assignments/Scraping_Pandas_inClass.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = requests.get(\"https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_population_density\")\n",
    "content = result.content\n",
    "soup=BeautifulSoup(content, \"html.parser\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By analyzing the HTML code, I found that the first table with class=sortable is the one I want\n",
    "\n",
    "I converted the fields, and created a dict to make things easier. We would also have used a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = soup.find('table',attrs={\"class\":\"sortable\"})\n",
    "\n",
    "my_dict = {}\n",
    "\n",
    "#I got the table that I want...\n",
    "#Now I will navigate:\n",
    "# For each row (<tr>), I will get the <td>s. \n",
    "for tr in table.findAll(\"tr\"):\n",
    "    td_list = tr.findAll(\"td\")\n",
    "\n",
    "    # JUST MAKING SURE THAT I AM GETTING THE TRs WITH TDs (not the THS)\n",
    "    if (len(td_list) > 0):\n",
    "\n",
    "        ### I USED THE EXISTENCE OF AN IMAGE (FLAG) TO FILTER THE LINES I WANT\n",
    "        if(len(td_list[1].findAll(\"img\")) > 0):\n",
    "\n",
    "            ##Looking at the table I want the 2nd, 3rd, 4th, and 5th columns\n",
    "            ### I know that se second column (second position in my list with the tds)\n",
    "            country_name = td_list[1].text.strip()\n",
    "            #for each field, I curated the numbers to remove the ',' so it is possible to convert to float/int\n",
    "            areakm = td_list[2].text.replace(',','')\n",
    "            areami = td_list[3].text.replace(',','')\n",
    "            population = td_list[4].text.replace(',','')\n",
    "            #created a dictionary to make it easier to work\n",
    "            my_dict[country_name] = {'area_km2': float(areakm),\n",
    "                                     'area_mi2': float(areami),  \n",
    "                                     'population': int(population)  \n",
    "            }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2\n",
    "* It is facilitated because I converted the fields as I was scraping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "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>area_km2</th>\n",
       "      <th>area_mi2</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abkhazia</th>\n",
       "      <td>8660.00</td>\n",
       "      <td>3344.00</td>\n",
       "      <td>243206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>645807.00</td>\n",
       "      <td>249347.00</td>\n",
       "      <td>31575018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>28703.00</td>\n",
       "      <td>11082.00</td>\n",
       "      <td>2862427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>2381741.00</td>\n",
       "      <td>919595.00</td>\n",
       "      <td>42545964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa (US)</th>\n",
       "      <td>197.00</td>\n",
       "      <td>76.00</td>\n",
       "      <td>56700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>464.00</td>\n",
       "      <td>179.00</td>\n",
       "      <td>76177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>1246700.00</td>\n",
       "      <td>481354.00</td>\n",
       "      <td>29250009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anguilla (UK)</th>\n",
       "      <td>96.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>13452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>442.00</td>\n",
       "      <td>171.00</td>\n",
       "      <td>104084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>2780400.00</td>\n",
       "      <td>1073518.00</td>\n",
       "      <td>44938712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>29743.00</td>\n",
       "      <td>11484.00</td>\n",
       "      <td>2962100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Artsakh</th>\n",
       "      <td>11458.00</td>\n",
       "      <td>4424.00</td>\n",
       "      <td>150932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba (Kingdom of the Netherlands)</th>\n",
       "      <td>180.00</td>\n",
       "      <td>69.00</td>\n",
       "      <td>111309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>7692024.00</td>\n",
       "      <td>2969907.00</td>\n",
       "      <td>25513109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>83879.00</td>\n",
       "      <td>32386.00</td>\n",
       "      <td>8877036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>86600.00</td>\n",
       "      <td>33436.00</td>\n",
       "      <td>10027874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahamas</th>\n",
       "      <td>13940.00</td>\n",
       "      <td>5382.00</td>\n",
       "      <td>386870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>778.00</td>\n",
       "      <td>300.00</td>\n",
       "      <td>1543300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>143998.00</td>\n",
       "      <td>55598.00</td>\n",
       "      <td>167539272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbados</th>\n",
       "      <td>430.00</td>\n",
       "      <td>166.00</td>\n",
       "      <td>287025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>207600.00</td>\n",
       "      <td>80155.00</td>\n",
       "      <td>9465300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>30528.00</td>\n",
       "      <td>11787.00</td>\n",
       "      <td>11485729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belize</th>\n",
       "      <td>22965.00</td>\n",
       "      <td>8867.00</td>\n",
       "      <td>398050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>112622.00</td>\n",
       "      <td>43484.00</td>\n",
       "      <td>11733059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bermuda (UK)</th>\n",
       "      <td>52.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>63779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhutan</th>\n",
       "      <td>38394.00</td>\n",
       "      <td>14824.00</td>\n",
       "      <td>819360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>1098581.00</td>\n",
       "      <td>424164.00</td>\n",
       "      <td>11307314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bonaire (Netherlands)</th>\n",
       "      <td>288.00</td>\n",
       "      <td>111.00</td>\n",
       "      <td>18905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>51209.00</td>\n",
       "      <td>19772.00</td>\n",
       "      <td>3511372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>581730.00</td>\n",
       "      <td>224607.00</td>\n",
       "      <td>2302878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>513120.00</td>\n",
       "      <td>198117.00</td>\n",
       "      <td>66431255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timor-Leste</th>\n",
       "      <td>14919.00</td>\n",
       "      <td>5760.00</td>\n",
       "      <td>1167242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Togo</th>\n",
       "      <td>56600.00</td>\n",
       "      <td>21853.00</td>\n",
       "      <td>7538000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokelau (NZ)</th>\n",
       "      <td>10.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonga</th>\n",
       "      <td>720.00</td>\n",
       "      <td>278.00</td>\n",
       "      <td>100651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transnistria</th>\n",
       "      <td>4163.00</td>\n",
       "      <td>1607.00</td>\n",
       "      <td>469000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad and Tobago</th>\n",
       "      <td>5155.00</td>\n",
       "      <td>1990.00</td>\n",
       "      <td>1359193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>163610.00</td>\n",
       "      <td>63170.00</td>\n",
       "      <td>11551448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>783562.00</td>\n",
       "      <td>302535.00</td>\n",
       "      <td>82003882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>491210.00</td>\n",
       "      <td>189657.00</td>\n",
       "      <td>5851466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turks and Caicos Islands (UK)</th>\n",
       "      <td>497.00</td>\n",
       "      <td>192.00</td>\n",
       "      <td>37910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuvalu</th>\n",
       "      <td>26.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uganda</th>\n",
       "      <td>241551.00</td>\n",
       "      <td>93263.00</td>\n",
       "      <td>40006700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine [note 5]</th>\n",
       "      <td>603000.00</td>\n",
       "      <td>232820.00</td>\n",
       "      <td>41990278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>83600.00</td>\n",
       "      <td>32278.00</td>\n",
       "      <td>9770529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>242910.00</td>\n",
       "      <td>93788.00</td>\n",
       "      <td>66435600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>9833517.00</td>\n",
       "      <td>3796742.00</td>\n",
       "      <td>330195272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States Virgin Islands (US)</th>\n",
       "      <td>352.00</td>\n",
       "      <td>136.00</td>\n",
       "      <td>104909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>176215.00</td>\n",
       "      <td>68037.00</td>\n",
       "      <td>2990452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>447400.00</td>\n",
       "      <td>172742.00</td>\n",
       "      <td>32653900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanuatu</th>\n",
       "      <td>12281.00</td>\n",
       "      <td>4742.00</td>\n",
       "      <td>304500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vatican City</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>916445.00</td>\n",
       "      <td>353841.00</td>\n",
       "      <td>31828110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>331212.00</td>\n",
       "      <td>127882.00</td>\n",
       "      <td>96208984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wallis &amp; Futuna (France)</th>\n",
       "      <td>142.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>11700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Sahara[note 7]</th>\n",
       "      <td>252120.00</td>\n",
       "      <td>97344.00</td>\n",
       "      <td>567421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>455000.00</td>\n",
       "      <td>175676.00</td>\n",
       "      <td>28915284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>752612.00</td>\n",
       "      <td>290585.00</td>\n",
       "      <td>16405229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>390757.00</td>\n",
       "      <td>150872.00</td>\n",
       "      <td>15159624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Åland Islands (Finland)</th>\n",
       "      <td>1552.00</td>\n",
       "      <td>599.00</td>\n",
       "      <td>28502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      area_km2    area_mi2  population\n",
       "Abkhazia                               8660.00     3344.00      243206\n",
       "Afghanistan                          645807.00   249347.00    31575018\n",
       "Albania                               28703.00    11082.00     2862427\n",
       "Algeria                             2381741.00   919595.00    42545964\n",
       "American Samoa (US)                     197.00       76.00       56700\n",
       "Andorra                                 464.00      179.00       76177\n",
       "Angola                              1246700.00   481354.00    29250009\n",
       "Anguilla (UK)                            96.00       37.00       13452\n",
       "Antigua and Barbuda                     442.00      171.00      104084\n",
       "Argentina                           2780400.00  1073518.00    44938712\n",
       "Armenia                               29743.00    11484.00     2962100\n",
       "Artsakh                               11458.00     4424.00      150932\n",
       "Aruba (Kingdom of the Netherlands)      180.00       69.00      111309\n",
       "Australia                           7692024.00  2969907.00    25513109\n",
       "Austria                               83879.00    32386.00     8877036\n",
       "Azerbaijan                            86600.00    33436.00    10027874\n",
       "Bahamas                               13940.00     5382.00      386870\n",
       "Bahrain                                 778.00      300.00     1543300\n",
       "Bangladesh                           143998.00    55598.00   167539272\n",
       "Barbados                                430.00      166.00      287025\n",
       "Belarus                              207600.00    80155.00     9465300\n",
       "Belgium                               30528.00    11787.00    11485729\n",
       "Belize                                22965.00     8867.00      398050\n",
       "Benin                                112622.00    43484.00    11733059\n",
       "Bermuda (UK)                             52.00       20.00       63779\n",
       "Bhutan                                38394.00    14824.00      819360\n",
       "Bolivia                             1098581.00   424164.00    11307314\n",
       "Bonaire (Netherlands)                   288.00      111.00       18905\n",
       "Bosnia and Herzegovina                51209.00    19772.00     3511372\n",
       "Botswana                             581730.00   224607.00     2302878\n",
       "...                                        ...         ...         ...\n",
       "Thailand                             513120.00   198117.00    66431255\n",
       "Timor-Leste                           14919.00     5760.00     1167242\n",
       "Togo                                  56600.00    21853.00     7538000\n",
       "Tokelau (NZ)                             10.00        4.00        1499\n",
       "Tonga                                   720.00      278.00      100651\n",
       "Transnistria                           4163.00     1607.00      469000\n",
       "Trinidad and Tobago                    5155.00     1990.00     1359193\n",
       "Tunisia                              163610.00    63170.00    11551448\n",
       "Turkey                               783562.00   302535.00    82003882\n",
       "Turkmenistan                         491210.00   189657.00     5851466\n",
       "Turks and Caicos Islands (UK)           497.00      192.00       37910\n",
       "Tuvalu                                   26.00       10.00       10200\n",
       "Uganda                               241551.00    93263.00    40006700\n",
       "Ukraine [note 5]                     603000.00   232820.00    41990278\n",
       "United Arab Emirates                  83600.00    32278.00     9770529\n",
       "United Kingdom                       242910.00    93788.00    66435600\n",
       "United States                       9833517.00  3796742.00   330195272\n",
       "United States Virgin Islands (US)       352.00      136.00      104909\n",
       "Uruguay                              176215.00    68037.00     2990452\n",
       "Uzbekistan                           447400.00   172742.00    32653900\n",
       "Vanuatu                               12281.00     4742.00      304500\n",
       "Vatican City                              0.44        0.17        1000\n",
       "Venezuela                            916445.00   353841.00    31828110\n",
       "Vietnam                              331212.00   127882.00    96208984\n",
       "Wallis & Futuna (France)                142.00       55.00       11700\n",
       "Western Sahara[note 7]               252120.00    97344.00      567421\n",
       "Yemen                                455000.00   175676.00    28915284\n",
       "Zambia                               752612.00   290585.00    16405229\n",
       "Zimbabwe                             390757.00   150872.00    15159624\n",
       "Åland Islands (Finland)                1552.00      599.00       28502\n",
       "\n",
       "[250 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# THis is the magical function I used to come from a dict to a dataframe\n",
    "# the parameter orient. If dict keys are row, you use index. If the dict keys are columns, you don't need the param.\n",
    "df = pandas.DataFrame.from_dict(my_dict, orient= 'index')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3\n",
    "* Summarizing our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>area_km2</th>\n",
       "      <th>area_mi2</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.500000e+02</td>\n",
       "      <td>2.500000e+02</td>\n",
       "      <td>2.500000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.454655e+05</td>\n",
       "      <td>2.106154e+05</td>\n",
       "      <td>3.047842e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.719106e+06</td>\n",
       "      <td>6.637473e+05</td>\n",
       "      <td>1.282811e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.400000e-01</td>\n",
       "      <td>1.700000e-01</td>\n",
       "      <td>5.600000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.686550e+03</td>\n",
       "      <td>7.362500e+02</td>\n",
       "      <td>2.634935e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.493100e+04</td>\n",
       "      <td>2.507000e+04</td>\n",
       "      <td>4.578766e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.533760e+05</td>\n",
       "      <td>1.364390e+05</td>\n",
       "      <td>1.765074e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.712524e+07</td>\n",
       "      <td>6.612093e+06</td>\n",
       "      <td>1.399881e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           area_km2      area_mi2    population\n",
       "count  2.500000e+02  2.500000e+02  2.500000e+02\n",
       "mean   5.454655e+05  2.106154e+05  3.047842e+07\n",
       "std    1.719106e+06  6.637473e+05  1.282811e+08\n",
       "min    4.400000e-01  1.700000e-01  5.600000e+01\n",
       "25%    1.686550e+03  7.362500e+02  2.634935e+05\n",
       "50%    6.493100e+04  2.507000e+04  4.578766e+06\n",
       "75%    3.533760e+05  1.364390e+05  1.765074e+07\n",
       "max    1.712524e+07  6.612093e+06  1.399881e+09"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 4\n",
    "* Creating the correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_edae2028_035e_11ea_b9a5_3af9d378f87brow0_col0 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_edae2028_035e_11ea_b9a5_3af9d378f87brow0_col1 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_edae2028_035e_11ea_b9a5_3af9d378f87brow0_col2 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_edae2028_035e_11ea_b9a5_3af9d378f87brow1_col0 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_edae2028_035e_11ea_b9a5_3af9d378f87brow1_col1 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_edae2028_035e_11ea_b9a5_3af9d378f87brow1_col2 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_edae2028_035e_11ea_b9a5_3af9d378f87brow2_col0 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_edae2028_035e_11ea_b9a5_3af9d378f87brow2_col1 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_edae2028_035e_11ea_b9a5_3af9d378f87brow2_col2 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_edae2028_035e_11ea_b9a5_3af9d378f87b\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >area_km2</th>        <th class=\"col_heading level0 col1\" >area_mi2</th>        <th class=\"col_heading level0 col2\" >population</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_edae2028_035e_11ea_b9a5_3af9d378f87blevel0_row0\" class=\"row_heading level0 row0\" >area_km2</th>\n",
       "                        <td id=\"T_edae2028_035e_11ea_b9a5_3af9d378f87brow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_edae2028_035e_11ea_b9a5_3af9d378f87brow0_col1\" class=\"data row0 col1\" >1</td>\n",
       "                        <td id=\"T_edae2028_035e_11ea_b9a5_3af9d378f87brow0_col2\" class=\"data row0 col2\" >0.458636</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_edae2028_035e_11ea_b9a5_3af9d378f87blevel0_row1\" class=\"row_heading level0 row1\" >area_mi2</th>\n",
       "                        <td id=\"T_edae2028_035e_11ea_b9a5_3af9d378f87brow1_col0\" class=\"data row1 col0\" >1</td>\n",
       "                        <td id=\"T_edae2028_035e_11ea_b9a5_3af9d378f87brow1_col1\" class=\"data row1 col1\" >1</td>\n",
       "                        <td id=\"T_edae2028_035e_11ea_b9a5_3af9d378f87brow1_col2\" class=\"data row1 col2\" >0.458635</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_edae2028_035e_11ea_b9a5_3af9d378f87blevel0_row2\" class=\"row_heading level0 row2\" >population</th>\n",
       "                        <td id=\"T_edae2028_035e_11ea_b9a5_3af9d378f87brow2_col0\" class=\"data row2 col0\" >0.458636</td>\n",
       "                        <td id=\"T_edae2028_035e_11ea_b9a5_3af9d378f87brow2_col1\" class=\"data row2 col1\" >0.458635</td>\n",
       "                        <td id=\"T_edae2028_035e_11ea_b9a5_3af9d378f87brow2_col2\" class=\"data row2 col2\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11e1de978>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(method='pearson').style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5\n",
    "* Scatterplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11efdd358>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x = \"area_km2\", y = \"population\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}