cia_factbook_sql.ipynb

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   "source": [
    "## Analyzing CIA Factbook Data\n",
    "#### SQLite and Python\n",
    "In this project, I will be analyzing data from the CIA World Factbook (https://www.cia.gov/library/publications/the-world-factbook/) which contains information about statistics concerning all countries on Earth. For example, the Factbook contrains data about population, population growth, area of land and water in a country, and more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   "outputs": [],
   "source": [
    "# import relevant packages\n",
    "import sqlite3\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we want to look at the information about the tables contained in the CIA World Factbook database, factbook.db:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <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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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get information about the tables in the database\n",
    "conn = sqlite3.connect(\"factbook.db\")\n",
    "query = '''\n",
    "        SELECT * \n",
    "        FROM sqlite_master \n",
    "        WHERE type = 'table';\n",
    "        '''\n",
    "pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the query above, we can see that we should be using the table called 'facts' to query the data we are looking for."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will run a query to explore the head of the facts table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "    </tr>\n",
       "  </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,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first five rows of facts table\n",
    "query = '''\n",
    "        SELECT * \n",
    "        FROM facts\n",
    "        LIMIT 5'''\n",
    "pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above query we see there are 11 columns in the table of varying data types. From here we can start to explore by gathering summary statistics of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MIN(population)</th>\n",
       "      <th>MAX(population)</th>\n",
       "      <th>MIN(population_growth)</th>\n",
       "      <th>MAX(population_growth)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7256490011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   MIN(population)  MAX(population)  MIN(population_growth)  \\\n",
       "0                0       7256490011                     0.0   \n",
       "\n",
       "   MAX(population_growth)  \n",
       "0                    4.02  "
      ]
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   "source": [
    "# Min and max population, min and max population growth\n",
    "query = '''\n",
    "        SELECT MIN(population),\n",
    "               MAX(population),\n",
    "               MIN(population_growth),\n",
    "               MAX(population_growth)\n",
    "               FROM facts\n",
    "        '''\n",
    "pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above summary statistics raise some concerns:\n",
    "1. The query suggests that at least one country have a population of 0\n",
    "2. The query suggests that a country has more than 7 billion people\n",
    "\n",
    "To look into these concerns further, we'll see which countries these outliers are connected to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Antarctica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "         name\n",
       "0  Antarctica"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "# country with population of 0\n",
    "query = '''\n",
    "        SELECT name\n",
    "        FROM facts\n",
    "        WHERE population = (\n",
    "                            SELECT MIN(population)\n",
    "                            FROM facts\n",
    "                            )\n",
    "        '''\n",
    "pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>World</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "    name\n",
       "0  World"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# country with population of 7.2 billion\n",
    "query = '''\n",
    "        SELECT name\n",
    "        FROM facts\n",
    "        WHERE population = (\n",
    "                            SELECT MAX(population)\n",
    "                            FROM facts\n",
    "                            )\n",
    "        '''\n",
    "pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above queries, we now see that the country with no population is Antarctica, which makes sense because nobody is a permanent resident of that country.\n",
    "\n",
    "Additionally, we see that the population of 7.2 billion was the world population, not a specific country's population.\n",
    "\n",
    "Knowing this, we should ignore name = 'Antarctica' and name = 'World' in our subsequent analyses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of Population, Population Growth, Birth Rate, and Death Rate\n",
    "\n",
    "To further explore the distribution of data for the non-outlier countries, let's create histograms of the columns for population, population growth, birth rate, and death rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# SQL query to create table, turn into pandas dataframe\n",
    "query = '''\n",
    "    SELECT population, \n",
    "           population_growth, \n",
    "           birth_rate, \n",
    "           death_rate \n",
    "    FROM facts\n",
    "'''\n",
    "hist_data = pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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    {
     "data": {
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       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f953d4bca20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f953d3fa080>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f953d3c4e10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f953d37def0>]],\n",
       "      dtype=object)"
      ]
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     "execution_count": 17,
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+lbX/7LPPOO9f2bjx9PTkPQ4MQZc1gJSNI75U/S2EcHR0hJubG27evAkAyMnJ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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f953d4d3780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# create histograms\n",
    "hist_data.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which countries have the highest population density?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Population density is the ratio of population to land area.\n",
    "\n",
    "In order to find which countries have the highest population density, we will first need to calculate the population densities for each country, excluding Antarctica and World. Then we can sort the table by population density in descending order.\n",
    "\n",
    "Because we have multiple steps in this problem (excluding certain countries and calculating population density), **subqueries** will come in handy for this SQL problem. We will also need to cast some columns as floating data types in order to make the division operation possible.\n",
    "\n",
    "We'll limit the results to the first 10 countries with the highest population densities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>country</th>\n",
       "      <th>population_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>8259.784571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>6655.271202</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country  population_density\n",
       "0       Macau        21168.964286\n",
       "1      Monaco        15267.500000\n",
       "2   Singapore         8259.784571\n",
       "3   Hong Kong         6655.271202\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"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = '''\n",
    "        SELECT name AS country,\n",
    "               CAST(population AS Float)/ CAST(area_land AS Float) AS population_density\n",
    "        FROM (\n",
    "              SELECT *\n",
    "              FROM facts\n",
    "              WHERE name NOT IN (\"Antarctica\", \"World\")\n",
    "              )\n",
    "        ORDER BY population_density DESC\n",
    "        LIMIT 10\n",
    "        '''\n",
    "pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To further explore the data, let's create a histogram to show the distribution of population densities amongst countries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f953d4b64e0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ghB0AAIMQdgAADELYAQAwCGEHAMAghB0AAIMQdgAADELYAQAwCGEHAMAghB0AAIMQdgAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f953d4b5d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query = '''\n",
    "        SELECT name AS country,\n",
    "               CAST(population AS Float)/ CAST(area_land AS Float) AS population_density\n",
    "        FROM (\n",
    "              SELECT *\n",
    "              FROM facts\n",
    "              WHERE name NOT IN (\"Antarctica\", \"World\")\n",
    "              )\n",
    "        ORDER BY population_density DESC\n",
    "        '''\n",
    "pd.read_sql_query(query, conn).hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's evident that the default bins for this histogram put the vast majority of the data into just one bin! It would be more useful to us to explore which bin parameter makes for the most informative visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f953c9dde80>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E448/zrp16wAoKioC6NSktIiIdJ2gzDkMHz6ciooKqqqqaG1tZdu2bYwfPz4Y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\nIwcTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f953c9ad940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run same query, but plot with more bins\n",
    "pd.read_sql_query(query, conn).hist(bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which countries have the highest ratios of water to land?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this query, we will calculate the ratio of area_water to area_land by casting the columns as floating numbers, and ordering by the water-to-land ratio in descending order. We will return the 10 countries with the highest ratios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>country</th>\n",
       "      <th>water_to_land_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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          country  water_to_land_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"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "        SELECT name AS country,\n",
    "               CAST(area_water AS Float) / CAST(area_land AS Float) AS water_to_land_ratio\n",
    "        FROM facts\n",
    "        ORDER BY 2 DESC\n",
    "        LIMIT 10\n",
    "        \"\"\"\n",
    "pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which countries have more water than land?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this query, we are simply comparing area_water to area_land. We don't need to limit this query because there are only two countries that qualify!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>country</th>\n",
       "      <th>area_water</th>\n",
       "      <th>area_land</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>British Indian Ocean Territory</td>\n",
       "      <td>54340</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>1564</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          country  area_water  area_land\n",
       "0  British Indian Ocean Territory       54340         60\n",
       "1                  Virgin Islands        1564        346"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "        SELECT name AS country,\n",
    "               area_water,\n",
    "               area_land\n",
    "        FROM facts\n",
    "        WHERE area_water > area_land\n",
    "        \"\"\"\n",
    "pd.read_sql_query(query, conn)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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