7장 세계은행 WBGAPI#
1. WBGAPI#
출처: WBGAPI Project
개요#
WBGAPI는 World Bank의 오픈 데이터 API에 접근하기 위한 파이썬 패키지다. 다른 일부 파이썬 패키지와 달리, 이 패키지는 전체 API 대신 단일 API 데이터베이스를 참조하므로, 얻는 데이터에 대해 더 높은 투명성과 제어력을 제공한다.
WBGAPI는 기본값(default)으로 World Development Indicators 데이터베이스(db=2)를 쿼리하지만, 이 기본값은 각 요청별 또는 전역적(globally)으로 변경할 수 있다. 이는 인구(SP.POP.TOTL)와 같은 지표(indicator)가 여러 다른 데이터베이스에 나타나거나, 데이터베이스마다 차원, 경제, 기간이 다를 때 혼란을 방지한다.
기타 주요 특징:
한 번의 요청으로 여러 시리즈, 경제(국가), 기간을 쉽게 선택할 수 있다.
개별 연도, 범위, 최신값(MRV: most recent value)을 선택할 수 있다.
메타데이터 쿼리 지원
설치
# 처음 한 번만 설치하면 됨
# %pip install wbgapi
WBGAPI는 API가 여러 독립적인 시리즈, 국가, 시간 차원을 가진 수십 개의 서로 다른 데이터베이스로 구성되어 있지만, 이들 간에 강한 중복이 존재한다는 사실을 바탕으로 설계되었다.
WBGAPI(wb)에는 API의 주요 구성요소에 해당하는 9개의 서브 모듈이 있다.
그룹명 |
내용 |
|---|---|
source |
데이터베이스 (예: WDI, Doing Business, International Debt) |
series |
지표 (예: |
economy |
국가 및 경제 (일부 데이터베이스의 경우 하위 국가 수준일 수도 있음) |
data |
데이터 |
topic |
World Bank 주제 (이것은 전역 리스트이며, 시리즈의 Topic 메타데이터 필드와는 구분됨) |
time |
시간 (일반적으로 연간, 때로는 분기별 또는 월별) |
region |
World Bank 지역 (모든 데이터베이스에 대해 전역적임) |
income |
World Bank 소득 그룹 (역시 전역적임) |
lending |
World Bank 대출 유형 (역시 전역적임) |
위의 각 그룹의 요소에 접근하기 위해 공통적으로 사용할 수 있는 함수들은 다음과 같다.
함수 |
설명 |
예시 |
|---|---|---|
info() |
사람이 읽기 쉬운 표 형식으로 반환 |
|
list() |
요소를 하나씩 반환 |
|
get() |
단일 요소를 반환 |
|
Series() |
pandas Series를 반환 |
|
둘러보기#
API나 특정 데이터베이스에 어떤 내용이 있는지 탐색하는 데 info 함수가 매우 유용하다.
import wbgapi as wb
# 데이터베이스의 종류
wb.source.info()
| id | name | code | concepts | lastupdated |
|---|---|---|---|---|
| 1 | Doing Business | DBS | 3 | 2021-08-18 |
| 2 | World Development Indicators | WDI | 3 | 2025-12-04 |
| 3 | Worldwide Governance Indicators | WGI | 3 | 2024-11-05 |
| 5 | Subnational Malnutrition Database | SNM | 3 | 2016-03-21 |
| 6 | International Debt Statistics | IDS | 4 | 2025-12-03 |
| 11 | Africa Development Indicators | ADI | 3 | 2013-02-22 |
| 12 | Education Statistics | EDS | 3 | 2024-06-25 |
| 13 | Enterprise Surveys | ESY | 3 | 2022-03-25 |
| 14 | Gender Statistics | GDS | 3 | 2025-11-11 |
| 15 | Global Economic Monitor | GEM | 3 | 2025-11-13 |
| 16 | Health Nutrition and Population Statistics | HNP | 3 | 2025-07-02 |
| 18 | IDA Results Measurement System | IDA | 3 | 2021-07-23 |
| 19 | Millennium Development Goals | MDG | 3 | 2018-09-19 |
| 20 | Quarterly Public Sector Debt | PSD | 3 | 2025-10-29 |
| 22 | Quarterly External Debt Statistics SDDS | QDS | 3 | 2025-10-31 |
| 23 | Quarterly External Debt Statistics GDDS | QDG | 3 | 2025-10-31 |
| 25 | Jobs | JOB | 3 | 2025-07-01 |
| 27 | Global Economic Prospects | GEP | 3 | 2025-06-10 |
| 28 | Global Findex database | FDX | 3 | 2025-10-06 |
| 29 | The Atlas of Social Protection: Indicators of Resilience and Equity | GSP | 3 | 2025-08-25 |
| 30 | Exporter Dynamics Database – Indicators at Country-Year Level | ED1 | 3 | 2016-03-31 |
| 31 | Country Policy and Institutional Assessment | CPI | 3 | 2025-07-12 |
| 32 | Global Financial Development | GFD | 3 | 2022-09-23 |
| 33 | G20 Financial Inclusion Indicators | G2F | 3 | 2019-02-27 |
| 34 | Global Partnership for Education | GPE | 3 | 2013-04-12 |
| 35 | Sustainable Energy for All | SE4 | 3 | 2018-06-30 |
| 37 | LAC Equity Lab | LEL | 3 | 2016-04-06 |
| 38 | Subnational Poverty | SNP | 3 | 2015-04-27 |
| 39 | Health Nutrition and Population Statistics by Wealth Quintile | HNQ | 3 | 2022-12-13 |
| 40 | Population estimates and projections | HPP | 3 | 2025-07-02 |
| 41 | Country Partnership Strategy for India (FY2013 - 17) | CPS | 3 | 2015-05-22 |
| 43 | Adjusted Net Savings | WAT | 3 | 2023-06-08 |
| 45 | Indonesia Database for Policy and Economic Research | IDD | 3 | 2022-08-11 |
| 46 | Sustainable Development Goals | SDG | 3 | 2022-07-22 |
| 50 | Subnational Population | SNT | 3 | 2017-09-21 |
| 54 | Joint External Debt Hub | JED | 3 | 2025-10-27 |
| 57 | WDI Database Archives | WDA | 4 | 2025-10-29 |
| 58 | Universal Health Coverage | UHC | 3 | 2019-04-25 |
| 59 | Wealth Accounts | WAC | 3 | 2024-10-08 |
| 60 | Economic Fitness | EFT | 3 | 2018-04-19 |
| 61 | PPPs Regulatory Quality | PRQ | 3 | 2019-04-25 |
| 62 | International Comparison Program (ICP) 2011 | ICP | 4 | 2020-08-24 |
| 63 | Human Capital Index | HCI | 3 | 2020-09-21 |
| 64 | Worldwide Bureaucracy Indicators | WBI | 3 | 2025-02-24 |
| 65 | Health Equity and Financial Protection Indicators | HPI | 3 | 2019-11-06 |
| 66 | Logistics Performance Index | LPI | 3 | 2024-03-26 |
| 67 | PEFA 2011 | PF1 | 4 | 2025-06-26 |
| 68 | PEFA 2016 | PF6 | 4 | 2025-12-08 |
| 69 | Global Financial Inclusion and Consumer Protection Survey | RFA | 3 | 2019-06-27 |
| 70 | Economic Fitness 2 | EF2 | 3 | 2024-11-30 |
| 71 | International Comparison Program (ICP) 2005 | IC5 | 4 | 2021-09-10 |
| 73 | Global Financial Inclusion and Consumer Protection Survey (Internal) | RFI | 3 | 2019-06-27 |
| 75 | Environment, Social and Governance (ESG) Data | ESG | 3 | 2025-07-30 |
| 76 | Remittance Prices Worldwide (Sending Countries) | RWS | 3 | 2020-12-07 |
| 77 | Remittance Prices Worldwide (Receiving Countries) | RWR | 3 | 2020-12-07 |
| 78 | ICP 2017 | IC7 | 4 | 2020-10-21 |
| 79 | PEFA_GRPFM | GRP | 4 | 2025-11-17 |
| 80 | Gender Disaggregated Labor Database (GDLD) | GDL | 4 | 2020-07-25 |
| 81 | International Debt Statistics: DSSI | DSI | 4 | 2025-12-03 |
| 82 | Global Public Procurement | GPP | 3 | 2021-03-24 |
| 83 | Statistical Performance Indicators (SPI) | SPI | 3 | 2024-11-11 |
| 84 | Education Policy | EDP | 3 | 2024-10-03 |
| 85 | PEFA_2021_SNG | SNG | 4 | 2025-10-24 |
| 86 | Global Jobs Indicators Database (JOIN) | JON | 3 | 2023-05-22 |
| 87 | Country Climate and Development Report (CCDR) | CCD | 3 | 2022-10-07 |
| 88 | Food Prices for Nutrition | FPN | 4 | 2025-07-31 |
| 89 | Identification for Development (ID4D) Data | ID4 | 3 | 2025-10-27 |
| 90 | ICP 2021 | IC2 | 4 | 2024-08-04 |
| 91 | PEFA_CRPFM | CRP | 4 | 2025-12-03 |
| 92 | Disability Data Hub (DDH) | DDH | 3 | 2025-03-28 |
| 93 | FPN Datahub Archive | FPA | 4 | 2025-10-22 |
| 71 elements |
# 개별 데이터베이스의 내용(지표), 너무 길어서 실행시키지 않음
# wb.series.info() # 기본값은 WDI(db=2)
wb.economy.info(db=6) # Debt Statistics 데이터베이스의 경제(국가) 정보
| id | value | region | incomeLevel |
|---|---|---|---|
| AFG | Afghanistan | MEA | LIC |
| AGO | Angola | SSF | LMC |
| ALB | Albania | ECS | UMC |
| ARG | Argentina | LCN | UMC |
| ARM | Armenia | ECS | UMC |
| AZE | Azerbaijan | ECS | UMC |
| BDI | Burundi | SSF | LIC |
| BEN | Benin | SSF | LMC |
| BFA | Burkina Faso | SSF | LIC |
| BGD | Bangladesh | SAS | LMC |
| BIH | Bosnia and Herzegovina | ECS | UMC |
| BLR | Belarus | ECS | UMC |
| BLZ | Belize | LCN | UMC |
| BOL | Bolivia | LCN | LMC |
| BRA | Brazil | LCN | UMC |
| BTN | Bhutan | SAS | LMC |
| BWA | Botswana | SSF | UMC |
| CAF | Central African Republic | SSF | LIC |
| CHN | China | EAS | UMC |
| CIV | Cote d'Ivoire | SSF | LMC |
| CMR | Cameroon | SSF | LMC |
| COD | Congo, Dem. Rep. | SSF | LIC |
| COG | Congo, Rep. | SSF | LMC |
| COL | Colombia | LCN | UMC |
| COM | Comoros | SSF | LMC |
| CPV | Cabo Verde | SSF | UMC |
| DJI | Djibouti | MEA | LMC |
| DMA | Dominica | LCN | UMC |
| DOM | Dominican Republic | LCN | UMC |
| DZA | Algeria | MEA | UMC |
| EAP | East Asia & Pacific (excluding high income) | ||
| ECA | Europe & Central Asia (excluding high income) | ||
| ECU | Ecuador | LCN | UMC |
| EGY | Egypt, Arab Rep. | MEA | LMC |
| ERI | Eritrea | SSF | LIC |
| ETH | Ethiopia | SSF | INX |
| FJI | Fiji | EAS | UMC |
| GAB | Gabon | SSF | UMC |
| GEO | Georgia | ECS | UMC |
| GHA | Ghana | SSF | LMC |
| GIN | Guinea | SSF | LMC |
| GMB | Gambia, The | SSF | LIC |
| GNB | Guinea-Bissau | SSF | LIC |
| GNQ | Equatorial Guinea | SSF | UMC |
| GRD | Grenada | LCN | UMC |
| GTM | Guatemala | LCN | UMC |
| GUY | Guyana | LCN | HIC |
| HND | Honduras | LCN | LMC |
| HTI | Haiti | LCN | LMC |
| IDA | IDA total | ||
| IDN | Indonesia | EAS | UMC |
| IDX | IDA only | ||
| IND | India | SAS | LMC |
| IRN | Iran, Islamic Rep. | MEA | UMC |
| IRQ | Iraq | MEA | UMC |
| JAM | Jamaica | LCN | UMC |
| JOR | Jordan | MEA | LMC |
| KAZ | Kazakhstan | ECS | UMC |
| KEN | Kenya | SSF | LMC |
| KGZ | Kyrgyz Republic | ECS | LMC |
| KHM | Cambodia | EAS | LMC |
| LAC | Latin America & Caribbean (excluding high income) | ||
| LAO | Lao PDR | EAS | LMC |
| LBN | Lebanon | MEA | LMC |
| LBR | Liberia | SSF | LIC |
| LCA | St. Lucia | LCN | UMC |
| LDC | Least developed countries: UN classification | ||
| LIC | Low income | ||
| LKA | Sri Lanka | SAS | LMC |
| LMC | Lower middle income | ||
| LMY | Low & middle income | ||
| LSO | Lesotho | SSF | LMC |
| MAR | Morocco | MEA | LMC |
| MDA | Moldova | ECS | UMC |
| MDG | Madagascar | SSF | LIC |
| MDV | Maldives | SAS | UMC |
| MEX | Mexico | LCN | UMC |
| MIC | Middle income | ||
| MKD | North Macedonia | ECS | UMC |
| MLI | Mali | SSF | LIC |
| MMR | Myanmar | EAS | LMC |
| MNA | Middle East, North Africa, Afghanistan & Pakistan (excluding high income) | ||
| MNE | Montenegro | ECS | UMC |
| MNG | Mongolia | EAS | UMC |
| MOZ | Mozambique | SSF | LIC |
| MRT | Mauritania | SSF | LMC |
| MUS | Mauritius | SSF | UMC |
| MWI | Malawi | SSF | LIC |
| NER | Niger | SSF | LIC |
| NGA | Nigeria | SSF | LMC |
| NIC | Nicaragua | LCN | LMC |
| NPL | Nepal | SAS | LMC |
| PAK | Pakistan | MEA | LMC |
| PER | Peru | LCN | UMC |
| PHL | Philippines | EAS | LMC |
| PNG | Papua New Guinea | EAS | LMC |
| PRY | Paraguay | LCN | UMC |
| RWA | Rwanda | SSF | LIC |
| SAS | South Asia | ||
| SDN | Sudan | SSF | LIC |
| SEN | Senegal | SSF | LMC |
| SLB | Solomon Islands | EAS | LMC |
| SLE | Sierra Leone | SSF | LIC |
| SLV | El Salvador | LCN | UMC |
| SOM | Somalia, Fed. Rep. | SSF | LIC |
| SRB | Serbia | ECS | UMC |
| SSA | Sub-Saharan Africa (excluding high income) | ||
| STP | Sao Tome and Principe | SSF | LMC |
| SUR | Suriname | LCN | UMC |
| SWZ | Eswatini | SSF | LMC |
| SYR | Syrian Arab Republic | MEA | LIC |
| TCD | Chad | SSF | LIC |
| TGO | Togo | SSF | LIC |
| THA | Thailand | EAS | UMC |
| TJK | Tajikistan | ECS | LMC |
| TKM | Turkmenistan | ECS | UMC |
| TLS | Timor-Leste | EAS | LMC |
| TON | Tonga | EAS | UMC |
| TUN | Tunisia | MEA | LMC |
| TUR | Turkiye | ECS | UMC |
| TZA | Tanzania | SSF | LMC |
| UGA | Uganda | SSF | LIC |
| UKR | Ukraine | ECS | UMC |
| UMC | Upper middle income | ||
| UZB | Uzbekistan | ECS | LMC |
| VCT | St. Vincent and the Grenadines | LCN | UMC |
| VNM | Viet Nam | EAS | LMC |
| VUT | Vanuatu | EAS | LMC |
| WSM | Samoa | EAS | UMC |
| XKX | Kosovo | ECS | UMC |
| YEM | Yemen, Rep. | MEA | LIC |
| ZAF | South Africa | SSF | UMC |
| ZMB | Zambia | SSF | LMC |
| ZWE | Zimbabwe | SSF | LMC |
| 134 elements |
기본 데이터베이스를 변경하려면 다음과 같이 하면 된다.
wb.db = 12 # Education Statistics 데이터베이스로 변경
# wb.series.info() # Education Statistics 지표
wb.db = 2 #기본값 데이터베이스 WDI로 돌아감
# wb.economy.info() # WDI 데이터베이스의 경제(국가) 정보
지표 등의 내용 확인 방법
wb.series.info('NY.GDP.PCAP.CD')
| id | value |
|---|---|
| NY.GDP.PCAP.CD | GDP per capita (current US$) |
| 1 elements |
wb.economy.info(['CAN', 'USA', 'MEX'])
| id | value | region | incomeLevel |
|---|---|---|---|
| CAN | Canada | NAC | HIC |
| MEX | Mexico | LCN | UMC |
| USA | United States | NAC | HIC |
| 3 elements |
키워드 쿼리
wb.series.info(q='co2')
| id | value |
|---|---|
| EN.GHG.CO2.AG.MT.CE.AR5 | Carbon dioxide (CO2) emissions from Agriculture (Mt CO2e) |
| EN.GHG.CO2.BU.MT.CE.AR5 | Carbon dioxide (CO2) emissions from Building (Energy) (Mt CO2e) |
| EN.GHG.CO2.FE.MT.CE.AR5 | Carbon dioxide (CO2) emissions from Fugitive Emissions (Energy) (Mt CO2e) |
| EN.GHG.CO2.IC.MT.CE.AR5 | Carbon dioxide (CO2) emissions from Industrial Combustion (Energy) (Mt CO2e) |
| EN.GHG.CO2.IP.MT.CE.AR5 | Carbon dioxide (CO2) emissions from Industrial Processes (Mt CO2e) |
| EN.GHG.CO2.LU.DF.MT.CE.AR5 | Carbon dioxide (CO2) net fluxes from LULUCF - Deforestation (Mt CO2e) |
| EN.GHG.CO2.LU.FL.MT.CE.AR5 | Carbon dioxide (CO2) net fluxes from LULUCF - Forest Land (Mt CO2e) |
| EN.GHG.CO2.LU.MT.CE.AR5 | Carbon dioxide (CO2) net fluxes from LULUCF - Total excluding non-tropical fires (Mt CO2e) |
| EN.GHG.CO2.LU.OL.MT.CE.AR5 | Carbon dioxide (CO2) net fluxes from LULUCF - Other Land (Mt CO2e) |
| EN.GHG.CO2.LU.OS.MT.CE.AR5 | Carbon dioxide (CO2) net fluxes from LULUCF - Organic Soil (Mt CO2e) |
| EN.GHG.CO2.MT.CE.AR5 | Carbon dioxide (CO2) emissions (total) excluding LULUCF (Mt CO2e) |
| EN.GHG.CO2.PC.CE.AR5 | Carbon dioxide (CO2) emissions excluding LULUCF per capita (t CO2e/capita) |
| EN.GHG.CO2.PI.MT.CE.AR5 | Carbon dioxide (CO2) emissions from Power Industry (Energy) (Mt CO2e) |
| EN.GHG.CO2.TR.MT.CE.AR5 | Carbon dioxide (CO2) emissions from Transport (Energy) (Mt CO2e) |
| EN.GHG.CO2.WA.MT.CE.AR5 | Carbon dioxide (CO2) emissions from Waste (Mt CO2e) |
| EN.GHG.CO2.ZG.AR5 | Carbon dioxide (CO2) emissions (total) excluding LULUCF (% change from 1990) |
| IC.FRM.CO2.ZS | Firms monitoring own CO2 emissions (% of firms) |
| 17 elements |
wb.economy.info(q='korea')
| id | value | region | incomeLevel |
|---|---|---|---|
| KOR | Korea, Rep. | EAS | HIC |
| PRK | Korea, Dem. People's Rep. | EAS | LIC |
| 2 elements |
참고: 키워드 쿼리는 지표 이름의 괄호 안 부분은 무시한다. 예를 들어, q='GDP'는 “Gross domestic savings (% of GDP)”는 배제한다. 괄호 안 부분까지 검색하려면, 느낌표를 추가하여 q='!GDP'와 같이 하면 된다.
만약 이것으로 원하는 결과가 나오지 않는다면, search 함수를 사용하여 현재 데이터베이스의 모든 메타데이터에 대해 더 깊은 검색을 수행할 수 있다. 이는 현재 데이터베이스의 메타데이터를 검색한다.
wb.search('Korea')
Country
| ID | Name | Field | Value |
|---|---|---|---|
| KOR | LongName | Republic of Korea | |
| KOR | ShortName | Korea | |
| KOR | TableName | Korea, Rep. | |
| PRK | LongName | Democratic People's Republic of Korea | |
| PRK | ShortName | Korea, Democratic People’s Republic of | |
| PRK | TableName | Korea, Dem. People's Rep. |
Country-Series
| ID | Name | Field | Value |
|---|---|---|---|
| KOR~IT.CEL.COVR.ZS | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.CEL.HOUS.ZS | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.CEL.SETS | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.CEL.SETS.P2 | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.CMP.HOUS.ZS | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.MLT.FALT.M2 | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.MLT.MAIN | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.MLT.MAIN.P2 | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.NET.BBND | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.NET.BBND.P2 | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.NET.BNDW | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.NET.BNDW.PC | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.NET.HOUS.ZS | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.NET.SUBR | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.NET.SUBR.P2 | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.NET.USER.ZS | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.POP.TOTL.ITU | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.TEL.EMPL.TO | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.TEL.INVS.CD | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.TEL.REVN.CD | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.TEL.TOTL | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.TVS.CABL | Country-Series | Ministry of Information and Communication. Korea Communications Commission. | |
| KOR~IT.TVS.HOUS.ZS | Country-Series | Ministry of Information and Communication. Korea Communications Commission. |
Series
| ID | Name | Field | Value |
|---|---|---|---|
| DC.DAC.AUSL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.AUSL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.AUTL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.AUTL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.BELL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.BELL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.CANL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.CANL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.CECL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.CECL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.CHEL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.CHEL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.CZEL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.CZEL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.DEUL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.DEUL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.DNKL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.DNKL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.ESPL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.ESPL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.ESTL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Lithuania, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal,... | |
| DC.DAC.ESTL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Lithuania, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal,... | |
| DC.DAC.FINL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.FINL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.FRAL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.FRAL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.GBRL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.GBRL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.GRCL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.GRCL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.HUNL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.HUNL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.IRLL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.IRLL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.ISLL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.ISLL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.ITAL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.ITAL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.JPNL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.JPNL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.KORL.CD | IndicatorName | Net bilateral aid flows from DAC donors, Korea, Rep. (current US$) | |
| DC.DAC.KORL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.KORL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.LTUL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Lithuania, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal,... | |
| DC.DAC.LTUL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Lithuania, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal,... | |
| DC.DAC.LUXL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.LUXL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.NLDL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.NLDL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.NORL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.NORL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.NZLL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.NZLL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.OTHR.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Lithuania, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal,... | |
| DC.DAC.POLL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.POLL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.PRTL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.PRTL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.SVKL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.SVKL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.SVNL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.SVNL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.SWEL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.SWEL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.TOTL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.TOTL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.USAL.CD | Longdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| DC.DAC.USAL.CD | Shortdefinition | ... Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak... | |
| FX.OWN.TOTL.40.ZS | Developmentrelevance | ...G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the... | |
| FX.OWN.TOTL.60.ZS | Developmentrelevance | ...(Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the... | |
| FX.OWN.TOTL.FE.ZS | Developmentrelevance | ...(Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the... | |
| FX.OWN.TOTL.MA.ZS | Developmentrelevance | ...(Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the... | |
| FX.OWN.TOTL.OL.ZS | Developmentrelevance | ...(Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the... | |
| FX.OWN.TOTL.PL.ZS | Developmentrelevance | ...(Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the... | |
| FX.OWN.TOTL.SO.ZS | Developmentrelevance | ...(Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the... | |
| FX.OWN.TOTL.YG.ZS | Developmentrelevance | ...(Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the... | |
| FX.OWN.TOTL.ZS | Developmentrelevance | ...(Demirgüç-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the... | |
| IP.TMK.NRCT | Statisticalconceptandmethodology | ...or a single filing system. For example, the offices of Japan, the Republic of Korea and the United States of America (US), as well as many European IP offices,... | |
| IP.TMK.RSCT | Statisticalconceptandmethodology | ...or a single filing system. For example, the offices of Japan, the Republic of Korea and the United States of America (US), as well as many European IP offices,... | |
| IS.RRS.GOOD.MT.K6 | Developmentrelevance | ...network covering virtually the whole continent. In India, China, South Korea and Japan, many millions use trains as regular transport. In the North America,... | |
| IS.RRS.PASG.KM | Developmentrelevance | ...network covering virtually the whole continent. In India, China, South Korea and Japan, many millions use trains as regular transport. In North America,... | |
| IS.RRS.TOTL.KM | Developmentrelevance | ...network covering virtually the whole continent. In India, China, South Korea and Japan, many millions use trains as regular transport. In the North America,... |
2. WBGAPI 레시피 모음#
출처: A Cookbook of WBGAPI Recipes
import wbgapi as wb
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
쿼리 및 검색#
info 함수는 API를 검색하기 위한 주요 도구다. info() 함수를 식별자(단일 값 또는 리스트)와 함께 호출하거나 키워드 검색을 추가하여 응답을 구할 수 있다.
wb.economy.info(q='korea')
| id | value | region | incomeLevel |
|---|---|---|---|
| KOR | Korea, Rep. | EAS | HIC |
| PRK | Korea, Dem. People's Rep. | EAS | LIC |
| 2 elements |
wb.search(q)는 또 다른 옵션이다. 이는 현재 데이터베이스의 메타데이터를 검색한다.
wb.search('won')
Country
| ID | Name | Field | Value |
|---|---|---|---|
| KOR | CurrencyUnit | Korean won | |
| PRK | CurrencyUnit | Democratic People's Republic of Korean won |
참고: wb.search는 출력에 적합한(print-friendly) 객체를 반환한다. 그렇지 않고 다음과 같이 for 루프를 사용하여 다음과 같이 프로그래밍적으로 검색 결과에 접근할 수 있다.
for k,v in wb.search('won').metadata.items():
print(k) # k is the concept name
for elem in v:
print(elem.id) # element ID (column 1)
print(elem.name) # element name (column 2)
print(elem.metadata) # dictionary of metadata fields and values
Country
KOR
None
{'CurrencyUnit': 'Korean won'}
PRK
None
{'CurrencyUnit': "Democratic People's Republic of Korean won"}
또한, info 함수의 결과는 list 또는 Series를 호출하여 프로그래밍적으로 접근할 수 있다.
for row in wb.series.list(q='Korea'):
print(row)
{'id': 'DC.DAC.KORL.CD', 'value': 'Net bilateral aid flows from DAC donors, Korea, Rep. (current US$)'}
데이터 접근#
data 서브 패키지는 현재 데이터베이스에서 series, economy, time의 조합에 해당하는 데이터를 요청한다.
fetch 함수를 사용하면 각 행을 딕셔너리 객체로 반환할 수 있다. 아래 예에서는 1인당 GDP(NY.GDP.PCAP.CD)와 인구(SP.POP.TOTL) 데이터를 반환해보자.
wb.series.info(['NY.GDP.PCAP.CD', 'SP.POP.TOTL'])
| id | value |
|---|---|
| NY.GDP.PCAP.CD | GDP per capita (current US$) |
| SP.POP.TOTL | Population, total |
| 2 elements |
for row in wb.data.fetch('SP.POP.TOTL', 'KOR', mrv=5): # 한국의 최근 5년 데이터
print(row)
{'value': 51751065, 'series': 'SP.POP.TOTL', 'economy': 'KOR', 'aggregate': False, 'time': 'YR2024'}
{'value': 51712619, 'series': 'SP.POP.TOTL', 'economy': 'KOR', 'aggregate': False, 'time': 'YR2023'}
{'value': 51672569, 'series': 'SP.POP.TOTL', 'economy': 'KOR', 'aggregate': False, 'time': 'YR2022'}
{'value': 51769539, 'series': 'SP.POP.TOTL', 'economy': 'KOR', 'aggregate': False, 'time': 'YR2021'}
{'value': 51836239, 'series': 'SP.POP.TOTL', 'economy': 'KOR', 'aggregate': False, 'time': 'YR2020'}
또는 fetch 대신 DataFrame 함수를 사용해 pandas데이터 프레임 형태로 데이터를 반환할 수 있다.
wb.data.DataFrame(['NY.GDP.PCAP.CD', 'SP.POP.TOTL'], 'KOR').T # 모든 연도에 대해
| series | NY.GDP.PCAP.CD | SP.POP.TOTL |
|---|---|---|
| YR1960 | 158.274136 | 25012374.0 |
| YR1961 | 93.831383 | 25765673.0 |
| YR1962 | 106.159703 | 26513030.0 |
| YR1963 | 146.302493 | 27261747.0 |
| YR1964 | 123.606374 | 27984155.0 |
| ... | ... | ... |
| YR2020 | 31721.298914 | 51836239.0 |
| YR2021 | 35125.522498 | 51769539.0 |
| YR2022 | 32394.683373 | 51672569.0 |
| YR2023 | 33121.371289 | 51712619.0 |
| YR2024 | NaN | 51751065.0 |
65 rows × 2 columns
각각의 파라미터(시리즈, 경제, 기간)는 단일 식별자, 식별자 리스트 또는 기본 키워드 'all'을 받을 수 있다.
# 아프리카 국가들의 인구 데이터를, 2010년부터 2019년까지 2년 간격으로 조회
wb.data.DataFrame('SP.POP.TOTL', wb.region.members('AFR'), range(2010, 2020, 2)).head()
| YR2010 | YR2012 | YR2014 | YR2016 | YR2018 | |
|---|---|---|---|---|---|
| economy | |||||
| AGO | 23294825.0 | 25177394.0 | 27160769.0 | 29183070.0 | 31297155.0 |
| BDI | 9376444.0 | 10071028.0 | 10799785.0 | 11239451.0 | 11859446.0 |
| BEN | 9797484.0 | 10397657.0 | 11030004.0 | 11697842.0 | 12383347.0 |
| BFA | 16176498.0 | 17172287.0 | 18229461.0 | 19334856.0 | 20438288.0 |
| BWA | 2033111.0 | 2108617.0 | 2172044.0 | 2234776.0 | 2299141.0 |
DataFrame 함수는 필요한 경우, 다중 인덱스(multi-index)를 사용한다(기본값을 변경하려면 index와 columns 파라미터를 사용하라).
wb.data.DataFrame(['SP.POP.TOTL', 'EN.ATM.CO2E.KT'], time=range(2000, 2020),
skipBlanks=True, columns='series')
| SP.POP.TOTL | ||
|---|---|---|
| economy | time | |
| ABW | YR2000 | 90588.0 |
| YR2001 | 91439.0 | |
| YR2002 | 92074.0 | |
| YR2003 | 93128.0 | |
| YR2004 | 95138.0 | |
| ... | ... | ... |
| ZWE | YR2015 | 14399013.0 |
| YR2016 | 14600294.0 | |
| YR2017 | 14812482.0 | |
| YR2018 | 15034452.0 | |
| YR2019 | 15271368.0 |
5300 rows × 1 columns
DataFrame의 reset_index 메서드를 사용하여 인덱스를 0부터 시작하는 정수로 대체한다.
wb.data.DataFrame('SP.POP.TOTL', time=2015, labels=True).reset_index()
| economy | Country | SP.POP.TOTL | |
|---|---|---|---|
| 0 | ZWE | Zimbabwe | 14399013.0 |
| 1 | ZMB | Zambia | 16399089.0 |
| 2 | YEM | Yemen, Rep. | 31159379.0 |
| 3 | PSE | West Bank and Gaza | 4270092.0 |
| 4 | VIR | Virgin Islands (U.S.) | 107712.0 |
| ... | ... | ... | ... |
| 261 | CEB | Central Europe and the Baltics | 102973167.0 |
| 262 | CSS | Caribbean small states | 4314191.0 |
| 263 | ARB | Arab World | 410190679.0 |
| 264 | AFW | Africa Western and Central | 418127845.0 |
| 265 | AFE | Africa Eastern and Southern | 607123269.0 |
266 rows × 3 columns
대부분의 세계은행 데이터베이스는 series, economy and time의 3차원으로 구성되지만, WDI Archives와 같이 일부 데이터베이스는 4차원을 포함하고 있다. 이에 대해서는 다음과 같이 접근할 수 있다.
wb.source.info(57)
| id | name | code | concepts | lastupdated |
|---|---|---|---|---|
| 57 | WDI Database Archives | WDA | 4 | 2025-10-29 |
| 1 elements |
wb.source.concepts(db=57)
{'economy': {'key': 'country', 'value': 'Country'},
'series': {'key': 'series', 'value': 'Series'},
'time': {'key': 'time', 'value': 'Time'},
'version': {'key': 'version', 'value': 'Version'}}
# 브라질의 인구 추정치가 시간에 따라 수정되었는지 확인해보자.
# 버전 식별자는 YYYYMM 형식이다. 이 예제는 2010년부터 2019년까지의 4월 버전을 조회한다.
wb.data.DataFrame('SP.POP.TOTL', 'BRA', range(2000,2005),
version=range(201004,202004,100), db=57)
| YR2000 | YR2001 | YR2002 | YR2003 | YR2004 | |
|---|---|---|---|---|---|
| version | |||||
| 201004 | 174174447.0 | 176659138.0 | 179123364.0 | 181537359.0 | 183863524.0 |
| 201104 | 174174447.0 | 176659138.0 | 179123364.0 | 181537359.0 | 183863524.0 |
| 201204 | 174425387.0 | 176877135.0 | 179289227.0 | 181633074.0 | 183873377.0 |
| 201304 | 174425387.0 | 176877135.0 | 179289227.0 | 181633074.0 | 183873377.0 |
| 201404 | 174504898.0 | 176968205.0 | 179393768.0 | 181752951.0 | 184010283.0 |
| 201504 | 174504898.0 | 176968205.0 | 179393768.0 | 181752951.0 | 184010283.0 |
| 201604 | 175786441.0 | 178419396.0 | 181045592.0 | 183627339.0 | 186116363.0 |
| 201704 | 175786441.0 | 178419396.0 | 181045592.0 | 183627339.0 | 186116363.0 |
| 201804 | 175287587.0 | 177750670.0 | 180151021.0 | 182482149.0 | 184738458.0 |
| 201904 | 175287587.0 | 177750670.0 | 180151021.0 | 182482149.0 | 184738458.0 |
차트 및 플로팅#
기본적인 pandas 차트(예: matplotlib 사용)는 매우 간단하다. 물론, seaborn, plotly 등 원하는 어떤 플로팅 패키지도 사용할 수 있다. 아래는 ‘BRA’, ‘ARG’, ‘COL’ 3개국에 대해 2000년부터 2019년까지 20년간 1인당 소득을 그린 것이다.
plt.rcParams['figure.dpi'] = 500 # 전역적으로 선명도(해상도)를 높인다.
wb.data.DataFrame('NY.GDP.PCAP.CD', ['BRA', 'ARG', 'COL'],
range(2000, 2020), index='time').plot(figsize=(10, 6))
plt.show()
빈곤인구비율 보고국 분포#
빈곤인구비율(SI.POV.NAHC) 보고국이 연도별로 몇 개 나라인지를 막대그래프로 그려보자. mrnev(most recent non-empty values) 파라미터를 1로 설정하면, 데이터 프레임에 시간 차원이 포함되지 않는다. 그러나 columns 또는 index 파라미터를 사용하여 이를 명시적으로 요청할 수 있으며, numericTimeKeys=True을 사용해 시간 차원의 값을 숫자형으로 반환하도록 요청할 수 있다.
wb.series.info('SI.POV.NAHC') # 국가 빈곤선 기준 빈곤 인구 비율 (전체 인구의 %)
| id | value |
|---|---|
| SI.POV.NAHC | Poverty headcount ratio at national poverty lines (% of population) |
| 1 elements |
df = wb.data.DataFrame('SI.POV.NAHC', mrnev=1, columns='time', numericTimeKeys=True)
df
| 1993 | 2006 | 2007 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| economy | |||||||||||||||||||
| AFG | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 47.1 | NaN | NaN | NaN | NaN | NaN |
| AGO | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 32.3 | NaN | NaN | NaN | NaN | NaN | NaN |
| ALB | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 19.7 | NaN | NaN |
| ARG | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 38.1 |
| ARM | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 23.7 | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| XKX | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 17.6 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| YEM | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 48.6 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| ZAF | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 55.5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| ZMB | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 60.0 | NaN | NaN |
| ZWE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 38.3 | NaN | NaN | NaN | NaN | NaN |
162 rows × 19 columns
counts = df.count()
counts
1993 1
2006 1
2007 1
2009 2
2010 1
2011 3
2012 5
2013 1
2014 3
2015 5
2016 8
2017 7
2018 11
2019 9
2020 8
2021 32
2022 25
2023 21
2024 18
dtype: int64
plt.figure(figsize=(8,5))
plt.bar(counts.index, counts)
plt.xticks(np.arange(2006, 2023, 2))
plt.title('Distribution of Poverty Rate MRVs')
plt.show()
4차원 데이터베이스#
대부분의 세계은행 데이터베이스는 series, economy and time의 3차원으로 구성되지만, WDI Archives와 같이 일부 데이터베이스는 4차원을 포함하고 있으며, 이전 버전의 지표들을 가져올 수 있다.
wb.source.info(57)
| id | name | code | concepts | lastupdated |
|---|---|---|---|---|
| 57 | WDI Database Archives | WDA | 4 | 2025-10-29 |
| 1 elements |
wb.source.concepts(db=57)
{'economy': {'key': 'country', 'value': 'Country'},
'series': {'key': 'series', 'value': 'Series'},
'time': {'key': 'time', 'value': 'Time'},
'version': {'key': 'version', 'value': 'Version'}}
wb.Series(wb.source.features('version', db=57))
198904 1989 Apr
199004 1990 Apr
199009 1990 Sep
199104 1991 Apr
199109 1991 Sep
...
202501 2025 Jan
202503 2025 Mar
202504 2025 Apr
202506 2025 Jun
202507 2025 Jul
Name: value, Length: 136, dtype: object
브라질의 인구 추정치가 시간에 따라 수정되었는지 확인해보자. 이 예제에서는 version 파라미터가 WDI 개정판의 연도와 월을 지정한다. 버전 식별자는 YYYYMM 형식이기 때문에 201004에서 시작해 100 단위로 버전을 요청하면 요청한 범위에서 매년 4월 릴리스를 가져올 수 있다. 이를 통해 서로 다른 데이터베이스 버전 간의 값을 비교하여 변경되었는지 확인할 수 있다.
df = wb.data.DataFrame('SP.POP.TOTL', 'BRA', db=57, time=range(2000, 2020),
version=range(201004, 202004, 100), index='time')
df
| 201004 | 201104 | 201204 | 201304 | 201404 | 201504 | 201604 | 201704 | 201804 | 201904 | |
|---|---|---|---|---|---|---|---|---|---|---|
| time | ||||||||||
| YR2000 | 174174447.0 | 174174447.0 | 174425387.0 | 174425387.0 | 174504898.0 | 174504898.0 | 175786441.0 | 175786441.0 | 175287587.0 | 175287587.0 |
| YR2001 | 176659138.0 | 176659138.0 | 176877135.0 | 176877135.0 | 176968205.0 | 176968205.0 | 178419396.0 | 178419396.0 | 177750670.0 | 177750670.0 |
| YR2002 | 179123364.0 | 179123364.0 | 179289227.0 | 179289227.0 | 179393768.0 | 179393768.0 | 181045592.0 | 181045592.0 | 180151021.0 | 180151021.0 |
| YR2003 | 181537359.0 | 181537359.0 | 181633074.0 | 181633074.0 | 181752951.0 | 181752951.0 | 183627339.0 | 183627339.0 | 182482149.0 | 182482149.0 |
| YR2004 | 183863524.0 | 183863524.0 | 183873377.0 | 183873377.0 | 184010283.0 | 184010283.0 | 186116363.0 | 186116363.0 | 184738458.0 | 184738458.0 |
| YR2005 | 186074634.0 | 186074634.0 | 185986964.0 | 185986964.0 | 186142403.0 | 186142403.0 | 188479240.0 | 188479240.0 | 186917361.0 | 186917361.0 |
| YR2006 | 188158438.0 | 188158438.0 | 187958211.0 | 187958211.0 | 188134315.0 | 188134315.0 | 190698241.0 | 190698241.0 | 189012412.0 | 189012412.0 |
| YR2007 | 190119995.0 | 190119995.0 | 189798070.0 | 189798070.0 | 189996976.0 | 189996976.0 | 192784521.0 | 192784521.0 | 191026637.0 | 191026637.0 |
| YR2008 | 191971506.0 | 191971506.0 | 191543237.0 | 191543237.0 | 191765567.0 | 191765567.0 | 194769696.0 | 194769696.0 | 192979029.0 | 192979029.0 |
| YR2009 | NaN | 193733795.0 | 193246610.0 | 193246610.0 | 193490922.0 | 193490922.0 | 196701298.0 | 196701298.0 | 194895996.0 | 194895996.0 |
| YR2010 | NaN | NaN | 194946000.0 | 194946470.0 | 195210154.0 | 195210154.0 | 198614208.0 | 198614208.0 | 196796269.0 | 196796269.0 |
| YR2011 | NaN | NaN | NaN | 196655014.0 | 196935134.0 | 196935134.0 | 200517584.0 | 200517584.0 | 198686688.0 | 198686688.0 |
| YR2012 | NaN | NaN | NaN | NaN | 198656019.0 | 198656019.0 | 202401584.0 | 202401584.0 | 200560983.0 | 200560983.0 |
| YR2013 | NaN | NaN | NaN | NaN | NaN | 200361925.0 | 204259377.0 | 204259377.0 | 202408632.0 | 202408632.0 |
| YR2014 | NaN | NaN | NaN | NaN | NaN | NaN | 206077898.0 | 206077898.0 | 204213133.0 | 204213133.0 |
| YR2015 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 207847528.0 | 205962108.0 | 205962108.0 |
| YR2016 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 207652865.0 | 207652865.0 |
| YR2017 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 209288000.0 | 209288278.0 |
| YR2018 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 210868000.0 |
| YR2019 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
ax = df.plot(figsize=(10, 6), title="Population estimates over time by WDI version")
집계#
pandas에서는 집계(aggregation)가 매우 간단하다. 예를 들어, 평균값은 다음과 같이 구한다.
df.mean()
아래에서는 예시적으로 브라질, 콜롬비아, 페루 3개국의 1990년부터 1995년에 걸친 EG.ELC.ACCS.ZS(전기접근성, 전체 인구의 %) 지표에 대해 각 연도별로 3개국의 평균값을 구해보자.
wb.series.info(['EG.ELC.ACCS.ZS'])
| id | value |
|---|---|
| EG.ELC.ACCS.ZS | Access to electricity (% of population) |
| 1 elements |
wb.economy.info(['BRA', 'COL', 'PER'])
| id | value | region | incomeLevel |
|---|---|---|---|
| BRA | Brazil | LCN | UMC |
| COL | Colombia | LCN | UMC |
| PER | Peru | LCN | UMC |
| 3 elements |
df = wb.data.DataFrame('EG.ELC.ACCS.ZS', ['BRA', 'COL', 'PER'], time=range(1990,1996))
df
| YR1990 | YR1991 | YR1992 | YR1993 | YR1994 | YR1995 | |
|---|---|---|---|---|---|---|
| economy | ||||||
| BRA | 87.475116 | 90.606544 | 88.803833 | 89.999927 | 91.883728 | 91.728598 |
| COL | 89.900000 | 91.836655 | 92.132660 | 92.427994 | 92.721962 | 91.200000 |
| PER | NaN | NaN | 70.100000 | 64.748993 | 66.052933 | 67.354836 |
df.mean()
YR1990 88.687558
YR1991 91.221600
YR1992 83.678831
YR1993 82.392305
YR1994 83.552874
YR1995 83.427811
dtype: float64
가중 평균
여기서는 예시적으로 인구를 가중치로 사용한다. 가중치를 적용할 값들과 동일한 차원을 가진 인구(SP.POP.TOTL) 데이터 프레임이 필요하다. 가장 간단한 방법은 동일한 economy와 time에 대해 두 지표를 단일의 데이터 프레임으로 만드는 것이다. 이 경우, 다음과 같은 방식으로 index 파라미터를 사용하여 다중 인덱스를 가진 데이터 프레임을 생성하면 된다.
combined = wb.data.DataFrame(['EG.ELC.ACCS.ZS', 'SP.POP.TOTL'], ['BRA', 'COL', 'PER'],
time=range(1990,1996), index=['series', 'economy'])
combined
| YR1990 | YR1991 | YR1992 | YR1993 | YR1994 | YR1995 | ||
|---|---|---|---|---|---|---|---|
| series | economy | ||||||
| EG.ELC.ACCS.ZS | BRA | 8.747512e+01 | 9.060654e+01 | 8.880383e+01 | 8.999993e+01 | 9.188373e+01 | 9.172860e+01 |
| COL | 8.990000e+01 | 9.183665e+01 | 9.213266e+01 | 9.242799e+01 | 9.272196e+01 | 9.120000e+01 | |
| PER | NaN | NaN | 7.010000e+01 | 6.474899e+01 | 6.605293e+01 | 6.735484e+01 | |
| SP.POP.TOTL | BRA | 1.491432e+08 | 1.517243e+08 | 1.542751e+08 | 1.567946e+08 | 1.592650e+08 | 1.617351e+08 |
| COL | 3.244007e+07 | 3.309837e+07 | 3.376057e+07 | 3.444147e+07 | 3.512741e+07 | 3.580466e+07 | |
| PER | 2.201477e+07 | 2.248378e+07 | 2.294634e+07 | 2.341378e+07 | 2.388149e+07 | 2.434892e+07 |
그런 다음, 집계 변수(EG.ELC.ACCS.ZS)에서 NaN으로 돼있는 관측값에 대해 가중치 변수(SP.POP.TOTL)에도 이들을 마스킹(제외) 처리해야 한다. 그렇지 않으면 가중 평균 계산에서 분모가 잘못 계산된다.
elec_access = combined.xs('EG.ELC.ACCS.ZS') # 집계 변수 데이터 프레임
combined = combined[elec_access.isna() == False] # 다른 변수들에서 NaN 값을 마스킹 처리함
combined
| YR1990 | YR1991 | YR1992 | YR1993 | YR1994 | YR1995 | ||
|---|---|---|---|---|---|---|---|
| series | economy | ||||||
| EG.ELC.ACCS.ZS | BRA | 8.747512e+01 | 9.060654e+01 | 8.880383e+01 | 8.999993e+01 | 9.188373e+01 | 9.172860e+01 |
| COL | 8.990000e+01 | 9.183665e+01 | 9.213266e+01 | 9.242799e+01 | 9.272196e+01 | 9.120000e+01 | |
| PER | NaN | NaN | 7.010000e+01 | 6.474899e+01 | 6.605293e+01 | 6.735484e+01 | |
| SP.POP.TOTL | BRA | 1.491432e+08 | 1.517243e+08 | 1.542751e+08 | 1.567946e+08 | 1.592650e+08 | 1.617351e+08 |
| COL | 3.244007e+07 | 3.309837e+07 | 3.376057e+07 | 3.444147e+07 | 3.512741e+07 | 3.580466e+07 | |
| PER | NaN | NaN | 2.294634e+07 | 2.341378e+07 | 2.388149e+07 | 2.434892e+07 |
이렇게 하면, 가중치가 적용된 값의 합을 가중치의 합으로 나눈 가중 평균을 쉽게 계산할 수 있다.
elec_access = combined.xs('EG.ELC.ACCS.ZS')
pop = combined.xs('SP.POP.TOTL')
(elec_access*pop).sum() / pop.sum()
YR1990 87.908324
YR1991 90.826835
YR1992 87.302276
YR1993 87.635175
YR1994 89.192463
YR1995 88.968650
dtype: float64
그룹 집계#
그룹 집계는 앞서 설명한 방법을 확장한 것이다. 평균을 계산하기 전에, 그룹을 정의하는 변수(예: 국가 이름의 첫 글자)를 기준으로 지표 데이터 프레임들을 결합하면 된다. 여기서는 국가 이름의 첫 글자를 기준으로 간단한 그룹을 정의해보자. 첫 번째 단계로, 국가의 첫 글자들로 이루어진 pandas 시리즈를 생성해야 한다.
c = wb.economy.DataFrame()
c['Initial'] = c['name'].str[0]
initial = c['Initial']
# another way to the same thing
initial = wb.economy.Series(name='Initial').apply(lambda n: n[0])
initial
ABW A
AFE A
AFG A
AFW A
AGO A
..
XKX K
YEM Y
ZAF S
ZMB Z
ZWE Z
Name: Initial, Length: 266, dtype: object
# 모든 국가를 불러온다. World나 Sub-Saharan Africa와 같은 집계 지역은 제외한다(skipAggs=True).
combined = wb.data.DataFrame(['EG.ELC.ACCS.ZS', 'SP.POP.TOTL'], skipAggs=True,
time=range(1990, 1996), index=['series', 'economy'])
elec_access = combined.xs('EG.ELC.ACCS.ZS')
# 집계 변수에 없는 가중치 변수의 관측값을 마스킹 처리한다.
combined = combined[elec_access.isna() == False]
pop = combined.xs('SP.POP.TOTL')
numerator = (elec_access * pop).join(initial).groupby('Initial').sum()
denominator = pop.join(initial).groupby('Initial').sum()
numerator / denominator
| YR1990 | YR1991 | YR1992 | YR1993 | YR1994 | YR1995 | |
|---|---|---|---|---|---|---|
| Initial | ||||||
| A | 95.780387 | 95.952820 | 96.126616 | 96.297750 | 96.468972 | 96.640102 |
| B | 89.838599 | 62.530803 | 59.826296 | 59.682781 | 62.342033 | 62.355179 |
| C | 95.191828 | 88.595383 | 88.954841 | 89.166747 | 83.116034 | 81.322167 |
| D | 100.000000 | 87.222188 | 89.663119 | 90.051993 | 90.447886 | 90.856901 |
| E | 100.000000 | 76.332513 | 92.076353 | 93.946373 | 94.263941 | 91.881157 |
| F | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 |
| G | 99.999760 | 99.999807 | 99.999854 | 88.494521 | 88.253563 | 86.068557 |
| H | 100.000000 | 89.096623 | 89.898608 | 90.270005 | 91.039354 | 76.030206 |
| I | 100.000000 | 62.177695 | 75.262775 | 54.384301 | 54.690727 | 56.554949 |
| J | 99.365228 | 99.515891 | 99.529516 | 99.543140 | 99.557136 | 99.571669 |
| K | 99.892055 | 99.918957 | 99.946693 | 69.348543 | 66.386924 | 72.708653 |
| L | 100.000000 | 100.000000 | 100.000000 | 71.539519 | 68.672293 | 67.803997 |
| M | 99.801353 | 99.808214 | 71.247142 | 73.941854 | 74.291011 | 74.398664 |
| N | 42.095137 | 48.265436 | 45.680094 | 46.882341 | 47.269657 | 47.825363 |
| O | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 |
| P | 98.644944 | 98.771710 | 90.286212 | 77.838038 | 79.140048 | 79.684509 |
| Q | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 |
| R | 100.000000 | 100.000000 | 95.806765 | 100.000000 | 100.000000 | 100.000000 |
| S | 85.925218 | 84.501869 | 84.505296 | 80.307182 | 80.289492 | 80.303349 |
| T | 88.700000 | 91.053909 | 6.839962 | 2.373276 | 23.054019 | 24.001586 |
| U | 100.000000 | 95.521974 | 99.967750 | 99.969418 | 94.955537 | 95.185852 |
| V | 100.000000 | 100.000000 | 97.831235 | 98.248316 | 97.735132 | 98.364642 |
| W | NaN | NaN | NaN | NaN | NaN | NaN |
| Y | NaN | NaN | 44.100000 | 40.774715 | 41.985134 | 43.193512 |
| Z | 13.900000 | 12.752703 | 24.302090 | 23.784013 | 22.297841 | 24.710103 |
3. 구매력평가 환율 데이터#
PPP 관련 지표#
WDI(World Development Indicators) 데이터베이스에서 PPP 관련 지표
Indicator |
English Description |
Korean Translation |
|---|---|---|
PA.NUS.FCRF |
Official exchange rate (LCU per USD, period average) |
공식 환율 (LCU per USD, 기간 평균) |
PA.NUS.PPP |
PPP conversion factor, GDP (LCU per international dollar) |
GDP에 대한 PPP 환산계수 (LCU per 국제 달러) |
PA.NUS.PPPC.RF |
Price level ratio of PPP conversion factor (GDP) to market exchange rate |
PPP 환산계수(GDP)와 시장 환율 간의 물가 수준 비율 |
PA.NUS.PRVT.PP |
PPP conversion factor, private consumption (LCU per international dollar) |
개인 소비에 대한 PPP 환산계수 (LCU per 국제 달러) |
한국 PPP 관련 데이터#
df = wb.data.DataFrame(
['PA.NUS.FCRF', 'PA.NUS.PPP', 'PA.NUS.PPPC.RF', 'PA.NUS.PRVT.PP'],
'KOR', columns='time', numericTimeKeys=True).T # 모든 연도에 대해
df
| series | PA.NUS.FCRF | PA.NUS.PPP | PA.NUS.PPPC.RF | PA.NUS.PRVT.PP |
|---|---|---|---|---|
| 1960 | 63.125000 | NaN | NaN | NaN |
| 1961 | 124.791667 | NaN | NaN | NaN |
| 1962 | 130.000000 | NaN | NaN | NaN |
| 1963 | 130.000000 | NaN | NaN | NaN |
| 1964 | 213.846667 | NaN | NaN | NaN |
| ... | ... | ... | ... | ... |
| 2020 | 1180.265833 | 829.359157 | 0.702688 | 971.479342 |
| 2021 | 1143.951667 | 829.867766 | 0.725440 | 960.988192 |
| 2022 | 1291.446667 | 816.607833 | 0.632320 | 933.201393 |
| 2023 | 1305.662500 | 828.391665 | 0.634461 | 932.176827 |
| 2024 | 1363.375000 | 838.897321 | 0.615309 | 932.737210 |
65 rows × 4 columns
plt.figure(figsize=(10, 6))
for series in df.columns:
plt.plot(df.index, df[series], marker='o', label=series)
plt.xlabel('Year')
plt.ylabel('Value')
plt.title('Yearly Trends for 4 Series')
plt.legend()
plt.tight_layout()
plt.show()
한국 명목환율 및 PPP 환율 추이#
df_sub = df[['PA.NUS.FCRF', 'PA.NUS.PPP', 'PA.NUS.PRVT.PP']]
df_sub.columns = ['Official exchange rate',
'PPP conversion factor (GDP)',
'PPP conversion factor (private consumption)']
df_sub = df_sub[df_sub.index.astype(int) >= 1990]
plt.figure(figsize=(10, 6))
for series in df_sub.columns:
plt.plot(df_sub.index, df_sub[series], marker='o', label=series)
plt.xlabel('Year')
plt.title('Trends in Official Exchange Rate and PPP Conversion Factor for Korea')
plt.legend()
plt.tight_layout()
plt.show()
