Basics.ipynb

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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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...  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import sqlite3\n",
    "conn = sqlite3.connect(\"factbook.db\")\n",
    "cursor = conn.cursor()\n",
    "q = \"SELECT * FROM sqlite_master WHERE type='table';\"\n",
    "pd.read_sql_query(q, conn)"
   ]
  },
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   "execution_count": 2,
   "metadata": {},
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       "\n",
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       "</style>\n",
       "<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  "
      ]
     },
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   ],
   "source": [
    "q2 = \"select * from facts limit 5\"\n",
    "pd.read_sql_query(q2, conn)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min_pop</th>\n",
       "      <th>max_pop</th>\n",
       "      <th>min_growth</th>\n",
       "      <th>max_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",
       "</div>"
      ],
      "text/plain": [
       "   min_pop     max_pop  min_growth  max_growth\n",
       "0        0  7256490011         0.0        4.02"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "q3 = \"SELECT MIN(population) min_pop, MAX(population) max_pop, MIN(population_growth) min_growth, MAX(population_growth) max_growth FROM facts\"\n",
    "pd.read_sql_query(q3, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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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>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",
       "</div>"
      ],
      "text/plain": [
       "         name\n",
       "0  Antarctica"
      ]
     },
     "execution_count": 4,
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   ],
   "source": [
    "#examining unexpected results of population 0 and population 7.2 Billion\n",
    "q4 = \"SELECT name FROM facts WHERE population = 0\"\n",
    "pd.read_sql_query(q4, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    {
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       "    <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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   ],
   "source": [
    "q5 = \"SELECT name FROM facts WHERE population > 7000000000\"\n",
    "pd.read_sql_query(q5, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- These outliers are not truly countries and may skew our results, as they did in the summary statistics above.\n",
    "- We might have guessed at the identities of both these outliers without running analyses, as the only significant landmass with 0 population is, of course, Antarctica, and it is common knowledge that the world only has a little more than 7 billion people.\n",
    "\n",
    "## Analyzing the Rest of the Data with Pandas"
   ]
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      "/Users/davidbrunetti/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2862: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
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eAtwB/Ptut/yvz7P8u4Gd3fIvAH4tyUlzXn9+V/dBwKXAW1dQo6ThcCzaW0zy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\nSQ0YsiRJkhowZEmSJDVgyJIkSWrg/wIu6NcRenJigAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a197afda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "%matplotlib inline\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 FROM facts WHERE (population > 0) AND (population != (SELECT MAX(population) FROM facts))\"\n",
    "pd.read_sql_query(q6, conn).hist(ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring Population Density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>population_density_per_sq_km</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",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>1297.977606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Sint Maarten</td>\n",
       "      <td>1167.323529</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>Taiwan</td>\n",
       "      <td>725.825356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>675.823256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Mauritius</td>\n",
       "      <td>660.013300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>623.122222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>604.565103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Saint Martin</td>\n",
       "      <td>588.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>San Marino</td>\n",
       "      <td>541.311475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>513.285755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Korea, South</td>\n",
       "      <td>506.760173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>500.041424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>West Bank</td>\n",
       "      <td>493.859220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Nauru</td>\n",
       "      <td>454.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>India</td>\n",
       "      <td>420.993721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>418.312928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>418.038462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>405.677227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>Niger</td>\n",
       "      <td>14.246253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>Congo, Republic of the</td>\n",
       "      <td>13.924149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>Mali</td>\n",
       "      <td>13.895816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>12.909915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Christmas Island</td>\n",
       "      <td>11.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>11.132343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>Oman</td>\n",
       "      <td>10.620149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>9.970342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>Chad</td>\n",
       "      <td>9.237179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>Russia</td>\n",
       "      <td>8.696179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>8.654378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>6.725607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>Gabon</td>\n",
       "      <td>6.618372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>Niue</td>\n",
       "      <td>4.576923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>Canada</td>\n",
       "      <td>3.859879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>3.851427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>Guyana</td>\n",
       "      <td>3.734954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>Suriname</td>\n",
       "      <td>3.715596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>Libya</td>\n",
       "      <td>3.644007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>3.489572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>3.310903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2.961485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Namibia</td>\n",
       "      <td>2.687154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>2.146113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>Mongolia</td>\n",
       "      <td>1.926489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>Pitcairn Islands</td>\n",
       "      <td>1.021277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>Falkland Islands (Islas Malvinas)</td>\n",
       "      <td>0.276103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>Svalbard</td>\n",
       "      <td>0.030172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>Greenland</td>\n",
       "      <td>0.026653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>Antarctica</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>233 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  name  population_density_per_sq_km\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\n",
       "10                          Bangladesh                   1297.977606\n",
       "11                        Sint Maarten                   1167.323529\n",
       "12                            Guernsey                    847.179487\n",
       "13                              Jersey                    838.741379\n",
       "14                              Taiwan                    725.825356\n",
       "15                            Barbados                    675.823256\n",
       "16                           Mauritius                    660.013300\n",
       "17                               Aruba                    623.122222\n",
       "18                             Lebanon                    604.565103\n",
       "19                        Saint Martin                    588.037037\n",
       "20                          San Marino                    541.311475\n",
       "21                              Rwanda                    513.285755\n",
       "22                        Korea, South                    506.760173\n",
       "23                         Netherlands                    500.041424\n",
       "24                           West Bank                    493.859220\n",
       "25                               Nauru                    454.285714\n",
       "26                               India                    420.993721\n",
       "27                             Burundi                    418.312928\n",
       "28                              Tuvalu                    418.038462\n",
       "29                         Puerto Rico                    405.677227\n",
       "..                                 ...                           ...\n",
       "203                              Niger                     14.246253\n",
       "204             Congo, Republic of the                     13.924149\n",
       "205                               Mali                     13.895816\n",
       "206                       Saudi Arabia                     12.909915\n",
       "207                   Christmas Island                     11.333333\n",
       "208                       Turkmenistan                     11.132343\n",
       "209                               Oman                     10.620149\n",
       "210                            Bolivia                      9.970342\n",
       "211                               Chad                      9.237179\n",
       "212                             Russia                      8.696179\n",
       "213           Central African Republic                      8.654378\n",
       "214                         Kazakhstan                      6.725607\n",
       "215                              Gabon                      6.618372\n",
       "216                               Niue                      4.576923\n",
       "217                             Canada                      3.859879\n",
       "218                           Botswana                      3.851427\n",
       "219                             Guyana                      3.734954\n",
       "220                           Suriname                      3.715596\n",
       "221                              Libya                      3.644007\n",
       "222                         Mauritania                      3.489572\n",
       "223                            Iceland                      3.310903\n",
       "224                          Australia                      2.961485\n",
       "225                            Namibia                      2.687154\n",
       "226                     Western Sahara                      2.146113\n",
       "227                           Mongolia                      1.926489\n",
       "228                   Pitcairn Islands                      1.021277\n",
       "229  Falkland Islands (Islas Malvinas)                      0.276103\n",
       "230                           Svalbard                      0.030172\n",
       "231                          Greenland                      0.026653\n",
       "232                         Antarctica                      0.000000\n",
       "\n",
       "[233 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q7 = '''\n",
    "SELECT name, CAST(population as float)/CAST(area_land as float) population_density_per_sq_km\n",
    "FROM facts\n",
    "ORDER BY population_density_per_sq_km DESC\n",
    "'''\n",
    "pd.read_sql_query(q7, conn).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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