Quick Population Decline Analysis using SQL.ipynb

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"
    }
   },
   "cell_type": "markdown",
   "id": "a880ae56",
   "metadata": {},
   "source": [
    "# Population Analysis with SQL\n",
    "---\n",
    "![Screenshot%202023-11-28%20at%201.51.31%20PM.png](attachment:Screenshot%202023-11-28%20at%201.51.31%20PM.png)\n",
    "\n",
    "The CIA Factbook, also known as the World Factbook, is an annual publication by the US Central Intelligence Agency. It serves as a comprehensive source of basic intelligence, offering summaries of information about countries and regions globally.\n",
    "\n",
    "In this Analysis, we will utilize SQL in jupyter notebook to analyze data from the CIA Factbook. In addition to population, we will also analyze other indicators such as mortality and fertility rates to understand what regions are at risk of population decline."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f561be16",
   "metadata": {},
   "source": [
    "## Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3ec15c5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%load_ext sql\n",
    "%sql sqlite:///factbook.db"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f47f9425",
   "metadata": {},
   "source": [
    "## Initial Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "374042ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>type</th>\n",
       "            <th>name</th>\n",
       "            <th>tbl_name</th>\n",
       "            <th>rootpage</th>\n",
       "            <th>sql</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\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",
       "            <td>table</td>\n",
       "            <td>facts</td>\n",
       "            <td>facts</td>\n",
       "            <td>47</td>\n",
       "            <td>CREATE TABLE &quot;facts&quot; (&quot;id&quot; INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, &quot;code&quot; varchar(255) NOT NULL, &quot;name&quot; varchar(255) NOT NULL, &quot;area&quot; integer, &quot;area_land&quot; integer, &quot;area_water&quot; integer, &quot;population&quot; integer, &quot;population_growth&quot; float, &quot;birth_rate&quot; float, &quot;death_rate&quot; float, &quot;migration_rate&quot; float)</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('table', 'sqlite_sequence', 'sqlite_sequence', 3, 'CREATE TABLE sqlite_sequence(name,seq)'),\n",
       " ('table', 'facts', 'facts', 47, 'CREATE TABLE \"facts\" (\"id\" INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, \"code\" varchar(255) NOT NULL, \"name\" varchar(255) NOT NULL, \"area\" integer, \"a ... (4 characters truncated) ... land\" integer, \"area_water\" integer, \"population\" integer, \"population_growth\" float, \"birth_rate\" float, \"death_rate\" float, \"migration_rate\" float)')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT * \n",
    "FROM sqlite_master\n",
    "WHERE TYPE='table';"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94352ff7",
   "metadata": {},
   "source": [
    "The `facts` table contains the relevant columns for our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1914e8d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\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",
       "            <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",
       "            <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>0.3</td>\n",
       "            <td>12.92</td>\n",
       "            <td>6.58</td>\n",
       "            <td>3.3</td>\n",
       "        </tr>\n",
       "        <tr>\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",
       "            <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>0.0</td>\n",
       "        </tr>\n",
       "        <tr>\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>"
      ],
      "text/plain": [
       "[(1, 'af', 'Afghanistan', 652230, 652230, 0, 32564342, 2.32, 38.57, 13.89, 1.51),\n",
       " (2, 'al', 'Albania', 28748, 27398, 1350, 3029278, 0.3, 12.92, 6.58, 3.3),\n",
       " (3, 'ag', 'Algeria', 2381741, 2381741, 0, 39542166, 1.84, 23.67, 4.31, 0.92),\n",
       " (4, 'an', 'Andorra', 468, 468, 0, 85580, 0.12, 8.13, 6.96, 0.0),\n",
       " (5, 'ao', 'Angola', 1246700, 1246700, 0, 19625353, 2.78, 38.78, 11.49, 0.46)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT *\n",
    "FROM facts\n",
    "LIMIT 5;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07170ea5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>num_entries</th>\n",
       "            <th>num_countries</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>261</td>\n",
       "            <td>261</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(261, 261)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT COUNT(*) AS num_entries,\n",
    "       COUNT(DISTINCT name) AS num_countries\n",
    "FROM facts;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed051527",
   "metadata": {},
   "source": [
    "Note only 195 countries exist, the remaining 66 countries are territories or other entities the Factbook includes. This will not affect our analysis because our scope is focused mainly on economically signifcant countries. \n",
    "\n",
    "Let's look at some of those entities below using the wildcard `%` operator and the `LIKE` clause:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e186b6d7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\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",
       "            <td>197</td>\n",
       "            <td>ee</td>\n",
       "            <td>European Union</td>\n",
       "            <td>4324782</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>513949445</td>\n",
       "            <td>0.25</td>\n",
       "            <td>10.2</td>\n",
       "            <td>10.2</td>\n",
       "            <td>2.5</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>199</td>\n",
       "            <td>kt</td>\n",
       "            <td>Christmas Island</td>\n",
       "            <td>135</td>\n",
       "            <td>135</td>\n",
       "            <td>0</td>\n",
       "            <td>1530</td>\n",
       "            <td>1.11</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>203</td>\n",
       "            <td>nf</td>\n",
       "            <td>Norfolk Island</td>\n",
       "            <td>36</td>\n",
       "            <td>36</td>\n",
       "            <td>0</td>\n",
       "            <td>2210</td>\n",
       "            <td>0.01</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>208</td>\n",
       "            <td>ip</td>\n",
       "            <td>Clipperton Island</td>\n",
       "            <td>6</td>\n",
       "            <td>6</td>\n",
       "            <td>0</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>222</td>\n",
       "            <td>bv</td>\n",
       "            <td>Bouvet Island</td>\n",
       "            <td>49</td>\n",
       "            <td>49</td>\n",
       "            <td>0</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>244</td>\n",
       "            <td>bq</td>\n",
       "            <td>Navassa Island</td>\n",
       "            <td>5</td>\n",
       "            <td>5</td>\n",
       "            <td>0</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>248</td>\n",
       "            <td>wq</td>\n",
       "            <td>Wake Island</td>\n",
       "            <td>6</td>\n",
       "            <td>6</td>\n",
       "            <td>0</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>256</td>\n",
       "            <td>xq</td>\n",
       "            <td>Arctic Ocean</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>257</td>\n",
       "            <td>zh</td>\n",
       "            <td>Atlantic Ocean</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>258</td>\n",
       "            <td>xo</td>\n",
       "            <td>Indian Ocean</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>259</td>\n",
       "            <td>zn</td>\n",
       "            <td>Pacific Ocean</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>260</td>\n",
       "            <td>oo</td>\n",
       "            <td>Southern Ocean</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "            <td>None</td>\n",
       "        </tr>\n",
       "        <tr>\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",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(197, 'ee', 'European Union', 4324782, None, None, 513949445, 0.25, 10.2, 10.2, 2.5),\n",
       " (199, 'kt', 'Christmas Island', 135, 135, 0, 1530, 1.11, None, None, None),\n",
       " (203, 'nf', 'Norfolk Island', 36, 36, 0, 2210, 0.01, None, None, None),\n",
       " (208, 'ip', 'Clipperton Island', 6, 6, 0, None, None, None, None, None),\n",
       " (222, 'bv', 'Bouvet Island', 49, 49, 0, None, None, None, None, None),\n",
       " (244, 'bq', 'Navassa Island', 5, 5, 0, None, None, None, None, None),\n",
       " (248, 'wq', 'Wake Island', 6, 6, 0, None, None, None, None, None),\n",
       " (256, 'xq', 'Arctic Ocean', None, None, None, None, None, None, None, None),\n",
       " (257, 'zh', 'Atlantic Ocean', None, None, None, None, None, None, None, None),\n",
       " (258, 'xo', 'Indian Ocean', None, None, None, None, None, None, None, None),\n",
       " (259, 'zn', 'Pacific Ocean', None, None, None, None, None, None, None, None),\n",
       " (260, 'oo', 'Southern Ocean', None, None, None, None, None, None, None, None),\n",
       " (261, 'xx', 'World', None, None, None, 7256490011, 1.08, 18.6, 7.8, None)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT *\n",
    "FROM facts\n",
    "WHERE name LIKE '%union'\n",
    "   OR name LIKE '%ocean'\n",
    "   OR name LIKE '%island'\n",
    "   OR name = 'World';"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c49012d7",
   "metadata": {},
   "source": [
    "## Limit to Economically Significant Countries\n",
    "For this analysis, we will only focus on economically significant countries. We can define this as Countries with a nominal GDP (current price of goods and services not adjusted to inflation or deflation) of over $1 trillion.\n",
    "\n",
    "As of 2022 data, those countries are: \n",
    " - United States\n",
    " - China\n",
    " - Japan\n",
    " - Germany\n",
    " - India\n",
    " - United Kingdom\n",
    " - France\n",
    " - Russia\n",
    " - Canada\n",
    " - Italy\n",
    " - Brazil\n",
    " - Australia\n",
    " - South Korea\n",
    " - Mexico\n",
    " - Spain\n",
    " - Indonesia\n",
    " - Saudi Arabia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2acfefa1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "(sqlite3.OperationalError) view sig_countries already exists\n",
      "[SQL: CREATE VIEW sig_countries AS\n",
      "SELECT *\n",
      "FROM facts\n",
      "WHERE name IN ('United States',\n",
      "               'China', \n",
      "               'India',\n",
      "               'Japan', \n",
      "               'Germany', \n",
      "               'India',\n",
      "               'United Kingdom',\n",
      "               'France', \n",
      "               'Russia',\n",
      "               'Canada', \n",
      "               'Italy', \n",
      "               'Brazil',\n",
      "               'Australia',\n",
      "               'Korea, South', \n",
      "               'Mexico',\n",
      "               'Spain', \n",
      "               'Indonesia', \n",
      "               'Saudi Arabia');]\n",
      "(Background on this error at: https://sqlalche.me/e/14/e3q8)\n"
     ]
    }
   ],
   "source": [
    "%%sql\n",
    "CREATE VIEW sig_countries AS\n",
    "SELECT *\n",
    "FROM facts\n",
    "WHERE name IN ('United States',\n",
    "               'China', \n",
    "               'India',\n",
    "               'Japan', \n",
    "               'Germany', \n",
    "               'India',\n",
    "               'United Kingdom',\n",
    "               'France', \n",
    "               'Russia',\n",
    "               'Canada', \n",
    "               'Italy', \n",
    "               'Brazil',\n",
    "               'Australia',\n",
    "               'Korea, South', \n",
    "               'Mexico',\n",
    "               'Spain', \n",
    "               'Indonesia', \n",
    "               'Saudi Arabia');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd33e197",
   "metadata": {},
   "source": [
    "*note the above kernel will showcase an error if run more than once due to the view already existing*.\n",
    "Below we look at this view we created for economically significant countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "552f56bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\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",
       "            <td>9</td>\n",
       "            <td>as</td>\n",
       "            <td>Australia</td>\n",
       "            <td>7741220</td>\n",
       "            <td>7682300</td>\n",
       "            <td>58920</td>\n",
       "            <td>22751014</td>\n",
       "            <td>1.07</td>\n",
       "            <td>12.15</td>\n",
       "            <td>7.14</td>\n",
       "            <td>5.65</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>24</td>\n",
       "            <td>br</td>\n",
       "            <td>Brazil</td>\n",
       "            <td>8515770</td>\n",
       "            <td>8358140</td>\n",
       "            <td>157630</td>\n",
       "            <td>204259812</td>\n",
       "            <td>0.77</td>\n",
       "            <td>14.46</td>\n",
       "            <td>6.58</td>\n",
       "            <td>0.14</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>32</td>\n",
       "            <td>ca</td>\n",
       "            <td>Canada</td>\n",
       "            <td>9984670</td>\n",
       "            <td>9093507</td>\n",
       "            <td>891163</td>\n",
       "            <td>35099836</td>\n",
       "            <td>0.75</td>\n",
       "            <td>10.28</td>\n",
       "            <td>8.42</td>\n",
       "            <td>5.66</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>37</td>\n",
       "            <td>ch</td>\n",
       "            <td>China</td>\n",
       "            <td>9596960</td>\n",
       "            <td>9326410</td>\n",
       "            <td>270550</td>\n",
       "            <td>1367485388</td>\n",
       "            <td>0.45</td>\n",
       "            <td>12.49</td>\n",
       "            <td>7.53</td>\n",
       "            <td>0.44</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>61</td>\n",
       "            <td>fr</td>\n",
       "            <td>France</td>\n",
       "            <td>643801</td>\n",
       "            <td>640427</td>\n",
       "            <td>3374</td>\n",
       "            <td>66553766</td>\n",
       "            <td>0.43</td>\n",
       "            <td>12.38</td>\n",
       "            <td>9.16</td>\n",
       "            <td>1.09</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>65</td>\n",
       "            <td>gm</td>\n",
       "            <td>Germany</td>\n",
       "            <td>357022</td>\n",
       "            <td>348672</td>\n",
       "            <td>8350</td>\n",
       "            <td>80854408</td>\n",
       "            <td>0.17</td>\n",
       "            <td>8.47</td>\n",
       "            <td>11.42</td>\n",
       "            <td>1.24</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>77</td>\n",
       "            <td>in</td>\n",
       "            <td>India</td>\n",
       "            <td>3287263</td>\n",
       "            <td>2973193</td>\n",
       "            <td>314070</td>\n",
       "            <td>1251695584</td>\n",
       "            <td>1.22</td>\n",
       "            <td>19.55</td>\n",
       "            <td>7.32</td>\n",
       "            <td>0.04</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>78</td>\n",
       "            <td>id</td>\n",
       "            <td>Indonesia</td>\n",
       "            <td>1904569</td>\n",
       "            <td>1811569</td>\n",
       "            <td>93000</td>\n",
       "            <td>255993674</td>\n",
       "            <td>0.92</td>\n",
       "            <td>16.72</td>\n",
       "            <td>6.37</td>\n",
       "            <td>1.16</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>83</td>\n",
       "            <td>it</td>\n",
       "            <td>Italy</td>\n",
       "            <td>301340</td>\n",
       "            <td>294140</td>\n",
       "            <td>7200</td>\n",
       "            <td>61855120</td>\n",
       "            <td>0.27</td>\n",
       "            <td>8.74</td>\n",
       "            <td>10.19</td>\n",
       "            <td>4.1</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>85</td>\n",
       "            <td>ja</td>\n",
       "            <td>Japan</td>\n",
       "            <td>377915</td>\n",
       "            <td>364485</td>\n",
       "            <td>13430</td>\n",
       "            <td>126919659</td>\n",
       "            <td>0.16</td>\n",
       "            <td>7.93</td>\n",
       "            <td>9.51</td>\n",
       "            <td>0.0</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>91</td>\n",
       "            <td>ks</td>\n",
       "            <td>Korea, South</td>\n",
       "            <td>99720</td>\n",
       "            <td>96920</td>\n",
       "            <td>2800</td>\n",
       "            <td>49115196</td>\n",
       "            <td>0.14</td>\n",
       "            <td>8.19</td>\n",
       "            <td>6.75</td>\n",
       "            <td>0.0</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>114</td>\n",
       "            <td>mx</td>\n",
       "            <td>Mexico</td>\n",
       "            <td>1964375</td>\n",
       "            <td>1943945</td>\n",
       "            <td>20430</td>\n",
       "            <td>121736809</td>\n",
       "            <td>1.18</td>\n",
       "            <td>18.78</td>\n",
       "            <td>5.26</td>\n",
       "            <td>1.68</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>143</td>\n",
       "            <td>rs</td>\n",
       "            <td>Russia</td>\n",
       "            <td>17098242</td>\n",
       "            <td>16377742</td>\n",
       "            <td>720500</td>\n",
       "            <td>142423773</td>\n",
       "            <td>0.04</td>\n",
       "            <td>11.6</td>\n",
       "            <td>13.69</td>\n",
       "            <td>1.69</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>151</td>\n",
       "            <td>sa</td>\n",
       "            <td>Saudi Arabia</td>\n",
       "            <td>2149690</td>\n",
       "            <td>2149690</td>\n",
       "            <td>0</td>\n",
       "            <td>27752316</td>\n",
       "            <td>1.46</td>\n",
       "            <td>18.51</td>\n",
       "            <td>3.33</td>\n",
       "            <td>0.55</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>163</td>\n",
       "            <td>sp</td>\n",
       "            <td>Spain</td>\n",
       "            <td>505370</td>\n",
       "            <td>498980</td>\n",
       "            <td>6390</td>\n",
       "            <td>48146134</td>\n",
       "            <td>0.89</td>\n",
       "            <td>9.64</td>\n",
       "            <td>9.04</td>\n",
       "            <td>8.31</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>185</td>\n",
       "            <td>uk</td>\n",
       "            <td>United Kingdom</td>\n",
       "            <td>243610</td>\n",
       "            <td>241930</td>\n",
       "            <td>1680</td>\n",
       "            <td>64088222</td>\n",
       "            <td>0.54</td>\n",
       "            <td>12.17</td>\n",
       "            <td>9.35</td>\n",
       "            <td>2.54</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>186</td>\n",
       "            <td>us</td>\n",
       "            <td>United States</td>\n",
       "            <td>9826675</td>\n",
       "            <td>9161966</td>\n",
       "            <td>664709</td>\n",
       "            <td>321368864</td>\n",
       "            <td>0.78</td>\n",
       "            <td>12.49</td>\n",
       "            <td>8.15</td>\n",
       "            <td>3.86</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(9, 'as', 'Australia', 7741220, 7682300, 58920, 22751014, 1.07, 12.15, 7.14, 5.65),\n",
       " (24, 'br', 'Brazil', 8515770, 8358140, 157630, 204259812, 0.77, 14.46, 6.58, 0.14),\n",
       " (32, 'ca', 'Canada', 9984670, 9093507, 891163, 35099836, 0.75, 10.28, 8.42, 5.66),\n",
       " (37, 'ch', 'China', 9596960, 9326410, 270550, 1367485388, 0.45, 12.49, 7.53, 0.44),\n",
       " (61, 'fr', 'France', 643801, 640427, 3374, 66553766, 0.43, 12.38, 9.16, 1.09),\n",
       " (65, 'gm', 'Germany', 357022, 348672, 8350, 80854408, 0.17, 8.47, 11.42, 1.24),\n",
       " (77, 'in', 'India', 3287263, 2973193, 314070, 1251695584, 1.22, 19.55, 7.32, 0.04),\n",
       " (78, 'id', 'Indonesia', 1904569, 1811569, 93000, 255993674, 0.92, 16.72, 6.37, 1.16),\n",
       " (83, 'it', 'Italy', 301340, 294140, 7200, 61855120, 0.27, 8.74, 10.19, 4.1),\n",
       " (85, 'ja', 'Japan', 377915, 364485, 13430, 126919659, 0.16, 7.93, 9.51, 0.0),\n",
       " (91, 'ks', 'Korea, South', 99720, 96920, 2800, 49115196, 0.14, 8.19, 6.75, 0.0),\n",
       " (114, 'mx', 'Mexico', 1964375, 1943945, 20430, 121736809, 1.18, 18.78, 5.26, 1.68),\n",
       " (143, 'rs', 'Russia', 17098242, 16377742, 720500, 142423773, 0.04, 11.6, 13.69, 1.69),\n",
       " (151, 'sa', 'Saudi Arabia', 2149690, 2149690, 0, 27752316, 1.46, 18.51, 3.33, 0.55),\n",
       " (163, 'sp', 'Spain', 505370, 498980, 6390, 48146134, 0.89, 9.64, 9.04, 8.31),\n",
       " (185, 'uk', 'United Kingdom', 243610, 241930, 1680, 64088222, 0.54, 12.17, 9.35, 2.54),\n",
       " (186, 'us', 'United States', 9826675, 9161966, 664709, 321368864, 0.78, 12.49, 8.15, 3.86)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT * FROM sig_countries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca977fd8",
   "metadata": {},
   "source": [
    "## Population\n",
    "Lets now focus our attention to each of the features for our economically significant countries to gain intuition around where each stand with respect to one another starting with population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0bcf8d74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Most Populated Countries</th>\n",
       "            <th>Population</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>China</td>\n",
       "            <td>1367485388</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>India</td>\n",
       "            <td>1251695584</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>United States</td>\n",
       "            <td>321368864</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Indonesia</td>\n",
       "            <td>255993674</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Brazil</td>\n",
       "            <td>204259812</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('China', 1367485388),\n",
       " ('India', 1251695584),\n",
       " ('United States', 321368864),\n",
       " ('Indonesia', 255993674),\n",
       " ('Brazil', 204259812)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT name AS 'Most Populated Countries',\n",
    "       population AS Population\n",
    "FROM sig_countries\n",
    "ORDER BY 2 DESC\n",
    "LIMIT 5;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5580711c",
   "metadata": {},
   "source": [
    "*Note this data is accumulated from 2015*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "92b4d5a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Least Populated Countries</th>\n",
       "            <th>Population</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Australia</td>\n",
       "            <td>22751014</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Saudi Arabia</td>\n",
       "            <td>27752316</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Canada</td>\n",
       "            <td>35099836</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Spain</td>\n",
       "            <td>48146134</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Korea, South</td>\n",
       "            <td>49115196</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Australia', 22751014),\n",
       " ('Saudi Arabia', 27752316),\n",
       " ('Canada', 35099836),\n",
       " ('Spain', 48146134),\n",
       " ('Korea, South', 49115196)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT name AS 'Least Populated Countries',\n",
    "       population AS Population\n",
    "FROM sig_countries\n",
    "ORDER BY 2\n",
    "LIMIT 5;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fe1d07f",
   "metadata": {},
   "source": [
    "## Population Density \n",
    "\n",
    "$ \\text{Population Density} = \\frac{\\text{Total Population}}{\\text{Land Area (km}^2\\text{)}} $"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8676943",
   "metadata": {},
   "source": [
    "### 5 Highest Population Densities of Economically Significant Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "00233883",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Country</th>\n",
       "            <th>Area</th>\n",
       "            <th>Population</th>\n",
       "            <th>Population_Density</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Korea, South</td>\n",
       "            <td>96920</td>\n",
       "            <td>49115196</td>\n",
       "            <td>506</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>India</td>\n",
       "            <td>2973193</td>\n",
       "            <td>1251695584</td>\n",
       "            <td>420</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Japan</td>\n",
       "            <td>364485</td>\n",
       "            <td>126919659</td>\n",
       "            <td>348</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>United Kingdom</td>\n",
       "            <td>241930</td>\n",
       "            <td>64088222</td>\n",
       "            <td>264</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Germany</td>\n",
       "            <td>348672</td>\n",
       "            <td>80854408</td>\n",
       "            <td>231</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Korea, South', 96920, 49115196, 506),\n",
       " ('India', 2973193, 1251695584, 420),\n",
       " ('Japan', 364485, 126919659, 348),\n",
       " ('United Kingdom', 241930, 64088222, 264),\n",
       " ('Germany', 348672, 80854408, 231)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT name AS Country, area_land AS Area, population AS Population,\n",
    "       CAST(\n",
    "            CAST(population AS Float) / \n",
    "            CAST(area_land AS Float) \n",
    "            AS Integer) \n",
    "       AS Population_Density\n",
    "FROM sig_countries\n",
    "ORDER BY 4 DESC\n",
    "LIMIT 5;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94599111",
   "metadata": {},
   "source": [
    "### 5 Lowest Population Densities of Economically Significant Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b20eff52",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Country</th>\n",
       "            <th>Area</th>\n",
       "            <th>Population</th>\n",
       "            <th>Population_Density</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Australia</td>\n",
       "            <td>7682300</td>\n",
       "            <td>22751014</td>\n",
       "            <td>2</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Canada</td>\n",
       "            <td>9093507</td>\n",
       "            <td>35099836</td>\n",
       "            <td>3</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Russia</td>\n",
       "            <td>16377742</td>\n",
       "            <td>142423773</td>\n",
       "            <td>8</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Saudi Arabia</td>\n",
       "            <td>2149690</td>\n",
       "            <td>27752316</td>\n",
       "            <td>12</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Brazil</td>\n",
       "            <td>8358140</td>\n",
       "            <td>204259812</td>\n",
       "            <td>24</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Australia', 7682300, 22751014, 2),\n",
       " ('Canada', 9093507, 35099836, 3),\n",
       " ('Russia', 16377742, 142423773, 8),\n",
       " ('Saudi Arabia', 2149690, 27752316, 12),\n",
       " ('Brazil', 8358140, 204259812, 24)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT name AS Country, area_land AS Area, population AS Population,\n",
    "       CAST(\n",
    "            CAST(population AS Float) / \n",
    "            CAST(area_land AS Float) \n",
    "            AS Integer) \n",
    "       AS Population_Density\n",
    "FROM sig_countries\n",
    "ORDER BY 4\n",
    "LIMIT 5;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dc7bc4b",
   "metadata": {},
   "source": [
    "## Mortality and Fertility"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2659cbb",
   "metadata": {},
   "source": [
    "### Birth Rates Among Economically Significant Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "893301ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Country</th>\n",
       "            <th>Birth Rate</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>India</td>\n",
       "            <td>19.55</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Mexico</td>\n",
       "            <td>18.78</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Saudi Arabia</td>\n",
       "            <td>18.51</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Indonesia</td>\n",
       "            <td>16.72</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Brazil</td>\n",
       "            <td>14.46</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>China</td>\n",
       "            <td>12.49</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>United States</td>\n",
       "            <td>12.49</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>France</td>\n",
       "            <td>12.38</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>United Kingdom</td>\n",
       "            <td>12.17</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Australia</td>\n",
       "            <td>12.15</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Russia</td>\n",
       "            <td>11.6</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Canada</td>\n",
       "            <td>10.28</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Spain</td>\n",
       "            <td>9.64</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Italy</td>\n",
       "            <td>8.74</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Germany</td>\n",
       "            <td>8.47</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Korea, South</td>\n",
       "            <td>8.19</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Japan</td>\n",
       "            <td>7.93</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('India', 19.55),\n",
       " ('Mexico', 18.78),\n",
       " ('Saudi Arabia', 18.51),\n",
       " ('Indonesia', 16.72),\n",
       " ('Brazil', 14.46),\n",
       " ('China', 12.49),\n",
       " ('United States', 12.49),\n",
       " ('France', 12.38),\n",
       " ('United Kingdom', 12.17),\n",
       " ('Australia', 12.15),\n",
       " ('Russia', 11.6),\n",
       " ('Canada', 10.28),\n",
       " ('Spain', 9.64),\n",
       " ('Italy', 8.74),\n",
       " ('Germany', 8.47),\n",
       " ('Korea, South', 8.19),\n",
       " ('Japan', 7.93)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql \n",
    "SELECT name AS Country, birth_rate AS 'Birth Rate'\n",
    "FROM sig_countries\n",
    "ORDER BY 2 DESC;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f9f6bd2",
   "metadata": {},
   "source": [
    "### Death Rates Among Economically Significant Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d7a97f51",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Country</th>\n",
       "            <th>Death Rate</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Russia</td>\n",
       "            <td>13.69</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Germany</td>\n",
       "            <td>11.42</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Italy</td>\n",
       "            <td>10.19</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Japan</td>\n",
       "            <td>9.51</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>United Kingdom</td>\n",
       "            <td>9.35</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>France</td>\n",
       "            <td>9.16</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Spain</td>\n",
       "            <td>9.04</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Canada</td>\n",
       "            <td>8.42</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>United States</td>\n",
       "            <td>8.15</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>China</td>\n",
       "            <td>7.53</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>India</td>\n",
       "            <td>7.32</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Australia</td>\n",
       "            <td>7.14</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Korea, South</td>\n",
       "            <td>6.75</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Brazil</td>\n",
       "            <td>6.58</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Indonesia</td>\n",
       "            <td>6.37</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Mexico</td>\n",
       "            <td>5.26</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Saudi Arabia</td>\n",
       "            <td>3.33</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Russia', 13.69),\n",
       " ('Germany', 11.42),\n",
       " ('Italy', 10.19),\n",
       " ('Japan', 9.51),\n",
       " ('United Kingdom', 9.35),\n",
       " ('France', 9.16),\n",
       " ('Spain', 9.04),\n",
       " ('Canada', 8.42),\n",
       " ('United States', 8.15),\n",
       " ('China', 7.53),\n",
       " ('India', 7.32),\n",
       " ('Australia', 7.14),\n",
       " ('Korea, South', 6.75),\n",
       " ('Brazil', 6.58),\n",
       " ('Indonesia', 6.37),\n",
       " ('Mexico', 5.26),\n",
       " ('Saudi Arabia', 3.33)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT name AS Country, death_rate AS 'Death Rate'\n",
    "FROM sig_countries\n",
    "ORDER BY 2 DESC;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "296b3835",
   "metadata": {},
   "source": [
    "### Birth to Death Ratio Among World"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e69a8718",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Birth Rate</th>\n",
       "            <th>Death Rate</th>\n",
       "            <th>World Birth to Death Ratio</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>18.6</td>\n",
       "            <td>7.8</td>\n",
       "            <td>2.38</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(18.6, 7.8, 2.38)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql \n",
    "SELECT birth_rate as 'Birth Rate', death_rate AS 'Death Rate',\n",
    "       ROUND((birth_rate / death_rate), 2) \n",
    "       AS 'World Birth to Death Ratio'\n",
    "FROM facts\n",
    "WHERE name = 'World';"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b10f3cc2",
   "metadata": {},
   "source": [
    "### Birth to Death Ratio Among Economically Significant Countries\n",
    "\n",
    "$ \\text{Birth to Death Ratio} = \\frac{\\text{Birth Rate}}{\\text{Death Rate}} $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "22d062f9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Country</th>\n",
       "            <th>Birth Rate</th>\n",
       "            <th>Death Rate</th>\n",
       "            <th>Birth to Death Ratio</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Saudi Arabia</td>\n",
       "            <td>18.51</td>\n",
       "            <td>3.33</td>\n",
       "            <td>5.56</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Mexico</td>\n",
       "            <td>18.78</td>\n",
       "            <td>5.26</td>\n",
       "            <td>3.57</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>India</td>\n",
       "            <td>19.55</td>\n",
       "            <td>7.32</td>\n",
       "            <td>2.67</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Indonesia</td>\n",
       "            <td>16.72</td>\n",
       "            <td>6.37</td>\n",
       "            <td>2.62</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Brazil</td>\n",
       "            <td>14.46</td>\n",
       "            <td>6.58</td>\n",
       "            <td>2.2</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Australia</td>\n",
       "            <td>12.15</td>\n",
       "            <td>7.14</td>\n",
       "            <td>1.7</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>China</td>\n",
       "            <td>12.49</td>\n",
       "            <td>7.53</td>\n",
       "            <td>1.66</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>United States</td>\n",
       "            <td>12.49</td>\n",
       "            <td>8.15</td>\n",
       "            <td>1.53</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>France</td>\n",
       "            <td>12.38</td>\n",
       "            <td>9.16</td>\n",
       "            <td>1.35</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>United Kingdom</td>\n",
       "            <td>12.17</td>\n",
       "            <td>9.35</td>\n",
       "            <td>1.3</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Canada</td>\n",
       "            <td>10.28</td>\n",
       "            <td>8.42</td>\n",
       "            <td>1.22</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Korea, South</td>\n",
       "            <td>8.19</td>\n",
       "            <td>6.75</td>\n",
       "            <td>1.21</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Spain</td>\n",
       "            <td>9.64</td>\n",
       "            <td>9.04</td>\n",
       "            <td>1.07</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Italy</td>\n",
       "            <td>8.74</td>\n",
       "            <td>10.19</td>\n",
       "            <td>0.86</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Russia</td>\n",
       "            <td>11.6</td>\n",
       "            <td>13.69</td>\n",
       "            <td>0.85</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Japan</td>\n",
       "            <td>7.93</td>\n",
       "            <td>9.51</td>\n",
       "            <td>0.83</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Germany</td>\n",
       "            <td>8.47</td>\n",
       "            <td>11.42</td>\n",
       "            <td>0.74</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Saudi Arabia', 18.51, 3.33, 5.56),\n",
       " ('Mexico', 18.78, 5.26, 3.57),\n",
       " ('India', 19.55, 7.32, 2.67),\n",
       " ('Indonesia', 16.72, 6.37, 2.62),\n",
       " ('Brazil', 14.46, 6.58, 2.2),\n",
       " ('Australia', 12.15, 7.14, 1.7),\n",
       " ('China', 12.49, 7.53, 1.66),\n",
       " ('United States', 12.49, 8.15, 1.53),\n",
       " ('France', 12.38, 9.16, 1.35),\n",
       " ('United Kingdom', 12.17, 9.35, 1.3),\n",
       " ('Canada', 10.28, 8.42, 1.22),\n",
       " ('Korea, South', 8.19, 6.75, 1.21),\n",
       " ('Spain', 9.64, 9.04, 1.07),\n",
       " ('Italy', 8.74, 10.19, 0.86),\n",
       " ('Russia', 11.6, 13.69, 0.85),\n",
       " ('Japan', 7.93, 9.51, 0.83),\n",
       " ('Germany', 8.47, 11.42, 0.74)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql \n",
    "SELECT name AS Country, birth_rate AS 'Birth Rate', death_rate as 'Death Rate',\n",
    "       ROUND((birth_rate / death_rate), 2) \n",
    "       AS 'Birth to Death Ratio'\n",
    "FROM sig_countries\n",
    "ORDER BY 4 DESC;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5649c601",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d5cd01",
   "metadata": {},
   "source": [
    "# High Risk Countries by Population Decline Criteria\n",
    "\n",
    "We define high risk as Countries with a Birth to Death Ratio of less than 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cbaf7a79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "CREATE VIEW hr_countries AS\n",
    "SELECT name AS Country, birth_rate AS 'Birth Rate', death_rate as 'Death Rate',\n",
    "       ROUND((birth_rate / death_rate), 2) \n",
    "       AS 'Birth_to_Death_Ratio'\n",
    " FROM sig_countries\n",
    "WHERE Birth_to_Death_Ratio < 1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1756789a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///factbook.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Country</th>\n",
       "            <th>Birth Rate</th>\n",
       "            <th>Death Rate</th>\n",
       "            <th>Birth_to_Death_Ratio</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Germany</td>\n",
       "            <td>8.47</td>\n",
       "            <td>11.42</td>\n",
       "            <td>0.74</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Japan</td>\n",
       "            <td>7.93</td>\n",
       "            <td>9.51</td>\n",
       "            <td>0.83</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Russia</td>\n",
       "            <td>11.6</td>\n",
       "            <td>13.69</td>\n",
       "            <td>0.85</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Italy</td>\n",
       "            <td>8.74</td>\n",
       "            <td>10.19</td>\n",
       "            <td>0.86</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Germany', 8.47, 11.42, 0.74),\n",
       " ('Japan', 7.93, 9.51, 0.83),\n",
       " ('Russia', 11.6, 13.69, 0.85),\n",
       " ('Italy', 8.74, 10.19, 0.86)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT * \n",
    "  FROM hr_countries\n",
    " ORDER BY 4;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac69dd2d",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "Based on our quick analysis, **Germany, Japan, Russia, and Italy** all exhibit significant risk to population decline."
   ]
  }
 ],
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