add first exercise

902b1366 · Corentin Vande Kerckhove · a7fe6da3 · 902b1366
--- a/data-collection/exercise1.ipynb
+++ b/data-collection/exercise1.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import urllib.request\n",
+    "import time\n",
+    "from bs4 import BeautifulSoup\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from urllib.request import urlopen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = 'https://en.wikipedia.org/wiki/Epidemiology_of_depression'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "html = urlopen(url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "soup = BeautifulSoup(html, 'html.parser')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tables = soup.find_all('table')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#convert number as string to integer\n",
+    "#re.sub() returns the substring that match the regrex\n",
+    "import re\n",
+    "def process_num(num):\n",
+    "    return float(re.sub(r'[^\\w\\s.]','',num))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1156.30'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num1 = re.sub(r'[^\\w\\s.]','','1,156.30')\n",
+    "num1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>Rank</th>\n",
+       "      <th>DALY rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>United States</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1454.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Nepal</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1424.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>East Timor</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1404.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Bangladesh</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1401.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>India</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1400.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Pakistan</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1400.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Brazil</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1396.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Maldives</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1391.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Bhutan</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1385.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1385.14</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Rank  DALY rate\n",
+       "United States     1    1454.74\n",
+       "Nepal             2    1424.48\n",
+       "East Timor        3    1404.10\n",
+       "Bangladesh        4    1401.53\n",
+       "India             5    1400.84\n",
+       "Pakistan          6    1400.42\n",
+       "Brazil            7    1396.10\n",
+       "Maldives          8    1391.61\n",
+       "Bhutan            9    1385.53\n",
+       "Afghanistan      10    1385.14"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ranks = []\n",
+    "rates = []\n",
+    "countries = []\n",
+    "links = []\n",
+    "\n",
+    "for table in tables:\n",
+    "    rows = table.find_all('tr')\n",
+    "    \n",
+    "    for row in rows:\n",
+    "        cells = row.find_all('td')\n",
+    "        \n",
+    "        if len(cells) > 1:\n",
+    "            rank = cells[0]\n",
+    "            ranks.append(int(rank.text))\n",
+    "            \n",
+    "            country = cells[1]\n",
+    "            countries.append(country.text.strip())\n",
+    "            \n",
+    "            rate = cells[2]\n",
+    "            rates.append(process_num(rate.text.strip()))\n",
+    "            \n",
+    "            link = cells[1].find('a').get('href')\n",
+    "            links.append('https://en.wikipedia.org/'+ link)\n",
+    "            \n",
+    "df1 = pd.DataFrame(ranks, index= countries, columns = ['Rank'])\n",
+    "df1['DALY rate'] = rates\n",
+    "\n",
+    "df1.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "789.14 3\n",
+      "Country: Benin, Sunshine Hours: 263.05\n",
+      "515.99 2\n",
+      "Country: Togo, Sunshine Hours: 258.0\n",
+      "710.25 3\n",
+      "Country: Ghana, Sunshine Hours: 236.75\n",
+      "866.0500000000001 4\n",
+      "Country: Cameroon, Sunshine Hours: 216.51\n",
+      "344.03999999999996 2\n",
+      "Country: Gabon, Sunshine Hours: 172.02\n",
+      "1334.54 5\n",
+      "Country: Nigeria, Sunshine Hours: 266.91\n",
+      "711.91 2\n",
+      "Country: Sudan, Sunshine Hours: 355.95\n",
+      "336.1 1\n",
+      "Country: Eritrea, Sunshine Hours: 336.1\n",
+      "641.8 2\n",
+      "Country: Burkina Faso, Sunshine Hours: 320.9\n",
+      "320.32 1\n",
+      "Country: Niger, Sunshine Hours: 320.32\n",
+      "670.6400000000001 2\n",
+      "Country: Chad, Sunshine Hours: 335.32\n",
+      "307.0 1\n",
+      "Country: Gambia, Sunshine Hours: 307.0\n",
+      "629.2 2\n",
+      "Country: Senegal, Sunshine Hours: 314.6\n",
+      "620.5999999999999 2\n",
+      "Country: Somalia, Sunshine Hours: 310.3\n",
+      "327.9 1\n",
+      "Country: Djibouti, Sunshine Hours: 327.9\n",
+      "964.0099999999999 3\n",
+      "Country: Mali, Sunshine Hours: 321.34\n",
+      "653.3 2\n",
+      "Country: Algeria, Sunshine Hours: 326.65\n",
+      "609.99 2\n",
+      "Country: Tunisia, Sunshine Hours: 305.0\n",
+      "946.64 3\n",
+      "Country: Morocco, Sunshine Hours: 315.55\n",
+      "2253.8500000000004 6\n",
+      "Country: Egypt, Sunshine Hours: 375.64\n",
+      "635.6199999999999 2\n",
+      "Country: Libya, Sunshine Hours: 317.81\n",
+      "1212.01 4\n",
+      "Country: Kenya, Sunshine Hours: 303.0\n",
+      "234.1 1\n",
+      "Country: Angola, Sunshine Hours: 234.1\n",
+      "1213.1399999999999 4\n",
+      "Country: Tanzania, Sunshine Hours: 303.28\n",
+      "556.97 2\n",
+      "Country: Ethiopia, Sunshine Hours: 278.49\n",
+      "666.5 2\n",
+      "Country: Mauritania, Sunshine Hours: 333.25\n",
+      "1884.79 6\n",
+      "Country: South Africa, Sunshine Hours: 314.13\n",
+      "1028.0 3\n",
+      "Country: Botswana, Sunshine Hours: 342.67\n",
+      "889.6400000000001 3\n",
+      "Country: Zambia, Sunshine Hours: 296.55\n",
+      "613.08 2\n",
+      "Country: Zimbabwe, Sunshine Hours: 306.54\n",
+      "838.76 3\n",
+      "Country: Malawi, Sunshine Hours: 279.59\n",
+      "1718.66 6\n",
+      "Country: Madagascar, Sunshine Hours: 286.44\n",
+      "283.8 1\n",
+      "Country: Mozambique, Sunshine Hours: 283.8\n",
+      "681.8 3\n",
+      "Country: Uganda, Sunshine Hours: 227.27\n",
+      "237.34 1\n",
+      "Country: Burundi, Sunshine Hours: 237.34\n",
+      "488.0 2\n",
+      "Country: Guinea, Sunshine Hours: 244.0\n",
+      "270.7 1\n",
+      "Country: Guinea-Bissau, Sunshine Hours: 270.7\n",
+      "309.79 2\n",
+      "Country: Equatorial Guinea, Sunshine Hours: 154.9\n",
+      "747.5 2\n",
+      "Country: Namibia, Sunshine Hours: 373.75\n",
+      "317.51 1\n",
+      "Country: Afghanistan, Sunshine Hours: 317.51\n",
+      "220.74 1\n",
+      "Country: Azerbaijan, Sunshine Hours: 220.74\n",
+      "206.6 1\n",
+      "Country: Bangladesh, Sunshine Hours: 206.6\n",
+      "1091.49 5\n",
+      "Country: China, Sunshine Hours: 218.3\n",
+      "973.66 4\n",
+      "Country: India, Sunshine Hours: 243.41\n",
+      "298.33000000000004 1\n",
+      "Country: Indonesia, Sunshine Hours: 298.33\n",
+      "282.61 1\n",
+      "Country: Iran, Sunshine Hours: 282.61\n",
+      "324.08000000000004 1\n",
+      "Country: Iraq, Sunshine Hours: 324.08\n",
+      "331.1 1\n",
+      "Country: Israel, Sunshine Hours: 331.1\n",
+      "361.71000000000004 2\n",
+      "Country: Japan, Sunshine Hours: 180.86\n",
+      "486.29999999999995 2\n",
+      "Country: Kazakhstan, Sunshine Hours: 243.15\n",
+      "279.15 1\n",
+      "Country: Mongolia, Sunshine Hours: 279.15\n",
+      "249.2 1\n",
+      "Country: North Korea, Sunshine Hours: 249.2\n",
+      "349.33000000000004 1\n",
+      "Country: Oman, Sunshine Hours: 349.33\n",
+      "598.4300000000001 2\n",
+      "Country: Pakistan, Sunshine Hours: 299.22\n",
+      "210.31 1\n",
+      "Country: Philippines, Sunshine Hours: 210.31\n",
+      "1578.2299999999998 8\n",
+      "Country: Russia, Sunshine Hours: 197.28\n",
+      "647.3 2\n",
+      "Country: Saudi Arabia, Sunshine Hours: 323.65\n",
+      "202.24 1\n",
+      "Country: Singapore, Sunshine Hours: 202.24\n",
+      "439.33000000000004 2\n",
+      "Country: South Korea, Sunshine Hours: 219.67\n",
+      "870.0099999999999 4\n",
+      "Country: Thailand, Sunshine Hours: 217.5\n",
+      "466.76 2\n",
+      "Country: Turkey, Sunshine Hours: 233.38\n",
+      "282.39 1\n",
+      "Country: Uzbekistan, Sunshine Hours: 282.39\n",
+      "849.4 4\n",
+      "Country: Vietnam, Sunshine Hours: 212.35\n",
+      "254.4 1\n",
+      "Country: Albania, Sunshine Hours: 254.4\n",
+      "247.4 1\n",
+      "Country: Armenia, Sunshine Hours: 247.4\n",
+      "188.4 1\n",
+      "Country: Austria, Sunshine Hours: 188.4\n",
+      "180.7 1\n",
+      "Country: Belarus, Sunshine Hours: 180.7\n",
+      "154.6 1\n",
+      "Country: Belgium, Sunshine Hours: 154.6\n",
+      "176.9 1\n",
+      "Country: Bosnia and Herzegovina, Sunshine Hours: 176.9\n",
+      "217.7 1\n",
+      "Country: Bulgaria, Sunshine Hours: 217.7\n",
+      "191.3 1\n",
+      "Country: Croatia, Sunshine Hours: 191.3\n",
+      "166.8 1\n",
+      "Country: Czech Republic, Sunshine Hours: 166.8\n",
+      "331.40999999999997 1\n",
+      "Country: Cyprus, Sunshine Hours: 331.41\n",
+      "173.9 1\n",
+      "Country: Denmark, Sunshine Hours: 173.9\n",
+      "182.6 1\n",
+      "Country: Estonia, Sunshine Hours: 182.6\n",
+      "185.8 1\n",
+      "Country: Finland, Sunshine Hours: 185.8\n",
+      "449.8 2\n",
+      "Country: France, Sunshine Hours: 224.9\n",
+      "204.6 1\n",
+      "Country: Georgia, Sunshine Hours: 204.6\n",
+      "328.79999999999995 2\n",
+      "Country: Germany, Sunshine Hours: 164.4\n",
+      "595.0 2\n",
+      "Country: Greece, Sunshine Hours: 297.5\n",
+      "198.8 1\n",
+      "Country: Hungary, Sunshine Hours: 198.8\n",
+      "132.6 1\n",
+      "Country: Iceland, Sunshine Hours: 132.6\n",
+      "145.3 1\n",
+      "Country: Ireland, Sunshine Hours: 145.3\n",
+      "438.8 2\n",
+      "Country: Italy, Sunshine Hours: 219.4\n",
+      "175.4 1\n",
+      "Country: Latvia, Sunshine Hours: 175.4\n",
+      "169.1 1\n",
+      "Country: Lithuania, Sunshine Hours: 169.1\n",
+      "305.4 1\n",
+      "Country: Malta, Sunshine Hours: 305.4\n",
+      "212.6 1\n",
+      "Country: Moldova, Sunshine Hours: 212.6\n",
+      "166.2 1\n",
+      "Country: Netherlands, Sunshine Hours: 166.2\n",
+      "166.8 1\n",
+      "Country: Norway, Sunshine Hours: 166.8\n",
+      "157.1 1\n",
+      "Country: Poland, Sunshine Hours: 157.1\n",
+      "280.6 1\n",
+      "Country: Portugal, Sunshine Hours: 280.6\n",
+      "211.5 1\n",
+      "Country: Romania, Sunshine Hours: 211.5\n",
+      "203.8 1\n",
+      "Country: Slovakia, Sunshine Hours: 203.8\n",
+      "197.4 1\n",
+      "Country: Slovenia, Sunshine Hours: 197.4\n",
+      "826.6 3\n",
+      "Country: Spain, Sunshine Hours: 275.53\n",
+      "374.29999999999995 2\n",
+      "Country: Sweden, Sunshine Hours: 187.15\n",
+      "156.6 1\n",
+      "Country: Switzerland, Sunshine Hours: 156.6\n",
+      "195.5 1\n",
+      "Country: Ukraine, Sunshine Hours: 195.5\n",
+      "306.0 2\n",
+      "Country: United Kingdom, Sunshine Hours: 153.0\n",
+      "1825.24 9\n",
+      "Country: Canada, Sunshine Hours: 202.8\n",
+      "225.98000000000002 1\n",
+      "Country: Honduras, Sunshine Hours: 225.98\n",
+      "1038.5 4\n",
+      "Country: Mexico, Sunshine Hours: 259.62\n",
+      "275.99 1\n",
+      "Country: Nicaragua, Sunshine Hours: 275.99\n",
+      "174.35 1\n",
+      "Country: Panama, Sunshine Hours: 174.35\n",
+      "295.7 1\n",
+      "Country: El Salvador, Sunshine Hours: 295.7\n",
+      "15218.579999999998 54\n",
+      "Country: United States, Sunshine Hours: 281.83\n",
+      "1149.52 5\n",
+      "Country: Argentina, Sunshine Hours: 229.9\n",
+      "228.89000000000001 1\n",
+      "Country: Bolivia, Sunshine Hours: 228.89\n",
+      "1322.58 6\n",
+      "Country: Brazil, Sunshine Hours: 220.43\n",
+      "953.81 6\n",
+      "Country: Colombia, Sunshine Hours: 158.97\n",
+      "1324.27 5\n",
+      "Country: Chile, Sunshine Hours: 264.85\n",
+      "381.90999999999997 2\n",
+      "Country: Ecuador, Sunshine Hours: 190.95\n",
+      "280.3 1\n",
+      "Country: Paraguay, Sunshine Hours: 280.3\n",
+      "604.0 3\n",
+      "Country: Peru, Sunshine Hours: 201.33\n",
+      "248.14000000000001 1\n",
+      "Country: Uruguay, Sunshine Hours: 248.14\n",
+      "579.0899999999999 2\n",
+      "Country: Venezuela, Sunshine Hours: 289.54\n",
+      "2553.15 9\n",
+      "Country: Australia, Sunshine Hours: 283.68\n",
+      "192.2 1\n",
+      "Country: Fiji, Sunshine Hours: 192.2\n",
+      "613.1999999999999 3\n",
+      "Country: New Zealand, Sunshine Hours: 204.4\n",
+      "246.3 1\n",
+      "Country: Papua New Guinea, Sunshine Hours: 246.3\n",
+      "233.0 1\n",
+      "Country: Solomon Islands, Sunshine Hours: 233.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "sun_url = urlopen('https://en.wikipedia.org/wiki/List_of_cities_by_sunshine_duration')\n",
+    "sun = BeautifulSoup(sun_url, 'html.parser')\n",
+    "tables = sun.find_all('table')\n",
+    "\n",
+    "#Dictionary to hold the name of the country and its corresponding temperature\n",
+    "country_suns = {}\n",
+    "\n",
+    "#Dictionary to hold the country and its frequency in the table\n",
+    "count = {}\n",
+    "for table in tables:\n",
+    "    if len(table) >1:\n",
+    "        rows = table.find_all('tr')\n",
+    "        \n",
+    "        #Skip the first row, which is the name of the columns\n",
+    "        for row in rows[1:]:\n",
+    "            cells = row.find_all('td')\n",
+    "            country = cells[0].text.strip()\n",
+    "            \n",
+    "            #If country in the list of country we found previously\n",
+    "            #append the country to the dictionary\n",
+    "            if country in countries:\n",
+    "                \n",
+    "                sun = cells[-2].text.strip()\n",
+    "                sun = process_num(sun)/10\n",
+    "                \n",
+    "                #If country is already in the dictionary\n",
+    "                #add to the existing sun hours of that country and the count to keep track of how many times we add\n",
+    "                if country in country_suns:\n",
+    "                    count[country] += 1\n",
+    "                    country_suns[country] += sun\n",
+    "                    \n",
+    "                else:\n",
+    "                    count[country] = 1\n",
+    "                    country_suns[country] = sun\n",
+    "                    \n",
+    "\n",
+    "#Find the average temperature of each country\n",
+    "for country in country_suns:\n",
+    "    print(country_suns[country],count[country])\n",
+    "    country_suns[country] = round(country_suns[country]/count[country],2)\n",
+    "    print('Country: {}, Sunshine Hours: {}'.format(country,country_suns[country]))\n",
+    "                "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 192 entries, United States to Japan\n",
+      "Data columns (total 3 columns):\n",
+      "Rank                   192 non-null int64\n",
+      "DALY rate              192 non-null float64\n",
+      "Sunshine Hours/Year    122 non-null float64\n",
+      "dtypes: float64(2), int64(1)\n",
+      "memory usage: 11.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df2 = pd.DataFrame.from_dict(country_suns,orient='index', columns = ['Sunshine Hours/Year'])\n",
+    "\n",
+    "df = df1.join(df2)\n",
+    "\n",
+    "df.info()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.dropna(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 122 entries, United States to Japan\n",
+      "Data columns (total 3 columns):\n",
+      "Rank                   122 non-null int64\n",
+      "DALY rate              122 non-null float64\n",
+      "Sunshine Hours/Year    122 non-null float64\n",
+      "dtypes: float64(2), int64(1)\n",
+      "memory usage: 8.8+ KB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a728410>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.info()\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.scatterplot('Rank', 'Sunshine Hours/Year', data=df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "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>Rank</th>\n",
+       "      <th>DALY rate</th>\n",
+       "      <th>Sunshine Hours/Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>Rank</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.963597</td>\n",
+       "      <td>0.346623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>DALY rate</td>\n",
+       "      <td>-0.963597</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.285906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Sunshine Hours/Year</td>\n",
+       "      <td>0.346623</td>\n",
+       "      <td>-0.285906</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         Rank  DALY rate  Sunshine Hours/Year\n",
+       "Rank                 1.000000  -0.963597             0.346623\n",
+       "DALY rate           -0.963597   1.000000            -0.285906\n",
+       "Sunshine Hours/Year  0.346623  -0.285906             1.000000"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr()"
+   ]
+  },
+  {
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
+%% Cell type:code id: tags:
+
+``` python
+import requests
+import urllib.request
+import time
+from bs4 import BeautifulSoup
+import numpy as np
+import pandas as pd
+from urllib.request import urlopen
+```
+
+%% Cell type:code id: tags:
+
+``` python
+url = 'https://en.wikipedia.org/wiki/Epidemiology_of_depression'
+```
+
+%% Cell type:code id: tags:
+
+``` python
+html = urlopen(url)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+soup = BeautifulSoup(html, 'html.parser')
+```
+
+%% Cell type:code id: tags:
+
+``` python
+tables = soup.find_all('table')
+```
+
+%% Cell type:code id: tags:
+
+``` python
+#convert number as string to integer
+#re.sub() returns the substring that match the regrex
+import re
+def process_num(num):
+    return float(re.sub(r'[^\w\s.]','',num))
+```
+
+%% Cell type:code id: tags:
+
+``` python
+num1 = re.sub(r'[^\w\s.]','','1,156.30')
+num1
+```
+
+%% Output
+
+    '1156.30'
+
+%% Cell type:code id: tags:
+
+``` python
+ranks = []
+rates = []
+countries = []
+links = []
+
+for table in tables:
+    rows = table.find_all('tr')
+
+    for row in rows:
+        cells = row.find_all('td')
+
+        if len(cells) > 1:
+            rank = cells[0]
+            ranks.append(int(rank.text))
+
+            country = cells[1]
+            countries.append(country.text.strip())
+
+            rate = cells[2]
+            rates.append(process_num(rate.text.strip()))
+
+            link = cells[1].find('a').get('href')
+            links.append('https://en.wikipedia.org/'+ link)
+
+df1 = pd.DataFrame(ranks, index= countries, columns = ['Rank'])
+df1['DALY rate'] = rates
+
+df1.head(10)
+```
+
+%% Output
+
+                   Rank  DALY rate
+    United States     1    1454.74
+    Nepal             2    1424.48
+    East Timor        3    1404.10
+    Bangladesh        4    1401.53
+    India             5    1400.84
+    Pakistan          6    1400.42
+    Brazil            7    1396.10
+    Maldives          8    1391.61
+    Bhutan            9    1385.53
+    Afghanistan      10    1385.14
+
+%% Cell type:code id: tags:
+
+``` python
+sun_url = urlopen('https://en.wikipedia.org/wiki/List_of_cities_by_sunshine_duration')
+sun = BeautifulSoup(sun_url, 'html.parser')
+tables = sun.find_all('table')
+
+#Dictionary to hold the name of the country and its corresponding temperature
+country_suns = {}
+
+#Dictionary to hold the country and its frequency in the table
+count = {}
+for table in tables:
+    if len(table) >1:
+        rows = table.find_all('tr')
+
+        #Skip the first row, which is the name of the columns
+        for row in rows[1:]:
+            cells = row.find_all('td')
+            country = cells[0].text.strip()
+
+            #If country in the list of country we found previously
+            #append the country to the dictionary
+            if country in countries:
+
+                sun = cells[-2].text.strip()
+                sun = process_num(sun)/10
+
+                #If country is already in the dictionary
+                #add to the existing sun hours of that country and the count to keep track of how many times we add
+                if country in country_suns:
+                    count[country] += 1
+                    country_suns[country] += sun
+
+                else:
+                    count[country] = 1
+                    country_suns[country] = sun
+
+
+#Find the average temperature of each country
+for country in country_suns:
+    print(country_suns[country],count[country])
+    country_suns[country] = round(country_suns[country]/count[country],2)
+    print('Country: {}, Sunshine Hours: {}'.format(country,country_suns[country]))
+
+```
+
+%% Output
+
+    789.14 3
+    Country: Benin, Sunshine Hours: 263.05
+    515.99 2
+    Country: Togo, Sunshine Hours: 258.0
+    710.25 3
+    Country: Ghana, Sunshine Hours: 236.75
+    866.0500000000001 4
+    Country: Cameroon, Sunshine Hours: 216.51
+    344.03999999999996 2
+    Country: Gabon, Sunshine Hours: 172.02
+    1334.54 5
+    Country: Nigeria, Sunshine Hours: 266.91
+    711.91 2
+    Country: Sudan, Sunshine Hours: 355.95
+    336.1 1
+    Country: Eritrea, Sunshine Hours: 336.1
+    641.8 2
+    Country: Burkina Faso, Sunshine Hours: 320.9
+    320.32 1
+    Country: Niger, Sunshine Hours: 320.32
+    670.6400000000001 2
+    Country: Chad, Sunshine Hours: 335.32
+    307.0 1
+    Country: Gambia, Sunshine Hours: 307.0
+    629.2 2
+    Country: Senegal, Sunshine Hours: 314.6
+    620.5999999999999 2
+    Country: Somalia, Sunshine Hours: 310.3
+    327.9 1
+    Country: Djibouti, Sunshine Hours: 327.9
+    964.0099999999999 3
+    Country: Mali, Sunshine Hours: 321.34
+    653.3 2
+    Country: Algeria, Sunshine Hours: 326.65
+    609.99 2
+    Country: Tunisia, Sunshine Hours: 305.0
+    946.64 3
+    Country: Morocco, Sunshine Hours: 315.55
+    2253.8500000000004 6
+    Country: Egypt, Sunshine Hours: 375.64
+    635.6199999999999 2
+    Country: Libya, Sunshine Hours: 317.81
+    1212.01 4
+    Country: Kenya, Sunshine Hours: 303.0
+    234.1 1
+    Country: Angola, Sunshine Hours: 234.1
+    1213.1399999999999 4
+    Country: Tanzania, Sunshine Hours: 303.28
+    556.97 2
+    Country: Ethiopia, Sunshine Hours: 278.49
+    666.5 2
+    Country: Mauritania, Sunshine Hours: 333.25
+    1884.79 6
+    Country: South Africa, Sunshine Hours: 314.13
+    1028.0 3
+    Country: Botswana, Sunshine Hours: 342.67
+    889.6400000000001 3
+    Country: Zambia, Sunshine Hours: 296.55
+    613.08 2
+    Country: Zimbabwe, Sunshine Hours: 306.54
+    838.76 3
+    Country: Malawi, Sunshine Hours: 279.59
+    1718.66 6
+    Country: Madagascar, Sunshine Hours: 286.44
+    283.8 1
+    Country: Mozambique, Sunshine Hours: 283.8
+    681.8 3
+    Country: Uganda, Sunshine Hours: 227.27
+    237.34 1
+    Country: Burundi, Sunshine Hours: 237.34
+    488.0 2
+    Country: Guinea, Sunshine Hours: 244.0
+    270.7 1
+    Country: Guinea-Bissau, Sunshine Hours: 270.7
+    309.79 2
+    Country: Equatorial Guinea, Sunshine Hours: 154.9
+    747.5 2
+    Country: Namibia, Sunshine Hours: 373.75
+    317.51 1
+    Country: Afghanistan, Sunshine Hours: 317.51
+    220.74 1
+    Country: Azerbaijan, Sunshine Hours: 220.74
+    206.6 1
+    Country: Bangladesh, Sunshine Hours: 206.6
+    1091.49 5
+    Country: China, Sunshine Hours: 218.3
+    973.66 4
+    Country: India, Sunshine Hours: 243.41
+    298.33000000000004 1
+    Country: Indonesia, Sunshine Hours: 298.33
+    282.61 1
+    Country: Iran, Sunshine Hours: 282.61
+    324.08000000000004 1
+    Country: Iraq, Sunshine Hours: 324.08
+    331.1 1
+    Country: Israel, Sunshine Hours: 331.1
+    361.71000000000004 2
+    Country: Japan, Sunshine Hours: 180.86
+    486.29999999999995 2
+    Country: Kazakhstan, Sunshine Hours: 243.15
+    279.15 1
+    Country: Mongolia, Sunshine Hours: 279.15
+    249.2 1
+    Country: North Korea, Sunshine Hours: 249.2
+    349.33000000000004 1
+    Country: Oman, Sunshine Hours: 349.33
+    598.4300000000001 2
+    Country: Pakistan, Sunshine Hours: 299.22
+    210.31 1
+    Country: Philippines, Sunshine Hours: 210.31
+    1578.2299999999998 8
+    Country: Russia, Sunshine Hours: 197.28
+    647.3 2
+    Country: Saudi Arabia, Sunshine Hours: 323.65
+    202.24 1
+    Country: Singapore, Sunshine Hours: 202.24
+    439.33000000000004 2
+    Country: South Korea, Sunshine Hours: 219.67
+    870.0099999999999 4
+    Country: Thailand, Sunshine Hours: 217.5
+    466.76 2
+    Country: Turkey, Sunshine Hours: 233.38
+    282.39 1
+    Country: Uzbekistan, Sunshine Hours: 282.39
+    849.4 4
+    Country: Vietnam, Sunshine Hours: 212.35
+    254.4 1
+    Country: Albania, Sunshine Hours: 254.4
+    247.4 1
+    Country: Armenia, Sunshine Hours: 247.4
+    188.4 1
+    Country: Austria, Sunshine Hours: 188.4
+    180.7 1
+    Country: Belarus, Sunshine Hours: 180.7
+    154.6 1
+    Country: Belgium, Sunshine Hours: 154.6
+    176.9 1
+    Country: Bosnia and Herzegovina, Sunshine Hours: 176.9
+    217.7 1
+    Country: Bulgaria, Sunshine Hours: 217.7
+    191.3 1
+    Country: Croatia, Sunshine Hours: 191.3
+    166.8 1
+    Country: Czech Republic, Sunshine Hours: 166.8
+    331.40999999999997 1
+    Country: Cyprus, Sunshine Hours: 331.41
+    173.9 1
+    Country: Denmark, Sunshine Hours: 173.9
+    182.6 1
+    Country: Estonia, Sunshine Hours: 182.6
+    185.8 1
+    Country: Finland, Sunshine Hours: 185.8
+    449.8 2
+    Country: France, Sunshine Hours: 224.9
+    204.6 1
+    Country: Georgia, Sunshine Hours: 204.6
+    328.79999999999995 2
+    Country: Germany, Sunshine Hours: 164.4
+    595.0 2
+    Country: Greece, Sunshine Hours: 297.5
+    198.8 1
+    Country: Hungary, Sunshine Hours: 198.8
+    132.6 1
+    Country: Iceland, Sunshine Hours: 132.6
+    145.3 1
+    Country: Ireland, Sunshine Hours: 145.3
+    438.8 2
+    Country: Italy, Sunshine Hours: 219.4
+    175.4 1
+    Country: Latvia, Sunshine Hours: 175.4
+    169.1 1
+    Country: Lithuania, Sunshine Hours: 169.1
+    305.4 1
+    Country: Malta, Sunshine Hours: 305.4
+    212.6 1
+    Country: Moldova, Sunshine Hours: 212.6
+    166.2 1
+    Country: Netherlands, Sunshine Hours: 166.2
+    166.8 1
+    Country: Norway, Sunshine Hours: 166.8
+    157.1 1
+    Country: Poland, Sunshine Hours: 157.1
+    280.6 1
+    Country: Portugal, Sunshine Hours: 280.6
+    211.5 1
+    Country: Romania, Sunshine Hours: 211.5
+    203.8 1
+    Country: Slovakia, Sunshine Hours: 203.8
+    197.4 1
+    Country: Slovenia, Sunshine Hours: 197.4
+    826.6 3
+    Country: Spain, Sunshine Hours: 275.53
+    374.29999999999995 2
+    Country: Sweden, Sunshine Hours: 187.15
+    156.6 1
+    Country: Switzerland, Sunshine Hours: 156.6
+    195.5 1
+    Country: Ukraine, Sunshine Hours: 195.5
+    306.0 2
+    Country: United Kingdom, Sunshine Hours: 153.0
+    1825.24 9
+    Country: Canada, Sunshine Hours: 202.8
+    225.98000000000002 1
+    Country: Honduras, Sunshine Hours: 225.98
+    1038.5 4
+    Country: Mexico, Sunshine Hours: 259.62
+    275.99 1
+    Country: Nicaragua, Sunshine Hours: 275.99
+    174.35 1
+    Country: Panama, Sunshine Hours: 174.35
+    295.7 1
+    Country: El Salvador, Sunshine Hours: 295.7
+    15218.579999999998 54
+    Country: United States, Sunshine Hours: 281.83
+    1149.52 5
+    Country: Argentina, Sunshine Hours: 229.9
+    228.89000000000001 1
+    Country: Bolivia, Sunshine Hours: 228.89
+    1322.58 6
+    Country: Brazil, Sunshine Hours: 220.43
+    953.81 6
+    Country: Colombia, Sunshine Hours: 158.97
+    1324.27 5
+    Country: Chile, Sunshine Hours: 264.85
+    381.90999999999997 2
+    Country: Ecuador, Sunshine Hours: 190.95
+    280.3 1
+    Country: Paraguay, Sunshine Hours: 280.3
+    604.0 3
+    Country: Peru, Sunshine Hours: 201.33
+    248.14000000000001 1
+    Country: Uruguay, Sunshine Hours: 248.14
+    579.0899999999999 2
+    Country: Venezuela, Sunshine Hours: 289.54
+    2553.15 9
+    Country: Australia, Sunshine Hours: 283.68
+    192.2 1
+    Country: Fiji, Sunshine Hours: 192.2
+    613.1999999999999 3
+    Country: New Zealand, Sunshine Hours: 204.4
+    246.3 1
+    Country: Papua New Guinea, Sunshine Hours: 246.3
+    233.0 1
+    Country: Solomon Islands, Sunshine Hours: 233.0
+
+%% Cell type:code id: tags:
+
+``` python
+df2 = pd.DataFrame.from_dict(country_suns,orient='index', columns = ['Sunshine Hours/Year'])
+
+df = df1.join(df2)
+
+df.info()
+```
+
+%% Output
+
+    <class 'pandas.core.frame.DataFrame'>
+    Index: 192 entries, United States to Japan
+    Data columns (total 3 columns):
+    Rank                   192 non-null int64
+    DALY rate              192 non-null float64
+    Sunshine Hours/Year    122 non-null float64
+    dtypes: float64(2), int64(1)
+    memory usage: 11.0+ KB
+
+%% Cell type:code id: tags:
+
+``` python
+df.dropna(inplace=True)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+df.info()
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+sns.scatterplot('Rank', 'Sunshine Hours/Year', data=df)
+```
+
+%% Output
+
+    <class 'pandas.core.frame.DataFrame'>
+    Index: 122 entries, United States to Japan
+    Data columns (total 3 columns):
+    Rank                   122 non-null int64
+    DALY rate              122 non-null float64
+    Sunshine Hours/Year    122 non-null float64
+    dtypes: float64(2), int64(1)
+    memory usage: 8.8+ KB
+
+    <matplotlib.axes._subplots.AxesSubplot at 0x1a1a728410>
+
+
+
+%% Cell type:code id: tags:
+
+``` python
+df.corr()
+```
+
+%% Output
+
+                             Rank  DALY rate  Sunshine Hours/Year
+    Rank                 1.000000  -0.963597             0.346623
+    DALY rate           -0.963597   1.000000            -0.285906
+    Sunshine Hours/Year  0.346623  -0.285906             1.000000
+
+%% Cell type:code id: tags:
+
+``` python
+```