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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 = ""
_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")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="train", version=VERSION, description="This part of my dataset covers the train set"),
datasets.BuilderConfig(name="validation", version=VERSION, description="This part of my dataset covers the validation set"),
]
DEFAULT_CONFIG_NAME = "train"
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
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