02-Choropleth Maps Exercise.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Choropleth Maps Exercise \n",
    "\n",
    "Welcome to the Choropleth Maps Exercise! In this exercise we will give you some simple datasets and ask you to create Choropleth Maps from them. Due to the Nature of Plotly we can't show you examples\n",
    "\n",
    "[Full Documentation Reference](https://plot.ly/python/reference/#choropleth)\n",
    "\n",
    "## Plotly Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objs as go \n",
    "from plotly.offline import init_notebook_mode,iplot\n",
    "init_notebook_mode(connected=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Import pandas and read the csv file: 2014_World_Power_Consumption**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('2014_World_Power_Consumption');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the head of the DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Power Consumption KWH</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>5.523000e+12</td>\n",
       "      <td>China 5,523,000,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States</td>\n",
       "      <td>3.832000e+12</td>\n",
       "      <td>United 3,832,000,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>European</td>\n",
       "      <td>2.771000e+12</td>\n",
       "      <td>European 2,771,000,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Russia</td>\n",
       "      <td>1.065000e+12</td>\n",
       "      <td>Russia 1,065,000,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japan</td>\n",
       "      <td>9.210000e+11</td>\n",
       "      <td>Japan 921,000,000,000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Power Consumption KWH                        Text\n",
       "0          China           5.523000e+12     China 5,523,000,000,000\n",
       "1  United States           3.832000e+12    United 3,832,000,000,000\n",
       "2       European           2.771000e+12  European 2,771,000,000,000\n",
       "3         Russia           1.065000e+12    Russia 1,065,000,000,000\n",
       "4          Japan           9.210000e+11       Japan 921,000,000,000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Referencing the lecture notes, create a Choropleth Plot of the Power Consumption for Countries using the data and layout dictionary. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = dict(type='choropleth',\n",
    "            locations = df['Country'],\n",
    "            locationmode = \"country names\", #this needs to adjusted according to the data\n",
    "            z = df['Power Consumption KWH'],\n",
    "            text = df['Text'],\n",
    "            colorbar = {'title':\"KWH\"}\n",
    "            ) \n",
    "layout = dict(\n",
    "    title = '2014 Global Power Consumption',\n",
    "    geo = dict(\n",
    "        showframe = False,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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        {
         "colorbar": {
          "title": {
           "text": "KWH"
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         },
         "locationmode": "country names",
         "locations": [
          "China",
          "United States",
          "European",
          "Russia",
          "Japan",
          "India",
          "Germany",
          "Canada",
          "Brazil",
          "Korea,",
          "France",
          "United Kingdom",
          "Italy",
          "Taiwan",
          "Spain",
          "Mexico",
          "Saudi",
          "Australia",
          "South",
          "Turkey",
          "Iran",
          "Indonesia",
          "Ukraine",
          "Thailand",
          "Poland",
          "Egypt",
          "Sweden",
          "Norway",
          "Malaysia",
          "Argentina",
          "Netherlands",
          "Vietnam",
          "Venezuela",
          "United Arab Emirates",
          "Finland",
          "Belgium",
          "Kazakhstan",
          "Pakistan",
          "Philippines",
          "Austria",
          "Chile",
          "Czechia",
          "Israel",
          "Switzerland",
          "Greece",
          "Iraq",
          "Romania",
          "Kuwait",
          "Colombia",
          "Singapore",
          "Portugal",
          "Uzbekistan",
          "Hong",
          "Algeria",
          "Bangladesh",
          "New",
          "Bulgaria",
          "Belarus",
          "Peru",
          "Denmark",
          "Qatar",
          "Slovakia",
          "Libya",
          "Serbia",
          "Morocco",
          "Syria",
          "Nigeria",
          "Ireland",
          "Hungary",
          "Oman",
          "Ecuador",
          "Puerto",
          "Azerbaijan",
          "Croatia",
          "Iceland",
          "Cuba",
          "Korea,",
          "Dominican",
          "Jordan",
          "Tajikistan",
          "Tunisia",
          "Slovenia",
          "Lebanon",
          "Bosnia",
          "Turkmenistan",
          "Bahrain",
          "Mozambique",
          "Ghana",
          "Sri",
          "Kyrgyzstan",
          "Lithuania",
          "Uruguay",
          "Costa",
          "Guatemala",
          "Georgia",
          "Trinidad",
          "Zambia",
          "Paraguay",
          "Albania",
          "Burma",
          "Estonia",
          "Congo,",
          "Panama",
          "Latvia",
          "Macedonia",
          "Zimbabwe",
          "Kenya",
          "Bolivia",
          "Luxembourg",
          "Sudan",
          "El",
          "Cameroon",
          "West",
          "Ethiopia",
          "Armenia",
          "Honduras",
          "Angola",
          "Cote",
          "Tanzania",
          "Nicaragua",
          "Moldova",
          "Cyprus",
          "Macau",
          "Namibia",
          "Mongolia",
          "Afghanistan",
          "Yemen",
          "Brunei",
          "Cambodia",
          "Montenegro",
          "Nepal",
          "Botswana",
          "Papua",
          "Jamaica",
          "Kosovo",
          "Laos",
          "Uganda",
          "New",
          "Mauritius",
          "Senegal",
          "Bhutan",
          "Malawi",
          "Madagascar",
          "Bahamas,",
          "Gabon",
          "Suriname",
          "Guam",
          "Liechtenstein",
          "Swaziland",
          "Burkina",
          "Togo",
          "Curacao",
          "Mauritania",
          "Barbados",
          "Niger",
          "Aruba",
          "Benin",
          "Guinea",
          "Mali",
          "Fiji",
          "Congo,",
          "Virgin",
          "Lesotho",
          "South",
          "Bermuda",
          "French",
          "Jersey",
          "Belize",
          "Andorra",
          "Guyana",
          "Cayman",
          "Haiti",
          "Rwanda",
          "Saint",
          "Djibouti",
          "Seychelles",
          "Somalia",
          "Antigua",
          "Greenland",
          "Cabo",
          "Eritrea",
          "Burundi",
          "Liberia",
          "Maldives",
          "Faroe",
          "Gambia,",
          "Chad",
          "Micronesia,",
          "Grenada",
          "Central",
          "Turks",
          "Gibraltar",
          "American",
          "Sierra",
          "Saint",
          "Saint",
          "Timor-Leste",
          "Equatorial",
          "Samoa",
          "Dominica",
          "Western",
          "Solomon",
          "Sao",
          "British",
          "Vanuatu",
          "Guinea-Bissau",
          "Tonga",
          "Saint",
          "Comoros",
          "Cook",
          "Kiribati",
          "Montserrat",
          "Nauru",
          "Falkland",
          "Saint",
          "Niue",
          "Gaza",
          "Malta",
          "Northern"
         ],
         "text": [
          "China 5,523,000,000,000",
          "United 3,832,000,000,000",
          "European 2,771,000,000,000",
          "Russia 1,065,000,000,000",
          "Japan 921,000,000,000",
          "India 864,700,000,000",
          "Germany 540,100,000,000",
          "Canada 511,000,000,000",
          "Brazil 483,500,000,000",
          "Korea, 482,400,000,000",
          "France 451,100,000,000",
          "United 319,100,000,000",
          "Italy 303,100,000,000",
          "Taiwan 249,500,000,000",
          "Spain 243,100,000,000",
          "Mexico 234,000,000,000",
          "Saudi 231,600,000,000",
          "Australia 222,600,000,000",
          "South 211,600,000,000",
          "Turkey 197,000,000,000",
          "Iran 195,300,000,000",
          "Indonesia 167,500,000,000",
          "Ukraine 159,800,000,000",
          "Thailand 155,900,000,000",
          "Poland 139,000,000,000",
          "Egypt 135,600,000,000",
          "Sweden 130,500,000,000",
          "Norway 126,400,000,000",
          "Malaysia 118,500,000,000",
          "Argentina 117,100,000,000",
          "Netherlands 116,800,000,000",
          "Vietnam 108,300,000,000",
          "Venezuela 97,690,000,000",
          "United 93,280,000,000",
          "Finland 82,040,000,000",
          "Belgium 81,890,000,000",
          "Kazakhstan 80,290,000,000",
          "Pakistan 78,890,000,000",
          "Philippines 75,270,000,000",
          "Austria 69,750,000,000",
          "Chile 63,390,000,000",
          "Czechia 60,550,000,000",
          "Israel 59,830,000,000",
          "Switzerland 58,010,000,000",
          "Greece 57,730,000,000",
          "Iraq 53,410,000,000",
          "Romania 50,730,000,000",
          "Kuwait 50,000,000,000",
          "Colombia 49,380,000,000",
          "Singapore 47,180,000,000",
          "Portugal 46,250,000,000",
          "Uzbekistan 45,210,000,000",
          "Hong 44,210,000,000",
          "Algeria 42,870,000,000",
          "Bangladesh 41,520,000,000",
          "New 40,300,000,000",
          "Bulgaria 37,990,000,000",
          "Belarus 37,880,000,000",
          "Peru 35,690,000,000",
          "Denmark 31,960,000,000",
          "Qatar 30,530,000,000",
          "Slovakia 28,360,000,000",
          "Libya 27,540,000,000",
          "Serbia 26,910,000,000",
          "Morocco 26,700,000,000",
          "Syria 25,700,000,000",
          "Nigeria 24,780,000,000",
          "Ireland 24,240,000,000",
          "Hungary 21,550,000,000",
          "Oman 20,360,000,000",
          "Ecuador 19,020,000,000",
          "Puerto 18,620,000,000",
          "Azerbaijan 17,790,000,000",
          "Croatia 16,970,000,000",
          "Iceland 16,940,000,000",
          "Cuba 16,200,000,000",
          "Korea, 16,000,000,000",
          "Dominican 15,140,000,000",
          "Jordan 14,560,000,000",
          "Tajikistan 14,420,000,000",
          "Tunisia 13,310,000,000",
          "Slovenia 13,020,000,000",
          "Lebanon 12,940,000,000",
          "Bosnia 12,560,000,000",
          "Turkmenistan 11,750,000,000",
          "Bahrain 11,690,000,000",
          "Mozambique 11,280,000,000",
          "Ghana 10,580,000,000",
          "Sri 10,170,000,000",
          "Kyrgyzstan 9,943,000,000",
          "Lithuania 9,664,000,000",
          "Uruguay 9,559,000,000",
          "Costa 8,987,000,000",
          "Guatemala 8,915,000,000",
          "Georgia 8,468,000,000",
          "Trinidad 8,365,000,000",
          "Zambia 8,327,000,000",
          "Paraguay 8,125,000,000",
          "Albania 7,793,000,000",
          "Burma 7,765,000,000",
          "Estonia 7,417,000,000",
          "Congo, 7,292,000,000",
          "Panama 7,144,000,000",
          "Latvia 7,141,000,000",
          "Macedonia 6,960,000,000",
          "Zimbabwe 6,831,000,000",
          "Kenya 6,627,000,000",
          "Bolivia 6,456,000,000",
          "Luxembourg 6,108,000,000",
          "Sudan 5,665,000,000",
          "El 5,665,000,000",
          "Cameroon 5,535,000,000",
          "West 5,312,000,000",
          "Ethiopia 5,227,000,000",
          "Armenia 5,043,000,000",
          "Honduras 5,036,000,000",
          "Angola 4,842,000,000",
          "Cote 4,731,000,000",
          "Tanzania 4,545,000,000",
          "Nicaragua 4,412,000,000",
          "Moldova 4,305,000,000",
          "Cyprus 4,296,000,000",
          "Macau 4,291,000,000",
          "Namibia 4,238,000,000",
          "Mongolia 4,204,000,000",
          "Afghanistan 3,893,000,000",
          "Yemen 3,838,000,000",
          "Brunei 3,766,000,000",
          "Cambodia 3,553,000,000",
          "Montenegro 3,465,000,000",
          "Nepal 3,239,000,000",
          "Botswana 3,213,000,000",
          "Papua 3,116,000,000",
          "Jamaica 3,008,000,000",
          "Kosovo 2,887,000,000",
          "Laos 2,874,000,000",
          "Uganda 2,821,000,000",
          "New 2,716,000,000",
          "Mauritius 2,658,000,000",
          "Senegal 2,586,000,000",
          "Bhutan 2,085,000,000",
          "Malawi 2,027,000,000",
          "Madagascar 1,883,000,000",
          "Bahamas, 1,716,000,000",
          "Gabon 1,680,000,000",
          "Suriname 1,572,000,000",
          "Guam 1,566,000,000",
          "Liechtenstein 1,360,000,000",
          "Swaziland 1,295,000,000",
          "Burkina 985,500,000",
          "Togo 976,000,000",
          "Curacao 968,000,000",
          "Mauritania 962,600,000",
          "Barbados 938,000,000",
          "Niger 930,200,000",
          "Aruba 920,700,000",
          "Benin 911,000,000",
          "Guinea 903,000,000",
          "Mali 882,600,000",
          "Fiji 777,600,000",
          "Congo, 740,000,000",
          "Virgin 723,500,000",
          "Lesotho 707,000,000",
          "South 694,100,000",
          "Bermuda 664,200,000",
          "French 652,900,000",
          "Jersey 630,100,000",
          "Belize 605,000,000",
          "Andorra 562,400,000",
          "Guyana 558,000,000",
          "Cayman 545,900,000",
          "Haiti 452,000,000",
          "Rwanda 365,500,000",
          "Saint 336,400,000",
          "Djibouti 311,600,000",
          "Seychelles 293,900,000",
          "Somalia 293,000,000",
          "Antigua 293,000,000",
          "Greenland 292,000,000",
          "Cabo 285,500,000",
          "Eritrea 284,000,000",
          "Burundi 282,900,000",
          "Liberia 276,900,000",
          "Maldives 267,100,000",
          "Faroe 261,300,000",
          "Gambia, 218,600,000",
          "Chad 190,700,000",
          "Micronesia, 178,600,000",
          "Grenada 178,000,000",
          "Central 168,300,000",
          "Turks 167,400,000",
          "Gibraltar 160,000,000",
          "American 146,000,000",
          "Sierra 134,900,000",
          "Saint 130,200,000",
          "Saint 127,400,000",
          "Timor-Leste 125,300,000",
          "Equatorial 93,000,000",
          "Samoa 90,400,000",
          "Dominica 89,750,000",
          "Western 83,700,000",
          "Solomon 79,050,000",
          "Sao 60,450,000",
          "British 51,150,000",
          "Vanuatu 49,290,000",
          "Guinea-Bissau 46,500,000",
          "Tonga 44,640,000",
          "Saint 39,990,000",
          "Comoros 39,990,000",
          "Cook 28,950,000",
          "Kiribati 24,180,000",
          "Montserrat 23,250,000",
          "Nauru 23,250,000",
          "Falkland 11,160,000",
          "Saint 7,440,000",
          "Niue 2,790,000",
          "Gaza 202,000",
          "Malta 174,700",
          "Northern 48,300"
         ],
         "type": "choropleth",
         "z": [
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19,020,000,000\", \"Puerto 18,620,000,000\", \"Azerbaijan 17,790,000,000\", \"Croatia 16,970,000,000\", \"Iceland 16,940,000,000\", \"Cuba 16,200,000,000\", \"Korea, 16,000,000,000\", \"Dominican 15,140,000,000\", \"Jordan 14,560,000,000\", \"Tajikistan 14,420,000,000\", \"Tunisia 13,310,000,000\", \"Slovenia 13,020,000,000\", \"Lebanon 12,940,000,000\", \"Bosnia 12,560,000,000\", \"Turkmenistan 11,750,000,000\", \"Bahrain 11,690,000,000\", \"Mozambique 11,280,000,000\", \"Ghana 10,580,000,000\", \"Sri 10,170,000,000\", \"Kyrgyzstan 9,943,000,000\", \"Lithuania 9,664,000,000\", \"Uruguay 9,559,000,000\", \"Costa 8,987,000,000\", \"Guatemala 8,915,000,000\", \"Georgia 8,468,000,000\", \"Trinidad 8,365,000,000\", \"Zambia 8,327,000,000\", \"Paraguay 8,125,000,000\", \"Albania 7,793,000,000\", \"Burma 7,765,000,000\", \"Estonia 7,417,000,000\", \"Congo, 7,292,000,000\", \"Panama 7,144,000,000\", \"Latvia 7,141,000,000\", \"Macedonia 6,960,000,000\", \"Zimbabwe 6,831,000,000\", \"Kenya 6,627,000,000\", \"Bolivia 6,456,000,000\", \"Luxembourg 6,108,000,000\", \"Sudan 5,665,000,000\", \"El 5,665,000,000\", \"Cameroon 5,535,000,000\", \"West 5,312,000,000\", \"Ethiopia 5,227,000,000\", \"Armenia 5,043,000,000\", \"Honduras 5,036,000,000\", \"Angola 4,842,000,000\", \"Cote 4,731,000,000\", \"Tanzania 4,545,000,000\", \"Nicaragua 4,412,000,000\", \"Moldova 4,305,000,000\", \"Cyprus 4,296,000,000\", \"Macau 4,291,000,000\", \"Namibia 4,238,000,000\", \"Mongolia 4,204,000,000\", \"Afghanistan 3,893,000,000\", \"Yemen 3,838,000,000\", \"Brunei 3,766,000,000\", \"Cambodia 3,553,000,000\", \"Montenegro 3,465,000,000\", \"Nepal 3,239,000,000\", \"Botswana 3,213,000,000\", \"Papua 3,116,000,000\", \"Jamaica 3,008,000,000\", \"Kosovo 2,887,000,000\", \"Laos 2,874,000,000\", \"Uganda 2,821,000,000\", \"New 2,716,000,000\", \"Mauritius 2,658,000,000\", \"Senegal 2,586,000,000\", \"Bhutan 2,085,000,000\", \"Malawi 2,027,000,000\", \"Madagascar 1,883,000,000\", \"Bahamas, 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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "choromap = go.Figure(data = [data],layout = layout)\n",
    "iplot(choromap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## USA Choropleth\n",
    "\n",
    "** Import the 2012_Election_Data csv file using pandas. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=pd.read_csv('2012_Election_Data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the head of the DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Year</th>\n",
       "      <th>ICPSR State Code</th>\n",
       "      <th>Alphanumeric State Code</th>\n",
       "      <th>State</th>\n",
       "      <th>VEP Total Ballots Counted</th>\n",
       "      <th>VEP Highest Office</th>\n",
       "      <th>VAP Highest Office</th>\n",
       "      <th>Total Ballots Counted</th>\n",
       "      <th>Highest Office</th>\n",
       "      <th>Voting-Eligible Population (VEP)</th>\n",
       "      <th>Voting-Age Population (VAP)</th>\n",
       "      <th>% Non-citizen</th>\n",
       "      <th>Prison</th>\n",
       "      <th>Probation</th>\n",
       "      <th>Parole</th>\n",
       "      <th>Total Ineligible Felon</th>\n",
       "      <th>State Abv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.6%</td>\n",
       "      <td>56.0%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,074,338</td>\n",
       "      <td>3,539,217</td>\n",
       "      <td>3707440.0</td>\n",
       "      <td>2.6%</td>\n",
       "      <td>32,232</td>\n",
       "      <td>57,993</td>\n",
       "      <td>8,616</td>\n",
       "      <td>71,584</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>81</td>\n",
       "      <td>2</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>58.9%</td>\n",
       "      <td>58.7%</td>\n",
       "      <td>55.3%</td>\n",
       "      <td>301,694</td>\n",
       "      <td>300,495</td>\n",
       "      <td>511,792</td>\n",
       "      <td>543763.0</td>\n",
       "      <td>3.8%</td>\n",
       "      <td>5,633</td>\n",
       "      <td>7,173</td>\n",
       "      <td>1,882</td>\n",
       "      <td>11,317</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>53.0%</td>\n",
       "      <td>52.6%</td>\n",
       "      <td>46.5%</td>\n",
       "      <td>2,323,579</td>\n",
       "      <td>2,306,559</td>\n",
       "      <td>4,387,900</td>\n",
       "      <td>4959270.0</td>\n",
       "      <td>9.9%</td>\n",
       "      <td>35,188</td>\n",
       "      <td>72,452</td>\n",
       "      <td>7,460</td>\n",
       "      <td>81,048</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>51.1%</td>\n",
       "      <td>50.7%</td>\n",
       "      <td>47.7%</td>\n",
       "      <td>1,078,548</td>\n",
       "      <td>1,069,468</td>\n",
       "      <td>2,109,847</td>\n",
       "      <td>2242740.0</td>\n",
       "      <td>3.5%</td>\n",
       "      <td>14,471</td>\n",
       "      <td>30,122</td>\n",
       "      <td>23,372</td>\n",
       "      <td>53,808</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>71</td>\n",
       "      <td>5</td>\n",
       "      <td>California</td>\n",
       "      <td>55.7%</td>\n",
       "      <td>55.1%</td>\n",
       "      <td>45.1%</td>\n",
       "      <td>13,202,158</td>\n",
       "      <td>13,038,547</td>\n",
       "      <td>23,681,837</td>\n",
       "      <td>28913129.0</td>\n",
       "      <td>17.4%</td>\n",
       "      <td>119,455</td>\n",
       "      <td>0</td>\n",
       "      <td>89,287</td>\n",
       "      <td>208,742</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  ICPSR State Code  Alphanumeric State Code       State  \\\n",
       "0  2012                41                        1     Alabama   \n",
       "1  2012                81                        2      Alaska   \n",
       "2  2012                61                        3     Arizona   \n",
       "3  2012                42                        4    Arkansas   \n",
       "4  2012                71                        5  California   \n",
       "\n",
       "  VEP Total Ballots Counted VEP Highest Office VAP Highest Office  \\\n",
       "0                       NaN              58.6%              56.0%   \n",
       "1                     58.9%              58.7%              55.3%   \n",
       "2                     53.0%              52.6%              46.5%   \n",
       "3                     51.1%              50.7%              47.7%   \n",
       "4                     55.7%              55.1%              45.1%   \n",
       "\n",
       "  Total Ballots Counted Highest Office Voting-Eligible Population (VEP)  \\\n",
       "0                   NaN      2,074,338                        3,539,217   \n",
       "1               301,694        300,495                          511,792   \n",
       "2             2,323,579      2,306,559                        4,387,900   \n",
       "3             1,078,548      1,069,468                        2,109,847   \n",
       "4            13,202,158     13,038,547                       23,681,837   \n",
       "\n",
       "   Voting-Age Population (VAP) % Non-citizen   Prison Probation  Parole  \\\n",
       "0                    3707440.0          2.6%   32,232    57,993   8,616   \n",
       "1                     543763.0          3.8%    5,633     7,173   1,882   \n",
       "2                    4959270.0          9.9%   35,188    72,452   7,460   \n",
       "3                    2242740.0          3.5%   14,471    30,122  23,372   \n",
       "4                   28913129.0         17.4%  119,455         0  89,287   \n",
       "\n",
       "  Total Ineligible Felon State Abv  \n",
       "0                 71,584        AL  \n",
       "1                 11,317        AK  \n",
       "2                 81,048        AZ  \n",
       "3                 53,808        AR  \n",
       "4                208,742        CA  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a plot that displays the Voting-Age Population (VAP) per state. If you later want to play around with other columns, make sure you consider their data type. VAP has already been transformed to a float for you. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = dict(type='choropleth',\n",
    "           locations = df2['State Abv'],\n",
    "            z = df2['Voting-Age Population (VAP)'],\n",
    "            locationmode = 'USA-states',\n",
    "            text = df2['State'],\n",
    "            colorbar = {'title':\"VEP\"}\n",
    "            ) \n",
    "layout = dict(\n",
    "    title = '2012 Voting Age per population',\n",
    "    geo = dict(scope='usa',\n",
    "    showlakes = True,\n",
    "    lakecolor = 'rgb(85,173,240)')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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          "DE",
          "District of Columbia",
          "FL",
          "GA",
          "HI",
          "ID",
          "IL",
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          "IA",
          "KS",
          "KY",
          "LA",
          "ME",
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