Geographical_Plotting_Exercise/Choropleth - Geographical Plotting.ipynb

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    "# Geographical Plotting\n",
    "\n",
    "## Exploration of geographical data visualization with choropleth maps.\n",
    "\n",
    "A [Choropleth](https://en.wikipedia.org/wiki/Choropleth_map) Map is a map composed of colored polygons. It is used to represent spatial variations of a quantity. The following simple exercises help in getting familiar with visualizing geographical data."
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   "source": [
    "### Imports"
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    "import chart_studio.plotly as py\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode,plot,iplot\n",
    "import plotly.graph_objs as go \n",
    "init_notebook_mode(connected = True)"
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    "### Basic Configuration\n",
    "\n",
    "Data is in JSON dictionary format. The following example shows how a data dictionary is setup.\n",
    "The keyword arg `z` contains the data to be visualized.\n",
    "\n",
    "**Refer to [Plotly documentation](https://plotly.com/python/choropleth-maps/) for upto date usage and best practices guides.**"
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    "data = dict(type = 'choropleth',\n",
    "            locations = ['AZ','CA','NY'],\n",
    "            locationmode = 'USA-states',\n",
    "            colorscale= 'Jet',\n",
    "            text= ['Arizona','California','NewYork'],\n",
    "            z=[1.0,2.0,3.0],\n",
    "            colorbar = {'title':'Values'})\n",
    "\n",
    "layout = dict(geo = {'scope':'usa'})"
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    "choromap = go.Figure(data = [data],layout = layout)"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iplot(choromap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Pandas to read in datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### US Agricultural exports 2011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 18 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   code           50 non-null     object \n",
      " 1   state          50 non-null     object \n",
      " 2   category       50 non-null     object \n",
      " 3   total exports  50 non-null     float64\n",
      " 4   beef           50 non-null     float64\n",
      " 5   pork           50 non-null     float64\n",
      " 6   poultry        50 non-null     float64\n",
      " 7   dairy          50 non-null     float64\n",
      " 8   fruits fresh   50 non-null     float64\n",
      " 9   fruits proc    50 non-null     float64\n",
      " 10  total fruits   50 non-null     float64\n",
      " 11  veggies fresh  50 non-null     float64\n",
      " 12  veggies proc   50 non-null     float64\n",
      " 13  total veggies  50 non-null     float64\n",
      " 14  corn           50 non-null     float64\n",
      " 15  wheat          50 non-null     float64\n",
      " 16  cotton         50 non-null     float64\n",
      " 17  text           50 non-null     object \n",
      "dtypes: float64(14), object(4)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Read in the data\n",
    "df = pd.read_csv('2011_US_AGRI_Exports')\n",
    "\n",
    "# Get basic information on the data\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create data variable from the dataset.**\n",
    "\n",
    "Note: Observe `locations` as passed in via the code column and `locationmode` is set to `USA-states`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "exports = dict(type='choropleth',\n",
    "           colorscale='ylorbr',\n",
    "           locations = df['code'],\n",
    "           locationmode = 'USA-states',\n",
    "           z = df['total exports'],\n",
    "           text = df['text'],\n",
    "           colorbar = {'title': 'Millions USD'},\n",
    "           marker = dict(line = dict(color = 'rgb(82, 82, 82)',width = 1))\n",
    "           )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create layout variable for the dataset**\n",
    "\n",
    "Note: The layout `scope` is set to `usa`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout = dict(title = '2011 US Agriculture Exports by State',\n",
    "              geo = dict(scope='usa',\n",
    "                         showlakes = True,\n",
    "                         lakecolor = 'rgb(85,173,240)')\n",
    "             ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "choromap = go.Figure(data = [exports],layout = layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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           "text": "Millions USD"
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          "AL",
          "AK",
          "AZ",
          "AR",
          "CA",
          "CO",
          "CT",
          "DE",
          "FL",
          "GA",
          "HI",
          "ID",
          "IL",
          "IN",
          "IA",
          "KS",
          "KY",
          "LA",
          "ME",
          "MD",
          "MA",
          "MI",
          "MN",
          "MS",
          "MO",
          "MT",
          "NE",
          "NV",
          "NH",
          "NJ",
          "NM",
          "NY",
          "NC",
          "ND",
          "OH",
          "OK",
          "OR",
          "PA",
          "RI",
          "SC",
          "SD",
          "TN",
          "TX",
          "UT",
          "VT",
          "VA",
          "WA",
          "WV",
          "WI",
          "WY"
         ],
         "marker": {
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         "text": [
          "Alabama<br>Beef 34.4 Dairy 4.06<br>Fruits 25.11 Veggies 14.33<br>Wheat 70.0 Corn 34.9",
          "Alaska<br>Beef 0.2 Dairy 0.19<br>Fruits 0.0 Veggies 1.56<br>Wheat 0.0 Corn 0.0",
          "Arizona<br>Beef 71.3 Dairy 105.48<br>Fruits 60.27 Veggies 386.91<br>Wheat 48.7 Corn 7.3",
          "Arkansas<br>Beef 53.2 Dairy 3.53<br>Fruits 6.88 Veggies 11.45<br>Wheat 114.5 Corn 69.5",
          " California<br>Beef 228.7 Dairy 929.95<br>Fruits 8736.4 Veggies 2106.79<br>Wheat 249.3 Corn 34.6",
          "Colorado<br>Beef 261.4 Dairy 71.94<br>Fruits 17.99 Veggies 118.27<br>Wheat 400.5 Corn 183.2",
          "Connecticut<br>Beef 1.1 Dairy 9.49<br>Fruits 13.1 Veggies 11.16<br>Wheat 0.0 Corn 0.0",
          "Delaware<br>Beef 0.4 Dairy 2.3<br>Fruits 1.53 Veggies 20.03<br>Wheat 22.9 Corn 26.9",
          "Florida<br>Beef 42.6 Dairy 66.31<br>Fruits 1371.36 Veggies 450.86<br>Wheat 1.8 Corn 3.5",
          "Georgia<br>Beef 31.0 Dairy 38.38<br>Fruits 233.51 Veggies 154.77<br>Wheat 65.4 Corn 57.8",
          "Hawaii<br>Beef 4.0 Dairy 1.16<br>Fruits 55.51 Veggies 24.83<br>Wheat 0.0 Corn 0.0",
          "Idaho<br>Beef 119.8 Dairy 294.6<br>Fruits 21.64 Veggies 319.19<br>Wheat 568.2 Corn 24.0",
          "Illinois<br>Beef 53.7 Dairy 45.82<br>Fruits 12.53 Veggies 39.95<br>Wheat 223.8 Corn 2228.5",
          "Indiana<br>Beef 21.9 Dairy 89.7<br>Fruits 12.98 Veggies 37.89<br>Wheat 114.0 Corn 1123.2",
          "Iowa<br>Beef 289.8 Dairy 107.0<br>Fruits 3.24 Veggies 7.1<br>Wheat 3.1 Corn 2529.8",
          "Kansas<br>Beef 659.3 Dairy 65.45<br>Fruits 3.11 Veggies 9.32<br>Wheat 1426.5 Corn 457.3",
          "Kentucky<br>Beef 54.8 Dairy 28.27<br>Fruits 6.6 Veggies 0.0<br>Wheat 149.3 Corn 179.1",
          "Louisiana<br>Beef 19.8 Dairy 6.02<br>Fruits 17.83 Veggies 17.25<br>Wheat 78.7 Corn 91.4",
          "Maine<br>Beef 1.4 Dairy 16.18<br>Fruits 52.01 Veggies 62.9<br>Wheat 0.0 Corn 0.0",
          "Maryland<br>Beef 5.6 Dairy 24.81<br>Fruits 12.9 Veggies 20.43<br>Wheat 55.8 Corn 54.1",
          "Massachusetts<br>Beef 0.6 Dairy 5.81<br>Fruits 80.83 Veggies 21.13<br>Wheat 0.0 Corn 0.0",
          "Michigan<br>Beef 37.7 Dairy 214.82<br>Fruits 257.69 Veggies 189.96<br>Wheat 247.0 Corn 381.5",
          "Minnesota<br>Beef 112.3 Dairy 218.05<br>Fruits 7.91 Veggies 120.37<br>Wheat 538.1 Corn 1264.3",
          "Mississippi<br>Beef 12.8 Dairy 5.45<br>Fruits 17.04 Veggies 27.87<br>Wheat 102.2 Corn 110.0",
          "Missouri<br>Beef 137.2 Dairy 34.26<br>Fruits 13.18 Veggies 17.9<br>Wheat 161.7 Corn 428.8",
          "Montana<br>Beef 105.0 Dairy 6.82<br>Fruits 3.3 Veggies 45.27<br>Wheat 1198.1 Corn 5.4",
          "Nebraska<br>Beef 762.2 Dairy 30.07<br>Fruits 2.16 Veggies 53.5<br>Wheat 292.3 Corn 1735.9",
          "Nevada<br>Beef 21.8 Dairy 16.57<br>Fruits 1.19 Veggies 27.93<br>Wheat 5.4 Corn 0.0",
          "New Hampshire<br>Beef 0.6 Dairy 7.46<br>Fruits 7.98 Veggies 4.5<br>Wheat 0.0 Corn 0.0",
          "New Jersey<br>Beef 0.8 Dairy 3.37<br>Fruits 109.45 Veggies 56.54<br>Wheat 6.7 Corn 10.1",
          "New Mexico<br>Beef 117.2 Dairy 191.01<br>Fruits 101.9 Veggies 43.88<br>Wheat 13.9 Corn 11.2",
          "New York<br>Beef 22.2 Dairy 331.8<br>Fruits 202.56 Veggies 143.37<br>Wheat 29.9 Corn 106.1",
          "North Carolina<br>Beef 24.8 Dairy 24.9<br>Fruits 74.47 Veggies 150.45<br>Wheat 200.3 Corn 92.2",
          "North Dakota<br>Beef 78.5 Dairy 8.14<br>Fruits 0.25 Veggies 130.79<br>Wheat 1664.5 Corn 236.1",
          "Ohio<br>Beef 36.2 Dairy 134.57<br>Fruits 27.21 Veggies 53.53<br>Wheat 207.4 Corn 535.1",
          "Oklahoma<br>Beef 337.6 Dairy 24.35<br>Fruits 9.24 Veggies 8.9<br>Wheat 324.8 Corn 27.5",
          "Oregon<br>Beef 58.8 Dairy 63.66<br>Fruits 315.04 Veggies 126.5<br>Wheat 320.3 Corn 11.7",
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34.26<br>Fruits 13.18 Veggies 17.9<br>Wheat 161.7 Corn 428.8\",\"Montana<br>Beef 105.0 Dairy 6.82<br>Fruits 3.3 Veggies 45.27<br>Wheat 1198.1 Corn 5.4\",\"Nebraska<br>Beef 762.2 Dairy 30.07<br>Fruits 2.16 Veggies 53.5<br>Wheat 292.3 Corn 1735.9\",\"Nevada<br>Beef 21.8 Dairy 16.57<br>Fruits 1.19 Veggies 27.93<br>Wheat 5.4 Corn 0.0\",\"New Hampshire<br>Beef 0.6 Dairy 7.46<br>Fruits 7.98 Veggies 4.5<br>Wheat 0.0 Corn 0.0\",\"New Jersey<br>Beef 0.8 Dairy 3.37<br>Fruits 109.45 Veggies 56.54<br>Wheat 6.7 Corn 10.1\",\"New Mexico<br>Beef 117.2 Dairy 191.01<br>Fruits 101.9 Veggies 43.88<br>Wheat 13.9 Corn 11.2\",\"New York<br>Beef 22.2 Dairy 331.8<br>Fruits 202.56 Veggies 143.37<br>Wheat 29.9 Corn 106.1\",\"North Carolina<br>Beef 24.8 Dairy 24.9<br>Fruits 74.47 Veggies 150.45<br>Wheat 200.3 Corn 92.2\",\"North Dakota<br>Beef 78.5 Dairy 8.14<br>Fruits 0.25 Veggies 130.79<br>Wheat 1664.5 Corn 236.1\",\"Ohio<br>Beef 36.2 Dairy 134.57<br>Fruits 27.21 Veggies 53.53<br>Wheat 207.4 Corn 535.1\",\"Oklahoma<br>Beef 337.6 Dairy 24.35<br>Fruits 9.24 Veggies 8.9<br>Wheat 324.8 Corn 27.5\",\"Oregon<br>Beef 58.8 Dairy 63.66<br>Fruits 315.04 Veggies 126.5<br>Wheat 320.3 Corn 11.7\",\"Pennsylvania<br>Beef 50.9 Dairy 280.87<br>Fruits 89.48 Veggies 38.26<br>Wheat 41.0 Corn 112.1\",\"Rhode Island<br>Beef 0.1 Dairy 0.52<br>Fruits 2.83 Veggies 3.02<br>Wheat 0.0 Corn 0.0\",\"South Carolina<br>Beef 15.2 Dairy 7.62<br>Fruits 53.45 Veggies 42.66<br>Wheat 55.3 Corn 32.1\",\"South Dakota<br>Beef 193.5 Dairy 46.77<br>Fruits 0.8 Veggies 4.06<br>Wheat 704.5 Corn 643.6\",\"Tennessee<br>Beef 51.1 Dairy 21.18<br>Fruits 6.23 Veggies 24.67<br>Wheat 100.0 Corn 88.8\",\"Texas<br>Beef 961.0 Dairy 240.55<br>Fruits 99.9 Veggies 115.23<br>Wheat 309.7 Corn 167.2\",\"Utah<br>Beef 27.9 Dairy 48.6<br>Fruits 12.34 Veggies 6.6<br>Wheat 42.8 Corn 5.3\",\"Vermont<br>Beef 6.2 Dairy 65.98<br>Fruits 8.01 Veggies 4.05<br>Wheat 0.0 Corn 0.0\",\"Virginia<br>Beef 39.5 Dairy 47.85<br>Fruits 36.48 Veggies 27.25<br>Wheat 77.5 Corn 39.5\",\"Washington<br>Beef 59.2 Dairy 154.18<br>Fruits 1738.57 Veggies 363.79<br>Wheat 786.3 Corn 29.5\",\"West Virginia<br>Beef 12.0 Dairy 3.9<br>Fruits 11.54 Veggies 0.0<br>Wheat 1.6 Corn 3.5\",\"Wisconsin<br>Beef 107.3 Dairy 633.6<br>Fruits 133.8 Veggies 148.99<br>Wheat 96.7 Corn 460.5\",\"Wyoming<br>Beef 75.1 Dairy 2.89<br>Fruits 0.17 Veggies 10.23<br>Wheat 20.7 Corn 9.0\"],\"z\":[1390.63,13.31,1463.17,3586.02,16472.88,1851.33,259.62,282.19,3764.09,2860.84,401.84,2078.89,8709.48,5050.23,11273.76,4589.01,1889.15,1914.23,278.37,692.75,248.65,3164.16,7192.33,2170.8,3933.42,1718.0,7114.13,139.89,73.06,500.4,751.58,1488.9,3806.05,3761.96,3979.79,1646.41,1794.57,1969.87,31.59,929.93,3770.19,1535.13,6648.22,453.39,180.14,1146.48,3894.81,138.89,3090.23,349.69],\"type\":\"choropleth\"}],                        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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iplot(choromap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Global GDP 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of             COUNTRY  GDP (BILLIONS) CODE\n",
       "0       Afghanistan           21.71  AFG\n",
       "1           Albania           13.40  ALB\n",
       "2           Algeria          227.80  DZA\n",
       "3    American Samoa            0.75  ASM\n",
       "4           Andorra            4.80  AND\n",
       "..              ...             ...  ...\n",
       "217  Virgin Islands            5.08  VGB\n",
       "218       West Bank            6.64  WBG\n",
       "219           Yemen           45.45  YEM\n",
       "220          Zambia           25.61  ZMB\n",
       "221        Zimbabwe           13.74  ZWE\n",
       "\n",
       "[222 rows x 3 columns]>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in data\n",
    "df = pd.read_csv('2014_World_GDP')\n",
    "# Get basic info of the data\n",
    "df.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create data variable from the dataset.**\n",
    "\n",
    "Note: Observe `locations` as passed in via the code column and the default `locationmode` is global"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp = dict(type='choropleth',\n",
    "           locations = df['CODE'],\n",
    "           z = df['GDP (BILLIONS)'],\n",
    "           text = df['COUNTRY'],\n",
    "           colorscale='ylorbr',\n",
    "           colorbar = {'title': 'GDP in Billions USD'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create layout variable for the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout = dict(title = '2014 Global GDP',\n",
    "              geo = dict(showframe = False,\n",
    "                         projection = {'type': 'natural earth'}\n",
    "             )) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "choromap = go.Figure(data=[gdp],layout = layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
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        "linkText": "Export to plot.ly",
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        "showLink": false
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       "data": [
        {
         "colorbar": {
          "title": {
           "text": "GDP in Billions USD"
          }
         },
         "colorscale": [
          [
           0,
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          [
           0.125,
           "rgb(255,247,188)"
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           0.25,
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           0.375,
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           0.5,
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           0.625,
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           0.75,
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          ],
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           0.875,
           "rgb(153,52,4)"
          ],
          [
           1,
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         ],
         "locations": [
          "AFG",
          "ALB",
          "DZA",
          "ASM",
          "AND",
          "AGO",
          "AIA",
          "ATG",
          "ARG",
          "ARM",
          "ABW",
          "AUS",
          "AUT",
          "AZE",
          "BHM",
          "BHR",
          "BGD",
          "BRB",
          "BLR",
          "BEL",
          "BLZ",
          "BEN",
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     "metadata": {},
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   ],
   "source": [
    "iplot(choromap)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Global Power Consumption 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 212 entries, 0 to 211\n",
      "Data columns (total 4 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Code                   212 non-null    object \n",
      " 1   Country                212 non-null    object \n",
      " 2   Power Consumption KWH  212 non-null    float64\n",
      " 3   Text                   212 non-null    object \n",
      "dtypes: float64(1), object(3)\n",
      "memory usage: 6.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Read in the data\n",
    "power = pd.read_csv('2014_World_Power_Consumption.csv')\n",
    "\n",
    "# Get some basic info on the data\n",
    "power.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</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>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3.893000e+09</td>\n",
       "      <td>Afghanistan 3893000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>7.793000e+09</td>\n",
       "      <td>Albania 7793000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>4.287000e+10</td>\n",
       "      <td>Algeria 42870000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASM</td>\n",
       "      <td>American</td>\n",
       "      <td>1.460000e+08</td>\n",
       "      <td>American 146000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AND</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>5.624000e+08</td>\n",
       "      <td>Andorra 562400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  Code      Country  Power Consumption KWH                    Text\n",
       "0  AFG  Afghanistan           3.893000e+09  Afghanistan 3893000000\n",
       "1  ALB      Albania           7.793000e+09      Albania 7793000000\n",
       "2  DZA      Algeria           4.287000e+10     Algeria 42870000000\n",
       "3  ASM     American           1.460000e+08      American 146000000\n",
       "4  AND      Andorra           5.624000e+08       Andorra 562400000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "power.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create data variable from the dataset.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp = dict(type='choropleth',\n",
    "           locations = power['Code'],\n",
    "           z = power['Power Consumption KWH'],\n",
    "           text = power['Text'],\n",
    "           colorscale='ylorbr',\n",
    "           colorbar = {'title': 'Global Power Consumption 2014 (WH)'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create layout variable for the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "layout = dict(title = 'Global Power Consumption 2014',\n",
    "              geo = dict(showframe = False,\n",
    "                         projection = {'type': 'natural earth'}\n",
    "             )) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "choromap = go.Figure(data=[gdp],layout = layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
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          "CUB",
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          "CYP",
          "CZE",
          "DNK",
          "DJI",
          "DMA",
          "DOM",
          "ECU",
          "EGY",
          "SLV",
          "GNQ",
          "ERI",
          "EST",
          "ETH",
          "FLK",
          "FRO",
          "FJI",
          "FIN",
          "FRA",
          "PYF",
          "GAB",
          "GMB",
          "GEO",
          "DEU",
          "GHA",
          "GIB",
          "GRC",
          "GRL",
          "GRD",
          "GUM",
          "GTM",
          "GIN",
          "GNB",
          "GUY",
          "HTI",
          "HND",
          "HKG",
          "HUN",
          "ISL",
          "IND",
          "IDN",
          "IRN",
          "IRQ",
          "IRL",
          "ISR",
          "ITA",
          "JAM",
          "JPN",
          "JEY",
          "JOR",
          "KAZ",
          "KEN",
          "KIR",
          "PRK",
          "KOR",
          "KSV",
          "KWT",
          "KGZ",
          "LAO",
          "LVA",
          "LBN",
          "LSO",
          "LBR",
          "LBY",
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          "PNG",
          "PRY",
          "PER",
          "PHL",
          "POL",
          "PRT",
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          "QAT",
          "ROU",
          "RUS",
          "RWA",
          "KNA",
          "LCA",
          "MAF",
          "SPM",
          "VCT",
          "WSM",
          "SMR",
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          "ZAF",
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          "ESP",
          "LKA",
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          "SUR",
          "SWZ",
          "SWE",
          "CHE",
          "SYR",
          "TWN",
          "TJK",
          "TZA",
          "THA",
          "TLS",
          "TGO",
          "TON",
          "TTO",
          "TUN",
          "TUR",
          "TKM",
          "TUV",
          "UGA",
          "UKR",
          "ARE",
          "GBR",
          "USA",
          "URY",
          "UZB",
          "VUT",
          "VEN",
          "VNM",
          "VGB",
          "WBG",
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          "Afghanistan 3893000000",
          "Albania 7793000000",
          "Algeria 42870000000",
          "American 146000000",
          "Andorra 562400000",
          "Angola 4842000000",
          "Antigua 293000000",
          "Argentina 117100000000",
          "Armenia 5043000000",
          "Aruba 920700000",
          "Australia 222600000000",
          "Austria 69750000000",
          "Azerbaijan 17790000000",
          "Bahamas 1716000000",
          "Bahrain 11690000000",
          "Bangladesh 41520000000",
          "Barbados 938000000",
          "Belarus 37880000000",
          "Belgium 81890000000",
          "Belize 605000000",
          "Benin 911000000",
          "Bermuda 664200000",
          "Bhutan 2085000000",
          "Bolivia 6456000000",
          "Bosnia 12560000000",
          "Botswana 3213000000",
          "Brazil 483500000000",
          "British 51150000",
          "Brunei 3766000000",
          "Bulgaria 37990000000",
          "Burkina 985500000",
          "Burma 7765000000",
          "Burundi 282900000",
          "Cabo 285500000",
          "Cambodia 3553000000",
          "Cameroon 5535000000",
          "Canada 511000000000",
          "Cayman 545900000",
          "Central 168300000",
          "Chad 190700000",
          "Chile 63390000000",
          "China 5523000000000",
          "Colombia 49380000000",
          "Comoros 39990000",
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