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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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
import csv
import json
import os
from typing import List
import datasets
import logging
import pandas as pd
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {NC Crime Dataset},
author={huggingface, Inc.
},
year={2024}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
The dataset, compiled from public police incident reports across various cities in North Carolina, covers a period from the early 2000s through to 2024. It is intended to facilitate the study of crime trends and patterns.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = ""
_URLS = ""
class NCCrimeDataset(datasets.GeneratorBasedBuilder):
"""Dataset for North Carolina Crime Incidents."""
_URLS = _URLS
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"year": datasets.Value("int64"),
"city": datasets.Value("string"),
"crime_major_category": datasets.Value("string"),
"crime_detail": datasets.Value("string"),
"latitude": datasets.Value("float64"),
"longitude": datasets.Value("float64"),
"occurance_time": datasets.Value("string"),
"clear_status": datasets.Value("string"),
"incident_address": datasets.Value("string"),
"notes": datasets.Value("string"),
}),
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
# Use the raw GitHub link to download the CSV file
downloaded_file_path = dl_manager.download_and_extract(
"https://raw.githubusercontent.com/zening-wang2023/NC-Crime-Dataset/main/Cary_new.csv")
# Return a list of split generators
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file_path})
]
def _generate_examples(self, filepath):
# Read the CSV file
df = pd.read_csv(filepath) ## just for test
# Iterate over the rows and yield examples
for i, row in df.iterrows():
yield i, {
"year": int(row["year"]),
"city": row["city"],
"crime_major_category": row["crime_major_category"],
"crime_detail": row["crime_detail"],
"latitude": float(row["latitude"]),
"longitude": float(row["longitude"]),
"occurance_time": row["occurance_time"],
"clear_status": row["clear_status"],
"incident_address": row["incident_address"],
"notes": row["notes"],
}
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