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README.md
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README.md
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# HomeHarvest
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<img src="https://github.com/ZacharyHampton/HomeHarvest/assets/78247585/d1a2bf8b-09f5-4c57-b33a-0ada8a34f12d" width="400">
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**HomeHarvest** is a simple but comprehensive real estate scraping library.
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**HomeHarvest** is a simple, yet comprehensive, real estate scraping library.
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[![Try with Replit](https://replit.com/badge?caption=Try%20with%20Replit)](https://replit.com/@ZacharyHampton/HomeHarvestDemo)
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[![Try with Replit](https://replit.com/badge?caption=Try%20with%20Replit)](https://replit.com/@ZacharyHampton/HomeHarvestDemo)
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*Looking to build a data-focused software product?* **[Book a call](https://calendly.com/zachary-products/15min)** *to work with us.*
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*Looking to build a data-focused software product?* **[Book a call](https://calendly.com/zachary-products/15min)** *to work with us.*
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## Features
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## Features
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- Scrapes properties from **Zillow**, **Realtor.com** & **Redfin** simultaneously
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- Scrapes properties from **Zillow**, **Realtor.com** & **Redfin** simultaneously
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- Aggregates the properties in a Pandas DataFrame
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- Aggregates the properties in a Pandas DataFrame
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@ -32,13 +32,11 @@ properties: pd.DataFrame = scrape_property(
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#: Note, to export to CSV or Excel, use properties.to_csv() or properties.to_excel().
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#: Note, to export to CSV or Excel, use properties.to_csv() or properties.to_excel().
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print(properties)
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print(properties)
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```
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```
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## Output
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## Output
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```py
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```py
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>> properties.head()
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>>> properties.head()
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street city ... mls_id description
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street city ... mls_id description
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0 420 N Scottsdale Rd Tempe ... NaN NaN
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0 420 N Scottsdale Rd Tempe ... NaN NaN
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1 1255 E University Dr Tempe ... NaN NaN
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1 1255 E University Dr Tempe ... NaN NaN
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2 1979 E Rio Salado Pkwy Tempe ... NaN NaN
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2 1979 E Rio Salado Pkwy Tempe ... NaN NaN
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@ -118,7 +116,7 @@ The following exceptions may be raised when using HomeHarvest:
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- `InvalidSite` - valid options: `zillow`, `redfin`, `realtor.com`
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- `InvalidSite` - valid options: `zillow`, `redfin`, `realtor.com`
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- `InvalidListingType` - valid options: `for_sale`, `for_rent`, `sold`
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- `InvalidListingType` - valid options: `for_sale`, `for_rent`, `sold`
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- `NoResultsFound` - no properties found from your input
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- `NoResultsFound` - no properties found from your input
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- `GeoCoordsNotFound` - if Zillow scraper is not able to find the geo-coordinates from the `location`
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- `GeoCoordsNotFound` - if Zillow scraper is not able to create geo-coordinates from the location you input
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## Frequently Asked Questions
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## Frequently Asked Questions
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@ -17,7 +17,6 @@ _scrapers = {
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"zillow": ZillowScraper,
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"zillow": ZillowScraper,
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}
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}
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def validate_input(site_name: str, listing_type: str) -> None:
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def validate_input(site_name: str, listing_type: str) -> None:
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if site_name.lower() not in _scrapers:
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if site_name.lower() not in _scrapers:
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raise InvalidSite(f"Provided site, '{site_name}', does not exist.")
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raise InvalidSite(f"Provided site, '{site_name}', does not exist.")
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@ -27,7 +26,6 @@ def validate_input(site_name: str, listing_type: str) -> None:
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f"Provided listing type, '{listing_type}', does not exist."
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f"Provided listing type, '{listing_type}', does not exist."
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)
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)
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def get_ordered_properties(result: Property) -> list[str]:
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def get_ordered_properties(result: Property) -> list[str]:
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return [
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return [
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"property_url",
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"property_url",
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@ -67,7 +65,6 @@ def get_ordered_properties(result: Property) -> list[str]:
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"longitude",
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"longitude",
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]
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]
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def process_result(result: Property) -> pd.DataFrame:
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def process_result(result: Property) -> pd.DataFrame:
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prop_data = result.__dict__
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prop_data = result.__dict__
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@ -93,7 +90,6 @@ def process_result(result: Property) -> pd.DataFrame:
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return properties_df
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return properties_df
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def _scrape_single_site(
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def _scrape_single_site(
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location: str, site_name: str, listing_type: str
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location: str, site_name: str, listing_type: str
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) -> pd.DataFrame:
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) -> pd.DataFrame:
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@ -112,6 +108,7 @@ def _scrape_single_site(
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results = site.search()
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results = site.search()
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properties_dfs = [process_result(result) for result in results]
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properties_dfs = [process_result(result) for result in results]
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properties_dfs = [df.dropna(axis=1, how='all') for df in properties_dfs if not df.empty]
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if not properties_dfs:
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if not properties_dfs:
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return pd.DataFrame()
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return pd.DataFrame()
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@ -154,6 +151,8 @@ def scrape_property(
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result = future.result()
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result = future.result()
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results.append(result)
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results.append(result)
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results = [df for df in results if not df.empty and not df.isna().all().all()]
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if not results:
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if not results:
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return pd.DataFrame()
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return pd.DataFrame()
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