advanced-2-wbgapi-cookbook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is downloaded from https://github.com/tgherzog/wbgapi/blob/master/examples/wbgapi-cookbook.ipynb\n",
    "# A Cookbook of WBGAPI Recipes\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). This notebook provides additional examples beyond the \"Getting Started\" information available at <https://pypi.org/project/wbgapi>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import wbgapi as wb   # I use wb as a namespace in all my work\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": [
    "## Querying and Searching ##\n",
    "\n",
    "The `info` functions are the primary tool for searching the API. You can limit the response by calling `info()` with identifiers (a single value or lists) or by adding a keyword search.\n",
    "\n",
    "    wb.source.info() # especially useful for seeing what databases are available\n",
    "    wb.series.info()\n",
    "    wb.economy.info('CAN')\n",
    "    wb.economy.info(['USA', 'MEX', 'ARG'])\n",
    "    wb.series.info(q='co2')\n",
    "    wb.economy.info(q='korea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>EN.ATM.CO2E.EG.ZS   </td><td>CO2 intensity (kg per kg of oil equivalent energy use)                                                                           </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.GF.KT   </td><td>CO2 emissions from gaseous fuel consumption (kt)                                                                                 </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.GF.ZS   </td><td>CO2 emissions from gaseous fuel consumption (% of total)                                                                         </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.KD.GD   </td><td>CO2 emissions (kg per 2015 US$ of GDP)                                                                                           </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.KT      </td><td>CO2 emissions (kt)                                                                                                               </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.LF.KT   </td><td>CO2 emissions from liquid fuel consumption (kt)                                                                                  </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.LF.ZS   </td><td>CO2 emissions from liquid fuel consumption (% of total)                                                                          </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.PC      </td><td>CO2 emissions (metric tons per capita)                                                                                           </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.PP.GD   </td><td>CO2 emissions (kg per PPP $ of GDP)                                                                                              </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.PP.GD.KD</td><td>CO2 emissions (kg per 2017 PPP $ of GDP)                                                                                         </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.SF.KT   </td><td>CO2 emissions from solid fuel consumption (kt)                                                                                   </td></tr>\n",
       "<tr><td>EN.ATM.CO2E.SF.ZS   </td><td>CO2 emissions from solid fuel consumption (% of total)                                                                           </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></tr>\n",
       "<tr><td>EN.CO2.ETOT.ZS      </td><td>CO2 emissions from electricity and heat production, total (% of total fuel combustion)                                           </td></tr>\n",
       "<tr><td>EN.CO2.MANF.ZS      </td><td>CO2 emissions from manufacturing industries and construction (% of total fuel combustion)                                        </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></tr>\n",
       "<tr><td>EN.CO2.TRAN.ZS      </td><td>CO2 emissions from transport (% of total fuel combustion)                                                                        </td></tr>\n",
       "<tr><td>                    </td><td>17 elements                                                                                                                      </td></tr>\n",
       "</tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "id                    value\n",
       "--------------------  ---------------------------------------------------------------------------------------------------------------------------------\n",
       "EN.ATM.CO2E.EG.ZS     CO2 intensity (kg per kg of oil equivalent energy use)\n",
       "EN.ATM.CO2E.GF.KT     CO2 emissions from gaseous fuel consumption (kt)\n",
       "EN.ATM.CO2E.GF.ZS     CO2 emissions from gaseous fuel consumption (% of total)\n",
       "EN.ATM.CO2E.KD.GD     CO2 emissions (kg per 2015 US$ of GDP)\n",
       "EN.ATM.CO2E.KT        CO2 emissions (kt)\n",
       "EN.ATM.CO2E.LF.KT     CO2 emissions from liquid fuel consumption (kt)\n",
       "EN.ATM.CO2E.LF.ZS     CO2 emissions from liquid fuel consumption (% of total)\n",
       "EN.ATM.CO2E.PC        CO2 emissions (metric tons per capita)\n",
       "EN.ATM.CO2E.PP.GD     CO2 emissions (kg per PPP $ of GDP)\n",
       "EN.ATM.CO2E.PP.GD.KD  CO2 emissions (kg per 2017 PPP $ of GDP)\n",
       "EN.ATM.CO2E.SF.KT     CO2 emissions from solid fuel consumption (kt)\n",
       "EN.ATM.CO2E.SF.ZS     CO2 emissions from solid fuel consumption (% of total)\n",
       "EN.CO2.BLDG.ZS        CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)\n",
       "EN.CO2.ETOT.ZS        CO2 emissions from electricity and heat production, total (% of total fuel combustion)\n",
       "EN.CO2.MANF.ZS        CO2 emissions from manufacturing industries and construction (% of total fuel combustion)\n",
       "EN.CO2.OTHX.ZS        CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion)\n",
       "EN.CO2.TRAN.ZS        CO2 emissions from transport (% of total fuel combustion)\n",
       "                      17 elements"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb.series.info(q='co2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`wb.search(q, brief=False)` is the other option; it searches the metadata of the current database. The `brief` parameter produces a more concise but less informative report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"wbgapi\"><h4>Country</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>CHI </td><td>Channel Islands     </td><td>CurrencyUnit</td><td>Pound sterling                                                                                                                                                           </td></tr>\n",
       "<tr><td>EGY </td><td>Egypt, Arab Rep.    </td><td>CurrencyUnit</td><td>Egyptian pound                                                                                                                                                           </td></tr>\n",
       "<tr><td>GBR </td><td>United Kingdom      </td><td>CurrencyUnit</td><td>Pound sterling                                                                                                                                                           </td></tr>\n",
       "<tr><td>GIB </td><td>Gibraltar           </td><td>CurrencyUnit</td><td>Gibraltar pound                                                                                                                                                          </td></tr>\n",
       "<tr><td>IMN </td><td>Isle of Man         </td><td>CurrencyUnit</td><td>Pound sterling                                                                                                                                                           </td></tr>\n",
       "<tr><td>IRL </td><td>Ireland             </td><td>SpecialNotes</td><td>...rate was adopted by the EU Council on January 1, 1999: 1 euro = 0.787564 Irish pound. Please note that historical data before 1999 are not actual euros and are not...</td></tr>\n",
       "<tr><td>LBN </td><td>Lebanon             </td><td>CurrencyUnit</td><td>Lebanese pound                                                                                                                                                           </td></tr>\n",
       "<tr><td>SDN </td><td>Sudan               </td><td>CurrencyUnit</td><td>Sudanese pound                                                                                                                                                           </td></tr>\n",
       "<tr><td>SSD </td><td>South Sudan         </td><td>CurrencyUnit</td><td>South Sudanese Pound                                                                                                                                                     </td></tr>\n",
       "<tr><td>SYR </td><td>Syrian Arab Republic</td><td>CurrencyUnit</td><td>Syrian pound                                                                                                                                                             </td></tr>\n",
       "</tbody>\n",
       "</table><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>DT.CUR.UKPS.ZS      </td><td>Currency composition of PPG debt, Pound sterling (%)                                          </td><td>IndicatorName                   </td><td>Currency composition of PPG debt, Pound sterling (%)                                                                                                                       </td></tr>\n",
       "<tr><td>DT.CUR.UKPS.ZS      </td><td>Currency composition of PPG debt, Pound sterling (%)                                          </td><td>Longdefinition                  </td><td>...of external long-term public and publicly-guaranteed debt contracted in U.K. pound sterling for the low- and middle-income countries....                                </td></tr>\n",
       "<tr><td>DT.CUR.UKPS.ZS      </td><td>Currency composition of PPG debt, Pound sterling (%)                                          </td><td>Shortdefinition                 </td><td>...of external long-term public and publicly-guaranteed debt contracted in U.K. pound sterling for the low- and middle-income countries....                                </td></tr>\n",
       "<tr><td>IS.SHP.DHL1.CD.IB.US</td><td>Cost to ship 1lb priority express air package through DHL from the United States (current US$)</td><td>Longdefinition                  </td><td>...is the DHL international U.S. outbound worldwide priority express rate for a 1 pound nondocument air package. Fuel, assessorial/surcharges, duties, and taxes are...    </td></tr>\n",
       "<tr><td>IS.SHP.DHL1.CD.IB.US</td><td>Cost to ship 1lb priority express air package through DHL from the United States (current US$)</td><td>Statisticalconceptandmethodology</td><td>...The data is the cost to ship a 1 pound priority air package from the United States using DHL's Express Worldwide...                                                     </td></tr>\n",
       "<tr><td>IS.SHP.DHL1.CD.OB.US</td><td>Cost to ship 1kg priority express air package through DHL to the United States (current US$)  </td><td>Statisticalconceptandmethodology</td><td>...billed weight. The actual weight uses a standard scale rounded to the next full pound. The dimensional weight uses the International Air Transport Association (IATA)...</td></tr>\n",
       "</tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "========\n",
       "Country: CHI, Channel Islands\n",
       "\n",
       "CurrencyUnit: Pound sterling\n",
       "========\n",
       "Country: EGY, Egypt, Arab Rep.\n",
       "\n",
       "CurrencyUnit: Egyptian pound\n",
       "========\n",
       "Country: GBR, United Kingdom\n",
       "\n",
       "CurrencyUnit: Pound sterling\n",
       "========\n",
       "Country: GIB, Gibraltar\n",
       "\n",
       "CurrencyUnit: Gibraltar pound\n",
       "========\n",
       "Country: IMN, Isle of Man\n",
       "\n",
       "CurrencyUnit: Pound sterling\n",
       "========\n",
       "Country: IRL, Ireland\n",
       "\n",
       "SpecialNotes: ...rate was adopted by the EU Council on January 1, 1999: 1 euro = 0.787564 Irish pound. Please note that historical data before 1999 are not actual euros and are not...\n",
       "========\n",
       "Country: LBN, Lebanon\n",
       "\n",
       "CurrencyUnit: Lebanese pound\n",
       "========\n",
       "Country: SDN, Sudan\n",
       "\n",
       "CurrencyUnit: Sudanese pound\n",
       "========\n",
       "Country: SSD, South Sudan\n",
       "\n",
       "CurrencyUnit: South Sudanese Pound\n",
       "========\n",
       "Country: SYR, Syrian Arab Republic\n",
       "\n",
       "CurrencyUnit: Syrian pound\n",
       "========\n",
       "Series: DT.CUR.UKPS.ZS, Currency composition of PPG debt, Pound sterling (%)\n",
       "\n",
       "IndicatorName: Currency composition of PPG debt, Pound sterling (%)\n",
       "--------\n",
       "Longdefinition: ...of external long-term public and publicly-guaranteed debt contracted in U.K. pound sterling for the low- and middle-income countries....\n",
       "--------\n",
       "Shortdefinition: ...of external long-term public and publicly-guaranteed debt contracted in U.K. pound sterling for the low- and middle-income countries....\n",
       "========\n",
       "Series: IS.SHP.DHL1.CD.IB.US, Cost to ship 1lb priority express air package through DHL from the United States (current US$)\n",
       "\n",
       "Longdefinition: ...is the DHL international U.S. outbound worldwide priority express rate for a 1 pound nondocument air package. Fuel, assessorial/surcharges, duties, and taxes are...\n",
       "--------\n",
       "Statisticalconceptandmethodology: ...The data is the cost to ship a 1 pound priority air package from the United States using DHL's Express Worldwide...\n",
       "========\n",
       "Series: IS.SHP.DHL1.CD.OB.US, Cost to ship 1kg priority express air package through DHL to the United States (current US$)\n",
       "\n",
       "Statisticalconceptandmethodology: ...billed weight. The actual weight uses a standard scale rounded to the next full pound. The dimensional weight uses the International Air Transport Association (IATA)..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb.search('pound')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** `wb.search` actually returns a print-friendly object. You can use a nested for loop\n",
    "to access search results programmatically, like this:\n",
    "\n",
    "    for k,v in wb.search('pound').metadata.items():\n",
    "        print(k) # k is the concept name\n",
    "        for elem in v:\n",
    "            print(elem.id)       # element ID (column 1)\n",
    "            print(elem.name)     # element name (column 2)\n",
    "            print(elem.metadata) # dictionary of metadata fields and values\n",
    "\n",
    "The results of the `info` functions can be accessed programmatically by calling `list` or `Series` instead:\n",
    "\n",
    "    for row in wb.series.list(q='co2'):\n",
    "        print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data queries ##\n",
    "\n",
    "WBGAPI can work with or without pandas, but pandas is a lot more interesting! Single identifiers and lists of identifiers can be mixed and matched. Here is a basic data query that returns a single indicator for all available economies and 5 even years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>YR2010</th>\n",
       "      <th>YR2012</th>\n",
       "      <th>YR2014</th>\n",
       "      <th>YR2016</th>\n",
       "      <th>YR2018</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ZWE</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>12697728.0</td>\n",
       "      <td>13115149.0</td>\n",
       "      <td>13586710.0</td>\n",
       "      <td>14030338.0</td>\n",
       "      <td>14438812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZMB</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>13605986.0</td>\n",
       "      <td>14465148.0</td>\n",
       "      <td>15399793.0</td>\n",
       "      <td>16363449.0</td>\n",
       "      <td>17351714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEM</th>\n",
       "      <td>Yemen, Rep.</td>\n",
       "      <td>23154854.0</td>\n",
       "      <td>24473176.0</td>\n",
       "      <td>25823488.0</td>\n",
       "      <td>27168210.0</td>\n",
       "      <td>28498683.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSE</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>3786161.0</td>\n",
       "      <td>3979998.0</td>\n",
       "      <td>4173398.0</td>\n",
       "      <td>4367088.0</td>\n",
       "      <td>4569087.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIR</th>\n",
       "      <td>Virgin Islands (U.S.)</td>\n",
       "      <td>108357.0</td>\n",
       "      <td>108188.0</td>\n",
       "      <td>107882.0</td>\n",
       "      <td>107516.0</td>\n",
       "      <td>107001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEB</th>\n",
       "      <td>Central Europe and the Baltics</td>\n",
       "      <td>104421447.0</td>\n",
       "      <td>103935318.0</td>\n",
       "      <td>103496179.0</td>\n",
       "      <td>102994278.0</td>\n",
       "      <td>102538451.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSS</th>\n",
       "      <td>Caribbean small states</td>\n",
       "      <td>6973193.0</td>\n",
       "      <td>7072640.0</td>\n",
       "      <td>7173443.0</td>\n",
       "      <td>7269385.0</td>\n",
       "      <td>7358929.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARB</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>354890097.0</td>\n",
       "      <td>371437642.0</td>\n",
       "      <td>387899835.0</td>\n",
       "      <td>404042892.0</td>\n",
       "      <td>419851989.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFW</th>\n",
       "      <td>Africa Western and Central</td>\n",
       "      <td>350556886.0</td>\n",
       "      <td>370243017.0</td>\n",
       "      <td>390882979.0</td>\n",
       "      <td>412551299.0</td>\n",
       "      <td>435229381.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFE</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>518468229.0</td>\n",
       "      <td>547482863.0</td>\n",
       "      <td>578075373.0</td>\n",
       "      <td>609978946.0</td>\n",
       "      <td>643090131.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Country       YR2010       YR2012  \\\n",
       "economy                                                             \n",
       "ZWE                            Zimbabwe   12697728.0   13115149.0   \n",
       "ZMB                              Zambia   13605986.0   14465148.0   \n",
       "YEM                         Yemen, Rep.   23154854.0   24473176.0   \n",
       "PSE                  West Bank and Gaza    3786161.0    3979998.0   \n",
       "VIR               Virgin Islands (U.S.)     108357.0     108188.0   \n",
       "...                                 ...          ...          ...   \n",
       "CEB      Central Europe and the Baltics  104421447.0  103935318.0   \n",
       "CSS              Caribbean small states    6973193.0    7072640.0   \n",
       "ARB                          Arab World  354890097.0  371437642.0   \n",
       "AFW          Africa Western and Central  350556886.0  370243017.0   \n",
       "AFE         Africa Eastern and Southern  518468229.0  547482863.0   \n",
       "\n",
       "              YR2014       YR2016       YR2018  \n",
       "economy                                         \n",
       "ZWE       13586710.0   14030338.0   14438812.0  \n",
       "ZMB       15399793.0   16363449.0   17351714.0  \n",
       "YEM       25823488.0   27168210.0   28498683.0  \n",
       "PSE        4173398.0    4367088.0    4569087.0  \n",
       "VIR         107882.0     107516.0     107001.0  \n",
       "...              ...          ...          ...  \n",
       "CEB      103496179.0  102994278.0  102538451.0  \n",
       "CSS        7173443.0    7269385.0    7358929.0  \n",
       "ARB      387899835.0  404042892.0  419851989.0  \n",
       "AFW      390882979.0  412551299.0  435229381.0  \n",
       "AFE      578075373.0  609978946.0  643090131.0  \n",
       "\n",
       "[266 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb.data.DataFrame('SP.POP.TOTL', time=range(2010, 2020, 2), labels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Charting and Plotting ##\n",
    "\n",
    "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": 5,
   "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'],\n",
    "                  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": 6,
   "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",
       "      <th>2020</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFG</th>\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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGO</th>\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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALB</th>\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>21.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARG</th>\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>NaN</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARM</th>\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>NaN</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>XKX</th>\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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEM</th>\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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZAF</th>\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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZMB</th>\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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZWE</th>\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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 16 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   NaN   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  2020  \n",
       "economy                                \n",
       "AFG      54.5   NaN   NaN   NaN   NaN  \n",
       "AGO       NaN   NaN  32.3   NaN   NaN  \n",
       "ALB       NaN   NaN   NaN  21.8   NaN  \n",
       "ARG       NaN   NaN   NaN   NaN  42.0  \n",
       "ARM       NaN   NaN   NaN   NaN  27.0  \n",
       "...       ...   ...   ...   ...   ...  \n",
       "XKX       NaN   NaN   NaN   NaN   NaN  \n",
       "YEM       NaN   NaN   NaN   NaN   NaN  \n",
       "ZAF       NaN   NaN   NaN   NaN   NaN  \n",
       "ZMB       NaN   NaN   NaN   NaN   NaN  \n",
       "ZWE       NaN   NaN   NaN  38.3   NaN  \n",
       "\n",
       "[156 rows x 16 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = wb.data.DataFrame('SI.POV.NAHC', mrnev=1, columns='time', numericTimeKeys=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here's the same data frame plotted as a bar graph\n",
    "counts = df.count()\n",
    "plt.bar(counts.index, counts)\n",
    "ax = plt.title('Distribution of Poverty Rate MRVs')"
   ]
  },
  {
   "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 Archives 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": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb.source.concepts(db=57)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "198904    1989 Apr\n",
       "199004    1990 Apr\n",
       "199009    1990 Sep\n",
       "199104    1991 Apr\n",
       "199109    1991 Sep\n",
       "            ...   \n",
       "202112    2021 Dec\n",
       "202202    2022 Feb\n",
       "202204    2022 Apr\n",
       "202207    2022 Jul\n",
       "202209    2022 Sep\n",
       "Name: value, Length: 117, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb.Series(wb.source.features('version', db=57))"
   ]
  },
  {
   "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": 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>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",
       "      <th>YR2000</th>\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",
       "      <th>YR2001</th>\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",
       "      <th>YR2002</th>\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",
       "      <th>YR2003</th>\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",
       "      <th>YR2004</th>\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",
       "      <th>YR2005</th>\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",
       "      <th>YR2006</th>\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",
       "      <th>YR2007</th>\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",
       "      <th>YR2008</th>\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",
       "      <th>YR2009</th>\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",
       "      <th>YR2010</th>\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",
       "      <th>YR2011</th>\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",
       "      <th>YR2012</th>\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",
       "      <th>YR2013</th>\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",
       "      <th>YR2014</th>\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",
       "      <th>YR2015</th>\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",
       "      <th>YR2016</th>\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",
       "      <th>YR2017</th>\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",
       "      <th>YR2018</th>\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",
       "      <th>YR2019</th>\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": 10,
     "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": 11,
   "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), title=\"Population estimates over time by WDI version\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregations\n",
    "\n",
    "Aggregations are easy in pandas. You simply use a summary function, something like this:\n",
    "\n",
    "       df.mean()\n",
    "\n",
    "For this demonstration let's start by getting a small sample of data to aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "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>YR1990</th>\n",
       "      <th>YR1991</th>\n",
       "      <th>YR1992</th>\n",
       "      <th>YR1993</th>\n",
       "      <th>YR1994</th>\n",
       "      <th>YR1995</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BRA</th>\n",
       "      <td>87.475116</td>\n",
       "      <td>90.606544</td>\n",
       "      <td>88.803833</td>\n",
       "      <td>89.999927</td>\n",
       "      <td>91.883728</td>\n",
       "      <td>91.728598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COL</th>\n",
       "      <td>89.900000</td>\n",
       "      <td>91.836655</td>\n",
       "      <td>92.132660</td>\n",
       "      <td>92.427994</td>\n",
       "      <td>92.721962</td>\n",
       "      <td>91.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PER</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.100000</td>\n",
       "      <td>64.748993</td>\n",
       "      <td>66.052933</td>\n",
       "      <td>67.354836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            YR1990     YR1991     YR1992     YR1993     YR1994     YR1995\n",
       "economy                                                                  \n",
       "BRA      87.475116  90.606544  88.803833  89.999927  91.883728  91.728598\n",
       "COL      89.900000  91.836655  92.132660  92.427994  92.721962  91.200000\n",
       "PER            NaN        NaN  70.100000  64.748993  66.052933  67.354836"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = wb.data.DataFrame('EG.ELC.ACCS.ZS', ['BRA', 'COL', 'PER'], time=range(1990,1996))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's calculate **unweighted** averages for each year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YR1990    88.687558\n",
       "YR1991    91.221600\n",
       "YR1992    83.678831\n",
       "YR1993    82.392305\n",
       "YR1994    83.552874\n",
       "YR1995    83.427811\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about **weighted** averages? Here, we need data for the weights,\n",
    "such as population, for each of values we're weighting. That means we need a data frame of population values with the same dimensions as the values we are weighting. There are a few ways to do this, but the most straight-forward is to fetch both indicators for the same set of economies and time periods in a single data frame. In this case, WBGAPI will create the dataframe with a multi-index, which you can (optionally) configure with the `index` parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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></th>\n",
       "      <th>YR1990</th>\n",
       "      <th>YR1991</th>\n",
       "      <th>YR1992</th>\n",
       "      <th>YR1993</th>\n",
       "      <th>YR1994</th>\n",
       "      <th>YR1995</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>series</th>\n",
       "      <th>economy</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",
       "      <th rowspan=\"3\" valign=\"top\">EG.ELC.ACCS.ZS</th>\n",
       "      <th>BRA</th>\n",
       "      <td>8.747512e+01</td>\n",
       "      <td>9.060654e+01</td>\n",
       "      <td>8.880383e+01</td>\n",
       "      <td>8.999993e+01</td>\n",
       "      <td>9.188373e+01</td>\n",
       "      <td>9.172860e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COL</th>\n",
       "      <td>8.990000e+01</td>\n",
       "      <td>9.183665e+01</td>\n",
       "      <td>9.213266e+01</td>\n",
       "      <td>9.242799e+01</td>\n",
       "      <td>9.272196e+01</td>\n",
       "      <td>9.120000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PER</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.010000e+01</td>\n",
       "      <td>6.474899e+01</td>\n",
       "      <td>6.605293e+01</td>\n",
       "      <td>6.735484e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">SP.POP.TOTL</th>\n",
       "      <th>BRA</th>\n",
       "      <td>1.490032e+08</td>\n",
       "      <td>1.516480e+08</td>\n",
       "      <td>1.542594e+08</td>\n",
       "      <td>1.568491e+08</td>\n",
       "      <td>1.594327e+08</td>\n",
       "      <td>1.620199e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COL</th>\n",
       "      <td>3.310257e+07</td>\n",
       "      <td>3.375833e+07</td>\n",
       "      <td>3.442257e+07</td>\n",
       "      <td>3.509127e+07</td>\n",
       "      <td>3.575898e+07</td>\n",
       "      <td>3.642144e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PER</th>\n",
       "      <td>2.207143e+07</td>\n",
       "      <td>2.252238e+07</td>\n",
       "      <td>2.296682e+07</td>\n",
       "      <td>2.340814e+07</td>\n",
       "      <td>2.385140e+07</td>\n",
       "      <td>2.429917e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              YR1990        YR1991        YR1992  \\\n",
       "series         economy                                             \n",
       "EG.ELC.ACCS.ZS BRA      8.747512e+01  9.060654e+01  8.880383e+01   \n",
       "               COL      8.990000e+01  9.183665e+01  9.213266e+01   \n",
       "               PER               NaN           NaN  7.010000e+01   \n",
       "SP.POP.TOTL    BRA      1.490032e+08  1.516480e+08  1.542594e+08   \n",
       "               COL      3.310257e+07  3.375833e+07  3.442257e+07   \n",
       "               PER      2.207143e+07  2.252238e+07  2.296682e+07   \n",
       "\n",
       "                              YR1993        YR1994        YR1995  \n",
       "series         economy                                            \n",
       "EG.ELC.ACCS.ZS BRA      8.999993e+01  9.188373e+01  9.172860e+01  \n",
       "               COL      9.242799e+01  9.272196e+01  9.120000e+01  \n",
       "               PER      6.474899e+01  6.605293e+01  6.735484e+01  \n",
       "SP.POP.TOTL    BRA      1.568491e+08  1.594327e+08  1.620199e+08  \n",
       "               COL      3.509127e+07  3.575898e+07  3.642144e+07  \n",
       "               PER      2.340814e+07  2.385140e+07  2.429917e+07  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = wb.data.DataFrame(['EG.ELC.ACCS.ZS', 'SP.POP.TOTL'], ['BRA', 'COL', 'PER'],\n",
    "                             time=range(1990,1996), index=['series', 'economy'])\n",
    "combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then (and very importantly) you must mask out the values in the weight variable that don't appear in the aggregated variable. If you don't then the denominator in the weighted average calculation will be wrong. Fortunately, pandas makes this easy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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></th>\n",
       "      <th>YR1990</th>\n",
       "      <th>YR1991</th>\n",
       "      <th>YR1992</th>\n",
       "      <th>YR1993</th>\n",
       "      <th>YR1994</th>\n",
       "      <th>YR1995</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>series</th>\n",
       "      <th>economy</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",
       "      <th rowspan=\"3\" valign=\"top\">EG.ELC.ACCS.ZS</th>\n",
       "      <th>BRA</th>\n",
       "      <td>8.747512e+01</td>\n",
       "      <td>9.060654e+01</td>\n",
       "      <td>8.880383e+01</td>\n",
       "      <td>8.999993e+01</td>\n",
       "      <td>9.188373e+01</td>\n",
       "      <td>9.172860e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COL</th>\n",
       "      <td>8.990000e+01</td>\n",
       "      <td>9.183665e+01</td>\n",
       "      <td>9.213266e+01</td>\n",
       "      <td>9.242799e+01</td>\n",
       "      <td>9.272196e+01</td>\n",
       "      <td>9.120000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PER</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.010000e+01</td>\n",
       "      <td>6.474899e+01</td>\n",
       "      <td>6.605293e+01</td>\n",
       "      <td>6.735484e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">SP.POP.TOTL</th>\n",
       "      <th>BRA</th>\n",
       "      <td>1.490032e+08</td>\n",
       "      <td>1.516480e+08</td>\n",
       "      <td>1.542594e+08</td>\n",
       "      <td>1.568491e+08</td>\n",
       "      <td>1.594327e+08</td>\n",
       "      <td>1.620199e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COL</th>\n",
       "      <td>3.310257e+07</td>\n",
       "      <td>3.375833e+07</td>\n",
       "      <td>3.442257e+07</td>\n",
       "      <td>3.509127e+07</td>\n",
       "      <td>3.575898e+07</td>\n",
       "      <td>3.642144e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PER</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.296682e+07</td>\n",
       "      <td>2.340814e+07</td>\n",
       "      <td>2.385140e+07</td>\n",
       "      <td>2.429917e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              YR1990        YR1991        YR1992  \\\n",
       "series         economy                                             \n",
       "EG.ELC.ACCS.ZS BRA      8.747512e+01  9.060654e+01  8.880383e+01   \n",
       "               COL      8.990000e+01  9.183665e+01  9.213266e+01   \n",
       "               PER               NaN           NaN  7.010000e+01   \n",
       "SP.POP.TOTL    BRA      1.490032e+08  1.516480e+08  1.542594e+08   \n",
       "               COL      3.310257e+07  3.375833e+07  3.442257e+07   \n",
       "               PER               NaN           NaN  2.296682e+07   \n",
       "\n",
       "                              YR1993        YR1994        YR1995  \n",
       "series         economy                                            \n",
       "EG.ELC.ACCS.ZS BRA      8.999993e+01  9.188373e+01  9.172860e+01  \n",
       "               COL      9.242799e+01  9.272196e+01  9.120000e+01  \n",
       "               PER      6.474899e+01  6.605293e+01  6.735484e+01  \n",
       "SP.POP.TOTL    BRA      1.568491e+08  1.594327e+08  1.620199e+08  \n",
       "               COL      3.509127e+07  3.575898e+07  3.642144e+07  \n",
       "               PER      2.340814e+07  2.385140e+07  2.429917e+07  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec_access = combined.xs('EG.ELC.ACCS.ZS')    # data frame for just a single variable\n",
    "combined = combined[elec_access.isna()==False] # mask out NaN values in other variables\n",
    "combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This makes it easy to calculate weighted averages, i.e. the sum of the weighted values divided by the sum of the weights:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YR1990    87.915903\n",
       "YR1991    90.830520\n",
       "YR1992    87.315609\n",
       "YR1993    87.650835\n",
       "YR1994    89.207879\n",
       "YR1995    88.983186\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec_access = combined.xs('EG.ELC.ACCS.ZS')\n",
    "pop = combined.xs('SP.POP.TOTL')\n",
    "(elec_access*pop).sum() / pop.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group Aggregations ##\n",
    "\n",
    "Group aggregations are just an extension of the above approach. You simply need to join\n",
    "the indicator data frames on the variable that defines your groups before calculating the\n",
    "averages. Here, let's define a toy grouping based on the first letter of the country name. As a first step, we need to create a pandas series of country first initials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ABW    A\n",
       "AFE    A\n",
       "AFG    A\n",
       "AFW    A\n",
       "AGO    A\n",
       "      ..\n",
       "XKX    K\n",
       "YEM    Y\n",
       "ZAF    S\n",
       "ZMB    Z\n",
       "ZWE    Z\n",
       "Name: Initial, Length: 266, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = wb.economy.DataFrame()\n",
    "c['Initial'] = c['name'].str[0]\n",
    "initial = c['Initial']\n",
    "\n",
    "# another way to the same thing\n",
    "initial = wb.economy.Series(name='Initial').apply(lambda n: n[0])\n",
    "\n",
    "initial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The technique for calculating a weighted average is just a variation on the previous approach:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>YR1990</th>\n",
       "      <th>YR1991</th>\n",
       "      <th>YR1992</th>\n",
       "      <th>YR1993</th>\n",
       "      <th>YR1994</th>\n",
       "      <th>YR1995</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Initial</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",
       "      <th>A</th>\n",
       "      <td>95.788657</td>\n",
       "      <td>95.961255</td>\n",
       "      <td>96.136204</td>\n",
       "      <td>96.308534</td>\n",
       "      <td>96.481401</td>\n",
       "      <td>96.654197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>89.839552</td>\n",
       "      <td>63.860951</td>\n",
       "      <td>61.129265</td>\n",
       "      <td>60.906808</td>\n",
       "      <td>63.478237</td>\n",
       "      <td>63.435008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>95.156949</td>\n",
       "      <td>88.332928</td>\n",
       "      <td>88.700518</td>\n",
       "      <td>88.911487</td>\n",
       "      <td>83.152378</td>\n",
       "      <td>81.431204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>87.243566</td>\n",
       "      <td>89.685448</td>\n",
       "      <td>90.077666</td>\n",
       "      <td>90.476783</td>\n",
       "      <td>90.888910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>76.481439</td>\n",
       "      <td>92.062789</td>\n",
       "      <td>93.925532</td>\n",
       "      <td>94.237194</td>\n",
       "      <td>91.615210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>99.999774</td>\n",
       "      <td>99.999817</td>\n",
       "      <td>99.999860</td>\n",
       "      <td>88.830781</td>\n",
       "      <td>88.562729</td>\n",
       "      <td>86.316742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>89.137363</td>\n",
       "      <td>89.937319</td>\n",
       "      <td>90.308847</td>\n",
       "      <td>91.075670</td>\n",
       "      <td>75.768477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>62.293907</td>\n",
       "      <td>75.340703</td>\n",
       "      <td>54.366609</td>\n",
       "      <td>54.654797</td>\n",
       "      <td>56.514981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J</th>\n",
       "      <td>99.357363</td>\n",
       "      <td>99.511279</td>\n",
       "      <td>99.525328</td>\n",
       "      <td>99.539295</td>\n",
       "      <td>99.553573</td>\n",
       "      <td>99.568358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <td>99.892744</td>\n",
       "      <td>99.919940</td>\n",
       "      <td>99.944659</td>\n",
       "      <td>67.770103</td>\n",
       "      <td>64.642489</td>\n",
       "      <td>71.534678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>71.773001</td>\n",
       "      <td>68.950658</td>\n",
       "      <td>68.115686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>99.801072</td>\n",
       "      <td>99.807991</td>\n",
       "      <td>71.765898</td>\n",
       "      <td>74.544849</td>\n",
       "      <td>74.802944</td>\n",
       "      <td>74.835415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N</th>\n",
       "      <td>42.296382</td>\n",
       "      <td>48.455403</td>\n",
       "      <td>45.885405</td>\n",
       "      <td>47.106669</td>\n",
       "      <td>47.497970</td>\n",
       "      <td>48.053402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>98.636873</td>\n",
       "      <td>98.764791</td>\n",
       "      <td>90.277839</td>\n",
       "      <td>77.918784</td>\n",
       "      <td>79.205871</td>\n",
       "      <td>79.737606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>96.322017</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>87.527261</td>\n",
       "      <td>86.113780</td>\n",
       "      <td>85.910763</td>\n",
       "      <td>81.683398</td>\n",
       "      <td>81.538076</td>\n",
       "      <td>81.439178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>88.700000</td>\n",
       "      <td>91.053909</td>\n",
       "      <td>6.842795</td>\n",
       "      <td>2.376779</td>\n",
       "      <td>23.303111</td>\n",
       "      <td>24.310313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>95.566763</td>\n",
       "      <td>99.967579</td>\n",
       "      <td>99.969195</td>\n",
       "      <td>95.028017</td>\n",
       "      <td>95.239458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>97.831355</td>\n",
       "      <td>98.248415</td>\n",
       "      <td>97.725368</td>\n",
       "      <td>98.353186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\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",
       "      <th>Y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.100000</td>\n",
       "      <td>40.774715</td>\n",
       "      <td>41.985134</td>\n",
       "      <td>43.193512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z</th>\n",
       "      <td>13.900000</td>\n",
       "      <td>12.752703</td>\n",
       "      <td>24.269519</td>\n",
       "      <td>23.733951</td>\n",
       "      <td>22.302605</td>\n",
       "      <td>24.726987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             YR1990      YR1991      YR1992      YR1993      YR1994  \\\n",
       "Initial                                                               \n",
       "A         95.788657   95.961255   96.136204   96.308534   96.481401   \n",
       "B         89.839552   63.860951   61.129265   60.906808   63.478237   \n",
       "C         95.156949   88.332928   88.700518   88.911487   83.152378   \n",
       "D        100.000000   87.243566   89.685448   90.077666   90.476783   \n",
       "E        100.000000   76.481439   92.062789   93.925532   94.237194   \n",
       "F        100.000000  100.000000  100.000000  100.000000  100.000000   \n",
       "G         99.999774   99.999817   99.999860   88.830781   88.562729   \n",
       "H        100.000000   89.137363   89.937319   90.308847   91.075670   \n",
       "I        100.000000   62.293907   75.340703   54.366609   54.654797   \n",
       "J         99.357363   99.511279   99.525328   99.539295   99.553573   \n",
       "K         99.892744   99.919940   99.944659   67.770103   64.642489   \n",
       "L        100.000000  100.000000  100.000000   71.773001   68.950658   \n",
       "M         99.801072   99.807991   71.765898   74.544849   74.802944   \n",
       "N         42.296382   48.455403   45.885405   47.106669   47.497970   \n",
       "O        100.000000  100.000000  100.000000  100.000000  100.000000   \n",
       "P         98.636873   98.764791   90.277839   77.918784   79.205871   \n",
       "Q        100.000000  100.000000  100.000000  100.000000  100.000000   \n",
       "R        100.000000  100.000000   96.322017  100.000000  100.000000   \n",
       "S         87.527261   86.113780   85.910763   81.683398   81.538076   \n",
       "T         88.700000   91.053909    6.842795    2.376779   23.303111   \n",
       "U        100.000000   95.566763   99.967579   99.969195   95.028017   \n",
       "V        100.000000  100.000000   97.831355   98.248415   97.725368   \n",
       "W               NaN         NaN         NaN         NaN         NaN   \n",
       "Y               NaN         NaN   44.100000   40.774715   41.985134   \n",
       "Z         13.900000   12.752703   24.269519   23.733951   22.302605   \n",
       "\n",
       "             YR1995  \n",
       "Initial              \n",
       "A         96.654197  \n",
       "B         63.435008  \n",
       "C         81.431204  \n",
       "D         90.888910  \n",
       "E         91.615210  \n",
       "F        100.000000  \n",
       "G         86.316742  \n",
       "H         75.768477  \n",
       "I         56.514981  \n",
       "J         99.568358  \n",
       "K         71.534678  \n",
       "L         68.115686  \n",
       "M         74.835415  \n",
       "N         48.053402  \n",
       "O        100.000000  \n",
       "P         79.737606  \n",
       "Q        100.000000  \n",
       "R        100.000000  \n",
       "S         81.439178  \n",
       "T         24.310313  \n",
       "U         95.239458  \n",
       "V         98.353186  \n",
       "W               NaN  \n",
       "Y         43.193512  \n",
       "Z         24.726987  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This time we'll pull all countries, not just 3. Be sure to exclude aggregate regions\n",
    "# like World or Sub-Saharan Africa\n",
    "combined = wb.data.DataFrame(['EG.ELC.ACCS.ZS', 'SP.POP.TOTL'], skipAggs=True,\n",
    "                             time=range(1990,1996), index=['series', 'economy'])\n",
    "elec_access = combined.xs('EG.ELC.ACCS.ZS')\n",
    "\n",
    "# mask out observations in the weight variable that are missing from the aggregated variable\n",
    "combined = combined[elec_access.isna()==False]\n",
    "\n",
    "pop = combined.xs('SP.POP.TOTL')\n",
    "\n",
    "numerator   = (elec_access * pop).join(initial).groupby('Initial').sum()\n",
    "denominator = pop.join(initial).groupby('Initial').sum()\n",
    "\n",
    "numerator / denominator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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