Project_11/Basics.ipynb

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    "# CIA Factbook Using SQLite and Python\n",
    "\n",
    "In this project, we are using a SQLite database, factbook.db, from the CIA World Factbook, which contains statistics about all of the countries on Earth."
   ]
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   "source": [
    "# imports and connect to the database\n",
    "import sqlite3\n",
    "import pandas as pd\n",
    "\n",
    "conn = sqlite3.connect('factbook.db')\n",
    "cursor = conn.cursor"
   ]
  },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>name</th>\n",
       "      <th>tbl_name</th>\n",
       "      <th>rootpage</th>\n",
       "      <th>sql</th>\n",
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       "      <td>table</td>\n",
       "      <td>sqlite_sequence</td>\n",
       "      <td>sqlite_sequence</td>\n",
       "      <td>3</td>\n",
       "      <td>CREATE TABLE sqlite_sequence(name,seq)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>table</td>\n",
       "      <td>facts</td>\n",
       "      <td>facts</td>\n",
       "      <td>47</td>\n",
       "      <td>CREATE TABLE \"facts\" (\"id\" INTEGER PRIMARY KEY...</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "    type             name         tbl_name  rootpage  \\\n",
       "0  table  sqlite_sequence  sqlite_sequence         3   \n",
       "1  table            facts            facts        47   \n",
       "\n",
       "                                                 sql  \n",
       "0             CREATE TABLE sqlite_sequence(name,seq)  \n",
       "1  CREATE TABLE \"facts\" (\"id\" INTEGER PRIMARY KEY...  "
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   "source": [
    "# info on the tables in the database\n",
    "q1 = \"SELECT * FROM sqlite_master WHERE type='table';\"\n",
    "pd.read_sql_query(q1, conn)"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>code</th>\n",
       "      <th>name</th>\n",
       "      <th>area</th>\n",
       "      <th>area_land</th>\n",
       "      <th>area_water</th>\n",
       "      <th>population</th>\n",
       "      <th>population_growth</th>\n",
       "      <th>birth_rate</th>\n",
       "      <th>death_rate</th>\n",
       "      <th>migration_rate</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>af</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>652230</td>\n",
       "      <td>652230</td>\n",
       "      <td>0</td>\n",
       "      <td>32564342</td>\n",
       "      <td>2.32</td>\n",
       "      <td>38.57</td>\n",
       "      <td>13.89</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>al</td>\n",
       "      <td>Albania</td>\n",
       "      <td>28748</td>\n",
       "      <td>27398</td>\n",
       "      <td>1350</td>\n",
       "      <td>3029278</td>\n",
       "      <td>0.30</td>\n",
       "      <td>12.92</td>\n",
       "      <td>6.58</td>\n",
       "      <td>3.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>ag</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2381741</td>\n",
       "      <td>2381741</td>\n",
       "      <td>0</td>\n",
       "      <td>39542166</td>\n",
       "      <td>1.84</td>\n",
       "      <td>23.67</td>\n",
       "      <td>4.31</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>an</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>468</td>\n",
       "      <td>468</td>\n",
       "      <td>0</td>\n",
       "      <td>85580</td>\n",
       "      <td>0.12</td>\n",
       "      <td>8.13</td>\n",
       "      <td>6.96</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>ao</td>\n",
       "      <td>Angola</td>\n",
       "      <td>1246700</td>\n",
       "      <td>1246700</td>\n",
       "      <td>0</td>\n",
       "      <td>19625353</td>\n",
       "      <td>2.78</td>\n",
       "      <td>38.78</td>\n",
       "      <td>11.49</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id code         name     area  area_land  area_water  population  \\\n",
       "0   1   af  Afghanistan   652230     652230           0    32564342   \n",
       "1   2   al      Albania    28748      27398        1350     3029278   \n",
       "2   3   ag      Algeria  2381741    2381741           0    39542166   \n",
       "3   4   an      Andorra      468        468           0       85580   \n",
       "4   5   ao       Angola  1246700    1246700           0    19625353   \n",
       "\n",
       "   population_growth  birth_rate  death_rate  migration_rate  \n",
       "0               2.32       38.57       13.89            1.51  \n",
       "1               0.30       12.92        6.58            3.30  \n",
       "2               1.84       23.67        4.31            0.92  \n",
       "3               0.12        8.13        6.96            0.00  \n",
       "4               2.78       38.78       11.49            0.46  "
      ]
     },
     "execution_count": 3,
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   ],
   "source": [
    "# create query to return first 5 rows of the facts table\n",
    "q2 = \"SELECT * FROM facts LIMIT 5;\"\n",
    "pd.read_sql_query(q2, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outliers\n",
    "\n",
    "Let's look at the data for any outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min_pop</th>\n",
       "      <th>max_pop</th>\n",
       "      <th>min_pop_growth</th>\n",
       "      <th>max_pop_growth</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7256490011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.02</td>\n",
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      "text/plain": [
       "   min_pop     max_pop  min_pop_growth  max_pop_growth\n",
       "0        0  7256490011             0.0            4.02"
      ]
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   "source": [
    "# create query to return the following:\n",
    "# min population, max population, min pop growth, max pop growth\n",
    "q3 = '''SELECT MIN(population) min_pop, MAX(population) max_pop, \n",
    "MIN(population_growth) min_pop_growth, MAX(population_growth) max_pop_growth\n",
    "FROM facts;'''\n",
    "pd.read_sql_query(q3, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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       "      <th>id</th>\n",
       "      <th>code</th>\n",
       "      <th>name</th>\n",
       "      <th>area</th>\n",
       "      <th>area_land</th>\n",
       "      <th>area_water</th>\n",
       "      <th>population</th>\n",
       "      <th>population_growth</th>\n",
       "      <th>birth_rate</th>\n",
       "      <th>death_rate</th>\n",
       "      <th>migration_rate</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>250</td>\n",
       "      <td>ay</td>\n",
       "      <td>Antarctica</td>\n",
       "      <td>None</td>\n",
       "      <td>280000</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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      "text/plain": [
       "    id code        name  area  area_land area_water  population  \\\n",
       "0  250   ay  Antarctica  None     280000       None           0   \n",
       "\n",
       "  population_growth birth_rate death_rate migration_rate  \n",
       "0              None       None       None           None  "
      ]
     },
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     "metadata": {},
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   "source": [
    "# look at the country(ies) with 0 population\n",
    "q4 = '''SELECT * FROM facts\n",
    "WHERE population == (SELECT MIN(population) FROM facts);'''\n",
    "pd.read_sql_query(q4, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
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       "      <th>id</th>\n",
       "      <th>code</th>\n",
       "      <th>name</th>\n",
       "      <th>area</th>\n",
       "      <th>area_land</th>\n",
       "      <th>area_water</th>\n",
       "      <th>population</th>\n",
       "      <th>population_growth</th>\n",
       "      <th>birth_rate</th>\n",
       "      <th>death_rate</th>\n",
       "      <th>migration_rate</th>\n",
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       "      <th>0</th>\n",
       "      <td>261</td>\n",
       "      <td>xx</td>\n",
       "      <td>World</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>7256490011</td>\n",
       "      <td>1.08</td>\n",
       "      <td>18.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>None</td>\n",
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      "text/plain": [
       "    id code   name  area area_land area_water  population  population_growth  \\\n",
       "0  261   xx  World  None      None       None  7256490011               1.08   \n",
       "\n",
       "   birth_rate  death_rate migration_rate  \n",
       "0        18.6         7.8           None  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# look at the country(ies) with max population\n",
    "q5 = '''SELECT * FROM facts\n",
    "WHERE population == (SELECT MAX(population) FROM facts);'''\n",
    "pd.read_sql_query(q5, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is one country, Antarctica, that has zero population and no population growth.  While this country is physically large, no one lives there.\n",
    "\n",
    "The other \"country\" with maximum population is actually the world.  It appears that the population is the total population of all of the countries, and the calculated rates for population growth, birth, and deaths are for the whole world.\n",
    "\n",
    "Depending on the analyses we perform later, we may need to exclude one or both of these."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms\n",
    "\n",
    "Let's first look at the histograms for population, population growth, birth rate, and death rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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      "/dataquest/system/env/python3/lib/python3.4/site-packages/IPython/core/interactiveshell.py:2885: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
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69Rg+9Zi+EHRkrpPaP991RmVlpU4c+UaOxuO2jFd/9Fsdqanw6fu1rz/2fH08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\nIdxqBAAgzlmBgBobG20d29TUNKzXW7ndbiUkJIQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff68e0316d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create histograms for pop, pop_growth, birth_rate, death_rate\n",
    "# ignore Antarctica and the World in these visualizations\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set_style('white')\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "q6 = '''SELECT population, population_growth, birth_rate, death_rate\n",
    "FROM facts\n",
    "WHERE population != (SELECT MIN(population) FROM facts)\n",
    "AND population != (SELECT MAX(population) FROM facts);'''\n",
    "\n",
    "pd.read_sql_query(q6, conn).hist(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Population Density\n",
    "\n",
    "Next, let's look at population density across countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "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>name</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Macau</td>\n",
       "      <td>21168.964286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monaco</td>\n",
       "      <td>15267.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>8141.279770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>6445.041516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gaza Strip</td>\n",
       "      <td>5191.819444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gibraltar</td>\n",
       "      <td>4876.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>1771.859211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Maldives</td>\n",
       "      <td>1319.640940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Malta</td>\n",
       "      <td>1310.015823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>1299.925926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sint Maarten</td>\n",
       "      <td>1167.323529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>1138.069143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Guernsey</td>\n",
       "      <td>847.179487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jersey</td>\n",
       "      <td>838.741379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>675.823256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name       density\n",
       "0          Macau  21168.964286\n",
       "1         Monaco  15267.500000\n",
       "2      Singapore   8141.279770\n",
       "3      Hong Kong   6445.041516\n",
       "4     Gaza Strip   5191.819444\n",
       "5      Gibraltar   4876.333333\n",
       "6        Bahrain   1771.859211\n",
       "7       Maldives   1319.640940\n",
       "8          Malta   1310.015823\n",
       "9        Bermuda   1299.925926\n",
       "10  Sint Maarten   1167.323529\n",
       "11    Bangladesh   1138.069143\n",
       "12      Guernsey    847.179487\n",
       "13        Jersey    838.741379\n",
       "14      Barbados    675.823256"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create query for highest population density countries\n",
    "q7 = '''SELECT name, CAST(population as float)/CAST(area as float) density\n",
    "FROM facts\n",
    "ORDER BY density DESC\n",
    "LIMIT 15;'''\n",
    "pd.read_sql_query(q7, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7ff68e0314e0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lpaV/pwYAOAjt8xmzk046KcOGDXvX9kaj8a5ty5cvz7Rp09La2ppRo0Zl9OjRWbNmTf9M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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff68e0315c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create histogram of population density\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "q8 = '''SELECT CAST(population as float)/CAST(area as float) density\n",
    "FROM facts\n",
    "WHERE population != (SELECT MIN(population) FROM facts)\n",
    "AND population != (SELECT MAX(population) FROM facts);'''\n",
    "\n",
    "pd.read_sql_query(q8, conn).hist(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Water versus Land\n",
    "\n",
    "Let's look at the highest ratios of water to land and which countries have more water than land."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "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>name</th>\n",
       "      <th>water_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>British Indian Ocean Territory</td>\n",
       "      <td>905.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>4.520231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>0.554791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bahamas, The</td>\n",
       "      <td>0.386613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>0.284673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>0.259396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0.225710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>0.222922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Eritrea</td>\n",
       "      <td>0.164356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>0.156240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>0.140509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Gambia, The</td>\n",
       "      <td>0.116601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>0.115313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Finland</td>\n",
       "      <td>0.112996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>India</td>\n",
       "      <td>0.105634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              name  water_ratio\n",
       "0   British Indian Ocean Territory   905.666667\n",
       "1                   Virgin Islands     4.520231\n",
       "2                      Puerto Rico     0.554791\n",
       "3                     Bahamas, The     0.386613\n",
       "4                    Guinea-Bissau     0.284673\n",
       "5                           Malawi     0.259396\n",
       "6                      Netherlands     0.225710\n",
       "7                           Uganda     0.222922\n",
       "8                          Eritrea     0.164356\n",
       "9                          Liberia     0.156240\n",
       "10                      Bangladesh     0.140509\n",
       "11                     Gambia, The     0.116601\n",
       "12                          Taiwan     0.115313\n",
       "13                         Finland     0.112996\n",
       "14                           India     0.105634"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create query for highest water/land ratio countries\n",
    "q9 = '''SELECT name, CAST(area_water as float)/CAST(area_land as float) water_ratio\n",
    "FROM facts\n",
    "ORDER BY water_ratio DESC\n",
    "LIMIT 15;'''\n",
    "pd.read_sql_query(q9, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above, we can see that only two countries, British Indian Ocean Territory and Virgin Islands, have more water than land (because the ratio is greater than one).\n",
    "\n",
    "The next highest ratios, which are less than one, include several islands, which seems reasonable.  Interestingly, there are some non-island countries included here as well, such as Guinea-Bissau, a country in West Africa, and Netherlands, a country in Western Europe.  Both of these countries have a large amount of the country that is at or near sea level and have borders that include more of the ocean."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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