pr/1 (#1)

- Curating cannabis licenses📜 | Completed CA + OR ✅ (14c4eafb52dcf9c57c7f00051cfd9c52641749c0)

.gitattributes

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+# Ignore environment variables.
+*.env
+
+# Ignore temporary files.
+*tmp
+
+# Ignore PDFs.
+*pdfs
+
+# Ignore external datasets.
+*.datasets

LICENSE

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README.md

@@ -1,3 +1,259 @@
 ---
-license: cc-by-4.0
+annotations_creators:
+  - expert-generated
+language_creators:
+  - expert-generated
+license:
+  - cc-by-4.0
+pretty_name: cannabis_licenses
+size_categories:
+  - 10K<n<100K
+source_datasets:
+  - original
+tags:
+  - cannabis
+  - licenses
+  - licensees
 ---
+
+# Cannabis Licenses, Curated by Cannlytics
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Data Collection and Normalization](#data-collection-and-normalization)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [License](#license)
+  - [Citation](#citation)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** <https://github.com/cannlytics/cannlytics>
+- **Repository:** <https://huggingface.co/datasets/cannlytics/cannabis_licenses>
+- **Point of Contact:** <dev@cannlytics.com>
+
+### Dataset Summary
+
+This dataset is a collection of cannabis license data for the licensees that have been permitted in the United States.
+
+## Dataset Structure
+
+The dataset is partitioned into subsets for each state.
+
+
+| State | Licenses |
+|-------|----------|
+|  [Alaska](#) |  |
+|  [Arizona](#) |  |
+|  [California](#) | ✅ |
+|  [Colorado](#) |  |
+|  [Connecticut](#) |  |
+|  [District of Columbia](#) |  |
+|  [Illinois](#) |  |
+|  [Maine](#) |  |
+|  [Massachusetts](#) |  |
+|  [Michigan](#) |  |
+|  [Montana](#) |  |
+|  [Nevada](#) |  |
+|  [New Hampshire](#) |  |
+|  [New Jersey](#) |  |
+|  [New Mexico](#) |  |
+|  [New York](#) |  |
+|  [Oregon](#) | ✅  |
+|  [Rhode Island](#) |  |
+|  [Vermont](#) |  |
+|  [Washington](#) |  |
+
+
+### Data Instances
+
+You can load the licenses for each state. For example:
+
+```py
+from datasets import load_dataset
+
+# Get the licenses for a specific state.
+dataset = load_dataset('cannlytics/cannabis_licenses', 'ca')
+data = dataset['data']
+assert len(data) > 0
+print('%i licenses.' % len(data))
+```
+
+### Data Fields
+
+Below is a non-exhaustive list of fields, used to standardize the various data that are encountered, that you may expect encounter in the parsed COA data.
+
+| Field | Example | Description |
+|-------|-----|-------------|
+| `id` | `"1046"` |  |
+| `license_number` | `"C10-0000423-LIC"` |  |
+| `license_status` | `"Active"` |  |
+| `license_status_date` | `""` |  |
+| `license_term` | `"Provisional"` |  |
+| `license_type` | `"Commercial -  Retailer"` |  |
+| `license_designation` | `"Adult-Use and Medicinal"` |  |
+| `issue_date` | `"2019-07-15T00:00:00"` |  |
+| `expiration_date` | `"2023-07-14T00:00:00"` |  |
+| `licensing_authority_id` | `"BCC"` |  |
+| `licensing_authority` | `"Bureau of Cannabis Control (BCC)"` |  |
+| `business_legal_name` | `"Movocan"` |  |
+| `business_dba_name` | `"Movocan"` |  |
+| `business_owner_name` | `"redacted"` |  |
+| `business_structure` | `"Corporation"` |  |
+| `activity` | `""` |  |
+| `premise_street_address` | `"1632 Gateway Rd"` |  |
+| `premise_city` | `"Calexico"` |  |
+| `premise_state` | `"CA"` |  |
+| `premise_county` | `"Imperial"` |  |
+| `premise_zip_code` | `"92231"` |  |
+| `business_email` | `"redacted@gmail.com"` |  |
+| `business_phone` | `"(555) 555-5555"` |  |
+| `parcel_number` | `""` |  |
+| `premise_latitude` | `32.69035693` |  |
+| `premise_longitude` | `-115.38987552` |  |
+| `data_refreshed_date` | `"2022-09-21T12:16:33.3866667"` |  |
+
+### Data Splits
+
+The data is split into subsets by state. You can retrieve all licenses by requesting the `all` subset.
+
+```py
+from datasets import load_dataset
+
+# Get all cannabis licenses.
+repo = 'cannlytics/cannabis_licenses'
+dataset = load_dataset(repo, 'all')
+data = dataset['data']
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+Data about organizations operating in the cannabis industry for each state is valuable for research.
+
+### Source Data
+
+| State | Data Source URL |
+|-------|-----------------|
+|  [Alaska](#) |  |
+|  [Arizona](https://azcarecheck.azdhs.gov/s/?licenseType=null) | <https://azcarecheck.azdhs.gov/s/?licenseType=null> |
+|  [California](https://search.cannabis.ca.gov/) | <https://search.cannabis.ca.gov/> |
+|  [Colorado](#) |  |
+|  [Connecticut](#) |  |
+|  [District of Columbia](#) |  |
+|  [Illinois](#) |  |
+|  [Maine](#) |  |
+|  [Massachusetts](#) |  |
+|  [Michigan](#) |  |
+|  [Montana](https://mtrevenue.gov/cannabis/#CannabisLicenses) | <https://mtrevenue.gov/cannabis/#CannabisLicenses> |
+|  [Nevada](https://ccb.nv.gov/list-of-licensees/) | <https://ccb.nv.gov/list-of-licensees/> |
+|  [New Hampshire](#) |  |
+|  [New Jersey](#) |  |
+|  [New Mexico](https://nmrldlpi.force.com/bcd/s/public-search-license?division=CCD&language=en_US) | <https://nmrldlpi.force.com/bcd/s/public-search-license?division=CCD&language=en_US> |
+|  [New York](#) |  |
+|  [Oregon](https://www.oregon.gov/olcc/marijuana/pages/recreational-marijuana-licensing.aspx) | <https://www.oregon.gov/olcc/marijuana/pages/recreational-marijuana-licensing.aspx> |
+|  [Rhode Island](#) |  |
+|  [Vermont](#) |  |
+|  [Washington](https://lcb.wa.gov/records/frequently-requested-lists) | <https://lcb.wa.gov/records/frequently-requested-lists> |
+
+#### Data Collection and Normalization
+
+In the `algorithms` directory, you can find the algorithms used for data collection. You can use these algorithms to recreate the dataset. First, you will need to clone the repository:
+
+```
+git clone https://huggingface.co/datasets/cannlytics/cannabis_licenses
+```
+
+You can then install the algorithm Python (3.9+) requirements:
+
+```
+cd cannabis_licenses
+pip install -r requirements.txt
+```
+
+Then you can run all of the data-collection algorithms:
+
+```
+python algorithms/main.py
+```
+
+Or you can run each algorithm individually. For example:
+
+```
+python algorithms/get_licenses_ca.py
+```
+
+### Personal and Sensitive Information
+
+This dataset includes names of individuals, public addresses, and contact information for cannabis licensees. It is important to take care to use these data points in a legal manner.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+Arguably, there is substantial social impact that could result from the study of permitted adult-use cannabis, therefore, researchers and data consumers alike should take the utmost care in the use of this dataset.
+
+### Discussion of Biases
+
+Cannlytics is a for-profit data and analytics company that primarily serves cannabis businesses. The data are not randomly collected and thus sampling bias should be taken into consideration.
+
+### Other Known Limitations
+
+The data is for adult-use cannabis licenses. It would be valuable to include medical cannabis licenses too.
+
+## Additional Information
+
+### Dataset Curators
+
+Curated by [🔥Cannlytics](https://cannlytics.com)<br>
+<dev@cannlytics.com>
+
+### License
+
+```
+Copyright (c) 2022 Cannlytics and the Cannabis Data Science Team
+
+The files associated with this dataset are licensed under a 
+Creative Commons Attribution 4.0 International license.
+
+You can share, copy and modify this dataset so long as you give
+appropriate credit, provide a link to the CC BY license, and
+indicate if changes were made, but you may not do so in a way
+that suggests the rights holder has endorsed you or your use of
+the dataset. Note that further permission may be required for
+any content within the dataset that is identified as belonging
+to a third party.
+```
+
+### Citation
+
+Please cite the following if you use the code examples in your research:
+
+```bibtex
+@misc{cannlytics2022,
+  title={Cannabis Data Science},
+  author={Skeate, Keegan},
+  journal={https://github.com/cannlytics/cannabis-data-science},
+  year={2022}
+}
+```
+
+### Contributions
+
+Thanks to [🔥Cannlytics](https://cannlytics.com), [@candy-o](https://github.com/candy-o), [@keeganskeate](https://github.com/keeganskeate), and the entire [Cannabis Data Science Team](https://meetup.com/cannabis-data-science/members) for their contributions.

algorithms/get_licenses_az.py

@@ -0,0 +1,100 @@
+"""
+Cannabis Licenses | Get Arizona Licenses
+Copyright (c) 2022 Cannlytics
+
+Authors:
+    Keegan Skeate <https://github.com/keeganskeate>
+Created: 9/27/2022
+Updated: 9/27/2022
+License: <https://github.com/cannlytics/cannlytics/blob/main/LICENSE>
+
+Description:
+
+    Collect Arizona cannabis license data.
+
+Data Source:
+
+    - Arizona Department of Health Services | Division of Licensing
+    URL: <https://azcarecheck.azdhs.gov/s/?licenseType=null>
+
+"""
+# Standard imports.
+from datetime import datetime
+import os
+from time import sleep
+
+# External imports.
+from cannlytics.utils import camel_to_snake
+from cannlytics.utils.constants import DEFAULT_HEADERS
+import matplotlib.pyplot as plt
+import pandas as pd
+import requests
+import seaborn as sns
+
+
+# Specify where your data lives.
+DATA_DIR = '../data/az'
+PDF_DIR = '../data/mt/pdfs'
+
+# Create directories if necessary.
+if not os.path.exists(DATA_DIR): os.makedirs(DATA_DIR)
+if not os.path.exists(PDF_DIR): os.makedirs(PDF_DIR)
+
+
+
+# # Define the license data API.
+# base = 'https://as-cdt-pub-vip-cannabis-ww-p-002.azurewebsites.net'
+# endpoint = '/licenses/filteredSearch'
+# query = f'{base}{endpoint}'
+# params = {'pageSize': 50, 'searchQuery': ''}
+
+# # Iterate over all of the pages to get all of the data.
+# iterate = True
+# page = 1
+# licenses = []
+# verbose = True
+# while(iterate):
+#     params['pageNumber'] = page
+#     response = requests.get(query, headers=DEFAULT_HEADERS, params=params)
+#     body = response.json()
+#     data = body['data']
+#     licenses.extend(data)
+#     if not body['metadata']['hasNext']:
+#         iterate = False
+#     if verbose:
+#         print('Recorded %i/%i pages.' % (page, body['metadata']['totalPages']))
+#     page += 1
+#     sleep(0.2)
+
+# # Standardize the licensee data.
+# license_data = pd.DataFrame(licenses)
+# columns = list(license_data.columns)
+# columns = [camel_to_snake(x) for x in columns]
+# license_data.columns = columns
+
+# # Save the data locally.
+# timestamp = datetime.now().isoformat()[:19].replace(':', '-')
+# license_data.to_excel(f'{DATA_DIR}/licenses-ca-{timestamp}.xlsx')
+
+# # Optional: Archive the licensee data in Firebase.
+# from cannlytics.firebase import initialize_firebase, update_documents
+# n = 420
+# database = initialize_firebase()
+# collection = 'public/data/licenses'
+# shards = [df[i:i + n] for i in range(0, df.shape[0], n)]
+# for shard in shards:
+#     refs = shard['license_number'].apply(lambda x: f'{collection}/{x}')
+#     docs = shard.to_dict('records')
+#     update_documents(refs, docs, database=database)
+
+# # Create a scatterplot of latitude and longitude with hue as license type.
+# sns.scatterplot(
+#     data=license_data.loc[
+#         (~license_data['premise_longitude'].isnull()) &
+#         (~license_data['premise_latitude'].isnull())
+#     ],
+#     x='premise_longitude',
+#     y='premise_latitude',
+#     hue='license_type',
+# )
+# plt.show()

algorithms/get_licenses_ca.py

@@ -0,0 +1,103 @@
+"""
+Cannabis Licenses | Get California Licenses
+Copyright (c) 2022 Cannlytics
+
+Authors:
+    Keegan Skeate <https://github.com/keeganskeate>
+Created: 9/16/2022
+Updated: 9/27/2022
+License: <https://github.com/cannlytics/cannlytics/blob/main/LICENSE>
+
+Description:
+
+    Collect California cannabis license data.
+
+Data Source:
+
+    - California Department of Cannabis Control Cannabis Unified License Search
+    URL: <https://search.cannabis.ca.gov/>
+
+"""
+# Standard imports.
+from datetime import datetime
+import os
+from time import sleep
+from typing import Optional
+
+# External imports.
+from cannlytics.utils import camel_to_snake
+from cannlytics.utils.constants import DEFAULT_HEADERS
+import pandas as pd
+import requests
+
+
+# Specify where your data lives.
+DATA_DIR = '../data/ca'
+
+
+def get_licenses_ca(
+        data_dir: Optional[str] = None,
+        page_size: Optional[int] = 50,
+        pause: Optional[float] = 0.2,
+        starting_page: Optional[int] = 1,
+        ending_page: Optional[int] = None,
+        verbose: Optional[bool] = False,
+        search: Optional[str] = '',
+    ):
+    """Get California cannabis license data."""
+
+    # Define the license data API.
+    base = 'https://as-cdt-pub-vip-cannabis-ww-p-002.azurewebsites.net'
+    endpoint = '/licenses/filteredSearch'
+    query = f'{base}{endpoint}'
+    params = {'pageSize': page_size, 'searchQuery': search}
+
+    # Iterate over all of the pages to get all of the data.
+    page = int(starting_page)
+    licenses = []
+    iterate = True
+    while(iterate):
+        params['pageNumber'] = page
+        response = requests.get(query, headers=DEFAULT_HEADERS, params=params)
+        body = response.json()
+        data = body['data']
+        licenses.extend(data)
+        if not body['metadata']['hasNext']:
+            iterate = False
+        if verbose:
+            print('Recorded %i/%i pages.' % (page, body['metadata']['totalPages']))
+        if ending_page is not None:
+            if page == ending_page:
+                iterate = False
+        page += 1
+        sleep(pause)
+
+    # Standardize the licensee data.
+    license_data = pd.DataFrame(licenses)
+    columns = list(license_data.columns)
+    columns = [camel_to_snake(x) for x in columns]
+    license_data.columns = columns
+
+    # Save and return the data.
+    if data_dir is not None:
+        if not os.path.exists(data_dir): os.makedirs(data_dir)
+        timestamp = datetime.now().isoformat()[:19].replace(':', '-')
+        license_data.to_excel(f'{data_dir}/licenses-ca-{timestamp}.xlsx')
+    return license_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)
+        # Future work: Support the rest of the arguments from the CL.
+        args = arg_parser.parse_args()
+    except SystemExit:
+        args = {'d': DATA_DIR}
+
+    # Get California licenses, saving them to the specified directory.
+    data_dir = args.get('d', args.get('data_dir'))
+    get_licenses_ca(data_dir)

algorithms/get_licenses_mt.py

@@ -0,0 +1,138 @@
+"""
+Cannabis Licenses | Get Montana Licenses
+Copyright (c) 2022 Cannlytics
+
+Authors:
+    Keegan Skeate <https://github.com/keeganskeate>
+Created: 9/27/2022
+Updated: 9/28/2022
+License: <https://github.com/cannlytics/cannlytics/blob/main/LICENSE>
+
+Description:
+
+    Collect Montana cannabis license data.
+
+Data Source:
+
+    - Montana Department of Revenue | Cannabis Control Division
+    URL: <https://mtrevenue.gov/cannabis/#CannabisLicenses>
+
+"""
+# Standard imports.
+from datetime import datetime
+import os
+from time import sleep
+
+# External imports.
+from bs4 import BeautifulSoup
+from cannlytics.utils import camel_to_snake
+from cannlytics.utils.constants import DEFAULT_HEADERS
+import pdfplumber
+import requests
+
+
+# Specify where your data lives.
+DATA_DIR = '../data/mt'
+PDF_DIR = '../data/mt/pdfs'
+
+MONTANA = {
+    'retailers': {
+        'url': 'https://mtrevenue.gov/?mdocs-file=60245',
+        'columns': ['city', 'dba', 'license_type', 'phone']
+    },
+    'processors': {'url': 'https://mtrevenue.gov/?mdocs-file=60250'},
+    'cultivators': {'url': 'https://mtrevenue.gov/?mdocs-file=60252'},
+    'labs': {'url': 'https://mtrevenue.gov/?mdocs-file=60248'},
+    'transporters': {'url': 'https://mtrevenue.gov/?mdocs-file=72489'},
+}
+
+# Create directories if necessary.
+if not os.path.exists(DATA_DIR): os.makedirs(DATA_DIR)
+if not os.path.exists(PDF_DIR): os.makedirs(PDF_DIR)
+
+
+# Download the retailers PDF.
+timestamp = datetime.now().isoformat()[:19].replace(':', '-')
+outfile = f'{PDF_DIR}/mt-retailers-{timestamp}.pdf'
+response = requests.get(MONTANA['retailers']['url'], headers=DEFAULT_HEADERS)
+with open(outfile, 'wb') as pdf:
+    pdf.write(response.content)
+
+# Extract the data from the PDF.
+rows = []
+skip_lines = ['GOVERNOR ', 'DIRECTOR ', 'Cannabis Control Division',
+'Licensed Dispensary locations', 'Please note', 'registered ',
+'City Location Name Sales Type Phone Number', 'Page ']
+doc = pdfplumber.open(outfile)
+for page in doc.pages:
+    text = page.extract_text()
+    lines = text.split('\n')
+    for line in lines:
+        skip = False
+        for skip_line in skip_lines:
+            if line.startswith(skip_line):
+                skip = True
+                break
+        if skip:
+            continue
+        rows.append(line)
+
+# Collect licensee data.
+licensees = []
+for row in rows:
+
+    # FIXME: Rows with double-line text get cut-off.
+    if '(' not in row:
+        continue
+
+    obs = {}
+    if 'Adult Use' in row:
+        parts = row.split('Adult Use')
+        obs['license_type'] = 'Adult Use'
+    else:
+        parts = row.split('Medical Only')
+        obs['license_type'] = 'Medical Only'
+    obs['dba'] = parts[0].strip()
+    obs['phone'] = parts[-1].strip()
+    licensees.append(obs)
+
+# Get a list of Montana cities.
+cities = []
+# response = requests.get('http://www.mlct.org/', headers=DEFAULT_HEADERS)
+# soup = BeautifulSoup(response.content, 'html.parser')
+# table = soup.find('table')
+# for tr in table.findAll('tr'):
+#     if not tr.text.strip().replace('\n', ''):
+#         continue
+#     city = tr.find('td').text
+#     if '©' in city or ',' in city or '\n' in city or city == 'Home' or city == 'City':
+#         continue
+#     cities.append(city)
+
+# remove_lines = ['RESOURCES', 'Official State Website', 'State Legislature',
+# 'Chamber of Commerce', 'Contact Us']
+# for ele in remove_lines:
+#     cities.remove(ele)
+
+# FIXME:
+url = 'https://dojmt.gov/wp-content/uploads/2011/05/mvmtcitiescountieszips.pdf'
+
+# TODO: Separate `city` from `dba` using list of Montana cities.
+for i, licensee in enumerate(licensees):
+    dba = licensee['dba']
+    city_found = False
+    for city in cities:
+        city_name = city.upper()
+        if city_name in dba:
+            licensees[i]['dba'] = dba.replace(city_name, '').strip()
+            licensees[i]['city'] = city
+            city_found = True
+            break
+    if not city_found:
+        print("Couldn't identify city:", dba)
+
+
+# TODO: Remove duplicates.
+
+
+# TODO: Lookup the address of the licenses?

algorithms/get_licenses_or.py

@@ -0,0 +1,192 @@
+"""
+Cannabis Licenses | Get Oregon Licenses
+Copyright (c) 2022 Cannlytics
+
+Authors:
+    Keegan Skeate <https://github.com/keeganskeate>
+Created: 9/28/2022
+Updated: 9/28/2022
+License: <https://github.com/cannlytics/cannlytics/blob/main/LICENSE>
+
+Description:
+
+    Collect Oregon cannabis license data.
+
+Data Source:
+
+    - Oregon Liquor and Cannabis Commission
+    URL: <https://www.oregon.gov/olcc/marijuana/pages/recreational-marijuana-licensing.aspx>
+
+"""
+# 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'
+
+# 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']
+    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'}
+
+    # 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)

analysis/license_map.py

@@ -0,0 +1,284 @@
+"""
+Interstate Cannabis Commerce
+Copyright (c) 2022 Cannlytics
+
+Authors:
+    Keegan Skeate <https://github.com/keeganskeate>
+Created: 9/22/2022
+Updated: 9/28/2022
+License: <https://github.com/cannlytics/cannabis-data-science/blob/main/LICENSE>
+
+Description: 
+
+    Map the adult-use cannabis retailers permitted in the United States.
+
+Data Sources (16):
+
+    - Alaska
+    URL: <>
+
+    - Arizona Department of Health Services | Division of Licensing
+    URL: <https://azcarecheck.azdhs.gov/s/?licenseType=null>
+
+    - Colorado
+    URL: <>
+
+    - Connecticut
+    URL: <>
+
+    - Illinois
+    URL: <>
+
+    - Maine
+    URL: <>
+
+    - Massachusetts
+    URL: <>
+
+    - Michigan
+    URL: <>
+
+    - Montana Department of Revenue | Cannabis Control Division
+    URL: <https://mtrevenue.gov/cannabis/#CannabisLicenses>
+
+    - New Mexico
+    URL: <https://nmrldlpi.force.com/bcd/s/public-search-license?division=CCD&language=en_US>
+    
+    - Nevada Cannabis Compliance Board | Nevada Cannabis Licensees
+    URL: <https://ccb.nv.gov/list-of-licensees/>
+
+    - New Jersey
+    URL: <>
+
+    - Oregon Liquor and Cannabis Commission
+    URL: <https://www.oregon.gov/olcc/marijuana/pages/recreational-marijuana-licensing.aspx>
+
+    - Rhode Island
+    URL: <>
+
+    - Vermont
+    URL: <>
+
+    - Washington
+    URL: <https://lcb.wa.gov/records/frequently-requested-lists>
+
+Coming Soon (3):
+    
+    - New York
+    - Virginia
+    - D.C.
+
+Medical (17):
+
+    - Utah
+    - Oklahoma
+    - North Dakota
+    - South Dakota
+    - Minnesota
+    - Missouri
+    - Arkansas
+    - Louisiana
+    - Mississippi
+    - Alabama
+    - Florida
+    - Ohio
+    - West Virginia
+    - Pennsylvania
+    - Maryland
+    - Delaware
+    - New Hampshire
+
+"""
+# Standard imports.
+
+# External imports.
+import folium
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+
+# Specify where your data lives.
+DATA_DIR = '../data'
+
+#-----------------------------------------------------------------------
+# Get the data.
+#-----------------------------------------------------------------------
+
+# California retailers.
+filename = f'{DATA_DIR}/ca/licenses-ca-2022-09-21T19-02-29.xlsx'
+ca_licenses = pd.read_excel(filename, index_col=0)
+
+# Alaska retailers.
+
+# Arizona retailers.
+
+# Colorado retailers.
+
+# Connecticut retailers.
+
+# Illinois retailers.
+
+# Maine retailers.
+
+# Massachusetts retailers.
+
+# Michigan retailers.
+
+# Montana retailers.
+
+# New Mexico retailers.
+
+# Nevada retailers.
+
+# New Jersey retailers.
+
+# Oregon retailers.
+filename = f'{DATA_DIR}/or/licenses-or-2022-09-28T10-11-12.xlsx'
+or_licenses = pd.read_excel(filename, index_col=0)
+
+# Rhode Island retailers.
+
+# Vermont retailers.
+
+# Washington retailers.
+
+
+#-----------------------------------------------------------------------
+# Look at the data!
+#-----------------------------------------------------------------------
+
+# Aggregate all of the retailer data.
+retailers = pd.concat([
+    ca_licenses,
+    or_licenses,
+])
+retailers = retailers.loc[
+    (~retailers['premise_longitude'].isnull()) &
+    (~retailers['premise_latitude'].isnull())
+]
+
+# Create a scatterplot of latitude and longitude with hue as license type.
+sns.scatterplot(
+    data=retailers,
+    x='premise_longitude',
+    y='premise_latitude',
+    hue='license_type',
+)
+plt.show()
+
+# Optional: Create a nice static map.
+
+# Create an interactive map.
+locations = retailers[['premise_latitude', 'premise_longitude']].to_numpy()
+m = folium.Map(
+    location=[45.5236, -122.6750],
+    zoom_start=4,
+    control_scale=True,
+)
+for index, row in retailers.iterrows():
+    folium.Circle(
+        radius=10,
+        location=[row['premise_latitude'], row['premise_longitude']],
+        color='crimson',
+    ).add_to(m)
+m.save('figures/cannabis-licenses-map.html')
+
+
+#-----------------------------------------------------------------------
+# Get supplementary data.
+#-----------------------------------------------------------------------
+
+from bs4 import BeautifulSoup
+from cannlytics.data.gis import get_state_population
+from cannlytics.utils.constants import state_names
+from dotenv import dotenv_values
+from fredapi import Fred
+import requests
+
+# Read your FRED API key.
+config = dotenv_values('../.env')
+fred_api_key = config['FRED_API_KEY']
+
+# Get the population for each state (in 2021).
+state_data = {}
+for state, abbv in state_names.items():
+    population = get_state_population(
+        abbv,
+        fred_api_key=fred_api_key,
+        obs_start='2021-01-01',
+    )
+    state_data[state] = {'population': population['population']}
+
+# Get the square miles of land for each state.
+url = 'https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_area'
+response = requests.get(url).text
+soup = BeautifulSoup(response, 'lxml')
+table = soup.find('table', class_='wikitable')
+for items in table.find_all('tr'):
+    data = items.find_all(['th', 'td'])
+    if data:
+        try:
+            rank = int(data[1].text)
+        except:
+            continue
+        state = data[0].text.replace('\n', '')
+        land_area = float(data[5].text.replace('\n', '').replace(',', ''))
+        state_data[state]['land_area_sq_mi']
+
+# Get the change in GDP for each state in 2022 Q1.
+code = 'NQGSP'
+fred = Fred(api_key=fred_api_key)
+for state, abbv in state_names.items():
+    try:
+        series = fred.get_series(abbv + code, '2021-10-01')
+    except ValueError:
+        continue
+    current, past = series[-1], series[-2]
+    change_gdp = ((current - past) / past) * 100
+    state_data[state]['change_gdp_2022_q1'] = change_gdp
+
+
+#-----------------------------------------------------------------------
+# Analyze the data.
+#-----------------------------------------------------------------------
+
+import statsmodels.api as sm
+
+# FIXME: Compile all of the state statistics.
+stats = pd.DataFrame()
+
+# TODO: Count the number of retailers by state.
+
+
+# TODO: Calculate retailers per capita (100,000) by state.
+
+
+# TODO: Calculate retailers per 100 square miles by state.
+
+
+# TODO: Create `adult_use` dummy variable. Assign 0 `retailers_per_capita`.
+
+
+# Regress GDP on adult-use status and retailers per capita.
+Y = stats['change_gdp_2022_q1']
+X = stats[['adult_use', 'retailers_per_capita']]
+X = sm.add_constant(X)
+regression = sm.OLS(Y, X).fit()
+print(regression.summary())
+
+# Interpret the relationships.
+beta = regression.params.adult_use
+statement = """If a state permitted adult-use at the start of 2022,
+then everything else held constant one would expect
+GDP in 2022 Q1 to change by {}.
+""".format(beta)
+print(statement)
+
+# Interpret the relationships.
+beta = regression.params.retailers_per_capita
+statement = """If retailers per 100,000 adults increases by 1,
+then everything else held constant one would expect
+GDP in 2022 Q1 to change by {}.
+""".format(beta)
+print(statement)

requirements.txt

@@ -0,0 +1,13 @@
+# Cannabis Licenses | Python Requirements
+# Created: 9/28/2022
+# Updated: 9/28/2022
+beautifulsoup4==4.11.1
+cannlytics==0.0.12
+firebase_admin==5.3.0
+folium==0.12.1.post1
+fredapi==0.5.0
+matplotlib==3.6.0
+pandas==1.4.4
+pdfplumber==0.7.4
+python-dotenv==0.21.0
+seaborn==0.12.0