Please use pandas library.

This is the content in olympics.csv?  0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 , Summer,01 !,02 !,03 !,Total, Winter,01 !,02 !,03 !,Total, Games,01 !,02 !,03 !,Combined total Afghanistan (AFG),13,0,0,2,2,0,0,0,0,0,13,0,0,2,2 Algeria (ALG),12,5,2,8,15,3,0,0,0,0,15,5,2,8,15 Argentina (ARG),23,18,24,28,70,18,0,0,0,0,41,18,24,28,70 Armenia (ARM),5,1,2,9,12,6,0,0,0,0,11,1,2,9,12 Australasia (ANZ) [ANZ],2,3,4,5,12,0,0,0,0,0,2,3,4,5,12 Australia (AUS) [AUS] [Z],25,139,152,177,468,18,5,3,4,12,43,144,155,181,480 Austria (AUT),26,18,33,35,86,22,59,78,81,218,48,77,111,116,304 Azerbaijan (AZE),5,6,5,15,26,5,0,0,0,0,10,6,5,15,26 Bahamas (BAH),15,5,2,5,12,0,0,0,0,0,15,5,2,5,12 Bahrain (BRN),8,0,0,1,1,0,0,0,0,0,8,0,0,1,1 Barbados (BAR) [BAR],11,0,0,1,1,0,0,0,0,0,11,0,0,1,1 Belarus (BLR),5,12,24,39,75,6,6,4,5,15,11,18,28,44,90 Belgium (BEL),25,37,52,53,142,20,1,1,3,5,45,38,53,56,147 Bermuda (BER),17,0,0,1,1,7,0,0,0,0,24,0,0,1,1 Bohemia (BOH) [BOH] [Z],3,0,1,3,4,0,0,0,0,0,3,0,1,3,4 Botswana (BOT),9,0,1,0,1,0,0,0,0,0,9,0,1,0,1 Brazil (BRA),21,23,30,55,108,7,0,0,0,0,28,23,30,55,108 British West Indies (BWI) [BWI],1,0,0,2,2,0,0,0,0,0,1,0,0,2,2 Bulgaria (BUL) [H],19,51,85,78,214,19,1,2,3,6,38,52,87,81,220 Burundi (BDI),5,1,0,0,1,0,0,0,0,0,5,1,0,0,1 Cameroon (CMR),13,3,1,1,5,1,0,0,0,0,14,3,1,1,5 Canada (CAN),25,59,99,121,279,22,62,56,52,170,47,121,155,173,449 Chile (CHI) [I],22,2,7,4,13,16,0,0,0,0,38,2,7,4,13 China (CHN) [CHN],9,201,146,126,473,10,12,22,19,53,19,213,168,145,526 Colombia (COL),18,2,6,11,19,1,0,0,0,0,19,2,6,11,19 Costa Rica (CRC),14,1,1,2,4,6,0,0,0,0,20,1,1,2,4 Ivory Coast (CIV) [CIV],12,0,1,0,1,0,0,0,0,0,12,0,1,0,1 Croatia (CRO),6,6,7,10,23,7,4,6,1,11,13,10,13,11,34 Cuba (CUB) [Z],19,72,67,70,209,0,0,0,0,0,19,72,67,70,209 Cyprus (CYP),9,0,1,0,1,10,0,0,0,0,19,0,1,0,1 Czech Republic (CZE) [CZE],5,14,15,15,44,6,7,9,8,24,11,21,24,23,68 Czechoslovakia (TCH) [TCH],16,49,49,45,143,16,2,8,15,25,32,51,57,60,168 Denmark (DEN) [Z],26,43,68,68,179,13,0,1,0,1,39,43,69,68,180 Djibouti (DJI) [B],7,0,0,1,1,0,0,0,0,0,7,0,0,1,1 Dominican Republic (DOM),13,3,2,1,6,0,0,0,0,0,13,3,2,1,6 Ecuador (ECU),13,1,1,0,2,0,0,0,0,0,13,1,1,0,2 Egypt (EGY) [EGY] [Z],21,7,9,10,26,1,0,0,0,0,22,7,9,10,26 Eritrea (ERI),4,0,0,1,1,0,0,0,0,0,4,0,0,1,1 Estonia (EST),11,9,9,15,33,9,4,2,1,7,20,13,11,16,40 Ethiopia (ETH),12,21,7,17,45,2,0,0,0,0,14,21,7,17,45 Finland (FIN),24,101,84,117,302,22,42,62,57,161,46,143,146,174,463 France (FRA) [O] [P] [Z],27,202,223,246,671,22,31,31,47,109,49,233,254,293,780 Gabon (GAB),9,0,1,0,1,0,0,0,0,0,9,0,1,0,1 Georgia (GEO),5,6,5,14,25,6,0,0,0,0,11,6,5,14,25 Germany (GER) [GER] [Z],15,174,182,217,573,11,78,78,53,209,26,252,260,270,782 United Team of Germany (EUA) [EUA],3,28,54,36,118,3,8,6,5,19,6,36,60,41,137 East Germany (GDR) [GDR],5,153,129,127,409,6,39,36,35,110,11,192,165,162,519 West Germany (FRG) [FRG],5,56,67,81,204,6,11,15,13,39,11,67,82,94,243 Ghana (GHA) [GHA],13,0,1,3,4,1,0,0,0,0,14,0,1,3,4 Great Britain (GBR) [GBR] [Z],27,236,272,272,780,22,10,4,12,26,49,246,276,284,806 Greece (GRE) [Z],27,30,42,39,111,18,0,0,0,0,45,30,42,39,111 Grenada (GRN),8,1,0,0,1,0,0,0,0,0,8,1,0,0,1 Guatemala (GUA),13,0,1,0,1,1,0,0,0,0,14,0,1,0,1 Guyana (GUY) [GUY],16,0,0,1,1,0,0,0,0,0,16,0,0,1,1 Haiti (HAI) [J],14,0,1,1,2,0,0,0,0,0,14,0,1,1,2 Hong Kong (HKG) [HKG],15,1,1,1,3,4,0,0,0,0,19,1,1,1,3 Hungary (HUN),25,167,144,165,476,22,0,2,4,6,47,167,146,169,482 Iceland (ISL),19,0,2,2,4,17,0,0,0,0,36,0,2,2,4 India (IND) [F],23,9,6,11,26,9,0,0,0,0,32,9,6,11,26 Indonesia (INA),14,6,10,11,27,0,0,0,0,0,14,6,10,11,27 Iran (IRI) [K],15,15,20,25,60,10,0,0,0,0,25,15,20,25,60 Iraq (IRQ),13,0,0,1,1,0,0,0,0,0,13,0,0,1,1 Ireland (IRL),20,9,8,12,29,6,0,0,0,0,26,9,8,12,29 Israel (ISR),15,1,1,5,7,6,0,0,0,0,21,1,1,5,7 Italy (ITA) [M] [S],26,198,166,185,549,22,37,34,43,114,48,235,200,228,663 Jamaica (JAM) [JAM],16,17,30,20,67,7,0,0,0,0,23,17,30,20,67 Japan (JPN),21,130,126,142,398,20,10,17,18,45,41,140,143,160,443 Kazakhstan (KAZ),5,16,17,19,52,6,1,3,3,7,11,17,20,22,59 Kenya (KEN),13,25,32,29,86,3,0,0,0,0,16,25,32,29,86 North Korea (PRK),9,14,12,21,47,8,0,1,1,2,17,14,13,22,49 South Korea (KOR),16,81,82,80,243,17,26,17,10,53,33,107,99,90,296 Kuwait (KUW),12,0,0,2,2,0,0,0,0,0,12,0,0,2,2 Kyrgyzstan (KGZ),5,0,1,2,3,6,0,0,0,0,11,0,1,2,3 Latvia (LAT),10,3,11,5,19,10,0,4,3,7,20,3,15,8,26 Lebanon (LIB),16,0,2,2,4,16,0,0,0,0,32,0,2,2,4 Liechtenstein (LIE),16,0,0,0,0,18,2,2,5,9,34,2,2,5,9 Lithuania (LTU),8,6,5,10,21,8,0,0,0,0,16,6,5,10,21 Luxembourg (LUX) [O],22,1,1,0,2,8,0,2,0,2,30,1,3,0,4 Macedonia (MKD),5,0,0,1,1,5,0,0,0,0,10,0,0,1,1 Malaysia (MAS) [MAS],12,0,3,3,6,0,0,0,0,0,12,0,3,3,6 Mauritius (MRI),8,0,0,1,1,0,0,0,0,0,8,0,0,1,1 Mexico (MEX),22,13,21,28,62,8,0,0,0,0,30,13,21,28,62 Moldova (MDA),5,0,2,5,7,6,0,0,0,0,11,0,2,5,7 Mongolia (MGL),12,2,9,13,24,13,0,0,0,0,25,2,9,13,24 Montenegro (MNE),2,0,1,0,1,2,0,0,0,0,4,0,1,0,1 Morocco (MAR),13,6,5,11,22,6,0,0,0,0,19,6,5,11,22 Mozambique (MOZ),9,1,0,1,2,0,0,0,0,0,9,1,0,1,2 Namibia (NAM),6,0,4,0,4,0,0,0,0,0,6,0,4,0,4 Netherlands (NED) [Z],25,77,85,104,266,20,37,38,35,110,45,114,123,139,376 Netherlands Antilles (AHO) [AHO] [I],13,0,1,0,1,2,0,0,0,0,15,0,1,0,1 New Zealand (NZL) [NZL],22,42,18,39,99,15,0,1,0,1,37,42,19,39,100 Niger (NIG),11,0,0,1,1,0,0,0,0,0,11,0,0,1,1 Nigeria (NGR),15,3,8,12,23,0,0,0,0,0,15,3,8,12,23 Norway (NOR) [Q],24,56,49,43,148,22,118,111,100,329,46,174,160,143,477 Pakistan (PAK),16,3,3,4,10,2,0,0,0,0,18,3,3,4,10 Panama (PAN),16,1,0,2,3,0,0,0,0,0,16,1,0,2,3 Paraguay (PAR),11,0,1,0,1,1,0,0,0,0,12,0,1,0,1 Peru (PER) [L],17,1,3,0,4,2,0,0,0,0,19,1,3,0,4 Philippines (PHI),20,0,2,7,9,4,0,0,0,0,24,0,2,7,9 Poland (POL),20,64,82,125,271,22,6,7,7,20,42,70,89,132,291 Portugal (POR),23,4,8,11,23,7,0,0,0,0,30,4,8,11,23 Puerto Rico (PUR),17,0,2,6,8,6,0,0,0,0,23,0,2,6,8 Qatar (QAT),8,0,0,4,4,0,0,0,0,0,8,0,0,4,4 Romania (ROU),20,88,94,119,301,20,0,0,1,1,40,88,94,120,302 Russia (RUS) [RUS],5,132,121,142,395,6,49,40,35,124,11,181,161,177,519 Russian Empire (RU1) [RU1],3,1,4,3,8,0,0,0,0,0,3,1,4,3,8 Soviet Union (URS) [URS],9,395,319,296,1010,9,78,57,59,194,18,473,376,355,1204 Unified Team (EUN) [EUN],1,45,38,29,112,1,9,6,8,23,2,54,44,37,135 Saudi Arabia (KSA),10,0,1,2,3,0,0,0,0,0,10,0,1,2,3 Senegal (SEN),13,0,1,0,1,5,0,0,0,0,18,0,1,0,1 Serbia (SRB) [SRB],3,1,2,4,7,2,0,0,0,0,5,1,2,4,7 Serbia and Montenegro (SCG) [SCG],3,2,4,3,9,3,0,0,0,0,6,2,4,3,9 Singapore (SIN),15,0,2,2,4,0,0,0,0,0,15,0,2,2,4 Slovakia (SVK) [SVK],5,7,9,8,24,6,2,2,1,5,11,9,11,9,29 Slovenia (SLO),6,4,6,9,19,7,2,4,9,15,13,6,10,18,34 South Africa (RSA),18,23,26,27,76,6,0,0,0,0,24,23,26,27,76 Spain (ESP) [Z],22,37,59,35,131,19,1,0,1,2,41,38,59,36,133 Sri Lanka (SRI) [SRI],16,0,2,0,2,0,0,0,0,0,16,0,2,0,2 Sudan (SUD),11,0,1,0,1,0,0,0,0,0,11,0,1,0,1 Suriname (SUR) [E],11,1,0,1,2,0,0,0,0,0,11,1,0,1,2 Sweden (SWE) [Z],26,143,164,176,483,22,50,40,54,144,48,193,204,230,627 Switzerland (SUI),27,47,73,65,185,22,50,40,48,138,49,97,113,113,323 Syria (SYR),12,1,1,1,3,0,0,0,0,0,12,1,1,1,3 Chinese Taipei (TPE) [TPE] [TPE2],13,2,7,12,21,11,0,0,0,0,24,2,7,12,21 Tajikistan (TJK),5,0,1,2,3,4,0,0,0,0,9,0,1,2,3 Tanzania (TAN) [TAN],12,0,2,0,2,0,0,0,0,0,12,0,2,0,2 Thailand (THA),15,7,6,11,24,3,0,0,0,0,18,7,6,11,24 Togo (TOG),9,0,0,1,1,1,0,0,0,0,10,0,0,1,1 Tonga (TGA),8,0,1,0,1,1,0,0,0,0,9,0,1,0,1 Trinidad and Tobago (TRI) [TRI],16,2,5,11,18,3,0,0,0,0,19,2,5,11,18 Tunisia (TUN),13,3,3,4,10,0,0,0,0,0,13,3,3,4,10 Turkey (TUR),21,39,25,24,88,16,0,0,0,0,37,39,25,24,88 Uganda (UGA),14,2,3,2,7,0,0,0,0,0,14,2,3,2,7 Ukraine (UKR),5,33,27,55,115,6,2,1,4,7,11,35,28,59,122 United Arab Emirates (UAE),8,1,0,0,1,0,0,0,0,0,8,1,0,0,1 United States (USA) [P] [Q] [R] [Z],26,976,757,666,2399,22,96,102,84,282,48,1072,859,750,2681 Uruguay (URU),20,2,2,6,10,1,0,0,0,0,21,2,2,6,10 Uzbekistan (UZB),5,5,5,10,20,6,1,0,0,1,11,6,5,10,21 Venezuela (VEN),17,2,2,8,12,4,0,0,0,0,21,2,2,8,12 Vietnam (VIE),14,0,2,0,2,0,0,0,0,0,14,0,2,0,2 Virgin Islands (ISV),11,0,1,0,1,7,0,0,0,0,18,0,1,0,1 Yugoslavia (YUG) [YUG],16,26,29,28,83,14,0,3,1,4,30,26,32,29,87 Independent Olympic Participants (IOP) [IOP],1,0,1,2,3,0,0,0,0,0,1,0,1,2,3 Zambia (ZAM) [ZAM],12,0,1,1,2,0,0,0,0,0,12,0,1,1,2 Zimbabwe (ZIM) [ZIM],12,3,4,1,8,1,0,0,0,0,13,3,4,1,8 Mixed team (ZZX) [ZZX],3,8,5,4,17,0,0,0,0,0,3,8,5,4,17 Totals,27,4809,4775,5130,14714,22,959,958,948,2865,49,5768,5733,6078,17579 

The Olympics Get The Data Download olympics.csv and place it in the same directory as your lab lab9.py The following function loads the olympics dataset olympics.csv), which was derived from the Wikipedia entry on All Time Olympic Games Medals, and does some basic data cleaning. def get0lympicData): df - pd.read_csv('olympics.csv, index_col-0, skiprows-1) #fix some column names for col in df.columns: if coll:2 '01': if coll:2]'2': if coll:21-'03' df.rename (columns-col: "Gold+col[4:]], inplace-True) df.rename (columns-col: 'Silver'+col[4:1, inplace-True) df. rename(coLumns-[col: 'Bronze'+col[4:1, inplace-True) df.rename(columns (col:+ol[l:], inplace-True) names ids-df. index. str, split(?N df. index-names-ids. str[0] # the [0] element is the country name (new index) df [, ID'1 = names-ids. str[1].str [:3] # the [1] element is the abbreviation or ID (take first 3 characters from that) df-df.drop(' Totals') # drop the totals row, we'll compute it if we need it return df (') # split the index by '(' Use getolymicData to load your data and assign it to a variable. Use the head) panda method to make sure the data looks ok. As in: olympicData-get0lympicData) olympicData.head() Note: In this table the columns ending in ".1" (as in "Gold.1") are medal counts for the winter olympics. The columns ending in ".2" (as in "Gold.2") are the sum of the summer and winter olympics. The columns with no number (e.g. "Gold") are for the summer olympics. Use the dataset to answer the questions below. The Olympics Get The Data Download olympics.csv and place it in the same directory as your lab lab9.py The following function loads the olympics dataset olympics.csv), which was derived from the Wikipedia entry on All Time Olympic Games Medals, and does some basic data cleaning. def get0lympicData): df - pd.read_csv('olympics.csv, index_col-0, skiprows-1) #fix some column names for col in df.columns: if coll:2 '01': if coll:2]'2': if coll:21-'03' df.rename (columns-col: "Gold+col[4:]], inplace-True) df.rename (columns-col: 'Silver'+col[4:1, inplace-True) df. rename(coLumns-[col: 'Bronze'+col[4:1, inplace-True) df.rename(columns (col:+ol[l:], inplace-True) names ids-df. index. str, split(?N df. index-names-ids. str[0] # the [0] element is the country name (new index) df [, ID'1 = names-ids. str[1].str [:3] # the [1] element is the abbreviation or ID (take first 3 characters from that) df-df.drop(' Totals') # drop the totals row, we'll compute it if we need it return df (') # split the index by '(' Use getolymicData to load your data and assign it to a variable. Use the head) panda method to make sure the data looks ok. As in: olympicData-get0lympicData) olympicData.head() Note: In this table the columns ending in ".1" (as in "Gold.1") are medal counts for the winter olympics. The columns ending in ".2" (as in "Gold.2") are the sum of the summer and winter olympics. The columns with no number (e.g. "Gold") are for the summer olympics. Use the dataset to answer the questions below