10000 GitHub - SatheeshMK/-Team_9_Python-Project: This repository contains our team's Python project, which explores and analyzes a comprehensive world airport dataset. Through data cleaning, manipulation, and visualization, we aim to uncover trends, patterns, and insights related to airport locations, passenger traffic, and infrastructure worldwide.
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This repository contains our team's Python project, which explores and analyzes a comprehensive world airport dataset. Through data cleaning, manipulation, and visualization, we aim to uncover trends, patterns, and insights related to airport locations, passenger traffic, and infrastructure worldwide.

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Team_9_Python-Project

This repository contains our team's Python project, which explores and analyzes a comprehensive world airport dataset. We aim to uncover trends, patterns, and insights related to airport locations, passenger traffic, and infrastructure worldwide through data cleaning, manipulation, and visualization.

Dataset Focus: The project uses the International Airports Dataset, offering a comprehensive overview of global airports.

Dataset Highlights The following CSV files were used in our analysis 1. airports.csv 2. countries.csv 3. navaids.csv 4. regions.csv 5. runways.csv

Data Dictionary:

airports.csv Columns:

  1. id: Unique identifier for each airport.
  2. ident: Airport’s identifier code (often an ICAO or IATA code).
  3. type: Type of airport (e.g., "heliport," "small_airport," "medium_airport").
  4. name: Full name of the airport.
  5. latitude_deg: Latitude coordinate of the airport in degrees.
  6. longitude_deg: Longitude coordinate of the airport in degrees.
  7. elevation_ft: Elevation of the airport above sea level, in feet.
  8. continent: Continent code where the airport is located.
  9. iso_country: ISO country code of the airport’s location.
  10. iso_region: ISO region code (state/province).
  11. municipality: Primary city or town served by the airport.
  12. scheduled_service: Indicates if the airport offers scheduled airline services ("yes" or "no").
  13. gps_code: GPS code (typically matching the ICAO code).
  14. iata_code: IATA code for the airport.
  15. local_code: Local code representing the airport. 16-18. Unnamed columns: Extra columns without descriptions or content.

countries.csv Columns:

  1. Unnamed: 0: Unlabeled unique identifier.
  2. code: Country code in ISO format.
  3. name: Country name.
  4. continent: Continent code for the country.

runways.csv Columns:

  1. id: Unique identifier for each runway.
  2. airport_ref: ID reference to the associated airport.
  3. airport_ident: Airport identifier where the runway is located.
  4. length_ft: Runway length in feet.
  5. width_ft: Runway width in feet.
  6. surface: Runway surface type (e.g., asphalt, grass).
  7. lighted: Indicates if the runway is lighted (1 for yes, 0 for no).
  8. closed: Indicates if the runway is closed (1 for yes, 0 for no).
  9. le_ident: Identifier for the runway end.

navaids.csv Columns:

  1. id: Unique identifier for each navigational aid.
  2. filename: Name associated with the navigational aid.
  3. ident: Identifier code for the navigational aid.
  4. name: Name of the navigational aid.
  5. type: Type of navigational aid (e.g., NDB, DME).
  6. frequency_khz: Frequency of the navigational aid in kHz.
  7. latitude_deg: Latitude of the navigational aid in degrees.
  8. longitude_deg: Longitude of the navigational aid in degrees.
  9. elevation_ft: Elevation above sea level in feet.
  10. iso_country: Country code where the navigational aid is located.
  11. magnetic_variation_deg: Magnetic variation at the location, in degrees.
  12. usageType: Type of usage (e.g., LO for low-level usage).
  13. power: Power level of the navigational aid.
  14. associated_airport: Identifier of the associated airport.

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This repository contains our team's Python project, which explores and analyzes a comprehensive world airport dataset. Through data cleaning, manipulation, and visualization, we aim to uncover trends, patterns, and insights related to airport locations, passenger traffic, and infrastructure worldwide.

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