Overview

This dataset is all about flights in the united states, including information about the number, length, and type of delays. The data is reported for individual months at every major airport for every carrier. Additional information is available: http://www.rita.dot.gov/bts/help/aviation/html/understanding.html

Explore Structure




Index Type Example Value
0 str "American Airlines Inc."
... ... ...
Index Type Example Value
0 dict { }
... ... ...
Key Type Example Value Comment
"Security" int 17 Number of delays or cancellations caused by evacuation of a terminal or concourse, re-boarding of aircraft because of security breach, inoperative screening equipment and/or long lines in excess of 29 minutes at screening areas in this month.
"Carrier" int 1009 The number of delays and cancellations due to circumstances within the airline's control (e.g. maintenance or crew problems, aircraft cleaning, baggage loading, fueling, etc.) in this month.
"Late Aircraft" int 1275 The number of delays and cancellations caused by a previous flight with the same aircraft arriving late, causing the present flight to depart late in this month.
"National Aviation System" int 3217 The number of delays and cancellations attributable to the national aviation system that refer to a broad set of conditions, such as non-extreme weather conditions, airport operations, heavy traffic volume, and air traffic control in this month.
"Weather" int 328 Number of delays or cancellations caused by significant meteorological conditions (actual or forecasted) that, in the judgment of the carrier, delays or prevents the operation of a flight such as tornado, blizzard or hurricane in this month.
Key Type Example Value Comment
"Cancelled" int 216 The number of flights that were cancelled in this month.
"On Time" int 23974 The number of flights that were on time in this month.
"Total" int 30060 The total number of flights in this month.
"Delayed" int 5843 The number of flights that were delayed in this month.
"Diverted" int 27 The number of flights that were diverted in this month.
Key Type Example Value Comment
"Late Aircraft" int 68335 The number of minutes delayed caused by a previous flight with the same aircraft arriving late, causing the present flight to depart late in this month.
"Weather" int 19474 Number of of minutes delayed caused by significant meteorological conditions (actual or forecasted) that, in the judgment of the carrier, delays or prevents the operation of a flight such as tornado, blizzard or hurricane in this month.
"Carrier" int 61606 The number of minutes delayed due to circumstances within the airline's control (e.g. maintenance or crew problems, aircraft cleaning, baggage loading, fueling, etc.) in this month.
"Security" int 518 Number of minutes delayed caused by evacuation of a terminal or concourse, re-boarding of aircraft because of security breach, inoperative screening equipment and/or long lines in excess of 29 minutes at screening areas in this month.
"Total" int 268764
"National Aviation System" int 118831 The number of minutes delayed attributable to the national aviation system that refer to a broad set of conditions, such as non-extreme weather conditions, airport operations, heavy traffic volume, and air traffic control in this month.
Key Type Example Value Comment
"Code" str "ATL"
[Preview ]
The 3 letter code for this airport, assigned by IATA. For more information, consult this List of Airport Codes.
"Name" str "Atlanta, GA: Hartsfield-Jackson Atlanta International"
[Preview ]
The full name of this airport.
Value Count
"LAS" 152
"ORD" 152
"LAX" 152
"PHX" 152
"SFO" 152
"LGA" 152
"PHL" 152
"MCO" 152
"SLC" 152
"EWR" 152
"CLT" 152
"DFW" 152
"IAD" 152
"PDX" 152
"TPA" 152
"IAH" 152
"MDW" 152
"BWI" 152
"SEA" 152
"DCA" 152
"SAN" 152
"DTW" 152
"DEN" 152
"FLL" 152
"MSP" 152
"BOS" 152
"ATL" 152
"MIA" 152
"JFK" 152
Value Count
"Salt Lake City, UT: Salt Lake City International" 152
"Newark, NJ: Newark Liberty International" 152
"Charlotte, NC: Charlotte Douglas International" 152
"Minneapolis, MN: Minneapolis-St Paul International" 152
"Seattle, WA: Seattle/Tacoma International" 152
"Detroit, MI: Detroit Metro Wayne County" 152
"Dallas/Fort Worth, TX: Dallas/Fort Worth International" 152
"Baltimore, MD: Baltimore/Washington International ..." 152
"Washington, DC: Ronald Reagan Washington National" 152
"Orlando, FL: Orlando International" 152
"San Francisco, CA: San Francisco International" 152
"Denver, CO: Denver International" 152
"New York, NY: LaGuardia" 152
"Boston, MA: Logan International" 152
"Los Angeles, CA: Los Angeles International" 152
"New York, NY: John F. Kennedy International" 152
"Las Vegas, NV: McCarran International" 152
"Fort Lauderdale, FL: Fort Lauderdale-Hollywood Int..." 152
"Portland, OR: Portland International" 152
"Chicago, IL: Chicago Midway International" 152
"Chicago, IL: Chicago O'Hare International" 152
"Miami, FL: Miami International" 152
"San Diego, CA: San Diego International" 152
"Philadelphia, PA: Philadelphia International" 152
"Houston, TX: George Bush Intercontinental/Houston" 152
"Tampa, FL: Tampa International" 152
"Phoenix, AZ: Phoenix Sky Harbor International" 152
"Washington, DC: Washington Dulles International" 152
"Atlanta, GA: Hartsfield-Jackson Atlanta International" 152
Key Type Example Value Comment
"Total" int 11 The number of carriers that reported flight information during this time period and at this location.
"Names" list [ ] The full names of the carriers that reported in.
Key Type Example Value Comment
"Airport" dict { }
"Statistics" dict { }
"Time" dict { }
Key Type Example Value Comment
"Flights" dict { }
"# of Delays" dict { }
"Minutes Delayed" dict { }
"Carriers" dict { }
Key Type Example Value Comment
"Month Name" str "June"
[Preview ]
"Year" int 2003 The reported year as a 4-digit number.
"Label" str "2003/06"
[Preview ]
The "year/month" reported as a string, to make it easier to sort by time periods.
"Month" int 6 The reported month as a number. 0 is January, 1 is February, etc.
Value Count
"November" 377
"July" 377
"January" 377
"June" 377
"December" 377
"September" 377
"August" 377
"October" 377
"May" 348
"April" 348
"Febuary" 348
"March" 348
Value Count
"2007/12" 29
"2007/11" 29
"2007/10" 29
"2012/11" 29
"2012/10" 29
"2012/12" 29
"2012/03" 29
"2012/06" 29
"2006/09" 29
"2006/08" 29
"2006/07" 29
"2006/06" 29
"2005/10" 29
"2006/04" 29
"2006/03" 29
"2006/02" 29
"2006/01" 29
"2012/05" 29
"2012/02" 29
"2004/06" 29
"2007/04" 29
"2007/05" 29
"2007/06" 29
"2007/07" 29
"2007/01" 29
"2007/02" 29
"2007/03" 29
"2007/08" 29
"2007/09" 29
"2004/09" 29
"2004/08" 29
"2004/01" 29
"2004/03" 29
"2004/02" 29
"2004/05" 29
"2004/04" 29
"2004/07" 29
"2012/08" 29
"2006/10" 29
"2006/11" 29
"2006/12" 29
"2010/02" 29
"2010/12" 29
"2012/09" 29
"2010/10" 29
"2004/12" 29
"2004/10" 29
"2004/11" 29
"2010/11" 29
"2016/01" 29
"2011/07" 29
"2014/12" 29
"2014/11" 29
"2014/10" 29
"2011/03" 29
"2011/02" 29
"2011/01" 29
"2013/12" 29
"2003/12" 29
"2011/09" 29
"2011/08" 29
"2005/11" 29
"2012/07" 29
"2012/04" 29
"2005/12" 29
"2010/08" 29
"2010/09" 29
"2012/01" 29
"2010/04" 29
"2010/05" 29
"2010/06" 29
"2010/07" 29
"2010/01" 29
"2013/11" 29
"2010/03" 29
"2008/05" 29
"2008/04" 29
"2013/01" 29
"2013/03" 29
"2013/02" 29
"2013/05" 29
"2013/04" 29
"2013/07" 29
"2013/06" 29
"2013/09" 29
"2013/08" 29
"2014/01" 29
"2015/09" 29
"2015/08" 29
"2014/04" 29
"2014/05" 29
"2014/06" 29
"2014/07" 29
"2014/08" 29
"2015/02" 29
"2015/01" 29
"2003/10" 29
"2015/07" 29
"2015/06" 29
"2015/05" 29
... ...

Downloads

Download all of the following files.

Usage

This library has 1 function you can use.
import airlines
list_of_airline = airlines.get_reports()
Additionally, some of the functions can return a sample of the Big Data using an extra argument. If you use this sampled Big Data, it may be much faster. When you are sure your code is correct, you can remove the argument to use the full dataset.
import airlines
# These may be slow!
list_of_airline = airlines.get_reports(test=True)

Documentation

 airlines.get_reports(test=False)

Returns a list of airline reports in the database.