geo_plotting_notes.ipynb

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   "id": "8030e686",
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   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'chart_studio'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-6605a698f08b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mchart_studio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotly\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'chart_studio'"
     ]
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   ],
   "source": [
    "import pip"
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   "execution_count": 5,
   "id": "20055f28",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.\n",
      "Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.\n",
      "To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.\n"
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     "text": [
      "Collecting chart-studio\n",
      "  Using cached chart_studio-1.1.0-py3-none-any.whl (64 kB)\n",
      "Collecting retrying>=1.3.3\n",
      "  Using cached retrying-1.3.3.tar.gz (10 kB)\n",
      "Requirement already satisfied: plotly in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from chart-studio) (5.1.0)\n",
      "Requirement already satisfied: six in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from chart-studio) (1.15.0)\n",
      "Requirement already satisfied: requests in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from chart-studio) (2.25.1)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from plotly->chart-studio) (8.0.1)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from requests->chart-studio) (2020.12.5)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from requests->chart-studio) (1.26.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from requests->chart-studio) (2.10)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from requests->chart-studio) (4.0.0)\n",
      "Building wheels for collected packages: retrying\n",
      "  Building wheel for retrying (setup.py): started\n",
      "  Building wheel for retrying (setup.py): finished with status 'done'\n",
      "  Created wheel for retrying: filename=retrying-1.3.3-py3-none-any.whl size=11429 sha256=bb21a1125d4c4766013c601e941c83117a7e3182a251d82f55b9d4c0248a2392\n",
      "  Stored in directory: c:\\users\\lenovo\\appdata\\local\\pip\\cache\\wheels\\c4\\a7\\48\\0a434133f6d56e878ca511c0e6c38326907c0792f67b476e56\n",
      "Successfully built retrying\n",
      "Installing collected packages: retrying, chart-studio\n",
      "Successfully installed chart-studio-1.1.0 retrying-1.3.3\n"
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   "source": [
    "pip.main(['install','chart-studio'])"
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   "cell_type": "code",
   "execution_count": 14,
   "id": "783a7cde",
   "metadata": {},
   "outputs": [],
   "source": [
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go"
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   "id": "3fcf5195",
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    "from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot"
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   "id": "99541191",
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       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
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       "        require(['plotly'], function(Plotly) {\n",
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   "execution_count": 28,
   "id": "8cfc0e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=dict(type='choropleth',\n",
    "         locations=['AZ','CA','NY'],#list of abbreviations\n",
    "         locationmode='USA-states',\n",
    "         colorscale='Greens',\n",
    "         text=['HAcı','maci','newi'],#list of text lines up eac of the locations\n",
    "         z=[1.0,2.0,3.0], #value for color\n",
    "         colorbar={'title':'Colorbar Title goes here'} #title for colorbar)"
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   "execution_count": 29,
   "id": "12fe62b4",
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       "{'type': 'choropleth',\n",
       " 'locations': ['AZ', 'CA', 'NY'],\n",
       " 'locationmode': 'USA-states',\n",
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       " 'z': [1.0, 2.0, 3.0],\n",
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    "data"
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  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "865b08b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "layout=dict(geo={'scope':'usa'})"
   ]
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   "id": "431165b3",
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       "      <th>3</th>\n",
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       "      <td>803.2</td>\n",
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       "      <td>34.6</td>\n",
       "      <td>249.3</td>\n",
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       "  code        state category  total exports   beef  pork  poultry   dairy  \\\n",
       "0   AL      Alabama    state        1390.63   34.4  10.6    481.0    4.06   \n",
       "1   AK       Alaska    state          13.31    0.2   0.1      0.0    0.19   \n",
       "2   AZ      Arizona    state        1463.17   71.3  17.9      0.0  105.48   \n",
       "3   AR     Arkansas    state        3586.02   53.2  29.4    562.9    3.53   \n",
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       "2          19.3         41.0         60.27          147.5         239.4   \n",
       "3           2.2          4.7          6.88            4.4           7.1   \n",
       "4        2791.8       5944.6       8736.40          803.2        1303.5   \n",
       "\n",
       "   total veggies  corn  wheat   cotton  \\\n",
       "0          14.33  34.9   70.0   317.61   \n",
       "1           1.56   0.0    0.0     0.00   \n",
       "2         386.91   7.3   48.7   423.95   \n",
       "3          11.45  69.5  114.5   665.44   \n",
       "4        2106.79  34.6  249.3  1064.95   \n",
       "\n",
       "                                                text  \n",
       "0  Alabama<br>Beef 34.4 Dairy 4.06<br>Fruits 25.1...  \n",
       "1  Alaska<br>Beef 0.2 Dairy 0.19<br>Fruits 0.0 Ve...  \n",
       "2  Arizona<br>Beef 71.3 Dairy 105.48<br>Fruits 60...  \n",
       "3  Arkansas<br>Beef 53.2 Dairy 3.53<br>Fruits 6.8...  \n",
       "4   California<br>Beef 228.7 Dairy 929.95<br>Frui...  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('2011_US_AGRI_Exports')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1e45509e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=dict(type='choropleth',\n",
    "         colorscale='Greens',\n",
    "         locations=df['code'],\n",
    "         locationmode='USA-states',\n",
    "         z=df['total exports'],\n",
    "         text=df['text'],\n",
    "         marker=dict(line=dict(color='rgb(12,12,12)',width=4)),\n",
    "         colorbar={'title':'Millions USD'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0d7bea5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'choropleth',\n",
       " 'colorscale': 'Greens',\n",
       " 'locations': 0     AL\n",
       " 1     AK\n",
       " 2     AZ\n",
       " 3     AR\n",
       " 4     CA\n",
       " 5     CO\n",
       " 6     CT\n",
       " 7     DE\n",
       " 8     FL\n",
       " 9     GA\n",
       " 10    HI\n",
       " 11    ID\n",
       " 12    IL\n",
       " 13    IN\n",
       " 14    IA\n",
       " 15    KS\n",
       " 16    KY\n",
       " 17    LA\n",
       " 18    ME\n",
       " 19    MD\n",
       " 20    MA\n",
       " 21    MI\n",
       " 22    MN\n",
       " 23    MS\n",
       " 24    MO\n",
       " 25    MT\n",
       " 26    NE\n",
       " 27    NV\n",
       " 28    NH\n",
       " 29    NJ\n",
       " 30    NM\n",
       " 31    NY\n",
       " 32    NC\n",
       " 33    ND\n",
       " 34    OH\n",
       " 35    OK\n",
       " 36    OR\n",
       " 37    PA\n",
       " 38    RI\n",
       " 39    SC\n",
       " 40    SD\n",
       " 41    TN\n",
       " 42    TX\n",
       " 43    UT\n",
       " 44    VT\n",
       " 45    VA\n",
       " 46    WA\n",
       " 47    WV\n",
       " 48    WI\n",
       " 49    WY\n",
       " Name: code, dtype: object,\n",
       " 'locationmode': 'USA-states',\n",
       " 'z': 0      1390.63\n",
       " 1        13.31\n",
       " 2      1463.17\n",
       " 3      3586.02\n",
       " 4     16472.88\n",
       " 5      1851.33\n",
       " 6       259.62\n",
       " 7       282.19\n",
       " 8      3764.09\n",
       " 9      2860.84\n",
       " 10      401.84\n",
       " 11     2078.89\n",
       " 12     8709.48\n",
       " 13     5050.23\n",
       " 14    11273.76\n",
       " 15     4589.01\n",
       " 16     1889.15\n",
       " 17     1914.23\n",
       " 18      278.37\n",
       " 19      692.75\n",
       " 20      248.65\n",
       " 21     3164.16\n",
       " 22     7192.33\n",
       " 23     2170.80\n",
       " 24     3933.42\n",
       " 25     1718.00\n",
       " 26     7114.13\n",
       " 27      139.89\n",
       " 28       73.06\n",
       " 29      500.40\n",
       " 30      751.58\n",
       " 31     1488.90\n",
       " 32     3806.05\n",
       " 33     3761.96\n",
       " 34     3979.79\n",
       " 35     1646.41\n",
       " 36     1794.57\n",
       " 37     1969.87\n",
       " 38       31.59\n",
       " 39      929.93\n",
       " 40     3770.19\n",
       " 41     1535.13\n",
       " 42     6648.22\n",
       " 43      453.39\n",
       " 44      180.14\n",
       " 45     1146.48\n",
       " 46     3894.81\n",
       " 47      138.89\n",
       " 48     3090.23\n",
       " 49      349.69\n",
       " Name: total exports, dtype: float64,\n",
       " 'text': 0     Alabama<br>Beef 34.4 Dairy 4.06<br>Fruits 25.1...\n",
       " 1     Alaska<br>Beef 0.2 Dairy 0.19<br>Fruits 0.0 Ve...\n",
       " 2     Arizona<br>Beef 71.3 Dairy 105.48<br>Fruits 60...\n",
       " 3     Arkansas<br>Beef 53.2 Dairy 3.53<br>Fruits 6.8...\n",
       " 4      California<br>Beef 228.7 Dairy 929.95<br>Frui...\n",
       " 5     Colorado<br>Beef 261.4 Dairy 71.94<br>Fruits 1...\n",
       " 6     Connecticut<br>Beef 1.1 Dairy 9.49<br>Fruits 1...\n",
       " 7     Delaware<br>Beef 0.4 Dairy 2.3<br>Fruits 1.53 ...\n",
       " 8     Florida<br>Beef 42.6 Dairy 66.31<br>Fruits 137...\n",
       " 9     Georgia<br>Beef 31.0 Dairy 38.38<br>Fruits 233...\n",
       " 10    Hawaii<br>Beef 4.0 Dairy 1.16<br>Fruits 55.51 ...\n",
       " 11    Idaho<br>Beef 119.8 Dairy 294.6<br>Fruits 21.6...\n",
       " 12    Illinois<br>Beef 53.7 Dairy 45.82<br>Fruits 12...\n",
       " 13    Indiana<br>Beef 21.9 Dairy 89.7<br>Fruits 12.9...\n",
       " 14    Iowa<br>Beef 289.8 Dairy 107.0<br>Fruits 3.24 ...\n",
       " 15    Kansas<br>Beef 659.3 Dairy 65.45<br>Fruits 3.1...\n",
       " 16    Kentucky<br>Beef 54.8 Dairy 28.27<br>Fruits 6....\n",
       " 17    Louisiana<br>Beef 19.8 Dairy 6.02<br>Fruits 17...\n",
       " 18    Maine<br>Beef 1.4 Dairy 16.18<br>Fruits 52.01 ...\n",
       " 19    Maryland<br>Beef 5.6 Dairy 24.81<br>Fruits 12....\n",
       " 20    Massachusetts<br>Beef 0.6 Dairy 5.81<br>Fruits...\n",
       " 21    Michigan<br>Beef 37.7 Dairy 214.82<br>Fruits 2...\n",
       " 22    Minnesota<br>Beef 112.3 Dairy 218.05<br>Fruits...\n",
       " 23    Mississippi<br>Beef 12.8 Dairy 5.45<br>Fruits ...\n",
       " 24    Missouri<br>Beef 137.2 Dairy 34.26<br>Fruits 1...\n",
       " 25    Montana<br>Beef 105.0 Dairy 6.82<br>Fruits 3.3...\n",
       " 26    Nebraska<br>Beef 762.2 Dairy 30.07<br>Fruits 2...\n",
       " 27    Nevada<br>Beef 21.8 Dairy 16.57<br>Fruits 1.19...\n",
       " 28    New Hampshire<br>Beef 0.6 Dairy 7.46<br>Fruits...\n",
       " 29    New Jersey<br>Beef 0.8 Dairy 3.37<br>Fruits 10...\n",
       " 30    New Mexico<br>Beef 117.2 Dairy 191.01<br>Fruit...\n",
       " 31    New York<br>Beef 22.2 Dairy 331.8<br>Fruits 20...\n",
       " 32    North Carolina<br>Beef 24.8 Dairy 24.9<br>Frui...\n",
       " 33    North Dakota<br>Beef 78.5 Dairy 8.14<br>Fruits...\n",
       " 34    Ohio<br>Beef 36.2 Dairy 134.57<br>Fruits 27.21...\n",
       " 35    Oklahoma<br>Beef 337.6 Dairy 24.35<br>Fruits 9...\n",
       " 36    Oregon<br>Beef 58.8 Dairy 63.66<br>Fruits 315....\n",
       " 37    Pennsylvania<br>Beef 50.9 Dairy 280.87<br>Frui...\n",
       " 38    Rhode Island<br>Beef 0.1 Dairy 0.52<br>Fruits ...\n",
       " 39    South Carolina<br>Beef 15.2 Dairy 7.62<br>Frui...\n",
       " 40    South Dakota<br>Beef 193.5 Dairy 46.77<br>Frui...\n",
       " 41    Tennessee<br>Beef 51.1 Dairy 21.18<br>Fruits 6...\n",
       " 42    Texas<br>Beef 961.0 Dairy 240.55<br>Fruits 99....\n",
       " 43    Utah<br>Beef 27.9 Dairy 48.6<br>Fruits 12.34 V...\n",
       " 44    Vermont<br>Beef 6.2 Dairy 65.98<br>Fruits 8.01...\n",
       " 45    Virginia<br>Beef 39.5 Dairy 47.85<br>Fruits 36...\n",
       " 46    Washington<br>Beef 59.2 Dairy 154.18<br>Fruits...\n",
       " 47    West Virginia<br>Beef 12.0 Dairy 3.9<br>Fruits...\n",
       " 48    Wisconsin<br>Beef 107.3 Dairy 633.6<br>Fruits ...\n",
       " 49    Wyoming<br>Beef 75.1 Dairy 2.89<br>Fruits 0.17...\n",
       " Name: text, dtype: object,\n",
       " 'marker': {'line': {'color': 'rgb(12,12,12)', 'width': 4}},\n",
       " 'colorbar': {'title': 'Millions USD'}}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ac61ae5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "layout=dict(title='2011_US_AGRI_Exports by State',\n",
    "           geo=dict(scope='usa',showlakes=True,lakecolor='rgb(85,173,240)'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c9e3fec3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'title': '2011_US_AGRI_Exports by State',\n",
       " 'geo': {'scope': 'usa', 'showlakes': True, 'lakecolor': 'rgb(85,173,240)'}}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cb4f9f6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "choromap2=go.Figure(data=[data],layout=layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8efc0ad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "colorbar": {
          "title": {
           "text": "Millions USD"
          }
         },
         "colorscale": [
          [
           0,
           "rgb(247,252,245)"
          ],
          [
           0.125,
           "rgb(229,245,224)"
          ],
          [
           0.25,
           "rgb(199,233,192)"
          ],
          [
           0.375,
           "rgb(161,217,155)"
          ],
          [
           0.5,
           "rgb(116,196,118)"
          ],
          [
           0.625,
           "rgb(65,171,93)"
          ],
          [
           0.75,
           "rgb(35,139,69)"
          ],
          [
           0.875,
           "rgb(0,109,44)"
          ],
          [
           1,
           "rgb(0,68,27)"
          ]
         ],
         "locationmode": "USA-states",
         "locations": [
          "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": {
          "line": {
           "color": "rgb(12,12,12)",
           "width": 4
          }
         },
         "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",
          "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"
         ],
         "type": "choropleth",
         "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,
          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
         ]
        }
       ],
       "layout": {
        "geo": {
         "lakecolor": "rgb(85,173,240)",
         "scope": "usa",
         "showlakes": true
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
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          "carpet": [
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            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
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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\"],\"type\":\"choropleth\",\"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]}],                        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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iplot(choromap2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe9c2d8f",
   "metadata": {},
   "source": [
    "# WORLD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2016ee85",
   "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",
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       "    }\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>GDP (BILLIONS)</th>\n",
       "      <th>CODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>21.71</td>\n",
       "      <td>AFG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>13.40</td>\n",
       "      <td>ALB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>227.80</td>\n",
       "      <td>DZA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>0.75</td>\n",
       "      <td>ASM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>4.80</td>\n",
       "      <td>AND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          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"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('2014_World_GDP')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f644addc",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=dict(type='choropleth',\n",
    "         locations=df['CODE'],\n",
    "         z=df['GDP (BILLIONS)'],\n",
    "         text=df['COUNTRY'],\n",
    "         colorbar={'title':'GDP in Billious USD'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d6484735",
   "metadata": {},
   "outputs": [],
   "source": [
    "layout=dict(title='2014_World_GDP  burada',\n",
    "           geo=dict(showframe=False,projection={'type':'stereographic'}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bf0c6bbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "choromap3=go.Figure(data=[data],layout=layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7b39be05",
   "metadata": {},
   "outputs": [
    {
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         "colorbar": {
          "title": {
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         "locations": [
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          "ERI",
          "EST",
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          "IRN",
          "IRQ",
          "IRL",
          "IMN",
          "ISR",
          "ITA",
          "JAM",
          "JPN",
          "JEY",
          "JOR",
          "KAZ",
          "KEN",
          "KIR",
          "KOR",
          "PRK",
          "KSV",
          "KWT",
          "KGZ",
          "LAO",
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          "LKA",
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