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‎data/spreadsheet/world_data.csv‎

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country,continent,population,gdp
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Fiji,Oceania,889953.0,5496
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Tanzania,Africa,58005463.0,63177
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W. Sahara,Africa,603253.0,907
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Canada,North America,37589262.0,1736425
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United States of America,North America,328239523.0,21433226
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Kazakhstan,Asia,18513930.0,181665
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Uzbekistan,Asia,33580650.0,57921
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Papua New Guinea,Oceania,8776109.0,24829
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Indonesia,Asia,270625568.0,1119190
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Argentina,South America,44938712.0,445445
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Chile,South America,18952038.0,282318
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Dem. Rep. Congo,Africa,86790567.0,50400
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Somalia,Africa,10192317.3,4719
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Kenya,Africa,52573973.0,95503
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Sudan,Africa,42813238.0,30513
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Chad,Africa,15946876.0,11314
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Haiti,North America,11263077.0,14332
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Dominican Rep.,North America,10738958.0,88941
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Russia,Europe,144373535.0,1699876
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Bahamas,North America,389482.0,13578
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Falkland Is.,South America,3398.0,282
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Norway,Europe,5347896.0,403336
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Greenland,North America,56225.0,3051
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Fr. S. Antarctic Lands,Seven seas (open ocean),140.0,16
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Timor-Leste,Asia,1293119.0,2017
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South Africa,Africa,58558270.0,351431
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Lesotho,Africa,2125268.0,2376
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Mexico,North America,127575529.0,1268870
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Uruguay,South America,3461734.0,56045
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Brazil,South America,211049527.0,1839758
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Bolivia,South America,11513100.0,40895
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Peru,South America,32510453.0,226848
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Colombia,South America,50339443.0,323615
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Panama,North America,4246439.0,66800
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Costa Rica,North America,5047561.0,61801
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Nicaragua,North America,6545502.0,12520
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Honduras,North America,9746117.0,25095
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El Salvador,North America,6453553.0,27022
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Guatemala,North America,16604026.0,76710
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Belize,North America,390353.0,1879
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Venezuela,South America,28515829.0,482359
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Guyana,South America,782766.0,5173
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Suriname,South America,581363.0,3697
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France,Europe,67059887.0,2715518
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Ecuador,South America,17373662.0,107435
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Puerto Rico,North America,3193694.0,104988
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Jamaica,North America,2948279.0,16458
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Cuba,North America,11333483.0,100023
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Zimbabwe,Africa,14645468.0,21440
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Botswana,Africa,2303697.0,18340
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Namibia,Africa,2494530.0,12366
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Senegal,Africa,16296364.0,23578
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Mali,Africa,19658031.0,17279
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Mauritania,Africa,4525696.0,7600
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Benin,Africa,11801151.0,14390
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Niger,Africa,23310715.0,12911
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Nigeria,Africa,200963599.0,448120
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Cameroon,Africa,25876380.0,39007
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Togo,Africa,8082366.0,5490
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Ghana,Africa,30417856.0,66983
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Côte d'Ivoire,Africa,25716544.0,58539
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Guinea,Africa,12771246.0,12296
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Guinea-Bissau,Africa,1920922.0,1339
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Liberia,Africa,4937374.0,3070
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Sierra Leone,Africa,7813215.0,4121
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Burkina Faso,Africa,20321378.0,15990
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Central African Rep.,Africa,4745185.0,2220
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Congo,Africa,5380508.0,12267
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Gabon,Africa,2172579.0,16874
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Eq. Guinea,Africa,1355986.0,11026
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Zambia,Africa,17861030.0,23309
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Malawi,Africa,18628747.0,7666
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Mozambique,Africa,30366036.0,15291
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eSwatini,Africa,1148130.0,4471
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Angola,Africa,31825295.0,88815
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Burundi,Africa,11530580.0,3012
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Israel,Asia,9053300.0,394652
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Lebanon,Asia,6855713.0,51991
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Madagascar,Africa,26969307.0,14114
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Palestine,Asia,4685306.0,16276
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Gambia,Africa,2347706.0,1826
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Tunisia,Africa,11694719.0,38796
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Algeria,Africa,43053054.0,171091
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Jordan,Asia,10101694.0,44502
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United Arab Emirates,Asia,9770529.0,421142
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Qatar,Asia,2832067.0,175837
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Kuwait,Asia,4207083.0,134628
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Iraq,Asia,39309783.0,234094
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Oman,Asia,4974986.0,76331
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Vanuatu,Oceania,299882.0,934
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Cambodia,Asia,16486542.0,27089
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Thailand,Asia,69625582.0,543548
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Laos,Asia,7169455.0,18173
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Myanmar,Asia,54045420.0,76085
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Vietnam,Asia,96462106.0,261921
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North Korea,Asia,25666161.0,40000
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South Korea,Asia,51709098.0,1646739
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Mongolia,Asia,3225167.0,13996
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India,Asia,1366417754.0,2868929
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Bangladesh,Asia,163046161.0,302571
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Bhutan,Asia,763092.0,2530
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Nepal,Asia,28608710.0,30641
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Pakistan,Asia,216565318.0,278221
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Afghanistan,Asia,38041754.0,19291
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Tajikistan,Asia,9321018.0,8116
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Kyrgyzstan,Asia,6456900.0,8454
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Turkmenistan,Asia,5942089.0,40761
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Iran,Asia,82913906.0,453996
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Syria,Asia,17070135.0,98830
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Armenia,Asia,2957731.0,13672
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Sweden,Europe,10285453.0,530883
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Belarus,Europe,9466856.0,63080
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Ukraine,Europe,44385155.0,153781
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Poland,Europe,37970874.0,595858
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Austria,Europe,8877067.0,445075
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Hungary,Europe,9769949.0,163469
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Moldova,Europe,2657637.0,11968
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Romania,Europe,19356544.0,250077
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Lithuania,Europe,2786844.0,54627
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Latvia,Europe,1912789.0,34102
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Estonia,Europe,1326590.0,31471
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Germany,Europe,83132799.0,3861123
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Bulgaria,Europe,6975761.0,68558
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Greece,Europe,10716322.0,209852
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Turkey,Asia,83429615.0,761425
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Albania,Europe,2854191.0,15279
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Croatia,Europe,4067500.0,60752
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Switzerland,Europe,8574832.0,703082
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Luxembourg,Europe,619896.0,71104
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Belgium,Europe,11484055.0,533097
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Netherlands,Europe,17332850.0,907050
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Portugal,Europe,10269417.0,238785
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Spain,Europe,47076781.0,1393490
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Ireland,Europe,4941444.0,388698
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New Caledonia,Oceania,287800.0,10770
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Solomon Is.,Oceania,669823.0,1589
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New Zealand,Oceania,4917000.0,206928
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Australia,Oceania,25364307.0,1396567
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Sri Lanka,Asia,21803000.0,84008
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China,Asia,1397715000.0,14342903
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Taiwan,Asia,23568378.0,1127000
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Italy,Europe,60297396.0,2003576
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Denmark,Europe,5818553.0,350104
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United Kingdom,Europe,66834405.0,2829108
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Iceland,Europe,361313.0,24188
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Azerbaijan,Asia,10023318.0,48047
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Georgia,Asia,3720382.0,17477
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Philippines,Asia,108116615.0,376795
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Malaysia,Asia,31949777.0,364681
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Brunei,Asia,433285.0,13469
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Slovenia,Europe,2087946.0,54174
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Finland,Europe,5520314.0,269296
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Slovakia,Europe,5454073.0,105079
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Czechia,Europe,10669709.0,250680
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Eritrea,Africa,6081196.0,2065
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Japan,Asia,126264931.0,5081769
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Paraguay,South America,7044636.0,38145
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Yemen,Asia,29161922.0,22581
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Saudi Arabia,Asia,34268528.0,792966
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Antarctica,Antarctica,4490.0,898
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N. Cyprus,Asia,326000.0,3600
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Cyprus,Asia,1198575.0,24948
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Morocco,Africa,36471769.0,119700
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Egypt,Africa,100388073.0,303092
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Libya,Africa,6777452.0,52091
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Ethiopia,Africa,112078730.0,95912
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Djibouti,Africa,973560.0,3324
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Somaliland,Africa,5096159.0,17836
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Uganda,Africa,44269594.0,35165
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Rwanda,Africa,12626950.0,10354
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Bosnia and Herz.,Europe,3301000.0,20164
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North Macedonia,Europe,2083459.0,12547
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Serbia,Europe,6944975.0,51475
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Montenegro,Europe,622137.0,5542
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Kosovo,Europe,1794248.0,7926
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Trinidad and Tobago,North America,1394973.0,24269
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S. Sudan,Africa,11062113.0,11998

‎data/spreadsheet/world_data.xlsx‎

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‎docs/part1/ch01_ecosystem.ipynb‎

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‎requirements.txt‎

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numpy
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pandas
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matplotlib
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geodatasets

‎scripts/county.py‎

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import geopandas as gpd
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import geodatasets
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# Use the available 'land' dataset from Natural Earth
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path = geodatasets.get_path('naturalearth.land')
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world = gpd.read_file(path)
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# Note: 'naturalearth.land' contains physical boundaries.
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# It does not contain 'pop_est' or 'gdp_md_est'.
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# Let's see what columns ARE available:
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print("Available columns:", world.columns.tolist())
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# Basic visualization to confirm it's working
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world.plot()
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print(world.head())

‎scripts/download_country.py‎

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import geopandas as gpd
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# URL direta para o dataset cultural (Admin 0 - Países) do Natural Earth
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url = "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip"
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# Carregando os dados
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world = gpd.read_file(url)
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# No dataset original, as colunas são:
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# 'NAME' -> Nome do país
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# 'CONTINENT' -> Continente
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# 'POP_EST' -> Estimativa de população
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# 'GDP_MD' -> PIB (Gross Domestic Product)
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# Selecionando e renomeando
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world_filtered = world[['NAME', 'CONTINENT', 'POP_EST', 'GDP_MD']]
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world_filtered.columns = ['country', 'continent', 'population', 'gdp']
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# Salvando em CSV
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world_filtered.to_csv("world_data.csv", index=False)
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print("Arquivo 'world_data.csv' salvo com sucesso!")
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print(world_filtered.head())

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