Datasets:

HathawayLiu
/

housing_dataset

Languages:

English

Size Categories:

Tags:

Dataset card Viewer Files Files and versions Community

housing_dataset / Housing_dataset.py

HathawayLiu

Rename housing_dataset.py to Housing_dataset.py

7a7a3b6 verified 2 months ago

raw history blame contribute delete

No virus

6.9 kB

	#
	# 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
	import pandas as pd

	_CITATION = """
	@InProceedings{huggingface:dataset,
	title = {Seattle Housing Permits Dataset},
	author={Xinyan(Hathaway) Liu
	},
	year={2024}
	}
	"""

	_DESCRIPTION = """
	This typical dataset contains all the building permits issued or in progress
	within the city of Seattle starting from 1990 to recent, and this dataset is
	still updating as time flows. Information includes permit records urls,
	detailed address, and building costs etc.
	"""

	_HOMEPAGE = "https://data.seattle.gov/Permitting/Building-Permits/76t5-zqzr/about_data"

	_LICENSE = " http://www.seattle.gov/sdci"

	_URL = "https://data.seattle.gov/Permitting/Building-Permits/76t5-zqzr/about_data"
	_URLS = {
	"train": "https://github.com/HathawayLiu/Housing_dataset/raw/main/housing_train_dataset.csv",
	"test": "https://github.com/HathawayLiu/Housing_dataset/raw/main/housing_test_dataset.csv",
	}

	class HousingDataset(datasets.GeneratorBasedBuilder):
	"""This dataset contains all building permits issued or in progress within
	the city of Seattle. It includes the original columns in the datasets, with
	new added columns for corresponding neighborhood district and parking lot
	near by each housing."""

	_URLS = _URLS
	VERSION = datasets.Version("1.1.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	# columns from original dataset
	"PermitNum": datasets.Value("string"),
	"PermitClass": datasets.Value("string"),
	"PermitClassMapped": datasets.Value("string"),
	"PermitTypeMapped": datasets.Value("string"),
	"PermitTypeDesc": datasets.Value("string"),
	"Description": datasets.Value("string"),
	"HousingUnits": datasets.Value("int64"),
	"HousingUnitsRemoved": datasets.Value("int64"),
	"HousingUnitsAdded": datasets.Value("int64"),
	"EstProjectCost": datasets.Value("float32"),
	"AppliedDate": datasets.Value("string"),
	"IssuedDate": datasets.Value("string"),
	"ExpiresDate": datasets.Value("string"),
	"CompletedDate": datasets.Value("string"),
	"StatusCurrent": datasets.Value("string"),
	"RelatedMup": datasets.Value("string"),
	"OriginalAddress1": datasets.Value("string"),
	"OriginalCity": datasets.Value("string"),
	"OriginalState": datasets.Value("string"),
	"OriginalZip": datasets.Value("int64"),
	"ContractorCompanyName": datasets.Value("string"),
	"Link": datasets.Value("string"),
	"Latitude": datasets.Value("float32"),
	"Longitude": datasets.Value("float32"),
	"Location1": datasets.Value("string"),
	# new added columns below
	"NeighborDistrict": datasets.Value("string")
	}
	),
	# No default supervised_keys (as we have to pass both question
	# and context as input).
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
	urls = self._URLS
	downloaded_files = dl_manager.download_and_extract(urls)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""
	logging.info("generating examples from = %s", filepath)
	with open(filepath) as f:
	housing_df = pd.read_csv(f)

	housing_df['EstProjectCost'] = housing_df["EstProjectCost"].replace('NA', 0)
	housing_df.dropna(subset = ['Latitude'], inplace = True)
	housing_df.dropna(subset = ['OriginalZip'], inplace = True)

	housing_df['Latitude'] = housing_df['Latitude'].astype(float)
	housing_df['Longitude'] = housing_df['Longitude'].astype(float)

	# Iterating through each row to generate examples
	for index, row in housing_df.iterrows():
	yield index, {
	"PermitNum": row.get("PermitNum", ""),
	"PermitClass": row.get("PermitClass", ""),
	"PermitClassMapped": row.get("PermitClassMapped", ""),
	"PermitTypeMapped": row.get("PermitTypeMapped", ""),
	"PermitTypeDesc": row.get("PermitTypeDesc", ""),
	"Description": row.get("Description", ""),
	"HousingUnits": int(row.get("HousingUnits", "")),
	"HousingUnitsRemoved": int(row.get("HousingUnitsRemoved", "")),
	"HousingUnitsAdded": int(row.get("HousingUnitsAdded", "")),
	"EstProjectCost": float(row.get("EstProjectCost", "")),
	"AppliedDate": str(row.get("AppliedDate", "")),
	"IssuedDate": str(row.get("IssuedDate", "")),
	"ExpiresDate": str(row.get("ExpiresDate", "")),
	"CompletedDate": str(row.get("CompletedDate", "")),
	"StatusCurrent": row.get("StatusCurrent", ""),
	"RelatedMup": row.get("RelatedMup", ""),
	"OriginalAddress1": row.get("OriginalAddress1", ""),
	"OriginalCity": row.get("OriginalCity", ""),
	"OriginalState": row.get("OriginalState", ""),
	"OriginalZip": int(row.get("OriginalZip", "")),
	"ContractorCompanyName": row.get("ContractorCompanyName", ""),
	"Link": row.get("Link", ""),
	"Latitude": row["Latitude"],
	"Longitude": row["Longitude"],
	"Location1": str(row["Latitude"]) + ", " + str(row["Longitude"]),
	"NeighborDistrict": row.get("NeighborDistrict", "")
	}