advanced-1-wbgapi-quickstart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is downloaded from https://github.com/tgherzog/wbgapi/blob/master/examples/wbgapi-quickstart.ipynb\n",
    "# A brief demo of the WBGAPI module\n",
    "\n",
    "[WBGAPI](https://pypi.org/project/wbgapi) is a python package for accessing the [World Bank's open data API](https://data.worldbank.org/developers). Unlike some other python packages, this one references a single API database at a time instead of the API as a whole, which provides greater transparency and control over the data you get.\n",
    "\n",
    "This notebook is a demo to get you started, based on a presentation given at the World Bank in July, 2020 with some revisions to include module improvements made since then.\n",
    "More information is available at <https://pypi.org/project/wbgapi>. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import wbgapi as wb\n",
    "import pandas as pd   # optional but highly recommended for wbgapi\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "wbgapi is designed around the fact that the API consists of several dozen different databases, each with independent series, countries and time dimensions, but with strong overlaps.\n",
    "\n",
    "wbgapi (wb) has 9 submodules that correspond to the major components of the API:\n",
    "\n",
    "Feature  | Description\n",
    "-------  | -----------\n",
    "source   | Databases\n",
    "series   | Indicators\n",
    "economy  | Countries/economies/aggregates\n",
    "data     | Data\n",
    "topic    | Indicator topics\n",
    "time     | Time\n",
    "region   | Regions\n",
    "income   | Income Groups\n",
    "lending  | Lending types\n",
    "\n",
    "And common functions for each feature:\n",
    "\n",
    "Function     | Description | Example\n",
    "------------ | ----------- | -------\n",
    "`info()`     | human readable table | `wb.time.info()`\n",
    "`list()`     | return elements one at a time | `wb.economy.list()`\n",
    "`get()`      | return a single element       | `wb.series.get('SP.POP.TOTL')`\n",
    "`Series()`   | pandas Series                 | `wb.income.Series()`\n",
    "\n",
    "And **lots** of inline help, e.g:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "?wb.economy.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"wbgapi\"><table>\n",
       "<thead>\n",
       "<tr><th>id  </th><th>name                                                                </th><th>lastupdated  </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>1   </td><td>Doing Business                                                      </td><td>2019-10-23   </td></tr>\n",
       "<tr><td>2   </td><td>World Development Indicators                                        </td><td>2020-12-16   </td></tr>\n",
       "<tr><td>3   </td><td>Worldwide Governance Indicators                                     </td><td>2020-09-28   </td></tr>\n",
       "<tr><td>5   </td><td>Subnational Malnutrition Database                                   </td><td>2016-03-21   </td></tr>\n",
       "<tr><td>6   </td><td>International Debt Statistics                                       </td><td>2021-01-21   </td></tr>\n",
       "<tr><td>11  </td><td>Africa Development Indicators                                       </td><td>2013-02-22   </td></tr>\n",
       "<tr><td>12  </td><td>Education Statistics                                                </td><td>2020-12-20   </td></tr>\n",
       "<tr><td>13  </td><td>Enterprise Surveys                                                  </td><td>2017-01-03   </td></tr>\n",
       "<tr><td>14  </td><td>Gender Statistics                                                   </td><td>2020-12-21   </td></tr>\n",
       "<tr><td>15  </td><td>Global Economic Monitor                                             </td><td>2020-07-27   </td></tr>\n",
       "<tr><td>16  </td><td>Health Nutrition and Population Statistics                          </td><td>2020-12-18   </td></tr>\n",
       "<tr><td>18  </td><td>IDA Results Measurement System                                      </td><td>2020-07-07   </td></tr>\n",
       "<tr><td>19  </td><td>Millennium Development Goals                                        </td><td>2018-09-19   </td></tr>\n",
       "<tr><td>20  </td><td>Quarterly Public Sector Debt                                        </td><td>2021-01-29   </td></tr>\n",
       "<tr><td>22  </td><td>Quarterly External Debt Statistics SDDS                             </td><td>2021-01-29   </td></tr>\n",
       "<tr><td>23  </td><td>Quarterly External Debt Statistics GDDS                             </td><td>2021-01-29   </td></tr>\n",
       "<tr><td>24  </td><td>Poverty and Equity                                                  </td><td>2020-10-07   </td></tr>\n",
       "<tr><td>25  </td><td>Jobs                                                                </td><td>2020-12-16   </td></tr>\n",
       "<tr><td>27  </td><td>Global Economic Prospects                                           </td><td>2021-01-05   </td></tr>\n",
       "<tr><td>28  </td><td>Global Financial Inclusion                                          </td><td>2018-10-15   </td></tr>\n",
       "<tr><td>29  </td><td>The Atlas of Social Protection: Indicators of Resilience and Equity </td><td>2020-09-09   </td></tr>\n",
       "<tr><td>30  </td><td>Exporter Dynamics Database – Indicators at Country-Year Level       </td><td>2016-03-31   </td></tr>\n",
       "<tr><td>31  </td><td>Country Policy and Institutional Assessment                         </td><td>2020-07-24   </td></tr>\n",
       "<tr><td>32  </td><td>Global Financial Development                                        </td><td>2019-10-30   </td></tr>\n",
       "<tr><td>33  </td><td>G20 Financial Inclusion Indicators                                  </td><td>2019-02-27   </td></tr>\n",
       "<tr><td>34  </td><td>Global Partnership for Education                                    </td><td>2013-04-12   </td></tr>\n",
       "<tr><td>35  </td><td>Sustainable Energy for All                                          </td><td>2018-06-30   </td></tr>\n",
       "<tr><td>36  </td><td>Statistical Capacity Indicators                                     </td><td>2021-02-03   </td></tr>\n",
       "<tr><td>37  </td><td>LAC Equity Lab                                                      </td><td>2016-04-06   </td></tr>\n",
       "<tr><td>38  </td><td>Subnational Poverty                                                 </td><td>2015-04-27   </td></tr>\n",
       "<tr><td>39  </td><td>Health Nutrition and Population Statistics by Wealth Quintile       </td><td>2020-12-21   </td></tr>\n",
       "<tr><td>40  </td><td>Population estimates and projections                                </td><td>2020-12-18   </td></tr>\n",
       "<tr><td>41  </td><td>Country Partnership Strategy for India (FY2013 - 17)                </td><td>2015-05-22   </td></tr>\n",
       "<tr><td>43  </td><td>Adjusted Net Savings                                                </td><td>2020-08-21   </td></tr>\n",
       "<tr><td>44  </td><td>Readiness for Investment in Sustainable Energy                      </td><td>2015-06-11   </td></tr>\n",
       "<tr><td>45  </td><td>Indonesia Database for Policy and Economic Research                 </td><td>2019-05-07   </td></tr>\n",
       "<tr><td>46  </td><td>Sustainable Development Goals                                       </td><td>2020-10-26   </td></tr>\n",
       "<tr><td>50  </td><td>Subnational Population                                              </td><td>2017-09-21   </td></tr>\n",
       "<tr><td>54  </td><td>Joint External Debt Hub                                             </td><td>2020-11-20   </td></tr>\n",
       "<tr><td>57  </td><td>WDI Database Archives                                               </td><td>2020-10-26   </td></tr>\n",
       "<tr><td>58  </td><td>Universal Health Coverage                                           </td><td>2019-04-25   </td></tr>\n",
       "<tr><td>59  </td><td>Wealth Accounts                                                     </td><td>2018-01-30   </td></tr>\n",
       "<tr><td>60  </td><td>Economic Fitness                                                    </td><td>2018-04-19   </td></tr>\n",
       "<tr><td>61  </td><td>PPPs Regulatory Quality                                             </td><td>2019-04-25   </td></tr>\n",
       "<tr><td>62  </td><td>International Comparison Program (ICP) 2011                         </td><td>2020-08-24   </td></tr>\n",
       "<tr><td>63  </td><td>Human Capital Index                                                 </td><td>2020-09-21   </td></tr>\n",
       "<tr><td>64  </td><td>Worldwide Bureaucracy Indicators                                    </td><td>2020-08-03   </td></tr>\n",
       "<tr><td>65  </td><td>Health Equity and Financial Protection Indicators                   </td><td>2019-11-06   </td></tr>\n",
       "<tr><td>66  </td><td>Logistics Performance Index                                         </td><td>2019-10-29   </td></tr>\n",
       "<tr><td>67  </td><td>PEFA 2011                                                           </td><td>2020-10-27   </td></tr>\n",
       "<tr><td>68  </td><td>PEFA 2016                                                           </td><td>2020-12-14   </td></tr>\n",
       "<tr><td>69  </td><td>Global Financial Inclusion and Consumer Protection Survey           </td><td>2019-06-27   </td></tr>\n",
       "<tr><td>70  </td><td>Economic Fitness 2                                                  </td><td>2019-04-26   </td></tr>\n",
       "<tr><td>71  </td><td>International Comparison Program (ICP) 2005                         </td><td>2020-08-24   </td></tr>\n",
       "<tr><td>72  </td><td>PEFA_Test                                                           </td><td>2019-09-10   </td></tr>\n",
       "<tr><td>73  </td><td>Global Financial Inclusion and Consumer Protection Survey (Internal)</td><td>2019-06-27   </td></tr>\n",
       "<tr><td>75  </td><td>Environment, Social and Governance (ESG) Data                       </td><td>2021-02-01   </td></tr>\n",
       "<tr><td>76  </td><td>Remittance Prices Worldwide (Sending Countries)                     </td><td>2020-12-07   </td></tr>\n",
       "<tr><td>77  </td><td>Remittance Prices Worldwide (Receiving Countries)                   </td><td>2020-12-07   </td></tr>\n",
       "<tr><td>78  </td><td>ICP 2017                                                            </td><td>2020-10-21   </td></tr>\n",
       "<tr><td>79  </td><td>PEFA_GRPFM                                                          </td><td>2020-12-02   </td></tr>\n",
       "<tr><td>80  </td><td>Gender Disaggregated Labor Database (GDLD)                          </td><td>2020-07-25   </td></tr>\n",
       "<tr><td>81  </td><td>International Debt Statistics: DSSI                                 </td><td>2021-01-21   </td></tr>\n",
       "<tr><td>    </td><td>63 elements                                                         </td><td>             </td></tr>\n",
       "</tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "id    name                                                                  lastupdated\n",
       "----  --------------------------------------------------------------------  -------------\n",
       "1     Doing Business                                                        2019-10-23\n",
       "2     World Development Indicators                                          2020-12-16\n",
       "3     Worldwide Governance Indicators                                       2020-09-28\n",
       "5     Subnational Malnutrition Database                                     2016-03-21\n",
       "6     International Debt Statistics                                         2021-01-21\n",
       "11    Africa Development Indicators                                         2013-02-22\n",
       "12    Education Statistics                                                  2020-12-20\n",
       "13    Enterprise Surveys                                                    2017-01-03\n",
       "14    Gender Statistics                                                     2020-12-21\n",
       "15    Global Economic Monitor                                               2020-07-27\n",
       "16    Health Nutrition and Population Statistics                            2020-12-18\n",
       "18    IDA Results Measurement System                                        2020-07-07\n",
       "19    Millennium Development Goals                                          2018-09-19\n",
       "20    Quarterly Public Sector Debt                                          2021-01-29\n",
       "22    Quarterly External Debt Statistics SDDS                               2021-01-29\n",
       "23    Quarterly External Debt Statistics GDDS                               2021-01-29\n",
       "24    Poverty and Equity                                                    2020-10-07\n",
       "25    Jobs                                                                  2020-12-16\n",
       "27    Global Economic Prospects                                             2021-01-05\n",
       "28    Global Financial Inclusion                                            2018-10-15\n",
       "29    The Atlas of Social Protection: Indicators of Resilience and Equity   2020-09-09\n",
       "30    Exporter Dynamics Database – Indicators at Country-Year Level         2016-03-31\n",
       "31    Country Policy and Institutional Assessment                           2020-07-24\n",
       "32    Global Financial Development                                          2019-10-30\n",
       "33    G20 Financial Inclusion Indicators                                    2019-02-27\n",
       "34    Global Partnership for Education                                      2013-04-12\n",
       "35    Sustainable Energy for All                                            2018-06-30\n",
       "36    Statistical Capacity Indicators                                       2021-02-03\n",
       "37    LAC Equity Lab                                                        2016-04-06\n",
       "38    Subnational Poverty                                                   2015-04-27\n",
       "39    Health Nutrition and Population Statistics by Wealth Quintile         2020-12-21\n",
       "40    Population estimates and projections                                  2020-12-18\n",
       "41    Country Partnership Strategy for India (FY2013 - 17)                  2015-05-22\n",
       "43    Adjusted Net Savings                                                  2020-08-21\n",
       "44    Readiness for Investment in Sustainable Energy                        2015-06-11\n",
       "45    Indonesia Database for Policy and Economic Research                   2019-05-07\n",
       "46    Sustainable Development Goals                                         2020-10-26\n",
       "50    Subnational Population                                                2017-09-21\n",
       "54    Joint External Debt Hub                                               2020-11-20\n",
       "57    WDI Database Archives                                                 2020-10-26\n",
       "58    Universal Health Coverage                                             2019-04-25\n",
       "59    Wealth Accounts                                                       2018-01-30\n",
       "60    Economic Fitness                                                      2018-04-19\n",
       "61    PPPs Regulatory Quality                                               2019-04-25\n",
       "62    International Comparison Program (ICP) 2011                           2020-08-24\n",
       "63    Human Capital Index                                                   2020-09-21\n",
       "64    Worldwide Bureaucracy Indicators                                      2020-08-03\n",
       "65    Health Equity and Financial Protection Indicators                     2019-11-06\n",
       "66    Logistics Performance Index                                           2019-10-29\n",
       "67    PEFA 2011                                                             2020-10-27\n",
       "68    PEFA 2016                                                             2020-12-14\n",
       "69    Global Financial Inclusion and Consumer Protection Survey             2019-06-27\n",
       "70    Economic Fitness 2                                                    2019-04-26\n",
       "71    International Comparison Program (ICP) 2005                           2020-08-24\n",
       "72    PEFA_Test                                                             2019-09-10\n",
       "73    Global Financial Inclusion and Consumer Protection Survey (Internal)  2019-06-27\n",
       "75    Environment, Social and Governance (ESG) Data                         2021-02-01\n",
       "76    Remittance Prices Worldwide (Sending Countries)                       2020-12-07\n",
       "77    Remittance Prices Worldwide (Receiving Countries)                     2020-12-07\n",
       "78    ICP 2017                                                              2020-10-21\n",
       "79    PEFA_GRPFM                                                            2020-12-02\n",
       "80    Gender Disaggregated Labor Database (GDLD)                            2020-07-25\n",
       "81    International Debt Statistics: DSSI                                   2021-01-21\n",
       "      63 elements"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb.source.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With WBGAPI you only target one database at a time, either through the `db` parameter passed to the call, or by setting a global variable. WDI (db=2) is the default global database, but you can change it either per call or globally:\n",
    "\n",
    "```\n",
    "wb.series.info(db=1) # fetch indicators from Doing Business\n",
    "\n",
    "# same thing\n",
    "wb.db = 1\n",
    "wb.series.info()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"wbgapi\"><table>\n",
       "<thead>\n",
       "<tr><th>id               </th><th>value                                                                                                       </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>LP.LPI.CUST.RK   </td><td>Efficiency of the clearance process, rank (1=highest performer)                                             </td></tr>\n",
       "<tr><td>LP.LPI.CUST.XQ   </td><td>Efficiency of the clearance process, score (1=low to 5=high)                                                </td></tr>\n",
       "<tr><td>LP.LPI.INFR.RK   </td><td>Quality- of trade and transport-related infrastructure, rank (1=highest performer)                          </td></tr>\n",
       "<tr><td>LP.LPI.INFR.XQ   </td><td>Quality of trade- and transport-related infrastructure, score (1=low to 5=high)                             </td></tr>\n",
       "<tr><td>LP.LPI.ITRN.RK   </td><td>Ease of arranging competitively priced international shipments, rank (1=highest performer)                  </td></tr>\n",
       "<tr><td>LP.LPI.ITRN.XQ   </td><td>Ease of arranging competitively priced international shipments, score (1=low to 5=high)                     </td></tr>\n",
       "<tr><td>LP.LPI.LOGS.RK   </td><td>Competence and quality of logistics services, rank (1=highest performer)                                    </td></tr>\n",
       "<tr><td>LP.LPI.LOGS.XQ   </td><td>Competence and quality of logistics services, score (1=low to 5=high)                                       </td></tr>\n",
       "<tr><td>LP.LPI.OVRL.RK   </td><td>Logistics performance index: Overall rank (1=highest performance)                                           </td></tr>\n",
       "<tr><td>LP.LPI.OVRL.RK.LB</td><td>Logistics performance index: Overall rank (1=highest performance), lower bound                              </td></tr>\n",
       "<tr><td>LP.LPI.OVRL.RK.UB</td><td>Logistics performance index: Overall rank (1=highest performance), upper bound                              </td></tr>\n",
       "<tr><td>LP.LPI.OVRL.RK.ZS</td><td>Logistics performance index: Percent of highest performer)                                                  </td></tr>\n",
       "<tr><td>LP.LPI.OVRL.XQ   </td><td>Logistics performance index: Overall score (1=low to 5=high)                                                </td></tr>\n",
       "<tr><td>LP.LPI.OVRL.XQ.LB</td><td>Logistics performance index: Overall score (1=low to 5=high), lower bound                                   </td></tr>\n",
       "<tr><td>LP.LPI.OVRL.XQ.UB</td><td>Logistics performance index: Overall score (1=low to 5=high), upper bound                                   </td></tr>\n",
       "<tr><td>LP.LPI.TIME.RK   </td><td>Frequency with which shipments reach consignee within scheduled or expected time, rank (1=highest performer)</td></tr>\n",
       "<tr><td>LP.LPI.TIME.XQ   </td><td>Frequency with which shipments reach consignee within scheduled or expected time, score (1=low to 5=high)   </td></tr>\n",
       "<tr><td>LP.LPI.TRAC.RK   </td><td>Ability to track and trace consignments, rank (1=highest performer)                                         </td></tr>\n",
       "<tr><td>LP.LPI.TRAC.XQ   </td><td>Ability to track and trace consignments, score (1=low to 5=high)                                            </td></tr>\n",
       "<tr><td>                 </td><td>19 elements                                                                                                 </td></tr>\n",
       "</tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "id                 value\n",
       "-----------------  ------------------------------------------------------------------------------------------------------------\n",
       "LP.LPI.CUST.RK     Efficiency of the clearance process, rank (1=highest performer)\n",
       "LP.LPI.CUST.XQ     Efficiency of the clearance process, score (1=low to 5=high)\n",
       "LP.LPI.INFR.RK     Quality- of trade and transport-related infrastructure, rank (1=highest performer)\n",
       "LP.LPI.INFR.XQ     Quality of trade- and transport-related infrastructure, score (1=low to 5=high)\n",
       "LP.LPI.ITRN.RK     Ease of arranging competitively priced international shipments, rank (1=highest performer)\n",
       "LP.LPI.ITRN.XQ     Ease of arranging competitively priced international shipments, score (1=low to 5=high)\n",
       "LP.LPI.LOGS.RK     Competence and quality of logistics services, rank (1=highest performer)\n",
       "LP.LPI.LOGS.XQ     Competence and quality of logistics services, score (1=low to 5=high)\n",
       "LP.LPI.OVRL.RK     Logistics performance index: Overall rank (1=highest performance)\n",
       "LP.LPI.OVRL.RK.LB  Logistics performance index: Overall rank (1=highest performance), lower bound\n",
       "LP.LPI.OVRL.RK.UB  Logistics performance index: Overall rank (1=highest performance), upper bound\n",
       "LP.LPI.OVRL.RK.ZS  Logistics performance index: Percent of highest performer)\n",
       "LP.LPI.OVRL.XQ     Logistics performance index: Overall score (1=low to 5=high)\n",
       "LP.LPI.OVRL.XQ.LB  Logistics performance index: Overall score (1=low to 5=high), lower bound\n",
       "LP.LPI.OVRL.XQ.UB  Logistics performance index: Overall score (1=low to 5=high), upper bound\n",
       "LP.LPI.TIME.RK     Frequency with which shipments reach consignee within scheduled or expected time, rank (1=highest performer)\n",
       "LP.LPI.TIME.XQ     Frequency with which shipments reach consignee within scheduled or expected time, score (1=low to 5=high)\n",
       "LP.LPI.TRAC.RK     Ability to track and trace consignments, rank (1=highest performer)\n",
       "LP.LPI.TRAC.XQ     Ability to track and trace consignments, score (1=low to 5=high)\n",
       "                   19 elements"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here, we fetch indicators from the Logistics Performance Indicators database only, not the default WDI\n",
    "wb.series.info(db=66)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"wbgapi\"><table>\n",
       "<thead>\n",
       "<tr><th>id  </th><th>value                   </th><th>region  </th><th>incomeLevel  </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>AFG </td><td>Afghanistan             </td><td>SAS     </td><td>LIC          </td></tr>\n",
       "<tr><td>AGO </td><td>Angola                  </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>ALB </td><td>Albania                 </td><td>ECS     </td><td>UMC          </td></tr>\n",
       "<tr><td>ARE </td><td>United Arab Emirates    </td><td>MEA     </td><td>HIC          </td></tr>\n",
       "<tr><td>ARG </td><td>Argentina               </td><td>LCN     </td><td>UMC          </td></tr>\n",
       "<tr><td>ARM </td><td>Armenia                 </td><td>ECS     </td><td>UMC          </td></tr>\n",
       "<tr><td>AUS </td><td>Australia               </td><td>EAS     </td><td>HIC          </td></tr>\n",
       "<tr><td>AUT </td><td>Austria                 </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>AZE </td><td>Azerbaijan              </td><td>ECS     </td><td>UMC          </td></tr>\n",
       "<tr><td>BDI </td><td>Burundi                 </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>BEL </td><td>Belgium                 </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>BEN </td><td>Benin                   </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>BFA </td><td>Burkina Faso            </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>BGD </td><td>Bangladesh              </td><td>SAS     </td><td>LMC          </td></tr>\n",
       "<tr><td>BGR </td><td>Bulgaria                </td><td>ECS     </td><td>UMC          </td></tr>\n",
       "<tr><td>BHR </td><td>Bahrain                 </td><td>MEA     </td><td>HIC          </td></tr>\n",
       "<tr><td>BHS </td><td>Bahamas, The            </td><td>LCN     </td><td>HIC          </td></tr>\n",
       "<tr><td>BIH </td><td>Bosnia and Herzegovina  </td><td>ECS     </td><td>UMC          </td></tr>\n",
       "<tr><td>BLR </td><td>Belarus                 </td><td>ECS     </td><td>UMC          </td></tr>\n",
       "<tr><td>BOL </td><td>Bolivia                 </td><td>LCN     </td><td>LMC          </td></tr>\n",
       "<tr><td>BRA </td><td>Brazil                  </td><td>LCN     </td><td>UMC          </td></tr>\n",
       "<tr><td>BRN </td><td>Brunei Darussalam       </td><td>EAS     </td><td>HIC          </td></tr>\n",
       "<tr><td>BTN </td><td>Bhutan                  </td><td>SAS     </td><td>LMC          </td></tr>\n",
       "<tr><td>BWA </td><td>Botswana                </td><td>SSF     </td><td>UMC          </td></tr>\n",
       "<tr><td>CAF </td><td>Central African Republic</td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>CAN </td><td>Canada                  </td><td>NAC     </td><td>HIC          </td></tr>\n",
       "<tr><td>CHE </td><td>Switzerland             </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>CHL </td><td>Chile                   </td><td>LCN     </td><td>HIC          </td></tr>\n",
       "<tr><td>CHN </td><td>China                   </td><td>EAS     </td><td>UMC          </td></tr>\n",
       "<tr><td>CIV </td><td>Cote d'Ivoire           </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>CMR </td><td>Cameroon                </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>COD </td><td>Congo, Dem. Rep.        </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>COG </td><td>Congo, Rep.             </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>COL </td><td>Colombia                </td><td>LCN     </td><td>UMC          </td></tr>\n",
       "<tr><td>COM </td><td>Comoros                 </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>CRI </td><td>Costa Rica              </td><td>LCN     </td><td>UMC          </td></tr>\n",
       "<tr><td>CUB </td><td>Cuba                    </td><td>LCN     </td><td>UMC          </td></tr>\n",
       "<tr><td>CYP </td><td>Cyprus                  </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>CZE </td><td>Czech Republic          </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>DEU </td><td>Germany                 </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>DJI </td><td>Djibouti                </td><td>MEA     </td><td>LMC          </td></tr>\n",
       "<tr><td>DNK </td><td>Denmark                 </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>DOM </td><td>Dominican Republic      </td><td>LCN     </td><td>UMC          </td></tr>\n",
       "<tr><td>DZA </td><td>Algeria                 </td><td>MEA     </td><td>LMC          </td></tr>\n",
       "<tr><td>ECU </td><td>Ecuador                 </td><td>LCN     </td><td>UMC          </td></tr>\n",
       "<tr><td>EGY </td><td>Egypt, Arab Rep.        </td><td>MEA     </td><td>LMC          </td></tr>\n",
       "<tr><td>ERI </td><td>Eritrea                 </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>ESP </td><td>Spain                   </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>EST </td><td>Estonia                 </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>ETH </td><td>Ethiopia                </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>FIN </td><td>Finland                 </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>FJI </td><td>Fiji                    </td><td>EAS     </td><td>UMC          </td></tr>\n",
       "<tr><td>FRA </td><td>France                  </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>GAB </td><td>Gabon                   </td><td>SSF     </td><td>UMC          </td></tr>\n",
       "<tr><td>GBR </td><td>United Kingdom          </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>GEO </td><td>Georgia                 </td><td>ECS     </td><td>UMC          </td></tr>\n",
       "<tr><td>GHA </td><td>Ghana                   </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>GIN </td><td>Guinea                  </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>GMB </td><td>Gambia, The             </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>GNB </td><td>Guinea-Bissau           </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>GNQ </td><td>Equatorial Guinea       </td><td>SSF     </td><td>UMC          </td></tr>\n",
       "<tr><td>GRC </td><td>Greece                  </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>GTM </td><td>Guatemala               </td><td>LCN     </td><td>UMC          </td></tr>\n",
       "<tr><td>GUY </td><td>Guyana                  </td><td>LCN     </td><td>UMC          </td></tr>\n",
       "<tr><td>HKG </td><td>Hong Kong SAR, China    </td><td>EAS     </td><td>HIC          </td></tr>\n",
       "<tr><td>HND </td><td>Honduras                </td><td>LCN     </td><td>LMC          </td></tr>\n",
       "<tr><td>HRV </td><td>Croatia                 </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>HTI </td><td>Haiti                   </td><td>LCN     </td><td>LIC          </td></tr>\n",
       "<tr><td>HUN </td><td>Hungary                 </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>IDN </td><td>Indonesia               </td><td>EAS     </td><td>UMC          </td></tr>\n",
       "<tr><td>IND </td><td>India                   </td><td>SAS     </td><td>LMC          </td></tr>\n",
       "<tr><td>IRL </td><td>Ireland                 </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>IRN </td><td>Iran, Islamic Rep.      </td><td>MEA     </td><td>UMC          </td></tr>\n",
       "<tr><td>IRQ </td><td>Iraq                    </td><td>MEA     </td><td>UMC          </td></tr>\n",
       "<tr><td>ISL </td><td>Iceland                 </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>ISR </td><td>Israel                  </td><td>MEA     </td><td>HIC          </td></tr>\n",
       "<tr><td>ITA </td><td>Italy                   </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>JAM </td><td>Jamaica                 </td><td>LCN     </td><td>UMC          </td></tr>\n",
       "<tr><td>JOR </td><td>Jordan                  </td><td>MEA     </td><td>UMC          </td></tr>\n",
       "<tr><td>JPN </td><td>Japan                   </td><td>EAS     </td><td>HIC          </td></tr>\n",
       "<tr><td>KAZ </td><td>Kazakhstan              </td><td>ECS     </td><td>UMC          </td></tr>\n",
       "<tr><td>KEN </td><td>Kenya                   </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>KGZ </td><td>Kyrgyz Republic         </td><td>ECS     </td><td>LMC          </td></tr>\n",
       "<tr><td>KHM </td><td>Cambodia                </td><td>EAS     </td><td>LMC          </td></tr>\n",
       "<tr><td>KOR </td><td>Korea, Rep.             </td><td>EAS     </td><td>HIC          </td></tr>\n",
       "<tr><td>KWT </td><td>Kuwait                  </td><td>MEA     </td><td>HIC          </td></tr>\n",
       "<tr><td>LAO </td><td>Lao PDR                 </td><td>EAS     </td><td>LMC          </td></tr>\n",
       "<tr><td>LBN </td><td>Lebanon                 </td><td>MEA     </td><td>UMC          </td></tr>\n",
       "<tr><td>LBR </td><td>Liberia                 </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>LBY </td><td>Libya                   </td><td>MEA     </td><td>UMC          </td></tr>\n",
       "<tr><td>LKA </td><td>Sri Lanka               </td><td>SAS     </td><td>LMC          </td></tr>\n",
       "<tr><td>LSO </td><td>Lesotho                 </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>LTU </td><td>Lithuania               </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>LUX </td><td>Luxembourg              </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>LVA </td><td>Latvia                  </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>MAR </td><td>Morocco                 </td><td>MEA     </td><td>LMC          </td></tr>\n",
       "<tr><td>MDA </td><td>Moldova                 </td><td>ECS     </td><td>LMC          </td></tr>\n",
       "<tr><td>MDG </td><td>Madagascar              </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>MDV </td><td>Maldives                </td><td>SAS     </td><td>UMC          </td></tr>\n",
       "<tr><td>MEX </td><td>Mexico                  </td><td>LCN     </td><td>UMC          </td></tr>\n",
       "<tr><td>MKD </td><td>Macedonia, FYR          </td><td>ECS     </td><td>UMC          </td></tr>\n",
       "<tr><td>MLI </td><td>Mali                    </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>MLT </td><td>Malta                   </td><td>MEA     </td><td>HIC          </td></tr>\n",
       "<tr><td>MMR </td><td>Myanmar                 </td><td>EAS     </td><td>LMC          </td></tr>\n",
       "<tr><td>MNE </td><td>Montenegro              </td><td>ECS     </td><td>UMC          </td></tr>\n",
       "<tr><td>MNG </td><td>Mongolia                </td><td>EAS     </td><td>LMC          </td></tr>\n",
       "<tr><td>MOZ </td><td>Mozambique              </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>MRT </td><td>Mauritania              </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>MUS </td><td>Mauritius               </td><td>SSF     </td><td>HIC          </td></tr>\n",
       "<tr><td>MWI </td><td>Malawi                  </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>MYS </td><td>Malaysia                </td><td>EAS     </td><td>UMC          </td></tr>\n",
       "<tr><td>NAM </td><td>Namibia                 </td><td>SSF     </td><td>UMC          </td></tr>\n",
       "<tr><td>NER </td><td>Niger                   </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>NGA </td><td>Nigeria                 </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>NIC </td><td>Nicaragua               </td><td>LCN     </td><td>LMC          </td></tr>\n",
       "<tr><td>NLD </td><td>Netherlands             </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>NOR </td><td>Norway                  </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>NPL </td><td>Nepal                   </td><td>SAS     </td><td>LMC          </td></tr>\n",
       "<tr><td>NZL </td><td>New Zealand             </td><td>EAS     </td><td>HIC          </td></tr>\n",
       "<tr><td>OMN </td><td>Oman                    </td><td>MEA     </td><td>HIC          </td></tr>\n",
       "<tr><td>PAK </td><td>Pakistan                </td><td>SAS     </td><td>LMC          </td></tr>\n",
       "<tr><td>PAN </td><td>Panama                  </td><td>LCN     </td><td>HIC          </td></tr>\n",
       "<tr><td>PER </td><td>Peru                    </td><td>LCN     </td><td>UMC          </td></tr>\n",
       "<tr><td>PHL </td><td>Philippines             </td><td>EAS     </td><td>LMC          </td></tr>\n",
       "<tr><td>PNG </td><td>Papua New Guinea        </td><td>EAS     </td><td>LMC          </td></tr>\n",
       "<tr><td>POL </td><td>Poland                  </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>PRT </td><td>Portugal                </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>PRY </td><td>Paraguay                </td><td>LCN     </td><td>UMC          </td></tr>\n",
       "<tr><td>QAT </td><td>Qatar                   </td><td>MEA     </td><td>HIC          </td></tr>\n",
       "<tr><td>ROU </td><td>Romania                 </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>RUS </td><td>Russian Federation      </td><td>ECS     </td><td>UMC          </td></tr>\n",
       "<tr><td>RWA </td><td>Rwanda                  </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>SAU </td><td>Saudi Arabia            </td><td>MEA     </td><td>HIC          </td></tr>\n",
       "<tr><td>SDN </td><td>Sudan                   </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>SEN </td><td>Senegal                 </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>SGP </td><td>Singapore               </td><td>EAS     </td><td>HIC          </td></tr>\n",
       "<tr><td>SLB </td><td>Solomon Islands         </td><td>EAS     </td><td>LMC          </td></tr>\n",
       "<tr><td>SLE </td><td>Sierra Leone            </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>SLV </td><td>El Salvador             </td><td>LCN     </td><td>LMC          </td></tr>\n",
       "<tr><td>SOM </td><td>Somalia                 </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>SRB </td><td>Serbia                  </td><td>ECS     </td><td>UMC          </td></tr>\n",
       "<tr><td>STP </td><td>São Tomé and Principe   </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>SVK </td><td>Slovak Republic         </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>SVN </td><td>Slovenia                </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>SWE </td><td>Sweden                  </td><td>ECS     </td><td>HIC          </td></tr>\n",
       "<tr><td>SYR </td><td>Syrian Arab Republic    </td><td>MEA     </td><td>LIC          </td></tr>\n",
       "<tr><td>TCD </td><td>Chad                    </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>TGO </td><td>Togo                    </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>THA </td><td>Thailand                </td><td>EAS     </td><td>UMC          </td></tr>\n",
       "<tr><td>TJK </td><td>Tajikistan              </td><td>ECS     </td><td>LIC          </td></tr>\n",
       "<tr><td>TKM </td><td>Turkmenistan            </td><td>ECS     </td><td>UMC          </td></tr>\n",
       "<tr><td>TLS </td><td>Timor-Leste             </td><td>EAS     </td><td>LMC          </td></tr>\n",
       "<tr><td>TTO </td><td>Trinidad and Tobago     </td><td>LCN     </td><td>HIC          </td></tr>\n",
       "<tr><td>TUN </td><td>Tunisia                 </td><td>MEA     </td><td>LMC          </td></tr>\n",
       "<tr><td>TUR </td><td>Turkey                  </td><td>ECS     </td><td>UMC          </td></tr>\n",
       "<tr><td>TWN </td><td>Taiwan, China           </td><td>EAS     </td><td>HIC          </td></tr>\n",
       "<tr><td>TZA </td><td>Tanzania                </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>UGA </td><td>Uganda                  </td><td>SSF     </td><td>LIC          </td></tr>\n",
       "<tr><td>UKR </td><td>Ukraine                 </td><td>ECS     </td><td>LMC          </td></tr>\n",
       "<tr><td>URY </td><td>Uruguay                 </td><td>LCN     </td><td>HIC          </td></tr>\n",
       "<tr><td>USA </td><td>United States           </td><td>NAC     </td><td>HIC          </td></tr>\n",
       "<tr><td>UZB </td><td>Uzbekistan              </td><td>ECS     </td><td>LMC          </td></tr>\n",
       "<tr><td>VEN </td><td>Venezuela, RB           </td><td>LCN     </td><td>UMC          </td></tr>\n",
       "<tr><td>VNM </td><td>Vietnam                 </td><td>EAS     </td><td>LMC          </td></tr>\n",
       "<tr><td>YEM </td><td>Yemen, Rep.             </td><td>MEA     </td><td>LIC          </td></tr>\n",
       "<tr><td>ZAF </td><td>South Africa            </td><td>SSF     </td><td>UMC          </td></tr>\n",
       "<tr><td>ZMB </td><td>Zambia                  </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>ZWE </td><td>Zimbabwe                </td><td>SSF     </td><td>LMC          </td></tr>\n",
       "<tr><td>    </td><td>168 elements            </td><td>        </td><td>             </td></tr>\n",
       "</tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "id    value                     region    incomeLevel\n",
       "----  ------------------------  --------  -------------\n",
       "AFG   Afghanistan               SAS       LIC\n",
       "AGO   Angola                    SSF       LMC\n",
       "ALB   Albania                   ECS       UMC\n",
       "ARE   United Arab Emirates      MEA       HIC\n",
       "ARG   Argentina                 LCN       UMC\n",
       "ARM   Armenia                   ECS       UMC\n",
       "AUS   Australia                 EAS       HIC\n",
       "AUT   Austria                   ECS       HIC\n",
       "AZE   Azerbaijan                ECS       UMC\n",
       "BDI   Burundi                   SSF       LIC\n",
       "BEL   Belgium                   ECS       HIC\n",
       "BEN   Benin                     SSF       LMC\n",
       "BFA   Burkina Faso              SSF       LIC\n",
       "BGD   Bangladesh                SAS       LMC\n",
       "BGR   Bulgaria                  ECS       UMC\n",
       "BHR   Bahrain                   MEA       HIC\n",
       "BHS   Bahamas, The              LCN       HIC\n",
       "BIH   Bosnia and Herzegovina    ECS       UMC\n",
       "BLR   Belarus                   ECS       UMC\n",
       "BOL   Bolivia                   LCN       LMC\n",
       "BRA   Brazil                    LCN       UMC\n",
       "BRN   Brunei Darussalam         EAS       HIC\n",
       "BTN   Bhutan                    SAS       LMC\n",
       "BWA   Botswana                  SSF       UMC\n",
       "CAF   Central African Republic  SSF       LIC\n",
       "CAN   Canada                    NAC       HIC\n",
       "CHE   Switzerland               ECS       HIC\n",
       "CHL   Chile                     LCN       HIC\n",
       "CHN   China                     EAS       UMC\n",
       "CIV   Cote d'Ivoire             SSF       LMC\n",
       "CMR   Cameroon                  SSF       LMC\n",
       "COD   Congo, Dem. Rep.          SSF       LIC\n",
       "COG   Congo, Rep.               SSF       LMC\n",
       "COL   Colombia                  LCN       UMC\n",
       "COM   Comoros                   SSF       LMC\n",
       "CRI   Costa Rica                LCN       UMC\n",
       "CUB   Cuba                      LCN       UMC\n",
       "CYP   Cyprus                    ECS       HIC\n",
       "CZE   Czech Republic            ECS       HIC\n",
       "DEU   Germany                   ECS       HIC\n",
       "DJI   Djibouti                  MEA       LMC\n",
       "DNK   Denmark                   ECS       HIC\n",
       "DOM   Dominican Republic        LCN       UMC\n",
       "DZA   Algeria                   MEA       LMC\n",
       "ECU   Ecuador                   LCN       UMC\n",
       "EGY   Egypt, Arab Rep.          MEA       LMC\n",
       "ERI   Eritrea                   SSF       LIC\n",
       "ESP   Spain                     ECS       HIC\n",
       "EST   Estonia                   ECS       HIC\n",
       "ETH   Ethiopia                  SSF       LIC\n",
       "FIN   Finland                   ECS       HIC\n",
       "FJI   Fiji                      EAS       UMC\n",
       "FRA   France                    ECS       HIC\n",
       "GAB   Gabon                     SSF       UMC\n",
       "GBR   United Kingdom            ECS       HIC\n",
       "GEO   Georgia                   ECS       UMC\n",
       "GHA   Ghana                     SSF       LMC\n",
       "GIN   Guinea                    SSF       LIC\n",
       "GMB   Gambia, The               SSF       LIC\n",
       "GNB   Guinea-Bissau             SSF       LIC\n",
       "GNQ   Equatorial Guinea         SSF       UMC\n",
       "GRC   Greece                    ECS       HIC\n",
       "GTM   Guatemala                 LCN       UMC\n",
       "GUY   Guyana                    LCN       UMC\n",
       "HKG   Hong Kong SAR, China      EAS       HIC\n",
       "HND   Honduras                  LCN       LMC\n",
       "HRV   Croatia                   ECS       HIC\n",
       "HTI   Haiti                     LCN       LIC\n",
       "HUN   Hungary                   ECS       HIC\n",
       "IDN   Indonesia                 EAS       UMC\n",
       "IND   India                     SAS       LMC\n",
       "IRL   Ireland                   ECS       HIC\n",
       "IRN   Iran, Islamic Rep.        MEA       UMC\n",
       "IRQ   Iraq                      MEA       UMC\n",
       "ISL   Iceland                   ECS       HIC\n",
       "ISR   Israel                    MEA       HIC\n",
       "ITA   Italy                     ECS       HIC\n",
       "JAM   Jamaica                   LCN       UMC\n",
       "JOR   Jordan                    MEA       UMC\n",
       "JPN   Japan                     EAS       HIC\n",
       "KAZ   Kazakhstan                ECS       UMC\n",
       "KEN   Kenya                     SSF       LMC\n",
       "KGZ   Kyrgyz Republic           ECS       LMC\n",
       "KHM   Cambodia                  EAS       LMC\n",
       "KOR   Korea, Rep.               EAS       HIC\n",
       "KWT   Kuwait                    MEA       HIC\n",
       "LAO   Lao PDR                   EAS       LMC\n",
       "LBN   Lebanon                   MEA       UMC\n",
       "LBR   Liberia                   SSF       LIC\n",
       "LBY   Libya                     MEA       UMC\n",
       "LKA   Sri Lanka                 SAS       LMC\n",
       "LSO   Lesotho                   SSF       LMC\n",
       "LTU   Lithuania                 ECS       HIC\n",
       "LUX   Luxembourg                ECS       HIC\n",
       "LVA   Latvia                    ECS       HIC\n",
       "MAR   Morocco                   MEA       LMC\n",
       "MDA   Moldova                   ECS       LMC\n",
       "MDG   Madagascar                SSF       LIC\n",
       "MDV   Maldives                  SAS       UMC\n",
       "MEX   Mexico                    LCN       UMC\n",
       "MKD   Macedonia, FYR            ECS       UMC\n",
       "MLI   Mali                      SSF       LIC\n",
       "MLT   Malta                     MEA       HIC\n",
       "MMR   Myanmar                   EAS       LMC\n",
       "MNE   Montenegro                ECS       UMC\n",
       "MNG   Mongolia                  EAS       LMC\n",
       "MOZ   Mozambique                SSF       LIC\n",
       "MRT   Mauritania                SSF       LMC\n",
       "MUS   Mauritius                 SSF       HIC\n",
       "MWI   Malawi                    SSF       LIC\n",
       "MYS   Malaysia                  EAS       UMC\n",
       "NAM   Namibia                   SSF       UMC\n",
       "NER   Niger                     SSF       LIC\n",
       "NGA   Nigeria                   SSF       LMC\n",
       "NIC   Nicaragua                 LCN       LMC\n",
       "NLD   Netherlands               ECS       HIC\n",
       "NOR   Norway                    ECS       HIC\n",
       "NPL   Nepal                     SAS       LMC\n",
       "NZL   New Zealand               EAS       HIC\n",
       "OMN   Oman                      MEA       HIC\n",
       "PAK   Pakistan                  SAS       LMC\n",
       "PAN   Panama                    LCN       HIC\n",
       "PER   Peru                      LCN       UMC\n",
       "PHL   Philippines               EAS       LMC\n",
       "PNG   Papua New Guinea          EAS       LMC\n",
       "POL   Poland                    ECS       HIC\n",
       "PRT   Portugal                  ECS       HIC\n",
       "PRY   Paraguay                  LCN       UMC\n",
       "QAT   Qatar                     MEA       HIC\n",
       "ROU   Romania                   ECS       HIC\n",
       "RUS   Russian Federation        ECS       UMC\n",
       "RWA   Rwanda                    SSF       LIC\n",
       "SAU   Saudi Arabia              MEA       HIC\n",
       "SDN   Sudan                     SSF       LIC\n",
       "SEN   Senegal                   SSF       LMC\n",
       "SGP   Singapore                 EAS       HIC\n",
       "SLB   Solomon Islands           EAS       LMC\n",
       "SLE   Sierra Leone              SSF       LIC\n",
       "SLV   El Salvador               LCN       LMC\n",
       "SOM   Somalia                   SSF       LIC\n",
       "SRB   Serbia                    ECS       UMC\n",
       "STP   São Tomé and Principe     SSF       LMC\n",
       "SVK   Slovak Republic           ECS       HIC\n",
       "SVN   Slovenia                  ECS       HIC\n",
       "SWE   Sweden                    ECS       HIC\n",
       "SYR   Syrian Arab Republic      MEA       LIC\n",
       "TCD   Chad                      SSF       LIC\n",
       "TGO   Togo                      SSF       LIC\n",
       "THA   Thailand                  EAS       UMC\n",
       "TJK   Tajikistan                ECS       LIC\n",
       "TKM   Turkmenistan              ECS       UMC\n",
       "TLS   Timor-Leste               EAS       LMC\n",
       "TTO   Trinidad and Tobago       LCN       HIC\n",
       "TUN   Tunisia                   MEA       LMC\n",
       "TUR   Turkey                    ECS       UMC\n",
       "TWN   Taiwan, China             EAS       HIC\n",
       "TZA   Tanzania                  SSF       LMC\n",
       "UGA   Uganda                    SSF       LIC\n",
       "UKR   Ukraine                   ECS       LMC\n",
       "URY   Uruguay                   LCN       HIC\n",
       "USA   United States             NAC       HIC\n",
       "UZB   Uzbekistan                ECS       LMC\n",
       "VEN   Venezuela, RB             LCN       UMC\n",
       "VNM   Vietnam                   EAS       LMC\n",
       "YEM   Yemen, Rep.               MEA       LIC\n",
       "ZAF   South Africa              SSF       UMC\n",
       "ZMB   Zambia                    SSF       LMC\n",
       "ZWE   Zimbabwe                  SSF       LMC\n",
       "      168 elements"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# same for economies; this is a shorter country list than in the WDI\n",
    "wb.economy.info(db=66)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"wbgapi\"><table>\n",
       "<thead>\n",
       "<tr><th>id    </th><th>value     </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>YR2007</td><td>2007      </td></tr>\n",
       "<tr><td>YR2010</td><td>2010      </td></tr>\n",
       "<tr><td>YR2012</td><td>2012      </td></tr>\n",
       "<tr><td>YR2014</td><td>2014      </td></tr>\n",
       "<tr><td>YR2016</td><td>2016      </td></tr>\n",
       "<tr><td>YR2018</td><td>2018      </td></tr>\n",
       "<tr><td>      </td><td>6 elements</td></tr>\n",
       "</tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "id      value\n",
       "------  ----------\n",
       "YR2007  2007\n",
       "YR2010  2010\n",
       "YR2012  2012\n",
       "YR2014  2014\n",
       "YR2016  2016\n",
       "YR2018  2018\n",
       "        6 elements"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and a much shorter time dimension\n",
    "wb.time.info(db=66)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the search feature to search metadata for keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"wbgapi\"><h4>Series</h4><table>\n",
       "<thead>\n",
       "<tr><th>ID                  </th><th>Name                                                                                                                             </th><th>Field                           </th><th>Value                                                                                                                                                                            </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>EG.ELC.COAL.KH      </td><td>Electricity production from coal sources (kWh)                                                                                   </td><td>Developmentrelevance            </td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EG.ELC.COAL.ZS      </td><td>Electricity production from coal sources (% of total)                                                                            </td><td>Developmentrelevance            </td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EG.ELC.HYRO.KH      </td><td>Electricity production from hydroelectric sources (kWh)                                                                          </td><td>Developmentrelevance            </td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EG.ELC.HYRO.ZS      </td><td>Electricity production from hydroelectric sources (% of total)                                                                   </td><td>Developmentrelevance            </td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EG.ELC.NGAS.KH      </td><td>Electricity production from natural gas sources (kWh)                                                                            </td><td>Developmentrelevance            </td><td>...2) lower carbon relative to other fossil fuels;...                                                                                                                            </td></tr>\n",
       "<tr><td>EG.ELC.NGAS.ZS      </td><td>Electricity production from natural gas sources (% of total)                                                                     </td><td>Developmentrelevance            </td><td>...2) lower carbon relative to other fossil fuels;...                                                                                                                            </td></tr>\n",
       "<tr><td>EG.ELC.NUCL.KH      </td><td>Electricity production from nuclear sources (kWh)                                                                                </td><td>Developmentrelevance            </td><td>...grew rapidly in the 1970s and 1980s as countries sought to reduce dependence on fossil fuels, especially after the oil crises of the 1970s. There was a renewed interest in...</td></tr>\n",
       "<tr><td>EG.ELC.NUCL.ZS      </td><td>Electricity production from nuclear sources (% of total)                                                                         </td><td>Developmentrelevance            </td><td>...grew rapidly in the 1970s and 1980s as countries sought to reduce dependence on fossil fuels, especially after the oil crises of the 1970s. There was a renewed interest in...</td></tr>\n",
       "<tr><td>EG.ELC.PETR.KH      </td><td>Electricity production from oil sources (kWh)                                                                                    </td><td>Developmentrelevance            </td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EG.ELC.PETR.ZS      </td><td>Electricity production from oil sources (% of total)                                                                             </td><td>Developmentrelevance            </td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EG.ELC.PROD.KH      </td><td>Electricity production (kWh)                                                                                                     </td><td>Developmentrelevance            </td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EG.GDP.PUSE.KO.PP.KD</td><td>GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)                                                        </td><td>Developmentrelevance            </td><td>...Fossil fuels are non-renewable resources because they take millions of years to form, and...                                                                                  </td></tr>\n",
       "<tr><td>EG.USE.COMM.CL.ZS   </td><td>Alternative and nuclear energy (% of total energy use)                                                                           </td><td>Developmentrelevance            </td><td>...energy is produced without the undesirable consequences of the burning of fossil fuels, such as high carbon dioxide emissions, which is considered to be the major...         </td></tr>\n",
       "<tr><td>EG.USE.COMM.FO.ZS   </td><td>Fossil fuel energy consumption (% of total)                                                                                      </td><td>Developmentrelevance            </td><td>...Fossil fuels are non-renewable resources because they take millions of years to form, and...                                                                                  </td></tr>\n",
       "<tr><td>EG.USE.COMM.KT.OE   </td><td>Energy use (kt of oil equivalent)                                                                                                </td><td>Developmentrelevance            </td><td>...Fossil fuels are non-renewable resources because they take millions of years to form, and...                                                                                  </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.EG.ZS   </td><td>CO2 intensity (kg per kg of oil equivalent energy use)                                                                           </td><td>Limitationsandexceptions        </td><td>...Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as...                      </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.EG.ZS   </td><td>CO2 intensity (kg per kg of oil equivalent energy use)                                                                           </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.GF.KT   </td><td>CO2 emissions from gaseous fuel consumption (kt)                                                                                 </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.GF.ZS   </td><td>CO2 emissions from gaseous fuel consumption (% of total)                                                                         </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.KD.GD   </td><td>CO2 emissions (kg per 2010 US$ of GDP)                                                                                           </td><td>Longdefinition                  </td><td>...Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during...                    </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.KT      </td><td>CO2 emissions (kt)                                                                                                               </td><td>Longdefinition                  </td><td>...Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during...                    </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.KT      </td><td>CO2 emissions (kt)                                                                                                               </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.LF.KT   </td><td>CO2 emissions from liquid fuel consumption (kt)                                                                                  </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.LF.ZS   </td><td>CO2 emissions from liquid fuel consumption (% of total)                                                                          </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.PC      </td><td>CO2 emissions (metric tons per capita)                                                                                           </td><td>Longdefinition                  </td><td>...Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during...                    </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.PC      </td><td>CO2 emissions (metric tons per capita)                                                                                           </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.PP.GD   </td><td>CO2 emissions (kg per PPP $ of GDP)                                                                                              </td><td>Longdefinition                  </td><td>...Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during...                    </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.PP.GD.KD</td><td>CO2 emissions (kg per 2017 PPP $ of GDP)                                                                                         </td><td>Limitationsandexceptions        </td><td>...Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as...                      </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.PP.GD.KD</td><td>CO2 emissions (kg per 2017 PPP $ of GDP)                                                                                         </td><td>Longdefinition                  </td><td>...Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during...                    </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.PP.GD.KD</td><td>CO2 emissions (kg per 2017 PPP $ of GDP)                                                                                         </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.SF.KT   </td><td>CO2 emissions from solid fuel consumption (kt)                                                                                   </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.ZG      </td><td>CO2 emissions (% change)                                                                                                         </td><td>Longdefinition                  </td><td>...Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during...                    </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.ZG      </td><td>CO2 emissions (% change)                                                                                                         </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.ATM.METH.EG.KT.CE</td><td>Methane emissions in energy sector (thousand metric tons of CO2 equivalent)                                                      </td><td>Longdefinition                  </td><td>...are emissions from the production, handling, transmission, and combustion of fossil fuels and biofuels....                                                                    </td></tr>\n",
       "<tr><td>EN.ATM.METH.EG.ZS   </td><td>Energy related methane emissions (% of total)                                                                                    </td><td>Longdefinition                  </td><td>...are emissions from the production, handling, transmission, and combustion of fossil fuels and biofuels....                                                                    </td></tr>\n",
       "<tr><td>EN.ATM.NOXE.EG.KT.CE</td><td>Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)                                                </td><td>Longdefinition                  </td><td>...emissions from energy processes are emissions produced by the combustion of fossil fuels and biofuels....                                                                     </td></tr>\n",
       "<tr><td>EN.ATM.NOXE.EG.ZS   </td><td>Nitrous oxide emissions in energy sector (% of total)                                                                            </td><td>Longdefinition                  </td><td>...emissions from energy processes are emissions produced by the combustion of fossil fuels and biofuels....                                                                     </td></tr>\n",
       "<tr><td>EN.CO2.BLDG.MT      </td><td>CO2 emissions from residential buildings and commercial and public services (million metric tons)                                </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.CO2.BLDG.ZS      </td><td>CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)                         </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.CO2.ETOT.MT      </td><td>CO2 emissions from electricity and heat production, total (million metric tons)                                                  </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.CO2.ETOT.ZS      </td><td>CO2 emissions from electricity and heat production, total (% of total fuel combustion)                                           </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.CO2.MANF.MT      </td><td>CO2 emissions from manufacturing industries and construction (million metric tons)                                               </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.CO2.MANF.ZS      </td><td>CO2 emissions from manufacturing industries and construction (% of total fuel combustion)                                        </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.CO2.OTHX.MT      </td><td>CO2 emissions from other sectors, excluding residential buildings and commercial and public services (million metric tons)       </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.CO2.OTHX.ZS      </td><td>CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion)</td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.CO2.TRAN.MT      </td><td>CO2 emissions from transport (million metric tons)                                                                               </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>EN.CO2.TRAN.ZS      </td><td>CO2 emissions from transport (% of total fuel combustion)                                                                        </td><td>Statisticalconceptandmethodology</td><td>...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...   </td></tr>\n",
       "<tr><td>NY.GDP.COAL.RT.ZS   </td><td>Coal rents (% of GDP)                                                                                                            </td><td>Developmentrelevance            </td><td>...development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings...  </td></tr>\n",
       "<tr><td>NY.GDP.FRST.RT.ZS   </td><td>Forest rents (% of GDP)                                                                                                          </td><td>Developmentrelevance            </td><td>...development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings...  </td></tr>\n",
       "<tr><td>NY.GDP.MINR.RT.ZS   </td><td>Mineral rents (% of GDP)                                                                                                         </td><td>Developmentrelevance            </td><td>...development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings...  </td></tr>\n",
       "<tr><td>NY.GDP.NGAS.RT.ZS   </td><td>Natural gas rents (% of GDP)                                                                                                     </td><td>Developmentrelevance            </td><td>...development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings...  </td></tr>\n",
       "<tr><td>NY.GDP.PETR.RT.ZS   </td><td>Oil rents (% of GDP)                                                                                                             </td><td>Developmentrelevance            </td><td>...development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings...  </td></tr>\n",
       "<tr><td>NY.GDP.TOTL.RT.ZS   </td><td>Total natural resources rents (% of GDP)                                                                                         </td><td>Developmentrelevance            </td><td>...development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings...  </td></tr>\n",
       "</tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "========\n",
       "Series: EG.ELC.COAL.KH, Electricity production from coal sources (kWh)\n",
       "\n",
       "Developmentrelevance: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EG.ELC.COAL.ZS, Electricity production from coal sources (% of total)\n",
       "\n",
       "Developmentrelevance: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EG.ELC.HYRO.KH, Electricity production from hydroelectric sources (kWh)\n",
       "\n",
       "Developmentrelevance: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EG.ELC.HYRO.ZS, Electricity production from hydroelectric sources (% of total)\n",
       "\n",
       "Developmentrelevance: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EG.ELC.NGAS.KH, Electricity production from natural gas sources (kWh)\n",
       "\n",
       "Developmentrelevance: ...2) lower carbon relative to other fossil fuels;...\n",
       "========\n",
       "Series: EG.ELC.NGAS.ZS, Electricity production from natural gas sources (% of total)\n",
       "\n",
       "Developmentrelevance: ...2) lower carbon relative to other fossil fuels;...\n",
       "========\n",
       "Series: EG.ELC.NUCL.KH, Electricity production from nuclear sources (kWh)\n",
       "\n",
       "Developmentrelevance: ...grew rapidly in the 1970s and 1980s as countries sought to reduce dependence on fossil fuels, especially after the oil crises of the 1970s. There was a renewed interest in...\n",
       "========\n",
       "Series: EG.ELC.NUCL.ZS, Electricity production from nuclear sources (% of total)\n",
       "\n",
       "Developmentrelevance: ...grew rapidly in the 1970s and 1980s as countries sought to reduce dependence on fossil fuels, especially after the oil crises of the 1970s. There was a renewed interest in...\n",
       "========\n",
       "Series: EG.ELC.PETR.KH, Electricity production from oil sources (kWh)\n",
       "\n",
       "Developmentrelevance: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EG.ELC.PETR.ZS, Electricity production from oil sources (% of total)\n",
       "\n",
       "Developmentrelevance: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EG.ELC.PROD.KH, Electricity production (kWh)\n",
       "\n",
       "Developmentrelevance: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EG.GDP.PUSE.KO.PP.KD, GDP per unit of energy use (constant 2017 PPP $ per kg of oil equivalent)\n",
       "\n",
       "Developmentrelevance: ...Fossil fuels are non-renewable resources because they take millions of years to form, and...\n",
       "========\n",
       "Series: EG.USE.COMM.CL.ZS, Alternative and nuclear energy (% of total energy use)\n",
       "\n",
       "Developmentrelevance: ...energy is produced without the undesirable consequences of the burning of fossil fuels, such as high carbon dioxide emissions, which is considered to be the major...\n",
       "========\n",
       "Series: EG.USE.COMM.FO.ZS, Fossil fuel energy consumption (% of total)\n",
       "\n",
       "Developmentrelevance: ...Fossil fuels are non-renewable resources because they take millions of years to form, and...\n",
       "========\n",
       "Series: EG.USE.COMM.KT.OE, Energy use (kt of oil equivalent)\n",
       "\n",
       "Developmentrelevance: ...Fossil fuels are non-renewable resources because they take millions of years to form, and...\n",
       "========\n",
       "Series: EN.ATM.CO2E.EG.ZS, CO2 intensity (kg per kg of oil equivalent energy use)\n",
       "\n",
       "Limitationsandexceptions: ...Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as...\n",
       "--------\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.ATM.CO2E.GF.KT, CO2 emissions from gaseous fuel consumption (kt)\n",
       "\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.ATM.CO2E.GF.ZS, CO2 emissions from gaseous fuel consumption (% of total)\n",
       "\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.ATM.CO2E.KD.GD, CO2 emissions (kg per 2010 US$ of GDP)\n",
       "\n",
       "Longdefinition: ...Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during...\n",
       "========\n",
       "Series: EN.ATM.CO2E.KT, CO2 emissions (kt)\n",
       "\n",
       "Longdefinition: ...Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during...\n",
       "--------\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.ATM.CO2E.LF.KT, CO2 emissions from liquid fuel consumption (kt)\n",
       "\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.ATM.CO2E.LF.ZS, CO2 emissions from liquid fuel consumption (% of total)\n",
       "\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.ATM.CO2E.PC, CO2 emissions (metric tons per capita)\n",
       "\n",
       "Longdefinition: ...Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during...\n",
       "--------\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.ATM.CO2E.PP.GD, CO2 emissions (kg per PPP $ of GDP)\n",
       "\n",
       "Longdefinition: ...Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during...\n",
       "========\n",
       "Series: EN.ATM.CO2E.PP.GD.KD, CO2 emissions (kg per 2017 PPP $ of GDP)\n",
       "\n",
       "Limitationsandexceptions: ...Data for carbon dioxide emissions include gases from the burning of fossil fuels and cement manufacture, but excludes emissions from land use such as...\n",
       "--------\n",
       "Longdefinition: ...Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during...\n",
       "--------\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.ATM.CO2E.SF.KT, CO2 emissions from solid fuel consumption (kt)\n",
       "\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.ATM.CO2E.ZG, CO2 emissions (% change)\n",
       "\n",
       "Longdefinition: ...Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during...\n",
       "--------\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.ATM.METH.EG.KT.CE, Methane emissions in energy sector (thousand metric tons of CO2 equivalent)\n",
       "\n",
       "Longdefinition: ...are emissions from the production, handling, transmission, and combustion of fossil fuels and biofuels....\n",
       "========\n",
       "Series: EN.ATM.METH.EG.ZS, Energy related methane emissions (% of total)\n",
       "\n",
       "Longdefinition: ...are emissions from the production, handling, transmission, and combustion of fossil fuels and biofuels....\n",
       "========\n",
       "Series: EN.ATM.NOXE.EG.KT.CE, Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)\n",
       "\n",
       "Longdefinition: ...emissions from energy processes are emissions produced by the combustion of fossil fuels and biofuels....\n",
       "========\n",
       "Series: EN.ATM.NOXE.EG.ZS, Nitrous oxide emissions in energy sector (% of total)\n",
       "\n",
       "Longdefinition: ...emissions from energy processes are emissions produced by the combustion of fossil fuels and biofuels....\n",
       "========\n",
       "Series: EN.CO2.BLDG.MT, CO2 emissions from residential buildings and commercial and public services (million metric tons)\n",
       "\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.CO2.BLDG.ZS, CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\n",
       "\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.CO2.ETOT.MT, CO2 emissions from electricity and heat production, total (million metric tons)\n",
       "\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.CO2.ETOT.ZS, CO2 emissions from electricity and heat production, total (% of total fuel combustion)\n",
       "\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.CO2.MANF.MT, CO2 emissions from manufacturing industries and construction (million metric tons)\n",
       "\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.CO2.MANF.ZS, CO2 emissions from manufacturing industries and construction (% of total fuel combustion)\n",
       "\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.CO2.OTHX.MT, CO2 emissions from other sectors, excluding residential buildings and commercial and public services (million metric tons)\n",
       "\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.CO2.OTHX.ZS, CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion)\n",
       "\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.CO2.TRAN.MT, CO2 emissions from transport (million metric tons)\n",
       "\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: EN.CO2.TRAN.ZS, CO2 emissions from transport (% of total fuel combustion)\n",
       "\n",
       "Statisticalconceptandmethodology: ...from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use:...\n",
       "========\n",
       "Series: NY.GDP.COAL.RT.ZS, Coal rents (% of GDP)\n",
       "\n",
       "Developmentrelevance: ...development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings...\n",
       "========\n",
       "Series: NY.GDP.FRST.RT.ZS, Forest rents (% of GDP)\n",
       "\n",
       "Developmentrelevance: ...development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings...\n",
       "========\n",
       "Series: NY.GDP.MINR.RT.ZS, Mineral rents (% of GDP)\n",
       "\n",
       "Developmentrelevance: ...development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings...\n",
       "========\n",
       "Series: NY.GDP.NGAS.RT.ZS, Natural gas rents (% of GDP)\n",
       "\n",
       "Developmentrelevance: ...development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings...\n",
       "========\n",
       "Series: NY.GDP.PETR.RT.ZS, Oil rents (% of GDP)\n",
       "\n",
       "Developmentrelevance: ...development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings...\n",
       "========\n",
       "Series: NY.GDP.TOTL.RT.ZS, Total natural resources rents (% of GDP)\n",
       "\n",
       "Developmentrelevance: ...development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb.search('fossil fuels')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a typical data query that returns rows as python dictionaries. You can pass series codes, country codes\n",
    "(ISO-3) or time codes as single values or lists, or omit them entirely to get all values. Time values can be\n",
    "numeric, and use python ranges to specify intervals. This example returns population data for 2 countries\n",
    "at 5-year intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'value': 17969353, 'series': 'SP.POP.TOTL', 'economy': 'CHL', 'aggregate': False, 'time': 'YR2015'}\n",
      "{'value': 17062536, 'series': 'SP.POP.TOTL', 'economy': 'CHL', 'aggregate': False, 'time': 'YR2010'}\n",
      "{'value': 16182721, 'series': 'SP.POP.TOTL', 'economy': 'CHL', 'aggregate': False, 'time': 'YR2005'}\n",
      "{'value': 15342353, 'series': 'SP.POP.TOTL', 'economy': 'CHL', 'aggregate': False, 'time': 'YR2000'}\n",
      "{'value': 14380866, 'series': 'SP.POP.TOTL', 'economy': 'CHL', 'aggregate': False, 'time': 'YR1995'}\n",
      "{'value': 13274623, 'series': 'SP.POP.TOTL', 'economy': 'CHL', 'aggregate': False, 'time': 'YR1990'}\n",
      "{'value': 12257236, 'series': 'SP.POP.TOTL', 'economy': 'CHL', 'aggregate': False, 'time': 'YR1985'}\n",
      "{'value': 11419348, 'series': 'SP.POP.TOTL', 'economy': 'CHL', 'aggregate': False, 'time': 'YR1980'}\n",
      "{'value': 43131966, 'series': 'SP.POP.TOTL', 'economy': 'ARG', 'aggregate': False, 'time': 'YR2015'}\n",
      "{'value': 40788453, 'series': 'SP.POP.TOTL', 'economy': 'ARG', 'aggregate': False, 'time': 'YR2010'}\n",
      "{'value': 38892931, 'series': 'SP.POP.TOTL', 'economy': 'ARG', 'aggregate': False, 'time': 'YR2005'}\n",
      "{'value': 36870787, 'series': 'SP.POP.TOTL', 'economy': 'ARG', 'aggregate': False, 'time': 'YR2000'}\n",
      "{'value': 34828170, 'series': 'SP.POP.TOTL', 'economy': 'ARG', 'aggregate': False, 'time': 'YR1995'}\n",
      "{'value': 32618651, 'series': 'SP.POP.TOTL', 'economy': 'ARG', 'aggregate': False, 'time': 'YR1990'}\n",
      "{'value': 30216279, 'series': 'SP.POP.TOTL', 'economy': 'ARG', 'aggregate': False, 'time': 'YR1985'}\n",
      "{'value': 27896528, 'series': 'SP.POP.TOTL', 'economy': 'ARG', 'aggregate': False, 'time': 'YR1980'}\n"
     ]
    }
   ],
   "source": [
    "for row in wb.data.fetch('SP.POP.TOTL', ['ARG', 'CHL'], time=range(1980, 2020, 5)):\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or you can return [pandas](https://pypi.org/project/pandas) dataframes. Here is a similar query, this time requesting per-capita income for all countries in the East Asia Pacific (EAP) region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>YR1980</th>\n",
       "      <th>YR1985</th>\n",
       "      <th>YR1990</th>\n",
       "      <th>YR1995</th>\n",
       "      <th>YR2000</th>\n",
       "      <th>YR2005</th>\n",
       "      <th>YR2010</th>\n",
       "      <th>YR2015</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>economy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>LAO</td>\n",
       "      <td>Lao PDR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>641.738631</td>\n",
       "      <td>203.255970</td>\n",
       "      <td>363.879602</td>\n",
       "      <td>325.186998</td>\n",
       "      <td>475.610714</td>\n",
       "      <td>1140.599205</td>\n",
       "      <td>2134.711796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TUV</td>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>990.064820</td>\n",
       "      <td>1185.840519</td>\n",
       "      <td>1462.854700</td>\n",
       "      <td>2183.909889</td>\n",
       "      <td>3022.176507</td>\n",
       "      <td>3197.772252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MNG</td>\n",
       "      <td>Mongolia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1137.690354</td>\n",
       "      <td>1172.443066</td>\n",
       "      <td>631.920090</td>\n",
       "      <td>474.216897</td>\n",
       "      <td>998.831365</td>\n",
       "      <td>2643.292914</td>\n",
       "      <td>3918.579174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WSM</td>\n",
       "      <td>Samoa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.363416</td>\n",
       "      <td>772.505849</td>\n",
       "      <td>1322.319565</td>\n",
       "      <td>1542.066736</td>\n",
       "      <td>2590.417791</td>\n",
       "      <td>3566.362378</td>\n",
       "      <td>4073.666010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SLB</td>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>768.398154</td>\n",
       "      <td>591.047692</td>\n",
       "      <td>666.965287</td>\n",
       "      <td>1275.665351</td>\n",
       "      <td>1040.007194</td>\n",
       "      <td>1014.855280</td>\n",
       "      <td>1604.205830</td>\n",
       "      <td>2167.173826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TON</td>\n",
       "      <td>Tonga</td>\n",
       "      <td>572.867641</td>\n",
       "      <td>639.629625</td>\n",
       "      <td>1194.541034</td>\n",
       "      <td>2176.290818</td>\n",
       "      <td>2090.878047</td>\n",
       "      <td>2594.495765</td>\n",
       "      <td>3527.782706</td>\n",
       "      <td>4336.196576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MMR</td>\n",
       "      <td>Myanmar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>190.606232</td>\n",
       "      <td>244.882350</td>\n",
       "      <td>979.051630</td>\n",
       "      <td>1287.430487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VNM</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>231.452328</td>\n",
       "      <td>95.188250</td>\n",
       "      <td>276.812667</td>\n",
       "      <td>390.093326</td>\n",
       "      <td>687.479736</td>\n",
       "      <td>1317.890706</td>\n",
       "      <td>2085.101484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MYS</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>1774.741454</td>\n",
       "      <td>2000.147901</td>\n",
       "      <td>2441.741991</td>\n",
       "      <td>4329.707364</td>\n",
       "      <td>4043.662051</td>\n",
       "      <td>5587.025650</td>\n",
       "      <td>9040.566251</td>\n",
       "      <td>9955.243708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>FSM</td>\n",
       "      <td>Micronesia, Fed. Sts.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1528.540721</td>\n",
       "      <td>2060.609696</td>\n",
       "      <td>2171.950243</td>\n",
       "      <td>2358.280018</td>\n",
       "      <td>2885.445676</td>\n",
       "      <td>2906.376785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ASM</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8444.981700</td>\n",
       "      <td>10271.224523</td>\n",
       "      <td>11843.331183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MHL</td>\n",
       "      <td>Marshall Islands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1144.679519</td>\n",
       "      <td>1660.129889</td>\n",
       "      <td>2382.821016</td>\n",
       "      <td>2272.722795</td>\n",
       "      <td>2568.361142</td>\n",
       "      <td>2876.994997</td>\n",
       "      <td>3213.837288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TLS</td>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>415.085949</td>\n",
       "      <td>464.521698</td>\n",
       "      <td>806.409102</td>\n",
       "      <td>1332.782859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>FJI</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>1892.891719</td>\n",
       "      <td>1603.329879</td>\n",
       "      <td>1835.128096</td>\n",
       "      <td>2540.990710</td>\n",
       "      <td>2076.568784</td>\n",
       "      <td>3659.579329</td>\n",
       "      <td>3652.527437</td>\n",
       "      <td>5390.745258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>IDN</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>491.579526</td>\n",
       "      <td>516.960993</td>\n",
       "      <td>585.076550</td>\n",
       "      <td>1026.393420</td>\n",
       "      <td>780.190201</td>\n",
       "      <td>1263.287321</td>\n",
       "      <td>3122.362815</td>\n",
       "      <td>3331.695128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VUT</td>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>1048.344659</td>\n",
       "      <td>1014.405009</td>\n",
       "      <td>1152.184968</td>\n",
       "      <td>1482.732014</td>\n",
       "      <td>1470.572265</td>\n",
       "      <td>1887.226576</td>\n",
       "      <td>2966.857116</td>\n",
       "      <td>2801.939848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PHL</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>685.221461</td>\n",
       "      <td>566.261999</td>\n",
       "      <td>715.913712</td>\n",
       "      <td>1062.133065</td>\n",
       "      <td>1072.809064</td>\n",
       "      <td>1244.349041</td>\n",
       "      <td>2217.474009</td>\n",
       "      <td>3001.040371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>THA</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>682.929843</td>\n",
       "      <td>747.703437</td>\n",
       "      <td>1508.942737</td>\n",
       "      <td>2846.586738</td>\n",
       "      <td>2007.735175</td>\n",
       "      <td>2894.062656</td>\n",
       "      <td>5076.340174</td>\n",
       "      <td>5840.046523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PRK</td>\n",
       "      <td>Korea, Dem. People’s Rep.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>KIR</td>\n",
       "      <td>Kiribati</td>\n",
       "      <td>652.865121</td>\n",
       "      <td>502.065271</td>\n",
       "      <td>549.855507</td>\n",
       "      <td>724.856583</td>\n",
       "      <td>796.888175</td>\n",
       "      <td>1214.556667</td>\n",
       "      <td>1516.811869</td>\n",
       "      <td>1542.575250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PNG</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>712.919871</td>\n",
       "      <td>593.814083</td>\n",
       "      <td>697.539573</td>\n",
       "      <td>893.773636</td>\n",
       "      <td>602.186902</td>\n",
       "      <td>749.186396</td>\n",
       "      <td>1949.352517</td>\n",
       "      <td>2679.345589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>KHM</td>\n",
       "      <td>Cambodia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>322.931788</td>\n",
       "      <td>302.577164</td>\n",
       "      <td>474.111228</td>\n",
       "      <td>785.502283</td>\n",
       "      <td>1162.904920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>194.804722</td>\n",
       "      <td>294.458849</td>\n",
       "      <td>317.884673</td>\n",
       "      <td>609.656679</td>\n",
       "      <td>959.372484</td>\n",
       "      <td>1753.417829</td>\n",
       "      <td>4550.453108</td>\n",
       "      <td>8066.942635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Country       YR1980       YR1985       YR1990  \\\n",
       "economy                                                                     \n",
       "LAO                        Lao PDR          NaN   641.738631   203.255970   \n",
       "TUV                         Tuvalu          NaN          NaN   990.064820   \n",
       "MNG                       Mongolia          NaN  1137.690354  1172.443066   \n",
       "WSM                          Samoa          NaN   597.363416   772.505849   \n",
       "SLB                Solomon Islands   768.398154   591.047692   666.965287   \n",
       "TON                          Tonga   572.867641   639.629625  1194.541034   \n",
       "MMR                        Myanmar          NaN          NaN          NaN   \n",
       "VNM                        Vietnam          NaN   231.452328    95.188250   \n",
       "MYS                       Malaysia  1774.741454  2000.147901  2441.741991   \n",
       "FSM          Micronesia, Fed. Sts.          NaN          NaN  1528.540721   \n",
       "ASM                 American Samoa          NaN          NaN          NaN   \n",
       "MHL               Marshall Islands          NaN  1144.679519  1660.129889   \n",
       "TLS                    Timor-Leste          NaN          NaN          NaN   \n",
       "FJI                           Fiji  1892.891719  1603.329879  1835.128096   \n",
       "IDN                      Indonesia   491.579526   516.960993   585.076550   \n",
       "VUT                        Vanuatu  1048.344659  1014.405009  1152.184968   \n",
       "PHL                    Philippines   685.221461   566.261999   715.913712   \n",
       "THA                       Thailand   682.929843   747.703437  1508.942737   \n",
       "PRK      Korea, Dem. People’s Rep.          NaN          NaN          NaN   \n",
       "KIR                       Kiribati   652.865121   502.065271   549.855507   \n",
       "PNG               Papua New Guinea   712.919871   593.814083   697.539573   \n",
       "KHM                       Cambodia          NaN          NaN          NaN   \n",
       "CHN                          China   194.804722   294.458849   317.884673   \n",
       "\n",
       "              YR1995       YR2000       YR2005        YR2010        YR2015  \n",
       "economy                                                                     \n",
       "LAO       363.879602   325.186998   475.610714   1140.599205   2134.711796  \n",
       "TUV      1185.840519  1462.854700  2183.909889   3022.176507   3197.772252  \n",
       "MNG       631.920090   474.216897   998.831365   2643.292914   3918.579174  \n",
       "WSM      1322.319565  1542.066736  2590.417791   3566.362378   4073.666010  \n",
       "SLB      1275.665351  1040.007194  1014.855280   1604.205830   2167.173826  \n",
       "TON      2176.290818  2090.878047  2594.495765   3527.782706   4336.196576  \n",
       "MMR              NaN   190.606232   244.882350    979.051630   1287.430487  \n",
       "VNM       276.812667   390.093326   687.479736   1317.890706   2085.101484  \n",
       "MYS      4329.707364  4043.662051  5587.025650   9040.566251   9955.243708  \n",
       "FSM      2060.609696  2171.950243  2358.280018   2885.445676   2906.376785  \n",
       "ASM              NaN          NaN  8444.981700  10271.224523  11843.331183  \n",
       "MHL      2382.821016  2272.722795  2568.361142   2876.994997   3213.837288  \n",
       "TLS              NaN   415.085949   464.521698    806.409102   1332.782859  \n",
       "FJI      2540.990710  2076.568784  3659.579329   3652.527437   5390.745258  \n",
       "IDN      1026.393420   780.190201  1263.287321   3122.362815   3331.695128  \n",
       "VUT      1482.732014  1470.572265  1887.226576   2966.857116   2801.939848  \n",
       "PHL      1062.133065  1072.809064  1244.349041   2217.474009   3001.040371  \n",
       "THA      2846.586738  2007.735175  2894.062656   5076.340174   5840.046523  \n",
       "PRK              NaN          NaN          NaN           NaN           NaN  \n",
       "KIR       724.856583   796.888175  1214.556667   1516.811869   1542.575250  \n",
       "PNG       893.773636   602.186902   749.186396   1949.352517   2679.345589  \n",
       "KHM       322.931788   302.577164   474.111228    785.502283   1162.904920  \n",
       "CHN       609.656679   959.372484  1753.417829   4550.453108   8066.942635  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = wb.data.DataFrame('NY.GDP.PCAP.CD', wb.region.members('EAP'), time=range(1980, 2020, 5), labels=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataframes give you access to all of pandas features for data computation and transformation. Here, we just convert the previous data frame from raw values to percent change (at 5-year intervals):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YR1980</th>\n",
       "      <th>YR1985</th>\n",
       "      <th>YR1990</th>\n",
       "      <th>YR1995</th>\n",
       "      <th>YR2000</th>\n",
       "      <th>YR2005</th>\n",
       "      <th>YR2010</th>\n",
       "      <th>YR2015</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Lao PDR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.683273</td>\n",
       "      <td>0.790253</td>\n",
       "      <td>-0.106334</td>\n",
       "      <td>0.462576</td>\n",
       "      <td>1.398178</td>\n",
       "      <td>0.871570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.197740</td>\n",
       "      <td>0.233602</td>\n",
       "      <td>0.492910</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>0.058102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mongolia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.030547</td>\n",
       "      <td>-0.461023</td>\n",
       "      <td>-0.249562</td>\n",
       "      <td>1.106275</td>\n",
       "      <td>1.646386</td>\n",
       "      <td>0.482461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Samoa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.293192</td>\n",
       "      <td>0.711728</td>\n",
       "      <td>0.166183</td>\n",
       "      <td>0.679835</td>\n",
       "      <td>0.376752</td>\n",
       "      <td>0.142247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.230805</td>\n",
       "      <td>0.128446</td>\n",
       "      <td>0.912641</td>\n",
       "      <td>-0.184734</td>\n",
       "      <td>-0.024184</td>\n",
       "      <td>0.580724</td>\n",
       "      <td>0.350933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Tonga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.116540</td>\n",
       "      <td>0.867551</td>\n",
       "      <td>0.821864</td>\n",
       "      <td>-0.039247</td>\n",
       "      <td>0.240864</td>\n",
       "      <td>0.359718</td>\n",
       "      <td>0.229156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Myanmar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.284755</td>\n",
       "      <td>2.998049</td>\n",
       "      <td>0.314977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vietnam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.588735</td>\n",
       "      <td>1.908055</td>\n",
       "      <td>0.409232</td>\n",
       "      <td>0.762347</td>\n",
       "      <td>0.916988</td>\n",
       "      <td>0.582151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Malaysia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.127008</td>\n",
       "      <td>0.220781</td>\n",
       "      <td>0.773204</td>\n",
       "      <td>-0.066066</td>\n",
       "      <td>0.381675</td>\n",
       "      <td>0.618136</td>\n",
       "      <td>0.101175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Micronesia, Fed. Sts.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.348089</td>\n",
       "      <td>0.054033</td>\n",
       "      <td>0.085789</td>\n",
       "      <td>0.223538</td>\n",
       "      <td>0.007254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>American Samoa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.216252</td>\n",
       "      <td>0.153059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Marshall Islands</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.450301</td>\n",
       "      <td>0.435322</td>\n",
       "      <td>-0.046205</td>\n",
       "      <td>0.130081</td>\n",
       "      <td>0.120168</td>\n",
       "      <td>0.117081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.119098</td>\n",
       "      <td>0.735999</td>\n",
       "      <td>0.652738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fiji</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.152973</td>\n",
       "      <td>0.144573</td>\n",
       "      <td>0.384639</td>\n",
       "      <td>-0.182772</td>\n",
       "      <td>0.762320</td>\n",
       "      <td>-0.001927</td>\n",
       "      <td>0.475895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Indonesia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.051632</td>\n",
       "      <td>0.131762</td>\n",
       "      <td>0.754289</td>\n",
       "      <td>-0.239872</td>\n",
       "      <td>0.619204</td>\n",
       "      <td>1.471617</td>\n",
       "      <td>0.067043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.032375</td>\n",
       "      <td>0.135823</td>\n",
       "      <td>0.286887</td>\n",
       "      <td>-0.008201</td>\n",
       "      <td>0.283328</td>\n",
       "      <td>0.572073</td>\n",
       "      <td>-0.055587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Philippines</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.173607</td>\n",
       "      <td>0.264280</td>\n",
       "      <td>0.483605</td>\n",
       "      <td>0.010051</td>\n",
       "      <td>0.159898</td>\n",
       "      <td>0.782035</td>\n",
       "      <td>0.353360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Thailand</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.094847</td>\n",
       "      <td>1.018103</td>\n",
       "      <td>0.886478</td>\n",
       "      <td>-0.294687</td>\n",
       "      <td>0.441456</td>\n",
       "      <td>0.754053</td>\n",
       "      <td>0.150444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Korea, Dem. People’s Rep.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kiribati</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.230982</td>\n",
       "      <td>0.095187</td>\n",
       "      <td>0.318267</td>\n",
       "      <td>0.099374</td>\n",
       "      <td>0.524124</td>\n",
       "      <td>0.248861</td>\n",
       "      <td>0.016985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.167068</td>\n",
       "      <td>0.174677</td>\n",
       "      <td>0.281323</td>\n",
       "      <td>-0.326242</td>\n",
       "      <td>0.244109</td>\n",
       "      <td>1.601959</td>\n",
       "      <td>0.374480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cambodia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.063031</td>\n",
       "      <td>0.566910</td>\n",
       "      <td>0.656789</td>\n",
       "      <td>0.480460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>China</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.511559</td>\n",
       "      <td>0.079556</td>\n",
       "      <td>0.917855</td>\n",
       "      <td>0.573627</td>\n",
       "      <td>0.827672</td>\n",
       "      <td>1.595190</td>\n",
       "      <td>0.772778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           YR1980    YR1985    YR1990    YR1995    YR2000  \\\n",
       "Country                                                                     \n",
       "Lao PDR                       NaN       NaN -0.683273  0.790253 -0.106334   \n",
       "Tuvalu                        NaN       NaN       NaN  0.197740  0.233602   \n",
       "Mongolia                      NaN       NaN  0.030547 -0.461023 -0.249562   \n",
       "Samoa                         NaN       NaN  0.293192  0.711728  0.166183   \n",
       "Solomon Islands               NaN -0.230805  0.128446  0.912641 -0.184734   \n",
       "Tonga                         NaN  0.116540  0.867551  0.821864 -0.039247   \n",
       "Myanmar                       NaN       NaN       NaN       NaN       NaN   \n",
       "Vietnam                       NaN       NaN -0.588735  1.908055  0.409232   \n",
       "Malaysia                      NaN  0.127008  0.220781  0.773204 -0.066066   \n",
       "Micronesia, Fed. Sts.         NaN       NaN       NaN  0.348089  0.054033   \n",
       "American Samoa                NaN       NaN       NaN       NaN       NaN   \n",
       "Marshall Islands              NaN       NaN  0.450301  0.435322 -0.046205   \n",
       "Timor-Leste                   NaN       NaN       NaN       NaN       NaN   \n",
       "Fiji                          NaN -0.152973  0.144573  0.384639 -0.182772   \n",
       "Indonesia                     NaN  0.051632  0.131762  0.754289 -0.239872   \n",
       "Vanuatu                       NaN -0.032375  0.135823  0.286887 -0.008201   \n",
       "Philippines                   NaN -0.173607  0.264280  0.483605  0.010051   \n",
       "Thailand                      NaN  0.094847  1.018103  0.886478 -0.294687   \n",
       "Korea, Dem. People’s Rep.     NaN       NaN       NaN       NaN       NaN   \n",
       "Kiribati                      NaN -0.230982  0.095187  0.318267  0.099374   \n",
       "Papua New Guinea              NaN -0.167068  0.174677  0.281323 -0.326242   \n",
       "Cambodia                      NaN       NaN       NaN       NaN -0.063031   \n",
       "China                         NaN  0.511559  0.079556  0.917855  0.573627   \n",
       "\n",
       "                             YR2005    YR2010    YR2015  \n",
       "Country                                                  \n",
       "Lao PDR                    0.462576  1.398178  0.871570  \n",
       "Tuvalu                     0.492910  0.383838  0.058102  \n",
       "Mongolia                   1.106275  1.646386  0.482461  \n",
       "Samoa                      0.679835  0.376752  0.142247  \n",
       "Solomon Islands           -0.024184  0.580724  0.350933  \n",
       "Tonga                      0.240864  0.359718  0.229156  \n",
       "Myanmar                    0.284755  2.998049  0.314977  \n",
       "Vietnam                    0.762347  0.916988  0.582151  \n",
       "Malaysia                   0.381675  0.618136  0.101175  \n",
       "Micronesia, Fed. Sts.      0.085789  0.223538  0.007254  \n",
       "American Samoa                  NaN  0.216252  0.153059  \n",
       "Marshall Islands           0.130081  0.120168  0.117081  \n",
       "Timor-Leste                0.119098  0.735999  0.652738  \n",
       "Fiji                       0.762320 -0.001927  0.475895  \n",
       "Indonesia                  0.619204  1.471617  0.067043  \n",
       "Vanuatu                    0.283328  0.572073 -0.055587  \n",
       "Philippines                0.159898  0.782035  0.353360  \n",
       "Thailand                   0.441456  0.754053  0.150444  \n",
       "Korea, Dem. People’s Rep.       NaN       NaN       NaN  \n",
       "Kiribati                   0.524124  0.248861  0.016985  \n",
       "Papua New Guinea           0.244109  1.601959  0.374480  \n",
       "Cambodia                   0.566910  0.656789  0.480460  \n",
       "China                      0.827672  1.595190  0.772778  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Country').pct_change(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic panda charts (using [matplotlib](https://pypi.org/project/matplotlib)) are straight forward. Of course, you can use any plotting package you like: [seaborn](https://pypi.org/project/seaborn), [plotly](https://pypi.org/project/plotly), etc. Here is a plot of per-capita income for 3 countries and 2 decades:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wb.data.DataFrame('NY.GDP.PCAP.CD', ['BRA', 'ARG', 'COL'], range(2000, 2020), index='time').plot(figsize=(10, 6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A few Stupid API Tricks\n",
    "\n",
    "#### What's the distribution of poverty estimates over time?\n",
    "\n",
    "The `mrnev` parameter lets you return the most recent non-empty values. If this is set to 1 then WBGAPI typically does not include a time dimension in the data frame. You can specifically request this with the `columns` or `index` parameter, as well as request numeric values for the time dimension. In this case, this gives an interesting view of the time variation for poverty estimates (SI.POV.NAHC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1993</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>economy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AFG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AGO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ALB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ARG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ARM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>XKX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YEM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZAF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZMB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZWE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         1993  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  \\\n",
       "economy                                                                     \n",
       "AFG       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "AGO       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "ALB       NaN   NaN   NaN   NaN   NaN   NaN   NaN  14.3   NaN   NaN   NaN   \n",
       "ARG       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "ARM       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
       "XKX       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  17.6   \n",
       "YEM       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  48.6   NaN   \n",
       "ZAF       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  55.5   NaN   \n",
       "ZMB       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  54.4   \n",
       "ZWE       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "         2016  2017  2018  2019  \n",
       "economy                          \n",
       "AFG      54.5   NaN   NaN   NaN  \n",
       "AGO       NaN   NaN  32.3   NaN  \n",
       "ALB       NaN   NaN   NaN   NaN  \n",
       "ARG       NaN   NaN   NaN  35.5  \n",
       "ARM       NaN   NaN  23.5   NaN  \n",
       "...       ...   ...   ...   ...  \n",
       "XKX       NaN   NaN   NaN   NaN  \n",
       "YEM       NaN   NaN   NaN   NaN  \n",
       "ZAF       NaN   NaN   NaN   NaN  \n",
       "ZMB       NaN   NaN   NaN   NaN  \n",
       "ZWE       NaN   NaN   NaN  38.3  \n",
       "\n",
       "[156 rows x 15 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = wb.data.DataFrame('SI.POV.NAHC', mrnev=1, columns='time', numericTimeKeys=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's an easier to understand view of the previous data frame using matplotlib to chart:\n",
    "\n",
    "(I assigned the plot result to a variable to avoid some output kruft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YdW+ttpcyemv2AHBf+7oOeD6jD333t9tz2/ww+iMnjwIPsuSIHka7pR5pX78z694m3SOjow0eZPTZyYPA9ln3toYef779PH+f0Qf6B4Hntfuuaz/bjwJ/PuveJtkfoyNC7m9fD6+X/sbs8d3Ak0vmLi55rlW/hp5RKkkdOa13v0hSbwx1SeqIoS5JHTHUJakjhrokdcRQl6SOGOqS1BFDXZI68n9DfL3zj6gOBgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = df.count()\n",
    "ax = plt.bar(counts.index, counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Do indicators vary across different versions of the WDI?\n",
    "\n",
    "Most databases in the API have 3 dimensions: series, country (or economy) and time. But some have 4 dimensions.\n",
    "The WDI database (db=57) includes a `version` dimension that lets you fetch indicators from previous vintages. Here\n",
    "is how you can explore non-standard dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'economy': {'key': 'country', 'value': 'Country'},\n",
       " 'series': {'key': 'series', 'value': 'Series'},\n",
       " 'time': {'key': 'time', 'value': 'Time'},\n",
       " 'version': {'key': 'version', 'value': 'Version'}}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb.source.concepts(db=57)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['201806', '201807', '201808', '201809', '201810', '201811', '201901', '201903', '201904', '201906', '201909', '201910', '201912', '202002', '202003', '202004', '202005', '202007', '202008', '202009']\n"
     ]
    }
   ],
   "source": [
    "versions = [row['id'] for row in wb.source.features('version', db=57)]\n",
    "print(versions[-20:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can pass non-standard dimensions as parameters to `wb.data.fetch` `wb.data.get` and `wb.data.DataFrame`.\n",
    "In this example, the version parameter specifies the year and month of the WDI revision. Requesting versions\n",
    "on intervals of 100 thus gives you the April release of every year in the requested range.\n",
    "You can compare values across different database versions to see if they've changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>201004</th>\n",
       "      <th>201104</th>\n",
       "      <th>201204</th>\n",
       "      <th>201304</th>\n",
       "      <th>201404</th>\n",
       "      <th>201504</th>\n",
       "      <th>201604</th>\n",
       "      <th>201704</th>\n",
       "      <th>201804</th>\n",
       "      <th>201904</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>YR2000</td>\n",
       "      <td>174174447.0</td>\n",
       "      <td>174174447.0</td>\n",
       "      <td>174425387.0</td>\n",
       "      <td>174425387.0</td>\n",
       "      <td>174504898.0</td>\n",
       "      <td>174504898.0</td>\n",
       "      <td>175786441.0</td>\n",
       "      <td>175786441.0</td>\n",
       "      <td>175287587.0</td>\n",
       "      <td>175287587.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2001</td>\n",
       "      <td>176659138.0</td>\n",
       "      <td>176659138.0</td>\n",
       "      <td>176877135.0</td>\n",
       "      <td>176877135.0</td>\n",
       "      <td>176968205.0</td>\n",
       "      <td>176968205.0</td>\n",
       "      <td>178419396.0</td>\n",
       "      <td>178419396.0</td>\n",
       "      <td>177750670.0</td>\n",
       "      <td>177750670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2002</td>\n",
       "      <td>179123364.0</td>\n",
       "      <td>179123364.0</td>\n",
       "      <td>179289227.0</td>\n",
       "      <td>179289227.0</td>\n",
       "      <td>179393768.0</td>\n",
       "      <td>179393768.0</td>\n",
       "      <td>181045592.0</td>\n",
       "      <td>181045592.0</td>\n",
       "      <td>180151021.0</td>\n",
       "      <td>180151021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2003</td>\n",
       "      <td>181537359.0</td>\n",
       "      <td>181537359.0</td>\n",
       "      <td>181633074.0</td>\n",
       "      <td>181633074.0</td>\n",
       "      <td>181752951.0</td>\n",
       "      <td>181752951.0</td>\n",
       "      <td>183627339.0</td>\n",
       "      <td>183627339.0</td>\n",
       "      <td>182482149.0</td>\n",
       "      <td>182482149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2004</td>\n",
       "      <td>183863524.0</td>\n",
       "      <td>183863524.0</td>\n",
       "      <td>183873377.0</td>\n",
       "      <td>183873377.0</td>\n",
       "      <td>184010283.0</td>\n",
       "      <td>184010283.0</td>\n",
       "      <td>186116363.0</td>\n",
       "      <td>186116363.0</td>\n",
       "      <td>184738458.0</td>\n",
       "      <td>184738458.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2005</td>\n",
       "      <td>186074634.0</td>\n",
       "      <td>186074634.0</td>\n",
       "      <td>185986964.0</td>\n",
       "      <td>185986964.0</td>\n",
       "      <td>186142403.0</td>\n",
       "      <td>186142403.0</td>\n",
       "      <td>188479240.0</td>\n",
       "      <td>188479240.0</td>\n",
       "      <td>186917361.0</td>\n",
       "      <td>186917361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2006</td>\n",
       "      <td>188158438.0</td>\n",
       "      <td>188158438.0</td>\n",
       "      <td>187958211.0</td>\n",
       "      <td>187958211.0</td>\n",
       "      <td>188134315.0</td>\n",
       "      <td>188134315.0</td>\n",
       "      <td>190698241.0</td>\n",
       "      <td>190698241.0</td>\n",
       "      <td>189012412.0</td>\n",
       "      <td>189012412.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2007</td>\n",
       "      <td>190119995.0</td>\n",
       "      <td>190119995.0</td>\n",
       "      <td>189798070.0</td>\n",
       "      <td>189798070.0</td>\n",
       "      <td>189996976.0</td>\n",
       "      <td>189996976.0</td>\n",
       "      <td>192784521.0</td>\n",
       "      <td>192784521.0</td>\n",
       "      <td>191026637.0</td>\n",
       "      <td>191026637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2008</td>\n",
       "      <td>191971506.0</td>\n",
       "      <td>191971506.0</td>\n",
       "      <td>191543237.0</td>\n",
       "      <td>191543237.0</td>\n",
       "      <td>191765567.0</td>\n",
       "      <td>191765567.0</td>\n",
       "      <td>194769696.0</td>\n",
       "      <td>194769696.0</td>\n",
       "      <td>192979029.0</td>\n",
       "      <td>192979029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>193733795.0</td>\n",
       "      <td>193246610.0</td>\n",
       "      <td>193246610.0</td>\n",
       "      <td>193490922.0</td>\n",
       "      <td>193490922.0</td>\n",
       "      <td>196701298.0</td>\n",
       "      <td>196701298.0</td>\n",
       "      <td>194895996.0</td>\n",
       "      <td>194895996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194946000.0</td>\n",
       "      <td>194946470.0</td>\n",
       "      <td>195210154.0</td>\n",
       "      <td>195210154.0</td>\n",
       "      <td>198614208.0</td>\n",
       "      <td>198614208.0</td>\n",
       "      <td>196796269.0</td>\n",
       "      <td>196796269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196655014.0</td>\n",
       "      <td>196935134.0</td>\n",
       "      <td>196935134.0</td>\n",
       "      <td>200517584.0</td>\n",
       "      <td>200517584.0</td>\n",
       "      <td>198686688.0</td>\n",
       "      <td>198686688.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198656019.0</td>\n",
       "      <td>198656019.0</td>\n",
       "      <td>202401584.0</td>\n",
       "      <td>202401584.0</td>\n",
       "      <td>200560983.0</td>\n",
       "      <td>200560983.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200361925.0</td>\n",
       "      <td>204259377.0</td>\n",
       "      <td>204259377.0</td>\n",
       "      <td>202408632.0</td>\n",
       "      <td>202408632.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>206077898.0</td>\n",
       "      <td>206077898.0</td>\n",
       "      <td>204213133.0</td>\n",
       "      <td>204213133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>207847528.0</td>\n",
       "      <td>205962108.0</td>\n",
       "      <td>205962108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>207652865.0</td>\n",
       "      <td>207652865.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>209288000.0</td>\n",
       "      <td>209288278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210868000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YR2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             201004       201104       201204       201304       201404  \\\n",
       "time                                                                      \n",
       "YR2000  174174447.0  174174447.0  174425387.0  174425387.0  174504898.0   \n",
       "YR2001  176659138.0  176659138.0  176877135.0  176877135.0  176968205.0   \n",
       "YR2002  179123364.0  179123364.0  179289227.0  179289227.0  179393768.0   \n",
       "YR2003  181537359.0  181537359.0  181633074.0  181633074.0  181752951.0   \n",
       "YR2004  183863524.0  183863524.0  183873377.0  183873377.0  184010283.0   \n",
       "YR2005  186074634.0  186074634.0  185986964.0  185986964.0  186142403.0   \n",
       "YR2006  188158438.0  188158438.0  187958211.0  187958211.0  188134315.0   \n",
       "YR2007  190119995.0  190119995.0  189798070.0  189798070.0  189996976.0   \n",
       "YR2008  191971506.0  191971506.0  191543237.0  191543237.0  191765567.0   \n",
       "YR2009          NaN  193733795.0  193246610.0  193246610.0  193490922.0   \n",
       "YR2010          NaN          NaN  194946000.0  194946470.0  195210154.0   \n",
       "YR2011          NaN          NaN          NaN  196655014.0  196935134.0   \n",
       "YR2012          NaN          NaN          NaN          NaN  198656019.0   \n",
       "YR2013          NaN          NaN          NaN          NaN          NaN   \n",
       "YR2014          NaN          NaN          NaN          NaN          NaN   \n",
       "YR2015          NaN          NaN          NaN          NaN          NaN   \n",
       "YR2016          NaN          NaN          NaN          NaN          NaN   \n",
       "YR2017          NaN          NaN          NaN          NaN          NaN   \n",
       "YR2018          NaN          NaN          NaN          NaN          NaN   \n",
       "YR2019          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "             201504       201604       201704       201804       201904  \n",
       "time                                                                     \n",
       "YR2000  174504898.0  175786441.0  175786441.0  175287587.0  175287587.0  \n",
       "YR2001  176968205.0  178419396.0  178419396.0  177750670.0  177750670.0  \n",
       "YR2002  179393768.0  181045592.0  181045592.0  180151021.0  180151021.0  \n",
       "YR2003  181752951.0  183627339.0  183627339.0  182482149.0  182482149.0  \n",
       "YR2004  184010283.0  186116363.0  186116363.0  184738458.0  184738458.0  \n",
       "YR2005  186142403.0  188479240.0  188479240.0  186917361.0  186917361.0  \n",
       "YR2006  188134315.0  190698241.0  190698241.0  189012412.0  189012412.0  \n",
       "YR2007  189996976.0  192784521.0  192784521.0  191026637.0  191026637.0  \n",
       "YR2008  191765567.0  194769696.0  194769696.0  192979029.0  192979029.0  \n",
       "YR2009  193490922.0  196701298.0  196701298.0  194895996.0  194895996.0  \n",
       "YR2010  195210154.0  198614208.0  198614208.0  196796269.0  196796269.0  \n",
       "YR2011  196935134.0  200517584.0  200517584.0  198686688.0  198686688.0  \n",
       "YR2012  198656019.0  202401584.0  202401584.0  200560983.0  200560983.0  \n",
       "YR2013  200361925.0  204259377.0  204259377.0  202408632.0  202408632.0  \n",
       "YR2014          NaN  206077898.0  206077898.0  204213133.0  204213133.0  \n",
       "YR2015          NaN          NaN  207847528.0  205962108.0  205962108.0  \n",
       "YR2016          NaN          NaN          NaN  207652865.0  207652865.0  \n",
       "YR2017          NaN          NaN          NaN  209288000.0  209288278.0  \n",
       "YR2018          NaN          NaN          NaN          NaN  210868000.0  \n",
       "YR2019          NaN          NaN          NaN          NaN          NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = wb.data.DataFrame('SP.POP.TOTL', 'BRA', db=57, time=range(2000, 2020),\n",
    "                       version=range(201004, 202004, 100), index='time')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the same data frame plotted for ease of understanding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A couple of handy utilities\n",
    "\n",
    "#### Metadata\n",
    "\n",
    "There are a set of metadata functions for fetching metadata at the indicator or economy level, or combinations.\n",
    "Footnotes are also possible (`wb.data.footnote`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"wbgapi\"><h4>Series: SP.POP.TOTL</h4><table>\n",
       "<thead>\n",
       "<tr><th>Field                           </th><th>Value                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>Aggregationmethod               </td><td>Sum                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            </td></tr>\n",
       "<tr><td>Developmentrelevance            </td><td>Increases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure.  This can place pressure on a country's sustainability.  A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure.</td></tr>\n",
       "<tr><td>Generalcomments                 </td><td>Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis.                                                                                                                                                                                                                                                                                                                                                                                                                                                         </td></tr>\n",
       "<tr><td>IndicatorName                   </td><td>Population, total                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              </td></tr>\n",
       "<tr><td>License_Type                    </td><td>CC BY-4.0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      </td></tr>\n",
       "<tr><td>License_URL                     </td><td>https://datacatalog.worldbank.org/public-licenses#cc-by                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        </td></tr>\n",
       "<tr><td>Limitationsandexceptions        </td><td>Current population estimates for developing countries that lack (i) reliable recent census data, and (ii) pre- and post-census estimates for countries with census data, are provided by the United Nations Population Division and other agencies. \n",
       "\n",
       "The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in both the model and the data. In the UN estimates the five-year age group is the cohort unit and five-year period data are used; therefore interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n",
       "\n",
       "Because future trends cannot be known with certainty, population projections have a wide range of uncertainty.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                </td></tr>\n",
       "<tr><td>Longdefinition                  </td><td>Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.                                                                                                                                                                                                                                                                                                                                                                                                                                              </td></tr>\n",
       "<tr><td>Periodicity                     </td><td>Annual                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         </td></tr>\n",
       "<tr><td>Source                          </td><td>(1) United Nations Population Division. World Population Prospects: 2019 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.                                                                                                                                                                          </td></tr>\n",
       "<tr><td>Statisticalconceptandmethodology</td><td>Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models.\n",
       "\n",
       "Errors and undercounting occur even in high-income countries.  In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census.\n",
       "\n",
       "The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data.\n",
       "\n",
       "The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census.\n",
       "\n",
       "The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems.\n",
       "\n",
       "International migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect.\n",
       "\n",
       "Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                </td></tr>\n",
       "<tr><td>Topic                           </td><td>Health: Population: Structure                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  </td></tr>\n",
       "</tbody>\n",
       "</table><h5>Series-Economy</h5><table>\n",
       "<thead>\n",
       "<tr><th>Field  </th><th>Value                                                 </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>KEN    </td><td>Data source: United Nations World Population Prospects</td></tr>\n",
       "<tr><td>TZA    </td><td>Data source: United Nations World Population Prospects</td></tr>\n",
       "</tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "========\n",
       "Series: SP.POP.TOTL\n",
       "\n",
       "Aggregationmethod: Sum\n",
       "--------\n",
       "Developmentrelevance: Increases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure.  This can place pressure on a country's sustainability.  A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure.\n",
       "--------\n",
       "Generalcomments: Relevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis.\n",
       "--------\n",
       "IndicatorName: Population, total\n",
       "--------\n",
       "License_Type: CC BY-4.0\n",
       "--------\n",
       "License_URL: https://datacatalog.worldbank.org/public-licenses#cc-by\n",
       "--------\n",
       "Limitationsandexceptions: Current population estimates for developing countries that lack (i) reliable recent census data, and (ii) pre- and post-census estimates for countries with census data, are provided by the United Nations Population Division and other agencies. \n",
       "\n",
       "The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in both the model and the data. In the UN estimates the five-year age group is the cohort unit and five-year period data are used; therefore interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n",
       "\n",
       "Because future trends cannot be known with certainty, population projections have a wide range of uncertainty.\n",
       "--------\n",
       "Longdefinition: Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.\n",
       "--------\n",
       "Periodicity: Annual\n",
       "--------\n",
       "Source: (1) United Nations Population Division. World Population Prospects: 2019 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.\n",
       "--------\n",
       "Statisticalconceptandmethodology: Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models.\n",
       "\n",
       "Errors and undercounting occur even in high-income countries.  In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census.\n",
       "\n",
       "The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data.\n",
       "\n",
       "The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census.\n",
       "\n",
       "The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems.\n",
       "\n",
       "International migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect.\n",
       "\n",
       "Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant.\n",
       "--------\n",
       "Topic: Health: Population: Structure\n",
       "========\n",
       "Series-Economy\n",
       "\n",
       "KEN: Data source: United Nations World Population Prospects\n",
       "--------\n",
       "TZA: Data source: United Nations World Population Prospects"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb.series.metadata.get('SP.POP.TOTL', economies=['KEN', 'TZA'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Country coding\n",
    "\n",
    "Another handy utility helps with determining the Bank's ISO code for countries when names can vary quite a bit.\n",
    "This relies on some regex sugar and hinting, using commonly used tables from the UN system, US State Department,\n",
    "and other sources (English only at the moment)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"wbgapi\"><table>\n",
       "<thead>\n",
       "<tr><th>ORIGINAL NAME  </th><th>WBG NAME       </th><th>ISO_CODE  </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>Argentina      </td><td>Argentina      </td><td>ARG       </td></tr>\n",
       "<tr><td>Slovakia       </td><td>Slovak Republic</td><td>SVK       </td></tr>\n",
       "<tr><td>Swaziland      </td><td>Eswatini       </td><td>SWZ       </td></tr>\n",
       "<tr><td>South Korea    </td><td>Korea, Rep.    </td><td>KOR       </td></tr>\n",
       "<tr><td>Burma          </td><td>Myanmar        </td><td>MMR       </td></tr>\n",
       "<tr><td>England        </td><td>United Kingdom </td><td>GBR       </td></tr>\n",
       "</tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "ORIGINAL NAME    WBG NAME         ISO_CODE\n",
       "---------------  ---------------  ----------\n",
       "Argentina        Argentina        ARG\n",
       "Slovakia         Slovak Republic  SVK\n",
       "Swaziland        Eswatini         SWZ\n",
       "South Korea      Korea, Rep.      KOR\n",
       "Burma            Myanmar          MMR\n",
       "England          United Kingdom   GBR"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb.economy.coder(['Argentina', 'Slovakia', 'Swaziland', 'South Korea', 'Burma', 'England'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Where to Get It\n",
    "\n",
    "Here's how to get started:\n",
    "\n",
    "* <https://pypi.org/project/wbgapi>\n",
    "* <https://github.com/tgherzog/wbgapi>\n",
    "* `pip install wbgapi`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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