Upload tidytuesday_script.py

tidytuesday_script.py

@@ -0,0 +1,122 @@
+# 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