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""" |
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Cannabis Licenses |
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Copyright (c) 2022-2023 Cannlytics |
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Authors: |
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Keegan Skeate <https://github.com/keeganskeate> |
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Candace O'Sullivan-Sutherland <https://github.com/candy-o> |
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Created: 9/29/2022 |
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Updated: 9/30/2023 |
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License: <https://huggingface.co/datasets/cannlytics/cannabis_licenses/blob/main/LICENSE> |
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""" |
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import datasets |
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import pandas as pd |
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_SCRIPT = 'cannabis_licenses.py' |
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_VERSION = '1.0.2' |
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_HOMEPAGE = 'https://huggingface.co/datasets/cannlytics/cannabis_licenses' |
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_LICENSE = "https://opendatacommons.org/licenses/by/4-0/" |
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_DESCRIPTION = """\ |
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Cannabis Licenses is a dataset of curated cannabis license data. The dataset consists of sub-datasets for each state with permitted adult-use cannabis, as well as a sub-dataset that includes all licenses. |
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""" |
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_CITATION = """\ |
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@inproceedings{cannlytics2023cannabis_licenses, |
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author = {Skeate, Keegan and O'Sullivan-Sutherland, Candace}, |
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title = {Cannabis Licenses}, |
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booktitle = {Cannabis Data Science}, |
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month = {August}, |
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year = {2023}, |
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address = {United States of America}, |
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publisher = {Cannlytics} |
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} |
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""" |
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SUBSETS = [ |
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'all', |
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'ak', |
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'az', |
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'ca', |
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'co', |
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'ct', |
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'il', |
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'ma', |
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'md', |
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'me', |
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'mi', |
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'mo', |
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'mt', |
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'nj', |
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'nm', |
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'ny', |
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'nv', |
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'or', |
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'ri', |
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'vt', |
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'wa', |
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] |
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FIELDS = datasets.Features({ |
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'id': datasets.Value(dtype='string'), |
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'license_number': datasets.Value(dtype='string'), |
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'license_status': datasets.Value(dtype='string'), |
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'license_status_date': datasets.Value(dtype='string'), |
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'license_term': datasets.Value(dtype='string'), |
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'license_type': datasets.Value(dtype='string'), |
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'license_designation': datasets.Value(dtype='string'), |
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'issue_date': datasets.Value(dtype='string'), |
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'expiration_date': datasets.Value(dtype='string'), |
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'licensing_authority_id': datasets.Value(dtype='string'), |
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'licensing_authority': datasets.Value(dtype='string'), |
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'business_legal_name': datasets.Value(dtype='string'), |
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'business_dba_name': datasets.Value(dtype='string'), |
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'business_image_url': datasets.Value(dtype='string'), |
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'business_owner_name': datasets.Value(dtype='string'), |
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'business_structure': datasets.Value(dtype='string'), |
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'business_website': datasets.Value(dtype='string'), |
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'activity': datasets.Value(dtype='string'), |
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'premise_street_address': datasets.Value(dtype='string'), |
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'premise_city': datasets.Value(dtype='string'), |
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'premise_state': datasets.Value(dtype='string'), |
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'premise_county': datasets.Value(dtype='string'), |
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'premise_zip_code': datasets.Value(dtype='string'), |
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'business_email': datasets.Value(dtype='string'), |
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'business_phone': datasets.Value(dtype='string'), |
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'parcel_number': datasets.Value(dtype='string'), |
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'premise_latitude': datasets.Value(dtype='string'), |
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'premise_longitude': datasets.Value(dtype='string'), |
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'data_refreshed_date': datasets.Value(dtype='string'), |
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}) |
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class CannabisLicensesConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Cannabis Licenses.""" |
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def __init__(self, name, **kwargs): |
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"""BuilderConfig for Cannabis Licenses. |
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Args: |
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name (str): Configuration name that determines setup. |
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**kwargs: Keyword arguments forwarded to super. |
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""" |
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description = _DESCRIPTION |
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description += f'This configuration is for the `{name}` subset.' |
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super().__init__( |
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data_dir='data', |
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description=description, |
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name=name, |
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**kwargs, |
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) |
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class CannabisLicenses(datasets.GeneratorBasedBuilder): |
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"""The Cannabis Licenses dataset.""" |
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VERSION = datasets.Version(_VERSION) |
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BUILDER_CONFIG_CLASS = CannabisLicensesConfig |
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BUILDER_CONFIGS = [CannabisLicensesConfig(s) for s in SUBSETS] |
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DEFAULT_CONFIG_NAME = 'all' |
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def _info(self): |
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"""Returns the dataset metadata.""" |
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return datasets.DatasetInfo( |
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features=FIELDS, |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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description=_DESCRIPTION, |
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license=_LICENSE, |
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version=_VERSION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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subset = self.config.name |
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data_url = f'data/{subset}/licenses-{subset}-latest.csv' |
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urls = {subset: data_url} |
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downloaded_files = dl_manager.download_and_extract(urls) |
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params = {'filepath': downloaded_files[subset]} |
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return [datasets.SplitGenerator(name='data', gen_kwargs=params)] |
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def _generate_examples(self, filepath): |
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"""Returns the examples in raw text form.""" |
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df = pd.read_csv(filepath) |
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for col in FIELDS.keys(): |
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if col not in df.columns: |
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df[col] = '' |
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df = df[list(FIELDS.keys())] |
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df.fillna('', inplace=True) |
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for index, row in df.iterrows(): |
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obs = row.to_dict() |
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yield index, obs |
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if __name__ == '__main__': |
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from datasets import load_dataset |
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for subset in SUBSETS: |
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dataset = load_dataset(_SCRIPT, subset) |
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data = dataset['data'] |
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assert len(data) > 0 |
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print('Read %i %s data points.' % (len(data), subset)) |
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