Overview

Collected statistics about the tallest buildings all over the world, including the materials used to develop them, the purpose of the buildings, and more.

http://www.skyscrapercenter.com/

Explore Structure




Index Type Example Value
0 dict { }
... ... ...
Key Type Example Value Comment
"bridge" bool False
"industrial" bool False
"office" bool True
"library" bool False
"education" bool False
"religious" bool False
"telecommunications" bool False
"belltower" bool False
"residential" bool True
"hospital" bool False
"other" bool False
"exhibition" bool False
"serviced apartments" bool False
"multiple" bool False
"government" bool True
"hotel" bool True
"commercial" bool False
"abandoned" bool False
"observation" bool False
"air traffic control tower" bool False
"casino" bool False
"retail" bool False
"museum" bool False
Key Type Example Value Comment
"number of purposes" int 4
"floors above" int 528
"rank" int 1
"height" float 1609.35998535
Key Type Example Value Comment
"city" str "Chicago"
[Preview ]
"city_id" int 1539
"country" str "US"
[Preview ]
"country id" int 163
"longitude" int 0
"latitude" int 0
Value Count
"New York City" 640
"Chicago" 419
"Miami" 130
"Houston" 68
"San Francisco" 68
"Seattle" 65
"Honolulu" 61
"Los Angeles" 55
"Las Vegas" 54
"Atlanta" 42
"Minneapolis" 41
"Pittsburgh" 39
"Philadelphia" 38
"Boston" 38
"Sunny Isles Beach" 29
"Dallas" 27
"Jersey City" 26
"San Diego" 24
"Austin" 21
"Detroit" 21
"Miami Beach" 21
"Milwaukee" 17
"Baltimore" 17
"Phoenix" 17
"Cleveland" 17
"Indianapolis" 16
"St. Louis" 15
"Columbus" 15
"Salt Lake City" 15
"Nashville" 13
"Denver" 13
"Cincinnati" 13
"Charlotte" 13
"Portland" 12
"Sacramento" 12
"Kansas City" 11
"New Orleans" 9
"Orlando" 9
"Rochester" 9
"Atlantic City" 8
"Buffalo" 8
"Fort Lauderdale" 7
"Tampa" 7
"Bellevue" 7
"Evanston" 6
"Albany" 6
"Fort Myers" 5
"Aventura" 5
"Louisville" 5
"San Antonio" 5
"Winston-Salem" 5
"Tulsa" 5
"St. Petersburg (FL)" 4
"Jacksonville" 4
"Hartford" 4
"Grand Rapids" 4
"Oklahoma City" 4
"White Plains" 4
"Gary" 4
"Guttenberg" 3
"St. Paul" 3
"Oakland" 3
"West Palm Beach" 3
"Southfield" 3
"Memphis" 3
"Dayton" 3
"Hollywood" 3
"Fort Lee" 3
"Birmingham (AL)" 3
"Hallandale Beach" 3
"Long Beach" 3
"Norfolk" 3
"Covington" 3
"Newark" 3
"Tempe" 2
"New Haven" 2
"Galveston" 2
"New Rochelle" 2
"Wilmington" 2
"Des Moines" 2
"Bartlesville" 2
"Worcester" 2
"Manchester (NH)" 2
"Akron" 2
"Springfield (MA)" 2
"Midland" 2
"South Padre Island" 2
"Syracuse" 2
"Boise" 2
"Peoria" 2
"Fort Worth" 2
"North Miami Beach" 2
"El Paso" 2
"Pearl City (Oahu)" 2
"Clayton" 2
"Toledo" 2
"Royal Oak" 1
"Reno" 1
"East Chicago" 1
"Tallahassee" 1
... ...
Value Count
"US" 2431
Key Type Example Value Comment
"status" dict { }
"statistics" dict { }
"name" str "The Illinois"
[Preview ]
"material" str "steel"
[Preview ]
"purposes" dict { }
"location" dict { }
"id" int 12
Value Count
"Wells Fargo Center" 5
"Bank of America Plaza" 5
"Park Tower" 4
"Bank of America Building" 4
"The Carlyle" 4
"Bank of America Center" 3
"Wells Fargo Plaza" 3
"Wells Fargo Tower" 3
"Chase Tower" 3
"Museum Tower" 3
"One World Trade Center" 3
"AT&T Building" 3
"Metropolitan Tower" 3
"Hilton Garden Inn" 2
"The Pinnacle" 2
"National City Bank Building" 2
"AT&T Center" 2
"The Residences at the Ritz Carlton, Westchester - ..." 2
"Citigroup Center" 2
"The Tower" 2
"The Century" 2
"Renaissance Tower" 2
"Equitable Building" 2
"City Hall" 2
"Fifth Third Center" 2
"Mellon Bank Center" 2
"Mercantile Building" 2
"The Mark" 2
"JPMorgan Chase Tower" 2
"City Place" 2
"The St. James" 2
"The Ritz-Carlton Residences" 2
"RBC Plaza" 2
"National City Center" 2
"Park Place Tower" 2
"Spire" 2
"2 World Trade Center" 2
"Trump International Hotel & Tower" 2
"U.S. Bank Center" 2
"Millennium Tower" 2
"McGraw-Hill Building" 2
"PNC Tower" 2
"Merchandise Mart" 2
"The Element" 2
"Fisher Building" 2
"Franklin Towers" 2
"Tribune Tower" 2
"Time-Life Building" 2
"The Atlantic" 2
"Trump Palace" 2
"Hearst Tower" 2
"Aon Center" 2
"Bank of America Tower" 2
"First National Bank Building" 2
"Capitol Square" 2
"60 Wall Street" 2
"Underwood Building" 1
"300 South Wacker" 1
"Meridian Plaza I" 1
"Marina Palms North Tower" 1
"The Oliver Cromwell" 1
"Montebello" 1
"Comerica Bank Tower" 1
"Daily Commerce Building" 1
"Hilton Milwaukee City Center, West Building" 1
"Southampton Apartments" 1
"177 Huntington" 1
"Park Avenue Place" 1
"Empire World Condo Tower" 1
"Echelon Place Shangri-La Tower" 1
"St. Nicholas Tower" 1
"111 South Wacker" 1
"Brickell World Plaza at 600 Brickell" 1
"ADA Building" 1
"Silver Tower I" 1
"Las Olas Grand" 1
"Eaton Center" 1
"Cathedral of Learning" 1
"225 Bush Street" 1
"West Ocean Condominiums I" 1
"Society Hill Towers I" 1
"1441 Broadway" 1
"Sky Las Vegas" 1
"712 Main Street" 1
"Elm Place" 1
"One Tequesta Point" 1
"The Gateway" 1
"Trump Plaza Residences" 1
"MetLife Building" 1
"Royal Capitol Plaza" 1
"Eight Spruce Street" 1
"Viceroy New York" 1
"Marque Chicago" 1
"Plaza Towers" 1
"Buncombe County Courthouse" 1
"Bank of America Corporate Center" 1
"Legg Mason Building" 1
"Price Tower Arts Center" 1
"Century Square" 1
"Paramount Miami Worldcenter" 1
... ...
Value Count
"concrete" 1366
"steel" 875
"composite" 123
"steel/concrete" 33
"concrete/steel" 22
"masonry" 9
"precast" 3
Key Type Example Value Comment
"is started" bool False
"year" int 0
Key Type Example Value Comment
"is completed" bool False
"year" int 0
Key Type Example Value Comment
"current" str "vision"
[Preview ]
"started" dict { }
"completed" dict { }
Value Count
"completed" 2081
"under construction" 132
"proposed" 76
"architecturally topped out" 45
"vision" 39
"demolished" 35
"never completed" 11
"structurally topped out" 7
"on hold" 5

Downloads

Download all of the following files.

Usage

This library has 1 function you can use.
import skyscrapers
list_of_skyscraper = skyscrapers.get_skyscrapers()
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 skyscrapers
# These may be slow!
list_of_skyscraper = skyscrapers.get_skyscrapers(test=True)

Documentation

 skyscrapers.get_skyscrapers(test=False)

Returns a list of the skyscrapers in the database.