Delete tidytuesday_script.py

tidytuesday_script.py

@@ -1,122 +0,0 @@
-# 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()
-            for record in tidytuesday_json:
-              yield record