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feat: add pandas

pull/1/head
Cullen Watson 2023-09-17 18:30:37 -05:00
parent b76c659f94
commit 3697b7cf2d
9 changed files with 393 additions and 30 deletions
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3
.gitignore vendored
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@ -2,4 +2,5 @@
**/dist/
**/__pycache__/
**/.pytest_cache/
*.pyc
*.pyc
/.ipynb_checkpoints/

73
HomeHarvest_Demo.ipynb Normal file
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@ -0,0 +1,73 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "cb48903e-5021-49fe-9688-45cd0bc05d0f",
"metadata": {},
"outputs": [],
"source": [
"from homeharvest import scrape_property\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "156488ce-0d5f-43c5-87f4-c33e9c427860",
"metadata": {},
"outputs": [],
"source": [
"pd.set_option('display.max_columns', None) # Show all columns\n",
"pd.set_option('display.max_rows', None) # Show all rows\n",
"pd.set_option('display.width', None) # Auto-adjust display width to fit console\n",
"pd.set_option('display.max_colwidth', 50) # Limit max column width to 50 characters"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1c8b9744-8606-4e9b-8add-b90371a249a7",
"metadata": {},
"outputs": [],
"source": [
"scrape_property(\n",
" location=\"dallas\", site_name=\"zillow\", listing_type=\"for_sale\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab7b4c21-da1d-4713-9df4-d7425d8ce21e",
"metadata": {},
"outputs": [],
"source": [
"scrape_property(\n",
" location=\"dallas\", site_name=\"redfin\", listing_type=\"for_sale\"\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

68
homeharvest/__init__.py
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@ -1,10 +1,11 @@
from .core.scrapers.redfin import RedfinScraper
from .core.scrapers.realtor import RealtorScraper
from .core.scrapers.zillow import ZillowScraper
from .core.scrapers.models import ListingType, Property, Building
from .core.scrapers.models import ListingType, Property, Building, SiteName
from .core.scrapers import ScraperInput
from .exceptions import InvalidSite, InvalidListingType
from typing import Union
import pandas as pd
_scrapers = {
@ -18,7 +19,7 @@ def scrape_property(
location: str,
site_name: str,
listing_type: str = "for_sale", #: for_sale, for_rent, sold
) -> Union[list[Building], list[Property]]: #: eventually, return pandas dataframe
) -> Union[list[Building], list[Property]]:
if site_name.lower() not in _scrapers:
raise InvalidSite(f"Provided site, '{site_name}', does not exist.")
@ -30,8 +31,69 @@ def scrape_property(
scraper_input = ScraperInput(
location=location,
listing_type=ListingType[listing_type.upper()],
site_name=SiteName[site_name.upper()],
)
site = _scrapers[site_name.lower()](scraper_input)
results = site.search()
return site.search()
properties_dfs = []
for result in results:
prop_data = result.__dict__
address_data = prop_data["address"]
prop_data["site_name"] = prop_data["site_name"].value
prop_data["listing_type"] = prop_data["listing_type"].value
prop_data["property_type"] = prop_data["property_type"].value.lower()
prop_data["address_one"] = address_data.address_one
prop_data["city"] = address_data.city
prop_data["state"] = address_data.state
prop_data["zip_code"] = address_data.zip_code
prop_data["address_two"] = address_data.address_two
del prop_data["address"]
if isinstance(result, Property):
desired_order = [
"listing_type",
"address_one",
"city",
"state",
"zip_code",
"address_two",
"url",
"property_type",
"price",
"beds",
"baths",
"square_feet",
"price_per_square_foot",
"lot_size",
"stories",
"year_built",
"agent_name",
"mls_id",
"description",
]
elif isinstance(result, Building):
desired_order = [
"address_one",
"city",
"state",
"zip_code",
"address_two",
"url",
"num_units",
"min_unit_price",
"max_unit_price",
"avg_unit_price",
"listing_type",
]
properties_df = pd.DataFrame([prop_data])
properties_df = properties_df[desired_order]
properties_dfs.append(properties_df)
return pd.concat(properties_dfs, ignore_index=True)

5
homeharvest/core/scrapers/__init__.py
View File

@ -1,12 +1,13 @@
from dataclasses import dataclass
import requests
from .models import Property, ListingType
from .models import Property, ListingType, SiteName
@dataclass
class ScraperInput:
location: str
listing_type: ListingType
site_name: SiteName
proxy_url: str | None = None
@ -14,6 +15,8 @@ class Scraper:
def __init__(self, scraper_input: ScraperInput):
self.location = scraper_input.location
self.session = requests.Session()
self.listing_type = scraper_input.listing_type
self.site_name = scraper_input.site_name
if scraper_input.proxy_url:
self.session.proxies = {

53
homeharvest/core/scrapers/models.py
View File

@ -2,12 +2,43 @@ from dataclasses import dataclass
from enum import Enum
class SiteName(Enum):
ZILLOW = "zillow"
REDFIN = "redfin"
REALTOR = "realtor.com"
class ListingType(Enum):
FOR_SALE = "for_sale"
FOR_RENT = "for_rent"
SOLD = "sold"
class PropertyType(Enum):
HOUSE = "HOUSE"
CONDO = "CONDO"
TOWNHOUSE = "townhousE"
SINGLE_FAMILY = "SINGLE_FAMILY"
MULTI_FAMILY = "MULTI_FAMILY"
LAND = "LAND"
OTHER = "OTHER"
@classmethod
def from_int_code(cls, code):
mapping = {
1: cls.HOUSE,
2: cls.CONDO,
3: cls.TOWNHOUSE,
4: cls.MULTI_FAMILY,
5: cls.LAND,
6: cls.OTHER,
8: cls.SINGLE_FAMILY,
13: cls.SINGLE_FAMILY,
}
return mapping.get(code, cls.OTHER)
@dataclass
class Address:
address_one: str
@ -18,35 +49,35 @@ class Address:
address_two: str | None = None
@dataclass
class Property:
@dataclass()
class Realty:
site_name: SiteName
address: Address
url: str
listing_type: ListingType | None = None
@dataclass
class Property(Realty):
price: int | None = None
beds: int | None = None
baths: float | None = None
stories: int | None = None
agent_name: str | None = None
year_built: int | None = None
square_feet: int | None = None
price_per_square_foot: int | None = None
year_built: int | None = None
price: int | None = None
mls_id: str | None = None
listing_type: ListingType | None = None
agent_name: str | None = None
property_type: PropertyType | None = None
lot_size: int | None = None
description: str | None = None
@dataclass
class Building:
address: Address
url: str
class Building(Realty):
num_units: int | None = None
min_unit_price: int | None = None
max_unit_price: int | None = None
avg_unit_price: int | None = None
listing_type: str | None = None

12
homeharvest/core/scrapers/redfin/__init__.py
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@ -1,5 +1,5 @@
import json
from ..models import Property, Address
from ..models import Property, Address, PropertyType
from .. import Scraper
from typing import Any
@ -7,6 +7,7 @@ from typing import Any
class RedfinScraper(Scraper):
def __init__(self, scraper_input):
super().__init__(scraper_input)
self.listing_type = scraper_input.listing_type
def _handle_location(self):
url = "https://www.redfin.com/stingray/do/location-autocomplete?v=2&al=1&location={}".format(
@ -31,8 +32,7 @@ class RedfinScraper(Scraper):
return target["id"].split("_")[1], get_region_type(target["type"])
@staticmethod
def _parse_home(home: dict, single_search: bool = False) -> Property:
def _parse_home(self, home: dict, single_search: bool = False) -> Property:
def get_value(key: str) -> Any | None:
if key in home and "value" in home[key]:
return home[key]["value"]
@ -53,10 +53,12 @@ class RedfinScraper(Scraper):
state=home["state"],
zip_code=home["zip"],
)
url = "https://www.redfin.com{}".format(home["url"])
property_type = home["propertyType"] if "propertyType" in home else None
return Property(
site_name=self.site_name,
listing_type=self.listing_type,
address=address,
url=url,
beds=home["beds"] if "beds" in home else None,
@ -68,6 +70,8 @@ class RedfinScraper(Scraper):
if not single_search
else home["yearBuilt"],
square_feet=get_value("sqFt"),
lot_size=home.get("lotSize", {}).get("value", None),
property_type=PropertyType.from_int_code(home.get("propertyType")),
price_per_square_foot=get_value("pricePerSqFt"),
price=get_value("price"),
mls_id=get_value("mlsId"),

23
homeharvest/core/scrapers/zillow/__init__.py
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@ -1,13 +1,11 @@
import re
import json
from ..models import Property, Address, Building, ListingType
from ..models import Property, Address, Building, ListingType, PropertyType
from ....exceptions import NoResultsFound, PropertyNotFound
from .. import Scraper
class ZillowScraper(Scraper):
listing_type: ListingType.FOR_SALE
def __init__(self, scraper_input):
super().__init__(scraper_input)
self.listing_type = scraper_input.listing_type
@ -65,15 +63,17 @@ class ZillowScraper(Scraper):
agent_name = self._extract_agent_name(home)
beds = home["hdpData"]["homeInfo"]["bedrooms"]
baths = home["hdpData"]["homeInfo"]["bathrooms"]
listing_type = home["hdpData"]["homeInfo"].get("homeType")
property_type = home["hdpData"]["homeInfo"].get("homeType")
return Property(
site_name=self.site_name,
address=address,
agent_name=agent_name,
url=url,
beds=beds,
baths=baths,
listing_type=listing_type,
listing_type=self.listing_type,
property_type=PropertyType(property_type),
**price_data,
)
else:
@ -83,10 +83,11 @@ class ZillowScraper(Scraper):
address = Address(address_one, city, state, zip_code, address_two)
building_info = self._extract_building_info(home)
return Building(address=address, url=url, **building_info)
return Building(
site_name=self.site_name, address=address, url=url, **building_info
)
@classmethod
def _get_single_property_page(cls, property_data: dict):
def _get_single_property_page(self, property_data: dict):
"""
This method is used when a user enters the exact location & zillow returns just one property
"""
@ -104,8 +105,11 @@ class ZillowScraper(Scraper):
state=address_data["state"],
zip_code=address_data["zipcode"],
)
property_type = property_data.get("homeType", None)
print(property_type)
return Property(
site_name=self.site_name,
address=address,
url=url,
beds=property_data.get("bedrooms", None),
@ -121,7 +125,8 @@ class ZillowScraper(Scraper):
"pricePerSquareFoot", None
),
square_feet=property_data.get("livingArea", None),
listing_type=property_data.get("homeType", None),
property_type=PropertyType(property_type),
listing_type=self.listing_type,
)
def _extract_building_info(self, home: dict) -> dict:

185
poetry.lock generated
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@ -142,6 +142,81 @@ files = [
{file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
]
[[package]]
name = "numpy"
version = "1.25.2"
description = "Fundamental package for array computing in Python"
optional = false
python-versions = ">=3.9"
files = [
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@ -190,6 +326,31 @@ tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
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1
pyproject.toml
View File

@ -9,6 +9,7 @@ readme = "README.md"
[tool.poetry.dependencies]
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requests = "^2.31.0"
pandas = "^2.1.0"
[tool.poetry.group.dev.dependencies]