Python using Jupyter

data set: worldbankhealth.csv below

1 country_name 2 Middle East & North Africa (IDA & IBRD countries) 3 Armenia 4 Georgia 5 Tanzania 6 Cambodia 7 Mongolia 8 Syrian Arab Republic 9 Solomon Islands 10 Zimbabwe 11 Kazakhstan 12 Norway 13 Congo, Dem. Rep. 14 Libya 15 Turkmenistan 16 Latin America & the Caribbean (IDA & IBRD countries) 17 Ethiopia 18 Guatemala 19 Canada 20 Pre-demographic dividend 21 Slovenia 22 Pakistan 23 Yemen, Rep. 24 Niger 25 Cambodia 26 Samoa Hong Kong SAR, China Trinidad and Tobago 29 Lebanon 30 Korea, Dem. PeopleTMs Rep. 31 Namibia 32 Hong Kong SAR, China 33 Namibia 34 Switzerland 35 Georgia 36 Uganda worldhankhealth data country_code indicator_name TMN Population ages 65 and above, male ARM Nurses and midwives (per 1,000 people) GEO Male population 25-29 TZA Labor force, total KHM Age population, age 16, female, interpolated MNG Female population 20-24 SYR Population ages 15-64, female (% of total) SLB Male population 50-54 ZWE Population ages 45-49, female (% of female population) KAZ Female population 80+ NOR Population, male COD Birth rate, crude (per 1,000 people) LBY Rural population (% of total population) TKM Mortality rate, adult, female (per 1,000 female adults) TLA Life expectancy at birth, total (years) ETH Age population, age 18, male, interpolated GTM Population ages 65 and above, female CAN Population, female (% of total) PRE Male population 65-69 SVN Age population, age 10, female, interpolated PAK Population ages 00-14% of total) YEM Population ages 05-09, female % of female population) NER Low-birthweight babies (% of births) KHM Population ages 15-64 (% of total) WSM School enrollment, secondary % gross) HKG Population ages 0-14, male TTO Age population, age 02, female, interpolated LBN Population ages 80 and older, male (% of male population) PRK Immunization, HepB3 (% of one-year-old children) NAM Age population, age 18, male, interpolated HKG Age population, age 15, male, interpolated NAM Male population 45-49 CHE Improved sanitation facilities (% of population with access) GEO Improved water source (% of population with access) UGA Death rate, crude (per 1,000 people) indicator_code SP.POP.65UP.MA.IN SH.MED.NUMW.P3 SP.POP.2529.MA SL.TLF.TOTLIN SP.POP.AG16.FE.IN SP.POP 2024.FE SP.POP.1564.FE.ZS SP.POP.5054.MA SP.POP.4549.FE.5Y SP.POP.8OUP.FE SP.POP.TOTL.MA.IN SP.DYN.CBRT.IN SP.RUR.TOTL.ZS SP.DYN.AMRT.FE SP.DYN.LEOO.IN SP.POP.AG18.MA.IN SP.POP.65UP.FE.IN SP.POP.TOTL.FE.ZS SP.POP.6569. MA SP.POP.AG10.FE.IN SP.POP.0014.TO.ZS SP.POP.0509.FE.5Y SH.STA.BRTW.ZS SP.POP.1564.TO.ZS SE.SEC.ENRR SP.POP.0014.MA.IN SP.POP.AGO2.FE.IN SP.POP.8OUP.MA.5Y SH.IMM.HEPB SP.POP.AG18.MA.IN SP.POP.AG15.MA.IN SP.POP.4549.MA SH.STA.ACSN SH.H2O.SAFE.ZS SP.DYN.CDRT.IN E F year value 1977 2598418 2006 4.92 1965 169351 2015 23025896 1991 84604 1989 105592 2015 58.92591 1960 1998 2009 2.569405 2002 120688 1972 1955275 2013 42.394 2004 23.226 1979 183.52 1985 65.76023 1985 380571 1996 207159 2006 50.43064 1983 2132405 2011 8721 2001 40.6329 1998 16.81394 2006 27 2012 63.64877 2001 80.40665 1995 616295 2002 8684 1996 0.796736 2014 93 1979 9465 1962 34705 1994 24098 1992 99.9 2011 97.6 1998 17.743 27 Hone 6) Use worldbankhealthdata.csv and reshape column "indicator_code" into multiple columns. Name your final datafram wbh_data_reshaped and it should have 99 rows and 84 columns [ ]: # solution 1 country_name 2 Middle East & North Africa (IDA & IBRD countries) 3 Armenia 4 Georgia 5 Tanzania 6 Cambodia 7 Mongolia 8 Syrian Arab Republic 9 Solomon Islands 10 Zimbabwe 11 Kazakhstan 12 Norway 13 Congo, Dem. Rep. 14 Libya 15 Turkmenistan 16 Latin America & the Caribbean (IDA & IBRD countries) 17 Ethiopia 18 Guatemala 19 Canada 20 Pre-demographic dividend 21 Slovenia 22 Pakistan 23 Yemen, Rep. 24 Niger 25 Cambodia 26 Samoa Hong Kong SAR, China Trinidad and Tobago 29 Lebanon 30 Korea, Dem. PeopleTMs Rep. 31 Namibia 32 Hong Kong SAR, China 33 Namibia 34 Switzerland 35 Georgia 36 Uganda worldhankhealth data country_code indicator_name TMN Population ages 65 and above, male ARM Nurses and midwives (per 1,000 people) GEO Male population 25-29 TZA Labor force, total KHM Age population, age 16, female, interpolated MNG Female population 20-24 SYR Population ages 15-64, female (% of total) SLB Male population 50-54 ZWE Population ages 45-49, female (% of female population) KAZ Female population 80+ NOR Population, male COD Birth rate, crude (per 1,000 people) LBY Rural population (% of total population) TKM Mortality rate, adult, female (per 1,000 female adults) TLA Life expectancy at birth, total (years) ETH Age population, age 18, male, interpolated GTM Population ages 65 and above, female CAN Population, female (% of total) PRE Male population 65-69 SVN Age population, age 10, female, interpolated PAK Population ages 00-14% of total) YEM Population ages 05-09, female % of female population) NER Low-birthweight babies (% of births) KHM Population ages 15-64 (% of total) WSM School enrollment, secondary % gross) HKG Population ages 0-14, male TTO Age population, age 02, female, interpolated LBN Population ages 80 and older, male (% of male population) PRK Immunization, HepB3 (% of one-year-old children) NAM Age population, age 18, male, interpolated HKG Age population, age 15, male, interpolated NAM Male population 45-49 CHE Improved sanitation facilities (% of population with access) GEO Improved water source (% of population with access) UGA Death rate, crude (per 1,000 people) indicator_code SP.POP.65UP.MA.IN SH.MED.NUMW.P3 SP.POP.2529.MA SL.TLF.TOTLIN SP.POP.AG16.FE.IN SP.POP 2024.FE SP.POP.1564.FE.ZS SP.POP.5054.MA SP.POP.4549.FE.5Y SP.POP.8OUP.FE SP.POP.TOTL.MA.IN SP.DYN.CBRT.IN SP.RUR.TOTL.ZS SP.DYN.AMRT.FE SP.DYN.LEOO.IN SP.POP.AG18.MA.IN SP.POP.65UP.FE.IN SP.POP.TOTL.FE.ZS SP.POP.6569. MA SP.POP.AG10.FE.IN SP.POP.0014.TO.ZS SP.POP.0509.FE.5Y SH.STA.BRTW.ZS SP.POP.1564.TO.ZS SE.SEC.ENRR SP.POP.0014.MA.IN SP.POP.AGO2.FE.IN SP.POP.8OUP.MA.5Y SH.IMM.HEPB SP.POP.AG18.MA.IN SP.POP.AG15.MA.IN SP.POP.4549.MA SH.STA.ACSN SH.H2O.SAFE.ZS SP.DYN.CDRT.IN E F year value 1977 2598418 2006 4.92 1965 169351 2015 23025896 1991 84604 1989 105592 2015 58.92591 1960 1998 2009 2.569405 2002 120688 1972 1955275 2013 42.394 2004 23.226 1979 183.52 1985 65.76023 1985 380571 1996 207159 2006 50.43064 1983 2132405 2011 8721 2001 40.6329 1998 16.81394 2006 27 2012 63.64877 2001 80.40665 1995 616295 2002 8684 1996 0.796736 2014 93 1979 9465 1962 34705 1994 24098 1992 99.9 2011 97.6 1998 17.743 27 Hone 6) Use worldbankhealthdata.csv and reshape column "indicator_code" into multiple columns. Name your final datafram wbh_data_reshaped and it should have 99 rows and 84 columns [ ]: # solution