#Salvador 84-lens | Explore Tumblr posts and blogs

ibnelhithem · 4 years ago

Text

third week assignment

in this blog I will provide the full output of the sub dataset after remove the missing values and also the frequency and the percentage for the variable that I use.

first I will show the code

import pandas as pd import numpy

#this options used to show all data in dataframe which is the data sctructire used in this application pd.set_option('display.max_rows', 500) pd.set_option('display.max_columns', 500) pd.set_option('display.width', 1000)

data = pd.read_csv('gapmiderds.csv',low_memory=False) # bug fix for display formats to avoid run time errors - put after code for loading data above pd.set_option('display.float_format', lambda x:'%f'%x)

#data.columns = map(str.upper(), data.columns) print(type(data)) print('the number of country that I have in the dataset =',len(data)) print('the number of all variable that we have is the data set', len(data.columns))

#I convert the empty values to 0 because I use int values # and the empty values is an empty spaces not NAN values so dropna=false will not work data = data.replace(r'^\s+$', '0', regex=True) #print('I replace all empty values with 0.0 because I want to take into considration the countries that did not provide data') # convert strings data to float numbers data['incomeperperson'] = data['incomeperperson'].astype(float) data['armedforcesrate'] = data['armedforcesrate'].astype(float) data['employrate'] = data['employrate'].astype(float) data['polityscore'] = data['polityscore'].astype(float)

sub1 = data[(data['incomeperperson'] > 0) & (data['armedforcesrate'] > 0)] sub1 = sub1[['country','incomeperperson','armedforcesrate', 'employrate']]

print('length of sbuset data that do not have empty variables = ',len(sub1)) print(type(sub1)) print(len(sub1)) print( len(sub1.columns)) print(sub1.head(len(sub1)))

sub1['sub1incomegroup'] = pd.cut(sub1.incomeperperson, [0, 1000, 10000,30000 ,1000000]) sub1incomegroup = sub1['sub1incomegroup'].value_counts(sort=False, dropna=True) print(sub1incomegroup)

sub1['sub1incomegrouppercent'] = pd.cut(sub1.incomeperperson, [0, 1000, 10000,30000 ,1000000]) sub1incomegrouppercent = sub1['sub1incomegrouppercent'].value_counts(sort=False, normalize=True) print(sub1incomegrouppercent)

print('armed forces statistics') sub1['armedforcesrategroup'] = pd.cut(sub1.armedforcesrate, [0, 0.8, 1.8, 3 ,10]) sub1armrategroup = sub1['armedforcesrategroup'].value_counts(sort=False, dropna=True) print(sub1armrategroup)

sub1['armedforcesrategrouppercent'] = pd.cut(sub1.armedforcesrate, [0, 0.8, 1.8, 3 ,10]) sub1armrategrouppercent = sub1['armedforcesrategrouppercent'].value_counts(sort=False, normalize=True) print(sub1armrategrouppercent)

print('employment statistics') sub1['employraterategroup'] = pd.cut(sub1.employrate, [0, 50, 65, 80 ,100]) sub1employraterategroup = sub1['employraterategroup'].value_counts(sort=False, dropna=True) print(sub1employraterategroup)

sub1['employraterategrouppercent'] = pd.cut(sub1.employrate, [0, 50, 65, 80 ,100]) sub1employraterategrouppercent = sub1['employraterategrouppercent'].value_counts(sort=False, normalize=True) print(sub1employraterategrouppercent)

then this is the output

country incomeperperson armedforcesrate employrate 1 Albania 1914.996551 1.024736 51.400002 2 Algeria 2231.993335 2.306817 50.500000 4 Angola 1381.004268 1.461329 75.699997 6 Argentina 10749.419238 0.560987 58.400002 7 Armenia 1326.741757 2.618438 40.099998 9 Australia 25249.986061 0.486280 61.500000 10 Austria 26692.984107 0.815580 57.099998 11 Azerbaijan 2344.896916 1.976746 60.900002 12 Bahamas 19630.540547 0.545286 66.599998 13 Bahrain 12505.212545 5.231143 60.400002 14 Bangladesh 558.062877 0.287892 68.099998 15 Barbados 9243.587053 0.663956 66.900002 16 Belarus 2737.670379 3.711930 53.400002 17 Belgium 24496.048264 0.815648 48.599998 18 Belize 3545.652174 0.758356 56.799999 19 Benin 377.039699 0.223399 71.599998 22 Bolivia 1232.794137 1.882198 70.400002 23 Bosnia and Herzegovina 2183.344867 0.462847 41.200001 24 Botswana 4189.436587 1.131910 46.000000 25 Brazil 4699.411262 0.721396 64.500000 26 Brunei 17092.460004 4.683989 63.799999 27 Bulgaria 2549.558474 2.043388 47.299999 28 Burkina Faso 276.200413 0.162904 81.300003 29 Burundi 115.305996 1.157750 83.199997 30 Cambodia 557.947513 2.536446 78.900002 31 Cameroon 713.639303 0.305758 59.099998 32 Canada 25575.352623 0.342976 63.500000 33 Cape Verde 1959.844472 0.483325 55.900002 35 Central African Rep. 239.518749 0.148659 71.300003 36 Chad 275.884287 0.835812 68.900002 37 Chile 6334.105194 1.341114 51.000000 38 China 2425.471293 0.371357 72.800003 39 Colombia 3233.423780 2.215621 63.200001 41 Congo, Dem. Rep. 103.775857 0.628381 66.199997 42 Congo, Rep. 1253.292015 0.771694 64.199997 44 Costa Rica 5188.900935 0.473654 58.599998 45 Cote d'Ivoire 591.067944 0.233915 59.900002 46 Croatia 6338.494668 1.103279 47.099998 47 Cuba 4495.046262 1.495383 56.000000 48 Cyprus 15313.859347 2.454913 59.099998 49 Czech Rep. 7381.312751 0.515706 56.000000 50 Denmark 30532.277044 1.012373 63.099998 51 Djibouti 895.318340 3.451546 0.000000 53 Dominican Rep. 4049.169629 1.469369 52.700001 54 Ecuador 1728.020976 1.017946 59.700001 55 Egypt 1975.551906 3.290807 42.400002 56 El Salvador 2557.433638 1.321155 58.200001 57 Equatorial Guinea 8654.536845 0.397654 61.700001 58 Eritrea 131.796207 9.820127 64.900002 59 Estonia 6238.537506 0.998428 56.500000 60 Ethiopia 220.891248 0.361708 80.699997 62 Fiji 2230.676374 1.177268 56.400002 63 Finland 27110.731591 1.177416 57.200001 64 France 22878.466567 1.233780 51.200001 66 Gabon 4180.765821 1.018906 59.000000 67 Gambia 354.599726 0.134591 71.699997 68 Georgia 1258.762596 1.450735 55.599998 69 Germany 25306.187193 0.575810 53.500000 70 Ghana 358.979540 0.131487 65.099998 72 Greece 13577.879885 3.093251 49.599998 77 Guatemala 1860.753895 0.660838 62.700001 78 Guinea 411.501447 0.402699 81.500000 79 Guinea-Bissau 161.317137 0.930108 65.900002 80 Guyana 1200.652075 0.877666 58.900002 82 Honduras 1392.411829 0.710823 56.299999 84 Hungary 5634.003948 0.866862 47.299999 85 Iceland 33945.314422 0.066100 73.599998 86 India 786.700098 0.573920 55.400002 87 Indonesia 1143.831514 0.515940 61.500000 88 Iran 2161.546510 2.026400 47.799999 89 Iraq 736.268054 7.737913 37.400002 90 Ireland 27595.091347 0.450024 59.900002 91 Israel 22275.751661 5.873143 51.299999 92 Italy 18982.269285 1.729346 46.400002 93 Jamaica 3665.348369 0.243739 58.200001 94 Japan 39309.478859 0.361858 57.299999 95 Jordan 2534.000380 5.909699 38.900002 96 Kazakhstan 2481.718918 0.956270 63.700001 97 Kenya 468.696044 0.159574 73.199997 100 Korea, Rep. 16372.499781 2.833966 58.900002 102 Kyrgyzstan 372.728414 0.837768 58.900002 103 Laos 554.879840 4.301576 78.199997 104 Latvia 5011.219456 1.316432 56.799999 105 Lebanon 6746.612632 5.406536 46.000000 106 Lesotho 495.734247 0.217653 56.299999 107 Liberia 155.033231 0.129953 66.000000 108 Libya 7885.468037 3.311674 48.599998 110 Lithuania 5332.238591 1.487831 53.099998 111 Luxembourg 52301.587179 0.731336 53.500000 113 Macedonia, FYR 2221.185664 2.101467 34.900002 114 Madagascar 242.677534 0.235185 83.000000 115 Malawi 184.141797 0.114592 71.800003 116 Malaysia 5184.709328 1.142127 60.500000 118 Mali 269.892881 0.326978 45.700001 119 Malta 11066.784145 1.144319 46.799999 122 Mauritania 609.131206 1.551262 46.900002 123 Mauritius 5182.143721 0.349544 54.500000 124 Mexico 6105.280743 0.612340 57.900002 126 Moldova 595.874535 0.541506 44.299999 128 Mongolia 772.933345 1.211869 52.099998 130 Morocco 1844.351028 2.085853 46.200001 131 Mozambique 389.763634 0.102269 77.000000 133 Namibia 2667.246710 1.972913 42.000000 135 Nepal 268.259450 1.012732 61.799999 136 Netherlands 26551.844238 0.526126 61.299999 139 New Zealand 14778.163929 0.389157 65.000000 140 Nicaragua 948.355952 0.526592 58.799999 141 Niger 180.083376 0.217717 60.400002 142 Nigeria 544.599477 0.333195 50.900002 144 Norway 39972.352768 0.720528 65.000000 145 Oman 11191.811007 4.465998 50.700001 146 Pakistan 668.547943 1.649451 51.000000 148 Panama 5900.616944 0.750210 59.799999 149 Papua New Guinea 744.239413 0.105115 70.400002 150 Paraguay 1621.177078 0.885386 73.099998 151 Peru 3180.430612 1.435633 68.300003 152 Philippines 1383.401869 0.388067 61.000000 153 Poland 6575.745044 0.810187 48.700001 154 Portugal 11744.834167 1.616886 57.599998 156 Qatar 33931.832079 1.334236 76.000000 158 Romania 2636.787800 1.536766 49.500000 159 Russia 2923.144355 1.941446 58.799999 160 Rwanda 338.266391 0.726855 79.800003 167 Saudi Arabia 9425.325870 2.618296 51.200001 168 Senegal 561.708585 0.362261 65.599998 172 Sierra Leone 268.331790 0.523548 63.799999 173 Singapore 32535.832512 6.394936 62.400002 174 Slovak Republic 8445.526689 0.628578 53.400002 175 Slovenia 12729.454400 1.160282 55.900002 178 South Africa 3745.649852 0.331863 41.099998 179 Spain 15461.758372 0.979537 52.500000 180 Sri Lanka 1295.742686 2.569643 55.099998 181 Sudan 523.950151 0.969499 47.299999 182 Suriname 2668.020519 1.058419 44.700001 184 Sweden 32292.482984 0.352092 60.700001 185 Switzerland 37662.751250 0.524220 64.300003 186 Syria 1525.780116 5.955847 44.799999 188 Tajikistan 279.180453 0.604873 54.599998 189 Tanzania 456.385712 0.134730 78.199997 190 Thailand 2712.517199 1.093651 72.000000 191 Timor-Leste 369.572954 0.234286 67.300003 192 Togo 285.224449 0.340031 63.900002 194 Trinidad and Tobago 10480.817203 0.581165 61.500000 195 Tunisia 3164.927693 1.266624 41.599998 196 Turkey 5348.597192 2.379415 42.799999 197 Turkmenistan 2062.125152 0.931418 58.500000 199 Uganda 377.421113 0.344518 83.199997 200 Ukraine 1036.830725 0.931169 54.400002 201 United Arab Emirates 21087.394125 1.812476 75.199997 202 United Kingdom 28033.489283 0.508018 59.299999 203 United States 37491.179523 0.972378 62.299999 204 Uruguay 9106.327234 1.579341 57.500000 205 Uzbekistan 952.827261 0.709630 57.500000 207 Venezuela 5528.363114 0.904025 59.900002 208 Vietnam 722.807559 1.085367 71.000000 210 Yemen, Rep. 610.357367 2.316235 39.000000 211 Zambia 432.226337 0.341335 61.000000 212 Zimbabwe 320.771890 1.032785 66.800003

divid the income to 4 groups and count countries in this groups (0, 1000] 51 (1000, 10000] 70 (10000, 30000] 27 (30000, 1000000] 10 divid the income to 4 groups and count countries percentage in this groups (0, 1000] 0.322785 (1000, 10000] 0.443038 (10000, 30000] 0.170886 (30000, 1000000] 0.063291

--------------------------------------------------------------------

armed forces statistics

the ratio of the armed forces to the full population in groups (0.0, 0.8] 70 (0.8, 1.8] 53 (1.8, 3.0] 19 (3.0, 10.0] 16

the percentage of the armed forces groups (0.0, 0.8] 0.443038 (0.8, 1.8] 0.335443 (1.8, 3.0] 0.120253 (3.0, 10.0] 0.101266

--------------------------------------------------------------------- employment statistics

the employment rate in groups (0, 50] 31 (50, 65] 88 (65, 80] 32 (80, 100] 6

the percentage of the employment ratio in group

(0, 50] 0.197452 (50, 65] 0.560510 (65, 80] 0.203822 (80, 100] 0.038217

Summary :-

from the above data it we can see that most of the countries in category 2 which is the average and a little bit below.

in this assignment I clean all empty values from the data so the data set length now is 158 countries not 213 as previous one so I exclude the countries that do not provide the data that I need.

also all my variable is quantities so I make this variables in group of 4 categories and I do not use second variable because I think I do not need it in this stage.

0 notes

ckknirsch · 8 years ago

Text

Každé dva roky zostavuje nezávislá medzinárodná organizácia The World Economic Forum rebríček turisticky najprívetivejších krajín. Rebríček zohľadňuje takmer všetko, najmä bezpečnosť, prírodné zdroje, pamiatky, infraštruktúru, hygienu ale tiež aj oblasť cien. Kde sa umiestnilo Slovensko a kam sa vybrať?

Celkovo sme sa umiestnili na chvoste prvej polovice zo 137 štátov, na 59. mieste obklopený štátmi ako Izrael, Kolumbia, Ekvádor a Litva. Takto sme sa umiestnili hlavne kvôli podnikateľskému prostrediu, vybudovanej infraštruktúre leteckej dopravy a stanoveniu priorít v oblasti cestovného ruchu. V týchto kategóriach sme celosvetovo na chvoste rebríčka. V kategóriách hygieny a environmentálnej udržiteľnosti sme zas v TOP 20 a ostatné zohľadňované kategórie nás posúvajú do priemeru.

V celkovom hodnotení uspelo Španielsko, Francúzsko, Nemecko, Japonsko s Anglickom, čo nie je až takým prekvapením, ale v kategóriach cenovej konkurencieschopnosti sú skôr na chvoste rebríčka. Anglicko konkrétne na predposlednom mieste.

Lacná dovolenka

Ak požadujete lacnú dovolenku, tak medzi najdostupnejšie aj s ohľadom na diaľku cestovania je Egypt (2. miesto, bezpečnosťou sú v posledných 10 miestach), Alžírsko či Tunis. Na skok máme do Poľska (23. miesto), Bulharsko (37. miesto), Ukrajina (45. miesto) či Maroko (47. miesto). Populárne Chorvátsko je na 100. mieste!

V čom excelujeme?

Rebríček treba brať rebríček s rezervou a zamerať sa čo skôr preferujete a s čím treba rátať. Je to skôr globálne pre celú krajinu a jednotlivé oblasti sa môžu mierne odlišovať. Hodnotí sa celkovo v 14 kategóriach, ktoré majú ešte svoje podkategórie a tam sme sa ocitli úplne na chvoste, ale máme aj zopár prvých miest: Náklady na vybavenie stavebných povolení, Výskyt terorizmu, výskyt malárie, prevencia HIV, Dostupnosť k lepšej pitnej vode a posledné hustota pokrytia mobilným signálom. Nabudúce keď budete nadávať, že niekde nie je dostupný mobilný signál, tak si spomeňte, že sme na tom celosvetovo najlepšie a všade to už je len horšie 🙂

#gallery-0-5 { margin: auto; } #gallery-0-5 .gallery-item { float: left; margin-top: 10px; text-align: center; width: 50%; } #gallery-0-5 img { border: 2px solid #cfcfcf; } #gallery-0-5 .gallery-caption { margin-left: 0; } /* see gallery_shortcode() in wp-includes/media.php */

Celkový rebríček

Miesto Štát Skóre Miesto Štát Skóre Miesto Štát Skóre 1 Spain 5.43 47 Qatar 4.08 93 Iran, Islamic Rep. 3.43 2 France 5.32 48 Chile 4.06 94 Lao PDR 3.40 3 Germany 5.28 49 Hungary 4.06 95 Serbia 3.38 4 Japan 5.26 50 Argentina 4.05 96 Lebanon 3.37 5 United Kingdom 5.20 51 Peru 4.04 97 Rwanda 3.36 6 United States 5.12 52 Cyprus 4.02 98 Albania 3.35 7 Australia 5.10 53 South Africa 4.01 99 Bolivia 3.34 8 Italy 4.99 54 Latvia 3.97 100 Kuwait 3.33 9 Canada 4.97 55 Mauritius 3.92 101 Cambodia 3.32 10 Switzerland 4.94 56 Lithuania 3.91 102 Mongolia 3.31 11 Hong Kong SAR 4.86 57 Ecuador 3.91 103 Nepal 3.28 12 Austria 4.86 58 Barbados 3.91 104 Venezuela 3.28 13 Singapore 4.85 59 Slovak Republic 3.90 105 El Salvador 3.28 14 Portugal 4.74 60 Bahrain 3.89 106 Uganda 3.20 15 China 4.72 61 Israel 3.84 107 Tajikistan 3.18 16 New Zealand 4.68 62 Colombia 3.83 108 Zambia 3.18 17 Netherlands 4.64 63 Saudi Arabia 3.82 109 Côte d’Ivoire 3.16 18 Norway 4.64 64 Sri Lanka 3.81 110 Paraguay 3.15 19 Korea, Rep. 4.57 65 Morocco 3.81 111 Senegal 3.14 20 Sweden 4.55 66 Oman 3.78 112 Gambia, The 3.12 21 Belgium 4.54 67 Vietnam 3.78 113 Bosnia and Herzegovina 3.12 22 Mexico 4.54 68 Romania 3.78 114 Zimbabwe 3.11 23 Ireland 4.53 69 Jamaica 3.71 115 Kyrgyz Republic 3.10 24 Greece 4.51 70 Georgia 3.70 116 Ethiopia 3.10 25 Iceland 4.50 71 Azerbaijan 3.70 117 Moldova 3.09 26 Malaysia 4.50 72 Montenegro 3.68 118 Algeria 3.07 27 Brazil 4.49 73 Trinidad and Tobago 3.67 119 Gabon 3.06 28 Luxembourg 4.49 74 Egypt 3.64 120 Ghana 3.04 29 United Arab Emirates 4.49 75 Jordan 3.63 121 Madagascar 2.99 30 Taiwan, China 4.47 76 Dominican Republic 3.62 122 Mozambique 2.91 31 Denmark 4.43 77 Uruguay 3.61 123 Malawi 2.91 32 Croatia 4.42 78 Bhutan 3.61 124 Pakistan 2.89 33 Finland 4.40 79 Philippines 3.60 125 Bangladesh 2.89 34 Thailand 4.38 80 Kenya 3.59 126 Cameroon 2.88 35 Panama 4.37 81 Kazakhstan 3.59 127 Benin 2.84 36 Malta 4.25 82 Namibia 3.59 128 Lesotho 2.84 37 Estonia 4.23 83 Cape Verde 3.55 129 Nigeria 2.82 38 Costa Rica 4.22 84 Armenia 3.53 130 Mali 2.78 39 Czech Republic 4.22 85 Botswana 3.52 131 Sierra Leone 2.69 40 India 4.18 86 Guatemala 3.51 132 Mauritania 2.64 41 Slovenia 4.18 87 Tunisia 3.50 133 Congo, Democratic Rep. 2.64 42 Indonesia 4.16 88 Ukraine 3.50 134 Burundi 2.57 43 Russian Federation 4.15 89 Macedonia, FYR 3.49 135 Chad 2.52 44 Turkey 4.14 90 Honduras 3.49 136 Yemen 2.44 45 Bulgaria 4.14 91 Tanzania 3.45 46 Poland 4.11 92 Nicaragua 3.44

Celý dokument si môžete stiahnúť aj od nás. Rebríčky jednotlivých kategórií začínajú na strane 33 a podrobnosti o jednotlivých štátoch na strane 77.

Vedeli ste, že Slovensko má najlepšie pokrytie mobilným signálom na svete? Každé dva roky zostavuje nezávislá medzinárodná organizácia The World Economic Forum rebríček turisticky najprívetivejších krajín. Rebríček zohľadňuje takmer všetko, najmä bezpečnosť, prírodné zdroje, pamiatky, infraštruktúru, hygienu ale tiež aj oblasť cien.

#dovolenka #lacno #Slovensko

0 notes

kunstlev · 9 years ago

Photo

#Hipstamatic #Salvador 84-lens #Kodama-filmrol

0 notes

hipstahobby-blog · 9 years ago

Photo

#Hipstamatic #Salvador 84-lens #DreamCanvas-filmrol

0 notes

jenviool · 10 years ago

Photo

Practice makes perfect...and sometimes a little dizzy ;-) 🎶

#Hipstamatic #Salvador 84-lens #DreamCanvas-filmrol

0 notes

zephyr-hipsta · 10 years ago

Photo

#Hipstamatic #Salvador 84-lens #BlacKeys Extra Fine-filmrol

16 notes · View notes

lostcollection · 7 years ago

Photo

The Lost Fidget Spinner Dodds St, Southbank 25 August, 2017 Salvador 84 Lens // T. Roosevelt 26 Film // Standard Flash

#lost #lost thing #lost collection #double exposure #dxp #Hipstamatic

1 note · View note

mazetman · 6 years ago

Photo

AGEN NEGA : Have a nice Salvador Sunday. \\ Lens : Salvador 84 - Film : Aristotle - Flash : Juicy Orange Gel // #hipstamatic #salvadorsunday #agen (at Ville d’Agen) https://www.instagram.com/p/BoV8uZOlq2_/?utm_source=ig_tumblr_share&igshid=mpo2q17v8ij9

#hipstamatic #salvadorsunday #agen

0 notes

uspscourse · 4 years ago

Text

Data Analyst - Gapminder Graph

1) Program

import pandas import numpy import seaborn import matplotlib.pyplot as plt

""" any additional libraries would be imported here """

""" Set PANDAS to show all columns in DataFrame """ pandas.set_option('display.max_columns', None)

"""Set PANDAS to show all rows in DataFrame """ pandas.set_option('display.max_rows', None)

""" bug fix for display formats to avoid run time errors """ pandas.set_option('display.float_format', lambda x:'%f'%x)

""" read in csv file """ data = pandas.read_csv('gapminder.csv', low_memory=False) data = data.replace(r'^\s*$', numpy.NaN, regex=True)

""" checking the format of your variables """ data['country'].dtype

""" setting variables you will be working with to numeric """ data['employrate'] = pandas.to_numeric(data['employrate'], errors='coerce') data['internetuserate'] = pandas.to_numeric(data['internetuserate'], errors='coerce') data['lifeexpectancy'] = pandas.to_numeric(data['lifeexpectancy'], errors='coerce')

""" Subset of employrate less than 76 and greater than 75 percent """ employ_sub1=data[(data['employrate'] <= 75)] employ_sub2=data[(data['employrate'] > 75)]

""" Subset of lifeexpectancy less than 51, greater than 50 but less 76 and greater than 75 """ life_sub1=data[(data['lifeexpectancy'] <= 50)] life_sub2=data[(data['lifeexpectancy'] > 50) & (data['lifeexpectancy'] <= 75)] life_sub3=data[(data['lifeexpectancy'] > 75)]

""" Subset of internetuserate less than 26, greater than 25 but less 51, greater than 50 but less than 76 and greater than 75 """ net_sub1=data[(data['internetuserate'] <= 25)] net_sub2=data[(data['internetuserate'] > 25) & (data['internetuserate'] <= 50)] net_sub3=data[(data['internetuserate'] > 50) & (data['internetuserate'] <= 75)] net_sub4=data[(data['internetuserate'] > 75)]

""" make a copy of all subsetted datas """ employ_copy1 = employ_sub1.copy() employ_copy2 = employ_sub2.copy() life_copy1 = life_sub1.copy() life_copy2 = life_sub2.copy() life_copy3 = life_sub3.copy() net_copy1 = net_sub1.copy() net_copy2 = net_sub2.copy() net_copy3 = net_sub3.copy() net_copy4 = net_sub4.copy()

""" displaying employ rate by break downs """ print ("counts and countries for original 'employrate' less than 76%") employ1 = employ_copy1['employrate'].value_counts(sort=False, dropna=False) print(len(employ_copy1['employrate']), "countries with less than 76% employ rate") print(employ_copy1['country'])

print ("counts and countries for original 'employrate' greater 75%") employ2 = employ_copy2['employrate'].value_counts(sort=False, dropna=False) print(len(employ_copy2['employrate']), "countries with employ rate greater than 75%") print(employ_copy2['country'])

""" displaying internet usage by break downs """ print ("counts and countries for original 'internetuserate' less than 26") net1 = net_copy1['internetuserate'].value_counts(sort=False, dropna=False) print(len(net_copy1['internetuserate']), "countries with internet usage rate below 25%") print(net_copy1['country'])

print ("counts and countries for original 'internetuserate' between 25 and 50") net2 = net_copy2['internetuserate'].value_counts(sort=False, dropna=False) print(len(net_copy2['internetuserate']), "countries with internet usage rate between 25 and 50%") print(net_copy2['country'])

print ("counts and countries for original 'internetuserate' between 50 and 75") net3 = net_copy3['internetuserate'].value_counts(sort=False, dropna=False) print(len(net_copy3['internetuserate']), "countries with internet usage rate between 50 and 75%") print(net_copy3['country'])

print ("counts and countries for original 'internetuserate' greater than 75") net4 = net_copy4['internetuserate'].value_counts(sort=False, dropna=False) print(len(net_copy4['internetuserate']), "countries with internet usage rate greater than 75%") print(net_copy4['country'])

""" displaying life expectancy by break downs """ print ("counts countries for original 'lifeexpectancy' less than 51") life1 = life_copy1['lifeexpectancy'].value_counts(sort=False, dropna=False) print(len(life_copy1['lifeexpectancy']), "countries with life expectancy below 51") print(life_copy1['country'])

print ("counts countries for original 'lifeexpectancy' less than 76") life2 = life_copy2['lifeexpectancy'].value_counts(sort=False, dropna=False) print(len(life_copy2['lifeexpectancy']), "countries with life expectancy between 50 and 75") print(life_copy2['country'])

print ("counts countries for original 'lifeexpectancy' greater than 75") life3 = life_copy3['lifeexpectancy'].value_counts(sort=False, dropna=False) print(len(life_copy3['lifeexpectancy']), "countries with life expectancy over 75") print(life_copy3['country'])

# recode missing values to python missing (NaN) employ_copy1['employrate']=employ_copy1['employrate'].replace(0, numpy.nan) employ_copy2['employrate']=employ_copy2['employrate'].replace(0, numpy.nan) net_copy1['internetuserate']=net_copy1['internetuserate'].replace(0, numpy.nan) net_copy2['internetuserate']=net_copy2['internetuserate'].replace(0, numpy.nan) net_copy3['internetuserate']=net_copy3['internetuserate'].replace(0, numpy.nan) net_copy4['internetuserate']=net_copy4['internetuserate'].replace(0, numpy.nan) life_copy1['lifeexpectancy']=life_copy1['lifeexpectancy'].replace(0, numpy.nan) life_copy2['lifeexpectancy']=life_copy2['lifeexpectancy'].replace(0, numpy.nan) life_copy3['lifeexpectancy']=life_copy3['lifeexpectancy'].replace(0, numpy.nan)

""" description of variables shapes and size """ print("decscription of variable: 'employrate'") desc_employ = data['employrate'].describe() print(desc_employ)

print("decscription of variable: 'internetuserate'") desc_net = data['internetuserate'].describe() print(desc_net)

print("decscription of variable: 'lifeexpectancy'") desc_life = data['lifeexpectancy'].describe() print(desc_life)

""" univariate bar graph for categorical variable 'employrate' """ seaborn.displot(data['employrate'].dropna()) plt.xlabel('employ rate (%)') plt.ylabel('nunmber of countries') plt.show()

""" univariate bar graph for categorical variable 'internetuserate' """ seaborn.displot(data['internetuserate'].dropna()) plt.xlabel('internet usage rate (%)') plt.ylabel('nunmber of countries') plt.show()

""" univariate bar graph for categorical variable 'lifeexpectancy' """ seaborn.displot(data['lifeexpectancy'].dropna()) plt.xlabel('life expectancy (year)') plt.ylabel('nunmber of countries') plt.show()

""" bivariate bar graph of 'employrate' and 'internetuserate' """ seaborn.catplot(x="employrate", y="internetuserate", data=data, kind="bar", ci=None) plt.xlabel("employ rate by country") plt.ylabel("internet use rate") plt.show()

2) output

counts and countries for original 'employrate' less than 76% 164 countries with less than 76% employ rate 0 Afghanistan 1 Albania 2 Algeria 6 Argentina 7 Armenia 9 Australia 10 Austria 11 Azerbaijan 12 Bahamas 13 Bahrain 14 Bangladesh 15 Barbados 16 Belarus 17 Belgium 18 Belize 19 Benin 21 Bhutan 22 Bolivia 23 Bosnia and Herzegovina 24 Botswana 25 Brazil 26 Brunei 27 Bulgaria 31 Cameroon 32 Canada 33 Cape Verde 35 Central African Rep. 36 Chad 37 Chile 38 China 39 Colombia 40 Comoros 41 Congo, Dem. Rep. 42 Congo, Rep. 44 Costa Rica 45 Cote d'Ivoire 46 Croatia 47 Cuba 48 Cyprus 49 Czech Rep. 50 Denmark 53 Dominican Rep. 54 Ecuador 55 Egypt 56 El Salvador 57 Equatorial Guinea 58 Eritrea 59 Estonia 62 Fiji 63 Finland 64 France 66 Gabon 67 Gambia 68 Georgia 69 Germany 70 Ghana 72 Greece 75 Guadeloupe 77 Guatemala 79 Guinea-Bissau 80 Guyana 81 Haiti 82 Honduras 83 Hong Kong, China 84 Hungary 85 Iceland 86 India 87 Indonesia 88 Iran 89 Iraq 90 Ireland 91 Israel 92 Italy 93 Jamaica 94 Japan 95 Jordan 96 Kazakhstan 97 Kenya 99 Korea, Dem. Rep. 100 Korea, Rep. 101 Kuwait 102 Kyrgyzstan 104 Latvia 105 Lebanon 106 Lesotho 107 Liberia 108 Libya 110 Lithuania 111 Luxembourg 112 Macao, China 113 Macedonia, FYR 115 Malawi 116 Malaysia 117 Maldives 118 Mali 119 Malta 121 Martinique 122 Mauritania 123 Mauritius 124 Mexico 126 Moldova 128 Mongolia 130 Morocco 132 Myanmar 133 Namibia 135 Nepal 136 Netherlands 137 Netherlands Antilles 139 New Zealand 140 Nicaragua 141 Niger 142 Nigeria 144 Norway 145 Oman 146 Pakistan 148 Panama 149 Papua New Guinea 150 Paraguay 151 Peru 152 Philippines 153 Poland 154 Portugal 155 Puerto Rico 157 Reunion 158 Romania 159 Russia 167 Saudi Arabia 168 Senegal 170 Serbia and Montenegro 172 Sierra Leone 173 Singapore 174 Slovak Republic 175 Slovenia 176 Solomon Islands 177 Somalia 178 South Africa 179 Spain 180 Sri Lanka 181 Sudan 182 Suriname 183 Swaziland 184 Sweden 185 Switzerland 186 Syria 187 Taiwan 188 Tajikistan 190 Thailand 191 Timor-Leste 192 Togo 194 Trinidad and Tobago 195 Tunisia 196 Turkey 197 Turkmenistan 200 Ukraine 202 United Kingdom 203 United States 204 Uruguay 205 Uzbekistan 207 Venezuela 208 Vietnam 209 West Bank and Gaza 210 Yemen, Rep. 211 Zambia 212 Zimbabwe Name: country, dtype: object counts and countries for original 'employrate' greater 75% 14 countries with employ rate greater than 75% 4 Angola 28 Burkina Faso 29 Burundi 30 Cambodia 60 Ethiopia 78 Guinea 103 Laos 114 Madagascar 131 Mozambique 156 Qatar 160 Rwanda 189 Tanzania 199 Uganda 201 United Arab Emirates Name: country, dtype: object counts and countries for original 'internetuserate' less than 26 82 countries with internet usage rate below 25% 0 Afghanistan 2 Algeria 4 Angola 14 Bangladesh 18 Belize 19 Benin 21 Bhutan 22 Bolivia 24 Botswana 28 Burkina Faso 29 Burundi 30 Cambodia 31 Cameroon 35 Central African Rep. 36 Chad 40 Comoros 41 Congo, Dem. Rep. 42 Congo, Rep. 45 Cote d'Ivoire 47 Cuba 51 Djibouti 56 El Salvador 57 Equatorial Guinea 58 Eritrea 60 Ethiopia 62 Fiji 66 Gabon 67 Gambia 70 Ghana 77 Guatemala 78 Guinea 79 Guinea-Bissau 81 Haiti 82 Honduras 86 India 87 Indonesia 88 Iran 89 Iraq 98 Kiribati 102 Kyrgyzstan 103 Laos 106 Lesotho 107 Liberia 108 Libya 114 Madagascar 115 Malawi 118 Mali 122 Mauritania 125 Micronesia, Fed. Sts. 128 Mongolia 131 Mozambique 133 Namibia 135 Nepal 140 Nicaragua 141 Niger 146 Pakistan 149 Papua New Guinea 150 Paraguay 152 �� Philippines 160 Rwanda 164 Samoa 166 Sao Tome and Principe 168 Senegal 176 Solomon Islands 178 South Africa 180 Sri Lanka 183 Swaziland 186 Syria 188 Tajikistan 189 Tanzania 190 Thailand 191 Timor-Leste 192 Togo 193 Tonga 197 Turkmenistan 198 Tuvalu 199 Uganda 205 Uzbekistan 206 Vanuatu 210 Yemen, Rep. 211 Zambia 212 Zimbabwe Name: country, dtype: object counts and countries for original 'internetuserate' between 25 and 50 54 countries with internet usage rate between 25 and 50% 1 Albania 6 Argentina 7 Armenia 8 Aruba 11 Azerbaijan 12 Bahamas 16 Belarus 25 Brazil 26 Brunei 27 Bulgaria 33 Cape Verde 37 Chile 38 China 39 Colombia 44 Costa Rica 52 Dominica 53 Dominican Rep. 54 Ecuador 55 Egypt 65 French Polynesia 68 Georgia 72 Greece 74 Grenada 80 Guyana 93 Jamaica 95 Jordan 96 Kazakhstan 97 Kenya 101 Kuwait 105 Lebanon 117 Maldives 123 Mauritius 124 Mexico 126 Moldova 130 Morocco 142 Nigeria 148 Panama 151 Peru 155 Puerto Rico 158 Romania 159 Russia 162 Saint Lucia 167 Saudi Arabia 169 Serbia 171 Seychelles 182 Suriname 194 Trinidad and Tobago 195 Tunisia 196 Turkey 200 Ukraine 204 Uruguay 207 Venezuela 208 Vietnam 209 West Bank and Gaza Name: country, dtype: object counts and countries for original 'internetuserate' between 50 and 75 31 countries with internet usage rate between 50 and 75% 10 Austria 13 Bahrain 15 Barbados 17 Belgium 23 Bosnia and Herzegovina 34 Cayman Islands 46 Croatia 48 Cyprus 49 Czech Rep. 59 Estonia 71 Gibraltar 73 Greenland 83 Hong Kong, China 84 Hungary 90 Ireland 91 Israel 92 Italy 104 Latvia 110 Lithuania 112 Macao, China 113 Macedonia, FYR 116 Malaysia 119 Malta 129 Montenegro 145 Oman 153 Poland 154 Portugal 173 Singapore 175 Slovenia 179 Spain 203 United States Name: country, dtype: object counts and countries for original 'internetuserate' greater than 75 25 countries with internet usage rate greater than 75% 3 Andorra 5 Antigua and Barbuda 9 Australia 20 Bermuda 32 Canada 50 Denmark 61 Faeroe Islands 63 Finland 64 France 69 Germany 85 Iceland 94 Japan 100 Korea, Rep. 109 Liechtenstein 111 Luxembourg 136 Netherlands 139 New Zealand 144 Norway 156 Qatar 161 Saint Kitts and Nevis 174 Slovak Republic 184 Sweden 185 Switzerland 201 United Arab Emirates 202 United Kingdom Name: country, dtype: object counts countries for original 'lifeexpectancy' less than 51 9 countries with life expectancy below 51 0 Afghanistan 35 Central African Rep. 36 Chad 41 Congo, Dem. Rep. 79 Guinea-Bissau 106 Lesotho 172 Sierra Leone 183 Swaziland 211 Zambia Name: country, dtype: object counts countries for original 'lifeexpectancy' less than 76 117 countries with life expectancy between 50 and 75 2 Algeria 4 Angola 7 Armenia 11 Azerbaijan 14 Bangladesh 16 Belarus 19 Benin 21 Bhutan 22 Bolivia 24 Botswana 25 Brazil 27 Bulgaria 28 Burkina Faso 29 Burundi 30 Cambodia 31 Cameroon 33 Cape Verde 38 China 39 Colombia 40 Comoros 42 Congo, Rep. 45 Cote d'Ivoire 51 Djibouti 53 Dominican Rep. 55 Egypt 56 El Salvador 57 Equatorial Guinea 58 Eritrea 59 Estonia 60 Ethiopia 62 Fiji 66 Gabon 67 Gambia 68 Georgia 70 Ghana 77 Guatemala 78 Guinea 80 Guyana 81 Haiti 82 Honduras 84 Hungary 86 India 87 Indonesia 88 Iran 89 Iraq 93 Jamaica 95 Jordan 96 Kazakhstan 97 Kenya 99 Korea, Dem. Rep. 101 Kuwait 102 Kyrgyzstan 103 Laos 104 Latvia 105 Lebanon 107 Liberia 108 Libya 110 Lithuania 113 Macedonia, FYR 114 Madagascar 115 Malawi 116 Malaysia 118 Mali 122 Mauritania 123 Mauritius 125 Micronesia, Fed. Sts. 126 Moldova 128 Mongolia 129 Montenegro 130 Morocco 131 Mozambique 132 Myanmar 133 Namibia 135 Nepal 140 Nicaragua 141 Niger 142 Nigeria 145 Oman 146 Pakistan 149 Papua New Guinea 150 Paraguay 151 Peru 152 Philippines 158 Romania 159 Russia 160 Rwanda 162 Saint Lucia 163 Saint Vincent and the Grenadines 164 Samoa 166 Sao Tome and Principe 167 Saudi Arabia 168 Senegal 169 Serbia 176 Solomon Islands 177 Somalia 178 South Africa 180 Sri Lanka 181 Sudan 182 Suriname 188 Tajikistan 189 Tanzania 190 Thailand 191 Timor-Leste 192 Togo 193 Tonga 194 Trinidad and Tobago 195 Tunisia 196 Turkey 197 Turkmenistan 199 Uganda 200 Ukraine 205 Uzbekistan 206 Vanuatu 207 Venezuela 209 West Bank and Gaza 210 Yemen, Rep. 212 Zimbabwe Name: country, dtype: object counts countries for original 'lifeexpectancy' greater than 75 65 countries with life expectancy over 75 1 Albania 6 Argentina 8 Aruba 9 Australia 10 Austria 12 Bahamas 13 Bahrain 15 Barbados 17 Belgium 18 Belize 23 Bosnia and Herzegovina 26 Brunei 32 Canada 37 Chile 44 Costa Rica 46 Croatia 47 Cuba 48 Cyprus 49 Czech Rep. 50 Denmark 54 Ecuador 63 Finland 64 France 65 French Polynesia 69 Germany 72 Greece 74 Grenada 75 Guadeloupe 76 Guam 83 Hong Kong, China 85 Iceland 90 Ireland 91 Israel 92 Italy 94 Japan 100 Korea, Rep. 111 Luxembourg 112 Macao, China 117 Maldives 119 Malta 121 Martinique 124 Mexico 136 Netherlands 137 Netherlands Antilles 138 New Caledonia 139 New Zealand 144 Norway 148 Panama 153 Poland 154 Portugal 155 Puerto Rico 156 Qatar 157 Reunion 173 Singapore 174 Slovak Republic 175 Slovenia 179 Spain 184 Sweden 185 Switzerland 186 Syria 201 United Arab Emirates 202 United Kingdom 203 United States 204 Uruguay 208 Vietnam Name: country, dtype: object decscription of variable: 'employrate' count 178.000000 mean 58.635955 std 10.519454 min 32.000000 25% 51.225000 50% 58.699999 75% 64.975000 max 83.199997 Name: employrate, dtype: float64 decscription of variable: 'internetuserate' count 192.000000 mean 35.632716 std 27.780285 min 0.210066 25% 9.999604 50% 31.810121 75% 56.416046 max 95.638113 Name: internetuserate, dtype: float64 decscription of variable: 'lifeexpectancy' count 191.000000 mean 69.753524 std 9.708621 min 47.794000 25% 64.447000 50% 73.131000 75% 76.593000 max 83.394000 Name: lifeexpectancy, dtype: float64

3) Graphs

The graphs show the percentage of internet usage and employ rate of each base on the countries. The life expectancy graph display the life expectancy year base on country count. The final graph trying to demonstrate the relationship between internet usage and employ rate.

0 notes