please code in python 3 in the skeleton code that is given
output given as well
We are interested in answering three questions: 1. If we add up the individual state unauthorized immigration population (at index 1), do we get the same value as in the summative row labeled U.S.? (Food for thought: why aren't they the same?) 2. Which states have a larger percentage unauthorized immigrant population (at index 2) than the value in the summative row labeled U.S."? 3. Consider the column on the industry with the largest number of unauthorized immigrant workers (at index 9). Which industry is listed as the largest in the most states and how many states? We provide a skeleton program with a main() and four function headers, one function to read the file plus one function for each question. 1. def read file (fp): this function takes a file pointer as an argument and returns a list of lists. The list of lists where each list is a list of the contents of each data row of the file- ignoring header rows, footnote rows, and empty rows. The list will contain 52 lists, one for each of the fifty states plus the District of Columbia and one summative row (the row labeled U.S."). The order of the list will be the same as the order in the file. (Hint: use csv.reader (), see notes below.) 2. def get totals (L): this function takes the list of lists returned by the read file () function and returns two values. The data of interest for this function is the column at index 1: unauthorized immigrant population. Return the value in the summative row (labeled U.S.) and the sum of the other 51 data rows. The purpose of this function is to gather data to answer question #1 above. def get_largest_states (L) : this function takes the list of lists returned by the read file () function and returns a list. The data of interest for this function is the column at index 2: unauthorized immigration % of population. The returned list is a list of states whose value is greater than the summative value (the value in the row labeled U.S."). Since District of Columbia" is in the file we will include it as a "state. The returned list will be in alphabetical order (the order of the original file). The purpose of this function is to gather data to answer question #2 above. 4. def get_industry_counts (L): this function takes the list of lists returned by the read file() function and returns a list of lists. The data of interest for this function is the column at index 9: industry with largest number of unauthorized immigrant workers. The list of lists returned is a list of industries and occurrences in the column (excluding the summative data from the row labeled U.S.). The returned list will be sorted by occurrences (largest first) and will look like this: [[industryl, counti], [industry2, count2], ...] The purpose of this function is to gather data to answer question #3 above. (Hint: use key=itemgetter (1) to sort on index 1 of the lists.) Notes and Suggestions 1. Using the CSV package: Remember import csv reader = csv.reader (fp) # attaches a reader to the file fp next (reader, None) # skips a line, such as a header line for line in reader: # line is a list import csv from operator import itemgetter INDUSTRIES = ['Agriculture', 'Business services', 'Construction', 'Leisure/hospitality', 'Manufacturing' ] def read_file(fp): I'Docstring'!! return None # temoprary return value so main runs def get_totals (L): "''Docstring'!! return None, None # temoprary return value so main runs def get_industry_counts (L): ''Docstring' return None # temoprary return value so main runs def get_largest_states (L): '' 'Docstring'!! return None # temoprary return value so main runs def main(): fp = open("immigration.csv") L = read_file(fp) us_pop, total_pop = get_totals (L) if us_pop and total pop: # if their values are not None print(" Data on illegal Immigration ") print("Summative:", us_pop) print("Total :", total_pop) states = get_largest_states (L) if states: # if their value is not None print(" States with large immigrant populations") for state in states: state = state.replace(' ','') print (state) counters = get_industry_counts (L) if counters: # if their value is not None print(" Industries with largest immigrant populations by state") print("{:24s} {:10s}".format("industry","count")) for tup in counters: print("{:24s} {:2d}".format(tup[0], tup[1])) if __name__ == "_main__": main() Player,s/c, Pos, GP, G, A, A1, A2,P, S, PIM, Hits, BKS, TOI, G/GP, A/GP, A1/GP, A2/GP,P/GP, S/GP, PIM/GP,Hits/GP, BkS/GP, TOI/GP Adam Oates, R, C, "1,337",341,"1,079",664,415,"1,420","2,392", 415,79,210,"10,512:35",0.26,0.81,0.5,0.31,1.06,1.79,0.31,--,--,- Alex Ovechkin, R,L,"1,129",684,569,351,218,"1,253","5,444",711,"2,972", 438,"23,635:51",0.61,0.5,0.31,0.19,1.11,4.82,0.63,2.63,0.39, 20:56 Alexander Mogilny,L,R,990, 473,559,374, 185,"1,032","2,966", 432,281,68,"8,418:43",0.48,0.56,0.38,0.19,1.04,3,0.44,--,--,-- Artemi Panarin, R, L, 365, 140, 241, 151, 90,381,949, 114, 114,72,"7,150:45",0.38,0.66,0.41,0.25, 1.04,2.6,0.31,0.31,0.2,19:35 Auston Matthews,L,C, 257, 142,117,75, 42, 259,886, 44,89, 213,"4,742:55",0.55,0.46,0.29,0.16,1.01,3.45,0.17,0.35,0.83, 18:27 Bernie Federko,L,C,"1,000",369,761,469,292,"1,130","2,074", 487,0,0,--,0.37,0.76,0.47,0.29,1.13,2.07,0.49,--,--,-- Bernie Nicholls,R,C,"1,127", 475,734,458,276,"1,209","3,231","1,292",65, 25,"1,011:55",0.42,0.65,0.41,0.24,1.07,2.87,1.15,--,--, Bill Cowley,L,C,549,195,354, 253,99,549,--,143,0,0,--,0.36,0.64,0.46,0.18,1,--,0.26,--,--,-- Bobby Clarke,L,C,"1,144", 358,852,541,311,"1,210","2,582","1,453",0,0,--,0.31,0.74,0.47,0.27,1.06,2.26,1.27,--,-- Bobby Hull,L,L,"1,063",610,560,366,194,"1,170","4,577",634,0,0,--,0.57,0.53,0.34,0.18,1.1,4.31,0.6,--,--,-- Bobby Orr,L,D, 657, 270, 645,326,319,915,"3,058",953,0,0,--,0.41,0.98,0.5, 0.49,1.39,4.65,1.45,--,--,-- Brett Hull,R,R,'1,269",741,650,388,262,"1,391","4,876",458,142,122,"9,648:19",0.58,0.51,0.31,0.21,1.1,3.84,0.36,--,--,-- Bryan Trottier,L,C,"1,279",524,901,542,359,"1,425","2,841", 912,0,0,--,0.41,0.7,0.42,0.28,1.11,2.22,0.71,--,-- Connor McDavid,L,C,333, 152, 290,184,106,442,"1,017",110,142,133,"7,149:33",0.46,0.87,0.55,0.32,1.33,3.05,0.33,0.43,0.4,21:28 Cy Denneny,L,L,329, 247,89,87,2,336,--,296,0,0,--,0.75,0.27,0.26,0.01,1.02,--,0.9,--,--,-- Dale Hawerchuk,L,C, "1, 1885,518,891,614,277,"1,409","3,754",740,0,0,--,0.44,0.75,0.52,0.23,1.19, 3.16,0.62,--,--,-- Darryl Sittler,L,C,"1,096", 484,637,420,217,"1,121","3,815",948,0,0,--,0.44,0.58,0.38,0.2,1.02,3.48,0.86,--,- Denis Savard, R,C,"1,196", 473,865,583,282,"1,338","3,142","1,336",0,0,--,0.4,0.72,0.49,0.24,1.12,2.63,1.12,--,--,-- Eric Lindros, R, C, 760, 372,493,304,189,865,"2,312","1,398", 739,146,"9,316:32",0.49,0.65,0.4,0.25,1.14,3.04,1.84,--,--,-- Evgeni Malkin,L,C,882,403,639,399,240,"1,042","2,979",932,531,363,"17,639:34",0.46,0.72,0.45,0.27,1.18,3.38,1.06,0.6,0.41,20:00 Gilbert Perreault,L,C,"1,191",512,814,516, 298,"1,326","3,077",500,0,0,--,0.43,0.68,0.43,0.25,1.11,2.58,0.42,--,--, Gordie Howe,R,R,"1,767",801,"1,049",746,303,"1,850","3,803","1,685",0,0,--,0.45,0.59,0.42,0.17,1.05,2.15,0.95,--,--,-- Guy Lafleur,R,R,"1,126",560, 793,492,301, "1,353","3,516", 399,0,0,--,0.5, 0.7,0.44,0.27, 1.2, 3.12,0.35,--,--,-- Harry Cameron, R,D,129,88,54,54,0,142,--,180,0,0,--,0.68,0.42,0.42,0,1.1,--,1.4,--,--,-- Jack Darragh,RR, 144,91,63,49,0,154,--, 129,0,0,--,0.63,0.44,0.34,0,1.07,--,0.9,--,--,-- Jari Kurri,R,R,"1,251",601,797, 463,334,"1,398","3,149",545,7,22,"1,044:26",0.48,0.64,0.37,0.27,1.12,2.52,0.44,--,--,-- Jaromir Jagr,L,R,'1,733",766,"1,155",684,471,"1,921","5,637","1,167", 322, 228,"24,972:04",0.44,0.67,0.39,0.27,1.11,3.25,0.67,--,-- Jean Beliveau,L,C,"1,125",507,712,444,268,"1,219","2,627","1,033",0,0,--,0.45,0.63,0.39,0.24,1.08,2.34,0.92,--,--,-- Joe Malone,L,C, 126, 143, 33, 32,1,176,--,57,0,0,--,1.13,0.26,0.25,0.01,1.4,--,0.45,--,--,-- Joe Mullen, R,R,"1,062",502,561,320, 241,"1,063","2,787", 241,0,0,--,0.47,0.53,0.3,0.23,1,2.62,0.23,--,--,-- Joe Sakic,L,C,"1,378",625,"1,016",577,439,"1,641","4,621", 614,282,343,"15,665:55",0.45,0.74,0.42,0.32,1.19,3.35,0.45,--,--,-- = = = = Kent Nilsson,L,C,553, 264,422,250, 172,686,"1,374", 116,0,0,--,0.48,0.76,0.45,0.31,1.24,2.48,0.21,--,--,-- Marcel Dionne,R,C,"1,348",731,"1,040", 662,378,"1,771","5,363", 600,0,0,--,0.54,0.77,0.49,0.28,1.31,3.98,0.45,--,--,-- Mario Lemieux, R, C, 915,690,"1,033",654,379,"1,723","3,633",834,52,45,"3,814:10",0.75,1.13,0.71,0.41,1.88,3.97,0.91,--,--,-- Mark Messier,L,C,"1,756",694,"1,193",751,442,"1,887","4,221","1,912", 272,210,"9,508:03",0.4,0.68,0.43,0.25,1.07,2.4,1.09,--,--, Mats Sundin,R,C,"1,346",564,785,512, 273, "1,349","4,015","1,093", 544,317,"16,319:30",0.42,0.58,0.38,0.2, 1, 2.98,0.81,--,--,-- Michel Goulet,L,L,"1,089",548,605,385, 220,"1,153","3,145", 825,0,0,--,0.5,0.56,0.35,0.2,1.06,2.89,0.76,--,--,- Mike Bossy,R,R,752,573,553,369,184,"1,126","2,707",210,0,0,--,0.76,0.74,0.49,0.24,1.5,3.6,0.28,--,--,-- Mike Rogers,L,C, 484, 202,317, 190, 127,519,"1,240", 184,0,0,--,0.42,0.65,0.39,0.26,1.07,2.56,0.38,--,--,-- Nikita Kucherov, L, R, 489,205,306,185,121,511,"1,392", 245, 267,155,"8,750:29",0.42,0.63,0.38,0.25,1.04,2.85,0.5,0.55,0.32,17:54 Pat LaFontaine, R, C, 865, 468,545, 350, 195,"1,013","2,664",552, 33, 14,"1,208:07",0.54,0.63,0.4,0.23,1.17,3.08,0.64,--,--,- Patrick Kane, L,R,948, 380, 614,385, 229,994,"3,096", 370, 257,196,"19,009:31",0.4,0.65,0.41,0.24,1.05,3.27,0.39,0.27,0.21, 20:03 Paul Coffey,L,D,"1,409", 396, "1,135",630,505, "1,531","4,389","1,802",72,155,"4,006:44",0.28,0.81,0.45,0.36,1.09, 3.11, 1.28,--,--,-- Paul Kariya, L,L,989,402,587,362, 225,989,"3,509",399,248,343,"16,590:29",0.41,0.59,0.37,0.23,1,3.55,0.4,--,--,-- Pavel Bure,L,R, 702,437,342,184,158,779,"3,130", 484,102,98,"8,041:38",0.62,0.49,0.26,0.23,1.11,4.46,0.69,--,- Peter Forsberg,L,C, 708, 249,636, 403, 233,885,"1,693", 690,521,160, "10,504:13",0.35,0.9,0.57,0.33,1.25,2.39,0.9 Peter Stastny,L,C,977,450,789,488,301,"1,239","2,374", 824,0,0,--,0.46,0.81,0.5,0.31,1.27,2.43,0.84,--,- Phil Esposito,L,C,"1,282",717,873,556, 317,"1,590","5, 166", 910,0,0,--,0.56,0.68,0.43,0.25,1.24, 4.03,0.71,--,--,-- Pierre Larouche, R, C, 812,395,427,273, 154,822,"2,298", 237,0,0,--,0.49,0.53,0.34,0.19,1.01,2.83,0.29,--,--, Pierre Turgeon,L,C,"1,294",515,812,520, 292,"1,327","3,154", 452,82,128,"9,036:58",0.4,0.63,0.4,0.23,1.03,2.44,0.35,--,--,-- Real Cloutier,L,R, 318, 146, 198, 120, 78, 344,918, 119,0,0,--,0.46,0.62,0.38,0.25, 1.08,2.89,0.37,--,--,-- Richard Martin, L,L,685,384,317, 198, 119,701,"2,526", 477,0,0,--,0.56,0.46,0.29,0.17,1.02,3.69,0.7,--,--,-- Ron Francis,L,C,"1,731",549,"1,249",797,452,"1,798","3,756",977, 176,301,"11,517:56",0.32,0.72,0.46,0.26,1.04,2.17,0.56,--,--, Sidney Crosby,L,C,960, 451,782,488, 294,"1,233","3,109",665,837,464,"20,035:29",0.47,0.81,0.51,0.31,1.28,3.24,0.69,0.87,0.48, 20:52 Stan Mikita,R,C,"1,396",541,926,585,341,"1,467","4,482","1,264",0,0,--,0.39,0.66,0.42,0.24,1.05,3.21,0.91,--,--,-- Steve Larmer,L,R,'1,006", 441,571,331, 240,"1,012","2,807",532,0,0,--,0.44,0.57,0.33,0.24,1.01,2.79,0.53,--,--,-- Steve Yzerman, R,C,"1,514", 692,"1,063", 650, 413,"1,755","4,602", 924, 268, 301, "9, 482:27",0.46,0.7,0.43,0.27,1.16,3.04,0.61,--,--,-- Steven Stamkos, R, C, 786, 410, 400, 249, 151,810,"2,447", 491,845, 335, "15, 243:35",0.52,0.51,0.32,0.19,1.03,3.11,0.62,1.07,0.43, 19:24 Syl Apps,L,C,423, 201, 231,172,59,432,--,56,0,0,--,0.48,0.55,0.41,0.14,1.02,--,0.13,--,-- Teemu Selanne, R,R,"1,451",684,773,493,280,"1,457","4,540",660,296,183,"20,679:07",0.47,0.53,0.34,0.19,1,3.13,0.45,--,- Theo Fleury,R,R,"1,084", 455,633,386, 247,"1,088","3,611","1,840", 378,172,"9,232:04",0.42,0.58,0.36,0.23,1,3.33,1.7,--,- Tim Kerr,R,R, 655, 370, 304,184,120,674,"1,985",596,0,0,--,0.56,0.46,0.28,0.18,1.03,3.03,0.91,--,--,-- Wayne Gretzky,L,C,"1,487",894,"1,963","1,324", 639,"2,857","5,088",577,11,22,"3,231:33",0.6,1.32,0.89,0.43,1.92,3.42,0.39,--,--,-- Ziggy Palffy,L,R,684,329,384, 226,158,713,"2,136",322,261, 202,"10,113:28",0.48,0.56,0.33,0.23,1.04,3.12,0.47,--,--,-- Enter filename: Scoring_per_Game.csv Shooting left: 39 right: 25 Position left: right: center: defense: 7 18 3 Off-side Shooter left-wing shooting right: right-wing shooting left: 8 Top Ten Points-Per-Game Player Position Points Per Game Wayne Gretzky 1.92 Mario Lemieux 1.88 Mike Bossy 1.50 Joe Malone 1.40 Bobby Orr 1.39 Connor McDavid 1.33 Marcel Dionne 1.31 Sidney Crosby 1.28 Peter Stastny 1.27 Peter Forsberg 1.25 ooooooooo Top Ten Games-Played Player Games Played Gordie Howe 1,767 Mark Messier 1,756 Jaromir Jagr 1,733 Ron Francis 1,731 Steve Yzerman 1,514 Wayne Gretzky 1,487 Teemu Selanne 1,451 Paul Coffey 1,409 Stan Mikita 1,396 Joe Sakic 1,378 Top Ten Shots-Taken Player Shots Taken Jaromir Jagr 5,637 Alex Ovechkin 5, 444 Marcel Dionne 5,363 Phil Esposito 5,166 Wayne Gretzky 5,088 Brett Hull 4,876 Joe Sakic 4,621 Steve Yzerman 4,602 Bobby Hull 4,577 Teemu Selanne 4,540 We are interested in answering three questions: 1. If we add up the individual state unauthorized immigration population (at index 1), do we get the same value as in the summative row labeled U.S.? (Food for thought: why aren't they the same?) 2. Which states have a larger percentage unauthorized immigrant population (at index 2) than the value in the summative row labeled U.S."? 3. Consider the column on the industry with the largest number of unauthorized immigrant workers (at index 9). Which industry is listed as the largest in the most states and how many states? We provide a skeleton program with a main() and four function headers, one function to read the file plus one function for each question. 1. def read file (fp): this function takes a file pointer as an argument and returns a list of lists. The list of lists where each list is a list of the contents of each data row of the file- ignoring header rows, footnote rows, and empty rows. The list will contain 52 lists, one for each of the fifty states plus the District of Columbia and one summative row (the row labeled U.S."). The order of the list will be the same as the order in the file. (Hint: use csv.reader (), see notes below.) 2. def get totals (L): this function takes the list of lists returned by the read file () function and returns two values. The data of interest for this function is the column at index 1: unauthorized immigrant population. Return the value in the summative row (labeled U.S.) and the sum of the other 51 data rows. The purpose of this function is to gather data to answer question #1 above. def get_largest_states (L) : this function takes the list of lists returned by the read file () function and returns a list. The data of interest for this function is the column at index 2: unauthorized immigration % of population. The returned list is a list of states whose value is greater than the summative value (the value in the row labeled U.S."). Since District of Columbia" is in the file we will include it as a "state. The returned list will be in alphabetical order (the order of the original file). The purpose of this function is to gather data to answer question #2 above. 4. def get_industry_counts (L): this function takes the list of lists returned by the read file() function and returns a list of lists. The data of interest for this function is the column at index 9: industry with largest number of unauthorized immigrant workers. The list of lists returned is a list of industries and occurrences in the column (excluding the summative data from the row labeled U.S.). The returned list will be sorted by occurrences (largest first) and will look like this: [[industryl, counti], [industry2, count2], ...] The purpose of this function is to gather data to answer question #3 above. (Hint: use key=itemgetter (1) to sort on index 1 of the lists.) Notes and Suggestions 1. Using the CSV package: Remember import csv reader = csv.reader (fp) # attaches a reader to the file fp next (reader, None) # skips a line, such as a header line for line in reader: # line is a list import csv from operator import itemgetter INDUSTRIES = ['Agriculture', 'Business services', 'Construction', 'Leisure/hospitality', 'Manufacturing' ] def read_file(fp): I'Docstring'!! return None # temoprary return value so main runs def get_totals (L): "''Docstring'!! return None, None # temoprary return value so main runs def get_industry_counts (L): ''Docstring' return None # temoprary return value so main runs def get_largest_states (L): '' 'Docstring'!! return None # temoprary return value so main runs def main(): fp = open("immigration.csv") L = read_file(fp) us_pop, total_pop = get_totals (L) if us_pop and total pop: # if their values are not None print(" Data on illegal Immigration ") print("Summative:", us_pop) print("Total :", total_pop) states = get_largest_states (L) if states: # if their value is not None print(" States with large immigrant populations") for state in states: state = state.replace(' ','') print (state) counters = get_industry_counts (L) if counters: # if their value is not None print(" Industries with largest immigrant populations by state") print("{:24s} {:10s}".format("industry","count")) for tup in counters: print("{:24s} {:2d}".format(tup[0], tup[1])) if __name__ == "_main__": main() Player,s/c, Pos, GP, G, A, A1, A2,P, S, PIM, Hits, BKS, TOI, G/GP, A/GP, A1/GP, A2/GP,P/GP, S/GP, PIM/GP,Hits/GP, BkS/GP, TOI/GP Adam Oates, R, C, "1,337",341,"1,079",664,415,"1,420","2,392", 415,79,210,"10,512:35",0.26,0.81,0.5,0.31,1.06,1.79,0.31,--,--,- Alex Ovechkin, R,L,"1,129",684,569,351,218,"1,253","5,444",711,"2,972", 438,"23,635:51",0.61,0.5,0.31,0.19,1.11,4.82,0.63,2.63,0.39, 20:56 Alexander Mogilny,L,R,990, 473,559,374, 185,"1,032","2,966", 432,281,68,"8,418:43",0.48,0.56,0.38,0.19,1.04,3,0.44,--,--,-- Artemi Panarin, R, L, 365, 140, 241, 151, 90,381,949, 114, 114,72,"7,150:45",0.38,0.66,0.41,0.25, 1.04,2.6,0.31,0.31,0.2,19:35 Auston Matthews,L,C, 257, 142,117,75, 42, 259,886, 44,89, 213,"4,742:55",0.55,0.46,0.29,0.16,1.01,3.45,0.17,0.35,0.83, 18:27 Bernie Federko,L,C,"1,000",369,761,469,292,"1,130","2,074", 487,0,0,--,0.37,0.76,0.47,0.29,1.13,2.07,0.49,--,--,-- Bernie Nicholls,R,C,"1,127", 475,734,458,276,"1,209","3,231","1,292",65, 25,"1,011:55",0.42,0.65,0.41,0.24,1.07,2.87,1.15,--,--, Bill Cowley,L,C,549,195,354, 253,99,549,--,143,0,0,--,0.36,0.64,0.46,0.18,1,--,0.26,--,--,-- Bobby Clarke,L,C,"1,144", 358,852,541,311,"1,210","2,582","1,453",0,0,--,0.31,0.74,0.47,0.27,1.06,2.26,1.27,--,-- Bobby Hull,L,L,"1,063",610,560,366,194,"1,170","4,577",634,0,0,--,0.57,0.53,0.34,0.18,1.1,4.31,0.6,--,--,-- Bobby Orr,L,D, 657, 270, 645,326,319,915,"3,058",953,0,0,--,0.41,0.98,0.5, 0.49,1.39,4.65,1.45,--,--,-- Brett Hull,R,R,'1,269",741,650,388,262,"1,391","4,876",458,142,122,"9,648:19",0.58,0.51,0.31,0.21,1.1,3.84,0.36,--,--,-- Bryan Trottier,L,C,"1,279",524,901,542,359,"1,425","2,841", 912,0,0,--,0.41,0.7,0.42,0.28,1.11,2.22,0.71,--,-- Connor McDavid,L,C,333, 152, 290,184,106,442,"1,017",110,142,133,"7,149:33",0.46,0.87,0.55,0.32,1.33,3.05,0.33,0.43,0.4,21:28 Cy Denneny,L,L,329, 247,89,87,2,336,--,296,0,0,--,0.75,0.27,0.26,0.01,1.02,--,0.9,--,--,-- Dale Hawerchuk,L,C, "1, 1885,518,891,614,277,"1,409","3,754",740,0,0,--,0.44,0.75,0.52,0.23,1.19, 3.16,0.62,--,--,-- Darryl Sittler,L,C,"1,096", 484,637,420,217,"1,121","3,815",948,0,0,--,0.44,0.58,0.38,0.2,1.02,3.48,0.86,--,- Denis Savard, R,C,"1,196", 473,865,583,282,"1,338","3,142","1,336",0,0,--,0.4,0.72,0.49,0.24,1.12,2.63,1.12,--,--,-- Eric Lindros, R, C, 760, 372,493,304,189,865,"2,312","1,398", 739,146,"9,316:32",0.49,0.65,0.4,0.25,1.14,3.04,1.84,--,--,-- Evgeni Malkin,L,C,882,403,639,399,240,"1,042","2,979",932,531,363,"17,639:34",0.46,0.72,0.45,0.27,1.18,3.38,1.06,0.6,0.41,20:00 Gilbert Perreault,L,C,"1,191",512,814,516, 298,"1,326","3,077",500,0,0,--,0.43,0.68,0.43,0.25,1.11,2.58,0.42,--,--, Gordie Howe,R,R,"1,767",801,"1,049",746,303,"1,850","3,803","1,685",0,0,--,0.45,0.59,0.42,0.17,1.05,2.15,0.95,--,--,-- Guy Lafleur,R,R,"1,126",560, 793,492,301, "1,353","3,516", 399,0,0,--,0.5, 0.7,0.44,0.27, 1.2, 3.12,0.35,--,--,-- Harry Cameron, R,D,129,88,54,54,0,142,--,180,0,0,--,0.68,0.42,0.42,0,1.1,--,1.4,--,--,-- Jack Darragh,RR, 144,91,63,49,0,154,--, 129,0,0,--,0.63,0.44,0.34,0,1.07,--,0.9,--,--,-- Jari Kurri,R,R,"1,251",601,797, 463,334,"1,398","3,149",545,7,22,"1,044:26",0.48,0.64,0.37,0.27,1.12,2.52,0.44,--,--,-- Jaromir Jagr,L,R,'1,733",766,"1,155",684,471,"1,921","5,637","1,167", 322, 228,"24,972:04",0.44,0.67,0.39,0.27,1.11,3.25,0.67,--,-- Jean Beliveau,L,C,"1,125",507,712,444,268,"1,219","2,627","1,033",0,0,--,0.45,0.63,0.39,0.24,1.08,2.34,0.92,--,--,-- Joe Malone,L,C, 126, 143, 33, 32,1,176,--,57,0,0,--,1.13,0.26,0.25,0.01,1.4,--,0.45,--,--,-- Joe Mullen, R,R,"1,062",502,561,320, 241,"1,063","2,787", 241,0,0,--,0.47,0.53,0.3,0.23,1,2.62,0.23,--,--,-- Joe Sakic,L,C,"1,378",625,"1,016",577,439,"1,641","4,621", 614,282,343,"15,665:55",0.45,0.74,0.42,0.32,1.19,3.35,0.45,--,--,-- = = = = Kent Nilsson,L,C,553, 264,422,250, 172,686,"1,374", 116,0,0,--,0.48,0.76,0.45,0.31,1.24,2.48,0.21,--,--,-- Marcel Dionne,R,C,"1,348",731,"1,040", 662,378,"1,771","5,363", 600,0,0,--,0.54,0.77,0.49,0.28,1.31,3.98,0.45,--,--,-- Mario Lemieux, R, C, 915,690,"1,033",654,379,"1,723","3,633",834,52,45,"3,814:10",0.75,1.13,0.71,0.41,1.88,3.97,0.91,--,--,-- Mark Messier,L,C,"1,756",694,"1,193",751,442,"1,887","4,221","1,912", 272,210,"9,508:03",0.4,0.68,0.43,0.25,1.07,2.4,1.09,--,--, Mats Sundin,R,C,"1,346",564,785,512, 273, "1,349","4,015","1,093", 544,317,"16,319:30",0.42,0.58,0.38,0.2, 1, 2.98,0.81,--,--,-- Michel Goulet,L,L,"1,089",548,605,385, 220,"1,153","3,145", 825,0,0,--,0.5,0.56,0.35,0.2,1.06,2.89,0.76,--,--,- Mike Bossy,R,R,752,573,553,369,184,"1,126","2,707",210,0,0,--,0.76,0.74,0.49,0.24,1.5,3.6,0.28,--,--,-- Mike Rogers,L,C, 484, 202,317, 190, 127,519,"1,240", 184,0,0,--,0.42,0.65,0.39,0.26,1.07,2.56,0.38,--,--,-- Nikita Kucherov, L, R, 489,205,306,185,121,511,"1,392", 245, 267,155,"8,750:29",0.42,0.63,0.38,0.25,1.04,2.85,0.5,0.55,0.32,17:54 Pat LaFontaine, R, C, 865, 468,545, 350, 195,"1,013","2,664",552, 33, 14,"1,208:07",0.54,0.63,0.4,0.23,1.17,3.08,0.64,--,--,- Patrick Kane, L,R,948, 380, 614,385, 229,994,"3,096", 370, 257,196,"19,009:31",0.4,0.65,0.41,0.24,1.05,3.27,0.39,0.27,0.21, 20:03 Paul Coffey,L,D,"1,409", 396, "1,135",630,505, "1,531","4,389","1,802",72,155,"4,006:44",0.28,0.81,0.45,0.36,1.09, 3.11, 1.28,--,--,-- Paul Kariya, L,L,989,402,587,362, 225,989,"3,509",399,248,343,"16,590:29",0.41,0.59,0.37,0.23,1,3.55,0.4,--,--,-- Pavel Bure,L,R, 702,437,342,184,158,779,"3,130", 484,102,98,"8,041:38",0.62,0.49,0.26,0.23,1.11,4.46,0.69,--,- Peter Forsberg,L,C, 708, 249,636, 403, 233,885,"1,693", 690,521,160, "10,504:13",0.35,0.9,0.57,0.33,1.25,2.39,0.9 Peter Stastny,L,C,977,450,789,488,301,"1,239","2,374", 824,0,0,--,0.46,0.81,0.5,0.31,1.27,2.43,0.84,--,- Phil Esposito,L,C,"1,282",717,873,556, 317,"1,590","5, 166", 910,0,0,--,0.56,0.68,0.43,0.25,1.24, 4.03,0.71,--,--,-- Pierre Larouche, R, C, 812,395,427,273, 154,822,"2,298", 237,0,0,--,0.49,0.53,0.34,0.19,1.01,2.83,0.29,--,--, Pierre Turgeon,L,C,"1,294",515,812,520, 292,"1,327","3,154", 452,82,128,"9,036:58",0.4,0.63,0.4,0.23,1.03,2.44,0.35,--,--,-- Real Cloutier,L,R, 318, 146, 198, 120, 78, 344,918, 119,0,0,--,0.46,0.62,0.38,0.25, 1.08,2.89,0.37,--,--,-- Richard Martin, L,L,685,384,317, 198, 119,701,"2,526", 477,0,0,--,0.56,0.46,0.29,0.17,1.02,3.69,0.7,--,--,-- Ron Francis,L,C,"1,731",549,"1,249",797,452,"1,798","3,756",977, 176,301,"11,517:56",0.32,0.72,0.46,0.26,1.04,2.17,0.56,--,--, Sidney Crosby,L,C,960, 451,782,488, 294,"1,233","3,109",665,837,464,"20,035:29",0.47,0.81,0.51,0.31,1.28,3.24,0.69,0.87,0.48, 20:52 Stan Mikita,R,C,"1,396",541,926,585,341,"1,467","4,482","1,264",0,0,--,0.39,0.66,0.42,0.24,1.05,3.21,0.91,--,--,-- Steve Larmer,L,R,'1,006", 441,571,331, 240,"1,012","2,807",532,0,0,--,0.44,0.57,0.33,0.24,1.01,2.79,0.53,--,--,-- Steve Yzerman, R,C,"1,514", 692,"1,063", 650, 413,"1,755","4,602", 924, 268, 301, "9, 482:27",0.46,0.7,0.43,0.27,1.16,3.04,0.61,--,--,-- Steven Stamkos, R, C, 786, 410, 400, 249, 151,810,"2,447", 491,845, 335, "15, 243:35",0.52,0.51,0.32,0.19,1.03,3.11,0.62,1.07,0.43, 19:24 Syl Apps,L,C,423, 201, 231,172,59,432,--,56,0,0,--,0.48,0.55,0.41,0.14,1.02,--,0.13,--,-- Teemu Selanne, R,R,"1,451",684,773,493,280,"1,457","4,540",660,296,183,"20,679:07",0.47,0.53,0.34,0.19,1,3.13,0.45,--,- Theo Fleury,R,R,"1,084", 455,633,386, 247,"1,088","3,611","1,840", 378,172,"9,232:04",0.42,0.58,0.36,0.23,1,3.33,1.7,--,- Tim Kerr,R,R, 655, 370, 304,184,120,674,"1,985",596,0,0,--,0.56,0.46,0.28,0.18,1.03,3.03,0.91,--,--,-- Wayne Gretzky,L,C,"1,487",894,"1,963","1,324", 639,"2,857","5,088",577,11,22,"3,231:33",0.6,1.32,0.89,0.43,1.92,3.42,0.39,--,--,-- Ziggy Palffy,L,R,684,329,384, 226,158,713,"2,136",322,261, 202,"10,113:28",0.48,0.56,0.33,0.23,1.04,3.12,0.47,--,--,-- Enter filename: Scoring_per_Game.csv Shooting left: 39 right: 25 Position left: right: center: defense: 7 18 3 Off-side Shooter left-wing shooting right: right-wing shooting left: 8 Top Ten Points-Per-Game Player Position Points Per Game Wayne Gretzky 1.92 Mario Lemieux 1.88 Mike Bossy 1.50 Joe Malone 1.40 Bobby Orr 1.39 Connor McDavid 1.33 Marcel Dionne 1.31 Sidney Crosby 1.28 Peter Stastny 1.27 Peter Forsberg 1.25 ooooooooo Top Ten Games-Played Player Games Played Gordie Howe 1,767 Mark Messier 1,756 Jaromir Jagr 1,733 Ron Francis 1,731 Steve Yzerman 1,514 Wayne Gretzky 1,487 Teemu Selanne 1,451 Paul Coffey 1,409 Stan Mikita 1,396 Joe Sakic 1,378 Top Ten Shots-Taken Player Shots Taken Jaromir Jagr 5,637 Alex Ovechkin 5, 444 Marcel Dionne 5,363 Phil Esposito 5,166 Wayne Gretzky 5,088 Brett Hull 4,876 Joe Sakic 4,621 Steve Yzerman 4,602 Bobby Hull 4,577 Teemu Selanne 4,540
