diff --git a/.gitignore b/.gitignore index 38bd445..14bebc1 100644 --- a/.gitignore +++ b/.gitignore @@ -127,33 +127,7 @@ dmypy.json # Pyre type checker .pyre/ -data/FAO_trade_matrix/Trade_DetailedTradeMatrix_E_All_Data_(Normalized)/Trade_DetailedTradeMatrix_E_All_Data_(Normalized).csv -data/FAO_trade_matrix/Trade_DetailedTradeMatrix_E_All_Data_(Normalized)/Trade_DetailedTradeMatrix_E_AreaCodes.csv -.gitignore -data/FAO_trade_matrix/Trade_DetailedTradeMatrix_E_All_Data_(Normalized)/Trade_DetailedTradeMatrix_E_Flags.csv -.gitignore -data/FAO_trade_matrix/Trade_DetailedTradeMatrix_E_Oceania/Trade_DetailedTradeMatrix_E_AreaCodes.csv -.gitignore -data/FAO_trade_matrix/Trade_DetailedTradeMatrix_E_Oceania/Trade_DetailedTradeMatrix_E_Oceania.csv -data/FAO_trade_matrix/Trade_DetailedTradeMatrix_E_Oceania/Trade_DetailedTradeMatrix_E_ItemCodes.csv -data/FAO_trade_matrix/Trade_DetailedTradeMatrix_E_Oceania/Trade_DetailedTradeMatrix_E_Flags.csv -.gitignore -data/FAO_trade_matrix/Trade_DetailedTradeMatrix_E_All_Data_(Normalized)/Trade_DetailedTradeMatrix_E_ItemCodes.csv -data/FAO_trade_matrix/Trade_DetailedTradeMatrix_E_Oceania/Trade_DetailedTradeMatrix_E_Oceania_NOFLAG.csv -.gitignore -data/FAO_trade_matrix/Trade_DetailedTradeMatrix_E_Oceania.zip -data/FAO_trade_matrix/Trade_DetailedTradeMatrix_E_All_Data_(Normalized).zip -data/oceania_only_for_testing/Trade_DetailedTradeMatrix_E_AreaCodes.csv -data/oceania_only_for_testing/Trade_DetailedTradeMatrix_E_Flags.csv -data/oceania_only_for_testing/Trade_DetailedTradeMatrix_E_ItemCodes.csv -data/oceania_only_for_testing/Trade_DetailedTradeMatrix_E_Oceania_NOFLAG.csv -data/oceania_only_for_testing/Trade_DetailedTradeMatrix_E_Oceania.csv -data/Trade_DetailedTradeMatrix_E_All_Data/Trade_DetailedTradeMatrix_E_All_Data_NOFLAG.csv -data/Trade_DetailedTradeMatrix_E_All_Data/Trade_DetailedTradeMatrix_E_All_Data.csv -data/Trade_DetailedTradeMatrix_E_All_Data/Trade_DetailedTradeMatrix_E_AreaCodes.csv -data/Trade_DetailedTradeMatrix_E_All_Data/Trade_DetailedTradeMatrix_E_Flags.csv -data/Trade_DetailedTradeMatrix_E_All_Data/Trade_DetailedTradeMatrix_E_ItemCodes.csv -data/data_raw/Trade_DetailedTradeMatrix_E_All_Data.zip - -# Ignores all pickle files + *.pkl +*.zip + diff --git a/LICENSE b/LICENSE index 261eeb9..2a13d49 100644 --- a/LICENSE +++ b/LICENSE @@ -175,18 +175,7 @@ END OF TERMS AND CONDITIONS - APPENDIX: How to apply the Apache License to your work. - - To apply the Apache License to your work, attach the following - boilerplate notice, with the fields enclosed by brackets "[]" - replaced with your own identifying information. (Don't include - the brackets!) The text should be enclosed in the appropriate - comment syntax for the file format. We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [yyyy] [name of copyright owner] + Copyright 2024 Florian Ulrich Jehn Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. diff --git a/data/data_raw/Production_Crops_Livestock_E_All_Data.zip b/data/data_raw/Production_Crops_Livestock_E_All_Data.zip deleted file mode 100644 index 6ebd1d8..0000000 Binary files a/data/data_raw/Production_Crops_Livestock_E_All_Data.zip and /dev/null differ diff --git a/data/data_raw/Production_Crops_Livestock_E_Oceania.zip b/data/data_raw/Production_Crops_Livestock_E_Oceania.zip deleted file mode 100644 index 80fa9f3..0000000 Binary files a/data/data_raw/Production_Crops_Livestock_E_Oceania.zip and /dev/null differ diff --git a/data/data_raw/Trade_DetailedTradeMatrix_E_Oceania.zip b/data/data_raw/Trade_DetailedTradeMatrix_E_Oceania.zip deleted file mode 100644 index 265f0a9..0000000 Binary files a/data/data_raw/Trade_DetailedTradeMatrix_E_Oceania.zip and /dev/null differ diff --git a/data/data_raw/readme.txt b/data/data_raw/readme.txt new file mode 100644 index 0000000..88a8ddd --- /dev/null +++ b/data/data_raw/readme.txt @@ -0,0 +1 @@ +Drop FAO production and trade zips here. diff --git a/data/preprocessed_data/Maize_Y2021_Global_production.csv b/data/preprocessed_data/Maize_Y2021_Global_production.csv new file mode 100644 index 0000000..2f0e9d7 --- /dev/null +++ b/data/preprocessed_data/Maize_Y2021_Global_production.csv @@ -0,0 +1,197 @@ +,Value +Afghanistan,180000.0 +Albania,414270.8 +Algeria,18491.0 +Angola,2970208.0 +Antigua and Barbuda,10.68 +Caribbean,502699.09 +Azerbaijan,279248.3 +Argentina,60525805.0 +Australia,305655.87 +Austria,2434900.0 +Bahamas,668.49 +Bahrain,0.0 +Bangladesh,4116438.0 +Armenia,6000.0 +Barbados,49.9 +Belgium,449890.0 +Micronesia,91.81 +Bhutan,30938.64 +Bolivia (Plurinational State of),1224719.66 +Bosnia and Herzegovina,893124.0 +Botswana,67436.11 +Brazil,88461943.0 +Belize,108743.0 +Solomon Islands,0.0 +Brunei Darussalam,0.0 +Bulgaria,3427310.0 +Myanmar,2300000.0 +Burundi,280000.0 +Belarus,1148000.0 +Cambodia,920000.0 +Cameroon,2100146.82 +Canada,13983859.0 +Cabo Verde,449.0 +Central African Republic,90000.0 +Sri Lanka,472443.9 +Chile,793821.78 +China; mainland,272552000.0 +China; Taiwan Province of,210124.0 +Colombia,1591400.0 +Comoros,6288.7 +Congo,13000.0 +Democratic Republic of the Congo,2243379.0 +Cook Islands,0.0 +Costa Rica,6246.0 +Croatia,2242120.0 +Cuba,238721.0 +Cyprus,0.0 +Czechia,988040.0 +Benin,1628148.88 +Denmark,45270.0 +Dominica,193.43 +Dominican Republic,54725.12 +Ecuador,1699369.61 +El Salvador,881000.0 +Equatorial Guinea,0.0 +Ethiopia,10722000.0 +Eritrea,20000.0 +Estonia,0.0 +Faroe Islands,0.0 +Fiji,1438.0 +Finland,0.0 +France,15358300.0 +French Polynesia,0.0 +Djibouti,16.99 +Gabon,45172.92 +Georgia,233000.0 +Gambia,20000.0 +Palestine,0.0 +Germany,4462400.0 +Ghana,3500000.0 +Kiribati,0.0 +Greece,1322940.0 +Grenada,382.66 +Guatemala,1960000.0 +Guinea,798327.0 +Guyana,4000.0 +Haiti,200000.0 +Honduras,673592.27 +China; Hong Kong SAR,0.0 +Hungary,6424630.0 +Iceland,0.0 +India,31650000.0 +Indonesia,20010000.0 +Iran (Islamic Republic of),320169.71 +Iraq,374400.0 +Ireland,0.0 +Israel,68224.0 +Italy,6079960.0 +Côte d'Ivoire,1140000.0 +Jamaica,2122.0 +Japan,166.5 +Kazakhstan,1129508.48 +Jordan,25000.0 +Kenya,3303000.0 +Republic of Korea,78000.0 +Kuwait,12833.0 +Kyrgyzstan,691139.0 +Lao People's Democratic Republic,1050000.0 +Lebanon,3000.0 +Lesotho,90000.0 +Latvia,0.0 +Liberia,0.0 +Libya,3452.03 +Lithuania,104680.0 +Luxembourg,460.0 +China; Macao SAR,0.0 +Madagascar,232026.0 +Malawi,4581000.0 +Malaysia,74718.65 +Maldives,143.75 +Mali,3603000.0 +Malta,0.0 +Mauritania,12000.0 +Mauritius,1106.0 +Mexico,27503477.82 +Mongolia,0.0 +Republic of Moldova,2792700.0 +Montenegro,2584.03 +Morocco,48637.3 +Mozambique,2100000.0 +Oman,15545.0 +Namibia,91869.0 +Nauru,0.0 +Nepal,2997733.0 +Netherlands (Kingdom of the),171420.0 +New Caledonia,7417.0 +Vanuatu,915.96 +New Zealand,209281.0 +Nicaragua,375726.0 +Niger,5681.0 +Nigeria,12745000.0 +Niue,0.0 +Norway,0.0 +Micronesia (Federated States of),91.81 +Marshall Islands,0.0 +Pakistan,10634831.0 +Panama,135425.0 +Papua New Guinea,12622.34 +Paraguay,4088093.0 +Peru,1582135.0 +Philippines,8300321.8 +Poland,7321910.0 +Portugal,752490.0 +Guinea-Bissau,16971.0 +Timor-Leste,56058.0 +Puerto Rico,24.02 +Qatar,2809.0 +Romania,14820690.0 +Russian Federation,15239865.1 +Rwanda,482682.0 +Saint Kitts and Nevis,0.0 +Saint Lucia,0.0 +Saint Vincent and the Grenadines,801.78 +Sao Tome and Principe,598.43 +Saudi Arabia,59000.0 +Senegal,754620.83 +Serbia,6027130.66 +Seychelles,0.0 +Sierra Leone,22544.65 +Singapore,0.0 +Slovakia,1579120.0 +Viet Nam,4446383.17 +Slovenia,388830.0 +Somalia,75000.0 +South Africa,16870705.0 +Zimbabwe,1469664.0 +Spain,4597660.0 +Sudan,23363.99 +Suriname,27.0 +Eswatini,100000.0 +Sweden,14900.0 +Switzerland,98216.0 +Syrian Arab Republic,309841.4 +Tajikistan,240000.0 +Thailand,5300000.0 +Togo,929000.0 +Tonga,0.0 +Trinidad and Tobago,5000.0 +United Arab Emirates,23638.73 +Tunisia,0.0 +Türkiye,6750000.0 +Turkmenistan,7699.22 +Uganda,2800000.0 +Ukraine,42109850.0 +North Macedonia,134036.0 +Egypt,7500000.0 +United Kingdom of Great Britain and Northern Ireland,0.0 +United Republic of Tanzania,7039000.0 +United States of America,383943000.0 +Burkina Faso,1913000.0 +Uruguay,769600.0 +Uzbekistan,590016.0 +Venezuela (Bolivarian Republic of),1541647.87 +Samoa,0.0 +Yemen,35000.0 +Zambia,3620243.9 diff --git a/data/preprocessed_data/Maize_Y2021_Global_trade.csv b/data/preprocessed_data/Maize_Y2021_Global_trade.csv new file mode 100644 index 0000000..579ab4c --- /dev/null +++ b/data/preprocessed_data/Maize_Y2021_Global_trade.csv @@ -0,0 +1,197 @@ +,Afghanistan,Albania,Algeria,Angola,Antigua and Barbuda,Caribbean,Azerbaijan,Argentina,Australia,Austria,Bahamas,Bahrain,Bangladesh,Armenia,Barbados,Belgium,Micronesia,Bhutan,Bolivia (Plurinational State of),Bosnia and Herzegovina,Botswana,Brazil,Belize,Solomon Islands,Brunei Darussalam,Bulgaria,Myanmar,Burundi,Belarus,Cambodia,Cameroon,Canada,Cabo Verde,Central African Republic,Sri Lanka,Chile,China; mainland,China; Taiwan Province of,Colombia,Comoros,Congo,Democratic Republic of the Congo,Cook Islands,Costa Rica,Croatia,Cuba,Cyprus,Czechia,Benin,Denmark,Dominica,Dominican Republic,Ecuador,El Salvador,Equatorial Guinea,Ethiopia,Eritrea,Estonia,Faroe Islands,Fiji,Finland,France,French Polynesia,Djibouti,Gabon,Georgia,Gambia,Palestine,Germany,Ghana,Kiribati,Greece,Grenada,Guatemala,Guinea,Guyana,Haiti,Honduras,China; Hong Kong SAR,Hungary,Iceland,India,Indonesia,Iran (Islamic Republic of),Iraq,Ireland,Israel,Italy,Côte d'Ivoire,Jamaica,Japan,Kazakhstan,Jordan,Kenya,Republic of Korea,Kuwait,Kyrgyzstan,Lao People's Democratic Republic,Lebanon,Lesotho,Latvia,Liberia,Libya,Lithuania,Luxembourg,China; Macao SAR,Madagascar,Malawi,Malaysia,Maldives,Mali,Malta,Mauritania,Mauritius,Mexico,Mongolia,Republic of Moldova,Montenegro,Morocco,Mozambique,Oman,Namibia,Nauru,Nepal,Netherlands (Kingdom of the),New Caledonia,Vanuatu,New Zealand,Nicaragua,Niger,Nigeria,Niue,Norway,Micronesia (Federated States of),Marshall Islands,Pakistan,Panama,Papua New Guinea,Paraguay,Peru,Philippines,Poland,Portugal,Guinea-Bissau,Timor-Leste,Puerto Rico,Qatar,Romania,Russian Federation,Rwanda,Saint Kitts and Nevis,Saint Lucia,Saint Vincent and the Grenadines,Sao Tome and Principe,Saudi Arabia,Senegal,Serbia,Seychelles,Sierra Leone,Singapore,Slovakia,Viet Nam,Slovenia,Somalia,South Africa,Zimbabwe,Spain,Sudan,Suriname,Eswatini,Sweden,Switzerland,Syrian Arab Republic,Tajikistan,Thailand,Togo,Tonga,Trinidad and Tobago,United Arab Emirates,Tunisia,Türkiye,Turkmenistan,Uganda,Ukraine,North Macedonia,Egypt,United Kingdom of Great Britain and Northern Ireland,United Republic of Tanzania,United States of America,Burkina Faso,Uruguay,Uzbekistan,Venezuela (Bolivarian Republic of),Samoa,Yemen,Zambia +Afghanistan,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,29.25,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,33.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Albania,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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from data/preprocessed_data/Maize_Y2021_production.csv rename to data/preprocessed_data/Maize_Y2021_Oceania_production.csv index f25005f..6caad21 100644 --- a/data/preprocessed_data/Maize_Y2021_production.csv +++ b/data/preprocessed_data/Maize_Y2021_Oceania_production.csv @@ -5,15 +5,15 @@ Bangladesh,0.0 Solomon Islands,0.0 Brunei Darussalam,0.0 Myanmar,0.0 -China,0.0 -"China, Taiwan Province of",0.0 +China; mainland,0.0 +China; Taiwan Province of,0.0 Cook Islands,0.0 Fiji,1438.0 France,0.0 French Polynesia,0.0 Germany,0.0 Kiribati,0.0 -"China, Hong Kong SAR",0.0 +China; Hong Kong SAR,0.0 Indonesia,0.0 Israel,0.0 Japan,0.0 @@ -36,6 +36,6 @@ Thailand,0.0 Tonga,0.0 United Arab Emirates,0.0 Türkiye,0.0 -United Kingdom,0.0 +United Kingdom of Great Britain and Northern Ireland,0.0 United States of America,0.0 Samoa,0.0 diff --git a/data/preprocessed_data/Maize_Y2021_trade.csv b/data/preprocessed_data/Maize_Y2021_Oceania_trade.csv similarity index 85% rename from data/preprocessed_data/Maize_Y2021_trade.csv rename to data/preprocessed_data/Maize_Y2021_Oceania_trade.csv index c8cd270..8ec8343 100644 --- a/data/preprocessed_data/Maize_Y2021_trade.csv +++ b/data/preprocessed_data/Maize_Y2021_Oceania_trade.csv @@ -1,19 +1,19 @@ -,Argentina,Australia,Bangladesh,Solomon Islands,Brunei Darussalam,Myanmar,China,"China, Taiwan Province of",Cook Islands,Fiji,France,French Polynesia,Germany,Kiribati,"China, Hong Kong SAR",Indonesia,Israel,Japan,Republic of Korea,Malaysia,Nauru,New Caledonia,Vanuatu,New Zealand,Niue,Micronesia (Federated States of),Marshall Islands,Pakistan,Papua New Guinea,Philippines,Timor-Leste,Singapore,South Africa,Thailand,Tonga,United Arab Emirates,Türkiye,United Kingdom,United States of America,Samoa +,Argentina,Australia,Bangladesh,Solomon Islands,Brunei Darussalam,Myanmar,China; mainland,China; Taiwan Province of,Cook Islands,Fiji,France,French Polynesia,Germany,Kiribati,China; Hong Kong SAR,Indonesia,Israel,Japan,Republic of Korea,Malaysia,Nauru,New Caledonia,Vanuatu,New Zealand,Niue,Micronesia (Federated States of),Marshall Islands,Pakistan,Papua New Guinea,Philippines,Timor-Leste,Singapore,South Africa,Thailand,Tonga,United Arab Emirates,Türkiye,United Kingdom of Great Britain and Northern Ireland,United States of America,Samoa Argentina,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Australia,0.12,0.0,4.0,0.0,0.32,0.65,12.44,237.32,0.0,14.99,0.0,247.77,4.26,0.0,30.65,300.0,0.05,3049.32,75632.76,5805.47,0.13,28.42,3.05,90.02,0.0,0.0,0.0,0.4,62.42,649.31,0.01,709.3,8.46,2.75,0.0,0.2,7.5,0.0,0.0,0.01 Bangladesh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Solomon Islands,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Brunei Darussalam,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Myanmar,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -China,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -"China, Taiwan Province of",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +China; mainland,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +China; Taiwan Province of,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Cook Islands,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Fiji,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,3.64,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.55,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0 France,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 French Polynesia,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Germany,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Kiribati,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -"China, Hong Kong SAR",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +China; Hong Kong SAR,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Indonesia,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Israel,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Japan,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 @@ -36,6 +36,6 @@ Thailand,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Tonga,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.12,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 United Arab Emirates,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Türkiye,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -United Kingdom,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +United Kingdom of Great Britain and Northern Ireland,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 United States of America,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Samoa,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/data/preprocessed_data/Rice_Y2021_Global_production.csv b/data/preprocessed_data/Rice_Y2021_Global_production.csv new file mode 100644 index 0000000..b5e2d74 --- /dev/null +++ b/data/preprocessed_data/Rice_Y2021_Global_production.csv @@ -0,0 +1,196 @@ +,Value +Afghanistan,0.0 +Albania,0.0 +Algeria,0.0 +Angola,0.0 +Antigua and Barbuda,0.0 +Azerbaijan,0.0 +Argentina,0.0 +Australia,0.0 +Austria,0.0 +Bahamas,0.0 +Bahrain,0.0 +Bangladesh,0.0 +Armenia,0.0 +Barbados,0.0 +Belgium,0.0 +Bhutan,0.0 +Bolivia (Plurinational State of),0.0 +Bosnia and Herzegovina,0.0 +Botswana,0.0 +Brazil,0.0 +Belize,0.0 +Solomon Islands,0.0 +Brunei Darussalam,0.0 +Bulgaria,0.0 +Myanmar,0.0 +Burundi,0.0 +Belarus,0.0 +Cambodia,0.0 +Cameroon,0.0 +Canada,0.0 +Cabo Verde,0.0 +Central African Republic,0.0 +Sri Lanka,0.0 +Chile,0.0 +China; mainland,0.0 +China; Taiwan Province of,0.0 +Colombia,0.0 +Comoros,0.0 +Congo,0.0 +Democratic Republic of the Congo,0.0 +Cook Islands,0.0 +Costa Rica,0.0 +Croatia,0.0 +Cuba,0.0 +Cyprus,0.0 +Czechia,0.0 +Benin,0.0 +Denmark,0.0 +Dominica,0.0 +Dominican Republic,0.0 +Ecuador,0.0 +El Salvador,0.0 +Equatorial Guinea,0.0 +Ethiopia,0.0 +Eritrea,0.0 +Estonia,0.0 +Faroe Islands,0.0 +Fiji,0.0 +Finland,0.0 +France,0.0 +French Polynesia,0.0 +Djibouti,0.0 +Gabon,0.0 +Georgia,0.0 +Gambia,0.0 +Palestine,0.0 +Germany,0.0 +Ghana,0.0 +Kiribati,0.0 +Greece,0.0 +Grenada,0.0 +Guatemala,0.0 +Guinea,0.0 +Guyana,0.0 +Haiti,0.0 +Honduras,0.0 +China; Hong Kong SAR,0.0 +Hungary,0.0 +Iceland,0.0 +India,0.0 +Indonesia,0.0 +Iran (Islamic Republic of),0.0 +Iraq,0.0 +Ireland,0.0 +Israel,0.0 +Italy,0.0 +Côte d'Ivoire,0.0 +Jamaica,0.0 +Japan,0.0 +Kazakhstan,0.0 +Jordan,0.0 +Kenya,0.0 +Republic of Korea,0.0 +Kuwait,0.0 +Kyrgyzstan,0.0 +Lao People's Democratic Republic,0.0 +Lebanon,0.0 +Lesotho,0.0 +Latvia,0.0 +Liberia,0.0 +Libya,0.0 +Lithuania,0.0 +Luxembourg,0.0 +China; Macao SAR,0.0 +Madagascar,0.0 +Malawi,0.0 +Malaysia,0.0 +Maldives,0.0 +Mali,0.0 +Malta,0.0 +Mauritania,0.0 +Mauritius,0.0 +Mexico,0.0 +Mongolia,0.0 +Republic of Moldova,0.0 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of),0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Samoa,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5238.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,10200.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Yemen,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Zambia,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,274.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,60.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/data/preprocessed_data/Rice_Y2021_production.csv b/data/preprocessed_data/Rice_Y2021_Oceania_production.csv similarity index 77% rename from data/preprocessed_data/Rice_Y2021_production.csv rename to data/preprocessed_data/Rice_Y2021_Oceania_production.csv index 60acb38..b5f8f0f 100644 --- a/data/preprocessed_data/Rice_Y2021_production.csv +++ b/data/preprocessed_data/Rice_Y2021_Oceania_production.csv @@ -7,9 +7,9 @@ Cook Islands,0.0 Fiji,0.0 French Polynesia,0.0 Kiribati,0.0 -"China, Hong Kong SAR",0.0 +China; Hong Kong SAR,0.0 India,0.0 -"China, Macao SAR",0.0 +China; Macao SAR,0.0 Nauru,0.0 New Caledonia,0.0 Vanuatu,0.0 @@ -23,6 +23,6 @@ Viet Nam,0.0 Tokelau,0.0 Tonga,0.0 Tuvalu,0.0 -United Kingdom,0.0 +United Kingdom of Great Britain and Northern Ireland,0.0 Uruguay,0.0 Samoa,0.0 diff --git a/data/preprocessed_data/Rice_Y2021_trade.csv b/data/preprocessed_data/Rice_Y2021_Oceania_trade.csv similarity index 80% rename from data/preprocessed_data/Rice_Y2021_trade.csv rename to data/preprocessed_data/Rice_Y2021_Oceania_trade.csv index 0e4bf49..1646f98 100644 --- a/data/preprocessed_data/Rice_Y2021_trade.csv +++ b/data/preprocessed_data/Rice_Y2021_Oceania_trade.csv @@ -1,4 +1,4 @@ -,Argentina,Australia,Bahrain,Solomon Islands,Cook Islands,Fiji,French Polynesia,Kiribati,"China, Hong Kong SAR",India,"China, Macao SAR",Nauru,New Caledonia,Vanuatu,New Zealand,Niue,Micronesia (Federated States of),Marshall Islands,Papua New Guinea,Singapore,Viet Nam,Tokelau,Tonga,Tuvalu,United Kingdom,Uruguay,Samoa +,Argentina,Australia,Bahrain,Solomon Islands,Cook Islands,Fiji,French Polynesia,Kiribati,China; Hong Kong SAR,India,China; Macao SAR,Nauru,New Caledonia,Vanuatu,New Zealand,Niue,Micronesia (Federated States of),Marshall Islands,Papua New Guinea,Singapore,Viet Nam,Tokelau,Tonga,Tuvalu,United Kingdom of Great Britain and Northern Ireland,Uruguay,Samoa Argentina,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Australia,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,12.0,0.0 Bahrain,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 @@ -7,9 +7,9 @@ Cook Islands,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Fiji,0.0,12.0,0.0,0.0,85.0,0.0,216.0,541.0,0.0,0.0,0.0,120.0,0.0,373.0,77.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,44.0,167.0,0.0,0.0,6.0 French Polynesia,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Kiribati,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -"China, Hong Kong SAR",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +China; Hong Kong SAR,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 India,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -"China, Macao SAR",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +China; Macao SAR,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Nauru,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 New Caledonia,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Vanuatu,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 @@ -23,6 +23,6 @@ Viet Nam,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Tokelau,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Tonga,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Tuvalu,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -United Kingdom,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +United Kingdom of Great Britain and Northern Ireland,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Uruguay,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Samoa,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5238.0,0.0,0.0,0.0,0.0,0.0,0.0,10200.0,0.0,0.0,0.0,0.0,0.0 diff --git a/data/preprocessed_data/Wheat_Y2021_Global_production.csv b/data/preprocessed_data/Wheat_Y2021_Global_production.csv new file mode 100644 index 0000000..a971fe6 --- /dev/null +++ b/data/preprocessed_data/Wheat_Y2021_Global_production.csv @@ -0,0 +1,185 @@ +,Value +Afghanistan,3900000.0 +Albania,225170.9 +Algeria,2168386.0 +Angola,2945.7 +Antigua and Barbuda,0.0 +Azerbaijan,1837188.4 +Argentina,17644277.0 +Australia,31922554.7 +Austria,1547600.0 +Bahamas,0.0 +Bahrain,0.0 +Bangladesh,1085368.0 +Armenia,97000.0 +Barbados,0.0 +Belgium,1629180.0 +Bhutan,1168.94 +Bolivia (Plurinational State of),336222.5 +Bosnia and Herzegovina,314382.0 +Botswana,1280.0 +Brazil,7874525.0 +Belize,0.0 +Solomon Islands,0.0 +Brunei Darussalam,0.0 +Bulgaria,7342990.0 +Myanmar,100000.0 +Burundi,8806.53 +Belarus,2441000.0 +Cambodia,0.0 +Cameroon,633.02 +Canada,22296100.0 +Cabo Verde,0.0 +Central African Republic,0.0 +Sri Lanka,0.0 +Chile,1353607.94 +China; mainland,136946000.0 +China; Taiwan Province of,6000.0 +Colombia,6965.0 +Comoros,0.0 +Congo,0.0 +Democratic Republic of the Congo,9000.0 +Costa Rica,0.0 +Croatia,986930.0 +Cuba,0.0 +Cyprus,25580.0 +Czechia,4960930.0 +Benin,0.0 +Denmark,4047090.0 +Dominica,0.0 +Dominican Republic,0.0 +Ecuador,10898.38 +El Salvador,0.0 +Equatorial Guinea,0.0 +Ethiopia,5214000.0 +Eritrea,25000.0 +Estonia,736270.0 +Faroe Islands,0.0 +Fiji,0.0 +Finland,687160.0 +France,36559450.0 +French Polynesia,0.0 +Djibouti,0.0 +Gabon,0.0 +Georgia,135900.0 +Gambia,0.0 +Palestine,34396.47 +Germany,21459200.0 +Ghana,0.0 +Greece,1058360.0 +Grenada,0.0 +Guatemala,291.97 +Guinea,0.0 +Guyana,0.0 +Haiti,0.0 +Honduras,1255.58 +China; Hong Kong SAR,0.0 +Hungary,5290140.0 +Iceland,0.0 +India,109590000.0 +Indonesia,0.0 +Iran (Islamic Republic of),10093578.64 +Iraq,4233714.0 +Ireland,628080.0 +Israel,150000.0 +Italy,7294570.0 +Côte d'Ivoire,0.0 +Jamaica,0.0 +Japan,1097000.0 +Kazakhstan,11814123.97 +Jordan,30000.0 +Kenya,245300.0 +Republic of Korea,30000.0 +Kuwait,36.74 +Kyrgyzstan,362711.0 +Lao People's Democratic Republic,0.0 +Lebanon,100000.0 +Lesotho,6000.0 +Latvia,2407700.0 +Liberia,0.0 +Libya,130000.0 +Lithuania,4248850.0 +Luxembourg,76140.0 +Madagascar,2000.0 +Malawi,1000.0 +Malaysia,0.0 +Maldives,0.0 +Mali,21000.0 +Malta,0.0 +Mauritania,7000.0 +Mauritius,0.0 +Mexico,3283613.73 +Mongolia,566273.03 +Republic of Moldova,1565200.0 +Montenegro,2391.58 +Morocco,7543847.9 +Mozambique,15000.0 +Oman,3542.0 +Namibia,18337.0 +Nepal,2127276.0 +Netherlands (Kingdom of the),947250.0 +New Caledonia,84.5 +Vanuatu,0.0 +New Zealand,422831.0 +Nicaragua,0.0 +Niger,4628.94 +Nigeria,90000.0 +Norway,266600.0 +Marshall Islands,0.0 +Pakistan,27464081.0 +Panama,0.0 +Papua New Guinea,0.0 +Paraguay,927776.0 +Peru,202725.0 +Philippines,0.0 +Poland,11893550.0 +Portugal,69440.0 +Guinea-Bissau,0.0 +Timor-Leste,0.0 +Qatar,252.0 +Romania,10433750.0 +Russian Federation,76057258.0 +Rwanda,13684.0 +Saint Lucia,0.0 +Saint Vincent and the Grenadines,0.0 +Sao Tome and Principe,0.0 +Saudi Arabia,612582.0 +Senegal,0.0 +Serbia,3442308.0 +Seychelles,0.0 +Sierra Leone,0.0 +Singapore,0.0 +Slovakia,2002240.0 +Viet Nam,0.0 +Slovenia,154450.0 +Somalia,1050.82 +South Africa,2257205.0 +Zimbabwe,337212.0 +Spain,8564630.0 +Sudan,600000.0 +Suriname,0.0 +Eswatini,709.46 +Sweden,3027800.0 +Switzerland,401202.0 +Syrian Arab Republic,1951805.93 +Tajikistan,852000.0 +Thailand,1317.53 +Togo,0.0 +Trinidad and Tobago,0.0 +United Arab Emirates,0.0 +Tunisia,1193000.0 +Türkiye,17650000.0 +Turkmenistan,1367092.11 +Uganda,25000.0 +Ukraine,32183300.0 +North Macedonia,243676.0 +Egypt,9000000.0 +United Kingdom of Great Britain and Northern Ireland,13988000.0 +United Republic of Tanzania,70000.0 +United States of America,44790360.0 +Burkina Faso,0.0 +Uruguay,936400.0 +Uzbekistan,5984756.0 +Venezuela (Bolivarian Republic of),550.84 +Yemen,125000.0 +Zambia,205881.95 diff --git a/data/preprocessed_data/Wheat_Y2021_Global_trade.csv b/data/preprocessed_data/Wheat_Y2021_Global_trade.csv new file mode 100644 index 0000000..2799604 --- /dev/null +++ b/data/preprocessed_data/Wheat_Y2021_Global_trade.csv @@ -0,0 +1,185 @@ +,Afghanistan,Albania,Algeria,Angola,Antigua and Barbuda,Azerbaijan,Argentina,Australia,Austria,Bahamas,Bahrain,Bangladesh,Armenia,Barbados,Belgium,Bhutan,Bolivia (Plurinational State of),Bosnia and Herzegovina,Botswana,Brazil,Belize,Solomon Islands,Brunei Darussalam,Bulgaria,Myanmar,Burundi,Belarus,Cambodia,Cameroon,Canada,Cabo Verde,Central African Republic,Sri Lanka,Chile,China; mainland,China; Taiwan Province of,Colombia,Comoros,Congo,Democratic Republic of the Congo,Costa Rica,Croatia,Cuba,Cyprus,Czechia,Benin,Denmark,Dominica,Dominican Republic,Ecuador,El Salvador,Equatorial Guinea,Ethiopia,Eritrea,Estonia,Faroe Islands,Fiji,Finland,France,French Polynesia,Djibouti,Gabon,Georgia,Gambia,Palestine,Germany,Ghana,Greece,Grenada,Guatemala,Guinea,Guyana,Haiti,Honduras,China; Hong Kong SAR,Hungary,Iceland,India,Indonesia,Iran (Islamic Republic of),Iraq,Ireland,Israel,Italy,Côte d'Ivoire,Jamaica,Japan,Kazakhstan,Jordan,Kenya,Republic of Korea,Kuwait,Kyrgyzstan,Lao People's Democratic Republic,Lebanon,Lesotho,Latvia,Liberia,Libya,Lithuania,Luxembourg,Madagascar,Malawi,Malaysia,Maldives,Mali,Malta,Mauritania,Mauritius,Mexico,Mongolia,Republic of Moldova,Montenegro,Morocco,Mozambique,Oman,Namibia,Nepal,Netherlands (Kingdom of the),New Caledonia,Vanuatu,New Zealand,Nicaragua,Niger,Nigeria,Norway,Marshall Islands,Pakistan,Panama,Papua New Guinea,Paraguay,Peru,Philippines,Poland,Portugal,Guinea-Bissau,Timor-Leste,Qatar,Romania,Russian Federation,Rwanda,Saint Lucia,Saint Vincent and the Grenadines,Sao Tome and Principe,Saudi Arabia,Senegal,Serbia,Seychelles,Sierra Leone,Singapore,Slovakia,Viet Nam,Slovenia,Somalia,South Africa,Zimbabwe,Spain,Sudan,Suriname,Eswatini,Sweden,Switzerland,Syrian Arab Republic,Tajikistan,Thailand,Togo,Trinidad and Tobago,United Arab Emirates,Tunisia,Türkiye,Turkmenistan,Uganda,Ukraine,North Macedonia,Egypt,United Kingdom of Great Britain and Northern Ireland,United Republic of Tanzania,United States of America,Burkina Faso,Uruguay,Uzbekistan,Venezuela (Bolivarian Republic of),Yemen,Zambia +Afghanistan,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Albania,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +Algeria,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,27.25,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2514.6,0.0,0.0,0.0,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.56,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0 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data/preprocessed_data/Wheat_Y2021_Oceania_production.csv index 2e276a6..af9aa83 100644 --- a/data/preprocessed_data/Wheat_Y2021_production.csv +++ b/data/preprocessed_data/Wheat_Y2021_Oceania_production.csv @@ -7,13 +7,13 @@ Myanmar,0.0 Burundi,0.0 Canada,0.0 Sri Lanka,0.0 -China,0.0 -"China, Taiwan Province of",0.0 +China; mainland,0.0 +China; Taiwan Province of,0.0 Fiji,0.0 Finland,0.0 French Polynesia,0.0 Germany,0.0 -"China, Hong Kong SAR",0.0 +China; Hong Kong SAR,0.0 Indonesia,0.0 Italy,0.0 Japan,0.0 @@ -45,7 +45,7 @@ Thailand,0.0 United Arab Emirates,0.0 Uganda,0.0 Egypt,0.0 -United Kingdom,0.0 +United Kingdom of Great Britain and Northern Ireland,0.0 United Republic of Tanzania,0.0 United States of America,0.0 Yemen,0.0 diff --git a/data/preprocessed_data/Wheat_Y2021_trade.csv b/data/preprocessed_data/Wheat_Y2021_Oceania_trade.csv similarity index 89% rename from data/preprocessed_data/Wheat_Y2021_trade.csv rename to data/preprocessed_data/Wheat_Y2021_Oceania_trade.csv index 76cd632..8e8b2e8 100644 --- a/data/preprocessed_data/Wheat_Y2021_trade.csv +++ b/data/preprocessed_data/Wheat_Y2021_Oceania_trade.csv @@ -1,4 +1,4 @@ -,Australia,Bahrain,Bangladesh,Solomon Islands,Myanmar,Burundi,Canada,Sri Lanka,China,"China, Taiwan Province of",Fiji,Finland,French Polynesia,Germany,"China, Hong Kong SAR",Indonesia,Italy,Japan,Kenya,Republic of Korea,Kuwait,Lao People's Democratic Republic,Madagascar,Malawi,Malaysia,Mauritius,Mozambique,Oman,New Caledonia,Vanuatu,New Zealand,Nigeria,Papua New Guinea,Philippines,Timor-Leste,Qatar,Rwanda,Saudi Arabia,Singapore,Viet Nam,South Africa,Sudan,Thailand,United Arab Emirates,Uganda,Egypt,United Kingdom,United Republic of Tanzania,United States of America,Yemen,Zambia +,Australia,Bahrain,Bangladesh,Solomon Islands,Myanmar,Burundi,Canada,Sri Lanka,China; mainland,China; Taiwan Province of,Fiji,Finland,French Polynesia,Germany,China; Hong Kong SAR,Indonesia,Italy,Japan,Kenya,Republic of Korea,Kuwait,Lao People's Democratic Republic,Madagascar,Malawi,Malaysia,Mauritius,Mozambique,Oman,New Caledonia,Vanuatu,New Zealand,Nigeria,Papua New Guinea,Philippines,Timor-Leste,Qatar,Rwanda,Saudi Arabia,Singapore,Viet Nam,South Africa,Sudan,Thailand,United Arab Emirates,Uganda,Egypt,United Kingdom of Great Britain and Northern Ireland,United Republic of Tanzania,United States of America,Yemen,Zambia Australia,0.0,82500.0,451240.6,13167.68,373348.5,23541.0,0.0,577683.1,2943393.26,282394.26,169735.78,0.0,625.28,0.0,1074.83,4938500.11,464616.4,1216260.6,508219.6,1133738.28,440514.6,2010.02,32307.0,113250.0,1151033.46,63.17,284279.6,75584.58,29851.56,0.0,546278.82,115280.0,252645.9,1969593.52,0.01,62783.2,42526.2,481946.6,108995.27,3028631.0,589422.1,226075.6,1053869.28,250157.3,123014.4,320570.4,6719.8,200866.82,0.0,856726.0,18000.0 Bahrain,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Bangladesh,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 @@ -7,13 +7,13 @@ Myanmar,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0, Burundi,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Canada,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Sri Lanka,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -China,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -"China, Taiwan Province of",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +China; mainland,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +China; Taiwan Province of,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Fiji,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Finland,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 French Polynesia,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Germany,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -"China, Hong Kong SAR",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +China; Hong Kong SAR,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Indonesia,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Italy,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Japan,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 @@ -45,7 +45,7 @@ Thailand,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 United Arab Emirates,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Uganda,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Egypt,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -United Kingdom,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +United Kingdom of Great Britain and Northern Ireland,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 United Republic of Tanzania,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 United States of America,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 Yemen,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/data/preprocessed_data/readme.txt b/data/preprocessed_data/readme.txt new file mode 100644 index 0000000..e3612a8 --- /dev/null +++ b/data/preprocessed_data/readme.txt @@ -0,0 +1 @@ +This folder contains the ordered production and trade data once the model has created them. diff --git a/data/supplemental_data/m49.csv b/data/supplemental_data/m49.csv deleted file mode 100644 index aa3b320..0000000 --- a/data/supplemental_data/m49.csv +++ /dev/null @@ -1,304 +0,0 @@ -"m49","country_name_en","country_name_es","country_name_fr","country_name_ar","country_name_zh","country_name_ru" -"-1","Abyei",,,,, -"4","Afghanistan","Afganistán","Afghanistan","أفغانستان","阿富汗","Афганистан" -"-20","Akrotiri",,,,, -"-2","Aksai Chin",,,,, -"8","Albania","Albania","Albanie","ألبانيا","阿尔巴尼亚","Албания" -"12","Algeria","Argelia","Algérie","الجزائر","阿尔及利亚","Алжир" -"16","American Samoa","Samoa Americana","Samoa américaines","ساموا الأمريكيّة","美属萨摩亚","Американское Самоа" -"20","Andorra","Andorra","Andorre","أندورا","安道尔","Андорра" -"24","Angola","Angola","Angola","أنغولا","安哥拉","Ангола" -"660","Anguilla","Anguila","Anguilla","أنغويلا","安圭拉","Ангилья" -"10","Antarctica","Antártida","Antarctique","أنتارتيكا","南极洲","Антарктика" -"28","Antigua and Barbuda","Antigua y Barbuda","Antigua-et-Barbuda","أنتيغوا وبربودا","安提瓜和巴布达","Антигуа и Барбуда" -"32","Argentina","Argentina","Argentine","الأرجنتين","阿根廷","Аргентина" -"51","Armenia","Armenia","Arménie","أرمينيا","亚美尼亚","Армения" -"533","Aruba","Aruba","Aruba","أَروبا","阿鲁巴岛","Аруба" -"-3","Arunachal Pradesh",,,,, -"36","Australia","Australia","Australie","أستراليا","澳大利亚","Австралия" -"40","Austria","Austria","Autriche","النمسا","奥地利","Австрия" -"31","Azerbaijan","Azerbaiyán","Azerbaïdjan","أذربيجان","阿塞拜疆","Азербайджан" -"44","Bahamas","Bahamas","Bahamas","جزر البهاما","巴哈马","Багамские Острова" -"48","Bahrain","Bahrein","Bahreïn","البحرين","巴林","Бахрейн" -"50","Bangladesh","Bangladesh","Bangladesh","بنغلاديش","孟加拉国","Бангладеш" -"52","Barbados","Barbados","Barbade","بربادوس","巴巴多斯","Барбадос" -"-4","Bassas da India",,,,, -"112","Belarus","Belarús","Bélarus","بيلاروس","白俄罗斯","Беларусь" -"56","Belgium","Bélgica","Belgique","بلجيكا","比利时","Бельгия" -"-24","Belgium-Luxembourg",,,,, -"84","Belize","Belice","Belize","بليز","伯利兹","Белиз" -"204","Benin","Benin","Bénin","بنن","贝宁","Бенин" -"60","Bermuda","Bermudas","Bermudes","برمودا","百慕大","Бермудские острова" -"64","Bhutan","Bhután","Bhoutan","بوتان","不丹","Бутан" -"-15","Bi'r Tawīl",,,,, -"68","Bolivia (Plurinational State of)","Bolivia (Estado Plurinacional de)","Bolivie (État plurinational de)","بوليفيا دولة) المتعددة (القوميات","玻利维亚（多民族国）","Боливия (Многонациональное Государство)" -"535","Bonaire, Sint Eustatius and Saba","Bonaire, Sint Eustatius y Saba","Bonaire, Sint Eustatius et Saba","بونير وسان يوستاتيوس وسابا","博纳尔、圣俄斯塔休斯和萨巴","Бонайре, Синт-Эстатиус и Саба" -"70","Bosnia and Herzegovina","Bosnia y Herzegovina","Bosnie-Herzégovine","البوسنة والهرسك","波斯尼亚和黑塞哥维那","Босния и Герцеговина" -"72","Botswana","Botswana","Botswana","بوتسوانا","博茨瓦纳","Ботсвана" -"74","Bouvet Island","Isla Bouvet","Île Bouvet","جزر بوفيه","博维特岛","остров Буве" -"76","Brazil","Brasil","Brésil","البرازيل","巴西","Бразилия" -"86","British Indian Ocean Territory","Territorio Británico del Océano Índico","Territoire britannique de l'océan Indien","المناطق البريطانيّة في المحيط الهندي","英属印度洋领地","Британская территория в Индийском океане" -"92","British Virgin Islands","Islas Vírgenes Británicas","Îles Vierges britanniques","جزر فيرجين البريطانيّة","英属维尔京群岛","Британские Виргинские острова" -"96","Brunei Darussalam","Brunei Darussalam","Brunéi Darussalam","بروني دار السلام","文莱达鲁萨兰国","Бруней-Даруссалам" -"100","Bulgaria","Bulgaria","Bulgarie","بلغاريا","保加利亚","Болгария" -"854","Burkina Faso","Burkina Faso","Burkina Faso","بوركينا فاسو","布基纳法索","Буркина-Фасо" -"108","Burundi","Burundi","Burundi","بوروندي","布隆迪","Бурунди" -"132","Cabo Verde","Cabo Verde","Cabo Verde","كابو فيردي","佛得角","Кабо-Верде" -"116","Cambodia","Camboya","Cambodge","كمبوديا","柬埔寨","Камбоджа" -"120","Cameroon","Camerún","Cameroun","الكاميرون","喀麦隆","Камерун" -"124","Canada","Canadá","Canada","كندا","加拿大","Канада" -"136","Cayman Islands","Islas Caimán","Îles Caïmanes","جزر كايمان","开曼群岛","Каймановы острова" -"140","Central African Republic","República Centroafricana","République centrafricaine","جمهوريّة أفريقيا الوسطى","中非共和国","Центральноафриканская Республика" -"148","Chad","Chad","Tchad","تشاد","乍得","Чад" -"-25","Channel Islands",,,,, -"152","Chile","Chile","Chili","شيلي","智利","Чили" -"156","China","China","Chine","الصين","中国","Китай" -"344","China, Hong Kong SAR","China, RAE de Hong Kong","Chine - RAS de Hong-Kong","الصين - هونغ كونغ منطقة إداريّة خاصة","中国香港特区","Китай, Специальный административный район Гонконг" -"446","China, Macao SAR","China, RAE de Macao","Chine - RAS de Macao","الصين - مكاو منطقة إداريّة خاصّة","中国澳门特区","Китай, Специальный административный район Макао" -"158","China, Taiwan Province of","Taiwan, provincia de China","Taiwan, Province de Chine","تايوان ، مقاطعة الصين","台湾，中国","Тайвань, провинция Китая" -"-26","China, mainland",,,,, -"162","Christmas Island","Isla de Navidad","Île Christmas","جزر كريسماس","圣诞岛","остров Рождества" -"166","Cocos (Keeling) Islands","Islas Cocos (Keeling)","Îles des Cocos (Keeling)","جزر كوكوس - كيلنغ","可可群岛","Кокосовые острова (Килинг)" -"170","Colombia","Colombia","Colombie","كولومبيا","哥伦比亚","Колумбия" -"174","Comoros","Comoras","Comores","جزر القمر","科摩罗","Коморские Острова" -"178","Congo","Congo","Congo","الكونغو","刚果","Конго" -"184","Cook Islands","Islas Cook","Îles Cook","جزر كوك","库克群岛","Острова Кука" -"188","Costa Rica","Costa Rica","Costa Rica","كوستاريكا","哥斯达黎加","Коста-Рика" -"191","Croatia","Croacia","Croatie","كرواتيا","克罗地亚","Хорватия" -"192","Cuba","Cuba","Cuba","كوبا","古巴","Куба" -"531","Curaçao","Curaçao","Curaçao","كوراساو","库拉索","Кюрасао" -"196","Cyprus","Chipre","Chypre","قبرص","塞浦路斯","Кипр" -"203","Czechia","Chequia","Tchéquie (la)","تشيكيا","捷克","Чехия" -"384","Côte d'Ivoire","Côte d'Ivoire","Côte d'Ivoire","كوت ديفوار","科特迪瓦","Кот-д`Ивуар" -"-21","Dekelia",,,,, -"408","Democratic People's Republic of Korea","República Popular Democrática de Corea","République populaire démocratique de Corée","جمهوريّة كوريا الشعبيّة الديمقراطية","朝鲜民主主义人民共和国","Корейская Народно-Демократическая Республика" -"180","Democratic Republic of the Congo","República Democrática del Congo","République démocratique du Congo","جمهوريّة الكونغو الديمقراطية","刚果民主共和国","Демократическая Республика Конго" -"208","Denmark","Dinamarca","Danemark","الدانمرك","丹麦","Дания" -"262","Djibouti","Djibouti","Djibouti","جيبوتي","吉布提","Джибути" -"212","Dominica","Dominica","Dominique","دومينيكا","多米尼克","Доминика" -"214","Dominican Republic","República Dominicana","République dominicaine","الجمهوريّة الدومينيكيّة","多米尼加共和国","Доминиканская Республика" -"218","Ecuador","Ecuador","Équateur","إكوادور","厄瓜多尔","Эквадор" -"818","Egypt","Egipto","Égypte","مصر","埃及","Египет" -"222","El Salvador","El Salvador","El Salvador","السلفادور","萨尔瓦多","Сальвадор" -"226","Equatorial Guinea","Guinea Ecuatorial","Guinée équatoriale","غينيا الاستوائيّة","赤道几内亚","Экваториальная Гвинея" -"232","Eritrea","Eritrea","Érythrée","إريتريا","厄立特里亚","Эритрея" -"233","Estonia","Estonia","Estonie","إستونيا","爱沙尼亚","Эстония" -"748","Eswatini","Eswatini","Eswatini","إسواتيني","斯威士兰","Эсватини" -"231","Ethiopia","Etiopía","Éthiopie","إثيوبيا","埃塞俄比亚","Эфиопия" -"-5","Europa Island",,,,, -"238","Falkland Islands (Malvinas)","Islas Malvinas (Falkland Islands)","Îles Falkland (Malvinas)","جزر فوكلاند - مالفيناس","福克兰群岛","Фолклендские (Мальвинские) острова" -"234","Faroe Islands","Islas Feroe (Miembro Asociado)","Îles Féroé (Membre associé)","جزر فيرويه - عضو منتسب","法罗群岛(准成员)","Фарерские Острова (ассоциированный член)" -"242","Fiji","Fiji","Fidji","فيجي","斐济","Фиджи" -"246","Finland","Finlandia","Finlande","فنلندا","芬兰","Финляндия" -"250","France","Francia","France","فرنسا","法国","Франция" -"254","French Guiana","Guayana Francesa","Guyane française","الفرنسيّة غيانا","法属圭亚那","Французская Гвиана" -"258","French Polynesia","Polinesia Francesa","Polynésie française","بولينيزيا الفرنسية","法属波利尼西亚","Французская Полинезия" -"260","French Southern and Antarctic Territories","Tierras Australes y Antárticas Francesas","Terres australes et antarctiques françaises","الأقاليم الفرنسيّة الجنوبيّة والواقعة في أنتارتيكا","法属南部和南极区领土","Французские южные и антарктические территории" -"266","Gabon","Gabón","Gabon","غابون","加蓬","Габон" -"270","Gambia","Gambia","Gambie","غامبيا","冈比亚","Гамбия" -"268","Georgia","Georgia","Géorgie","جورجيا","格鲁吉亚","Грузия" -"276","Germany","Alemania","Allemagne","ألمانيا","德国","Германия" -"288","Ghana","Ghana","Ghana","غانا","加纳","Гана" -"292","Gibraltar","Gibraltar","Gibraltar","جبل طارق","直布罗陀","Гибралтар" -"-6","Glorioso Islands",,,,, -"300","Greece","Grecia","Grèce","اليونان","希腊","Греция" -"304","Greenland","Groenlandia","Groenland","غرينلاند","格陵兰","Гренландия" -"308","Grenada","Granada","Grenade","غرينادا","格林纳达","Гренада" -"312","Guadeloupe","Guadalupe","Guadeloupe","غوادلوب","瓜德罗普","Гваделупа" -"316","Guam","Guam","Guam","غوام","关岛","Гуам" -"320","Guatemala","Guatemala","Guatemala","غواتيمالا","危地马拉","Гватемала" -"831","Guernsey","Isla de Guernesey","Guernesey","بيلويك التابعة لغويرنسي","根西","Гернси" -"324","Guinea","Guinea","Guinée","غينيا","几内亚","Гвинея" -"624","Guinea-Bissau","Guinea-Bissau","Guinée-Bissau","غينيا - بيساو","几内亚比绍","Гвинея-Бисау" -"328","Guyana","Guyana","Guyana","غيانا","圭亚那","Гайана" -"332","Haiti","Haití","Haïti","هايتي","海地","Гаити" -"-9","Hala'ib Triangle",,,,, -"-10","Hala'ib Triangle",,,,, -"334","Heard and McDonald Islands","Islas Heard y McDonald","Îles Heard et McDonald","جزيرة هيرد وجزر ماكدونالد","赫德和麦克唐纳群岛","острова Херд и Макдональд" -"336","Holy See","Santa Sede","Saint-Siège","الكرسي الرسولي","教廷","Святой Престол" -"340","Honduras","Honduras","Honduras","هندوراس","洪都拉斯","Гондурас" -"348","Hungary","Hungría","Hongrie","هنغاريا","匈牙利","Венгрия" -"352","Iceland","Islandia","Islande","آيسلندا","冰岛","Исландия" -"-11","Ilemi Triangle",,,,, -"356","India","India","Inde","الهند","印度","Индия" -"360","Indonesia","Indonesia","Indonésie","إندونيسيا","印度尼西亚","Индонезия" -"364","Iran (Islamic Republic of)","Irán (República Islámica del)","Iran (République islamique d')","إيران جمهوريّة) - (الإسلامية","伊朗(伊斯兰共和国)","Иран (Исламская Республика)" -"368","Iraq","Iraq","Iraq","العراق","伊拉克","Ирак" -"372","Ireland","Irlanda","Irlande","آيرلندا","爱尔兰","Ирландия" -"833","Isle of Man","Isla de Man","Île de Man","جزيرة مان","马恩岛","остров Мэн" -"376","Israel","Israel","Israël","إسرائيل","以色列","Израиль" -"380","Italy","Italia","Italie","إيطاليا","意大利","Италия" -"388","Jamaica","Jamaica","Jamaïque","جامايكا","牙买加","Ямайка" -"-12","Jammu and Kashmir",,,,, -"392","Japan","Japón","Japon","اليابان","日本","Япония" -"832","Jersey","Jersey","Jersey","جيرسي","泽西","Джерси" -"400","Jordan","Jordania","Jordanie","الأردن","约旦","Иордания" -"-7","Juan de Nova Island",,,,, -"398","Kazakhstan","Kazajstán","Kazakhstan","كازاخستان","哈萨克斯坦","Казахстан" -"404","Kenya","Kenya","Kenya","كينيا","肯尼亚","Кения" -"296","Kiribati","Kiribati","Kiribati","كيريباس","基里巴斯","Кирибати" -"-14","Kuril Islands",,,,, -"414","Kuwait","Kuwait","Koweït","الكويت","科威特","Кувейт" -"417","Kyrgyzstan","Kirguistán","Kirghizistan","قيرغيزستان","吉尔吉斯斯坦","Кыргызстан" -"418","Lao People's Democratic Republic","República Democrática Popular Lao","République démocratique populaire lao","جمهوريّة لاو الديمقراطية الشعبيّة","老挝人民民主共和国","Лаосская Народно-Демократическая Республика" -"428","Latvia","Letonia","Lettonie","لاتفيا","拉脱维亚","Латвия" -"422","Lebanon","Líbano","Liban","لبنان","黎巴嫩","Ливан" -"426","Lesotho","Lesotho","Lesotho","ليسوتو","莱索托","Лесото" -"430","Liberia","Liberia","Libéria","ليبريا","利比里亚","Либерия" -"434","Libya","Libia","Libye","ليبيا","利比亚","Ливия" -"438","Liechtenstein","Liechtenstein","Liechtenstein","ليختنشتاين","列支敦士登","Лихтенштейн" -"440","Lithuania","Lituania","Lituanie","ليتوانيا","立陶宛","Литва" -"442","Luxembourg","Luxemburgo","Luxembourg","لكسمبرغ","卢森堡","Люксембург" -"450","Madagascar","Madagascar","Madagascar","مدغشقر","马达加斯加","Мадагаскар" -"454","Malawi","Malawi","Malawi","ملاوي","马拉维","Малави" -"458","Malaysia","Malasia","Malaisie","ماليزيا","马来西亚","Малайзия" -"462","Maldives","Maldivas","Maldives","ملديف","马尔代夫","Мальдивы" -"466","Mali","Malí","Mali","مالي","马里","Мали" -"470","Malta","Malta","Malte","مالطة","马耳他","Мальта" -"584","Marshall Islands","Islas Marshall","Îles Marshall","جزر مارشال","马绍尔群岛","Маршалловы Острова" -"474","Martinique","Martinica","Martinique","مارتينيك","马提尼克","Мартиника" -"478","Mauritania","Mauritania","Mauritanie","موريتانيا","毛里塔尼亚","Мавритания" -"480","Mauritius","Mauricio","Maurice","موريشيوس","毛里求斯","Маврикий" -"175","Mayotte","Mayotte","Mayotte","مايوت","马约特","Майотта" -"484","Mexico","México","Mexique","المكسيك","墨西哥","Мексика" -"583","Micronesia (Federated States of)","Micronesia (Estados Federados de)","Micronésie (États fédérés de)","ميكرونيزيا","密克罗尼西亚(联邦)","Микронезия (Федеративные Штаты)" -"492","Monaco","Mónaco","Monaco","موناكو","摩纳哥","Монако" -"496","Mongolia","Mongolia","Mongolie","منغوليا","蒙古","Монголия" -"499","Montenegro","Montenegro","Monténégro","الجبل الأسود","黑山","Черногория" -"500","Montserrat","Montserrat","Montserrat","مونتيسيرات","蒙特塞拉特","Монтсеррат" -"504","Morocco","Marruecos","Maroc","المغرب","摩洛哥","Марокко" -"508","Mozambique","Mozambique","Mozambique","موزامبيق","莫桑比克","Мозамбик" -"104","Myanmar","Myanmar","Myanmar","ميانمار","缅甸","Мьянма" -"516","Namibia","Namibia","Namibie","ناميبيا","纳米比亚","Намибия" -"520","Nauru","Nauru","Nauru","ناورو","瑙鲁","Науру" -"524","Nepal","Nepal","Népal","نيبال","尼泊尔","Непал" -"528","Netherlands","Países Bajos","Pays-Bas","هولندا","荷兰","Нидерланды" -"540","New Caledonia","Nueva Caledonia","Nouvelle-Calédonie","كاليدونيا الجديدة","新喀里多尼亚","Новая Каледония" -"554","New Zealand","Nueva Zelandia","Nouvelle-Zélande","نيوزيلندا","新西兰","Новая Зеландия" -"558","Nicaragua","Nicaragua","Nicaragua","نيكاراغوا","尼加拉瓜","Никарагуа" -"562","Niger","Níger","Niger","النيجر","尼日尔","Нигер" -"566","Nigeria","Nigeria","Nigéria","نيجيريا","尼日利亚","Нигерия" -"570","Niue","Niue","Nioué","نيوي","纽埃","Ниуэ" -"574","Norfolk Island","Isla Norfolk","Île Norfolk","جزيرة نورفولك","诺福克岛","остров Норфолк" -"807","North Macedonia","Macedonia del Norte","Macédoine du Nord","مقدونيا الشمالية","北马其顿","Северная Македония" -"580","Northern Mariana Islands","Islas Marianas septentrionales","Îles Mariannes du Nord","جزر ماريانا الشمالية","北马里亚纳群岛","Северные Марианские острова" -"578","Norway","Noruega","Norvège","النرويج","挪威","Норвегия" -"512","Oman","Omán","Oman","عمان","阿曼","Оман" -"586","Pakistan","Pakistán","Pakistan","باكستان","巴基斯坦","Пакистан" -"585","Palau","Palau","Palaos","بالاو","帕劳","Палау" -"275","Palestine","Territorio Palestino Ocupado","Territoire palestinien occupé","فلسطين","巴勒斯坦被占领土","Палестина" -"591","Panama","Panamá","Panama","بنما","巴拿马","Панама" -"598","Papua New Guinea","Papua Nueva Guinea","Papouasie-Nouvelle-Guinée","بابوا غينيا الجديدة","巴布亚新几内亚","Папуа-Новая Гвинея" -"-16","Paracel Islands",,,,, -"600","Paraguay","Paraguay","Paraguay","باراغواي","巴拉圭","Парагвай" -"604","Peru","Perú","Pérou","بيرو","秘鲁","Перу" -"608","Philippines","Filipinas","Philippines","الفلبين","菲律宾","Филиппины" -"612","Pitcairn Islands","Islas Pitcairn","Îles Pitcairn","جزر بيتكيرن","皮特克恩岛","Питкэрн" -"616","Poland","Polonia","Pologne","بولندا","波兰","Польша" -"620","Portugal","Portugal","Portugal","البرتغال","葡萄牙","Португалия" -"630","Puerto Rico","Puerto Rico","Porto Rico","بورتوريكو","波多黎各","Пуэрто-Рико" -"634","Qatar","Qatar","Qatar","قطر","卡塔尔","Катар" -"410","Republic of Korea","República de Corea","République de Corée","جمهوريّة كوريا","大韩民国","Республика Корея" -"498","Republic of Moldova","República de Moldova","République de Moldova","جمهوريّة مولدوفا","摩尔多瓦共和国","Республика Молдова" -"642","Romania","Rumania","Roumanie","رومانيا","罗马尼亚","Румыния" -"643","Russian Federation","Federación de Rusia","Fédération de Russie","الاتحاد الروسي","俄罗斯联邦","Российская Федерация" -"646","Rwanda","Rwanda","Rwanda","رواندا","卢旺达","Руанда" -"638","Réunion","Reunión","Réunion","رييونيون","留尼汪","Реюньон" -"652","Saint Barthélemy","San Bartolomé","Saint-Barthélemy","سان بارتليمي","圣巴泰勒米","Сен-Бартельми" -"654","Saint Helena, Ascension and Tristan da Cunha","Santa Elena, Ascensión y Tristán de Acuña","Sainte-Hélène, Ascension et Tristan da Cunha","سانت هيلانة وأسنسيون وتريستان دا كونا","圣赫勒拿、阿森松和特里斯坦-达库尼亚","острова Святой Елены, Вознесения и Тристан-да-Кунья" -"659","Saint Kitts and Nevis","Saint Kitts y Nevis","Saint-Kitts-et-Nevis","سانت كيتس ونيفيس","圣基茨和尼维斯","Сент-Китс и Невис" -"662","Saint Lucia","Santa Lucía","Sainte-Lucie","سانت لوسيا","圣卢西亚","Сент-Люсия" -"666","Saint Pierre and Miquelon","Saint-Pierre y Miquelon","Saint-Pierre-et-Miquelon","سان بيير وميكلون","圣皮埃尔和密克隆","Сен-Пьер и Микелон" -"670","Saint Vincent and the Grenadines","San Vicente y las Granadinas","Saint-Vincent-et-les Grenadines","سانت فنسنت وجزر غرينادين","圣文森特和格林纳丁斯","Сент-Винсент и Гренадины" -"663","Saint-Martin (French Part)","Saint-Martin (parte francesa)","Saint-Martin (partie française)","سان مارتن - الجزء الفرنسي","圣马丁（法属部分）","Сен-Мартен (французская часть)" -"882","Samoa","Samoa","Samoa","ساموا","萨摩亚","Самоа" -"674","San Marino","San Marino","Saint-Marin","سان مارينو","圣马力诺","Сан-Марино" -"678","Sao Tome and Principe","Santo Tomé y Príncipe","Sao Tomé-et-Principe","سان تومي وبرنسيبي","圣多美和普林西比","Сан-Томе и Принсипи" -"682","Saudi Arabia","Arabia Saudita","Arabie saoudite","المملكة العربيّة السعوديّة","沙特阿拉伯","Саудовская Аравия" -"-19","Scarborough Reef",,,,, -"686","Senegal","Senegal","Sénégal","السنغال","塞内加尔","Сенегал" -"-18","Senkaku Islands",,,,, -"688","Serbia","Serbia","Serbie","صربيا","塞尔维亚","Сербия" -"690","Seychelles","Seychelles","Seychelles","سيشيل","塞舌尔","Сейшельские Острова" -"694","Sierra Leone","Sierra Leona","Sierra Leone","سيراليون","塞拉利昂","Сьерра-Леоне" -"702","Singapore","Singapur","Singapour","سنغافورة","新加坡","Сингапур" -"534","Sint Maarten (Dutch Part)",,,"سينت مارتن",, -"703","Slovakia","Eslovaquia","Slovaquie","سلوفاكيا","斯洛伐克","Словакия" -"705","Slovenia","Eslovenia","Slovénie","سلوفينيا","斯洛文尼亚","Словения" -"90","Solomon Islands","Islas Salomón","Îles Salomon","جزر سليمان","所罗门群岛","Соломоновы Острова" -"706","Somalia","Somalia","Somalie","الصومال","索马里","Сомали" -"710","South Africa","Sudáfrica","Afrique du Sud","جنوب أفريقيا","南非","Южная Африка" -"239","South Georgia and the South Sandwich Islands","Islas Georgias del Sur y Sandwich del Sur","Îles Géorgie du Sud et Sandwich du Sud","جزر جنوب جورجيا وساندويتش","南乔治亚岛和南桑威奇群岛","остров Южная Георгия и Южные Сандвичевы острова" -"728","South Sudan","Sudán del Sur","Soudan du Sud","جنوب السودان","南苏丹","Южный Судан" -"724","Spain","España","Espagne","إسبانيا","西班牙","Испания" -"-17","Spratly Islands",,,,, -"144","Sri Lanka","Sri Lanka","Sri Lanka","سري لانكا","斯里兰卡","Шри-Ланка" -"729","Sudan","Sudán","Soudan","السودان","苏丹","Судан" -"740","Suriname","Suriname","Suriname","سورينام","苏里南","Суринам" -"744","Svalbard and Jan Mayen Islands","Islas Svalbard y Jan Mayen","Îles Svalbard et Jan Mayen","جزر سفالبارد وجان مايين","斯瓦尔巴和扬马延群岛","острова Свальбард и Ян-Майен" -"752","Sweden","Suecia","Suède","السويد","瑞典","Швеция" -"756","Switzerland","Suiza","Suisse","سويسرا","瑞士","Швейцария" -"760","Syrian Arab Republic","República Árabe Siria","République arabe syrienne","الجمهوريّة العربيّة السوريّة","阿拉伯叙利亚共和国","Сирийская Арабская Республика" -"762","Tajikistan","Tayikistán","Tadjikistan","طاجيكستان","塔吉克斯坦","Таджикистан" -"764","Thailand","Tailandia","Thaïlande","تايلند","泰国","Таиланд" -"626","Timor-Leste","Timor-Leste","Timor-Leste","تيمور - ليشتي","东帝汶","Тимор-Лешти" -"768","Togo","Togo","Togo","توغو","多哥","Того" -"772","Tokelau","Tokelau (Miembro Asociado)","Tokélaou (Membre associé)","توكيلاو - عضو منتسب","托克劳（准成员）","Токелау (ассоциированный член)" -"776","Tonga","Tonga","Tonga","تونغا","汤加","Тонга" -"780","Trinidad and Tobago","Trinidad y Tabago","Trinité-et-Tobago","ترينيداد وتوباغو","特立尼达和多巴哥","Тринидад и Тобаго" -"-8","Tromelin Island",,,,, -"788","Tunisia","Túnez","Tunisie","تونس","突尼斯","Тунис" -"792","Türkiye","Türkiye","Türkiye","تركيا","土耳其","Турция" -"795","Turkmenistan","Turkmenistán","Turkménistan","تركمانستان","土库曼斯坦","Туркменистан" -"796","Turks and Caicos Islands","Islas Turcas y Caicos","Îles Turques et Caïques","جزر تركس وكايكوس","特克斯和凯科斯群岛","острова Тёркс и Кайкос" -"798","Tuvalu","Tuvalu","Tuvalu","توفالو","图瓦卢","Тувалу" -"581","US Minor Is.","Islas Menores Periféricas de los Estados Unidos","Îles mineures éloignées des États-Unis","جزر الولايات المتحدة الصغرى","美国小离岛","малые отдаленные острова Соединенных Штатов Америки" -"800","Uganda","Uganda","Ouganda","أوغندا","乌干达","Уганда" -"804","Ukraine","Ucrania","Ukraine","أوكرانيا","乌克兰","Украина" -"784","United Arab Emirates","Emiratos Árabes Unidos","Émirats arabes unis","الإمارات العربيّة المتحدة","阿拉伯联合酋长国","Объединенные Арабские Эмираты" -"826","United Kingdom","Reino Unido","Royaume-Uni","المملكة المتحدة","联合王国","Соединенное Королевство" -"834","United Republic of Tanzania","República Unida de Tanzanía","République-Unie de Tanzanie","جمهوريّة تنزانيا المتحدة","坦桑尼亚联合共和国","Объединенная Республика Танзания" -"850","United States Virgin Islands","Islas Vírgenes (EE.UU.)","Îles Vierges américaines","جزر فيرجين التابعة للولايات المتحدة","美属维尔京群岛","Виргинские острова США" -"840","United States of America","Estados Unidos de América","États-Unis d'Amérique","الولايات المتحدة الأمريكيّة","美国","Соединенные Штаты Америки" -"858","Uruguay","Uruguay","Uruguay","أوروغواي","乌拉圭","Уругвай" -"860","Uzbekistan","Uzbekistán","Ouzbékistan","أوزبكستان","乌兹别克斯坦","Узбекистан" -"548","Vanuatu","Vanuatu","Vanuatu","فانواتو","瓦努阿图","Вануату" -"862","Venezuela (Bolivarian Republic of)","Venezuela (República Bolivariana de)","Venezuela (République bolivarienne du)","فنزويلا الجمهوريّة) - (البوليفاريّة","委内瑞拉(玻利瓦尔共和国)","Венесуэла (Боливарианская Республика)" -"704","Viet Nam","Viet Nam","Viet Nam","فييت نام","越南","Вьетнам" -"876","Wallis and Futuna Islands","Islas Wallis y Futuna","Îles Wallis et Futuna","جزر واليس وفوتونا","瓦利斯和富图纳群岛","острова Уоллис и Футуна" -"732","Western Sahara","Sáhara occidental","Sahara occidental","الصحراء الغربيّة","西撒哈拉","Западная Сахара" -"887","Yemen","Yemen","Yémen","اليمن","也门","Йемен" -"894","Zambia","Zambia","Zambie","زامبيا","赞比亚","Замбия" -"716","Zimbabwe","Zimbabwe","Zimbabwe","زمبابوي","津巴布韦","Зимбабве" -"248","Åland Islands","Islas Åland","Îles d'Åland","جزر ألاند","奥兰群岛","Аландские острова" -"151","Eastern Europe",,,,, -"11","Western Africa",,,,, -"39","Southern Europe",,,,, -"17","Middle Africa",,,,, -"142","Asia",,,,, -"57","Micronesia",,,,, -"34","Southern Asia",,,,, -"18","Southern Africa",,,,, -"143","Central Asia",,,,, -"145","Western Asia",,,,, -"-13","---",,,,, -"2","Africa",,,,, -"15","Northern Africa",,,,, -"13","Central America",,,,, -"21","Northern America",,,,, -"5","South America",,,,, -"-22","---",,,,, -"19","Americas",,,,, -"202","Sub-Saharan Africa",,,,, -"30","Eastern Asia",,,,, -"155","Western Europe",,,,, -"29","Caribbean",,,,, -"54","Melanesia",,,,, -"53","Australia and New Zealand",,,,, -"61","Polynesia",,,,, -"14","Eastern Africa",,,,, -"154","Northern Europe",,,,, -"35","South-eastern Asia",,,,, -"150","Europe",,,,, -"9","Oceania",,,,, -"-5707","European union",,,,, diff --git a/data/temp_files/readme.txt b/data/temp_files/readme.txt new file mode 100644 index 0000000..d44ed12 --- /dev/null +++ b/data/temp_files/readme.txt @@ -0,0 +1 @@ +This folder contains the temporary pickle files once the model has run. diff --git a/data/validation_data_from_Hedlung_2022/All_Data/NEW_Ex_wheat_2021.csv b/data/validation_data_from_Hedlung_2022/All_Data/NEW_Ex_wheat_2021.csv new file mode 100644 index 0000000..82eab6e --- /dev/null +++ b/data/validation_data_from_Hedlung_2022/All_Data/NEW_Ex_wheat_2021.csv @@ -0,0 +1,184 @@ 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faostat.list_datasets_df(https_proxy=None) -print(datasets) -for label, code in zip(datasets["label"], datasets["code"]): - print(label, code) - -trade_matrix = faostat.get_data_df( - "TM", - show_flags=False, - null_values=False, - https_proxy=None, - pars={'years': 2018} -) -trade_matrix.to_csv("trade_matrix.csv", index=False) diff --git a/src/model.py b/src/model.py index 28cf937..ea11815 100644 --- a/src/model.py +++ b/src/model.py @@ -19,22 +19,22 @@ class PyTradeShifts: Returns: None """ - def __init__(self, crop, base_year, percentile=0.75): + def __init__(self, crop, base_year, percentile=0.75, region="Global", testing=False): self.base_year = base_year self.crop = crop self.percentile = percentile - # Read in the data - self.trade_data, self.production_data = self.load_data( - self.crop, - self.base_year, - ) - self.trade_matrix = self.build_trade_matrix() - + self.region = region + + # Don't run the methods if we are testing, so we can test them individually + if not testing: + # Read in the data + self.load_data() + # Prebalance the trade matrix + self.prebalance(self.production_data, self.trade_matrix) + def load_data( self, - crop: str, - base_year: int, - ) -> tuple[pd.DataFrame, pd.Series]: + ): """ Loads the data into a pandas dataframe and cleans it of countries with trade below a certain percentile. @@ -48,29 +48,24 @@ def load_data( and only the relevant crop. """ # Read in the data - trade_data = pd.read_csv( + self.trade_matrix = pd.read_csv( "." + os.sep + "data" + os.sep + - f"trade_data_only_relevant_{base_year}.csv" + "preprocessed_data" + os.sep + + f"{self.crop}_{self.base_year}_{self.region}_trade.csv", + index_col=0, ) - production_data = pd.read_csv( + self.production_data = pd.read_csv( "." + os.sep + "data" + os.sep + - f"production_data_only_relevant_{base_year}.csv" - ) - - # Only use crop of interest - crop_trade_data = trade_data[trade_data["Item"] == crop] - production_data_data = production_data[production_data["Item"] == crop] - - # Reshape the production data to a vector - production_data_data.index = production_data_data["Area"] - production_data_data = production_data_data["Production"] + "preprocessed_data" + os.sep + + f"{self.crop}_{self.base_year}_{self.region}_production.csv", + index_col=0, + ).squeeze() - print(f"Loaded data for {crop} in {base_year}.") + print(f"Loaded data for {self.crop} in {self.base_year}.") - return (crop_trade_data, production_data_data) def remove_above_percentile( self, @@ -131,7 +126,7 @@ def remove_re_exports(self): assert trade_matrix.shape[0] == production_data.shape[0] print("Ignoring countries without production data.") - + trade_matrix = self.prebalance(production_data, trade_matrix) production_data, trade_matrix = self.remove_net_zero_countries( production_data, @@ -143,67 +138,71 @@ def remove_re_exports(self): # of the total trade of all countries for this crop trade_matrix = self.remove_above_percentile(trade_matrix, self.percentile) self.trade_matrix = trade_matrix - + def prebalance( self, - production_data: pd.Series, - trade_matrix: pd.DataFrame, precision=10**-3 - ) -> pd.DataFrame: + ): """ - Return prebalaced trading data. + Prebalance trading data. Arguments: - production_data (pd.Series): Vector of production data. - trade_matrix (pd.DataFrame): Trade matrix of the crop specified precision (float, optional): Specifies precision of the prebalancing. Returns: - pandas.DataFrame: Prebalanced trade matrix. + None """ + # Make a local copy of the trade matrix and production data + trade_matrix = self.trade_matrix.copy() + production_data = self.production_data.copy() + # this is virtually 1:1 as in Croft et al. test = production_data + trade_matrix.sum(axis=0) - trade_matrix.sum(axis=1) while (test <= -precision).any(): - sf = (production_data + trade_matrix.sum(axis=0)) / trade_matrix.sum(axis=1) + sf = (production_data + trade_matrix.sum(axis=0)) / trade_matrix.sum( + axis=1 + ) multiplier = ((test < 0) * sf).replace(0, 1) trade_matrix = pd.DataFrame( np.diag(multiplier) @ trade_matrix.values, index=trade_matrix.index, columns=trade_matrix.columns, ) - test = production_data + trade_matrix.sum(axis=0) - trade_matrix.sum(axis=1) - return trade_matrix + test = production_data + trade_matrix.sum(axis=0) - trade_matrix.sum( + axis=1 + ) def remove_net_zero_countries( self, - production_data: pd.Series, - trade_matrix: pd.DataFrame, - ) -> tuple[pd.Series, pd.DataFrame]: + ): """ Return production and trading data with "all zero" countries removed. "All zero" countries are such states that has production = 0 and sum of rows in trade matrix = 0, and sum of columns = 0. Arguments: - production_data (pd.Series): Vector of production data. - trade_matrix (pd.DataFrame): Trade matrix of the crop specified + None Returns: - tuple[pd.Series, pd.DataFrame]: Production data and trade matrix - without "all zero" countries. + None """ + # Make a local copy of the trade matrix and production data + trade_matrix = self.trade_matrix.copy() + production_data = self.production_data.copy() + # b_ signifies boolean here, these are filtering masks b_zero_prod = production_data == 0 b_zero_colsum = trade_matrix.sum(axis=0) == 0 b_zero_rowsum = trade_matrix.sum(axis=1) == 0 b_filter = ~(b_zero_prod & b_zero_rowsum & b_zero_colsum) - return production_data[b_filter], trade_matrix.loc[b_filter, b_filter] + + # Filter out the countries with all zeroes + self.production_data = production_data[b_filter] + self.trade_matrix = trade_matrix.loc[b_filter, b_filter] def correct_reexports( self, - production_data: pd.Series, - trade_matrix: pd.DataFrame, - ) -> pd.DataFrame: + ): """ Return trading data after correcting for re-exports. @@ -211,12 +210,15 @@ def correct_reexports( removed. Arguments: - production_data (pd.Series): Vector of production data. - trade_matrix (pd.DataFrame): Trade matrix of the crop specified - + None + Returns: - pd.DataFrame: Trade matrix without re-exports. + None """ + # Make a local copy of the trade matrix and production data + trade_matrix = self.trade_matrix.copy() + production_data = self.production_data.copy() + # I know that the variable names here are confusing, but this is a conversion # by the original R code from Johanna Hedlung/Croft et al.. The variable names are the # same as in the R code and we leave them this way, so we can more easily @@ -234,4 +236,4 @@ def correct_reexports( # we could consider parametrising this but Croft had this hard coded R[R < 0.001] = 0 - return pd.DataFrame(R, index=trade_matrix.index, columns=trade_matrix.columns) + self.trade_matrix = pd.DataFrame(R, index=trade_matrix.index, columns=trade_matrix.columns) diff --git a/src/preprocessing.py b/src/preprocessing.py index 62cbb56..344b378 100644 --- a/src/preprocessing.py +++ b/src/preprocessing.py @@ -41,12 +41,17 @@ def read_faostat_bulk(faostat_zip: str) -> pd.DataFrame: Returns: pd.DataFrame: The FAOSTAT data. """ + print("Unzipping file") zip_file = ZipFile(faostat_zip) - return pd.read_csv( - zip_file.open(faostat_zip[faostat_zip.rfind("/") + 1 :].replace("zip", "csv")), + print("Finished unzipping file") + print("Reading csv from zip") + df = pd.read_csv( + zip_file.open(faostat_zip[faostat_zip.rfind("/") + 1:].replace("zip", "csv")), encoding="latin1", low_memory=False, ) + print("Finished reading csv from zip") + return df def serialise_faostat_bulk(faostat_zip: str) -> None: @@ -61,11 +66,13 @@ def serialise_faostat_bulk(faostat_zip: str) -> None: None """ data = read_faostat_bulk(faostat_zip) + print("Starting to convert zip to pickle") data.to_pickle( f"data{os.sep}temp_files{os.sep}{faostat_zip[faostat_zip.rfind('/') + 1:].replace( 'zip', 'pkl' )}" ) + print("Finished converting zip to pickle") return None @@ -79,6 +86,7 @@ def _melt_year_cols(data: pd.Series | pd.DataFrame) -> pd.Series | pd.DataFrame: Returns: pd.Series | pd.DataFrame: The melted data. """ + print("Melt year columns") # there are columns of format: Y2021N, Y2021F, Y2021 # where 2021 can be any year. We only want to keep of format Y2021 data = data[ @@ -87,11 +95,14 @@ def _melt_year_cols(data: pd.Series | pd.DataFrame) -> pd.Series | pd.DataFrame: # and then we want to melt all those year columns (Y2019, Y2020, Y2021 etc.) # so that we have a "Year" and "Value" columns # there are other ways of handling this but this is consistent with Croft et al. - return data.melt( + # Though this implementation floods the RAM, sorry about that. + melted = data.melt( id_vars=[c for c in data.columns if c[0] != "Y"], var_name="Year", value_name="Value", ).dropna(subset="Value") + print("Finished melting year columns") + return melted def _prep_trade_matrix( @@ -117,6 +128,7 @@ def _prep_trade_matrix( """ trad = pd.read_pickle(trade_pkl) trad = _melt_year_cols(trad) + print("Filter trade matrix") trad = trad[ ( (trad["Item"] == item) @@ -126,17 +138,22 @@ def _prep_trade_matrix( ) ] trad = trad[["Reporter Country Code (M49)", "Partner Country Code (M49)", "Value"]] + print("Finished filtering trade matrix") + print("Pivot trade matrix") trad = trad.pivot( columns="Partner Country Code (M49)", index="Reporter Country Code (M49)", values="Value" ) - # Remoev the ' from the index and columns - trad.index = trad.index.str.replace("'", "") - trad.columns = trad.columns.str.replace("'", "") + print("Finished pivoting trade matrix") + + # Remove entries which are not countries + trad = remove_entries_from_data(trad) return trad -def _prep_production_vector(production_pkl: str, item="Wheat", unit="t", year="Y2021") -> pd.DataFrame: +def _prep_production_vector( + production_pkl: str, item="Wheat", unit="t", year="Y2021" +) -> pd.DataFrame: """ Return properly formatted production vector. @@ -156,13 +173,17 @@ def _prep_production_vector(production_pkl: str, item="Wheat", unit="t", year="Y """ prod = pd.read_pickle(production_pkl) prod = _melt_year_cols(prod) + print("Filter production vector") prod = prod[ ((prod["Item"] == item) & (prod["Unit"] == unit) & (prod["Year"] == year)) ] prod = prod[["Area Code (M49)", "Value"]] + print("Finished filtering production vector") prod = prod.set_index("Area Code (M49)") - # Remoev the ' from the index - prod.index = prod.index.str.replace("'", "") + + # Remove entries which are not countries + prod = remove_entries_from_data(prod) + return prod @@ -183,11 +204,13 @@ def _unify_indices( tuple[pd.Series, pd.DataFrame]: The production vector and trade matrix with unified indices/columns. """ + print("Unify indices") index = trade_matrix.index.union(trade_matrix.columns).union(production_vector.index) index = index.sort_values() trade_matrix = trade_matrix.reindex(index=index, columns=index).fillna(0) production_vector = production_vector.reindex(index=index).fillna(0) production_vector = production_vector.squeeze() + print("Finished unifying indices") return (production_vector, trade_matrix) @@ -221,11 +244,122 @@ def format_prod_trad_data( depend on particular datasets. E.g., unit can be "tonnes" in one file and "t" in another. """ + print("Started to format production data") production_vector = _prep_production_vector(production_pkl, item, production_unit, year) + print("Finished formatting production data\n") + print("Started to format trade data") trade_matrix = _prep_trade_matrix(trade_pkl, item, trade_unit, element, year) + print("Finished formatting trade data\n") return _unify_indices(production_vector, trade_matrix) +def rename_countries( + data: pd.Series | pd.DataFrame, + region: str, + filename: str, + code_type: str = "M49 Code", +) -> pd.DataFrame: + """ + Rename country codes with country names in either production or trade data. + + Arguments: + data (pd.DataFrame): The data to be renamed. + region (str): The region of the data. + filename (str): The filename for the country codes CSV file. + code_type (str): The type of country code to be used. + after_union (bool): Whether the index/columns of the data are already unified. + + Returns: + pd.DataFrame: The data with country codes replaced by country names. + """ + # Read in the country codes from the zip file + faostat_zip = f"data{os.sep}data_raw{os.sep}{filename}_{region}.zip" + zip_file = ZipFile(faostat_zip) + # Open the csv file in the zip Production_Crops_Livestock_E_AreaCodes.csv + # and read it into a dataframe + codes = pd.read_csv( + zip_file.open(filename + "_AreaCodes.csv"), + encoding="latin1", + low_memory=False, + ) + # Create a dictionary with the country codes as keys and country names as values + codes_dict = dict(zip(codes[code_type], codes["Area"])) + + print(f"Replacing country codes with country names in {filename.split('_')[0]} data") + for code in data.index: + data.rename(index={code: codes_dict[code]}, inplace=True) + + if isinstance(data, pd.DataFrame): + for code in data.columns: + data.rename(columns={code: codes_dict[code]}, inplace=True) + + return data + + +def remove_entries_from_data(data: pd.Series | pd.DataFrame) -> pd.Series | pd.DataFrame: + """ + Removes a bunch of entries from the data, which do not actually represent countries + or where no trade data is available. + + Arguments: + data (pd.Series | pd.DataFrame): The data to be filtered. + + Returns: + pd.Series | pd.DataFrame: The filtered data. + """ + entries_to_remove = { + # Groups of countries by region + "World": "'001", + "Africa": "'002", + "South America": "'005", + "Oceania": "'009", + "Western Africa": "'011", + "Central America": "'013", + "Eastern Africa": "'014", + "Northern Africa": "'015", + "Middle Africa": "'017", + "Southern Africa": "'018", + "Americas": "'019", + "Northern America": "'021", + "Eastern Asia": "'030", + "Southern Asia": "'034", + "South-eastern Asia": "'035", + "Southern Europe": "'039", + "Australia and New Zealand": "'053", + "Melanesia": "'054", + "European Union (27)": "'097", + "Asia": "'142", + "Central Asia": "'143", + "Western Asia": "'145", + "Europe": "'150", + "Eastern Europe": "'151", + "Northern Europe": "'154", + "Western Europe": "'155", + # Groups of countries by property + "Least Developed Countries": "'199", + "Land Locked Developing Countries": "'432", + "Small Island Developing States": "'722", + "Low Income Food Deficit Countries": "'901", + "Net Food Importing Developing Countries": "'902", + # We want to look at China and Taiwan seperately, so this is not needed + # as 159 refers to China incl. Taiwan + "China": "'159", + # No reliable data available, therefore they are excluded + # This assessment is based on the fact that all of these countries + # don't show up as a partner country in the trade data of the FAO + "Democratic People's Republic of Korea": "'408", + "Chad": "'148", + "South Sudan": "'728", + } + + # Remove the entries + data = data[~data.index.isin(entries_to_remove.values())] + if isinstance(data, pd.DataFrame): + data = data.loc[:, ~data.columns.isin(entries_to_remove.values())] + + return data + + def main( region: str, item: str, @@ -256,8 +390,12 @@ def main( serialise_faostat_bulk(production_zip) serialise_faostat_bulk(trade_zip) print(f"Pickles created. Reading in data for {item} in {region}...") + # Replace the zip with the pickle and link to the temp files folder production_pkl = production_zip.replace("zip", "pkl") trade_pkl = trade_zip.replace("zip", "pkl") + production_pkl = production_pkl.replace("data_raw", "temp_files") + trade_pkl = trade_pkl.replace("data_raw", "temp_files") + production, trade_matrix = format_prod_trad_data( production_pkl, trade_pkl, @@ -267,49 +405,40 @@ def main( element, year, ) - # Replace the area codes with country names. This is based on the M49 codes - # https://data.apps.fao.org/catalog/dataset/m49-code-list-global-region-country - codes = pd.read_csv( - f"data{os.sep}supplemental_data{os.sep}m49.csv" - ) - # Create a dictionary of country codes and names with the m49 column as the key - # and the country_names_en column as the value. This also adds two leading zeros - # to the codes with only one character and one leading zero to the codes with - # two characters. This is to match the codes in the production and trade matrix - # which have two leading zeros. This is not the case for the codes in the m49 - # file for unknown reasons. Would be too much to ask for the UN to be consistent. - # Convert the codes to strings to allow for the leading zeros - codes_dict = { - str(code).zfill(3): country_name - for code, country_name in zip(codes["m49"], codes["country_name_en"]) - } - - # Go through the index of production and index/columns of trade matrix - # and replace the codes with country names - for code in production.index: - production.rename(index={code: codes_dict[code]}, inplace=True) - for code in trade_matrix.index: - trade_matrix.rename(index={code: codes_dict[code]}, inplace=True) - trade_matrix.rename(columns={code: codes_dict[code]}, inplace=True) + + # Replace country codes with country names + trade_matrix = rename_countries(trade_matrix, region, "Trade_DetailedTradeMatrix_E") + production = rename_countries(production, region, "Production_Crops_Livestock_E") # Rename the item for readability item = rename_item(item) + # Make sure that production index and trade matrix index/columns are the same + assert production.index.equals(trade_matrix.index) + assert production.index.equals(trade_matrix.columns) + + # Replace "All_Data" with "global" for readability + if region == "All_Data": + region = "Global" + # Save to CSV - production.to_csv(f"data{os.sep}preprocessed_data{os.sep}{item}_{year}_production.csv") - trade_matrix.to_csv(f"data{os.sep}preprocessed_data{os.sep}{item}_{year}_trade.csv") + production.to_csv(f"data{os.sep}preprocessed_data{os.sep}{item}_{year}_{region}_production.csv") + trade_matrix.to_csv(f"data{os.sep}preprocessed_data{os.sep}{item}_{year}_{region}_trade.csv") if __name__ == "__main__": # Define values - year = 2018 + year = "Y2021" items_trade = ["Maize (corn)", "Wheat", "Rice, paddy (rice milled equivalent)"] - items_production = ["Maize (corn)", "Wheat", "Rice"] # Define regions for which the data is processed + # "Oceania" is used for testing, as it has the least amount of countries + # to run with all data use: "All_Data" for region region = "Oceania" - + print("\n") for item in items_trade: main( region, item, + year=year, ) + print("\n\n") diff --git a/tests/test_model.py b/tests/test_model.py new file mode 100644 index 0000000..e2d6f30 --- /dev/null +++ b/tests/test_model.py @@ -0,0 +1,147 @@ +from src.model import PyTradeShifts +import pandas as pd +import os + + +def loading(region): + """ + Just loads the data to make sure it is loaded correctly. + """ + Wheat2021 = PyTradeShifts( + "Wheat", + "Y2021", + region=region, + testing=True + ) + Wheat2021.load_data() + + # Make sure the data is loaded correctly + assert isinstance(Wheat2021.trade_matrix, pd.DataFrame) + assert isinstance(Wheat2021.production_data, pd.Series) + + +def test_loading_oceania(): + """ + Loads the data for Oceania. + """ + loading("Oceania") + + +def test_loading_global(): + """ + Loads the data for the world. + """ + loading("Global") + + +def prebalancing(region): + """ + Runs the model with prebalancing and compares the results with the + results from the R script. + """ + Wheat2021 = PyTradeShifts( + "Wheat", + "Y2021", + region=region, + testing=True + ) + + # Load the data + Wheat2021.load_data() + + # Run the prebalancing + Wheat2021.prebalance() + + if region == "Global": + region = "All_Data" + # Load the data from the R script + wheat2021_from_R = pd.read_csv( + "data" + os.sep + + "validation_data_from_Hedlung_2022" + os.sep + + region + os.sep + + "TEST_prebalanced_trade_wheat_2021.csv", + index_col=0, + ) + + # Compare the results + assert wheat2021_from_R.shape == Wheat2021.trade_matrix.shape + # Comparing the sum this has only be correct to 2 decimal places + assert round(wheat2021_from_R.sum().sum(), 2) == round(Wheat2021.trade_matrix.sum().sum(), 2) + + +def test_prebalancing_oceania(): + """ + Runs the model with prebalancing and compares the results with the + results from the R script. + """ + prebalancing("Oceania") + + +def test_prebalancing_global(): + """ + Runs the model with prebalancing and compares the results with the + results from the R script. + """ + prebalancing("Global") + + +def removing_countries_with_zeros_and_reexport(region): + """ + Runs the model with prebalancing and compares the results with the + results from the R script. + """ + Wheat2021 = PyTradeShifts( + "Wheat", + "Y2021", + region=region, + testing=True + ) + + # Load the data + Wheat2021.load_data() + + # Run the prebalancing + Wheat2021.prebalance() + + # Remove countries with zero trade + Wheat2021.remove_net_zero_countries() + + # Reexport + Wheat2021.correct_reexports() + + if region == "Global": + region = "All_Data" + + # Load the data from the R script + wheat2021_from_R = pd.read_csv( + "data" + os.sep + + "validation_data_from_Hedlung_2022" + os.sep + + region + os.sep + + "NEW_Ex_wheat_2021.csv", + index_col=0, + ) + + # Compare the results + assert wheat2021_from_R.shape == Wheat2021.trade_matrix.shape + # Comparing the sum this has only be correct to 2 decimal places + assert round(wheat2021_from_R.sum().sum(), 2) == round(Wheat2021.trade_matrix.sum().sum(), 2) + + +def test_removing_countries_with_zeros_and_reexport_oceania(): + """ + Runs the model with prebalancing and compares the results with the + results from the R script. + """ + removing_countries_with_zeros_and_reexport("Oceania") + + +def test_removing_countries_with_zeros_and_reexport_global(): + """ + Runs the model with prebalancing and compares the results with the + results from the R script. + """ + removing_countries_with_zeros_and_reexport("Global") + +if __name__ == "__main__": + test_prebalancing_oceania() + test_prebalancing_global() diff --git a/tests/test_preprocessing.py b/tests/test_preprocessing.py index 1b2854b..4f7fb9f 100644 --- a/tests/test_preprocessing.py +++ b/tests/test_preprocessing.py @@ -1,64 +1,174 @@ - -from src.preprocessing import read_in_raw_trade_data -from src.preprocessing import read_in_raw_production_data -from src.preprocessing import extract_relevant_trade_data -from src.preprocessing import extract_relevant_production_data import pandas as pd +from src.preprocessing import format_prod_trad_data +from src.preprocessing import rename_countries +import os + + +def test_format_prod_trad_data_oceania(): + region = "Oceania" + production, trade = format_prod_trad_data( + f"data{os.sep}temp_files{os.sep}Production_Crops_Livestock_E_Oceania.pkl", + f"data{os.sep}temp_files{os.sep}Trade_DetailedTradeMatrix_E_Oceania.pkl", + item="Wheat", + ) + + production_from_R = pd.read_csv( + f"data{os.sep}validation_data_from_Hedlung_2022{os.sep}" + f"{region}{os.sep}NEW_production_wheat_2021.csv" + )[["Area", "value"]] + + production_from_R.set_index("Area", inplace=True) + production_from_R.sort_index(inplace=True) + production_from_R = production_from_R.squeeze() + + trade_from_R = pd.read_csv( + f"data{os.sep}validation_data_from_Hedlung_2022{os.sep}" + f"{region}{os.sep}NEW_trade_wheat_2021.csv" + ) + + trade_from_R.drop(columns="Unnamed: 0", inplace=True) + trade_from_R.columns = [int(c) for c in trade_from_R.columns] + trade_from_R.index = trade_from_R.columns + trade_from_R = trade_from_R[sorted(trade_from_R.columns)] + trade_from_R.sort_index(inplace=True) + + # Replace country codes with country names in the R files + trade_from_R = rename_countries( + trade_from_R, + region, + "Trade_DetailedTradeMatrix_E", + "Area Code" + ) + production_from_R = rename_countries( + production_from_R, + region, + "Production_Crops_Livestock_E", + "Area Code" + ) + trade = rename_countries( + trade, + region, + "Trade_DetailedTradeMatrix_E", + ) + production = rename_countries( + production, + region, + "Production_Crops_Livestock_E", + ) + + trade.sort_index(inplace=True) + production.sort_index(inplace=True) + trade_from_R.sort_index(inplace=True) + production_from_R.sort_index(inplace=True) + trade = trade[sorted(trade.columns)] + trade_from_R = trade_from_R[sorted(trade_from_R.columns)] + + print(production_from_R.shape) + print(production.shape) + + assert production_from_R.shape == production.shape + assert trade_from_R.shape == trade.shape + + assert production_from_R.sum() == production.sum() + assert trade_from_R.sum().sum() == trade.sum().sum() + + assert production_from_R.index.equals(production.index) + assert trade_from_R.index.equals(trade.index) + assert trade_from_R.columns.equals(trade.columns) + + assert (production_from_R == production).all(axis=None) + assert (trade_from_R == trade).all(axis=None) + + +def test_format_prod_trad_data_global(): + region = "All_Data" + + try: + production = pd.read_csv( + f"data{os.sep}preprocessed_data{os.sep}Wheat_Y2021_Global_production.csv", + index_col=0, + ).squeeze() + trade = pd.read_csv( + f"data{os.sep}preprocessed_data{os.sep}Wheat_Y2021_Global_trade.csv", + index_col=0, + ) + except FileNotFoundError: + print("Files not found, loading from pickle files to redo preprocessing") + production, trade = format_prod_trad_data( + f"data{os.sep}temp_files{os.sep}Production_Crops_Livestock_E_All_Data.pkl", + f"data{os.sep}temp_files{os.sep}Trade_DetailedTradeMatrix_E_All_Data.pkl", + item="Wheat", + ) + + production_from_R = pd.read_csv( + f"data{os.sep}validation_data_from_Hedlung_2022{os.sep}" + f"{region}{os.sep}NEW_production_wheat_2021.csv" + )[["Area", "value"]] + + production_from_R.set_index("Area", inplace=True) + production_from_R.sort_index(inplace=True) + production_from_R = production_from_R.squeeze() + + trade_from_R = pd.read_csv( + f"data{os.sep}validation_data_from_Hedlung_2022{os.sep}" + f"{region}{os.sep}NEW_trade_wheat_2021.csv" + ) + trade_from_R.drop(columns="Unnamed: 0", inplace=True) + trade_from_R.columns = [int(c) for c in trade_from_R.columns] + trade_from_R.index = trade_from_R.columns + trade_from_R = trade_from_R[sorted(trade_from_R.columns)] + trade_from_R.sort_index(inplace=True) + + # Replace country codes with country names in the R files + trade_from_R = rename_countries( + trade_from_R, + region, + "Trade_DetailedTradeMatrix_E", + "Area Code" + ) + production_from_R = rename_countries( + production_from_R, + region, + "Production_Crops_Livestock_E", + "Area Code" + ) + + # print all the countries that only exist in one of the dataframes + for c in production_from_R.index: + if c not in production.index: + print("This country is present in R, but not in Python:") + print(c) + + # and now the other way around + for c in production.index: + if c not in production_from_R.index: + print("This country is present in Python, but not in R:") + print(c) + + # Sort the Python and R files to make sure they are in the same order + production.sort_index(inplace=True) + trade.sort_index(inplace=True) + trade_from_R.sort_index(inplace=True) + production_from_R.sort_index(inplace=True) + + # Also sort the columns of the trade dataframes + trade = trade[sorted(trade.columns)] + trade_from_R = trade_from_R[sorted(trade_from_R.columns)] + + assert production_from_R.shape == production.shape + assert trade_from_R.shape == trade.shape -import pytest - - -def test_returns_correct_dataframe_trade(): - # Only read in the Oceanian subset of the data for testing purposes. - # This is to reduce the time it takes to run the tests. - result = read_in_raw_trade_data(testing=True) - assert isinstance(result, pd.DataFrame) - # This is the shape of the Oceanian subset of the data. - assert result.shape == (215548, 84) - # The first country in the dataset is Australia. - assert result.iloc[0, 2] == "Australia" - - -def test_extracts_right_data_trade(): - # Only read in the Oceanian subset of the data for testing purposes. - # This is to reduce the time it takes to run the tests. - result = read_in_raw_trade_data(testing=True) - # Extract only the relevant data for the trade model. - result = extract_relevant_trade_data(result, ["Maize (corn)", "Wheat", "Rice, paddy (rice milled equivalent)"], year=2018) - assert isinstance(result, pd.DataFrame) - # This is the shape of the Oceanian subset of the data. - assert result.shape == (139, 6) - # The first country in the dataset is Australia. - assert result.iloc[0, 0] == "Australia" - # The first item in the dataset is Bananas. - assert result.iloc[0, 3] == "Maize" - - -def test_returns_correct_dataframe_production(): - result = read_in_raw_production_data() - assert isinstance(result, pd.DataFrame) - # This is the shape of the complete dataset. - assert result.shape == (79297, 70) - # The first country in the dataset is Afghanistan. - assert result.iloc[0, 2] == "Afghanistan" - - -def test_extracts_right_data_production(): - result = read_in_raw_production_data() - # Extract only the relevant data for the production model. - result = extract_relevant_production_data(result, [ - "Maize (corn)", - "Wheat", - "Rice, paddy (rice milled equivalent)" - ], year=2018) - assert isinstance(result, pd.DataFrame) - # This is the shape of the complete dataset. - assert result.shape == (362, 5) - # The first country in the dataset is Afghanistan. - assert result.iloc[0, 0] == "Afghanistan" - # The first item in the dataset is Bananas. - assert result.iloc[0, 1] == "Maize" + assert production_from_R.sum() == production.sum() + assert trade_from_R.sum().sum() == trade.sum().sum() + assert production_from_R.index.equals(production.index) + assert trade_from_R.index.equals(trade.index) + assert trade_from_R.columns.equals(trade.columns) + assert (production_from_R == production).all(axis=None) + assert (trade_from_R == trade).all(axis=None) +if __name__ == "__main__": + test_format_prod_trad_data_global() + test_format_prod_trad_data_oceania()