 'https://www.oregon.gov/olcc/marijuana/Documents/Approved_Retail_Licenses.xlsx', 'columns': { 'TRADE NAME': 'business_dba_name', 'POSTAL CITY': 'premise_city', 'COUNTY': 'premise_county', 'STREET ADDRESS': 'premise_street_address', 'ZIP': 'premise_zip_code', 'Med Grade': 'medicinal', 'Delivery': 'delivery', }, }, } def get_licenses_or( data_dir: Optional[str] = None, env_file: Optional[str] = '.env', # Optional: Add print statements. # verbose: Optional[bool] = False, ): """Get California 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) # Download the data workbooks. timestamp = datetime.now().isoformat()[:19].replace(':', '-') outfile = f'{file_dir}/retailers-or-{timestamp}.xlsx' response = requests.get(OREGON['retailers']['url']) with open(outfile, 'wb') as doc: doc.write(response.content) # Extract data from the workbooks, removing the footnote. data = pd.read_excel(outfile, skiprows=3) data = data[:-1] data.rename(columns=OREGON['retailers']['columns'], inplace=True) # Optional: Remove licenses with an asterisk (*). # Curate the data. data['licensing_authority_id'] = OREGON['licensing_authority_id'] data['licensing_authority'] = OREGON['licensing_authority'] data['license_status'] = 'Active' data['license_designation'] = 'Adult-Use' data['premise_state'] = 'OR' data.loc[data['medicinal'] == 'Yes', 'license_designation'] = 'Adult-Use and Medicinal' # Convert `medicinal` and `delivery` columns to boolean. data['medicinal'] = data['medicinal'].map(dict(Yes=1)) data['delivery'] = data['delivery'].map(dict(Yes=1)) data['medicinal'].fillna(0, inplace=True) data['delivery'].fillna(0, inplace=True) # Convert certain columns from upper case title case. cols = ['business_dba_name', 'premise_city', 'premise_county', 'premise_street_address'] for col in cols: data[col] = data[col].apply(lambda x: x.title().strip()) # Convert zip code to a string. data.loc[:, 'premise_zip_code'] = data['premise_zip_code'].apply(lambda x: str(int(x))) # Get the `data_refreshed_date`. df = pd.read_excel(outfile, index_col=None, usecols='C', header=1, nrows=0) header = df.columns.values[0] date = pd.to_datetime(header.split(' ')[-1]) data['data_refreshed_date'] = date.isoformat() # Get the `license_number` and `license_type` from license list. license_file = f'{file_dir}/licenses-or-{timestamp}.xlsx' response = requests.get(OREGON['licenses']['url']) with open(license_file, 'wb') as doc: doc.write(response.content) licenses = pd.read_excel(license_file, skiprows=2) licenses['BUSINESS NAME'] = licenses['BUSINESS NAME'].apply( lambda x: str(x).title().strip(), ) licenses = licenses.loc[licenses['LICENSE TYPE'] == 'Recreational Retailer'] data = pd.merge( data, licenses[['BUSINESS NAME', 'COUNTY', 'LICENSE NUMBER', 'LICENSE TYPE']], left_on=['business_dba_name', 'premise_county'], right_on=['BUSINESS NAME', 'COUNTY'], how='left', ) # Clean the merged columns. data.drop_duplicates(subset='premise_street_address', inplace=True) columns = { 'LICENSE NUMBER': 'license_number', 'LICENSE TYPE': 'license_type', } data.rename(columns=columns, inplace=True) data.drop(columns=['BUSINESS NAME', 'COUNTY'], inplace=True) # Geocode licenses to get `premise_latitude` and `premise_longitude`. config = dotenv_values(env_file) google_maps_api_key = config['GOOGLE_MAPS_API_KEY'] cols = ['premise_street_address', 'premise_city', 'premise_state', 'premise_zip_code'] data['address'] = data[cols].apply( lambda row: ', '.join(row.values.astype(str)), axis=1, ) data = geocode_addresses( data, api_key=google_maps_api_key, address_field='address', ) drop_cols = ['state', 'state_name', 'county', 'address', 'formatted_address'] data.drop(columns=drop_cols, inplace=True) gis_cols = { 'latitude': 'premise_latitude', 'longitude': 'premise_longitude' } data.rename(columns=gis_cols, inplace=True) # Optional: Lookup details by searching for business' websites. # - business_email # - business_phone # Optional: Create fields for standardization: # - id # Save the license data. if data_dir is not None: timestamp = datetime.now().isoformat()[:19].replace(':', '-') data.to_excel(f'{data_dir}/licenses-or-{timestamp}.xlsx') return data 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 California licenses, saving them to the specified directory. data_dir = args.get('d', args.get('data_dir')) env_file = args.get('env_file') get_licenses_or(data_dir, env_file=env_file)