Guided Project - Analyzing CIA Factbook Data.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Working with SQlite and python pandas. Will be analyzing CIA factbook file \"factbook.db\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "  <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",
       "</div>"
      ],
      "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 sqlite3\n",
    "import pandas as pd\n",
    "\n",
    "conn = sqlite3.connect(\"factbook.db\")\n",
    "\n",
    "q1 = \"SELECT * FROM sqlite_master WHERE type='table';\"\n",
    "pd.read_sql_query(q1, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
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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>652230.0</td>\n",
       "      <td>652230.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32564342.0</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>28748.0</td>\n",
       "      <td>27398.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>3029278.0</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>2381741.0</td>\n",
       "      <td>2381741.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39542166.0</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>468.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85580.0</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>1246700.0</td>\n",
       "      <td>1246700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19625353.0</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   652230.0   652230.0         0.0  32564342.0   \n",
       "1   2   al      Albania    28748.0    27398.0      1350.0   3029278.0   \n",
       "2   3   ag      Algeria  2381741.0  2381741.0         0.0  39542166.0   \n",
       "3   4   an      Andorra      468.0      468.0         0.0     85580.0   \n",
       "4   5   ao       Angola  1246700.0  1246700.0         0.0  19625353.0   \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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q2 = \"SELECT * FROM facts\"\n",
    "\n",
    "data = pd.read_sql_query(q2, conn)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  <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",
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       "</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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3 = \"SELECT MIN(population), MAX(population), MIN(population_growth), MAX(population_growth) FROM facts\"\n",
    "data = pd.read_sql_query(q3, conn)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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       "      <th>id</th>\n",
       "      <th>code</th>\n",
       "      <th>name</th>\n",
       "      <th>area</th>\n",
       "      <th>area_land</th>\n",
       "      <th>area_water</th>\n",
       "      <th>population</th>\n",
       "      <th>population_growth</th>\n",
       "      <th>birth_rate</th>\n",
       "      <th>death_rate</th>\n",
       "      <th>migration_rate</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>250</td>\n",
       "      <td>ay</td>\n",
       "      <td>Antarctica</td>\n",
       "      <td>None</td>\n",
       "      <td>280000</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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      ],
      "text/plain": [
       "    id code        name  area  area_land area_water  population  \\\n",
       "0  250   ay  Antarctica  None     280000       None           0   \n",
       "\n",
       "  population_growth birth_rate death_rate migration_rate  \n",
       "0              None       None       None           None  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q4 = '''\n",
    "SELECT * FROM facts \n",
    "WHERE population = 0;\n",
    "'''\n",
    "data = pd.read_sql_query(q4, conn)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>code</th>\n",
       "      <th>name</th>\n",
       "      <th>area</th>\n",
       "      <th>area_land</th>\n",
       "      <th>area_water</th>\n",
       "      <th>population</th>\n",
       "      <th>population_growth</th>\n",
       "      <th>birth_rate</th>\n",
       "      <th>death_rate</th>\n",
       "      <th>migration_rate</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>261</td>\n",
       "      <td>xx</td>\n",
       "      <td>World</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>7256490011</td>\n",
       "      <td>1.08</td>\n",
       "      <td>18.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>None</td>\n",
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      ],
      "text/plain": [
       "    id code   name  area area_land area_water  population  population_growth  \\\n",
       "0  261   xx  World  None      None       None  7256490011               1.08   \n",
       "\n",
       "   birth_rate  death_rate migration_rate  \n",
       "0        18.6         7.8           None  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q5 = '''\n",
    "SELECT * FROM facts \n",
    "WHERE population = 7256490011;\n",
    "'''\n",
    "data = pd.read_sql_query(q5, conn)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It doesn't make such sense to include Antarctica and the whole world."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>population_growth</th>\n",
       "      <th>birth_rate</th>\n",
       "      <th>death_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32564342</td>\n",
       "      <td>2.32</td>\n",
       "      <td>38.57</td>\n",
       "      <td>13.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3029278</td>\n",
       "      <td>0.30</td>\n",
       "      <td>12.92</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39542166</td>\n",
       "      <td>1.84</td>\n",
       "      <td>23.67</td>\n",
       "      <td>4.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>85580</td>\n",
       "      <td>0.12</td>\n",
       "      <td>8.13</td>\n",
       "      <td>6.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19625353</td>\n",
       "      <td>2.78</td>\n",
       "      <td>38.78</td>\n",
       "      <td>11.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   population  population_growth  birth_rate  death_rate\n",
       "0    32564342               2.32       38.57       13.89\n",
       "1     3029278               0.30       12.92        6.58\n",
       "2    39542166               1.84       23.67        4.31\n",
       "3       85580               0.12        8.13        6.96\n",
       "4    19625353               2.78       38.78       11.49"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "q6 = '''\n",
    "SELECT population, population_growth, birth_rate, death_rate\n",
    "FROM facts\n",
    "WHERE population != 0\n",
    "AND population != 7256490011\n",
    "'''\n",
    "\n",
    "data = pd.read_sql_query(q6, conn)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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pqqoDRsZqaoqR19ui6uqDRsYDhjoCFgAMUVVVB7R0/RaNjks0NmZjzT4lpKQZGw8YqghY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\nCILC/g931VRFQdKFHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5edf4622e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "ax1 = fig.add_subplot(2,2,1)\n",
    "ax2 = fig.add_subplot(2,2,2)\n",
    "ax3 = fig.add_subplot(2,2,3)\n",
    "ax4 = fig.add_subplot(2,2,4)\n",
    "\n",
    "data[\"birth_rate\"].hist(ax=ax1)\n",
    "ax1.set_xlabel(\"birth_rate\")\n",
    "data[\"death_rate\"].hist(ax=ax2)\n",
    "ax2.set_xlabel(\"death_rate\")\n",
    "data[\"population_growth\"].hist(ax=ax3)\n",
    "ax3.set_xlabel(\"population_growth\")\n",
    "data[\"population\"].hist(ax=ax4)\n",
    "ax4.set_xlabel(\"population\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Birth Rate and Population Growth does make sense since both show a right-skewed distribution. It is hard to understand death rate because of Outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Macau</td>\n",
       "      <td>21168.964286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monaco</td>\n",
       "      <td>15267.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>8141.279770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>6445.041516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gaza Strip</td>\n",
       "      <td>5191.819444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name       density\n",
       "0       Macau  21168.964286\n",
       "1      Monaco  15267.500000\n",
       "2   Singapore   8141.279770\n",
       "3   Hong Kong   6445.041516\n",
       "4  Gaza Strip   5191.819444"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q7 = '''\n",
    "SELECT name, CAST(population as float)/CAST(area as float) \"density\"\n",
    "FROM facts\n",
    "WHERE population != 0\n",
    "AND population != 7256490011\n",
    "ORDER BY density DESC\n",
    "'''\n",
    "\n",
    "data = pd.read_sql_query(q7, conn)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting Macau has the highest population density."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5cbq0mpyc1Hvv/e0z9/nnPxdrZOSm6diSR5mZ7r268vWvf1OZmZmuHR9YqFyJZzweV15e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\n0tRrs3kG5wwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5edf462198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "data['density'].hist()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hard to read because of number of outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5edcf8df60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "data['density'].hist(bins=500)\n",
    "ax.set_xlim(0, 2000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-11-e32a1baad747>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-11-e32a1baad747>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    Learning Summary\u001b[0m\n\u001b[1;37m                   ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "Learning Summary\n",
    "SQL with Python Pandas : sqlite3, pandas, SQL queries, SQL subqueries, matplotlib, seaborn, histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}