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@ -121,7 +121,10 @@ class RealtorScraper(Scraper):
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property_info = response_json["data"]["home"]
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return [self.process_property(property_info)]
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if self.return_type != ReturnType.raw:
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return [self.process_property(property_info)]
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else:
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return [property_info]
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@staticmethod
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def process_advertisers(advertisers: list[dict] | None) -> Advertisers | None:
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@ -1,6 +1,6 @@
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[tool.poetry]
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name = "homeharvest"
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version = "0.4.8"
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version = "0.4.9"
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description = "Real estate scraping library"
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authors = ["Zachary Hampton <zachary@bunsly.com>", "Cullen Watson <cullen@bunsly.com>"]
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homepage = "https://github.com/Bunsly/HomeHarvest"
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@ -292,11 +292,14 @@ def test_phone_number_matching():
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def test_return_type():
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results = {
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"pandas": scrape_property(location="Surprise, AZ", listing_type="for_rent", limit=100),
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"pydantic": scrape_property(location="Surprise, AZ", listing_type="for_rent", limit=100, return_type="pydantic"),
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"raw": scrape_property(location="Surprise, AZ", listing_type="for_rent", limit=100, return_type="raw"),
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"pandas": [scrape_property(location="Surprise, AZ", listing_type="for_rent", limit=100)],
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"pydantic": [scrape_property(location="Surprise, AZ", listing_type="for_rent", limit=100, return_type="pydantic")],
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"raw": [
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scrape_property(location="Surprise, AZ", listing_type="for_rent", limit=100, return_type="raw"),
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scrape_property(location="66642", listing_type="for_rent", limit=100, return_type="raw"),
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],
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}
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assert isinstance(results["pandas"], pd.DataFrame)
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assert isinstance(results["pydantic"][0], Property)
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assert isinstance(results["raw"][0], dict)
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assert all(isinstance(result, pd.DataFrame) for result in results["pandas"])
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assert all(isinstance(result[0], Property) for result in results["pydantic"])
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assert all(isinstance(result[0], dict) for result in results["raw"])
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