It is the third time that I upload the same question and nobody helps me, please help, help

Use the file happyscore_income.csv Get information about the data types of the file 1 point. Read the first ten records of the file 1 point. Check for null values 2 points. Create a boxplot with the values of the happyScore field 2 points. Create a histogram with the values of any field. 1 point point. Place titles on graphs. 2 points ................. chegg does not allow me to upload files or links to Google Drive ............

please provide the data in photos and an example of the python code i need, kindly use python .......... qualifying conditions ....... import CSV , you can use any library for the graphics, also you can't use Pandas .... to solve the questions you must use Loops, while , for , if , dictionary lists .... I put the data and photos to help me solve the questions, since it is the third time that I publish the same question and they have not been able to help me, help ....... I can not upload files, nor links, just solve the questions with all this information and photos kindly provided

country,adjusted_satisfaction,avg_satisfaction,std_satisfaction,avg_income,median_income,income_inequality,region,happyScore,GDP,country

Armenia,37.0,4.9,2.42,2096.7599999999998,1731.5066666666667,31.445555555555554, 'Central and Eastern Europe',4.35,0.7682100000000001,Armenia

Angola,26.0,4.3,3.19,1448.88,1044.24,42.72,'Sub-Saharan Africa',4.033,0.75778,Angola

Argentina,60.0,7.1,1.91,7101.12,5109.4,45.475555555555566,'Latin America and Caribbean',6.574,1.05351,Argentina

Austria,59.0,7.2,2.11,19457.039999999997,16879.62,30.29625,'Western Europe',7.2,1.33723,Austria

Australia,65.0,7.6,1.8,19917.0,15846.060000000001,35.285,'Australia and New Zealand',7.284,1.33358,Australia

Azerbaijan,46.0,5.8,2.27,3381.6000000000004,2931.48,24.215,'Central and Eastern Europe',5.212000000000001,1.02389,Azerbaijan

Bangladesh,43.0,5.3,2.1,1265.34,994.1400000000001,32.665000000000006,'Southern Asia',4.694,0.39753,Bangladesh

Belgium,63.0,7.2,1.72,17168.505,15166.455,28.745,'Western Europe',6.937,1.30782,Belgium

Burkina Faso,37.0,4.4,2.02,870.84,630.24,39.76,'Sub-Saharan Africa',3.5869999999999997,0.25811999999999996,Burkina Faso

Bulgaria,34.0,4.6,2.57,5354.82,4523.565,34.1625,'Central and Eastern Europe',4.218,1.01216,Bulgaria

Burundi,25.0,2.9,1.96,572.88,436.92,33.36,'Sub-Saharan Africa',2.905,0.0153,Burundi

Benin,20.0,3.0,2.7,989.04,657.0,43.44,'Sub-Saharan Africa',3.34,0.28665,Benin

Bolivia,53.0,6.3,1.9,3985.7100000000005,2584.4700000000003,51.61,'Latin America and Caribbean',5.89,0.68133,Bolivia

Brazil,56.0,6.9,2.19,5567.235,3294.18,54.33375,'Latin America and Caribbean',6.983,0.98124,Brazil

Botswana,36.0,4.7,2.42,3484.68,1632.6,60.46,'Sub-Saharan Africa',4.332,0.99355,Botswana

Belarus,47.0,5.5,1.99,5453.933333333334,4814.453333333334,27.754444444444445,'Central and Eastern Europe',5.813,1.0319200000000002,Belarus

Canada,69.0,8.0,1.71,20190.78,16829.1,33.79,'North America',7.4270000000000005,1.32629,Canada

NM LA NO happyscore_income.py X 1 import csv with open("happyscore_income.csv') as f: read=csv.reader(f) for row in read: print country: {0}, adjusted satisfaction: {1}, avg_satisfaction: {2}, std_satisfaction: {3}, avg_income:{4},median_income:{5}, income_inequality:{6},region:{7}, happyScore:{8}, GDP:{9}, country:{10} .format(row[@], row[1], row[ 2], row[3], row[4], row[5], row[6], row[7], row[ 8 ], row[9], row[10])) 2 3 4 5 6 7 8 9 L22 X fic A B C D E F G H 1 country, adjusted_satisfaction, avg_satisfaction,std_satisfaction, avg_income,median_income, income_inequality,region, happyScore, GDP country 2 Armenia, 37.0,4.9,2.42,2096.7599999999998,1731.5066666666667,31.445555555555554, 'Central and Eastern Europe',4.35,0.7682100000000001, Armenia 3 Angola, 26.0,4.3,3.19,1448.88,1044.24,42.72, 'Sub-Saharan Africa', 4.033,0.75778,Angola 4 Argentina, 60.0,7.1,1.91,7101.12,5109.4,45.475555555555566,'Latin America and Caribbean',6.574,1.05351, Argentina 5 Austria,59.0,7.2,2.11,19457.039999999997,16879.62,30.29625, 'Western Europe', 7.2,1.33723, Austria 6 Australia, 65.0,7.6,1.8,19917.0,15846.060000000001,35.285,'Australia and New Zealand', 7.284,1.33358, Australia 7 Azerbaijan, 46.0,5.8,2.27,3381.6000000000004,2931.48,24.215, 'Central and Eastern Europe', 5.212000000000001,1.02389, Azerbaijan 8 Bangladesh,43.0,5.3,2.1,1265.34,994.1400000000001,32.665000000000006, 'Southern Asia',4.694,0.39753, Bangladesh 9 Belgium, 63.0,7.2,1.72,17168.505,15166.455,28.745, 'Western Europe',6.937,1.30782,Belgium 10 Burkina Faso,37.0,4.4,2.02,870.84,630.24,39.76,'Sub-Saharan Africa',3.5869999999999997,0.25811999999999996, Burkina Faso 11 Bulgaria, 34.0,4.6,2.57,5354.82,4523.565,34.1625, 'Central and Eastern Europe',4.218,1.01216, Bulgaria 12 Burundi 25.0,2.9,1.96,572.88,436.92,33.36, 'Sub-Saharan Africa', 2.905,0.0153,Burundi 13 Benin, 20.0,3.0,2.7,989.04,657.0,43.44, 'Sub-Saharan Africa', 3.34,0.28665, Benin 14 Bolivia,53.0,6.3,1.9,3985.7100000000005,2584.4700000000003,51.61,'Latin America and Caribbean',5.89,0.68133, Bolivia 15 Brazil,56.0,6.9,2.19,5567.235,3294.18,54.33375, 'Latin America and Caribbean', 6.983,0.98124, Brazil 16 Botswana, 36.0,4.7,2.42,3484.68,1632.6,60.46,'Sub-Saharan Africa',4.332,0.99355, Botswana 17 Belarus, 47.0,5.5,1.99,5453.933333333334,4814.453333333334,27.754444444444445, 'Central and Eastern Europe',5.813,1.0319200000000002, Belarus 18 Canada,69.0,8.0,1.71,20190.78,16829.1,33.79,'North America', 7.4270000000000005,1.32629, Canada happyscore_income.csv country adjusted_satisfaction, avg_satisfaction,std_satisfaction, avg_income,median_income, income_inequality, region, happyScore,GDP country Armenia, 37.0,4.9,2.42,2096.7599999999998,1731.5066666666667,31.445555555555554, 'Central and Eastern Europe',4.35,0.7682100000000001, Armenia Angola,26.0.4.3,3.19,1448.88,1044.24,42.72, 'Sub-Saharan Africa', 4.033,0.75778, Angola Argentina, 60.0,7.1,1.91,7101.12,5109.4,45.475555555555566, 'Latin America and Caribbean', 6.574,1.05351, Argentina Austria,59.0,7.2.2.11,19457.039999999997,16879.62,30.29625, 'Western Europe', 7.2,1.33723,Austria Australia,65.0,7.6,1.8,19917.0,15846.060000000001,35.285, 'Australia and New Zealand', 7.284,1.33358, Australia Azerbaijan, 46.0,5.8,2.27,3381.6000000000004,2931.48,24.215,'Central and Eastern Europe', 5.212000000000001,1.02389, Azerbaijan Bangladesh,43.0,5.3,2.1,1265.34,994.1400000000001,32.665000000000006, 'Southern Asia', 4.694,0.39753, Bangladesh Belgium,63.0,7.2,1.72,17168.505,15166.455,28.745,'Western Europe', 6.937,1.30782,Belgium Burkina Faso,37.0,4.4,2.02,870.84,630.24,39.76,'Sub-Saharan Africa', 3.5869999999999997,0.25811999999999996, Burkina Faso Bulgaria,34.0,4.6,2.57,5354.82,4523.565,34.1625, 'Central and Eastern Europe',4.218,1.01216, Bulgaria Burundi, 25.0,2.9,1.96,572.88,436.92,33.36,'Sub-Saharan Africa', 2.905,0.0153,Burundi Benin,20.0,3.0,2.7,989.04,657.0,43.44, 'Sub-Saharan Africa',3.34,0.28665, Benin Bolivia,53.0,6.3,1.9,3985.7100000000005,2584.4700000000003,51.61, Latin America and Caribbean', 5.89,0.68133, Bolivia Brazil,56.0,6.9,2.19,5567.235,3294.18,54.33375,'Latin America and Caribbean', 6.983,0.98124, Brazil Botswana,36.0,4.7,2.42,3484.68,1632.6,60.46, 'Sub-Saharan Africa', 4.332,0.99355, Botswana Belarus,47.0,5.5,1.99,5453.933333333334,4814.453333333334,27.754444444444445, 'Central and Eastern Europe',5.813,1.0319200000000002, Belarus Canada,69.0,8.0,1.71,20190.78,16829.1,33.79,'North America', 7.4270000000000005,1.32629, Canada