File size: 4,179 Bytes
b638057 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
# 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
|