feat: add pandas

homeharvest/__init__.py

@@ -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)