Datasets:

kraina
/

airbnb

	import datasets
	from enum import Enum
	from dataclasses import dataclass
	from typing import List
	import pandas as pd

	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@dataset{gyodi_kristof_2021_4446043,
	author = {Gyódi, Kristóf and
	Nawaro, Łukasz},
	title = {{Determinants of Airbnb prices in European cities:
	A spatial econometrics approach (Supplementary
	Material)}},
	month = jan,
	year = 2021,
	note = {{This research was supported by National Science
	Centre, Poland: Project number 2017/27/N/HS4/00951}},
	publisher = {Zenodo},
	doi = {10.5281/zenodo.4446043},
	url = {https://doi.org/10.5281/zenodo.4446043}
	}"""

	_DESCRIPTION = """\
	This dataset contains accommodation offers from the AirBnb platform from 10 European cities.
	It has been copied from https://zenodo.org/record/4446043#.ZEV8d-zMI-R to make it available as a Huggingface Dataset.
	It was originally published as supplementary material for the article: Determinants of Airbnb prices in European cities: A spatial econometrics approach
	(DOI: https://doi.org/10.1016/j.tourman.2021.104319)"""

	_CITIES = [
	"Amsterdam",
	"Athens",
	"Barcelona",
	"Berlin",
	"Budapest",
	"Lisbon",
	"London",
	"Paris",
	"Rome",
	"Vienna"
	]

	_BASE_URL = "data/"
	_URL_TEMPLATE = _BASE_URL + "{city}_{day_type}.csv"

	class DayType(str, Enum):
	WEEKDAYS = "weekdays"
	WEEKENDS = "weekends"


	@dataclass
	class AirbnbFile:
	"""A file from the Airbnb dataset."""

	city: str
	day_type: DayType
	@property
	def url(self) -> str:
	return _URL_TEMPLATE.format(city=self.city.lower(), day_type=self.day_type.value)



	class AirbnbConfig(datasets.BuilderConfig):
	"""BuilderConfig for Airbnb."""

	def __init__(self, files: List[AirbnbFile], **kwargs):
	"""BuilderConfig for Airbnb.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(AirbnbConfig, self).__init__(**kwargs)
	self.files = files

	_WEEKDAY_FILES = [AirbnbFile(city=city, day_type=DayType.WEEKDAYS) for city in _CITIES]
	_WEEKEND_FILES = [AirbnbFile(city=city, day_type=DayType.WEEKENDS) for city in _CITIES]

	_DATASET_VERSION = "2.0.0"
	class Airbnb(datasets.GeneratorBasedBuilder):
	""""""

	BUILDER_CONFIGS = [
	AirbnbConfig(
	name=DayType.WEEKDAYS.value,
	files=_WEEKDAY_FILES,
	version=datasets.Version(_DATASET_VERSION),
	),
	AirbnbConfig(
	name=DayType.WEEKENDS.value,
	files=_WEEKEND_FILES,
	version=datasets.Version(_DATASET_VERSION),
	),
	AirbnbConfig(
	name="all",
	files=_WEEKDAY_FILES + _WEEKEND_FILES,
	version=datasets.Version(_DATASET_VERSION),
	),
	]

	def _info(self):
	features = datasets.Features(
	{
	"_id": datasets.Value("string"),
	"city": datasets.Value("string"),
	"realSum": datasets.Value(dtype="float64"),
	"room_type": datasets.Value(dtype="string"),
	"room_shared": datasets.Value(dtype="bool"),
	"room_private": datasets.Value(dtype="bool"),
	"person_capacity": datasets.Value(dtype="float64"),
	"host_is_superhost": datasets.Value(dtype="bool"),
	"multi": datasets.Value(dtype="int64"),
	"biz": datasets.Value(dtype="int64"),
	"cleanliness_rating": datasets.Value(dtype="float64"),
	"guest_satisfaction_overall": datasets.Value(dtype="float64"),
	"bedrooms": datasets.Value(dtype="int64"),
	"dist": datasets.Value(dtype="float64"),
	"metro_dist": datasets.Value(dtype="float64"),
	"attr_index": datasets.Value(dtype="float64"),
	"attr_index_norm": datasets.Value(dtype="float64"),
	"rest_index": datasets.Value(dtype="float64"),
	"rest_index_norm": datasets.Value(dtype="float64"),
	"lng": datasets.Value(dtype="float64"),
	"lat": datasets.Value(dtype="float64")
	})
	if self.config.name == "all":
	features["day_type"] = datasets.Value(dtype="string")

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage="https://zenodo.org/record/4446043#.ZEV8d-zMI-R",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	config_files: List[AirbnbFile] = self.config.files
	urls = [file.url for file in config_files]
	downloaded_files = dl_manager.download_and_extract(urls)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"paths": downloaded_files})
	]

	def _generate_examples(self, paths: List[str]):
	_id = 0
	config_files: List[AirbnbFile] = self.config.files
	include_day_type = self.config.name == "all"
	for file, path in zip(config_files, paths):
	logger.info("generating examples from = %s", path)
	df = pd.read_csv(path, index_col=0, header=0)
	for row in df.itertuples():
	city = file.city
	data = {
	"_id": _id,
	"city": city,
	"realSum": row.realSum,
	"room_type": row.room_type,
	"room_shared": row.room_shared,
	"room_private": row.room_private,
	"person_capacity": row.person_capacity,
	"host_is_superhost": row.host_is_superhost,
	"multi": row.multi,
	"biz": row.biz,
	"cleanliness_rating": row.cleanliness_rating,
	"guest_satisfaction_overall": row.guest_satisfaction_overall,
	"bedrooms": row.bedrooms,
	"dist": row.dist,
	"metro_dist": row.metro_dist,
	"attr_index": row.attr_index,
	"attr_index_norm": row.attr_index_norm,
	"rest_index": row.rest_index,
	"rest_index_norm": row.rest_index_norm,
	"lng": row.lng,
	"lat": row.lat
	}
	if include_day_type:
	data["day_type"] = file.day_type.value
	yield _id, data
	_id += 1