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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "World Bank Indicators Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook provides a brief demonstration of how to access the World Bank Indicators data using `pandas`.\n",
      "\n",
      "A wrapper for the API is provided as part of the main `pandas` distribution, as part of the [Remote Data Access](http://pandas.pydata.org/pandas-docs/stable/remote_data.html) support."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#First of all we need to load in the pandas library...\n",
      "import pandas as pd\n",
      "\n",
      "#...and the pandas remote data access support for calls to the World Bank Indicators API\n",
      "from pandas.io import wb"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Searching for Indicators"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The easiest way to identify an indicator is to search for it by name using a keyword or key phrase."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wb.search('fertility rate')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>name</th>\n",
        "      <th>source</th>\n",
        "      <th>sourceNote</th>\n",
        "      <th>sourceOrganization</th>\n",
        "      <th>topics</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>6554</th>\n",
        "      <td>    SP.ADO.TFRT</td>\n",
        "      <td> Adolescent fertility rate (births per 1,000 wo...</td>\n",
        "      <td>                      World Development Indicators</td>\n",
        "      <td> Adolescent fertility rate is the number of bir...</td>\n",
        "      <td> b'United Nations Population Division, World Po...</td>\n",
        "      <td> Social Development  ; Health  ; Gender</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6594</th>\n",
        "      <td> SP.DYN.TFRT.IN</td>\n",
        "      <td>          Fertility rate, total (births per woman)</td>\n",
        "      <td>                      World Development Indicators</td>\n",
        "      <td> Total fertility rate represents the number of ...</td>\n",
        "      <td> b'(1) United Nations Population Division. Worl...</td>\n",
        "      <td>                       Gender ; Health </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6595</th>\n",
        "      <td> SP.DYN.TFRT.Q1</td>\n",
        "      <td>  Total fertility rate (TFR) (births per woman)...</td>\n",
        "      <td> Health Nutrition and Population Statistics by ...</td>\n",
        "      <td> Total fertility rate (TFR): The number of chil...</td>\n",
        "      <td>                  b'Household Surveys (DHS, MICS)'</td>\n",
        "      <td>                                Health </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6596</th>\n",
        "      <td> SP.DYN.TFRT.Q2</td>\n",
        "      <td>  Total fertility rate (TFR) (births per woman)...</td>\n",
        "      <td> Health Nutrition and Population Statistics by ...</td>\n",
        "      <td> Total fertility rate (TFR): The number of chil...</td>\n",
        "      <td>                  b'Household Surveys (DHS, MICS)'</td>\n",
        "      <td>                                Health </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6597</th>\n",
        "      <td> SP.DYN.TFRT.Q3</td>\n",
        "      <td>  Total fertility rate (TFR) (births per woman)...</td>\n",
        "      <td> Health Nutrition and Population Statistics by ...</td>\n",
        "      <td> Total fertility rate (TFR): The number of chil...</td>\n",
        "      <td>                  b'Household Surveys (DHS, MICS)'</td>\n",
        "      <td>                                Health </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6598</th>\n",
        "      <td> SP.DYN.TFRT.Q4</td>\n",
        "      <td>  Total fertility rate (TFR) (births per woman)...</td>\n",
        "      <td> Health Nutrition and Population Statistics by ...</td>\n",
        "      <td> Total fertility rate (TFR): The number of chil...</td>\n",
        "      <td>                  b'Household Surveys (DHS, MICS)'</td>\n",
        "      <td>                                Health </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6599</th>\n",
        "      <td> SP.DYN.TFRT.Q5</td>\n",
        "      <td>  Total fertility rate (TFR) (births per woman)...</td>\n",
        "      <td> Health Nutrition and Population Statistics by ...</td>\n",
        "      <td> Total fertility rate (TFR): The number of chil...</td>\n",
        "      <td>                  b'Household Surveys (DHS, MICS)'</td>\n",
        "      <td>                                Health </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6602</th>\n",
        "      <td>    SP.DYN.WFRT</td>\n",
        "      <td>          Wanted fertility rate (births per woman)</td>\n",
        "      <td>                      World Development Indicators</td>\n",
        "      <td> Wanted fertility rate is an estimate of what t...</td>\n",
        "      <td> b'Demographic and Health Surveys by ICF Intern...</td>\n",
        "      <td>                       Health  ; Gender</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6603</th>\n",
        "      <td> SP.DYN.WFRT.Q1</td>\n",
        "      <td> Total wanted fertility rate (births per woman)...</td>\n",
        "      <td> Health Nutrition and Population Statistics by ...</td>\n",
        "      <td> Total wanted fertility rate: Total wanted fert...</td>\n",
        "      <td>                  b'Household Surveys (DHS, MICS)'</td>\n",
        "      <td>                                Health </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6604</th>\n",
        "      <td> SP.DYN.WFRT.Q2</td>\n",
        "      <td> Total wanted fertility rate (births per woman)...</td>\n",
        "      <td> Health Nutrition and Population Statistics by ...</td>\n",
        "      <td> Total wanted fertility rate: Total wanted fert...</td>\n",
        "      <td>                  b'Household Surveys (DHS, MICS)'</td>\n",
        "      <td>                                Health </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6605</th>\n",
        "      <td> SP.DYN.WFRT.Q3</td>\n",
        "      <td> Total wanted fertility rate (births per woman)...</td>\n",
        "      <td> Health Nutrition and Population Statistics by ...</td>\n",
        "      <td> Total wanted fertility rate: Total wanted fert...</td>\n",
        "      <td>                  b'Household Surveys (DHS, MICS)'</td>\n",
        "      <td>                                Health </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6606</th>\n",
        "      <td> SP.DYN.WFRT.Q4</td>\n",
        "      <td> Total wanted fertility rate (births per woman)...</td>\n",
        "      <td> Health Nutrition and Population Statistics by ...</td>\n",
        "      <td> Total wanted fertility rate: Total wanted fert...</td>\n",
        "      <td>                  b'Household Surveys (DHS, MICS)'</td>\n",
        "      <td>                                Health </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6607</th>\n",
        "      <td> SP.DYN.WFRT.Q5</td>\n",
        "      <td> Total wanted fertility rate (births per woman)...</td>\n",
        "      <td> Health Nutrition and Population Statistics by ...</td>\n",
        "      <td> Total wanted fertility rate: Total wanted fert...</td>\n",
        "      <td>                  b'Household Surveys (DHS, MICS)'</td>\n",
        "      <td>                                Health </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "                  id                                               name  \\\n",
        "6554     SP.ADO.TFRT  Adolescent fertility rate (births per 1,000 wo...   \n",
        "6594  SP.DYN.TFRT.IN           Fertility rate, total (births per woman)   \n",
        "6595  SP.DYN.TFRT.Q1   Total fertility rate (TFR) (births per woman)...   \n",
        "6596  SP.DYN.TFRT.Q2   Total fertility rate (TFR) (births per woman)...   \n",
        "6597  SP.DYN.TFRT.Q3   Total fertility rate (TFR) (births per woman)...   \n",
        "6598  SP.DYN.TFRT.Q4   Total fertility rate (TFR) (births per woman)...   \n",
        "6599  SP.DYN.TFRT.Q5   Total fertility rate (TFR) (births per woman)...   \n",
        "6602     SP.DYN.WFRT           Wanted fertility rate (births per woman)   \n",
        "6603  SP.DYN.WFRT.Q1  Total wanted fertility rate (births per woman)...   \n",
        "6604  SP.DYN.WFRT.Q2  Total wanted fertility rate (births per woman)...   \n",
        "6605  SP.DYN.WFRT.Q3  Total wanted fertility rate (births per woman)...   \n",
        "6606  SP.DYN.WFRT.Q4  Total wanted fertility rate (births per woman)...   \n",
        "6607  SP.DYN.WFRT.Q5  Total wanted fertility rate (births per woman)...   \n",
        "\n",
        "                                                 source  \\\n",
        "6554                       World Development Indicators   \n",
        "6594                       World Development Indicators   \n",
        "6595  Health Nutrition and Population Statistics by ...   \n",
        "6596  Health Nutrition and Population Statistics by ...   \n",
        "6597  Health Nutrition and Population Statistics by ...   \n",
        "6598  Health Nutrition and Population Statistics by ...   \n",
        "6599  Health Nutrition and Population Statistics by ...   \n",
        "6602                       World Development Indicators   \n",
        "6603  Health Nutrition and Population Statistics by ...   \n",
        "6604  Health Nutrition and Population Statistics by ...   \n",
        "6605  Health Nutrition and Population Statistics by ...   \n",
        "6606  Health Nutrition and Population Statistics by ...   \n",
        "6607  Health Nutrition and Population Statistics by ...   \n",
        "\n",
        "                                             sourceNote  \\\n",
        "6554  Adolescent fertility rate is the number of bir...   \n",
        "6594  Total fertility rate represents the number of ...   \n",
        "6595  Total fertility rate (TFR): The number of chil...   \n",
        "6596  Total fertility rate (TFR): The number of chil...   \n",
        "6597  Total fertility rate (TFR): The number of chil...   \n",
        "6598  Total fertility rate (TFR): The number of chil...   \n",
        "6599  Total fertility rate (TFR): The number of chil...   \n",
        "6602  Wanted fertility rate is an estimate of what t...   \n",
        "6603  Total wanted fertility rate: Total wanted fert...   \n",
        "6604  Total wanted fertility rate: Total wanted fert...   \n",
        "6605  Total wanted fertility rate: Total wanted fert...   \n",
        "6606  Total wanted fertility rate: Total wanted fert...   \n",
        "6607  Total wanted fertility rate: Total wanted fert...   \n",
        "\n",
        "                                     sourceOrganization  \\\n",
        "6554  b'United Nations Population Division, World Po...   \n",
        "6594  b'(1) United Nations Population Division. Worl...   \n",
        "6595                   b'Household Surveys (DHS, MICS)'   \n",
        "6596                   b'Household Surveys (DHS, MICS)'   \n",
        "6597                   b'Household Surveys (DHS, MICS)'   \n",
        "6598                   b'Household Surveys (DHS, MICS)'   \n",
        "6599                   b'Household Surveys (DHS, MICS)'   \n",
        "6602  b'Demographic and Health Surveys by ICF Intern...   \n",
        "6603                   b'Household Surveys (DHS, MICS)'   \n",
        "6604                   b'Household Surveys (DHS, MICS)'   \n",
        "6605                   b'Household Surveys (DHS, MICS)'   \n",
        "6606                   b'Household Surveys (DHS, MICS)'   \n",
        "6607                   b'Household Surveys (DHS, MICS)'   \n",
        "\n",
        "                                      topics  \n",
        "6554  Social Development  ; Health  ; Gender  \n",
        "6594                        Gender ; Health   \n",
        "6595                                 Health   \n",
        "6596                                 Health   \n",
        "6597                                 Health   \n",
        "6598                                 Health   \n",
        "6599                                 Health   \n",
        "6602                        Health  ; Gender  \n",
        "6603                                 Health   \n",
        "6604                                 Health   \n",
        "6605                                 Health   \n",
        "6606                                 Health   \n",
        "6607                                 Health   "
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#We can also get a full list of indicators\n",
      "indicators=wb.get_indicators()\n",
      "\n",
      "#Preview first few rows of indicators list\n",
      "indicators[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>name</th>\n",
        "      <th>source</th>\n",
        "      <th>sourceNote</th>\n",
        "      <th>sourceOrganization</th>\n",
        "      <th>topics</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>   1.0.HCount.1.25usd</td>\n",
        "      <td>          Poverty Headcount ($1.25 a day)</td>\n",
        "      <td> LAC Equity Lab</td>\n",
        "      <td> The poverty headcount index measures the propo...</td>\n",
        "      <td> b'LAC Equity Lab tabulations of SEDLAC (CEDLAS...</td>\n",
        "      <td> Poverty </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>     1.0.HCount.10usd</td>\n",
        "      <td> Under Middle Class ($10 a day) Headcount</td>\n",
        "      <td> LAC Equity Lab</td>\n",
        "      <td> The poverty headcount index measures the propo...</td>\n",
        "      <td> b'LAC Equity Lab tabulations of SEDLAC (CEDLAS...</td>\n",
        "      <td> Poverty </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>    1.0.HCount.2.5usd</td>\n",
        "      <td>          Poverty Headcount ($2.50 a day)</td>\n",
        "      <td> LAC Equity Lab</td>\n",
        "      <td> The poverty headcount index measures the propo...</td>\n",
        "      <td> b'LAC Equity Lab tabulations of SEDLAC (CEDLAS...</td>\n",
        "      <td> Poverty </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1.0.HCount.Mid10to50</td>\n",
        "      <td>    Middle Class ($10-50 a day) Headcount</td>\n",
        "      <td> LAC Equity Lab</td>\n",
        "      <td> The poverty headcount index measures the propo...</td>\n",
        "      <td> b'LAC Equity Lab tabulations of SEDLAC (CEDLAS...</td>\n",
        "      <td> Poverty </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>      1.0.HCount.Ofcl</td>\n",
        "      <td>  Official Moderate Poverty Rate-National</td>\n",
        "      <td> LAC Equity Lab</td>\n",
        "      <td> The poverty headcount index measures the propo...</td>\n",
        "      <td> b'LAC Equity Lab tabulations of data from Nati...</td>\n",
        "      <td> Poverty </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "                     id                                      name  \\\n",
        "0    1.0.HCount.1.25usd           Poverty Headcount ($1.25 a day)   \n",
        "1      1.0.HCount.10usd  Under Middle Class ($10 a day) Headcount   \n",
        "2     1.0.HCount.2.5usd           Poverty Headcount ($2.50 a day)   \n",
        "3  1.0.HCount.Mid10to50     Middle Class ($10-50 a day) Headcount   \n",
        "4       1.0.HCount.Ofcl   Official Moderate Poverty Rate-National   \n",
        "\n",
        "           source                                         sourceNote  \\\n",
        "0  LAC Equity Lab  The poverty headcount index measures the propo...   \n",
        "1  LAC Equity Lab  The poverty headcount index measures the propo...   \n",
        "2  LAC Equity Lab  The poverty headcount index measures the propo...   \n",
        "3  LAC Equity Lab  The poverty headcount index measures the propo...   \n",
        "4  LAC Equity Lab  The poverty headcount index measures the propo...   \n",
        "\n",
        "                                  sourceOrganization    topics  \n",
        "0  b'LAC Equity Lab tabulations of SEDLAC (CEDLAS...  Poverty   \n",
        "1  b'LAC Equity Lab tabulations of SEDLAC (CEDLAS...  Poverty   \n",
        "2  b'LAC Equity Lab tabulations of SEDLAC (CEDLAS...  Poverty   \n",
        "3  b'LAC Equity Lab tabulations of SEDLAC (CEDLAS...  Poverty   \n",
        "4  b'LAC Equity Lab tabulations of data from Nati...  Poverty   "
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If you know the identifier  - or part of the identifier - for a particular indicator, you can look up details for it directly. Use the * character as a wildcard character."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wb.search('gdp.*capita.*const')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>name</th>\n",
        "      <th>source</th>\n",
        "      <th>sourceNote</th>\n",
        "      <th>sourceOrganization</th>\n",
        "      <th>topics</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>700 </th>\n",
        "      <td> 6.0.GDPpc_constant</td>\n",
        "      <td> GDP per capita, PPP (constant 2011 internation...</td>\n",
        "      <td>               LAC Equity Lab</td>\n",
        "      <td> GDP per capita based on purchasing power parit...</td>\n",
        "      <td>  b'NULWorld Development Indicators (World Bank)L'</td>\n",
        "      <td> Economy &amp; Growth</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3496</th>\n",
        "      <td>            GDPPCKD</td>\n",
        "      <td>            GDP per Capita, constant US$, millions</td>\n",
        "      <td>       GEP Economic Prospects</td>\n",
        "      <td> GDP per capita is gross domestic product divid...</td>\n",
        "      <td> b'World Bank staff calculations based on World...</td>\n",
        "      <td> Economy &amp; Growth</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5530</th>\n",
        "      <td>     NY.GDP.PCAP.KD</td>\n",
        "      <td>                GDP per capita (constant 2005 US$)</td>\n",
        "      <td> World Development Indicators</td>\n",
        "      <td> GDP per capita is gross domestic product divid...</td>\n",
        "      <td> b'World Bank national accounts data, and OECD ...</td>\n",
        "      <td> Economy &amp; Growth</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5532</th>\n",
        "      <td>     NY.GDP.PCAP.KN</td>\n",
        "      <td>                     GDP per capita (constant LCU)</td>\n",
        "      <td> World Development Indicators</td>\n",
        "      <td> GDP per capita is gross domestic product divid...</td>\n",
        "      <td> b'World Bank national accounts data, and OECD ...</td>\n",
        "      <td> Economy &amp; Growth</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5534</th>\n",
        "      <td>  NY.GDP.PCAP.PP.KD</td>\n",
        "      <td> GDP per capita, PPP (constant 2011 internation...</td>\n",
        "      <td> World Development Indicators</td>\n",
        "      <td> GDP per capita based on purchasing power parit...</td>\n",
        "      <td> b'World Bank, International Comparison Program...</td>\n",
        "      <td> Economy &amp; Growth</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "                      id                                               name  \\\n",
        "700   6.0.GDPpc_constant  GDP per capita, PPP (constant 2011 internation...   \n",
        "3496             GDPPCKD             GDP per Capita, constant US$, millions   \n",
        "5530      NY.GDP.PCAP.KD                 GDP per capita (constant 2005 US$)   \n",
        "5532      NY.GDP.PCAP.KN                      GDP per capita (constant LCU)   \n",
        "5534   NY.GDP.PCAP.PP.KD  GDP per capita, PPP (constant 2011 internation...   \n",
        "\n",
        "                            source  \\\n",
        "700                 LAC Equity Lab   \n",
        "3496        GEP Economic Prospects   \n",
        "5530  World Development Indicators   \n",
        "5532  World Development Indicators   \n",
        "5534  World Development Indicators   \n",
        "\n",
        "                                             sourceNote  \\\n",
        "700   GDP per capita based on purchasing power parit...   \n",
        "3496  GDP per capita is gross domestic product divid...   \n",
        "5530  GDP per capita is gross domestic product divid...   \n",
        "5532  GDP per capita is gross domestic product divid...   \n",
        "5534  GDP per capita based on purchasing power parit...   \n",
        "\n",
        "                                     sourceOrganization            topics  \n",
        "700    b'NULWorld Development Indicators (World Bank)L'  Economy & Growth  \n",
        "3496  b'World Bank staff calculations based on World...  Economy & Growth  \n",
        "5530  b'World Bank national accounts data, and OECD ...  Economy & Growth  \n",
        "5532  b'World Bank national accounts data, and OECD ...  Economy & Growth  \n",
        "5534  b'World Bank, International Comparison Program...  Economy & Growth  "
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Identifying Countries"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "When retrieving a dataset, we can specifiy which country, countries or regions we want the data for. The locations are identified using the appropriate ISO-2 code. To look up countries we can download the full country list."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#We can get a list of the countries and regions that indicator data may be available for\n",
      "countries=wb.get_countries()\n",
      "\n",
      "#Preview first few rows of countries list\n",
      "countries[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>adminregion</th>\n",
        "      <th>capitalCity</th>\n",
        "      <th>iso3c</th>\n",
        "      <th>incomeLevel</th>\n",
        "      <th>iso2c</th>\n",
        "      <th>latitude</th>\n",
        "      <th>lendingType</th>\n",
        "      <th>longitude</th>\n",
        "      <th>name</th>\n",
        "      <th>region</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>                                        </td>\n",
        "      <td> Oranjestad</td>\n",
        "      <td> ABW</td>\n",
        "      <td> High income: nonOECD</td>\n",
        "      <td> AW</td>\n",
        "      <td>  12.5167</td>\n",
        "      <td> Not classified</td>\n",
        "      <td> -70.0167</td>\n",
        "      <td>       Aruba</td>\n",
        "      <td> Latin America &amp; Caribbean (all income levels)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>                              South Asia</td>\n",
        "      <td>      Kabul</td>\n",
        "      <td> AFG</td>\n",
        "      <td>           Low income</td>\n",
        "      <td> AF</td>\n",
        "      <td>  34.5228</td>\n",
        "      <td>            IDA</td>\n",
        "      <td>  69.1761</td>\n",
        "      <td> Afghanistan</td>\n",
        "      <td>                                    South Asia</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>                                        </td>\n",
        "      <td>           </td>\n",
        "      <td> AFR</td>\n",
        "      <td>           Aggregates</td>\n",
        "      <td> A9</td>\n",
        "      <td>         </td>\n",
        "      <td>     Aggregates</td>\n",
        "      <td>         </td>\n",
        "      <td>      Africa</td>\n",
        "      <td>                                    Aggregates</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>    Sub-Saharan Africa (developing only)</td>\n",
        "      <td>     Luanda</td>\n",
        "      <td> AGO</td>\n",
        "      <td>  Upper middle income</td>\n",
        "      <td> AO</td>\n",
        "      <td> -8.81155</td>\n",
        "      <td>           IBRD</td>\n",
        "      <td>   13.242</td>\n",
        "      <td>      Angola</td>\n",
        "      <td>        Sub-Saharan Africa (all income levels)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> Europe &amp; Central Asia (developing only)</td>\n",
        "      <td>     Tirane</td>\n",
        "      <td> ALB</td>\n",
        "      <td>  Upper middle income</td>\n",
        "      <td> AL</td>\n",
        "      <td>  41.3317</td>\n",
        "      <td>           IBRD</td>\n",
        "      <td>  19.8172</td>\n",
        "      <td>     Albania</td>\n",
        "      <td>     Europe &amp; Central Asia (all income levels)</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "                               adminregion capitalCity iso3c  \\\n",
        "0                                           Oranjestad   ABW   \n",
        "1                               South Asia       Kabul   AFG   \n",
        "2                                                        AFR   \n",
        "3     Sub-Saharan Africa (developing only)      Luanda   AGO   \n",
        "4  Europe & Central Asia (developing only)      Tirane   ALB   \n",
        "\n",
        "            incomeLevel iso2c  latitude     lendingType longitude  \\\n",
        "0  High income: nonOECD    AW   12.5167  Not classified  -70.0167   \n",
        "1            Low income    AF   34.5228             IDA   69.1761   \n",
        "2            Aggregates    A9                Aggregates             \n",
        "3   Upper middle income    AO  -8.81155            IBRD    13.242   \n",
        "4   Upper middle income    AL   41.3317            IBRD   19.8172   \n",
        "\n",
        "          name                                         region  \n",
        "0        Aruba  Latin America & Caribbean (all income levels)  \n",
        "1  Afghanistan                                     South Asia  \n",
        "2       Africa                                     Aggregates  \n",
        "3       Angola         Sub-Saharan Africa (all income levels)  \n",
        "4      Albania      Europe & Central Asia (all income levels)  "
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#pandas dataframes allow us to search within the country list for a particular country\n",
      "countries[ countries['name'] == 'Angola' ]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>adminregion</th>\n",
        "      <th>capitalCity</th>\n",
        "      <th>iso3c</th>\n",
        "      <th>incomeLevel</th>\n",
        "      <th>iso2c</th>\n",
        "      <th>latitude</th>\n",
        "      <th>lendingType</th>\n",
        "      <th>longitude</th>\n",
        "      <th>name</th>\n",
        "      <th>region</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Sub-Saharan Africa (developing only)</td>\n",
        "      <td> Luanda</td>\n",
        "      <td> AGO</td>\n",
        "      <td> Upper middle income</td>\n",
        "      <td> AO</td>\n",
        "      <td> -8.81155</td>\n",
        "      <td> IBRD</td>\n",
        "      <td> 13.242</td>\n",
        "      <td> Angola</td>\n",
        "      <td> Sub-Saharan Africa (all income levels)</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "                            adminregion capitalCity iso3c  \\\n",
        "3  Sub-Saharan Africa (developing only)      Luanda   AGO   \n",
        "\n",
        "           incomeLevel iso2c  latitude lendingType longitude    name  \\\n",
        "3  Upper middle income    AO  -8.81155        IBRD    13.242  Angola   \n",
        "\n",
        "                                   region  \n",
        "3  Sub-Saharan Africa (all income levels)  "
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Download Data for a Particular Indicator"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Once you have identified one or more indicators for which you would like to download a dataset, you need to identify the year or range of years, and the country, countries or regions (identified via their ISO-2 code) for which you would like the data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Download data from the World Bank API into a dataframe\n",
      "\n",
      "df = wb.download(\n",
      "                    #Use the indicator attribute to identify which indicator or indicators to download\n",
      "                    indicator='NY.GDP.PCAP.KD',\n",
      "                    #Use the country attribute to identify the countries you want data for\n",
      "                    country=['US', 'CA', 'MX'],\n",
      "                    #Identify the first year for which you want the data, as an integer or a string\n",
      "                    start='2008',\n",
      "                    #Identify the last year for which you want the data, as an integer or a string\n",
      "                    end=2010\n",
      "                )\n",
      "\n",
      "#Show the dataframe \n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>NY.GDP.PCAP.KD</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>country</th>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">Canada</th>\n",
        "      <th>2010</th>\n",
        "      <td> 36466.815112</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 35671.659294</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 37088.020368</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">Mexico</th>\n",
        "      <th>2010</th>\n",
        "      <td>  8084.629000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td>  7788.271761</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td>  8275.809458</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">United States</th>\n",
        "      <th>2010</th>\n",
        "      <td> 43952.436548</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 43234.451155</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 44872.653626</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "                    NY.GDP.PCAP.KD\n",
        "country       year                \n",
        "Canada        2010    36466.815112\n",
        "              2009    35671.659294\n",
        "              2008    37088.020368\n",
        "Mexico        2010     8084.629000\n",
        "              2009     7788.271761\n",
        "              2008     8275.809458\n",
        "United States 2010    43952.436548\n",
        "              2009    43234.451155\n",
        "              2008    44872.653626"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#To download data for multiple indicators, specify them as a list\n",
      "wb.download( indicator=['SP.DYN.TFRT.IN','NY.GDP.PCAP.KD'], country=['US','GB'], start=2008, end=2010 )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>SP.DYN.TFRT.IN</th>\n",
        "      <th>NY.GDP.PCAP.KD</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>country</th>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">United Kingdom</th>\n",
        "      <th>2010</th>\n",
        "      <td> 1.920</td>\n",
        "      <td> 37600.293399</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 1.890</td>\n",
        "      <td> 37277.481537</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 1.910</td>\n",
        "      <td> 39608.431481</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">United States</th>\n",
        "      <th>2010</th>\n",
        "      <td> 1.931</td>\n",
        "      <td> 43952.436548</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 2.002</td>\n",
        "      <td> 43234.451155</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 2.072</td>\n",
        "      <td> 44872.653626</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "                     SP.DYN.TFRT.IN  NY.GDP.PCAP.KD\n",
        "country        year                                \n",
        "United Kingdom 2010           1.920    37600.293399\n",
        "               2009           1.890    37277.481537\n",
        "               2008           1.910    39608.431481\n",
        "United States  2010           1.931    43952.436548\n",
        "               2009           2.002    43234.451155\n",
        "               2008           2.072    44872.653626"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#We can download data for a single year by setting the start and end dates to the same year\n",
      "#To download data for a single country, you do not need to specify it as a list\n",
      "df = wb.download( indicator='NY.GDP.PCAP.KD', country='US', start=2008, end=2008 )\n",
      "\n",
      "#Show the dataframe \n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>NY.GDP.PCAP.KD</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>country</th>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>United States</th>\n",
        "      <th>2008</th>\n",
        "      <td> 44872.653626</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "                    NY.GDP.PCAP.KD\n",
        "country       year                \n",
        "United States 2008    44872.653626"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#To download the data for all countries, set the country attribute to 'all'\n",
      "df = wb.download( indicator='SP.DYN.TFRT.IN', country='all', start=2010, end=2010 )\n",
      "\n",
      "#Show a preview of the the first few rows of the dataframe \n",
      "df[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>SP.DYN.TFRT.IN</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>country</th>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Andean Region</th>\n",
        "      <th>2010</th>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arab World</th>\n",
        "      <th>2010</th>\n",
        "      <td> 3.297409</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Caribbean small states</th>\n",
        "      <th>2010</th>\n",
        "      <td> 2.224484</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Central Europe and the Baltics</th>\n",
        "      <th>2010</th>\n",
        "      <td> 1.445135</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>East Asia &amp; Pacific (all income levels)</th>\n",
        "      <th>2010</th>\n",
        "      <td> 1.817921</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>East Asia &amp; Pacific (developing only)</th>\n",
        "      <th>2010</th>\n",
        "      <td> 1.856321</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>East Asia and the Pacific (IFC classification)</th>\n",
        "      <th>2010</th>\n",
        "      <td>      NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Euro area</th>\n",
        "      <th>2010</th>\n",
        "      <td> 1.573820</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Europe &amp; Central Asia (all income levels)</th>\n",
        "      <th>2010</th>\n",
        "      <td> 1.723861</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Europe &amp; Central Asia (developing only)</th>\n",
        "      <th>2010</th>\n",
        "      <td> 1.979588</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "                                                     SP.DYN.TFRT.IN\n",
        "country                                        year                \n",
        "Andean Region                                  2010             NaN\n",
        "Arab World                                     2010        3.297409\n",
        "Caribbean small states                         2010        2.224484\n",
        "Central Europe and the Baltics                 2010        1.445135\n",
        "East Asia & Pacific (all income levels)        2010        1.817921\n",
        "East Asia & Pacific (developing only)          2010        1.856321\n",
        "East Asia and the Pacific (IFC classification) 2010             NaN\n",
        "Euro area                                      2010        1.573820\n",
        "Europe & Central Asia (all income levels)      2010        1.723861\n",
        "Europe & Central Asia (developing only)        2010        1.979588"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice that selecting *all* countries also pulls indicators back for different regional groupings as well as countries."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Summary"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`pandas` support for remote data access makes it easy for us to get data from the World Bank Indicators API into a `pandas` dataframe, where we can start to work with it."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}