Tirsvad
225d28feb7
- Add project structure with domain-driven design organization - Implement FastAPI endpoints for hotel booking reports - Add data cleaning, statistics, and LLM report generation services - Include configuration management and security utilities - Add repository layer for bookings and metadata access - Setup testing framework with conftest.py - Include example files and documentation
108 lines
4.2 KiB
Python
108 lines
4.2 KiB
Python
import pandas as pd
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from nf_hotel_api.services.cleaning import DataCleaningService
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def test_clean_removes_duplicate_bookings(dirty_bookings_df, hotel_metadata):
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result = DataCleaningService(hotel_metadata).clean(dirty_bookings_df)
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# booking 2 is a duplicate of booking 1 (ignoring booking_id) and must go.
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assert result["booking_id"].tolist() == [1]
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def test_clean_coerces_dates_and_drops_unparseable_rows(hotel_metadata):
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df = pd.DataFrame(
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[
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{"booking_id": 1, "booking_date": "2024-01-01", "arrival_date": "2024-02-01"},
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{"booking_id": 2, "booking_date": "2024-01-01", "arrival_date": "garbage"},
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]
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)
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result = DataCleaningService(hotel_metadata)._fix_wrong_format(df)
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assert pd.api.types.is_datetime64_any_dtype(result["arrival_date"])
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cleaned = DataCleaningService(hotel_metadata)._clean_empty_cells(result)
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assert cleaned["booking_id"].tolist() == [1]
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def test_clean_replaces_blank_categoricals_with_unknown_placeholder(hotel_metadata):
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df = pd.DataFrame([{"hotel": "NF Hotel", "meal": "", "country": "Portugal"}])
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result = DataCleaningService(hotel_metadata)._clean_empty_cells(df)
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assert result.loc[0, "meal"] == "Unknown"
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assert result.loc[0, "country"] == "Portugal"
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def test_clean_caps_implausible_guest_counts_and_fixes_zero_guest_rows(hotel_metadata):
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df = pd.DataFrame(
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[
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{"adults": 55, "children": 0, "babies": 0},
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{"adults": 0, "children": 0, "babies": 0},
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]
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)
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result = DataCleaningService(hotel_metadata)._fix_wrong_data(df)
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assert result.loc[0, "adults"] <= 10
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assert result.loc[1, "adults"] == 1
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def test_clean_parses_day_first_and_iso_dates_without_dropping_rows(hotel_metadata):
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df = pd.DataFrame(
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[
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{"booking_id": 1, "booking_date": "13-07-2018", "arrival_date": "02-06-2018"},
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{"booking_id": 2, "booking_date": "2018-07-14", "arrival_date": "2018-06-03"},
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]
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)
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result = DataCleaningService(hotel_metadata).clean(df)
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assert result["booking_id"].tolist() == [1, 2]
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assert result.loc[0, "booking_date"] == pd.Timestamp(2018, 7, 13)
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assert result.loc[0, "arrival_date"] == pd.Timestamp(2018, 6, 2)
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assert result.loc[1, "arrival_date"] == pd.Timestamp(2018, 6, 3)
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def test_add_pricing_keeps_price_paid_and_fills_missing_from_standard_price(hotel_metadata):
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df = pd.DataFrame(
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[
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{"assigned_room_type": "A", "prize_per_nigth": 15}, # discounted, kept
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{"assigned_room_type": "B", "prize_per_nigth": None}, # missing -> 25
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{"assigned_room_type": "A", "prize_per_nigth": -3}, # invalid -> 20
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{"assigned_room_type": "A", "prize_per_nigth": 0}, # free stay, kept
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{"assigned_room_type": "C", "prize_per_nigth": None}, # not in metadata
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]
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)
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result = DataCleaningService(hotel_metadata)._add_pricing(df)
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assert result["prize_per_nigth"].iloc[:4].tolist() == [15, 25, 20, 0]
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assert pd.isna(result.loc[4, "prize_per_nigth"])
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assert result["room_size"].tolist() == ["Small", "Large", "Small", "Small", "Unknown"]
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def test_add_pricing_revenue_is_nights_times_price_paid_and_zero_when_canceled(hotel_metadata):
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df = pd.DataFrame(
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[
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{"assigned_room_type": "A", "is_canceled": 0, "prize_per_nigth": 15,
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"stays_in_weekend_nights": 1, "stays_in_week_nights": 2},
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{"assigned_room_type": "B", "is_canceled": 0,
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"stays_in_weekend_nights": 0, "stays_in_week_nights": 3},
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{"assigned_room_type": "B", "is_canceled": 1,
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"stays_in_weekend_nights": 2, "stays_in_week_nights": 2},
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]
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)
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result = DataCleaningService(hotel_metadata)._add_pricing(df)
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assert result["revenue"].tolist() == [45, 75, 0]
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def test_clean_adds_pricing_columns_to_full_pipeline(dirty_bookings_df, hotel_metadata):
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result = DataCleaningService(hotel_metadata).clean(dirty_bookings_df)
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assert result.loc[0, "prize_per_nigth"] == 20
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assert result.loc[0, "room_size"] == "Small"
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assert result.loc[0, "revenue"] == 60 # 3 nights x $20
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