Overview

This library holds data about over Broadway shows, grouped over weeklong periods. Only shows that reported capacity were included, so the dataset stretches back to the 1990s. The dataset is made available by the Broadway League (the national trade association for the Broadway industry), and you can view the data online at http://www.broadwayleague.com/. This dataset

Explore Structure




Index Type Example Value
0 dict { }
... ... ...
Key Type Example Value Comment
"Full" str "08/26/1990"
[Preview ]
The full date representation that this performance's week ended on in "Month/Day/Year" format.
"Day" int 26 The day of the month that this performance's week ended on.
"Month" int 8 The numeric month that this performance's week ended in (1 = January, 2 = February, etc.).
"Year" int 1990 The year that this week of performances occurred in.
Value Count
"05/06/2012" 39
"04/29/2012" 39
"04/08/2012" 39
"11/28/2010" 39
"04/10/2011" 39
"04/22/2012" 39
"04/15/2012" 39
"04/03/2011" 38
"11/07/2010" 38
"04/23/2006" 38
"11/14/2010" 38
"11/21/2010" 38
"04/30/2006" 38
"04/17/2011" 38
"11/29/2015" 38
"11/22/2015" 38
"04/22/2007" 38
"04/24/2011" 38
"05/13/2012" 38
"04/20/2014" 37
"05/01/2016" 37
"04/27/2014" 37
"05/04/2014" 37
"05/11/2008" 37
"12/06/2015" 37
"05/08/2016" 37
"04/17/2005" 37
"12/27/2015" 37
"12/13/2015" 37
"12/20/2015" 37
"05/20/2012" 37
"05/01/2005" 37
"05/04/2008" 37
"01/03/2016" 37
"04/24/2016" 37
"04/13/2014" 37
"04/17/2016" 37
"04/16/2000" 37
"05/14/2006" 37
"12/12/2010" 37
"04/22/2001" 37
"05/13/2001" 37
"05/06/2001" 37
"05/29/2016" 37
"04/29/2001" 37
"04/29/2007" 37
"05/07/2006" 37
"06/05/2016" 36
"05/25/2008" 36
"04/03/2016" 36
"04/06/2014" 36
"05/18/2014" 36
"11/23/2014" 36
"12/14/2014" 36
"06/08/2014" 36
"05/01/2011" 36
"04/05/2015" 36
"05/21/2006" 36
"05/27/2012" 36
"04/26/1998" 36
"11/15/2015" 36
"04/23/2000" 36
"05/11/2014" 36
"06/10/2007" 36
"10/31/2010" 36
"05/14/2000" 36
"04/09/2000" 36
"05/15/2005" 36
"04/10/2016" 36
"11/30/2014" 36
"12/05/2010" 36
"12/26/2010" 36
"05/18/2008" 36
"12/19/2010" 36
"12/07/2014" 36
"05/02/1999" 36
"04/30/2000" 36
"06/15/2014" 36
"06/03/2007" 36
"12/21/2014" 36
"12/28/2014" 36
"04/15/2001" 36
"05/15/2011" 36
"11/16/2003" 36
"05/15/2016" 36
"05/13/2007" 36
"11/23/2003" 36
"01/04/2015" 36
"05/27/2007" 36
"06/03/2012" 36
"05/07/2000" 36
"05/06/2007" 36
"05/22/2016" 36
"05/08/2005" 36
"06/22/2008" 36
"04/25/1999" 36
"04/26/2009" 35
"04/19/2009" 35
"05/20/2007" 35
"06/15/2008" 35
... ...
Key Type Example Value Comment
"Gross" int 134456 The "Gross Gross" of this performance, or how much it made in total across the entire week. Measured in dollars.
"Performances" int 8 The number of performances that occurred this week.
"Attendance" int 5500 The total number of people who attended performances over the week.
"Capacity" int 88 The percentage of the theatre that was filled during that week.
"Gross Potential" int 0 The Gross Potential is the maximum amount an engagement can possibly earn based on calculations involving ticket prices, seating capacity, and the number of performances. This number is expressed here as a percentage of what could have been achieved (Gross Gross / Gross Potential). In case the GP could not be calculated, it was replaced with 0%.
Key Type Example Value Comment
"Date" dict { }
"Statistics" dict { }
"Show" dict { }
Key Type Example Value Comment
"Type" str "Play"
[Preview ]
Whether it is a "Musical", "Play", or "Special".
"Name" str "Tru"
[Preview ]
The name of the production.
"Theatre" str "Booth"
[Preview ]
The name of the theatre.
Value Count
"Musical" 22551
"Play" 8406
"Special" 339
Value Count
"The Phantom Of The Opera" 1053
"Chicago" 1032
"The Lion King" 980
"Mamma Mia!" 725
"Beauty And The Beast" 690
"Wicked" 669
"Rent" 639
"Jersey Boys" 565
"Miss Saigon" 489
"Les Misrables" 365
"Hairspray" 336
"Mary Poppins" 334
"Avenue Q" 321
"The Producers" 318
"Cabaret 98" 304
"Rock Of Ages" 295
"The Book Of Mormon" 286
"Smokey Joe'S Cafe" 258
"Cats" 251
"Aida" 237
"Monty Python'S Spamalot" 202
"Jekyll & Hyde" 199
"42Nd Street '01" 193
"Kinky Boots" 181
"Matilda" 180
"Billy Elliot: The Musical" 171
"Movin' Out" 167
"Spider-Man Turn Off The Dark" 160
"In The Heights" 152
"Memphis" 150
"Once" 149
"The 25Th Annual Putnam County Spelling Bee" 145
"Bring In Da Noise, Bring In Da Funk" 144
"Beautiful" 143
"Fosse" 140
"Annie Get Your Gun" 135
"Contact" 131
"South Pacific" 130
"Aladdin" 129
"Les Misrables '14" 129
"Newsies" 128
"Urinetown" 125
"The Color Purple" 119
"Thoroughly Modern Millie" 118
"Proof" 117
"A Gentleman'S Guide To Love And Murder" 116
"Kiss Me, Kate" 114
"Spring Awakening" 112
"Ragtime" 108
"Grease" 107
"Titanic" 105
"Fiddler On The Roof 04" 103
"The Scarlet Pimpernel" 103
"Cinderella" 102
"The Curious Incident Of The Dog In The Night-Time" 101
"The Full Monty" 101
"The Tale Of The Allergist'S Wife" 101
"The 39 Steps" 100
"Sunset Boulevard" 99
"A Chorus Line '06" 97
"West Side Story" 97
"Motown The Musical" 97
"Next To Normal" 95
"The Addams Family" 95
"War Horse" 95
"A Funny Thing Happened On The Way To The Forum" 94
"Pippin" 94
"The Little Mermaid" 94
"Show Boat" 94
"The King And I 96" 91
"Footloose" 91
"The Music Man" 91
"The Drowsy Chaperone" 89
"August: Osage County" 84
"Dirty Rotten Scoundrels" 83
"Legally Blonde" 79
"Art" 78
"Riverdance" 78
"Defending The Caveman" 77
"Hedwig And The Angry Inch" 77
"Finding Neverland" 75
"An American In Paris" 75
"Sister Act" 75
"Grease 07" 74
"Something Rotten!" 73
"The Last Night Of Ballyhoo" 73
"Fun Home" 73
"The Sisters Rosensweig" 72
"The Sound Of Music" 72
"Xanadu" 71
"Anything Goes '11" 70
"Hair" 69
"Priscilla Queen Of The Desert" 69
"Victor/Victoria" 69
"Doubt" 69
"The Light In The Piazza" 68
"Side Man" 68
"Tarzan" 68
"Curtains" 68
"The King And I 2015" 68
... ...
Value Count
"Majestic" 1053
"Broadway" 1045
"Gershwin" 980
"Palace" 970
"Shubert" 967
"Ambassador" 963
"New Amsterdam" 916
"Nederlander" 911
"Lunt-Fontanne" 910
"Minskoff" 905
"Imperial" 903
"Eugene O'Neill" 878
"Helen Hayes" 868
"Vivian Beaumont" 842
"Walter Kerr" 827
"St. James" 817
"Richard Rodgers" 810
"Ethel Barrymore" 797
"Golden" 781
"Neil Simon" 754
"Marquis" 736
"Booth" 726
"Winter Garden" 719
"Broadhurst" 706
"Brooks Atkinson" 690
"American Airlines" 650
"Music Box" 642
"Circle In The Square" 639
"Al Hirschfeld" 570
"August Wilson" 563
"Lyceum" 546
"Cort" 501
"Belasco" 471
"Jacobs" 441
"Virginia" 424
"Longacre" 416
"Plymouth" 383
"Studio 54" 362
"Schoenfeld" 351
"Royale" 312
"Friedman" 291
"Martin Beck" 290
"Studio 54 ('98)" 269
"Stephen Sondheim" 252
"Cadillac Winter Garden" 242
"Ford Center" 193
"Biltmore" 167
"Foxwoods" 160
"Hilton Theatre" 146
"Criterion" 145
"Henry Miller" 125
"Ford Center (Livent)" 108
"Lyric" 78
"Kit Kat Klub" 35
"Henry Miller (Rndabt)" 26
"Ford Center (Tcn)" 24

Downloads

Download all of the following files.

Usage

This library has 1 function you can use.
import broadway
list_of_production = broadway.get_shows()
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 broadway
# These may be slow!
list_of_production = broadway.get_shows(test=True)

Documentation

 broadway.get_shows(test=False)

Returns information about all the shows