Notebooks/23_Intro_Scraping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Python  \n",
    "\n",
    "### Introduction to Scraping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import pylab\n",
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import requests\n",
    "import urllib\n",
    "from bs4 import BeautifulSoup as bs\n",
    "\n",
    "%matplotlib inline\n",
    "#%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scraping HTML tables with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url1=\"https://en.wikipedia.org/wiki/List_of_cities_and_towns_in_Austria\"\n",
    "list_df_tables = pd.read_html(url1)\n",
    "len(list_df_tables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Population</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>1991</th>\n",
       "      <th>2001</th>\n",
       "      <th>2015[1]</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>1539848</td>\n",
       "      <td>1550123</td>\n",
       "      <td>1840573</td>\n",
       "      <td>Vienna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Graz</td>\n",
       "      <td>237810</td>\n",
       "      <td>226244</td>\n",
       "      <td>273838</td>\n",
       "      <td>Styria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Linz</td>\n",
       "      <td>203044</td>\n",
       "      <td>183504</td>\n",
       "      <td>198181</td>\n",
       "      <td>Upper Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Salzburg</td>\n",
       "      <td>143978</td>\n",
       "      <td>142662</td>\n",
       "      <td>148420</td>\n",
       "      <td>Salzburg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Innsbruck</td>\n",
       "      <td>118112</td>\n",
       "      <td>113392</td>\n",
       "      <td>126851</td>\n",
       "      <td>Tyrol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Rank       Name Population                            State\n",
       "  Rank       Name       1991     2001  2015[1]          State\n",
       "0  1.0     Vienna    1539848  1550123  1840573         Vienna\n",
       "1  2.0       Graz     237810   226244   273838         Styria\n",
       "2  3.0       Linz     203044   183504   198181  Upper Austria\n",
       "3  4.0   Salzburg     143978   142662   148420       Salzburg\n",
       "4  5.0  Innsbruck     118112   113392   126851          Tyrol"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_df_tables[0].head()"
   ]
  },
  {
   "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>Federal state</th>\n",
       "      <th>Community Name</th>\n",
       "      <th>Status</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Burgenland</td>\n",
       "      <td>Andau</td>\n",
       "      <td>Market town</td>\n",
       "      <td>2285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Burgenland</td>\n",
       "      <td>Antau</td>\n",
       "      <td>Other municipality</td>\n",
       "      <td>758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Burgenland</td>\n",
       "      <td>Apetlon</td>\n",
       "      <td>Market town</td>\n",
       "      <td>1760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Burgenland</td>\n",
       "      <td>Bad Sauerbrunn</td>\n",
       "      <td>Other municipality</td>\n",
       "      <td>2219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Burgenland</td>\n",
       "      <td>Bad Tatzmannsdorf</td>\n",
       "      <td>Other municipality</td>\n",
       "      <td>1554</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Federal state     Community Name              Status  Population\n",
       "0    Burgenland              Andau         Market town        2285\n",
       "1    Burgenland              Antau  Other municipality         758\n",
       "2    Burgenland            Apetlon         Market town        1760\n",
       "3    Burgenland     Bad Sauerbrunn  Other municipality        2219\n",
       "4    Burgenland  Bad Tatzmannsdorf  Other municipality        1554"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_df_tables[2].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scraping using [Requests](https://docs.python-requests.org/en/master/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n"
     ]
    }
   ],
   "source": [
    "url2 = 'https://www.wienerzeitung.at/nachrichten/politik/oesterreich/2107909-Haertere-steirische-Impfanreize.html' \n",
    "content = requests.get(url2)\n",
    "print(content.status_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check [HTTP status codes](https://developer.mozilla.org/pt-BR/docs/Web/HTTP/Status) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting html content of the page "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<!DOCTYPE html>\\n<html lang=\"de\">\\n<head>\\n<meta charset=\"utf-8\" />\\n<meta http-equiv=\"cache-control\" content=\"no-cache\" />\\n<meta http-equiv=\"pragma\" content=\"no-cache\" />\\n<meta name=\"robots\" content=\"index, follow, noarchive\" />\\n<meta name=\"content-language\" content=\"de\" />\\n<meta name=\"description\" content=\"Am Donnerstag ging es zwar &quot;nur&quot; um eine Impfkampagne. Der steirische Landeshauptmann Hermann Sch&uuml;tzenh&ouml;fer plant aber die Corona-Impfung als Voraussetzung bei Neueinstellung von Kindergartenpersonal.\" />\\n<meta name=\"author\" content=\"Martina Madner\" />\\n<meta name=\"copyright\" content=\"Wiener Zeitung Online\" />\\n<meta property=\"og:type\" content=\"article\" />\\n<meta property=\"og:title\" content=\"Impfpflicht - H&auml;rtere steirische Impfanreize\" />\\n<meta property=\"og:description\" content=\"Am Donnerstag ging es zwar &quot;nur&quot; um eine Impfkampagne. Der steirische Landeshauptmann Hermann Sch&uuml;tzenh&ouml;fer plant aber die Corona-Impfung als...\" />\\n<meta property=\"og'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.text[0:1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using [Beautiful Soup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'bs4.BeautifulSoup'>\n"
     ]
    }
   ],
   "source": [
    "soup = bs(content.text,'lxml')\n",
    "print(type(soup))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A quick note on parsers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Parser</th>\n",
       "      <th>Typical usage</th>\n",
       "      <th>Advantages</th>\n",
       "      <th>Disadvantages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Python’s html.parser</td>\n",
       "      <td>BeautifulSoup(markup, \"html.parser\")</td>\n",
       "      <td>Batteries included Decent speed Lenient (As of...</td>\n",
       "      <td>Not as fast as lxml, less lenient than html5lib.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lxml’s HTML parser</td>\n",
       "      <td>BeautifulSoup(markup, \"lxml\")</td>\n",
       "      <td>Very fast Lenient</td>\n",
       "      <td>External C dependency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lxml’s XML parser</td>\n",
       "      <td>BeautifulSoup(markup, \"lxml-xml\") BeautifulSou...</td>\n",
       "      <td>Very fast The only currently supported XML parser</td>\n",
       "      <td>External C dependency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>html5lib</td>\n",
       "      <td>BeautifulSoup(markup, \"html5lib\")</td>\n",
       "      <td>Extremely lenient Parses pages the same way a ...</td>\n",
       "      <td>Very slow External Python dependency</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0                Parser                                      Typical usage  \\\n",
       "1  Python’s html.parser               BeautifulSoup(markup, \"html.parser\")   \n",
       "2    lxml’s HTML parser                      BeautifulSoup(markup, \"lxml\")   \n",
       "3     lxml’s XML parser  BeautifulSoup(markup, \"lxml-xml\") BeautifulSou...   \n",
       "4              html5lib                  BeautifulSoup(markup, \"html5lib\")   \n",
       "\n",
       "0                                         Advantages  \\\n",
       "1  Batteries included Decent speed Lenient (As of...   \n",
       "2                                  Very fast Lenient   \n",
       "3  Very fast The only currently supported XML parser   \n",
       "4  Extremely lenient Parses pages the same way a ...   \n",
       "\n",
       "0                                     Disadvantages  \n",
       "1  Not as fast as lxml, less lenient than html5lib.  \n",
       "2                             External C dependency  \n",
       "3                             External C dependency  \n",
       "4              Very slow External Python dependency  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_parsers = pd.read_html(\"https://www.crummy.com/software/BeautifulSoup/bs4/doc/\")[0]\n",
    "df_parsers.columns = df_parsers.iloc[0]\n",
    "df_parsers.drop(df_parsers.index[0], inplace=True)\n",
    "df_parsers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting formatted html content of the page "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<!DOCTYPE html>\n",
      "<html lang=\"de\">\n",
      " <head>\n",
      "  <meta charset=\"utf-8\"/>\n",
      "  <meta content=\"no-cache\" http-equiv=\"cache-control\"/>\n",
      "  <meta content=\"no-cache\" http-equiv=\"pragma\"/>\n",
      "  <meta content=\"index, follow, noarchive\" name=\"robots\"/>\n",
      "  <meta content=\"de\" name=\"content-language\"/>\n",
      "  <meta content='Am Donnerstag ging es zwar \"nur\" um eine Impfkampagne. Der steirische Landeshauptmann Hermann Schützenhöfer plant aber die Corona-Impfung als Voraussetzung bei Neueinstellung von Kindergartenpersonal.' name=\"description\"/>\n",
      "  <meta content=\"Martina Madner\" name=\"author\"/>\n",
      "  <meta content=\"Wiener Zeitung Online\" name=\"copyright\"/>\n",
      "  <meta content=\"article\" property=\"og:type\"/>\n",
      "  <meta content=\"Impfpflicht - Härtere steirische Impfanreize\" property=\"og:title\"/>\n",
      "  <meta content='Am Donnerstag ging es zwar \"nur\" um eine Impfkampagne. Der steirische Landeshauptmann Hermann Schützenhöfer plant aber die Corona-Impfung als...' property=\"og:description\"/>\n",
      "  <meta content=\"Österreich Politik - Nachrichten -\n"
     ]
    }
   ],
   "source": [
    "print(soup.prettify()[0:1000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting text of the page "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "un gestartete Werbekampagne f&uuml;rs Impfen in der Steiermark. Es spricht von einer \\\"gewissen Impfm&uuml;digkeit, die eingetreten ist, da rede ich nicht um den hei&szlig;en Brei herum: Die Anmeldungen sind zur&uuml;ckgegangen.\\\" 560.000 von 1,1 Millionen impfbaren Steirerinnen und Steirern sind erstgeimpft, 227.000 auch ein zweites Mal. Sein Appell: \\\"Wir werden alles versuchen, damit sich die Menschen impfen lassen. Es ist noch nicht vorbei.\\\"<\\/p>\\n<p>In der ORF-\\\"Pressestunde\\\" sprach Sch&uuml;tzenh&ouml;fer von einem \\\"sch&auml;bigem Verhalten\\\" der Impfunwilligen. Auf die Frage, ob er sich eine Sars-CoV-2-Schutzimpfungspflicht f&uuml;r das Kindergartenpersonal vorstellen k&ouml;nne, sagte er: \\\"Ich w&auml;re pers&ouml;nlich daf&uuml;r, der Verfassungsdienst des Bundes sagt aber: Nein, das geht nicht\\\" - und lie&szlig; dann aufhorchen: \\\"Aber: Jedes Land kann selber etwas bei der Aufnahme tun. Das will ich in den Bereichen des Landes selbstverst&auml;ndlich so machen.\\\"<\\/p>\\n<p>\n"
     ]
    }
   ],
   "source": [
    "print(soup.get_text()[9000:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<title>Impfpflicht - Härtere steirische Impfanreize - Wiener Zeitung Online</title>\n"
     ]
    }
   ],
   "source": [
    "print(soup.title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extracting anchors and links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<a href=\"/abonnement\">Abo</a>,\n",
       " <a href=\"/epaper\" target=\"extern\">E-Paper</a>,\n",
       " <a href=\"/beilagen\">Magazine</a>,\n",
       " <a href=\"/\">\n",
       " <i class=\"fal fa-history fa-lg fa-fw\"></i><span class=\"notification-counter meldung-counter\"></span>\n",
       " </a>,\n",
       " <a class=\"mobile-search-button mobile-search-closed\" data-overlay=\"overlay-search\" href=\"javascript:void(0);\" title=\"Suche\"><i class=\"far fa-search\"></i></a>,\n",
       " <a class=\"logo-wzo\" href=\"/\" itemprop=\"url\" title=\"\">\n",
       " <img alt=\"Wiener Zeitung\" height=\"30\" src=\"/_em_daten/wzo/assets/img/WZ-Logo.svg\"/>\n",
       " <img class=\"hidden\" height=\"0\" itemprop=\"logo\" src=\"/_em_daten/wzo/assets/img/favicons/wzo-512.png\"/>\n",
       " </a>,\n",
       " <a class=\"mobile-menu-button\" href=\"javascript:void(0);\" title=\"Hauptmenü\"><i class=\"far fa-bars\"></i></a>,\n",
       " <a data-preview=\"\" data-preview-count=\"4\" href=\"https://www.wienerzeitung.at/nachrichten/politik/\" target=\"_top\" title=\"Politik\">Politik</a>,\n",
       " <a data-preview=\"\" data-preview-count=\"4\" href=\"https://www.wienerzeitung.at/nachrichten/kultur/\" target=\"_top\" title=\"Kultur\">Kultur</a>,\n",
       " <a data-preview=\"\" data-preview-count=\"4\" href=\"https://www.wienerzeitung.at/nachrichten/wirtschaft/\" target=\"_top\" title=\"Wirtschaft\">Wirtschaft</a>,\n",
       " <a data-preview=\"\" data-preview-count=\"4\" href=\"https://www.wienerzeitung.at/amtsblatt/aktuelle-ausgabe/\" target=\"_top\" title=\"Amtsblatt\">Amtsblatt</a>,\n",
       " <a data-preview=\"\" data-preview-count=\"4\" href=\"https://www.wienerzeitung.at/dossiers/\" target=\"_top\" title=\"Dossiers\">Dossiers</a>,\n",
       " <a data-preview=\"\" data-preview-count=\"4\" href=\"https://www.wienerzeitung.at/meinung/\" target=\"_top\" title=\"Meinung\">Meinung</a>,\n",
       " <a class=\"overlay-close\" href=\"javascript:void(0);\" id=\"overlay_close\" title=\"Suche schließen\"><i class=\"fal fa-times fa-lg\"></i></a>,\n",
       " <a href=\"https://www.wienerzeitung.at/nachrichten/politik/\" target=\"_top\" title=\"Nachrichten\">Nachrichten</a>,\n",
       " <a href=\"https://www.wienerzeitung.at/nachrichten/politik/\" target=\"_top\" title=\"Politik\">Politik</a>,\n",
       " <a href=\"https://www.wienerzeitung.at/nachrichten/wirtschaft/\" target=\"_top\" title=\"Wirtschaft\">Wirtschaft</a>,\n",
       " <a href=\"https://www.wienerzeitung.at/nachrichten/kultur/\" target=\"_top\" title=\"Kultur\">Kultur</a>,\n",
       " <a href=\"https://www.wienerzeitung.at/nachrichten/chronik/\" target=\"_top\" title=\"Chronik\">Chronik</a>,\n",
       " <a href=\"https://www.wienerzeitung.at/nachrichten/wissen/\" target=\"_top\" title=\"Wissen\">Wissen</a>]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup.find_all('a')[0:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extracting only the *href* of anchors and links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/abonnement\n",
      "/epaper\n",
      "/beilagen\n",
      "/\n",
      "javascript:void(0);\n",
      "/\n",
      "javascript:void(0);\n",
      "https://www.wienerzeitung.at/nachrichten/politik/\n",
      "https://www.wienerzeitung.at/nachrichten/kultur/\n",
      "https://www.wienerzeitung.at/nachrichten/wirtschaft/\n"
     ]
    }
   ],
   "source": [
    "for link in soup.find_all('a')[0:10]:\n",
    "    print(link.get('href'))\n",
    "    #print(link.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extracting only *http* and *https* from *href* links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://www.wienerzeitung.at/nachrichten/politik/\n",
      "https://www.wienerzeitung.at/nachrichten/kultur/\n",
      "https://www.wienerzeitung.at/nachrichten/wirtschaft/\n",
      "https://www.wienerzeitung.at/amtsblatt/aktuelle-ausgabe/\n",
      "https://www.wienerzeitung.at/dossiers/\n",
      "https://www.wienerzeitung.at/meinung/\n",
      "https://www.wienerzeitung.at/nachrichten/politik/\n",
      "https://www.wienerzeitung.at/nachrichten/politik/\n",
      "https://www.wienerzeitung.at/nachrichten/wirtschaft/\n",
      "https://www.wienerzeitung.at/nachrichten/kultur/\n",
      "https://www.wienerzeitung.at/nachrichten/chronik/\n",
      "https://www.wienerzeitung.at/nachrichten/wissen/\n",
      "https://www.wienerzeitung.at/nachrichten/sport/\n",
      "https://www.wienerzeitung.at/nachrichten/wahlen/\n",
      "https://www.wienerzeitung.at/nachrichten/zeitreisen-rubriken/\n",
      "https://www.wienerzeitung.at/nachrichten/reflexionen/\n",
      "https://www.wienerzeitung.at/nachrichten/digital/\n",
      "https://www.wienerzeitung.at/amtsblatt/aktuelle-ausgabe/\n",
      "https://www.wienerzeitung.at/amtsblatt/amtliche-veroeffentlichungen-zu-corona/\n",
      "https://www.wienerzeitung.at/amtsblatt/suche/\n",
      "https://www.wienerzeitung.at/amtsblatt/aktuelle-ausgabe/\n",
      "https://www.wienerzeitung.at/amtsblatt/alte-ausgaben/\n",
      "https://www.auftrag.at/tenders.aspx\n",
      "https://www.wienerzeitung.at/amtsblatt/firmeninsolvenzen/\n",
      "https://www.wienerzeitung.at/amtsblatt/privatkonkurse/\n",
      "https://www.wienerzeitung.at/amtsblatt/mein-bereich/\n",
      "https://www.wienerzeitung.at/amtsblatt/jobs/\n",
      "https://www.wienerzeitung.at/amtsblatt/info/\n",
      "https://www.wienerzeitung.at/amtsblatt/lehrerstellen/\n",
      "https://www.wienerzeitung.at/dossiers/\n",
      "https://www.wienerzeitung.at/dossiers/100-jahre-verfassung/\n",
      "https://www.wienerzeitung.at/dossiers/autismus/\n",
      "https://www.wienerzeitung.at/dossiers/digitaler-humanismus/\n",
      "https://www.wienerzeitung.at/dossiers/bob-dylan/\n",
      "https://www.wienerzeitung.at/dossiers/shakespeare/\n",
      "https://www.wienerzeitung.at/dossiers/wald/\n",
      "https://www.wienerzeitung.at/dossiers/wie-wir-die-krise-meistern/\n",
      "https://www.wienerzeitung.at/dossiers/ludwig-wittgenstein/\n",
      "https://www.wienerzeitung.at/themen/\n",
      "https://www.wienerzeitung.at/themen/brexit/\n",
      "https://www.wienerzeitung.at/themen/bundestagswahl-2021/\n",
      "https://www.wienerzeitung.at/themen/comics-und-mangas/\n",
      "https://www.wienerzeitung.at/themen/energiewende/\n",
      "https://www.wienerzeitung.at/themen/pflege/\n",
      "https://www.wienerzeitung.at/themen/recht/\n",
      "https://www.wienerzeitung.at/themen/sars-cov-2/\n",
      "https://www.wienerzeitung.at/themen/vor-gericht/\n",
      "https://www.wienerzeitung.at/themen/wiener-festwochen/\n",
      "https://www.wienerzeitung.at/meinung/\n",
      "https://www.wienerzeitung.at/meinung/leitartikel/\n",
      "https://www.wienerzeitung.at/meinung/glossen/\n",
      "https://www.wienerzeitung.at/meinung/gastkommentare/\n",
      "https://www.wienerzeitung.at/meinung/kommentare/\n",
      "https://www.wienerzeitung.at/meinung/blogs/\n",
      "https://www.wienerzeitung.at/meinung/leserforum/\n",
      "https://www.wienerzeitung.at/multimedia/\n",
      "https://www.wienerzeitung.at/multimedia/fotostrecken/\n",
      "https://www.wienerzeitung.at/multimedia/videos/\n",
      "https://www.wienerzeitung.at/multimedia/podcasts/\n",
      "https://www.wienerzeitung.at/multimedia/scroll-stories/\n",
      "https://www.wienerzeitung.at/multimedia/podcast-ueber-wien/\n",
      "https://www.wienerzeitung.at/multimedia/wie-jetzt-der-demokratie-podcast/\n",
      "https://www.wienerzeitung.at/multimedia/spiele/\n",
      "https://www.wienerzeitung.at/abonnement/\n",
      "https://www.wienerzeitung.at/abonnement/samstagsabo/\n",
      "https://www.wienerzeitung.at/abonnement/abo-service/\n",
      "https://www.wienerzeitung.at/abonnement/abo-vorteile/\n",
      "https://www.wienerzeitung.at/mediasales/\n",
      "https://www.wienerzeitung.at/mediasales/tageszeitung/\n",
      "https://www.wienerzeitung.at/mediasales/amtsblatt/\n",
      "https://www.wienerzeitung.at/mediasales/wiener-journal/\n",
      "https://www.wienerzeitung.at/mediasales/magazin-die-republik/\n",
      "https://www.wienerzeitung.at/mediasales/digital/\n",
      "https://www.wienerzeitung.at/mediasales/beilagen/\n",
      "https://www.wienerzeitung.at/mediasales/spezifikationen/\n",
      "https://www.wienerzeitung.at/mediasales/ansprechpartner/\n",
      "https://www.wienerzeitung.at/magazine/\n",
      "https://www.wienerzeitung.at/magazine/wiener-journal/\n",
      "https://epaper.wienerzeitung.at/shelf.act?filter=Extra\n",
      "https://www.wienerzeitung.at/magazine/zeitreisen/\n",
      "https://www.wienerzeitung.at/verlagsbeilagen/\n",
      "https://www.wienerzeitung.at/verlagsbeilagen/die-republik-das-verwaltungsmagazin/\n",
      "https://www.wienerzeitung.at/verlagsbeilagen/weinzeit/\n",
      "https://www.wienerzeitung.at/verlagsbeilagen/digitale-republik/\n",
      "https://www.wienerzeitung.at/verlagsbeilagen/plus-beilagen/\n",
      "https://www.wienerzeitung.at/verlagsaktivitaeten/\n",
      "https://www.wienerzeitung.at/verlagsaktivitaeten/veranstaltungen/\n",
      "https://www.wienerzeitung.at/verlagsaktivitaeten/einen-klick-voraus/\n",
      "https://www.wienerzeitung.at/verlagsaktivitaeten/future-challenge/\n",
      "https://www.wienerzeitung.at/verlagsaktivitaeten/medienpartnerschaften/\n",
      "https://www.wienerzeitung.at/unternehmen/\n",
      "https://www.wienerzeitung.at/unternehmen/marken-und-produkte/\n",
      "https://www.wienerzeitung.at/unternehmen/team/\n",
      "https://www.wienerzeitung.at/unternehmen/redaktionsstatut/\n",
      "https://www.wienerzeitung.at/unternehmen/geschichte/\n",
      "https://www.wienerzeitung.at/unternehmen/akademie/\n",
      "https://www.wienerzeitung.at/unternehmen/karriere/\n",
      "https://www.wienerzeitung.at/unternehmen/apps/\n",
      "https://www.wienerzeitung.at/unternehmen/english/\n",
      "https://www.wienerzeitung.at/unternehmen/kontakt/\n",
      "https://www.wienerzeitung.at/unternehmen/cookies/\n",
      "https://www.wienerzeitung.at/unternehmen/agb/\n",
      "https://www.wienerzeitung.at/unternehmen/datenschutz/\n",
      "https://www.wienerzeitung.at/unternehmen/impressum/\n",
      "https://www.wienerzeitung.at/volltextsuche/\n",
      "https://www.wienerzeitung.at/nachrichten/politik/\n",
      "https://www.wienerzeitung.at/nachrichten/politik/oesterreich/\n",
      "http://stock.adobe.com\n",
      "http://www.facebook.com\n",
      "http://www.twitter.com\n",
      "http://www.facebook.com\n",
      "http://www.twitter.com\n",
      "https://info.gesundheitsministerium.at/\n",
      "https://www.wienerzeitung.at/nachrichten/politik/oesterreich/2101620-Infektionshort-Kindergarten.html\n",
      "http://www.facebook.com\n",
      "http://www.twitter.com\n",
      "http://www.facebook.com\n",
      "http://www.twitter.com\n",
      "https://www.wienerzeitung.at/nachrichten/politik/oesterreich/2107935-Kogler-verteidigt-Justiz-gegen-OeVP-Angriffe.html\n",
      "https://www.wienerzeitung.at/nachrichten/politik/oesterreich/2107935-Kogler-verteidigt-Justiz-gegen-OeVP-Angriffe.html\n",
      "https://www.wienerzeitung.at/nachrichten/politik/oesterreich/2107923-Keine-Ministeranklage-gegen-Bluemel-und-Schramboeck.html\n",
      "https://www.wienerzeitung.at/nachrichten/politik/oesterreich/2107923-Keine-Ministeranklage-gegen-Bluemel-und-Schramboeck.html\n",
      "https://www.wienerzeitung.at/nachrichten/politik/oesterreich/2107913-Kleiner-U-Ausschuss-zu-Corona-Beschaffung-endete.html\n",
      "https://www.wienerzeitung.at/nachrichten/politik/oesterreich/2107913-Kleiner-U-Ausschuss-zu-Corona-Beschaffung-endete.html\n",
      "https://www.wienerzeitung.at/nachrichten/politik/oesterreich/2107796-Corona-als-Stolperstein-fuer-die-Integration.html\n",
      "https://www.wienerzeitung.at/nachrichten/politik/oesterreich/2107796-Corona-als-Stolperstein-fuer-die-Integration.html\n",
      "https://epaper.wienerzeitung.at\n",
      "https://www.facebook.com/WienerZeitung\n",
      "https://twitter.com/WienerZeitung\n",
      "https://www.instagram.com/wienerzeitung\n",
      "https://www.linkedin.com/company/wiener-zeitung/\n"
     ]
    }
   ],
   "source": [
    "for link in soup.findAll('a', attrs={'href': re.compile(\"^http[s]?://\")}):\n",
    "    print(link.get('href'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which one to use: [urllib or requests?](https://stackoverflow.com/questions/2018026/what-are-the-differences-between-the-urllib-urllib2-urllib3-and-requests-modul)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a small generic function to extract links:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['https://arstechnica.com', 'http://video.arstechnica.com/', 'http://arstechnica.com/?view=grid', 'http://arstechnica.com/?view=archive', 'http://arstechnica.com/?theme=light', 'http://arstechnica.com/?theme=dark', 'https://arstechnica.com/civis/ucp.php?mode=login&return_to=%2F', 'https://arstechnica.com/civis/ucp.php?mode=register', 'https://arstechnica.com/gadgets/2021/06/rtx-3070-ti-review-nvidia-has-flipped-its-turn-signal-out-of-the-gpu-fast-lane/', 'https://arstechnica.com/gadgets/2021/06/rtx-3070-ti-review-nvidia-has-flipped-its-turn-signal-out-of-the-gpu-fast-lane/', 'https://arstechnica.com/gadgets/2021/06/rtx-3070-ti-review-nvidia-has-flipped-its-turn-signal-out-of-the-gpu-fast-lane/', 'https://arstechnica.com/author/samred/', 'https://arstechnica.com/gadgets/2021/06/rtx-3070-ti-review-nvidia-has-flipped-its-turn-signal-out-of-the-gpu-fast-lane/?comments=1', 'https://arstechnica.com/tech-policy/2021/06/some-people-cant-get-fcc-subsidy-because-street-isnt-the-same-as-st/', 'https://arstechnica.com/tech-policy/2021/06/some-people-cant-get-fcc-subsidy-because-street-isnt-the-same-as-st/', 'https://arstechnica.com/tech-policy/2021/06/some-people-cant-get-fcc-subsidy-because-street-isnt-the-same-as-st/', 'https://arstechnica.com/author/jon-brodkin/', 'https://arstechnica.com/tech-policy/2021/06/some-people-cant-get-fcc-subsidy-because-street-isnt-the-same-as-st/?comments=1', 'https://arstechnica.com/gaming/2021/06/review-our-favorite-trickster-god-is-charismatic-as-ever-in-loki-premiere/', 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'https://arstechnica.com/science/2021/06/a-disgraceful-decision-researchers-blast-fda-for-approving-alzheimers-drug/', 'https://arstechnica.com/science/2021/06/a-disgraceful-decision-researchers-blast-fda-for-approving-alzheimers-drug/', 'https://arstechnica.com/science/2021/06/a-disgraceful-decision-researchers-blast-fda-for-approving-alzheimers-drug/', 'https://arstechnica.com/author/beth/', 'https://arstechnica.com/science/2021/06/a-disgraceful-decision-researchers-blast-fda-for-approving-alzheimers-drug/?comments=1', 'https://arstechnica.com/tech-policy/2021/06/spurred-by-clarence-thomas-ohio-ag-wants-google-declared-a-public-utility/', 'https://arstechnica.com/tech-policy/2021/06/spurred-by-clarence-thomas-ohio-ag-wants-google-declared-a-public-utility/', 'https://arstechnica.com/tech-policy/2021/06/spurred-by-clarence-thomas-ohio-ag-wants-google-declared-a-public-utility/', 'https://arstechnica.com/author/jon-brodkin/', 'https://arstechnica.com/tech-policy/2021/06/spurred-by-clarence-thomas-ohio-ag-wants-google-declared-a-public-utility/?comments=1', 'https://arstechnica.com/gadgets/2021/06/fast-ly-broke-the-internet-for-an-hour-this-morning/', 'https://arstechnica.com/gadgets/2021/06/fast-ly-broke-the-internet-for-an-hour-this-morning/', 'https://arstechnica.com/gadgets/2021/06/fast-ly-broke-the-internet-for-an-hour-this-morning/', 'https://arstechnica.com/author/jimsalter/', 'https://arstechnica.com/gadgets/2021/06/fast-ly-broke-the-internet-for-an-hour-this-morning/?comments=1', 'https://arstechnica.com/staff/2021/06/amazon-deal-anker-noise-canceling-headphones/', 'https://arstechnica.com/staff/2021/06/amazon-deal-anker-noise-canceling-headphones/', 'https://arstechnica.com/staff/2021/06/amazon-deal-anker-noise-canceling-headphones/', 'https://arstechnica.com/author/ars-staff/', 'https://arstechnica.com/staff/2021/06/amazon-deal-anker-noise-canceling-headphones/?comments=1', 'https://arstechnica.com/gadgets/2021/06/here-are-some-macos-monterey-features-the-wwdc-keynote-didnt-mention/', 'https://arstechnica.com/gadgets/2021/06/here-are-some-macos-monterey-features-the-wwdc-keynote-didnt-mention/', 'https://arstechnica.com/gadgets/2021/06/here-are-some-macos-monterey-features-the-wwdc-keynote-didnt-mention/', 'https://arstechnica.com/author/samuelaxon/', 'https://arstechnica.com/gadgets/2021/06/here-are-some-macos-monterey-features-the-wwdc-keynote-didnt-mention/?comments=1', 'https://arstechnica.com/gaming/2021/06/apple-tvs-summer-preview-includes-tantalizing-glimpses-of-foundation-series/', 'https://arstechnica.com/gaming/2021/06/apple-tvs-summer-preview-includes-tantalizing-glimpses-of-foundation-series/', 'https://arstechnica.com/gaming/2021/06/apple-tvs-summer-preview-includes-tantalizing-glimpses-of-foundation-series/', 'https://arstechnica.com/author/jenniferouellette/', 'https://arstechnica.com/gaming/2021/06/apple-tvs-summer-preview-includes-tantalizing-glimpses-of-foundation-series/?comments=1', 'https://arstechnica.com/science/2021/06/will-sir-richard-come-out-of-the-blue-and-beat-bezos-into-space/', 'https://arstechnica.com/science/2021/06/will-sir-richard-come-out-of-the-blue-and-beat-bezos-into-space/', 'https://arstechnica.com/science/2021/06/will-sir-richard-come-out-of-the-blue-and-beat-bezos-into-space/', 'https://arstechnica.com/author/ericberger/', 'https://arstechnica.com/science/2021/06/will-sir-richard-come-out-of-the-blue-and-beat-bezos-into-space/?comments=1', 'https://arstechnica.com/tech-policy/2021/06/fbi-sold-phones-to-organized-crime-and-read-27-million-encrypted-messages/', 'https://arstechnica.com/tech-policy/2021/06/fbi-sold-phones-to-organized-crime-and-read-27-million-encrypted-messages/', 'https://arstechnica.com/tech-policy/2021/06/fbi-sold-phones-to-organized-crime-and-read-27-million-encrypted-messages/', 'https://arstechnica.com/author/jon-brodkin/', 'https://arstechnica.com/tech-policy/2021/06/fbi-sold-phones-to-organized-crime-and-read-27-million-encrypted-messages/?comments=1', 'http://arstechnica.com/?view=mobile', 'http://condenast.com/', 'https://www.condenast.com/user-agreement/', 'https://www.condenast.com/privacy-policy/', 'https://www.condenast.com/privacy-policy/#california', 'https://www.condenast.com/online-behavioral-advertising-oba-and-how-to-opt-out-of-oba/#clickheretoreadmoreaboutonlinebehavioraladvertising(oba)']\n"
     ]
    }
   ],
   "source": [
    "def getLinks(url):\n",
    "    raw_page = requests.get(url)\n",
    "    html_page = raw_page.text\n",
    "    #html_page = urllib.request.urlopen(url)\n",
    "    soup = bs(html_page)\n",
    "    links = []\n",
    "    for link in soup.findAll('a', attrs={'href': re.compile(\"^http[s]?://\")}):\n",
    "        links.append(link.get('href'))\n",
    "    return links\n",
    "\n",
    "print(getLinks(\"https://arstechnica.com\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting specific information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "url3 = 'https://thegreenestworkforce.ca/index.php/en/schools/' \n",
    "content = requests.get(url3).text\n",
    "soup = bs(content,'lxml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\\n\\nA complete list of Canadian Universities and Colleges - The Greenest Workforce : The Greenest Workforce\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nif(typeof stLight != \"undefined\" ){\\n\\tstLight.options({\\n\\t\\tpublisher: \"df7e08d0-fa61-487f-be1c-0db70716d1aa\",\\n\\t\\tsnapsets:false,\\n\\t\\tservicePopup:true,\\n\\t\\tonhover: false\\n\\t\\t}); \\n\\t//stLight.options({snapsets:false});\\n}\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n{\"@context\":\"https://schema.org\",\"@graph\":[{\"@type\":\"WebSite\",\"@id\":\"http://thegreenestworkforce.ca/index.php/en/home/#website\",\"url\":\"http://thegreenestworkforce.ca/index.php/en/home/\",\"name\":\"The Greenest Workforce\",\"description\":\"\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":\"http://thegreenestworkforce.ca/index.php/en/home/?s={search_term_string}\",\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-CA\"},{\"@type\":\"WebPage\",\"@id\":\"https://thegreenestworkforce.ca/index.php/en/schools/#webpage\",\"url\":\"https://thegreenestworkforce.ca/index.php/en/schools/\",\"name\":\"A complete list of Canadian Universities and Colleges - The Greenest Workforce\",\"isPartOf\":{\"@id\":\"http://thegreenestworkforce.ca/index.php/en/home/#website\"},\"datePublished\":\"2015-01-15T02:24:10+00:00\",\"dateModified\":\"2015-01-15T02:26:55+00:00\",\"breadcrumb\":{\"@id\":\"https://thegreenestworkforce.ca/index.php/en/schools/#breadcrumb\"},\"inLanguage\":\"en-CA\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https://thegreenestworkforce.ca/index.php/en/schools/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https://thegreenestworkforce.ca/index.php/en/schools/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"item\":{\"@type\":\"WebPage\",\"@id\":\"https://thegreenestworkforce.ca/index.php/en/home/\",\"url\":\"https://thegreenestworkforce.ca/index.php/en/home/\",\"name\":\"Home\"}},{\"@type\":\"ListItem\",\"position\":2,\"item\":{\"@type\":\"WebPage\",\"@id\":\"https://thegreenestworkforce.ca/index.php/en/\",\"url\":\"https://thegreenestworkforce.ca/index.php/en/\",\"name\":\"en\"}},{\"@type\":\"ListItem\",\"position\":3,\"item\":{\"@id\":\"https://thegreenestworkforce.ca/index.php/en/schools/#webpage\"}}]}]}\\n\\n\\n\\n\\n\\n\\t\\t\\twindow._wpemojiSettings = {\"baseUrl\":\"https:\\\\/\\\\/s.w.org\\\\/images\\\\/core\\\\/emoji\\\\/13.0.1\\\\/72x72\\\\/\",\"ext\":\".png\",\"svgUrl\":\"https:\\\\/\\\\/s.w.org\\\\/images\\\\/core\\\\/emoji\\\\/13.0.1\\\\/svg\\\\/\",\"svgExt\":\".svg\",\"source\":{\"concatemoji\":\"https:\\\\/\\\\/thegreenestworkforce.ca\\\\/wp-includes\\\\/js\\\\/wp-emoji-release.min.js?ver=5.7.2\"}};\\n\\t\\t\\t!function(e,a,t){var n,r,o,i=a.createElement(\"canvas\"),p=i.getContext&&i.getContext(\"2d\");function s(e,t){var a=String.fromCharCode;p.clearRect(0,0,i.width,i.height),p.fillText(a.apply(this,e),0,0);e=i.toDataURL();return p.clearRect(0,0,i.width,i.height),p.fillText(a.apply(this,t),0,0),e===i.toDataURL()}function c(e){var t=a.createElement(\"script\");t.src=e,t.defer=t.type=\"text/javascript\",a.getElementsByTagName(\"head\")[0].appendChild(t)}for(o=Array(\"flag\",\"emoji\"),t.supports={everything:!0,everythingExceptFlag:!0},r=0;r<o.length;r++)t.supports[o[r]]=function(e){if(!p||!p.fillText)return!1;switch(p.textBaseline=\"top\",p.font=\"600 32px Arial\",e){case\"flag\":return s([127987,65039,8205,9895,65039],[127987,65039,8203,9895,65039])?!1:!s([55356,56826,55356,56819],[55356,56826,8203,55356,56819])&&!s([55356,57332,56128,56423,56128,56418,56128,56421,56128,56430,56128,56423,56128,56447],[55356,57332,8203,56128,56423,8203,56128,56418,8203,56128,56421,8203,56128,56430,8203,56128,56423,8203,56128,56447]);case\"emoji\":return!s([55357,56424,8205,55356,57212],[55357,56424,8203,55356,57212])}return!1}(o[r]),t.supports.everything=t.supports.everything&&t.supports[o[r]],\"flag\"!==o[r]&&(t.supports.everythingExceptFlag=t.supports.everythingExceptFlag&&t.supports[o[r]]);t.supports.everythingExceptFlag=t.supports.everythingExceptFlag&&!t.supports.flag,t.DOMReady=!1,t.readyCallback=function(){t.DOMReady=!0},t.supports.everything||(n=function(){t.readyCallback()},a.addEventListener?(a.addEventListener(\"DOMContentLoaded\",n,!1),e.addEventListener(\"load\",n,!1)):(e.attachEvent(\"onload\",n),a.attachEvent(\"onreadystatechange\",function(){\"complete\"===a.readyState&&t.readyC'"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup.get_text()[0:4000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inspect page source code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "My_table = soup.find('article',{'id':'post-17896'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['\\n',\n",
       " <h2>Universities</h2>,\n",
       " '\\n',\n",
       " <h3>British Columbia</h3>,\n",
       " '\\n',\n",
       " <p><a href=\"http://www.viu.ca/\" target=\"_blank\" title=\"Vancouver Island University\">Vancouver Island University</a><br/>\n",
       " <a href=\"http://www.royalroads.ca/\" target=\"_blank\" title=\"Royal Roads University\">Royal Roads University</a><br/>\n",
       " <a href=\"http://www.sfu.ca/\" target=\"_blank\" title=\"Simon Fraser University\">Simon Fraser University</a><br/>\n",
       " <a href=\"https://www.twu.ca/\" target=\"_blank\" title=\"Trinity Western University\">Trinity Western University</a><br/>\n",
       " <a href=\"http://www.tru.ca/\" target=\"_blank\" title=\"Thompson River University\">Thompson River University</a><br/>\n",
       " <a href=\"http://www.ufv.ca/\" target=\"_blank\" title=\"University of the Fraser Valley\">University of the Fraser Valley</a><br/>\n",
       " <a href=\"http://www.ubc.ca/\" target=\"_blank\" title=\"University of British Columbia\">University of British Columbia</a><br/>\n",
       " <a href=\"http://www.unbc.ca/\" target=\"_blank\" title=\"University of Northern British Columbia\">University of Northern British Columbia</a><br/>\n",
       " <a href=\"http://www.uvic.ca/\" target=\"_blank\" title=\"University of Victoria\">University of Victoria</a></p>,\n",
       " '\\n',\n",
       " <h3>Saskatchewan</h3>,\n",
       " '\\n',\n",
       " <p><a href=\"http://www.fnuniv.ca/\" target=\"_blank\" title=\"First Nations University of Canada\">First Nations University of Canada</a><br/>\n",
       " <a href=\"http://www.uregina.ca/\" target=\"_blank\" title=\"University of Regina\">University of Regina</a><br/>\n",
       " <a href=\"http://www.usask.ca/\" target=\"_blank\" title=\"University of Saskatchewan\">University of Saskatchewan</a></p>]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "My_table.contents[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<a href=\"http://www.viu.ca/\" target=\"_blank\" title=\"Vancouver Island University\">Vancouver Island University</a>,\n",
       " <a href=\"http://www.royalroads.ca/\" target=\"_blank\" title=\"Royal Roads University\">Royal Roads University</a>,\n",
       " <a href=\"http://www.sfu.ca/\" target=\"_blank\" title=\"Simon Fraser University\">Simon Fraser University</a>,\n",
       " <a href=\"https://www.twu.ca/\" target=\"_blank\" title=\"Trinity Western University\">Trinity Western University</a>,\n",
       " <a href=\"http://www.tru.ca/\" target=\"_blank\" title=\"Thompson River University\">Thompson River University</a>,\n",
       " <a href=\"http://www.ufv.ca/\" target=\"_blank\" title=\"University of the Fraser Valley\">University of the Fraser Valley</a>,\n",
       " <a href=\"http://www.ubc.ca/\" target=\"_blank\" title=\"University of British Columbia\">University of British Columbia</a>,\n",
       " <a href=\"http://www.unbc.ca/\" target=\"_blank\" title=\"University of Northern British Columbia\">University of Northern British Columbia</a>,\n",
       " <a href=\"http://www.uvic.ca/\" target=\"_blank\" title=\"University of Victoria\">University of Victoria</a>,\n",
       " <a href=\"http://www.fnuniv.ca/\" target=\"_blank\" title=\"First Nations University of Canada\">First Nations University of Canada</a>]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links = My_table.findAll('a')\n",
    "links[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['http://www.viu.ca/', 'http://www.royalroads.ca/', 'http://www.sfu.ca/', 'https://www.twu.ca/', 'http://www.tru.ca/', 'http://www.ufv.ca/', 'http://www.ubc.ca/', 'http://www.unbc.ca/', 'http://www.uvic.ca/', 'http://www.fnuniv.ca/', 'http://www.uregina.ca/', 'http://www.usask.ca/', 'http://www.athabascau.ca/', 'http://www.augustana.ualberta.ca/', 'http://concordia.ab.ca/', 'http://www.kingsu.ca/', 'http://www.ualberta.ca/', 'http://www.ucalgary.ca/', 'http://www.uleth.ca/', 'http://www.macewan.ca/wcm/index.htm', 'http://www.mtroyal.ca/', 'http://www.brandonu.ca/', 'http://umanitoba.ca/', 'http://www.uwinnipeg.ca/', 'http://www.cmu.ca/', 'http://umanitoba.ca/stpauls/', 'http://ustboniface.ca/', 'http://www.algomau.ca/', 'http://brescia.uwo.ca/', 'http://www.brocku.ca/', 'http://carleton.ca/', 'http://www.dominicanu.ca/', 'http://www.huronuc.on.ca/Home', 'http://www.kings.uwo.ca/', 'https://www.lakeheadu.ca/', 'http://laurentian.ca/', 'http://www.mcmaster.ca/', 'http://www.nipissingu.ca/', 'http://www.ocadu.ca/', 'http://www.queensu.ca/', 'http://www.redeemer.ca/', 'http://www.rmc.ca/', 'http://www.ryerson.ca/index.html', 'http://ustpaul.ca/', 'http://www.sju.ca/', 'http://www.trentu.ca/', 'http://www.uoguelph.ca/', 'http://www.uoit.ca/', 'http://www.uottawa.ca/en', 'http://stmikes.utoronto.ca/', 'http://www.usudbury.ca/', 'http://www.utoronto.ca/', 'http://www.trinity.utoronto.ca/', 'https://uwaterloo.ca/', 'http://www.uwo.ca/', 'http://www.uwindsor.ca/', 'http://www.vicu.utoronto.ca/home', 'https://www.wlu.ca/', 'http://futurestudents.yorku.ca/', 'http://www.ubishops.ca/index.html', 'https://www.concordia.ca/', 'http://www.etsmtl.ca/', 'http://www.etsmtl.ca/', 'http://www.polymtl.ca/', 'http://www.hec.ca/en/', 'http://www.inrs.ca/english/homepage', 'http://www.mcgill.ca/', 'http://www.teluq.ca/', 'http://www.umontreal.ca/english/', 'http://www.usherbrooke.ca/', 'http://www.uQuébec.ca/reseau/', 'http://www.uqac.ca/', 'http://www.uqam.ca/', 'http://www.uqar.ca/english/', 'http://www.uqtr.ca/', 'http://www.uqat.ca/en/', 'http://uqo.ca/', 'http://www2.ulaval.ca/en/home.html', 'http://home.upei.ca/', 'http://www2.acadiau.ca/', 'http://www.cbu.ca/', 'http://www.dal.ca/', 'http://www.msvu.ca/en/home/default.aspx', 'http://nscad.ca/en/home/default.aspx', 'http://www.smu.ca/', 'http://www.stfx.ca/', 'https://www.usainteanne.ca/english', 'http://www.ukings.ca/', 'http://www.mta.ca/Prospective/Default.aspx', 'http://w3.stu.ca/stu/default.aspx', 'http://www.umoncton.ca/', 'http://www.unb.ca/', 'http://www.mun.ca/', 'http://www.bcit.ca/', 'http://www.okanagan.bc.ca/', 'http://www.ashtoncollege.com/', 'https://www.brightoncollege.org.uk/', 'http://www.columbiacollege.ca/', 'http://camosun.ca/', 'http://ccbst.ca/', 'http://www.capilanou.ca/', 'http://www.educacentre.com/en/', 'http://www.cnc.bc.ca/', 'http://www.cotr.bc.ca/', 'https://www.douglascollege.ca/', 'http://www.etoncollege.com/', 'http://www.jibc.ca/', 'http://www.kpu.ca/', 'http://www.langara.bc.ca/', 'http://www.necvancouver.org/', 'http://www.nvit.ca/', 'http://www.nic.bc.ca/', 'http://www.nlc.bc.ca/', 'http://www.nwcc.bc.ca/', 'http://www.okanagan.bc.ca/', 'http://selkirk.ca/', 'http://sprottshaw.com/', 'http://www.vcc.ca/', 'http://www.vanarts.com/', 'http://eal.wtc.mb.ca/', 'http://public.assiniboine.net/default.aspx', 'https://www.ucn.ca/default.aspx', 'http://www.rrc.mb.ca/', 'http://ustboniface.ca/etp', 'http://www.mitt.ca/wp/', 'http://www.nbccd.ca/en/home/default.aspx', 'http://www.nbcc.ca/', 'http://mcft.ca/en', 'http://nbbi.ca/', 'http://www.cna.nl.ca/', 'http://www.mi.mun.ca/', 'http://www.cns.nf.ca/', 'https://www.uoguelph.ca/alfred/fr/bienvenue', 'http://www.algonquincollege.com/', 'http://www.collegeboreal.ca/accueil', 'http://www.cambriancollege.ca/Pages/Home.aspx', 'http://www.canadorecollege.ca/', 'http://www.centennialcollege.ca/', 'https://www.collegelacite.ca/', 'http://www.conestogac.on.ca/', 'http://www.confederationc.on.ca/programs', 'http://www.durhamcollege.ca/', 'http://www.fanshawec.ca/', 'http://flemingcollege.ca/', 'http://www.georgebrown.ca/', 'http://www.georgiancollege.ca/', 'http://www.uoguelph.ca/campus/regional/', 'https://www.lambton.on.ca/', 'http://www.loyalistcollege.com/', 'http://michener.ca/', 'http://www.mohawkcollege.ca/', 'http://www.niagaracollege.ca/content/', 'http://www.northernc.on.ca/', 'http://www.stclaircollege.ca/', 'http://www.stlawrencecollege.ca/', 'http://www.saultcollege.ca/', 'http://www.senecacollege.ca/', 'https://www.sheridancollege.ca/', 'http://www.auroracollege.nt.ca/_live/pages/wpPages/home.aspx', 'http://www.academyoflearning.com/', 'http://www.ccg-gcc.gc.ca/eng/CCG/College/welcome', 'http://www.gaeliccollege.edu/', 'http://www.kbca.ca/page.aspx?site=kbca', 'http://www.nscc.ca/', 'http://www.arcticcollege.ca/', 'http://www.hollandcollege.com/', 'http://www.mccpei.com/', 'http://www.collegeacadieipe.ca/greenest/index.php/fr/', 'http://www.cegepat.qc.ca/accueil', 'http://www.collegeahuntsic.qc.ca/', 'http://www.claurendeau.qc.ca/accueil', 'http://www.cegep-baie-comeau.qc.ca/accueil', 'http://www.cegepba.qc.ca/', 'http://bdeb.qc.ca/', 'http://admin.crc-sher.qc.ca/', 'http://www.cegep-chicoutimi.qc.ca/', 'http://www.dawsoncollege.qc.ca/', 'http://www.cdrummond.qc.ca/', 'http://www.college-em.qc.ca/college-cegep-edouard-montpetit-longueuil-ecole-nationale-daerotechnique', 'http://www.cegepgarneau.ca/en/', 'http://www.cegepgim.ca/', 'http://www.cgodin.qc.ca/accueil', 'http://www.cegepgranby.qc.ca/accueil', 'http://www.cegep-heritage.qc.ca/', 'http://www.johnabbott.qc.ca/', 'http://www.cegepjonquiere.ca/cegep/', 'http://www.cegeplapocatiere.qc.ca/MyScriptorWeb/scripto.asp?resultat=533169', 'http://www.cegep-lanaudiere.qc.ca/', 'http://cll.qc.ca/', 'http://www.cegeplimoilou.ca/', 'http://www.clg.qc.ca/', 'http://www.cmaisonneuve.qc.ca/cegep-montreal-dec-preuniversitaire-technique-accueil', 'http://www.collegemv.qc.ca/', 'http://www.cegep-matane.qc.ca/', 'http://www.cmontmorency.qc.ca/', 'http://www.cegepoutaouais.qc.ca/', 'http://www.cegep-rimouski.qc.ca/', 'http://www.crosemont.qc.ca/accueil', 'http://www.cegepstfe.ca/', 'http://www.cegepsth.qc.ca/', 'http://www.cstjean.qc.ca/accueil', 'http://www.cstj.qc.ca/', 'http://www.cegepsl.qc.ca/', 'http://www.cegep-ste-foy.qc.ca/csf4/index.php', 'http://www.cegep-sept-iles.qc.ca/', 'http://www.collegeshawinigan.ca/', 'http://cegepsherbrooke.qc.ca/', 'http://www.cegepst.qc.ca/', 'http://www.cegepth.qc.ca/accueil', 'http://www.cegeptr.qc.ca/', 'https://www.colval.qc.ca/', 'http://www.vaniercollege.qc.ca/', 'http://www.cgpvicto.qc.ca/formation-collegiale/index.aspx', 'http://www.cvm.qc.ca/Pages/index.aspx', 'http://bart.ca/', 'http://www.brebeuf.qc.ca/', 'http://www.clafleche.qc.ca/', 'http://www.collegelasalle.com/', 'http://www.marianopolis.edu/', 'http://www.cimf.ca/', 'https://www.merici.ca/', 'http://www.osullivan.edu/en/', 'http://www.osullivan-Québec.qc.ca/', 'http://www.stanislas.qc.ca/', 'http://www.emvi.qc.ca/', 'http://www.nationalcircusschool.ca/en/home', 'http://www.seminaire-sherbrooke.qc.ca/', 'http://www.trebas.com/', 'http://bethany.sk.ca/', 'http://www.briercrest.ca/', 'http://www.ctrc.sk.ca/', 'http://www.horizon.edu/home', 'http://www.collegemathieu.sk.ca/', 'https://www.cumberlandcollege.sk.ca/', 'http://www.estoncollege.ca/', 'http://www.greatplainscollege.ca/', 'http://nipawin.org/', 'http://trainnorth.ca/', 'http://www.nwrc.sk.ca/', 'http://www.parklandcollege.sk.ca/', 'http://www.siit.sk.ca/', 'http://saskpolytech.ca/', 'http://www.southeastcollege.org/', 'http://www.stpeterscollege.ca/', 'http://www.wabcwesternacademy.com/', 'http://www.yukoncollege.yk.ca/']\n"
     ]
    }
   ],
   "source": [
    "colleges = []\n",
    "for link in links:\n",
    "    colleges.append(link.get('href'))\n",
    "    \n",
    "print(colleges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>University</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://www.viu.ca/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://www.royalroads.ca/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://www.sfu.ca/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.twu.ca/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://www.tru.ca/</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  University\n",
       "0         http://www.viu.ca/\n",
       "1  http://www.royalroads.ca/\n",
       "2         http://www.sfu.ca/\n",
       "3        https://www.twu.ca/\n",
       "4         http://www.tru.ca/"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "df['University'] = colleges\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Requests with Parameters\n",
    "\n",
    "[Example](https://towardsdatascience.com/web-scraping-101-in-python-35f8653b1c97) of using [parameters with *requests*](https://docs.python-requests.org/en/master/user/quickstart/) on [IMDB](https://www.imdb.com/search/title/?count=100&groups=top_1000&sort=user_rating):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After taking a look at the IMDB webpage, we’ll set out to extract (all highlighted in the above screenshot of the page):\n",
    "\n",
    "+ Movie title\n",
    "+ Release year\n",
    "+ Runtime\n",
    "+ Audience rating\n",
    "+ Genre\n",
    "+ IMDB rating\n",
    "+ Number of votes\n",
    "+ Box office earnings\n",
    "+ Director\n",
    "+ Primary actors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "url4 = 'https://www.imdb.com/search/title/?count=100&groups=top_1000&sort=user_rating'\n",
    "\n",
    "def get_page_contents(url):\n",
    "    page = requests.get(url, headers={\"Accept-Language\": \"en-US\"})\n",
    "    return bs(page.text, \"html.parser\")\n",
    "\n",
    "soup = get_page_contents(url4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extracting text: Movie titles and release year\n",
    "\n",
    "Since we’ve already located the HTML tag containing each movie card, we can get a list of all distinct movies and their corresponding HTML by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<h3 class=\"lister-item-header\">\n",
       " <span class=\"lister-item-index unbold text-primary\">1.</span>\n",
       " <a href=\"/title/tt0111161/\">The Shawshank Redemption</a>\n",
       " <span class=\"lister-item-year text-muted unbold\">(1994)</span>\n",
       " </h3>,\n",
       " <h3 class=\"lister-item-header\">\n",
       " <span class=\"lister-item-index unbold text-primary\">2.</span>\n",
       " <a href=\"/title/tt0068646/\">The Godfather</a>\n",
       " <span class=\"lister-item-year text-muted unbold\">(1972)</span>\n",
       " </h3>,\n",
       " <h3 class=\"lister-item-header\">\n",
       " <span class=\"lister-item-index unbold text-primary\">3.</span>\n",
       " <a href=\"/title/tt10189514/\">Soorarai Pottru</a>\n",
       " <span class=\"lister-item-year text-muted unbold\">(2020)</span>\n",
       " </h3>,\n",
       " <h3 class=\"lister-item-header\">\n",
       " <span class=\"lister-item-index unbold text-primary\">4.</span>\n",
       " <a href=\"/title/tt0468569/\">The Dark Knight</a>\n",
       " <span class=\"lister-item-year text-muted unbold\">(2008)</span>\n",
       " </h3>,\n",
       " <h3 class=\"lister-item-header\">\n",
       " <span class=\"lister-item-index unbold text-primary\">5.</span>\n",
       " <a href=\"/title/tt0071562/\">The Godfather: Part II</a>\n",
       " <span class=\"lister-item-year text-muted unbold\">(1974)</span>\n",
       " </h3>]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = soup.findAll('h3', class_='lister-item-header')\n",
    "movies[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The findAll method creates a list where each entry contains the HTML that’s captured within the h3 tag and list-item-header class.  \n",
    "By taking a deeper look at the first movies HTML and see that the movie title can be found under the first a tag.\n",
    "\n",
    "To capture this attribute we can loop through all movies and either call findAll and grab the first element of the list, or we can use the find method which automatically grabs the first tag it finds. Thus, we can construct a list of all movie titles (with a little help from list comprehensions for efficiency) through:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['The Shawshank Redemption',\n",
       " 'The Godfather',\n",
       " 'Soorarai Pottru',\n",
       " 'The Dark Knight',\n",
       " 'The Godfather: Part II',\n",
       " '12 Angry Men',\n",
       " 'The Lord of the Rings: The Return of the King',\n",
       " 'Pulp Fiction',\n",
       " \"Schindler's List\",\n",
       " 'Inception']"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles = [movie.find('a').text for movie in movies]\n",
    "titles[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Release years can be found under the tag span and class lister-item-year text-muted unbold. To grab these, we can follow a similar approach as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['(1994)',\n",
       " '(1972)',\n",
       " '(2020)',\n",
       " '(2008)',\n",
       " '(1974)',\n",
       " '(1957)',\n",
       " '(2003)',\n",
       " '(1994)',\n",
       " '(1993)',\n",
       " '(2010)']"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "release = [movie.find('span', class_='lister-item-year text-muted unbold').text for movie in movies]\n",
    "release[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extracting Numerical Values:\n",
    "\n",
    "In the case of IMDB ratings, number of votes and box office earnings we can see that while these may be available as string values, can also grab the actual numerical values from the data-value attribute within each respective tag.\n",
    "\n",
    "Here’s the IMDB rating of The Godfather:  \n",
    "\\<div class=\"inline-block ratings-imdb-rating\" name=\"ir\" data-value=\"9.2\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'9.3'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup.find('div', class_='inline-block ratings-imdb-rating')['data-value']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the case of number of votes and earnings we don’t have a class attribute to filter for. Here are the number of votes and estimated box office earnings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2404057', '28,341,469', '1664187', '134,966,411', '82317', '2364103', '534,858,444', '1156483', '57,300,000', '708658', '4,360,000', '1677032', '377,845,905', '1868122', '107,928,762', '1239852', '96,898,818', '2121603', '292,576,195', '1898194', '37,030,102', '1697864', '315,544,750', '1857924', '330,252,182', '702227', '6,100,000', '26531', '1516385', '342,551,365', '1715841', '171,479,930', '1046031', '46,836,394', '1181890', '290,475,067', '935674', '112,000,000', '616592', '53,367,844', '1566414', '188,020,017', '710849', '7,563,397', '673653', '10,055,859', '1266411', '216,540,909', '1177221', '136,801,374', '638535', '57,598,247', '1481893', '100,125,643', '1302569', '130,742,922', '1253912', '322,740,140', '45502', '25607', '536,364', '322211', '269,061', '413454', '30747', '742248', '13,092,000', '780749', '13,182,281', '1219542', '53,089,891', '1216552', '132,384,315', '751501', '32,572,577', '1370694', '187,705,427', '1051520', '6,719,864', '1010052', '23,341,568', '1059327', '19,501,238', '964172', '422,783,777', '1013540', '204,843,350', '237500', '11,990,401', '244203', '1085727', '210,609,762', '308464', '5,321,508', '617266', '32,000,000', '453396', '36,764,313', '531589', '1,024,560', '223364', '163,245', '171634', '19,181', '65649', '69856', '1,661,096', '1008333', '335,451,311', '35818', '31201', '211080', '5,017,246', '164698', '12,391,761', '403791', '190,241,310', '884465', '858,373,000', '883973', '678,815,482', '409205', '209,726,015', '1393773', '162,805,434', '1549516', '448,139,099', '357283', '6,532,908', '175527', '1,223,869', '1022180', '223,808,164', '366269', '11,286,112', '528750', '707,481', '1147875', '25,544,867', '353916', '2,375,308', '319223', '5,321,508', '899682', '248,159,971', '920520', '44,017,374', '618718', '83,471,511', '805418', '78,900,000', '36909', '458444', '275,902', '112359', '8,175,000', '182583', '205908', '207439', '288,475', '38258', '898,575', '465216', '159,227,644', '32595', '36136', '293911', '687,185', '225823', '7,098,492', '156196', '6,857,096', '34732', '67320', '81184', '1302351', '120,540,719', '930111', '34,400,301', '714903', '33,225,499', '796618', '30,328,156']\n"
     ]
    }
   ],
   "source": [
    "votes_earnings = soup.findAll('span', {'name':'nv'})\n",
    "print([ve['data-value'] for ve in votes_earnings])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2404057', '1664187', '82317', '534,858,444', '57,300,000']\n",
      "['28,341,469', '134,966,411', '2364103', '1156483', '708658']\n"
     ]
    }
   ],
   "source": [
    "votes = []\n",
    "earnings = []\n",
    "idx = 0\n",
    "while idx < len(votes_earnings)-1:\n",
    "    votes.append(votes_earnings[idx]['data-value'])\n",
    "    earnings.append(votes_earnings[idx+1]['data-value'])\n",
    "    idx+=2\n",
    "print(votes[0:5])\n",
    "print(earnings[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nested Values\n",
    "\n",
    "In the case where the data we need is located within multiple levels of generic tags, we’ll need to dig into this nested structure to extract what we need.\n",
    "\n",
    "In the case of the movie directors and actors we’ll need to do just that. From inspecting the HTML we see that the director information is located within an initial p tag and thereafter an a tag — both without class attributes making it necessary to unnest the data. We’ll do this by calling find and findAll repeatedly.\n",
    "\n",
    "Since the director is the 1st a tag, we can extract this information through:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      "<a href=\"/name/nm0001104/\">Frank Darabont</a>\n",
      "Frank Darabont\n",
      "<a href=\"/name/nm0000209/\">Tim Robbins</a>\n",
      "Tim Robbins\n",
      "<a href=\"/name/nm0000151/\">Morgan Freeman</a>\n",
      "Morgan Freeman\n",
      "<a href=\"/name/nm0348409/\">Bob Gunton</a>\n",
      "Bob Gunton\n",
      "<a href=\"/name/nm0006669/\">William Sadler</a>\n",
      "William Sadler\n",
      "---\n",
      "<a href=\"/name/nm0000338/\">Francis Ford Coppola</a>\n",
      "Francis Ford Coppola\n",
      "<a href=\"/name/nm0000008/\">Marlon Brando</a>\n",
      "Marlon Brando\n",
      "<a href=\"/name/nm0000199/\">Al Pacino</a>\n",
      "Al Pacino\n",
      "<a href=\"/name/nm0001001/\">James Caan</a>\n",
      "James Caan\n",
      "<a href=\"/name/nm0000473/\">Diane Keaton</a>\n",
      "Diane Keaton\n"
     ]
    }
   ],
   "source": [
    "for l1 in soup.findAll('p')[0:10]:\n",
    "    if l1.find('a'):\n",
    "        print('---')\n",
    "        for l2 in l1.findAll('a'):\n",
    "            print(l2)\n",
    "            print(l2.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Frank Darabont',\n",
       " 'Tim Robbins',\n",
       " 'Morgan Freeman',\n",
       " 'Bob Gunton',\n",
       " 'William Sadler']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors = [actor.text for actor in soup.findAll('p')[2].findAll('a')]\n",
    "actors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating functions to automate information scraping on IMDB:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numeric_value(movie, tag, class_=None, order=None):\n",
    "    if order:\n",
    "        if len(movie.findAll(tag, class_)) > 1:\n",
    "            to_extract = movie.findAll(tag, class_)[order]['data-value']\n",
    "        else:\n",
    "            to_extract = None\n",
    "    else:\n",
    "        to_extract = movie.find(tag, class_)['data-value']\n",
    "\n",
    "    return to_extract\n",
    "\n",
    "\n",
    "def text_value(movie, tag, class_=None):\n",
    "    if movie.find(tag, class_):\n",
    "        return movie.find(tag, class_).text\n",
    "    else:\n",
    "        return\n",
    "\n",
    "\n",
    "def nested_text_value(movie, tag_1, class_1, tag_2, class_2, order=None):\n",
    "    if not order:\n",
    "        return movie.find(tag_1, class_1).find(tag_2, class_2).text\n",
    "    else:\n",
    "        return [val.text for val in movie.find(tag_1, class_1).findAll(tag_2, class_2)[order]]\n",
    "\n",
    "\n",
    "def extract_attribute(soup, tag_1, class_1='', tag_2='', class_2='',\n",
    "                      text_attribute=True, order=None, nested=False):\n",
    "    movies = soup.findAll('div', class_='lister-item-content')\n",
    "    data_list = []\n",
    "    for movie in movies:\n",
    "        if text_attribute:\n",
    "            if nested:\n",
    "                data_list.append(nested_text_value(movie, tag_1, class_1, tag_2, class_2, order))\n",
    "            else:\n",
    "                data_list.append(text_value(movie, tag_1, class_1))\n",
    "        else:\n",
    "            data_list.append(numeric_value(movie, tag_1, class_1, order))\n",
    "\n",
    "    return data_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating a Dataframe with the information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Title</th>\n",
       "      <th>Relase</th>\n",
       "      <th>Audience Rating</th>\n",
       "      <th>Runtime</th>\n",
       "      <th>Genre</th>\n",
       "      <th>IMDB Rating</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Box Office Earnings</th>\n",
       "      <th>Director</th>\n",
       "      <th>Actors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>(1994)</td>\n",
       "      <td>R</td>\n",
       "      <td>142 min</td>\n",
       "      <td>\\nDrama</td>\n",
       "      <td>\\n\\n9.3\\n</td>\n",
       "      <td>2,404,057</td>\n",
       "      <td>2,404,057</td>\n",
       "      <td>Frank Darabont</td>\n",
       "      <td>[Tim Robbins, Morgan Freeman, Bob Gunton, Will...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>(1972)</td>\n",
       "      <td>R</td>\n",
       "      <td>175 min</td>\n",
       "      <td>\\nCrime, Drama</td>\n",
       "      <td>\\n\\n9.2\\n</td>\n",
       "      <td>1,664,187</td>\n",
       "      <td>1,664,187</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
       "      <td>[Marlon Brando, Al Pacino, James Caan, Diane K...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Soorarai Pottru</td>\n",
       "      <td>(2020)</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>153 min</td>\n",
       "      <td>\\nDrama</td>\n",
       "      <td>\\n\\n9.1\\n</td>\n",
       "      <td>82,317</td>\n",
       "      <td>82,317</td>\n",
       "      <td>Sudha Kongara</td>\n",
       "      <td>[Madhavan, Suriya, Paresh Rawal, Aparna Balamu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>(2008)</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>152 min</td>\n",
       "      <td>\\nAction, Crime, Drama</td>\n",
       "      <td>\\n\\n9.0\\n</td>\n",
       "      <td>2,364,103</td>\n",
       "      <td>2,364,103</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>[Christian Bale, Heath Ledger, Aaron Eckhart, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>(1974)</td>\n",
       "      <td>R</td>\n",
       "      <td>202 min</td>\n",
       "      <td>\\nCrime, Drama</td>\n",
       "      <td>\\n\\n9.0\\n</td>\n",
       "      <td>1,156,483</td>\n",
       "      <td>1,156,483</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
       "      <td>[Al Pacino, Robert De Niro, Robert Duvall, Dia...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Title  Relase Audience Rating  Runtime  \\\n",
       "0  The Shawshank Redemption  (1994)               R  142 min   \n",
       "1             The Godfather  (1972)               R  175 min   \n",
       "2           Soorarai Pottru  (2020)           TV-MA  153 min   \n",
       "3           The Dark Knight  (2008)           PG-13  152 min   \n",
       "4    The Godfather: Part II  (1974)               R  202 min   \n",
       "\n",
       "                                Genre IMDB Rating      Votes  \\\n",
       "0                 \\nDrama               \\n\\n9.3\\n  2,404,057   \n",
       "1          \\nCrime, Drama               \\n\\n9.2\\n  1,664,187   \n",
       "2                 \\nDrama               \\n\\n9.1\\n     82,317   \n",
       "3  \\nAction, Crime, Drama               \\n\\n9.0\\n  2,364,103   \n",
       "4          \\nCrime, Drama               \\n\\n9.0\\n  1,156,483   \n",
       "\n",
       "  Box Office Earnings              Director  \\\n",
       "0           2,404,057        Frank Darabont   \n",
       "1           1,664,187  Francis Ford Coppola   \n",
       "2              82,317         Sudha Kongara   \n",
       "3           2,364,103     Christopher Nolan   \n",
       "4           1,156,483  Francis Ford Coppola   \n",
       "\n",
       "                                              Actors  \n",
       "0  [Tim Robbins, Morgan Freeman, Bob Gunton, Will...  \n",
       "1  [Marlon Brando, Al Pacino, James Caan, Diane K...  \n",
       "2  [Madhavan, Suriya, Paresh Rawal, Aparna Balamu...  \n",
       "3  [Christian Bale, Heath Ledger, Aaron Eckhart, ...  \n",
       "4  [Al Pacino, Robert De Niro, Robert Duvall, Dia...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles = extract_attribute(soup, 'a')\n",
    "release = extract_attribute(soup, 'span', 'lister-item-year text-muted unbold')\n",
    "audience_rating = extract_attribute(soup, 'span', 'certificate')\n",
    "runtime = extract_attribute(soup, 'span', 'runtime')\n",
    "genre = extract_attribute(soup, 'span', 'genre')\n",
    "imdb_rating = extract_attribute(soup, 'div', 'inline-block ratings-imdb-rating', False)\n",
    "votes = extract_attribute(soup, 'span' , {'name' : 'nv'}, False, 0)\n",
    "earnings = extract_attribute(soup, 'span' , {'name' : 'nv'}, False, 1)\n",
    "directors = extract_attribute(soup, 'p', '', 'a', '', True, 0, True)\n",
    "actors = extract_attribute(soup, 'p', '', 'a', '', True, slice(1, 5, None), True)\n",
    "\n",
    "\n",
    "df_dict = {'Title': titles, 'Relase': release, 'Audience Rating': audience_rating,\n",
    "           'Runtime': runtime, 'Genre': genre, 'IMDB Rating': imdb_rating,\n",
    "           'Votes': votes, 'Box Office Earnings': earnings, 'Director': directors,\n",
    "           'Actors': actors}\n",
    "\n",
    "df = pd.DataFrame(df_dict)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manually creating a Dataframe and starting analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "url5 = \"http://www.hubertiming.com/results/2017GPTR10K\"\n",
    "html = urllib.request.urlopen(url5)\n",
    "soup = bs(html, 'lxml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<title>Race results for the 2017 Intel Great Place to Run \\ Urban Clash Games!</title>\n"
     ]
    }
   ],
   "source": [
    "# Get the title\n",
    "title = soup.title\n",
    "print(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\\n\\n\\n\\n\\n\\nRace results for the 2017 Intel Great Place to Run \\\\ Urban Clash Games!\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n 2017 Intel Great Place to Run 10K \\\\ Urban Clash Games\\n Hillsboro Stadium, Hillsboro, OR \\n June 2nd,'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the text\n",
    "text = soup.get_text()\n",
    "text[0:200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<a href=\"mailto:timing@hubertiming.com\">timing@hubertiming.com</a>,\n",
       " <a href=\"https://www.hubertiming.com/\">Huber Timing Home</a>,\n",
       " <a class=\"btn btn-primary btn-lg\" href=\"/results/2017GPTR\" role=\"button\" style=\"margin: 0px 0px 5px 5px\"><i aria-hidden=\"true\" class=\"fa fa-user\"></i> 5K Individual</a>,\n",
       " <a class=\"btn btn-primary btn-lg\" href=\"/results/team/2017GPTR\" role=\"button\" style=\"margin: 0px 0px 5px 5px\"><i aria-hidden=\"true\" class=\"fa fa-users\"></i> 5K Team</a>,\n",
       " <a class=\"btn btn-primary btn-lg\" href=\"/results/team/2017GPTR10K\" role=\"button\" style=\"margin: 0px 0px 5px 5px\"><i aria-hidden=\"true\" class=\"fa fa-users\"></i> 10K Team</a>,\n",
       " <a class=\"btn btn-primary btn-lg\" href=\"/results/summary/2017GPTR10K\" role=\"button\" style=\"margin: 0px 0px 5px 5px\"><i class=\"fa fa-stream\"></i> Summary</a>,\n",
       " <a id=\"individual\" name=\"individual\"></a>,\n",
       " <a data-url=\"/results/2017GPTR10K\" href=\"#tabs-1\" id=\"rootTab\" style=\"font-size: 18px\">10K Results</a>,\n",
       " <a href=\"https://www.hubertiming.com/\"><img height=\"65\" src=\"https://www.hubertiming.com//sites/all/themes/hubertiming/images/clockWithFinishSign_small.png\" width=\"50\"/>Huber Timing</a>,\n",
       " <a href=\"https://facebook.com/hubertiming/\"><img src=\"https://www.hubertiming.com/results/FB-f-Logo__blue_50.png\"/></a>,\n",
       " <a class=\"small\" id=\"bestFeatureEver\" style=\"color:#007bff\">Dark Mode</a>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup.find_all('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mailto:timing@hubertiming.com\n",
      "https://www.hubertiming.com/\n",
      "/results/2017GPTR\n",
      "/results/team/2017GPTR\n",
      "/results/team/2017GPTR10K\n",
      "/results/summary/2017GPTR10K\n",
      "None\n",
      "#tabs-1\n",
      "https://www.hubertiming.com/\n",
      "https://facebook.com/hubertiming/\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "all_links = soup.find_all(\"a\")\n",
    "for link in all_links:\n",
    "    print(link.get(\"href\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<tr colspan=\"2\">\n",
      "<b>10K:</b>\n",
      "</tr>, <tr>\n",
      "<td>Finishers:</td>\n",
      "<td>577</td>\n",
      "</tr>, <tr>\n",
      "<td>Male:</td>\n",
      "<td>414</td>\n",
      "</tr>, <tr>\n",
      "<td>Female:</td>\n",
      "<td>163</td>\n",
      "</tr>, <tr class=\"header\">\n",
      "<th>Place</th>\n",
      "<th>Bib</th>\n",
      "<th>Name</th>\n",
      "<th>Gender</th>\n",
      "<th>City</th>\n",
      "<th>State</th>\n",
      "<th>Chip Time</th>\n",
      "<th>Chip Pace</th>\n",
      "<th>Gun Time</th>\n",
      "<th>Team</th>\n",
      "</tr>, <tr data-bib=\"814\">\n",
      "<td>1</td>\n",
      "<td>814</td>\n",
      "<td>\n",
      "\n",
      "                    JARED WILSON\n",
      "\n",
      "                </td>\n",
      "<td>M</td>\n",
      "<td>TIGARD</td>\n",
      "<td>OR</td>\n",
      "<td>36:21</td>\n",
      "<td>5:51</td>\n",
      "<td>36:24</td>\n",
      "<td></td>\n",
      "</tr>, <tr data-bib=\"573\">\n",
      "<td>2</td>\n",
      "<td>573</td>\n",
      "<td>\n",
      "\n",
      "                    NATHAN A SUSTERSIC\n",
      "\n",
      "                </td>\n",
      "<td>M</td>\n",
      "<td>PORTLAND</td>\n",
      "<td>OR</td>\n",
      "<td>36:42</td>\n",
      "<td>5:55</td>\n",
      "<td>36:45</td>\n",
      "<td>\n",
      "<img class=\"lazy teamThumbs\" data-src=\"/teamLogoThumbnail/logo?teamName=INTEL%20TEAM%20F&amp;raceId=1251&amp;state=OR\"/>\n",
      "                            INTEL TEAM F\n",
      "                        </td>\n",
      "</tr>, <tr data-bib=\"687\">\n",
      "<td>3</td>\n",
      "<td>687</td>\n",
      "<td>\n",
      "\n",
      "                    FRANCISCO MAYA\n",
      "\n",
      "                </td>\n",
      "<td>M</td>\n",
      "<td>PORTLAND</td>\n",
      "<td>OR</td>\n",
      "<td>37:44</td>\n",
      "<td>6:05</td>\n",
      "<td>37:48</td>\n",
      "<td></td>\n",
      "</tr>]\n"
     ]
    }
   ],
   "source": [
    "# Print the first 8 rows for sanity check\n",
    "rows = soup.find_all('tr')\n",
    "print(rows[:8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<td>577</td>, <td>443</td>, <td>\n",
      "\n",
      "                    LIBBY B MITCHELL\n",
      "\n",
      "                </td>, <td>F</td>, <td>HILLSBORO</td>, <td>OR</td>, <td>1:41:18</td>, <td>16:20</td>, <td>1:42:10</td>, <td></td>]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "bs4.element.ResultSet"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for row in rows:\n",
    "    row_td = row.find_all('td')\n",
    "print(row_td)\n",
    "type(row_td)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[577, 443, \n",
      "\n",
      "                    LIBBY B MITCHELL\n",
      "\n",
      "                , F, HILLSBORO, OR, 1:41:18, 16:20, 1:42:10, ]\n"
     ]
    }
   ],
   "source": [
    "str_cells = str(row_td)\n",
    "cleantext = bs(str_cells, \"lxml\").get_text()\n",
    "print(cleantext)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now processing the whole page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[577, 443, \n",
      "\n",
      "                    LIBBY B MITCHELL\n",
      "\n",
      "                , F, HILLSBORO, OR, 1:41:18, 16:20, 1:42:10, ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_rows = []\n",
    "for row in rows:\n",
    "    cells = row.find_all('td')\n",
    "    str_cells = str(cells)\n",
    "    clean = re.compile('<.*?>')\n",
    "    clean2 = (re.sub(clean, '',str_cells))\n",
    "    list_rows.append(clean2)\n",
    "print(clean2)\n",
    "type(clean2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Finishers:, 577]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Male:, 414]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[Female:, 163]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[1, 814, \\r\\n\\r\\n                    JARED WIL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[2, 573, \\r\\n\\r\\n                    NATHAN A ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[3, 687, \\r\\n\\r\\n                    FRANCISCO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[4, 623, \\r\\n\\r\\n                    PAUL MORR...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[5, 569, \\r\\n\\r\\n                    DEREK G O...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 raw\n",
       "0                                                 []\n",
       "1                                  [Finishers:, 577]\n",
       "2                                       [Male:, 414]\n",
       "3                                     [Female:, 163]\n",
       "4                                                 []\n",
       "5  [1, 814, \\r\\n\\r\\n                    JARED WIL...\n",
       "6  [2, 573, \\r\\n\\r\\n                    NATHAN A ...\n",
       "7  [3, 687, \\r\\n\\r\\n                    FRANCISCO...\n",
       "8  [4, 623, \\r\\n\\r\\n                    PAUL MORR...\n",
       "9  [5, 569, \\r\\n\\r\\n                    DEREK G O..."
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(list_rows, columns=['raw'])\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning the fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Finishers:</td>\n",
       "      <td>577]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Male:</td>\n",
       "      <td>414]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[Female:</td>\n",
       "      <td>163]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[1</td>\n",
       "      <td>814</td>\n",
       "      <td>\\r\\n\\r\\n                    JARED WILSON\\r\\n\\...</td>\n",
       "      <td>M</td>\n",
       "      <td>TIGARD</td>\n",
       "      <td>OR</td>\n",
       "      <td>36:21</td>\n",
       "      <td>5:51</td>\n",
       "      <td>36:24</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[2</td>\n",
       "      <td>573</td>\n",
       "      <td>\\r\\n\\r\\n                    NATHAN A SUSTERSI...</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>36:42</td>\n",
       "      <td>5:55</td>\n",
       "      <td>36:45</td>\n",
       "      <td>\\n\\r\\n                            INTEL TEAM ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[3</td>\n",
       "      <td>687</td>\n",
       "      <td>\\r\\n\\r\\n                    FRANCISCO MAYA\\r\\...</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>37:44</td>\n",
       "      <td>6:05</td>\n",
       "      <td>37:48</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[4</td>\n",
       "      <td>623</td>\n",
       "      <td>\\r\\n\\r\\n                    PAUL MORROW\\r\\n\\r...</td>\n",
       "      <td>M</td>\n",
       "      <td>BEAVERTON</td>\n",
       "      <td>OR</td>\n",
       "      <td>38:34</td>\n",
       "      <td>6:13</td>\n",
       "      <td>38:37</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[5</td>\n",
       "      <td>569</td>\n",
       "      <td>\\r\\n\\r\\n                    DEREK G OSBORNE\\r...</td>\n",
       "      <td>M</td>\n",
       "      <td>HILLSBORO</td>\n",
       "      <td>OR</td>\n",
       "      <td>39:21</td>\n",
       "      <td>6:20</td>\n",
       "      <td>39:24</td>\n",
       "      <td>\\n\\r\\n                            INTEL TEAM ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0      1                                                  2  \\\n",
       "0           []   None                                               None   \n",
       "1  [Finishers:   577]                                               None   \n",
       "2       [Male:   414]                                               None   \n",
       "3     [Female:   163]                                               None   \n",
       "4           []   None                                               None   \n",
       "5           [1    814   \\r\\n\\r\\n                    JARED WILSON\\r\\n\\...   \n",
       "6           [2    573   \\r\\n\\r\\n                    NATHAN A SUSTERSI...   \n",
       "7           [3    687   \\r\\n\\r\\n                    FRANCISCO MAYA\\r\\...   \n",
       "8           [4    623   \\r\\n\\r\\n                    PAUL MORROW\\r\\n\\r...   \n",
       "9           [5    569   \\r\\n\\r\\n                    DEREK G OSBORNE\\r...   \n",
       "\n",
       "      3           4     5       6      7       8  \\\n",
       "0  None        None  None    None   None    None   \n",
       "1  None        None  None    None   None    None   \n",
       "2  None        None  None    None   None    None   \n",
       "3  None        None  None    None   None    None   \n",
       "4  None        None  None    None   None    None   \n",
       "5     M      TIGARD    OR   36:21   5:51   36:24   \n",
       "6     M    PORTLAND    OR   36:42   5:55   36:45   \n",
       "7     M    PORTLAND    OR   37:44   6:05   37:48   \n",
       "8     M   BEAVERTON    OR   38:34   6:13   38:37   \n",
       "9     M   HILLSBORO    OR   39:21   6:20   39:24   \n",
       "\n",
       "                                                   9  \n",
       "0                                               None  \n",
       "1                                               None  \n",
       "2                                               None  \n",
       "3                                               None  \n",
       "4                                               None  \n",
       "5                                                  ]  \n",
       "6   \\n\\r\\n                            INTEL TEAM ...  \n",
       "7                                                  ]  \n",
       "8                                                  ]  \n",
       "9   \\n\\r\\n                            INTEL TEAM ...  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df['raw'].str.split(',', expand=True)\n",
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Finishers:</td>\n",
       "      <td>577]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male:</td>\n",
       "      <td>414]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female:</td>\n",
       "      <td>163]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>814</td>\n",
       "      <td>\\r\\n\\r\\n                    JARED WILSON\\r\\n\\...</td>\n",
       "      <td>M</td>\n",
       "      <td>TIGARD</td>\n",
       "      <td>OR</td>\n",
       "      <td>36:21</td>\n",
       "      <td>5:51</td>\n",
       "      <td>36:24</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>573</td>\n",
       "      <td>\\r\\n\\r\\n                    NATHAN A SUSTERSI...</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>36:42</td>\n",
       "      <td>5:55</td>\n",
       "      <td>36:45</td>\n",
       "      <td>\\n\\r\\n                            INTEL TEAM ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>687</td>\n",
       "      <td>\\r\\n\\r\\n                    FRANCISCO MAYA\\r\\...</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>37:44</td>\n",
       "      <td>6:05</td>\n",
       "      <td>37:48</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>623</td>\n",
       "      <td>\\r\\n\\r\\n                    PAUL MORROW\\r\\n\\r...</td>\n",
       "      <td>M</td>\n",
       "      <td>BEAVERTON</td>\n",
       "      <td>OR</td>\n",
       "      <td>38:34</td>\n",
       "      <td>6:13</td>\n",
       "      <td>38:37</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>569</td>\n",
       "      <td>\\r\\n\\r\\n                    DEREK G OSBORNE\\r...</td>\n",
       "      <td>M</td>\n",
       "      <td>HILLSBORO</td>\n",
       "      <td>OR</td>\n",
       "      <td>39:21</td>\n",
       "      <td>6:20</td>\n",
       "      <td>39:24</td>\n",
       "      <td>\\n\\r\\n                            INTEL TEAM ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0      1                                                  2     3  \\\n",
       "0           ]   None                                               None  None   \n",
       "1  Finishers:   577]                                               None  None   \n",
       "2       Male:   414]                                               None  None   \n",
       "3     Female:   163]                                               None  None   \n",
       "4           ]   None                                               None  None   \n",
       "5           1    814   \\r\\n\\r\\n                    JARED WILSON\\r\\n\\...     M   \n",
       "6           2    573   \\r\\n\\r\\n                    NATHAN A SUSTERSI...     M   \n",
       "7           3    687   \\r\\n\\r\\n                    FRANCISCO MAYA\\r\\...     M   \n",
       "8           4    623   \\r\\n\\r\\n                    PAUL MORROW\\r\\n\\r...     M   \n",
       "9           5    569   \\r\\n\\r\\n                    DEREK G OSBORNE\\r...     M   \n",
       "\n",
       "            4     5       6      7       8  \\\n",
       "0        None  None    None   None    None   \n",
       "1        None  None    None   None    None   \n",
       "2        None  None    None   None    None   \n",
       "3        None  None    None   None    None   \n",
       "4        None  None    None   None    None   \n",
       "5      TIGARD    OR   36:21   5:51   36:24   \n",
       "6    PORTLAND    OR   36:42   5:55   36:45   \n",
       "7    PORTLAND    OR   37:44   6:05   37:48   \n",
       "8   BEAVERTON    OR   38:34   6:13   38:37   \n",
       "9   HILLSBORO    OR   39:21   6:20   39:24   \n",
       "\n",
       "                                                   9  \n",
       "0                                               None  \n",
       "1                                               None  \n",
       "2                                               None  \n",
       "3                                               None  \n",
       "4                                               None  \n",
       "5                                                  ]  \n",
       "6   \\n\\r\\n                            INTEL TEAM ...  \n",
       "7                                                  ]  \n",
       "8                                                  ]  \n",
       "9   \\n\\r\\n                            INTEL TEAM ...  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[0] = df1[0].str.strip('[')\n",
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_labels = soup.find_all('th')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[Place, Bib, Name, Gender, City, State, Chip Time, Chip Pace, Gun Time, Team]']\n"
     ]
    }
   ],
   "source": [
    "all_header = []\n",
    "col_str = str(col_labels)\n",
    "cleantext2 = bs(col_str, \"lxml\").get_text()\n",
    "all_header.append(cleantext2)\n",
    "print(all_header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[Place, Bib, Name, Gender, City, State, Chip T...</td>\n",
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       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "                                                   0\n",
       "0  [Place, Bib, Name, Gender, City, State, Chip T..."
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(all_header)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "      <td>Gender</td>\n",
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       "      <td>State</td>\n",
       "      <td>Chip Time</td>\n",
       "      <td>Chip Pace</td>\n",
       "      <td>Gun Time</td>\n",
       "      <td>Team]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0     1      2        3      4       5           6           7  \\\n",
       "0  [Place   Bib   Name   Gender   City   State   Chip Time   Chip Pace   \n",
       "\n",
       "           8       9  \n",
       "0   Gun Time   Team]  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2[0].str.split(',', expand=True)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Finishers:</td>\n",
       "      <td>577]</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male:</td>\n",
       "      <td>414]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female:</td>\n",
       "      <td>163]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>814</td>\n",
       "      <td>\\r\\n\\r\\n                    JARED WILSON\\r\\n\\...</td>\n",
       "      <td>M</td>\n",
       "      <td>TIGARD</td>\n",
       "      <td>OR</td>\n",
       "      <td>36:21</td>\n",
       "      <td>5:51</td>\n",
       "      <td>36:24</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>573</td>\n",
       "      <td>\\r\\n\\r\\n                    NATHAN A SUSTERSI...</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>36:42</td>\n",
       "      <td>5:55</td>\n",
       "      <td>36:45</td>\n",
       "      <td>\\n\\r\\n                            INTEL TEAM ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>687</td>\n",
       "      <td>\\r\\n\\r\\n                    FRANCISCO MAYA\\r\\...</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>37:44</td>\n",
       "      <td>6:05</td>\n",
       "      <td>37:48</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>623</td>\n",
       "      <td>\\r\\n\\r\\n                    PAUL MORROW\\r\\n\\r...</td>\n",
       "      <td>M</td>\n",
       "      <td>BEAVERTON</td>\n",
       "      <td>OR</td>\n",
       "      <td>38:34</td>\n",
       "      <td>6:13</td>\n",
       "      <td>38:37</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0      1                                                  2  \\\n",
       "0      [Place    Bib                                               Name   \n",
       "0           ]   None                                               None   \n",
       "1  Finishers:   577]                                               None   \n",
       "2       Male:   414]                                               None   \n",
       "3     Female:   163]                                               None   \n",
       "4           ]   None                                               None   \n",
       "5           1    814   \\r\\n\\r\\n                    JARED WILSON\\r\\n\\...   \n",
       "6           2    573   \\r\\n\\r\\n                    NATHAN A SUSTERSI...   \n",
       "7           3    687   \\r\\n\\r\\n                    FRANCISCO MAYA\\r\\...   \n",
       "8           4    623   \\r\\n\\r\\n                    PAUL MORROW\\r\\n\\r...   \n",
       "\n",
       "         3           4       5           6           7          8  \\\n",
       "0   Gender        City   State   Chip Time   Chip Pace   Gun Time   \n",
       "0     None        None    None        None        None       None   \n",
       "1     None        None    None        None        None       None   \n",
       "2     None        None    None        None        None       None   \n",
       "3     None        None    None        None        None       None   \n",
       "4     None        None    None        None        None       None   \n",
       "5        M      TIGARD      OR       36:21        5:51      36:24   \n",
       "6        M    PORTLAND      OR       36:42        5:55      36:45   \n",
       "7        M    PORTLAND      OR       37:44        6:05      37:48   \n",
       "8        M   BEAVERTON      OR       38:34        6:13      38:37   \n",
       "\n",
       "                                                   9  \n",
       "0                                              Team]  \n",
       "0                                               None  \n",
       "1                                               None  \n",
       "2                                               None  \n",
       "3                                               None  \n",
       "4                                               None  \n",
       "5                                                  ]  \n",
       "6   \\n\\r\\n                            INTEL TEAM ...  \n",
       "7                                                  ]  \n",
       "8                                                  ]  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [df3, df1]\n",
    "\n",
    "df4 = pd.concat(frames)\n",
    "df4.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>Gender</th>\n",
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       "      <th>0</th>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Finishers:</td>\n",
       "      <td>577]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male:</td>\n",
       "      <td>414]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female:</td>\n",
       "      <td>163]</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       [Place    Bib   Name   Gender   City   State   Chip Time   Chip Pace  \\\n",
       "0      [Place    Bib   Name   Gender   City   State   Chip Time   Chip Pace   \n",
       "0           ]   None   None     None   None    None        None        None   \n",
       "1  Finishers:   577]   None     None   None    None        None        None   \n",
       "2       Male:   414]   None     None   None    None        None        None   \n",
       "3     Female:   163]   None     None   None    None        None        None   \n",
       "\n",
       "    Gun Time   Team]  \n",
       "0   Gun Time   Team]  \n",
       "0       None    None  \n",
       "1       None    None  \n",
       "2       None    None  \n",
       "3       None    None  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df4.rename(columns=df4.iloc[0])\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 583 entries, 0 to 581\n",
      "Data columns (total 10 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   [Place      583 non-null    object\n",
      " 1    Bib        581 non-null    object\n",
      " 2    Name       578 non-null    object\n",
      " 3    Gender     578 non-null    object\n",
      " 4    City       578 non-null    object\n",
      " 5    State      578 non-null    object\n",
      " 6    Chip Time  578 non-null    object\n",
      " 7    Chip Pace  578 non-null    object\n",
      " 8    Gun Time   578 non-null    object\n",
      " 9    Team]      578 non-null    object\n",
      "dtypes: object(10)\n",
      "memory usage: 50.1+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(583, 10)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.info()\n",
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df6 = df5.dropna(axis=0, how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>\\n\\r\\n                            INTEL TEAM ...</td>\n",
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       "      <th>7</th>\n",
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       "      <td>6:05</td>\n",
       "      <td>37:48</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>623</td>\n",
       "      <td>\\r\\n\\r\\n                    PAUL MORROW\\r\\n\\r...</td>\n",
       "      <td>M</td>\n",
       "      <td>BEAVERTON</td>\n",
       "      <td>OR</td>\n",
       "      <td>38:34</td>\n",
       "      <td>6:13</td>\n",
       "      <td>38:37</td>\n",
       "      <td>]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>569</td>\n",
       "      <td>\\r\\n\\r\\n                    DEREK G OSBORNE\\r...</td>\n",
       "      <td>M</td>\n",
       "      <td>HILLSBORO</td>\n",
       "      <td>OR</td>\n",
       "      <td>39:21</td>\n",
       "      <td>6:20</td>\n",
       "      <td>39:24</td>\n",
       "      <td>\\n\\r\\n                            INTEL TEAM ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  [Place   Bib                                               Name  Gender  \\\n",
       "5      1   814   \\r\\n\\r\\n                    JARED WILSON\\r\\n\\...       M   \n",
       "6      2   573   \\r\\n\\r\\n                    NATHAN A SUSTERSI...       M   \n",
       "7      3   687   \\r\\n\\r\\n                    FRANCISCO MAYA\\r\\...       M   \n",
       "8      4   623   \\r\\n\\r\\n                    PAUL MORROW\\r\\n\\r...       M   \n",
       "9      5   569   \\r\\n\\r\\n                    DEREK G OSBORNE\\r...       M   \n",
       "\n",
       "         City  State  Chip Time  Chip Pace  Gun Time  \\\n",
       "5      TIGARD     OR      36:21       5:51     36:24   \n",
       "6    PORTLAND     OR      36:42       5:55     36:45   \n",
       "7    PORTLAND     OR      37:44       6:05     37:48   \n",
       "8   BEAVERTON     OR      38:34       6:13     38:37   \n",
       "9   HILLSBORO     OR      39:21       6:20     39:24   \n",
       "\n",
       "                                               Team]  \n",
       "5                                                  ]  \n",
       "6   \\n\\r\\n                            INTEL TEAM ...  \n",
       "7                                                  ]  \n",
       "8                                                  ]  \n",
       "9   \\n\\r\\n                            INTEL TEAM ...  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7 = df6.drop(df6.index[0])\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>HILLSBORO</td>\n",
       "      <td>OR</td>\n",
       "      <td>39:21</td>\n",
       "      <td>6:20</td>\n",
       "      <td>39:24</td>\n",
       "      <td>\\n\\r\\n                            INTEL TEAM ...</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Place   Bib                                               Name  Gender  \\\n",
       "5     1   814   \\r\\n\\r\\n                    JARED WILSON\\r\\n\\...       M   \n",
       "6     2   573   \\r\\n\\r\\n                    NATHAN A SUSTERSI...       M   \n",
       "7     3   687   \\r\\n\\r\\n                    FRANCISCO MAYA\\r\\...       M   \n",
       "8     4   623   \\r\\n\\r\\n                    PAUL MORROW\\r\\n\\r...       M   \n",
       "9     5   569   \\r\\n\\r\\n                    DEREK G OSBORNE\\r...       M   \n",
       "\n",
       "         City  State  Chip Time  Chip Pace  Gun Time  \\\n",
       "5      TIGARD     OR      36:21       5:51     36:24   \n",
       "6    PORTLAND     OR      36:42       5:55     36:45   \n",
       "7    PORTLAND     OR      37:44       6:05     37:48   \n",
       "8   BEAVERTON     OR      38:34       6:13     38:37   \n",
       "9   HILLSBORO     OR      39:21       6:20     39:24   \n",
       "\n",
       "                                                Team  \n",
       "5                                                  ]  \n",
       "6   \\n\\r\\n                            INTEL TEAM ...  \n",
       "7                                                  ]  \n",
       "8                                                  ]  \n",
       "9   \\n\\r\\n                            INTEL TEAM ...  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.rename(columns={'[Place': 'Place'},inplace=True)\n",
    "df7.rename(columns={' Team]': 'Team'},inplace=True)\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>M</td>\n",
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       "      <td>OR</td>\n",
       "      <td>38:34</td>\n",
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       "      <td>38:37</td>\n",
       "      <td></td>\n",
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       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>569</td>\n",
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       "      <td>M</td>\n",
       "      <td>HILLSBORO</td>\n",
       "      <td>OR</td>\n",
       "      <td>39:21</td>\n",
       "      <td>6:20</td>\n",
       "      <td>39:24</td>\n",
       "      <td>\\n\\r\\n                            INTEL TEAM ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  Place   Bib                                               Name  Gender  \\\n",
       "5     1   814   \\r\\n\\r\\n                    JARED WILSON\\r\\n\\...       M   \n",
       "6     2   573   \\r\\n\\r\\n                    NATHAN A SUSTERSI...       M   \n",
       "7     3   687   \\r\\n\\r\\n                    FRANCISCO MAYA\\r\\...       M   \n",
       "8     4   623   \\r\\n\\r\\n                    PAUL MORROW\\r\\n\\r...       M   \n",
       "9     5   569   \\r\\n\\r\\n                    DEREK G OSBORNE\\r...       M   \n",
       "\n",
       "         City  State  Chip Time  Chip Pace  Gun Time  \\\n",
       "5      TIGARD     OR      36:21       5:51     36:24   \n",
       "6    PORTLAND     OR      36:42       5:55     36:45   \n",
       "7    PORTLAND     OR      37:44       6:05     37:48   \n",
       "8   BEAVERTON     OR      38:34       6:13     38:37   \n",
       "9   HILLSBORO     OR      39:21       6:20     39:24   \n",
       "\n",
       "                                                Team  \n",
       "5                                                     \n",
       "6   \\n\\r\\n                            INTEL TEAM ...  \n",
       "7                                                     \n",
       "8                                                     \n",
       "9   \\n\\r\\n                            INTEL TEAM ...  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7['Team'] = df7['Team'].str.strip(']')\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_list = df7[' Chip Time'].tolist()\n",
    "\n",
    "# You can use a for loop to convert 'Chip Time' to minutes\n",
    "\n",
    "time_mins = []\n",
    "for i in time_list:\n",
    "    try:\n",
    "        h, m, s = i.split(':')\n",
    "        math = (int(h) * 3600 + int(m) * 60 + int(s))/60\n",
    "    except:\n",
    "        h, m = i.split(':')\n",
    "        math = (int(h) * 3600 + int(m) * 60) # + int(s))/60\n",
    "    time_mins.append(math)\n",
    "#print(time_mins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Gun Time</th>\n",
       "      <th>Team</th>\n",
       "      <th>Runner_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>814</td>\n",
       "      <td>\\r\\n\\r\\n                    JARED WILSON\\r\\n\\...</td>\n",
       "      <td>M</td>\n",
       "      <td>TIGARD</td>\n",
       "      <td>OR</td>\n",
       "      <td>36:21</td>\n",
       "      <td>5:51</td>\n",
       "      <td>36:24</td>\n",
       "      <td></td>\n",
       "      <td>130860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>573</td>\n",
       "      <td>\\r\\n\\r\\n                    NATHAN A SUSTERSI...</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>36:42</td>\n",
       "      <td>5:55</td>\n",
       "      <td>36:45</td>\n",
       "      <td>\\n\\r\\n                            INTEL TEAM ...</td>\n",
       "      <td>132120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>687</td>\n",
       "      <td>\\r\\n\\r\\n                    FRANCISCO MAYA\\r\\...</td>\n",
       "      <td>M</td>\n",
       "      <td>PORTLAND</td>\n",
       "      <td>OR</td>\n",
       "      <td>37:44</td>\n",
       "      <td>6:05</td>\n",
       "      <td>37:48</td>\n",
       "      <td></td>\n",
       "      <td>135840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>623</td>\n",
       "      <td>\\r\\n\\r\\n                    PAUL MORROW\\r\\n\\r...</td>\n",
       "      <td>M</td>\n",
       "      <td>BEAVERTON</td>\n",
       "      <td>OR</td>\n",
       "      <td>38:34</td>\n",
       "      <td>6:13</td>\n",
       "      <td>38:37</td>\n",
       "      <td></td>\n",
       "      <td>138840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>569</td>\n",
       "      <td>\\r\\n\\r\\n                    DEREK G OSBORNE\\r...</td>\n",
       "      <td>M</td>\n",
       "      <td>HILLSBORO</td>\n",
       "      <td>OR</td>\n",
       "      <td>39:21</td>\n",
       "      <td>6:20</td>\n",
       "      <td>39:24</td>\n",
       "      <td>\\n\\r\\n                            INTEL TEAM ...</td>\n",
       "      <td>141660.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Place   Bib                                               Name  Gender  \\\n",
       "5     1   814   \\r\\n\\r\\n                    JARED WILSON\\r\\n\\...       M   \n",
       "6     2   573   \\r\\n\\r\\n                    NATHAN A SUSTERSI...       M   \n",
       "7     3   687   \\r\\n\\r\\n                    FRANCISCO MAYA\\r\\...       M   \n",
       "8     4   623   \\r\\n\\r\\n                    PAUL MORROW\\r\\n\\r...       M   \n",
       "9     5   569   \\r\\n\\r\\n                    DEREK G OSBORNE\\r...       M   \n",
       "\n",
       "         City  State  Chip Time  Chip Pace  Gun Time  \\\n",
       "5      TIGARD     OR      36:21       5:51     36:24   \n",
       "6    PORTLAND     OR      36:42       5:55     36:45   \n",
       "7    PORTLAND     OR      37:44       6:05     37:48   \n",
       "8   BEAVERTON     OR      38:34       6:13     38:37   \n",
       "9   HILLSBORO     OR      39:21       6:20     39:24   \n",
       "\n",
       "                                                Team  Runner_mins  \n",
       "5                                                        130860.0  \n",
       "6   \\n\\r\\n                            INTEL TEAM ...     132120.0  \n",
       "7                                                        135840.0  \n",
       "8                                                        138840.0  \n",
       "9   \\n\\r\\n                            INTEL TEAM ...     141660.0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7['Runner_mins'] = time_mins\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Runner_mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>577.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>97617.157972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>92975.700498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>60.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>68.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>152580.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>186540.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>215700.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Runner_mins\n",
       "count     577.000000\n",
       "mean    97617.157972\n",
       "std     92975.700498\n",
       "min        60.016667\n",
       "25%        68.300000\n",
       "50%    152580.000000\n",
       "75%    186540.000000\n",
       "max    215700.000000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.describe(include=[np.number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f0932d2da00>], [Text(1, 0, 'Runners')])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df7.boxplot(column='Runner_mins')\n",
    "plt.grid(True, axis='y')\n",
    "plt.ylabel('Chip Time')\n",
    "plt.xticks([1], ['Runners'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rsouza/environments/default_env/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df7['Runner_mins']\n",
    "ax = sns.distplot(x, hist=True, kde=True, rug=False, color='m', bins=25, hist_kws={'edgecolor':'black'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rsouza/environments/default_env/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/home/rsouza/environments/default_env/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0930c36970>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_fuko = df7.loc[df7[' Gender']==' F']['Runner_mins']\n",
    "m_fuko = df7.loc[df7[' Gender']==' M']['Runner_mins']\n",
    "sns.distplot(f_fuko, hist=True, kde=True, rug=False, hist_kws={'edgecolor':'black'}, label='Female')\n",
    "sns.distplot(m_fuko, hist=False, kde=True, rug=False, hist_kws={'edgecolor':'black'}, label='Male')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Runner_mins                                                     \\\n",
      "              count           mean           std        min        25%   \n",
      " Gender                                                                  \n",
      " F            163.0   61844.168609  91100.389419  60.200000  66.550000   \n",
      " M            414.0  111701.692432  89982.758495  60.016667  69.320833   \n",
      "\n",
      "                                            \n",
      "                   50%       75%       max  \n",
      " Gender                                     \n",
      " F           75.216667  173280.0  215700.0  \n",
      " M       163320.000000  188745.0  215580.0  \n"
     ]
    }
   ],
   "source": [
    "g_stats = df7.groupby(\" Gender\", as_index=True).describe()\n",
    "print(g_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df7.boxplot(column='Runner_mins', by=' Gender')\n",
    "plt.ylabel('Chip Time')\n",
    "plt.suptitle(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now it is your turn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://www.4icu.org/at/vienna/'"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}