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tidytuesday_for_python

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tidytuesday_for_python / tidytuesday_for_python.py

hollyyfc

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	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.


	import csv
	import json
	import os
	from typing import List
	import datasets
	import logging


	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@InProceedings{huggingface:dataset,
	title = {TidyTuesday for Python},
	author={Holly Cui
	},
	year={2024}
	}
	"""


	_DESCRIPTION = """\
	This dataset compiles TidyTuesday datasets from 2023-2024, aiming to make resources in the R community more accessible for Python users.
	"""


	_HOMEPAGE = "https://huggingface.co/datasets/hollyyfc/tidytuesday_for_python"


	_LICENSE = ""


	_URLS = {
	"full": "https://raw.githubusercontent.com/hollyyfc/tidytuesday-for-python/main/tidytuesday_json.json",
	"train": "https://raw.githubusercontent.com/hollyyfc/tidytuesday-for-python/main/tidytuesday_json_train.json",
	"validation": "https://raw.githubusercontent.com/hollyyfc/tidytuesday-for-python/main/tidytuesday_json_val.json"
	}


	class TidyTuesdayPython(datasets.GeneratorBasedBuilder):

	_URLS = _URLS
	VERSION = datasets.Version("1.1.0")


	def _info(self):

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"date_posted": datasets.Value("string"),
	"project_name": datasets.Value("string"),
	"project_source": datasets.features.Sequence(datasets.Value("string")),
	"description": datasets.Value("string"),
	"data_source_url": datasets.Value("string"),
	"data_dictionary": datasets.features.Sequence(
	{
	"variable": datasets.Value("string"),
	"class": datasets.Value("string"),
	"description": datasets.Value("string"),
	}
	),
	"data": datasets.features.Sequence(
	{
	"file_name": datasets.Value("string"),
	"file_url": datasets.Value("string"),
	}
	),
	"data_load": datasets.features.Sequence(
	{
	"file_name": datasets.Value("string"),
	"file_url": datasets.Value("string"),
	}
	),
	}
	),
	# No default supervised_keys (as we have to pass both premise
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
	urls_to_download = self._URLS
	downloaded_files = dl_manager.download_and_extract(urls_to_download)
	return [
	datasets.SplitGenerator(
	name="full",
	gen_kwargs={
	"filepath": downloaded_files["full"]
	}
	),
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": downloaded_files["train"]
	}
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": downloaded_files["validation"]
	}
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath):
	logging.info("generating examples from = %s", filepath)
	with open(filepath, "r") as j:
	tidytuesday_json = json.load(j)

	for record in tidytuesday_json:
	id_ = record['date_posted']
	yield id_, record
	'''
	yield id_, {
	"project_name": record["project_name"],
	"project_source": record["project_source"],
	"description": record["description"],
	"data_source_url": record["data_source_url"],
	"data_dictionary": record["data_dictionary"],
	"data": record["data"],
	}
	'''