55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
import pandas as pd
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import pytest
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from funda_scraper.preprocess import preprocess_data
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@pytest.fixture
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def input_data():
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data = {
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"url": ["https://www.funda.nl/koop/utrecht/appartement-00000000-dummy-100/"],
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"price": ["na"],
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"address": ["dummy 10"],
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"descrip": ["dummy"],
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"listed_since": ["Verkocht"],
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"zip_code": ["1111 AA"],
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"size": ["100 m²"],
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"year": ["2000"],
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"living_area": ["78 m²"],
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"perceel_area": ["78 m²"],
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"kind_of_house": ["Eengezinswoning"],
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"building_type": ["Bestaande bouw"],
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"num_of_rooms": ["4 kamers (3 slaapkamers)"],
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"num_of_bathrooms": ["1 badkamer en 1 apart toilet"],
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"layout": ["Aantal kamers 4 kamers (3 slaapkamers)"],
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"energy_label": ["A++++"],
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"insulation": ["Dubbel glas"],
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"heating": ["dummy"],
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"ownership": ["dummy"],
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"exteriors": ["dummy"],
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"parking": ["dummy"],
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"neighborhood_name": ["dummy"],
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"date_list": ["30 juni 2023"],
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"date_sold": ["13 juli 2023"],
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"term": ["13 dagen"],
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"price_sold": ["€ 500.000 k.k."],
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"last_ask_price": ["€ 500.000 kosten koper"],
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"last_ask_price_m2": ["dummy"],
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"city": ["utrecht"],
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"log_id": ["dummy"],
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"photo": ['dummy']
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}
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return pd.DataFrame(data)
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class TestPreprocessData:
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def test_is_past_true(self, input_data):
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df = preprocess_data(df=input_data, is_past=True)
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assert df.shape == (1, 17)
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assert df["house_type"].item() == "appartement"
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assert df["price"].item() == 500000
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assert df["room"].item() == 4
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assert df["bedroom"].item() == 3
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# assert df["term_days"].item() == 13
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assert df["energy_label"].item() == ">A+"
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