{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":105399,"databundleVersionId":12733338,"sourceType":"competition"}],"dockerImageVersionId":31040,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Notebook Purpose\n\nThis notebook helps **reorder and rename columns** in the FlightRank 2025 dataset for easier exploration and cleaning. I found digesting the the data very overwhelming.\n\nThe raw Parquet files contain:\n- Columns that are **not grouped logically** (e.g. user info, flight info, pricing),\n- Column names that are **not intuitive** (e.g. `Id`, `profileId`, `legs0_segments1_...`).\n\nThis script restructures the columns into logical groups and applies more readable names.  \nHopefully, this makes your **EDA and data cleaning** smoother and more intuitive.\n\n> **Note:** If you're applying this to the **test set**, you'll need to adjust for missing target columns.  \n> **Reminder:** Before submission, make sure to **rename columns back** to match the original submission format.","metadata":{}},{"cell_type":"code","source":"import polars as pl","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-26T15:35:53.726110Z","iopub.execute_input":"2025-06-26T15:35:53.726470Z","iopub.status.idle":"2025-06-26T15:35:54.551795Z","shell.execute_reply.started":"2025-06-26T15:35:53.726445Z","shell.execute_reply":"2025-06-26T15:35:54.550616Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# **Define the column order**","metadata":{}},{"cell_type":"code","source":"# Define Column Groups\n\nidentifiers = [\"Id\", \"ranker_id\", \"profileId\", \"companyID\"]\nuser_info = [\"sex\", \"nationality\", \"frequentFlyer\", \"isVip\", \"bySelf\", \"isAccess3D\"]\ncompany_info = [\"corporateTariffCode\"]\nsearch_info = [\"searchRoute\", \"requestDate\"]\npricing_info = [\"totalPrice\", \"taxes\"]\ntiming_info = [\n    \"legs0_departureAt\", \"legs0_arrivalAt\", \"legs0_duration\",\n    \"legs1_departureAt\", \"legs1_arrivalAt\", \"legs1_duration\"\n]\ncancellation_rules = [\n    \"miniRules0_monetaryAmount\", \"miniRules0_percentage\", \"miniRules0_statusInfos\",\n    \"miniRules1_monetaryAmount\", \"miniRules1_percentage\", \"miniRules1_statusInfos\"\n]\npolicy_info = [\"pricingInfo_isAccessTP\", \"pricingInfo_passengerCount\"]\ntarget = [\"selected\"]\n\n# Dynamic segment columns\ngeography_route, airline_flight_details, service_characteristics = [], [], []\nfor leg in [0, 1]:\n    for seg in range(4):\n        prefix = f\"legs{leg}_segments{seg}_\"\n        geography_route.extend([\n            f\"{prefix}departureFrom_airport_iata\",\n            f\"{prefix}arrivalTo_airport_iata\",\n            f\"{prefix}arrivalTo_airport_city_iata\"\n        ])\n        airline_flight_details.extend([\n            f\"{prefix}marketingCarrier_code\",\n            f\"{prefix}operatingCarrier_code\",\n            f\"{prefix}aircraft_code\",\n            f\"{prefix}flightNumber\",\n            f\"{prefix}duration\"\n        ])\n        service_characteristics.extend([\n            f\"{prefix}baggageAllowance_quantity\",\n            f\"{prefix}baggageAllowance_weightMeasurementType\",\n            f\"{prefix}cabinClass\",\n            f\"{prefix}seatsAvailable\"\n        ])\n\n# Final desired order\nfinal_col_order = (\n    identifiers + user_info + company_info + search_info + pricing_info +\n    timing_info + geography_route + airline_flight_details +\n    service_characteristics + cancellation_rules + policy_info + target\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-26T15:36:29.936667Z","iopub.execute_input":"2025-06-26T15:36:29.937014Z","iopub.status.idle":"2025-06-26T15:36:29.946439Z","shell.execute_reply.started":"2025-06-26T15:36:29.936990Z","shell.execute_reply":"2025-06-26T15:36:29.945185Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# **Rename the columns**","metadata":{}},{"cell_type":"code","source":"# Define Rename Map\nrename_map = {\n    \"Id\": \"flight_option_id\", \"ranker_id\": \"search_session_id\",\n    \"profileId\": \"user_id\", \"companyID\": \"company_id\",\n    \"sex\": \"user_gender\", \"nationality\": \"user_nationality\",\n    \"frequentFlyer\": \"frequent_flyer_status\", \"isVip\": \"vip_status\",\n    \"bySelf\": \"booked_by_self\", \"isAccess3D\": \"internal_flag_3d\",\n    \"corporateTariffCode\": \"corporate_tariff_code\", \"searchRoute\": \"route_type\",\n    \"requestDate\": \"search_time\", \"totalPrice\": \"price_total\",\n    \"taxes\": \"price_taxes\", \"legs0_departureAt\": \"outbound_departure_time\",\n    \"legs0_arrivalAt\": \"outbound_arrival_time\", \"legs0_duration\": \"outbound_duration\",\n    \"legs1_departureAt\": \"return_departure_time\", \"legs1_arrivalAt\": \"return_arrival_time\",\n    \"legs1_duration\": \"return_duration\", \"miniRules0_monetaryAmount\": \"cancellation_fee_amount\",\n    \"miniRules0_percentage\": \"cancellation_fee_percent\", \"miniRules0_statusInfos\": \"cancellation_rule_status\",\n    \"miniRules1_monetaryAmount\": \"exchange_fee_amount\", \"miniRules1_percentage\": \"exchange_fee_percent\",\n    \"miniRules1_statusInfos\": \"exchange_rule_status\",\n    \"pricingInfo_isAccessTP\": \"is_compliant_with_corporate_travel_policy\",\n    \"pricingInfo_passengerCount\": \"num_of_passengers\", \"selected\": \"flight_is_selected\"\n}\n\n# Dynamic segment renames\nfor leg in [0, 1]:\n    for seg in range(4):\n        prefix = f\"legs{leg}_segments{seg}_\"\n        rename_map.update({\n            f\"{prefix}departureFrom_airport_iata\": f\"leg{leg}_seg{seg}_departure_airport\",\n            f\"{prefix}arrivalTo_airport_iata\": f\"leg{leg}_seg{seg}_arrival_airport\",\n            f\"{prefix}arrivalTo_airport_city_iata\": f\"leg{leg}_seg{seg}_arrival_city\",\n            f\"{prefix}marketingCarrier_code\": f\"leg{leg}_seg{seg}_marketing_airline\",\n            f\"{prefix}operatingCarrier_code\": f\"leg{leg}_seg{seg}_operating_airline\",\n            f\"{prefix}aircraft_code\": f\"leg{leg}_seg{seg}_aircraft_type\",\n            f\"{prefix}flightNumber\": f\"leg{leg}_seg{seg}_flight_number\",\n            f\"{prefix}duration\": f\"leg{leg}_seg{seg}_segment_duration\",\n            f\"{prefix}baggageAllowance_quantity\": f\"leg{leg}_seg{seg}_baggage_quantity\",\n            f\"{prefix}baggageAllowance_weightMeasurementType\": f\"leg{leg}_seg{seg}_baggage_unit\",\n            f\"{prefix}cabinClass\": f\"leg{leg}_seg{seg}_cabin_class\",\n            f\"{prefix}seatsAvailable\": f\"leg{leg}_seg{seg}_seats_available\"\n        })","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-26T15:36:44.006679Z","iopub.execute_input":"2025-06-26T15:36:44.007031Z","iopub.status.idle":"2025-06-26T15:36:44.017389Z","shell.execute_reply.started":"2025-06-26T15:36:44.007007Z","shell.execute_reply":"2025-06-26T15:36:44.016148Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# **Run & Save**","metadata":{}},{"cell_type":"code","source":"# Load, Select, Rename and Write (with Polars)\n\n# Read lazily\ndf = pl.read_parquet(\"/kaggle/input/aeroclub-recsys-2025/train.parquet\")\n\n# Keep only the columns that exist\nexisting_cols = [col for col in final_col_order if col in df.columns]\ndf = df.select(existing_cols)\n\n# Rename\ndf = df.rename({k: v for k, v in rename_map.items() if k in df.columns})\n\n# Save\ndf.write_parquet(\"/kaggle/working/train_processed.parquet\", compression=\"snappy\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-26T15:37:39.067512Z","iopub.execute_input":"2025-06-26T15:37:39.069015Z","iopub.status.idle":"2025-06-26T15:38:21.507848Z","shell.execute_reply.started":"2025-06-26T15:37:39.068970Z","shell.execute_reply":"2025-06-26T15:38:21.506841Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"![image.png](attachment:f77b5f72-df51-4d59-a3a5-b8e5c0fe320d.png)\n* you can find the output parquet in the right hand sidebar of your kaggle notebook.\n* click the 3 dots to download the file","metadata":{},"attachments":{"f77b5f72-df51-4d59-a3a5-b8e5c0fe320d.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# **Visual Check**","metadata":{}},{"cell_type":"code","source":"# Visual Inspection\ndf.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-26T15:38:26.439506Z","iopub.execute_input":"2025-06-26T15:38:26.439922Z","iopub.status.idle":"2025-06-26T15:38:26.467273Z","shell.execute_reply.started":"2025-06-26T15:38:26.439888Z","shell.execute_reply":"2025-06-26T15:38:26.466287Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Print column order and names\nfor col in df.columns:\n    print(col)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-26T15:38:59.213959Z","iopub.execute_input":"2025-06-26T15:38:59.214285Z","iopub.status.idle":"2025-06-26T15:38:59.221471Z","shell.execute_reply.started":"2025-06-26T15:38:59.214261Z","shell.execute_reply":"2025-06-26T15:38:59.220412Z"}},"outputs":[],"execution_count":null}]}