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Flight data analysis in python using numpy, pandas, matplotlib and seaborn

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Flight Data Analysis

We have dataset contains information about all flights that departed from NYC in 2013, Contains around 336,776 total flights.

Software's and Libraries:

  1. Jupyter Notebook
  2. Python 3.x
  3. Numpy
  4. Pandas
  5. MatplotLib
  6. Seaborn
  7. Pandas Profiling

This dataset is composed by the following variables:

  1. year: 2013
  2. month: 1-12
  3. day: Day of month(1-31)
  4. dep_time: Departure times, local timezone
  5. sched_dep_time: Scheduled departure time
  6. dep_delay: Departure delay, in minutes, Negative times represent early departures
  7. arr_time: Arrival times, local timezone
  8. sched_arr_time: Scheduled arrival time
  9. arr_delay: Arrival delay, in minutes, Negative times represent early arrivals
  10. carrier: Two letter carrier abbreviation
  11. flight: Flight number
  12. tailnum: Plane tail number
  13. origin: Airport codes for origin
  14. dest: Airport codes for destination
  15. air_time: Amount of time spent in the air, in minutes
  16. distance: Distance flown, in miles
  17. hour: Time of departure broken in to hour
  18. minute: Time of departure broken in to minutes
  19. time_hour: Timestamp

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Flight data analysis in python using numpy, pandas, matplotlib and seaborn

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