tidytuesday_for_python.py

# 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 = ""


_LICENSE = ""


_URLS = {
    "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"),
                            "load_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=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"],
                }
                '''