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21 Commits

Author SHA1 Message Date
Cullen Watson
5b6a9943cc Merge pull request #42 from Bunsly/street_dirction
fix: add street direction
2023-11-08 16:53:29 -06:00
Cullen Watson
9816defaf3 chore: version 2023-11-08 16:53:05 -06:00
Cullen Watson
f692b438b2 fix: add street direction 2023-11-08 16:52:06 -06:00
Zachary Hampton
30f48f54c8 Update README.md 2023-11-06 22:13:01 -07:00
Cullen Watson
7f86f69610 docs: readme 2023-11-03 18:53:46 -05:00
Cullen Watson
cc64dacdb0 docs: readme - date_from, date_to 2023-11-03 18:52:22 -05:00
Cullen Watson
d3268d8e5a Merge pull request #40 from Bunsly/date_range
Add date_to and date_from params
2023-11-03 18:42:13 -05:00
Cullen Watson
4edad901c5 [enh] date_to and date_from 2023-11-03 18:40:34 -05:00
Zachary Hampton
c597a78191 - None address bug fix 2023-10-18 16:32:43 -07:00
Zachary Hampton
11a7d854f0 - remove pending listings from for_sale 2023-10-18 14:41:41 -07:00
Zachary Hampton
f726548cc6 Update pyproject.toml 2023-10-18 09:35:48 -07:00
Zachary Hampton
fad7d670eb Update README.md 2023-10-18 08:37:42 -07:00
Zachary Hampton
89a6f93c9f Update pyproject.toml 2023-10-18 08:37:26 -07:00
Zachary Hampton
e1090b06e4 Update README.md 2023-10-17 20:22:25 -07:00
Cullen Watson
5036e74b60 Merge branch 'master' of https://github.com/ZacharyHampton/HomeHarvest 2023-10-09 11:30:17 -05:00
Cullen Watson
2cb544bc8d [chore] display clickable URLs in jupyter 2023-10-09 11:28:56 -05:00
Zachary Hampton
68cb365e03 Merge pull request #34 from ZacharyHampton/days_on_mls
[enh] days_on_mls attr
2023-10-09 09:04:59 -07:00
Cullen Watson
23876d5725 [chore] function types 2023-10-09 11:02:51 -05:00
Cullen Watson
b59d55f6b5 [enh] days_on_mls attr 2023-10-09 11:00:36 -05:00
Cullen Watson
3c3adb5f29 [docs] update video 2023-10-05 20:24:23 -05:00
Zachary Hampton
6ede8622cc - pending listing support
- removal of pending_or_contingent param
2023-10-05 11:43:00 -07:00
11 changed files with 283 additions and 131 deletions

View File

@@ -6,9 +6,9 @@
**Not technical?** Try out the web scraping tool on our site at [tryhomeharvest.com](https://tryhomeharvest.com).
*Looking to build a data-focused software product?* **[Book a call](https://calendly.com/zachary-products/15min)** *to work with us.*
*Looking to build a data-focused software product?* **[Book a call](https://bunsly.com)** *to work with us.*
Check out another project we wrote: ***[JobSpy](https://github.com/cullenwatson/JobSpy)** a Python package for job scraping*
Check out another project we wrote: ***[JobSpy](https://github.com/Bunsly/JobSpy)** a Python package for job scraping*
## HomeHarvest Features
@@ -20,7 +20,7 @@ Check out another project we wrote: ***[JobSpy](https://github.com/cullenwatson/
- **CLI**: For users who prefer command-line operations.
[Video Guide for HomeHarvest](https://youtu.be/JnV7eR2Ve2o) - _updated for release v0.2.7_
[Video Guide for HomeHarvest](https://youtu.be/J1qgNPgmSLI) - _updated for release v0.3.4_
![homeharvest](https://github.com/ZacharyHampton/HomeHarvest/assets/78247585/b3d5d727-e67b-4a9f-85d8-1e65fd18620a)
@@ -45,9 +45,12 @@ filename = f"HomeHarvest_{current_timestamp}.csv"
properties = scrape_property(
location="San Diego, CA",
listing_type="sold", # or (for_sale, for_rent)
past_days=30, # sold in last 30 days - listed in last x days if (for_sale, for_rent)
# pending_or_contingent=True # use on for_sale listings to find pending / contingent listings
listing_type="sold", # or (for_sale, for_rent, pending)
past_days=30, # sold in last 30 days - listed in last 30 days if (for_sale, for_rent)
# date_from="2023-05-01", # alternative to past_days
# date_to="2023-05-28",
# mls_only=True, # only fetch MLS listings
# proxy="http://user:pass@host:port" # use a proxy to change your IP address
)
@@ -58,37 +61,6 @@ properties.to_csv(filename, index=False)
print(properties.head())
```
### CLI
```
usage: homeharvest [-l {for_sale,for_rent,sold}] [-o {excel,csv}] [-f FILENAME] [-p PROXY] [-d DAYS] [-r RADIUS] [-m] [-c] location
Home Harvest Property Scraper
positional arguments:
location Location to scrape (e.g., San Francisco, CA)
options:
-l {for_sale,for_rent,sold}, --listing_type {for_sale,for_rent,sold}
Listing type to scrape
-o {excel,csv}, --output {excel,csv}
Output format
-f FILENAME, --filename FILENAME
Name of the output file (without extension)
-p PROXY, --proxy PROXY
Proxy to use for scraping
-d DAYS, --days DAYS Sold/listed in last _ days filter.
-r RADIUS, --radius RADIUS
Get comparable properties within _ (e.g., 0.0) miles. Only applicable for individual addresses.
-m, --mls_only If set, fetches only MLS listings.
-c, --pending_or_contingent
If set, fetches only pending or contingent listings. Only applicable for for_sale listings from general area searches.
```
```bash
homeharvest "San Francisco, CA" -l for_rent -o excel -f HomeHarvest
```
## Output
```plaintext
@@ -110,6 +82,7 @@ Required
- 'for_rent'
- 'for_sale'
- 'sold'
- 'pending'
Optional
├── radius (decimal): Radius in miles to find comparable properties based on individual addresses.
@@ -117,14 +90,47 @@ Optional
├── past_days (integer): Number of past days to filter properties. Utilizes 'last_sold_date' for 'sold' listing types, and 'list_date' for others (for_rent, for_sale).
│ Example: 30 (fetches properties listed/sold in the last 30 days)
|
├── pending_or_contingent (True/False): If set, fetches only pending or contingent listings. Only applicable for `for_sale listings` from general area searches.
├── date_from, date_to (string): Start and end dates to filter properties listed or sold, both dates are required.
} (use this to get properties in chunks as there's a 10k result limit)
│ Format for both must be "YYYY-MM-DD".
│ Example: "2023-05-01", "2023-05-15" (fetches properties listed/sold between these dates)
├── mls_only (True/False): If set, fetches only MLS listings (mainly applicable to 'sold' listings)
└── proxy (string): In format 'http://user:pass@host:port'
```
### CLI
```
usage: homeharvest [-l {for_sale,for_rent,sold}] [-o {excel,csv}] [-f FILENAME] [-p PROXY] [-d DAYS] [-r RADIUS] [-m] [-c] location
Home Harvest Property Scraper
positional arguments:
location Location to scrape (e.g., San Francisco, CA)
options:
-l {for_sale,for_rent,sold,pending}, --listing_type {for_sale,for_rent,sold,pending}
Listing type to scrape
-o {excel,csv}, --output {excel,csv}
Output format
-f FILENAME, --filename FILENAME
Name of the output file (without extension)
-p PROXY, --proxy PROXY
Proxy to use for scraping
-d DAYS, --days DAYS Sold/listed in last _ days filter.
-r RADIUS, --radius RADIUS
Get comparable properties within _ (e.g., 0.0) miles. Only applicable for individual addresses.
-m, --mls_only If set, fetches only MLS listings.
```
```bash
homeharvest "San Francisco, CA" -l for_rent -o excel -f HomeHarvest
```
### Property Schema
```plaintext
Property
@@ -152,6 +158,7 @@ Property
│ └── lot_sqft
├── Property Listing Details:
│ ├── days_on_mls
│ ├── list_price
│ ├── list_date
│ ├── sold_price
@@ -171,7 +178,7 @@ Property
The following exceptions may be raised when using HomeHarvest:
- `InvalidListingType` - valid options: `for_sale`, `for_rent`, `sold`
- `NoResultsFound` - no properties found from your search
- `InvalidDate` - date_from or date_to is not in the format YYYY-MM-DD
## Frequently Asked Questions

View File

@@ -4,7 +4,9 @@
"cell_type": "code",
"execution_count": null,
"id": "cb48903e-5021-49fe-9688-45cd0bc05d0f",
"metadata": {},
"metadata": {
"is_executing": true
},
"outputs": [],
"source": [
"from homeharvest import scrape_property\n",
@@ -84,10 +86,34 @@
"outputs": [],
"source": [
"# check sold properties\n",
"scrape_property(\n",
"properties = scrape_property(\n",
" location=\"90210\",\n",
" listing_type=\"sold\"\n",
")"
" listing_type=\"sold\",\n",
" past_days=10\n",
")\n",
"display(properties)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "628c1ce2",
"metadata": {
"collapsed": false,
"is_executing": true,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"# display clickable URLs\n",
"from IPython.display import display, HTML\n",
"properties['property_url'] = '<a href=\"' + properties['property_url'] + '\" target=\"_blank\">' + properties['property_url'] + '</a>'\n",
"\n",
"html = properties.to_html(escape=False)\n",
"truncate_width = f'<style>.dataframe td {{ max-width: 200px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }}</style>{html}'\n",
"display(HTML(truncate_width))"
]
}
],

View File

@@ -1,10 +1,9 @@
import warnings
import pandas as pd
from .core.scrapers import ScraperInput
from .utils import process_result, ordered_properties, validate_input
from .utils import process_result, ordered_properties, validate_input, validate_dates
from .core.scrapers.realtor import RealtorScraper
from .core.scrapers.models import ListingType
from .exceptions import InvalidListingType, NoResultsFound
def scrape_property(
@@ -13,8 +12,9 @@ def scrape_property(
radius: float = None,
mls_only: bool = False,
past_days: int = None,
pending_or_contingent: bool = False,
proxy: str = None,
date_from: str = None,
date_to: str = None,
) -> pd.DataFrame:
"""
Scrape properties from Realtor.com based on a given location and listing type.
@@ -23,10 +23,11 @@ def scrape_property(
:param radius: Get properties within _ (e.g. 1.0) miles. Only applicable for individual addresses.
:param mls_only: If set, fetches only listings with MLS IDs.
:param past_days: Get properties sold or listed (dependent on your listing_type) in the last _ days.
:param pending_or_contingent: If set, fetches only pending or contingent listings. Only applicable for for_sale listings from general area searches.
:param date_from, date_to: Get properties sold or listed (dependent on your listing_type) between these dates. format: 2021-01-28
:param proxy: Proxy to use for scraping
"""
validate_input(listing_type)
validate_dates(date_from, date_to)
scraper_input = ScraperInput(
location=location,
@@ -35,7 +36,8 @@ def scrape_property(
radius=radius,
mls_only=mls_only,
last_x_days=past_days,
pending_or_contingent=pending_or_contingent,
date_from=date_from,
date_to=date_to,
)
site = RealtorScraper(scraper_input)
@@ -43,7 +45,7 @@ def scrape_property(
properties_dfs = [process_result(result) for result in results]
if not properties_dfs:
raise NoResultsFound("no results found for the query")
return pd.DataFrame()
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=FutureWarning)

View File

@@ -14,7 +14,7 @@ def main():
"--listing_type",
type=str,
default="for_sale",
choices=["for_sale", "for_rent", "sold"],
choices=["for_sale", "for_rent", "sold", "pending"],
help="Listing type to scrape",
)
@@ -60,13 +60,6 @@ def main():
help="If set, fetches only MLS listings.",
)
parser.add_argument(
"-c",
"--pending_or_contingent",
action="store_true",
help="If set, fetches only pending or contingent listings. Only applicable for for_sale listings from general area searches.",
)
args = parser.parse_args()
result = scrape_property(
@@ -76,7 +69,6 @@ def main():
proxy=args.proxy,
mls_only=args.mls_only,
past_days=args.days,
pending_or_contingent=args.pending_or_contingent,
)
if not args.filename:

View File

@@ -11,7 +11,8 @@ class ScraperInput:
mls_only: bool | None = None
proxy: str | None = None
last_x_days: int | None = None
pending_or_contingent: bool | None = None
date_from: str | None = None
date_to: str | None = None
class Scraper:
@@ -37,7 +38,8 @@ class Scraper:
self.radius = scraper_input.radius
self.last_x_days = scraper_input.last_x_days
self.mls_only = scraper_input.mls_only
self.pending_or_contingent = scraper_input.pending_or_contingent
self.date_from = scraper_input.date_from
self.date_to = scraper_input.date_to
def search(self) -> list[Property]:
...

View File

@@ -19,6 +19,7 @@ class SiteName(Enum):
class ListingType(Enum):
FOR_SALE = "FOR_SALE"
FOR_RENT = "FOR_RENT"
PENDING = "PENDING"
SOLD = "SOLD"
@@ -58,6 +59,7 @@ class Property:
last_sold_date: str | None = None
prc_sqft: int | None = None
hoa_fee: int | None = None
days_on_mls: int | None = None
description: Description | None = None
latitude: float | None = None

View File

@@ -4,11 +4,11 @@ homeharvest.realtor.__init__
This module implements the scraper for realtor.com
"""
from datetime import datetime
from typing import Dict, Union, Optional
from concurrent.futures import ThreadPoolExecutor, as_completed
from .. import Scraper
from ....exceptions import NoResultsFound
from ..models import Property, Address, ListingType, Description
@@ -18,7 +18,6 @@ class RealtorScraper(Scraper):
ADDRESS_AUTOCOMPLETE_URL = "https://parser-external.geo.moveaws.com/suggest"
def __init__(self, scraper_input):
self.counter = 1
super().__init__(scraper_input)
def handle_location(self):
@@ -38,7 +37,7 @@ class RealtorScraper(Scraper):
result = response_json["autocomplete"]
if not result:
raise NoResultsFound("No results found for location: " + self.location)
return None
return result[0]
@@ -50,6 +49,7 @@ class RealtorScraper(Scraper):
listing_id
}
address {
street_direction
street_number
street_name
street_suffix
@@ -105,11 +105,29 @@ class RealtorScraper(Scraper):
)
able_to_get_lat_long = (
property_info
and property_info.get("address")
and property_info["address"].get("location")
and property_info["address"]["location"].get("coordinate")
property_info
and property_info.get("address")
and property_info["address"].get("location")
and property_info["address"]["location"].get("coordinate")
)
list_date_str = property_info["basic"]["list_date"].split("T")[0] if property_info["basic"].get(
"list_date") else None
last_sold_date_str = property_info["basic"]["sold_date"].split("T")[0] if property_info["basic"].get(
"sold_date") else None
list_date = datetime.strptime(list_date_str, "%Y-%m-%d") if list_date_str else None
last_sold_date = datetime.strptime(last_sold_date_str, "%Y-%m-%d") if last_sold_date_str else None
today = datetime.now()
days_on_mls = None
status = property_info["basic"]["status"].lower()
if list_date:
if status == "sold" and last_sold_date:
days_on_mls = (last_sold_date - list_date).days
elif status in ('for_sale', 'for_rent'):
days_on_mls = (today - list_date).days
if days_on_mls and days_on_mls < 0:
days_on_mls = None
listing = Property(
mls=mls,
@@ -119,17 +137,13 @@ class RealtorScraper(Scraper):
property_url=f"{self.PROPERTY_URL}{property_info['details']['permalink']}",
status=property_info["basic"]["status"].upper(),
list_price=property_info["basic"]["price"],
list_date=property_info["basic"]["list_date"].split("T")[0]
if property_info["basic"].get("list_date")
else None,
list_date=list_date,
prc_sqft=property_info["basic"].get("price")
/ property_info["basic"].get("sqft")
/ property_info["basic"].get("sqft")
if property_info["basic"].get("price")
and property_info["basic"].get("sqft")
else None,
last_sold_date=property_info["basic"]["sold_date"].split("T")[0]
if property_info["basic"].get("sold_date")
and property_info["basic"].get("sqft")
else None,
last_sold_date=last_sold_date,
latitude=property_info["address"]["location"]["coordinate"].get("lat")
if able_to_get_lat_long
else None,
@@ -149,6 +163,7 @@ class RealtorScraper(Scraper):
garage=property_info["details"].get("garage"),
stories=property_info["details"].get("stories"),
),
days_on_mls=days_on_mls
)
return [listing]
@@ -201,6 +216,7 @@ class RealtorScraper(Scraper):
stories
}
address {
street_direction
street_number
street_name
street_suffix
@@ -274,6 +290,10 @@ class RealtorScraper(Scraper):
last_sold_date
list_price
price_per_sqft
flags {
is_contingent
is_pending
}
description {
sqft
beds
@@ -297,6 +317,7 @@ class RealtorScraper(Scraper):
}
location {
address {
street_direction
street_number
street_name
street_suffix
@@ -317,15 +338,17 @@ class RealtorScraper(Scraper):
}
}"""
date_param = (
'sold_date: { min: "$today-%sD" }' % self.last_x_days
if self.listing_type == ListingType.SOLD and self.last_x_days
else (
'list_date: { min: "$today-%sD" }' % self.last_x_days
if self.last_x_days
else ""
)
)
date_param = ""
if self.listing_type == ListingType.SOLD:
if self.date_from and self.date_to:
date_param = f'sold_date: {{ min: "{self.date_from}", max: "{self.date_to}" }}'
elif self.last_x_days:
date_param = f'sold_date: {{ min: "$today-{self.last_x_days}D" }}'
else:
if self.date_from and self.date_to:
date_param = f'list_date: {{ min: "{self.date_from}", max: "{self.date_to}" }}'
elif self.last_x_days:
date_param = f'list_date: {{ min: "$today-{self.last_x_days}D" }}'
sort_param = (
"sort: [{ field: sold_date, direction: desc }]"
@@ -335,17 +358,19 @@ class RealtorScraper(Scraper):
pending_or_contingent_param = (
"or_filters: { contingent: true, pending: true }"
if self.pending_or_contingent
if self.listing_type == ListingType.PENDING
else ""
)
listing_type = ListingType.FOR_SALE if self.listing_type == ListingType.PENDING else self.listing_type
if search_type == "comps": #: comps search, came from an address
query = """query Property_search(
$coordinates: [Float]!
$radius: String!
$offset: Int!,
) {
property_search(
home_search(
query: {
nearby: {
coordinates: $coordinates
@@ -353,13 +378,15 @@ class RealtorScraper(Scraper):
}
status: %s
%s
%s
}
%s
limit: 200
offset: $offset
) %s""" % (
self.listing_type.value.lower(),
listing_type.value.lower(),
date_param,
pending_or_contingent_param,
sort_param,
results_query,
)
@@ -385,7 +412,7 @@ class RealtorScraper(Scraper):
limit: 200
offset: $offset
) %s""" % (
self.listing_type.value.lower(),
listing_type.value.lower(),
date_param,
pending_or_contingent_param,
sort_param,
@@ -415,7 +442,7 @@ class RealtorScraper(Scraper):
response = self.session.post(self.SEARCH_GQL_URL, json=payload)
response.raise_for_status()
response_json = response.json()
search_key = "home_search" if search_type == "area" else "property_search"
search_key = "home_search" if "home_search" in query else "property_search"
properties: list[Property] = []
@@ -430,7 +457,6 @@ class RealtorScraper(Scraper):
return {"total": 0, "properties": []}
for result in response_json["data"][search_key]["results"]:
self.counter += 1
mls = (
result["source"].get("id")
if "source" in result and isinstance(result["source"], dict)
@@ -447,13 +473,18 @@ class RealtorScraper(Scraper):
and result["location"]["address"].get("coordinate")
)
is_pending = result["flags"].get("is_pending") or result["flags"].get("is_contingent")
if is_pending and self.listing_type != ListingType.PENDING:
continue
realty_property = Property(
mls=mls,
mls_id=result["source"].get("listing_id")
if "source" in result and isinstance(result["source"], dict)
else None,
property_url=f"{self.PROPERTY_URL}{result['property_id']}",
status=result["status"].upper(),
status="PENDING" if is_pending else result["status"].upper(),
list_price=result["list_price"],
list_date=result["list_date"].split("T")[0]
if result.get("list_date")
@@ -470,8 +501,8 @@ class RealtorScraper(Scraper):
if able_to_get_lat_long
else None,
address=self._parse_address(result, search_type="general_search"),
#: neighborhoods=self._parse_neighborhoods(result),
description=self._parse_description(result),
days_on_mls=self.calculate_days_on_mls(result)
)
properties.append(realty_property)
@@ -482,6 +513,9 @@ class RealtorScraper(Scraper):
def search(self):
location_info = self.handle_location()
if not location_info:
return []
location_type = location_info["area_type"]
search_variables = {
@@ -560,21 +594,29 @@ class RealtorScraper(Scraper):
return ", ".join(neighborhoods_list) if neighborhoods_list else None
@staticmethod
def _parse_address(result: dict, search_type):
def handle_none_safely(address_part):
if address_part is None:
return ""
return address_part
def _parse_address(self, result: dict, search_type):
if search_type == "general_search":
return Address(
street=f"{result['location']['address']['street_number']} {result['location']['address']['street_name']} {result['location']['address']['street_suffix']}",
unit=result["location"]["address"]["unit"],
city=result["location"]["address"]["city"],
state=result["location"]["address"]["state_code"],
zip=result["location"]["address"]["postal_code"],
)
address = result['location']['address']
else:
address = result["address"]
return Address(
street=f"{result['address']['street_number']} {result['address']['street_name']} {result['address']['street_suffix']}",
unit=result["address"]["unit"],
city=result["address"]["city"],
state=result["address"]["state_code"],
zip=result["address"]["postal_code"],
street=" ".join([
self.handle_none_safely(address.get('street_number')),
self.handle_none_safely(address.get('street_direction')),
self.handle_none_safely(address.get('street_name')),
self.handle_none_safely(address.get('street_suffix')),
]).strip(),
unit=address["unit"],
city=address["city"],
state=address["state_code"],
zip=address["postal_code"],
)
@staticmethod
@@ -582,7 +624,6 @@ class RealtorScraper(Scraper):
description_data = result.get("description", {})
if description_data is None or not isinstance(description_data, dict):
print("Warning: description_data is invalid!")
description_data = {}
style = description_data.get("type", "")
@@ -601,3 +642,22 @@ class RealtorScraper(Scraper):
garage=description_data.get("garage"),
stories=description_data.get("stories"),
)
@staticmethod
def calculate_days_on_mls(result: dict) -> Optional[int]:
list_date_str = result.get("list_date")
list_date = datetime.strptime(list_date_str.split("T")[0], "%Y-%m-%d") if list_date_str else None
last_sold_date_str = result.get("last_sold_date")
last_sold_date = datetime.strptime(last_sold_date_str, "%Y-%m-%d") if last_sold_date_str else None
today = datetime.now()
if list_date:
if result["status"] == 'sold':
if last_sold_date:
days = (last_sold_date - list_date).days
if days >= 0:
return days
elif result["status"] in ('for_sale', 'for_rent'):
days = (today - list_date).days
if days >= 0:
return days

View File

@@ -1,6 +1,5 @@
class InvalidListingType(Exception):
"""Raised when a provided listing type is does not exist."""
class NoResultsFound(Exception):
"""Raised when no results are found for the given location"""
class InvalidDate(Exception):
"""Raised when only one of date_from or date_to is provided or not in the correct format. ex: 2023-10-23 """

View File

@@ -1,6 +1,7 @@
from .core.scrapers.models import Property, ListingType
import pandas as pd
from .exceptions import InvalidListingType
from datetime import datetime
from .core.scrapers.models import Property, ListingType
from .exceptions import InvalidListingType, InvalidDate
ordered_properties = [
"property_url",
@@ -18,6 +19,7 @@ ordered_properties = [
"half_baths",
"sqft",
"year_built",
"days_on_mls",
"list_price",
"list_date",
"sold_price",
@@ -69,3 +71,18 @@ def validate_input(listing_type: str) -> None:
raise InvalidListingType(
f"Provided listing type, '{listing_type}', does not exist."
)
def validate_dates(date_from: str | None, date_to: str | None) -> None:
if (date_from is not None and date_to is None) or (date_from is None and date_to is not None):
raise InvalidDate("Both date_from and date_to must be provided.")
if date_from and date_to:
try:
date_from_obj = datetime.strptime(date_from, "%Y-%m-%d")
date_to_obj = datetime.strptime(date_to, "%Y-%m-%d")
if date_to_obj < date_from_obj:
raise InvalidDate("date_to must be after date_from.")
except ValueError as e:
raise InvalidDate(f"Invalid date format or range")

View File

@@ -1,9 +1,9 @@
[tool.poetry]
name = "homeharvest"
version = "0.3.3"
version = "0.3.9"
description = "Real estate scraping library supporting Zillow, Realtor.com & Redfin."
authors = ["Zachary Hampton <zachary@zacharysproducts.com>", "Cullen Watson <cullen@cullen.ai>"]
homepage = "https://github.com/ZacharyHampton/HomeHarvest"
homepage = "https://github.com/Bunsly/HomeHarvest"
readme = "README.md"
[tool.poetry.scripts]

View File

@@ -1,20 +1,15 @@
from homeharvest import scrape_property
from homeharvest.exceptions import (
InvalidListingType,
NoResultsFound,
)
def test_realtor_pending_or_contingent():
pending_or_contingent_result = scrape_property(
location="Surprise, AZ",
pending_or_contingent=True,
location="Surprise, AZ", listing_type="pending"
)
regular_result = scrape_property(
location="Surprise, AZ",
pending_or_contingent=False,
)
regular_result = scrape_property(location="Surprise, AZ", listing_type="for_sale")
assert all(
[
@@ -25,6 +20,45 @@ def test_realtor_pending_or_contingent():
assert len(pending_or_contingent_result) != len(regular_result)
def test_realtor_pending_comps():
pending_comps = scrape_property(
location="2530 Al Lipscomb Way",
radius=5,
past_days=180,
listing_type="pending",
)
for_sale_comps = scrape_property(
location="2530 Al Lipscomb Way",
radius=5,
past_days=180,
listing_type="for_sale",
)
sold_comps = scrape_property(
location="2530 Al Lipscomb Way",
radius=5,
past_days=180,
listing_type="sold",
)
results = [pending_comps, for_sale_comps, sold_comps]
assert all([result is not None for result in results])
#: assert all lengths are different
assert len(set([len(result) for result in results])) == len(results)
def test_realtor_sold_past():
result = scrape_property(
location="San Diego, CA",
past_days=30,
listing_type="sold",
)
assert result is not None and len(result) > 0
def test_realtor_comps():
result = scrape_property(
location="2530 Al Lipscomb Way",
@@ -50,6 +84,20 @@ def test_realtor_last_x_days_sold():
) and len(days_result_30) != len(days_result_10)
def test_realtor_date_range_sold():
days_result_30 = scrape_property(
location="Dallas, TX", listing_type="sold", date_from="2023-05-01", date_to="2023-05-28"
)
days_result_60 = scrape_property(
location="Dallas, TX", listing_type="sold", date_from="2023-04-01", date_to="2023-06-10"
)
assert all(
[result is not None for result in [days_result_30, days_result_60]]
) and len(days_result_30) < len(days_result_60)
def test_realtor_single_property():
results = [
scrape_property(
@@ -82,15 +130,12 @@ def test_realtor():
assert all([result is not None for result in results])
bad_results = []
try:
bad_results += [
scrape_property(
location="abceefg ju098ot498hh9",
listing_type="for_sale",
)
]
except (InvalidListingType, NoResultsFound):
def test_realtor_bad_address():
bad_results = scrape_property(
location="abceefg ju098ot498hh9",
listing_type="for_sale",
)
if len(bad_results) == 0:
assert True
assert all([result is None for result in bad_results])