Notebooks/23_Intro_Scraping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Python  \n",
    "\n",
    "### Introduction to Scraping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 2,
     "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": 3,
   "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>Name</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andau</td>\n",
       "      <td>Market town</td>\n",
       "      <td>2285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Antau</td>\n",
       "      <td>Other municipality</td>\n",
       "      <td>758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apetlon</td>\n",
       "      <td>Market town</td>\n",
       "      <td>1760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bad Sauerbrunn</td>\n",
       "      <td>Other municipality</td>\n",
       "      <td>2219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bad Tatzmannsdorf</td>\n",
       "      <td>Other municipality</td>\n",
       "      <td>1554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Badersdorf</td>\n",
       "      <td>Other municipality</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Baumgarten</td>\n",
       "      <td>Other municipality</td>\n",
       "      <td>894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bernstein</td>\n",
       "      <td>Market town</td>\n",
       "      <td>2123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bildein</td>\n",
       "      <td>Other municipality</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bocksdorf</td>\n",
       "      <td>Other municipality</td>\n",
       "      <td>813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name         Designation  Population\n",
       "0              Andau         Market town        2285\n",
       "1              Antau  Other municipality         758\n",
       "2            Apetlon         Market town        1760\n",
       "3     Bad Sauerbrunn  Other municipality        2219\n",
       "4  Bad Tatzmannsdorf  Other municipality        1554\n",
       "5         Badersdorf  Other municipality         287\n",
       "6         Baumgarten  Other municipality         894\n",
       "7          Bernstein         Market town        2123\n",
       "8            Bildein  Other municipality         343\n",
       "9          Bocksdorf  Other municipality         813"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_df_tables[1].head(10)"
   ]
  },
  {
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afritz am See</td>\n",
       "      <td>Other municipality</td>\n",
       "      <td>1429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albeck</td>\n",
       "      <td>Other municipality</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Althofen</td>\n",
       "      <td>Other city</td>\n",
       "      <td>4759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arnoldstein</td>\n",
       "      <td>Market town</td>\n",
       "      <td>7096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arriach</td>\n",
       "      <td>Other municipality</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name         Designation  Population\n",
       "0  Afritz am See  Other municipality        1429\n",
       "1         Albeck  Other municipality         995\n",
       "2       Althofen          Other city        4759\n",
       "3    Arnoldstein         Market town        7096\n",
       "4        Arriach  Other municipality        1349"
      ]
     },
     "execution_count": 4,
     "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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n"
     ]
    }
   ],
   "source": [
    "url2 = 'https://wetter.orf.at/oes/' \n",
    "content = requests.get(url2)\n",
    "print(content.status_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check [HTTP status codes](https://developer.mozilla.org/en-US/docs/Web/HTTP/Status) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting html content of the page "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<!DOCTYPE html >\\n<html lang=\"de\" dir=\"ltr\">\\n<head>\\n   <meta name=\"google-site-verification\" content=\"x-x0HlFf8WLZAZ0GyCaEfTFs8bav35iVLSj30TnJA-w\" />\\n   <meta charset=\"utf-8\" />\\n   <meta http-equiv=\"X-UA-Compatible\" content=\"IE=edge\" />\\n   <link rel=\"preload\" href=\"//orf.at/fonts/OrfOn-Regular.woff2\" as=\"font\" type=\"font/woff2\" crossorigin>\\n   <link rel=\"preload\" href=\"//orf.at/fonts/OrfOnSC-CondensedRegular.woff2\" as=\"font\" type=\"font/woff2\" crossorigin>\\n   <link rel=\"preload\" href=\"//orf.at/fonts/OrfOn-CondensedMedium.woff2\" as=\"font\" type=\"font/woff2\" crossorigin>\\n   <link rel=\"preload\" href=\"//orf.at/fonts/OrfOn-CondensedRegular.woff2\" as=\"font\" type=\"font/woff2\" crossorigin>\\n   <link rel=\"preload\" href=\"//orf.at/fonts/OrfOn-CondensedBold.woff2\" as=\"font\" type=\"font/woff2\" crossorigin>\\n\\n   <title>Österreich - wetter.ORF.at</title>\\n   <link rel=\"stylesheet\" type=\"text/css\" href=\"https://orf.at/mojo/1_3/storyserver/common/fonts/fonts.css\">\\n   <link rel=\"stylesheet\" type=\"text/css\" med'"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "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": 8,
   "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": 8,
     "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 dir=\"ltr\" lang=\"de\">\n",
      " <head>\n",
      "  <meta content=\"x-x0HlFf8WLZAZ0GyCaEfTFs8bav35iVLSj30TnJA-w\" name=\"google-site-verification\"/>\n",
      "  <meta charset=\"utf-8\"/>\n",
      "  <meta content=\"IE=edge\" http-equiv=\"X-UA-Compatible\"/>\n",
      "  <link as=\"font\" crossorigin=\"\" href=\"//orf.at/fonts/OrfOn-Regular.woff2\" rel=\"preload\" type=\"font/woff2\"/>\n",
      "  <link as=\"font\" crossorigin=\"\" href=\"//orf.at/fonts/OrfOnSC-CondensedRegular.woff2\" rel=\"preload\" type=\"font/woff2\"/>\n",
      "  <link as=\"font\" crossorigin=\"\" href=\"//orf.at/fonts/OrfOn-CondensedMedium.woff2\" rel=\"preload\" type=\"font/woff2\"/>\n",
      "  <link as=\"font\" crossorigin=\"\" href=\"//orf.at/fonts/OrfOn-CondensedRegular.woff2\" rel=\"preload\" type=\"font/woff2\"/>\n",
      "  <link as=\"font\" crossorigin=\"\" href=\"//orf.at/fonts/OrfOn-CondensedBold.woff2\" rel=\"preload\" type=\"font/woff2\"/>\n",
      "  <title>\n",
      "   Österreich - wetter.ORF.at\n",
      "  </title>\n",
      "  <link href=\"https://orf.at/mojo/1_3/storyserver/common/fonts/fonts.css\" rel=\"stylesheet\" type=\"text/css\"/>\n",
      "  <link href=\"/static/wetter/compressed//main.css?v=1.44\" media=\"screen, handheld\" rel=\"stylesheet\" title=\"CSS Stylesheet\" type=\"text/css\"/>\n",
      "  <link href=\"/static/wetter/compressed//sprites.css?v=1.44\" media=\"all\" rel=\"stylesheet\" title=\"CSS Stylesheet\" type=\"text/css\"/>\n",
      "  <!-- For mobile devices use reduced styles -->\n",
      "  <link href=\"/static/wetter/3_3//css/maps_mobile.css\" media=\"all and (max-device-width: 800px)\" rel=\"stylesheet\" type=\"text/css\"/>\n",
      "  <link href=\"/static/wetter/3_3//css/print.css\" media=\"print\" rel=\"stylesheet\" title=\"CSS Stylesheet\" type=\"text/css\"/>\n",
      "  <script type=\"text/javascript\">\n",
      "   var settings = {\n",
      "         /* Network Navigation Config */\n",
      "         \"sitemap\": {\n",
      "            \"url\": \"//orf.at/mojo/1_3/storyserver/common/sitemap/js/sitemap.js\",\n",
      "            \"opacity\": 0.9\n",
      "         },\n",
      "         /* For RaphaelJS image loading */\n",
      "         \"staticUrl\": \"/static/wetter/3_3/\",\n",
      "         \"baseUri\": \"https://wetter.ORF.at\",\n",
      "         \"searchUrl\": \"/search/json\",\n",
      "         \"stationTemplate\": function(data) {\n",
      "             var sOrC = (data.state || data.country);\n",
      "             if (sOrC) {\n",
      "                 return ['<span class=\"station\" data-href=\"', data.href, '\">', data.name, ', ', sOrC.name, '</span>'].join(\"\");\n",
      "             }\n",
      "             return ['<span class=\"station\" data-href=\"', data.href, '\">', data.name, '</span>'].join(\"\");\n",
      "         },\n",
      "         \"countryTemplate\": function(data) {\n",
      "             return ['<span class=\"Country\" data-href=\"', data.href, '\">', data.name.toUpperCase(), ' (', data.stations.length, ')</span>'].join(\"\");\n",
      "         },\n",
      "         \"videobar\": {\n",
      "             vodUrl: \"//tubestatic.orf.at/tvthek/vod/wetter.json\",\n",
      "             playerUrl: \"/video/\",\n",
      "             items: 5,\n",
      "             responsive: {\n",
      "                1000: {\n",
      "                   items: 6\n",
      "                },\n",
      "                768: {\n",
      "                   items: 5\n",
      "                },\n",
      "                479: {\n",
      "                   items: 4\n",
      "                },\n",
      "                390: {\n",
      "                   items: 2\n",
      "                },\n",
      "                0: {\n",
      "                   items: 2\n",
      "                }\n",
      "             }\n",
      "          },\n",
      "          videoMissingImageUrl: \"/static/wetter/3_3//images/tvthek/not_found.png\"\n",
      "      };\n",
      "      var loc = {};\n",
      "      var ads = {\n",
      "              'path': '/oes/front',\n",
      "              'profile': {\"temp\":6,\"day\":1,\"cloud\":0,\"rain\":0,\"snow\":0,\"warning\":0,\"subcategory\":\"oesterreichwetter\"},\n",
      "              'type': 'bill',\n",
      "              'jsonURL': '/static/wetter/3_3//wetter.json'\n",
      "          };\n",
      "      ads.profile.viewport = 'full';\n",
      "      ads.profile.platform = 'web';\n",
      "  </script>\n",
      "  <script src=\"/static/wetter/compressed//main.js?v=1.44\" type=\"text/javascript\">\n",
      "  </script>\n",
      "  <script src=\"//orf.at/oon/media/6.0/oonmedia.js?v=20200727\">\n",
      "  </script>\n",
      "  <link href=\"//orf.at/mojo.compressed/1_4_1/news/news/dsgvo-banner.css?v=1.44\" media=\"screen, handheld\" rel=\"stylesheet\" type=\"text/css\"/>\n",
      "  <script src=\"//orf.at/mojo.compressed/1_4_1/news/news/dsgvo-banner.js?v=1.44\" type=\"text/javascript\">\n",
      "  </script>\n",
      "  <meta content=\"index, follow, noodp\" name=\"robots\"/>\n",
      "  <meta content=\"telephone=no\" name=\"format-detection\"/>\n",
      "  <meta content=\"wetter.ORF.at\" name=\"application-name\"/>\n",
      "  <meta content=\"//wetter.orf.at/static/wetter/3_3/images/ms-metro-icon.png\" name=\"msapplication-TileImage\"/>\n",
      "  <meta content=\"#5286C1\" name=\"msapplication-TileColor\"/>\n",
      "  <meta content=\"//orf.at/mojo/1_3/storyserver/common/msapplicationtiles/target.128x128.png\" name=\"msapplication-square70x70logo\"/>\n",
      "  <meta content=\"//orf.at/mojo/1_3/storyserver/common/msapplicationtiles/target.270x270.png\" name=\"msapplication-square150x150logo\"/>\n",
      "  <meta content=\"//orf.at/mojo/1_3/storyserver/common/msapplicationtiles/target.558x270.png\" name=\"msapplication-wide310x150logo\"/>\n",
      "  <meta content=\"//orf.at/mojo/1_3/storyserver/common/msapplicationtiles/target.558x558.png\" name=\"msapplication-square310x310logo\"/>\n",
      "  <link href=\"//orf.at/favicon.ico\" rel=\"shortcut icon\" type=\"image/x-icon\"/>\n",
      "  <link href=\"//orf.at/favicon.ico?newfav=on\" rel=\"icon\" type=\"image/x-icon\"/>\n",
      "  <link href=\"/static/wetter/3_3//images/icons/apple-touch-icon-wetter.png\" rel=\"apple-touch-icon\"/>\n",
      "  <meta content=\"//wetter.orf.at/static/wetter/3_3//images/og-image-fb.png\" property=\"og:image\"/>\n",
      "  <meta content=\"Die aktuellen Wetterwerte und Prognosen für Österreich, Europa und die Welt, mit Bergwetterprognosen, Karten, Tabellen und Wettervideos.\" name=\"description\"/>\n",
      "  <!--[if lte IE 8]>\n",
      "      <link rel=\"stylesheet\" type=\"text/css\" href=\"/static/wetter/3_3//css/ie-fixes.css?v=2\" />\n",
      "   <![endif]-->\n",
      "  <link href=\"/m/oes/\" media=\"only screen and (max-width: 599px)\" rel=\"alternate\"/>\n",
      "  <link href=\"/m/oes/\" media=\"handheld\" rel=\"alternate\"/>\n",
      " </head>\n",
      " <body class=\"ss wetter main winterMaps day\" id=\"top\">\n",
      "  <div aria-hidden=\"true\" class=\"lightVersionMessage\" id=\"ss-lightVersionMessage\">\n",
      "   <a href=\"https://orf.at/light?redirect=https://wetter.orf.at/m/\">\n",
      "    Möchten Sie zur Mobilversion wechseln?\n",
      "   </a>\n",
      "  </div>\n",
      "  <div id=\"skiplinks\">\n",
      "   <a accesskey=\"1\" href=\"#content\">\n",
      "    Zum Inhalt [AK+1]\n",
      "   </a>\n",
      "   <span class=\"hidden offscreen\">\n",
      "    /\n",
      "   </span>\n",
      "   <span id=\"ss-dynamicSkiplink\">\n",
      "   </span>\n",
      "   <a accesskey=\"3\" href=\"#nnav\">\n",
      "    Zur ORF.at-Navigation [AK+3]\n",
      "   </a>\n",
      "  </div>\n",
      "  <div class=\"wrapper\">\n",
      "   <div id=\"advert\">\n",
      "   </div>\n",
      "   <div class=\"header\" role=\"banner\">\n",
      "    <a class=\"hdpiLogo\" href=\"https://wetter.ORF.at/\" id=\"wetterLogo\">\n",
      "     <img alt=\"zur wetter.ORF.at-Startseite\" height=\"39px\" src=\"/static/wetter/3_3//images/wetter_v2.svg\" title=\"wetter.ORF.at\" width=\"82px\"/>\n",
      "    </a>\n",
      "    <a class=\"hdpiLogo\" href=\"//orf.at\" id=\"orfonTarget\">\n",
      "     <img alt=\"zur ORF.at-Startseite\" height=\"39px\" src=\"/static/wetter/3_3//images/target_news.svg\" title=\"ORF.at\" width=\"101px\"/>\n",
      "    </a>\n",
      "    <img alt=\"\" height=\"0\" id=\"ss-printLogo\" src=\"/static/wetter/3_3//images/logo.wetterorfat.print.png\" title=\"\" width=\"0\"/>\n",
      "    <h2 class=\"offscreen\" id=\"nnav\">\n",
      "     Navigation\n",
      "    </h2>\n",
      "    <nav role=\"navigation\">\n",
      "     <ul class=\"networkNavigation\" id=\"ss-networkNavigation\" role=\"navigation\">\n",
      "      <li>\n",
      "       <a href=\"//tv.orf.at\">\n",
      "        Fernsehen\n",
      "       </a>\n",
      "      </li>\n",
      "      <li>\n",
      "       <a href=\"//tvthek.orf.at\">\n",
      "        TVthek\n",
      "       </a>\n",
      "      </li>\n",
      "      <li>\n",
      "       <a href=\"//radiothek.orf.at\">\n",
      "        Radiothek\n",
      "       </a>\n",
      "      </li>\n",
      "      <li>\n",
      "       <a href=\"//debatte.orf.at\">\n",
      "        Debatte\n",
      "       </a>\n",
      "      </li>\n",
      "      <li>\n",
      "       <a href=\"//oesterreich.orf.at\">\n",
      "        Österreich\n",
      "       </a>\n",
      "      </li>\n",
      "      <li>\n",
      "       <a class=\"current\" href=\"//wetter.orf.at\">\n",
      "        Wetter\n",
      "       </a>\n",
      "      </li>\n",
      "      <li>\n",
      "       <a href=\"//sport.orf.at\">\n",
      "        Sport\n",
      "       </a>\n",
      "      </li>\n",
      "      <li>\n",
      "       <a href=\"//orf.at\" lang=\"en\">\n",
      "        News\n",
      "       </a>\n",
      "      </li>\n",
      "      <li class=\"sitemapMenu\" id=\"ss-sitemapMenu\">\n",
      "       <a aria-controls=\"ss-sitemap\" aria-haspopup=\"true\" href=\"//orf.at/uebersicht\" id=\"ss-showSitemap\" role=\"button\">\n",
      "        ORF.at im Überblick\n",
      "       </a>\n",
      "       <div aria-labelledby=\"ss-showSitemap\" class=\"sitemap\" id=\"ss-sitemap\">\n",
      "        <ul class=\"list\">\n",
      "        </ul>\n",
      "        <div class=\"background\" id=\"ss-sitemapBackground\">\n",
      "        </div>\n",
      "       </div>\n",
      "      </li>\n",
      "     </ul>\n",
      "    </nav>\n",
      "   </div>\n",
      "   <div class=\"content\" id=\"content\" role=\"main\">\n",
      "    <div class=\"regionNavigation\">\n",
      "     <h2 class=\"offscreen\" id=\"ss-regionLabel\" tabindex=\"-1\">\n",
      "      Regionenauswahl\n",
      "     </h2>\n",
      "     <div role=\"menubar\">\n",
      "      <ul id=\"ss-regionMenubar\" role=\"presentation\">\n",
      "       <li class=\"toplevel level2 nodeExternalID9999999975\" role=\"presentation\">\n",
      "        <a aria-haspopup=\"true\" href=\"https://wetter.ORF.at/welt/\" id=\"ss-navnode-9999999975\" role=\"menuitem\">\n",
      "         <h3>\n",
      "          Welt\n",
      "          <span class=\"topnavi_bckg_png\">\n",
      "          </span>\n",
      "         </h3>\n",
      "        </a>\n",
      "        <ul class=\"sublevelList\" role=\"menu\">\n",
      "         <li role=\"presentation\">\n",
      "          <a href=\"https://wetter.ORF.at/afrika/\" id=\"ss-subitem-9999999996\" role=\"menuitem\">\n",
      "           Afrika\n",
      "          </a>\n",
      "         </li>\n",
      "         <li role=\"presentation\">\n",
      "          <a href=\"https://wetter.ORF.at/asien/\" id=\"ss-subitem-9999999995\" role=\"menuitem\">\n",
      "           Asien\n",
      "          </a>\n",
      "         </li>\n",
      "         <li role=\"presentation\">\n",
      "          <a href=\"https://wetter.ORF.at/australienozeanien/\" id=\"ss-subitem-9999999969\" role=\"menuitem\">\n",
      "           Australien &amp; Ozeanien\n",
      "          </a>\n",
      "         </li>\n",
      "         <li role=\"presentation\">\n",
      "          <a href=\"https://wetter.ORF.at/europa/\" id=\"ss-subitem-9999999984\" role=\"menuitem\">\n",
      "           Europa\n",
      "          </a>\n",
      "         </li>\n",
      "         <li role=\"presentation\">\n",
      "          <a href=\"https://wetter.ORF.at/naherosten/\" id=\"ss-subitem-9999999966\" role=\"menuitem\">\n",
      "           Naher Osten\n",
      "          </a>\n",
      "         </li>\n",
      "         <li role=\"presentation\">\n",
      "          <a href=\"https://wetter.ORF.at/nordamerika/\" id=\"ss-subitem-9999999981\" role=\"menuitem\">\n",
      "           Nord- &amp; Mittelamerika\n",
      "          </a>\n",
      "         </li>\n",
      "         <li role=\"presentation\">\n",
      "          <a href=\"https://wetter.ORF.at/s\n"
     ]
    }
   ],
   "source": [
    "print(soup.prettify()[0:10000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting text of the page "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Österreich - wetter.ORF.at\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Möchten Sie zur Mobilversion wechseln?\n",
      "\n",
      "\n",
      "Zum Inhalt [AK+1] / \n",
      "\n",
      "Zur ORF.at-Navigation [AK+3]\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Navigation\n",
      "\n",
      "\n",
      "Fernsehen\n",
      "TVthek\n",
      "Radiothek\n",
      "Debatte\n",
      "Österreich\n",
      "Wetter\n",
      "Sport\n",
      "News\n",
      "\n",
      "ORF.at im Überblick\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Regionenauswahl\n",
      "Welt\n",
      "\n",
      "Afrika\n",
      "Asien\n",
      "Australien & Ozeanien\n",
      "Europa\n",
      "Naher Osten\n",
      "Nord- & Mittelamerika\n",
      "Südamerika\n",
      "\n",
      "\n",
      "\n",
      "Europa\n",
      "\n",
      "Deutschland, Schweiz\n",
      "Frankreich, Benelux\n",
      "Großbritannien, Irland\n",
      "Italien, Adria-Raum\n",
      "Nordeuropa\n",
      "Österreich\n",
      "Osteuropa\n",
      "Polen, Baltikum\n",
      "Spanien, Portugal\n",
      "Südosteuropa\n",
      "Ungarn, Tschechien, Slowakei \n",
      "\n",
      "\n",
      "\n",
      "Österreich\n",
      "\n",
      "Burgenland\n",
      "Kärnten\n",
      "Niederösterreich\n",
      "Oberösterreich\n",
      "Salzburg\n",
      "Steiermark\n",
      "Tirol\n",
      "Vorarlberg\n",
      "Wien\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Wien-Hohe Warte:\n",
      "\n",
      "\n",
      "Sonnenaufgang um 07:41 Uhr.\n",
      "Sonnenuntergang um 16:27 Uhr.\n",
      "\n",
      "\n",
      "Mondphase: Zunehmender Halbmond.\n",
      "Mondaufgang um 13:19 Uhr.\n",
      "Monduntergang um 04:57 Uhr.\n",
      "\n",
      "\n",
      "\n",
      "Tageslänge: 8:46 h\n",
      "Dämmerung Morgen: 07:06 Uhr\n",
      "Dämmerung Abend:17:02 Uhr\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Wien\n",
      "9,7 °C, wolkenlos.\n",
      "43 km/h, West.\n",
      "0,0 m\n"
     ]
    }
   ],
   "source": [
    "print(soup.get_text()[0:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<title>Österreich - wetter.ORF.at</title>\n"
     ]
    }
   ],
   "source": [
    "print(soup.title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extracting anchors and links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<a href=\"https://orf.at/light?redirect=https://wetter.orf.at/m/\">Möchten Sie zur Mobilversion wechseln?</a>,\n",
       " <a accesskey=\"1\" href=\"#content\">Zum Inhalt [AK+1]</a>,\n",
       " <a accesskey=\"3\" href=\"#nnav\">Zur ORF.at-Navigation [AK+3]</a>,\n",
       " <a class=\"hdpiLogo\" href=\"https://wetter.ORF.at/\" id=\"wetterLogo\">\n",
       " <img alt=\"zur wetter.ORF.at-Startseite\" height=\"39px\" src=\"/static/wetter/3_3//images/wetter_v2.svg\" title=\"wetter.ORF.at\" width=\"82px\"/>\n",
       " </a>,\n",
       " <a class=\"hdpiLogo\" href=\"//orf.at\" id=\"orfonTarget\">\n",
       " <img alt=\"zur ORF.at-Startseite\" height=\"39px\" src=\"/static/wetter/3_3//images/target_news.svg\" title=\"ORF.at\" width=\"101px\"/>\n",
       " </a>,\n",
       " <a href=\"//tv.orf.at\">Fernsehen</a>,\n",
       " <a href=\"//tvthek.orf.at\">TVthek</a>,\n",
       " <a href=\"//radiothek.orf.at\">Radiothek</a>,\n",
       " <a href=\"//debatte.orf.at\">Debatte</a>,\n",
       " <a href=\"//oesterreich.orf.at\">Österreich</a>,\n",
       " <a class=\"current\" href=\"//wetter.orf.at\">Wetter</a>,\n",
       " <a href=\"//sport.orf.at\">Sport</a>,\n",
       " <a href=\"//orf.at\" lang=\"en\">News</a>,\n",
       " <a aria-controls=\"ss-sitemap\" aria-haspopup=\"true\" href=\"//orf.at/uebersicht\" id=\"ss-showSitemap\" role=\"button\">ORF.at im Überblick</a>,\n",
       " <a aria-haspopup=\"true\" href=\"https://wetter.ORF.at/welt/\" id=\"ss-navnode-9999999975\" role=\"menuitem\"><h3>Welt<span class=\"topnavi_bckg_png\"></span></h3></a>,\n",
       " <a href=\"https://wetter.ORF.at/afrika/\" id=\"ss-subitem-9999999996\" role=\"menuitem\">Afrika</a>,\n",
       " <a href=\"https://wetter.ORF.at/asien/\" id=\"ss-subitem-9999999995\" role=\"menuitem\">Asien</a>,\n",
       " <a href=\"https://wetter.ORF.at/australienozeanien/\" id=\"ss-subitem-9999999969\" role=\"menuitem\">Australien &amp; Ozeanien</a>,\n",
       " <a href=\"https://wetter.ORF.at/europa/\" id=\"ss-subitem-9999999984\" role=\"menuitem\">Europa</a>,\n",
       " <a href=\"https://wetter.ORF.at/naherosten/\" id=\"ss-subitem-9999999966\" role=\"menuitem\">Naher Osten</a>]"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://orf.at/light?redirect=https://wetter.orf.at/m/\n",
      "#content\n",
      "#nnav\n",
      "https://wetter.ORF.at/\n",
      "//orf.at\n",
      "//tv.orf.at\n",
      "//tvthek.orf.at\n",
      "//radiothek.orf.at\n",
      "//debatte.orf.at\n",
      "//oesterreich.orf.at\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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://orf.at/light?redirect=https://wetter.orf.at/m/\n",
      "https://wetter.ORF.at/\n",
      "https://wetter.ORF.at/welt/\n",
      "https://wetter.ORF.at/afrika/\n",
      "https://wetter.ORF.at/asien/\n",
      "https://wetter.ORF.at/australienozeanien/\n",
      "https://wetter.ORF.at/europa/\n",
      "https://wetter.ORF.at/naherosten/\n",
      "https://wetter.ORF.at/nordamerika/\n",
      "https://wetter.ORF.at/suedamerika/\n",
      "https://wetter.ORF.at/europa/\n",
      "https://wetter.ORF.at/deutschlandschweiz/\n",
      "https://wetter.ORF.at/frankreichbenelux/\n",
      "https://wetter.ORF.at/britischeinseln/\n",
      "https://wetter.ORF.at/italien-adria/\n",
      "https://wetter.ORF.at/nordeuropa/\n",
      "https://wetter.ORF.at/oes/\n",
      "https://wetter.ORF.at/osteuropa/\n",
      "https://wetter.ORF.at/polenbaltikum/\n",
      "https://wetter.ORF.at/spanienportugal/\n",
      "https://wetter.ORF.at/suedosteuropa/\n",
      "https://wetter.ORF.at/ungarntschechienslo/\n",
      "https://wetter.ORF.at/oes/\n",
      "https://wetter.ORF.at/burgenland/\n",
      "https://wetter.ORF.at/kaernten/\n",
      "https://wetter.ORF.at/niederoesterreich/\n",
      "https://wetter.ORF.at/oberoesterreich/\n",
      "https://wetter.ORF.at/salzburg/\n",
      "https://wetter.ORF.at/steiermark/\n",
      "https://wetter.ORF.at/tirol/\n",
      "https://wetter.ORF.at/vorarlberg/\n",
      "https://wetter.ORF.at/wien/\n",
      "https://wetter.ORF.at/wien/\n",
      "https://wetter.ORF.at/burgenland/\n",
      "https://wetter.ORF.at/niederoesterreich/\n",
      "https://wetter.ORF.at/oberoesterreich/\n",
      "https://wetter.ORF.at/steiermark/\n",
      "https://wetter.ORF.at/salzburg/\n",
      "https://wetter.ORF.at/kaernten/\n",
      "https://wetter.ORF.at/tirol/\n",
      "https://wetter.ORF.at/vorarlberg/\n",
      "https://wetter.ORF.at/ungarntschechienslo/\n",
      "https://wetter.ORF.at/ungarntschechienslo/\n",
      "https://wetter.ORF.at/deutschlandschweiz/\n",
      "https://wetter.ORF.at/deutschlandschweiz/\n",
      "https://wetter.ORF.at/deutschlandschweiz/\n",
      "https://wetter.ORF.at/italien-adria/\n",
      "https://wetter.ORF.at/italien-adria/\n",
      "https://wetter.ORF.at/ungarntschechienslo/\n",
      "https://wetter.ORF.at/oes/\n",
      "https://wetter.ORF.at/oes/prognose\n",
      "https://wetter.ORF.at/oes/bergwetter\n",
      "https://wetter.ORF.at/kaernten/bergwetter\n",
      "https://wetter.ORF.at/niederoesterreich/bergwetter\n",
      "https://wetter.ORF.at/oberoesterreich/bergwetter\n",
      "https://wetter.ORF.at/salzburg/bergwetter\n",
      "https://wetter.ORF.at/steiermark/bergwetter\n",
      "https://wetter.ORF.at/tirol/bergwetter\n",
      "https://wetter.ORF.at/vorarlberg/bergwetter\n",
      "https://wetter.ORF.at/kaernten/schneelagen\n",
      "https://wetter.ORF.at/niederoesterreich/schneelagen\n",
      "https://wetter.ORF.at/oberoesterreich/schneelagen\n",
      "https://wetter.ORF.at/salzburg/schneelagen\n",
      "https://wetter.ORF.at/steiermark/schneelagen\n",
      "https://wetter.ORF.at/tirol/schneelagen\n",
      "https://wetter.ORF.at/vorarlberg/schneelagen\n",
      "https://wetter.ORF.at/wien/schneelagen\n",
      "https://wetter.ORF.at/kaernten/lawinen\n",
      "https://wetter.ORF.at/niederoesterreich/lawinen\n",
      "https://wetter.ORF.at/oberoesterreich/lawinen\n",
      "https://wetter.ORF.at/salzburg/lawinen\n",
      "https://wetter.ORF.at/steiermark/lawinen\n",
      "https://wetter.ORF.at/tirol/lawinen\n",
      "https://wetter.ORF.at/oes/wetterverlauf\n",
      "https://wetter.ORF.at/oes/wetterprognosen\n",
      "https://wetter.ORF.at/burgenland/webcam\n",
      "https://wetter.ORF.at/kaernten/webcam\n",
      "https://wetter.ORF.at/niederoesterreich/webcam\n",
      "https://wetter.ORF.at/oberoesterreich/webcam\n",
      "https://wetter.ORF.at/salzburg/webcam\n",
      "https://wetter.ORF.at/steiermark/webcam\n",
      "https://wetter.ORF.at/tirol/webcam\n",
      "https://wetter.ORF.at/vorarlberg/webcam\n",
      "https://wetter.ORF.at/wien/webcam\n",
      "https://wetter.ORF.at/oes/umweltdaten\n",
      "https://wetter.ORF.at/oes/extremwerte\n",
      "https://wetter.ORF.at/oes/prognose\n",
      "https://wetter.ORF.at/oes/bergwetter/\n",
      "https://tvthek.orf.at/profiles/genre/Wetter/2703827\n",
      "https://wetter.ORF.at/feedback\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": 15,
   "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/science/2022/01/immune-system-vs-virus-why-omicron-had-experts-worried-form-the-start/', 'https://arstechnica.com/science/2022/01/immune-system-vs-virus-why-omicron-had-experts-worried-form-the-start/', 'https://arstechnica.com/science/2022/01/immune-system-vs-virus-why-omicron-had-experts-worried-form-the-start/', 'https://arstechnica.com/author/john-timmer/', 'https://arstechnica.com/science/2022/01/immune-system-vs-virus-why-omicron-had-experts-worried-form-the-start/?comments=1', 'https://arstechnica.com/science/2022/01/rocket-report-india-test-fires-upper-stage-engine-astra-gets-a-takedown/', 'https://arstechnica.com/science/2022/01/rocket-report-india-test-fires-upper-stage-engine-astra-gets-a-takedown/', 'https://arstechnica.com/science/2022/01/rocket-report-india-test-fires-upper-stage-engine-astra-gets-a-takedown/', 'https://arstechnica.com/author/ericberger/', 'https://arstechnica.com/science/2022/01/rocket-report-india-test-fires-upper-stage-engine-astra-gets-a-takedown/?comments=1', 'https://arstechnica.com/gaming/2022/01/humble-subscription-service-is-dumping-mac-linux-access-in-18-days/', 'https://arstechnica.com/gaming/2022/01/humble-subscription-service-is-dumping-mac-linux-access-in-18-days/', 'https://arstechnica.com/gaming/2022/01/humble-subscription-service-is-dumping-mac-linux-access-in-18-days/', 'https://arstechnica.com/author/samred/', 'https://arstechnica.com/gaming/2022/01/humble-subscription-service-is-dumping-mac-linux-access-in-18-days/?comments=1', 'https://arstechnica.com/gaming/2022/01/raised-by-wolves-s2-trailer-promises-another-wild-crazy-possibly-maddening-ride/', 'https://arstechnica.com/gaming/2022/01/raised-by-wolves-s2-trailer-promises-another-wild-crazy-possibly-maddening-ride/', 'https://arstechnica.com/gaming/2022/01/raised-by-wolves-s2-trailer-promises-another-wild-crazy-possibly-maddening-ride/', 'https://arstechnica.com/author/jenniferouellette/', 'https://arstechnica.com/gaming/2022/01/raised-by-wolves-s2-trailer-promises-another-wild-crazy-possibly-maddening-ride/?comments=1', 'https://arstechnica.com/gadgets/2022/01/android-13-leaks-more-material-you-options-opt-in-to-app-notifications/', 'https://arstechnica.com/gadgets/2022/01/android-13-leaks-more-material-you-options-opt-in-to-app-notifications/', 'https://arstechnica.com/gadgets/2022/01/android-13-leaks-more-material-you-options-opt-in-to-app-notifications/', 'https://arstechnica.com/author/ronamadeo/', 'https://arstechnica.com/gadgets/2022/01/android-13-leaks-more-material-you-options-opt-in-to-app-notifications/?comments=1', 'https://arstechnica.com/tech-policy/2022/01/comcast-trying-to-torpedo-biden-fcc-pick-gigi-sohn-advocacy-group-says/', 'https://arstechnica.com/tech-policy/2022/01/comcast-trying-to-torpedo-biden-fcc-pick-gigi-sohn-advocacy-group-says/', 'https://arstechnica.com/tech-policy/2022/01/comcast-trying-to-torpedo-biden-fcc-pick-gigi-sohn-advocacy-group-says/', 'https://arstechnica.com/author/jon-brodkin/', 'https://arstechnica.com/tech-policy/2022/01/comcast-trying-to-torpedo-biden-fcc-pick-gigi-sohn-advocacy-group-says/?comments=1', 'https://arstechnica.com/information-technology/2022/01/new-chrome-security-measure-aims-to-curtail-an-entire-class-of-web-attack/', 'https://arstechnica.com/information-technology/2022/01/new-chrome-security-measure-aims-to-curtail-an-entire-class-of-web-attack/', 'https://arstechnica.com/information-technology/2022/01/new-chrome-security-measure-aims-to-curtail-an-entire-class-of-web-attack/', 'https://arstechnica.com/author/dan-goodin/', 'https://arstechnica.com/information-technology/2022/01/new-chrome-security-measure-aims-to-curtail-an-entire-class-of-web-attack/?comments=1', 'https://arstechnica.com/gadgets/2022/01/microwave-hack-replaces-flat-keypad-with-mechanical-keyboard-switches/', 'https://arstechnica.com/gadgets/2022/01/microwave-hack-replaces-flat-keypad-with-mechanical-keyboard-switches/', 'https://arstechnica.com/gadgets/2022/01/microwave-hack-replaces-flat-keypad-with-mechanical-keyboard-switches/', 'https://arstechnica.com/author/scharonharding/', 'https://arstechnica.com/gadgets/2022/01/microwave-hack-replaces-flat-keypad-with-mechanical-keyboard-switches/?comments=1', 'https://arstechnica.com/science/2022/01/a-very-common-virus-may-be-the-trigger-for-multiple-sclerosis/', 'https://arstechnica.com/science/2022/01/a-very-common-virus-may-be-the-trigger-for-multiple-sclerosis/', 'https://arstechnica.com/science/2022/01/a-very-common-virus-may-be-the-trigger-for-multiple-sclerosis/', 'https://arstechnica.com/author/beth/', 'https://arstechnica.com/science/2022/01/a-very-common-virus-may-be-the-trigger-for-multiple-sclerosis/?comments=1', 'https://arstechnica.com/tech-policy/2022/01/supreme-court-on-vaccine-mandates-hospitals-ok-general-employment-a-no/', 'https://arstechnica.com/tech-policy/2022/01/supreme-court-on-vaccine-mandates-hospitals-ok-general-employment-a-no/', 'https://arstechnica.com/tech-policy/2022/01/supreme-court-on-vaccine-mandates-hospitals-ok-general-employment-a-no/', 'https://arstechnica.com/author/john-timmer/', 'https://arstechnica.com/tech-policy/2022/01/supreme-court-on-vaccine-mandates-hospitals-ok-general-employment-a-no/?comments=1', 'https://arstechnica.com/science/2022/01/2021-was-the-worlds-sixth-warmest-year-on-record/', 'https://arstechnica.com/science/2022/01/2021-was-the-worlds-sixth-warmest-year-on-record/', 'https://arstechnica.com/science/2022/01/2021-was-the-worlds-sixth-warmest-year-on-record/', 'https://arstechnica.com/author/scott-johnson/', 'https://arstechnica.com/science/2022/01/2021-was-the-worlds-sixth-warmest-year-on-record/?comments=1', 'https://arstechnica.com/science/2022/01/ancient-peruvians-partied-hard-spiked-their-beer-with-hallucinogens-to-win-friends/', 'https://arstechnica.com/science/2022/01/ancient-peruvians-partied-hard-spiked-their-beer-with-hallucinogens-to-win-friends/', 'https://arstechnica.com/science/2022/01/ancient-peruvians-partied-hard-spiked-their-beer-with-hallucinogens-to-win-friends/', 'https://arstechnica.com/author/jenniferouellette/', 'https://arstechnica.com/science/2022/01/ancient-peruvians-partied-hard-spiked-their-beer-with-hallucinogens-to-win-friends/?comments=1', 'https://arstechnica.com/gaming/2022/01/one-of-nintendo-switchs-best-puzzle-games-is-now-mostly-free-for-7-days/', 'https://arstechnica.com/gaming/2022/01/one-of-nintendo-switchs-best-puzzle-games-is-now-mostly-free-for-7-days/', 'https://arstechnica.com/gaming/2022/01/one-of-nintendo-switchs-best-puzzle-games-is-now-mostly-free-for-7-days/', 'https://arstechnica.com/author/samred/', 'https://arstechnica.com/gaming/2022/01/one-of-nintendo-switchs-best-puzzle-games-is-now-mostly-free-for-7-days/?comments=1', 'https://arstechnica.com/tech-policy/2022/01/holmes-to-face-maximum-of-80-years-in-prison-when-shes-sentenced-in-september/', 'https://arstechnica.com/tech-policy/2022/01/holmes-to-face-maximum-of-80-years-in-prison-when-shes-sentenced-in-september/', 'https://arstechnica.com/tech-policy/2022/01/holmes-to-face-maximum-of-80-years-in-prison-when-shes-sentenced-in-september/', 'https://arstechnica.com/author/tim-de-chant-2/', 'https://arstechnica.com/tech-policy/2022/01/holmes-to-face-maximum-of-80-years-in-prison-when-shes-sentenced-in-september/?comments=1', 'https://arstechnica.com/gadgets/2022/01/decade-old-volume-indicator-gets-a-new-look-in-latest-windows-11-preview/', 'https://arstechnica.com/gadgets/2022/01/decade-old-volume-indicator-gets-a-new-look-in-latest-windows-11-preview/', 'https://arstechnica.com/gadgets/2022/01/decade-old-volume-indicator-gets-a-new-look-in-latest-windows-11-preview/', 'https://arstechnica.com/author/andrew_cunningham/', 'https://arstechnica.com/gadgets/2022/01/decade-old-volume-indicator-gets-a-new-look-in-latest-windows-11-preview/?comments=1', 'https://arstechnica.com/gadgets/2022/01/dropbox-starts-testing-native-apple-m1-support/', 'https://arstechnica.com/gadgets/2022/01/dropbox-starts-testing-native-apple-m1-support/', 'https://arstechnica.com/gadgets/2022/01/dropbox-starts-testing-native-apple-m1-support/', 'https://arstechnica.com/author/scharonharding/', 'https://arstechnica.com/gadgets/2022/01/dropbox-starts-testing-native-apple-m1-support/?comments=1', 'https://arstechnica.com/tech-policy/2022/01/spacex-aims-to-launch-2nd-gen-starlink-satellites-soon-but-amazon-seeks-delay/', 'https://arstechnica.com/tech-policy/2022/01/spacex-aims-to-launch-2nd-gen-starlink-satellites-soon-but-amazon-seeks-delay/', 'https://arstechnica.com/tech-policy/2022/01/spacex-aims-to-launch-2nd-gen-starlink-satellites-soon-but-amazon-seeks-delay/', 'https://arstechnica.com/author/jon-brodkin/', 'https://arstechnica.com/tech-policy/2022/01/spacex-aims-to-launch-2nd-gen-starlink-satellites-soon-but-amazon-seeks-delay/?comments=1', 'https://arstechnica.com/gaming/2022/01/sony-reportedly-producing-more-ps4s-to-make-up-for-ps5-shortages/', 'https://arstechnica.com/gaming/2022/01/sony-reportedly-producing-more-ps4s-to-make-up-for-ps5-shortages/', 'https://arstechnica.com/gaming/2022/01/sony-reportedly-producing-more-ps4s-to-make-up-for-ps5-shortages/', 'https://arstechnica.com/author/kyle-orland/', 'https://arstechnica.com/gaming/2022/01/sony-reportedly-producing-more-ps4s-to-make-up-for-ps5-shortages/?comments=1', 'https://arstechnica.com/gadgets/2022/01/tsmc-invests-in-new-capacity-despite-forecasts-chip-demand-will-ease/', 'https://arstechnica.com/gadgets/2022/01/tsmc-invests-in-new-capacity-despite-forecasts-chip-demand-will-ease/', 'https://arstechnica.com/gadgets/2022/01/tsmc-invests-in-new-capacity-despite-forecasts-chip-demand-will-ease/', 'https://arstechnica.com/author/financialtimes/', 'https://arstechnica.com/gadgets/2022/01/tsmc-invests-in-new-capacity-despite-forecasts-chip-demand-will-ease/?comments=1', 'https://arstechnica.com/science/2022/01/with-thursdays-launch-spacex-continues-to-increase-cadence-of-booster-reuse/', 'https://arstechnica.com/science/2022/01/with-thursdays-launch-spacex-continues-to-increase-cadence-of-booster-reuse/', 'https://arstechnica.com/science/2022/01/with-thursdays-launch-spacex-continues-to-increase-cadence-of-booster-reuse/', 'https://arstechnica.com/author/ericberger/', 'https://arstechnica.com/science/2022/01/with-thursdays-launch-spacex-continues-to-increase-cadence-of-booster-reuse/?comments=1', 'https://www.facebook.com/arstechnica', 'https://twitter.com/arstechnica', 'https://www.youtube.com/user/arstechnicavideos', 'https://www.instagram.com/arstechnica/', 'https://video.arstechnica.com', 'https://arstechnica.com/gaming/2021/12/war-stories-it-was-surprisingly-hard-to-make-deus-ex-all-things-to-all-players/', 'https://arstechnica.com/gaming/2021/12/war-stories-it-was-surprisingly-hard-to-make-deus-ex-all-things-to-all-players/', 'https://arstechnica.com/gaming/2021/12/war-stories-it-was-surprisingly-hard-to-make-deus-ex-all-things-to-all-players/', 'https://arstechnica.com/gaming/2022/01/prime-video-drops-red-band-trailer-for-legend-of-vox-machina-animated-series/', 'https://arstechnica.com/gaming/2022/01/prime-video-drops-red-band-trailer-for-legend-of-vox-machina-animated-series/', 'https://arstechnica.com/gaming/2022/01/prime-video-drops-red-band-trailer-for-legend-of-vox-machina-animated-series/', 'https://arstechnica.com/author/jenniferouellette/', 'https://arstechnica.com/gaming/2022/01/prime-video-drops-red-band-trailer-for-legend-of-vox-machina-animated-series/?comments=1', 'https://arstechnica.com/science/2022/01/cdc-to-update-advice-on-best-masks-but-just-wants-you-to-wear-one-any-of-them/', 'https://arstechnica.com/science/2022/01/cdc-to-update-advice-on-best-masks-but-just-wants-you-to-wear-one-any-of-them/', 'https://arstechnica.com/science/2022/01/cdc-to-update-advice-on-best-masks-but-just-wants-you-to-wear-one-any-of-them/', 'https://arstechnica.com/author/beth/', 'https://arstechnica.com/science/2022/01/cdc-to-update-advice-on-best-masks-but-just-wants-you-to-wear-one-any-of-them/?comments=1', 'https://arstechnica.com/gadgets/2022/01/sonys-compact-xperia-5-iii-comes-to-the-us-for-1000/', 'https://arstechnica.com/gadgets/2022/01/sonys-compact-xperia-5-iii-comes-to-the-us-for-1000/', 'https://arstechnica.com/gadgets/2022/01/sonys-compact-xperia-5-iii-comes-to-the-us-for-1000/', 'https://arstechnica.com/author/ronamadeo/', 'https://arstechnica.com/gadgets/2022/01/sonys-compact-xperia-5-iii-comes-to-the-us-for-1000/?comments=1', 'https://arstechnica.com/gadgets/2022/01/pci-express-6-0-spec-is-finalized-doubling-bandwidth-for-ssds-gpus-and-more/', 'https://arstechnica.com/gadgets/2022/01/pci-express-6-0-spec-is-finalized-doubling-bandwidth-for-ssds-gpus-and-more/', 'https://arstechnica.com/gadgets/2022/01/pci-express-6-0-spec-is-finalized-doubling-bandwidth-for-ssds-gpus-and-more/', 'https://arstechnica.com/author/andrew_cunningham/', 'https://arstechnica.com/gadgets/2022/01/pci-express-6-0-spec-is-finalized-doubling-bandwidth-for-ssds-gpus-and-more/?comments=1', 'https://arstechnica.com/cars/2022/01/new-cars-have-lost-their-steering-feel-heres-why-that-matters/', 'https://arstechnica.com/cars/2022/01/new-cars-have-lost-their-steering-feel-heres-why-that-matters/', 'https://arstechnica.com/cars/2022/01/new-cars-have-lost-their-steering-feel-heres-why-that-matters/', 'https://arstechnica.com/author/jonathan-m-gitlin/', 'https://arstechnica.com/cars/2022/01/new-cars-have-lost-their-steering-feel-heres-why-that-matters/?comments=1', 'https://arstechnica.com/tech-policy/2022/01/scammers-put-fake-qr-codes-on-parking-meters-to-intercept-parkers-payments/', 'https://arstechnica.com/tech-policy/2022/01/scammers-put-fake-qr-codes-on-parking-meters-to-intercept-parkers-payments/', 'https://arstechnica.com/tech-policy/2022/01/scammers-put-fake-qr-codes-on-parking-meters-to-intercept-parkers-payments/', 'https://arstechnica.com/author/jon-brodkin/', 'https://arstechnica.com/tech-policy/2022/01/scammers-put-fake-qr-codes-on-parking-meters-to-intercept-parkers-payments/?comments=1', 'https://arstechnica.com/gaming/2022/01/wordle-and-ip-law-what-happens-when-a-hot-game-gets-cloned/', 'https://arstechnica.com/gaming/2022/01/wordle-and-ip-law-what-happens-when-a-hot-game-gets-cloned/', 'https://arstechnica.com/gaming/2022/01/wordle-and-ip-law-what-happens-when-a-hot-game-gets-cloned/', 'https://arstechnica.com/author/kyle-orland/', 'https://arstechnica.com/gaming/2022/01/wordle-and-ip-law-what-happens-when-a-hot-game-gets-cloned/?comments=1', 'https://arstechnica.com/tech-policy/2022/01/facebook-loses-bid-to-kill-ftc-antitrust-lawsuit/', 'https://arstechnica.com/tech-policy/2022/01/facebook-loses-bid-to-kill-ftc-antitrust-lawsuit/', 'https://arstechnica.com/tech-policy/2022/01/facebook-loses-bid-to-kill-ftc-antitrust-lawsuit/', 'https://arstechnica.com/author/tim-de-chant-2/', 'https://arstechnica.com/tech-policy/2022/01/facebook-loses-bid-to-kill-ftc-antitrust-lawsuit/?comments=1', 'https://arstechnica.com/science/2022/01/ships-from-1581-ports-may-go-to-antarctica-bringing-unwanted-guests/', 'https://arstechnica.com/science/2022/01/ships-from-1581-ports-may-go-to-antarctica-bringing-unwanted-guests/', 'https://arstechnica.com/science/2022/01/ships-from-1581-ports-may-go-to-antarctica-bringing-unwanted-guests/', 'https://arstechnica.com/author/dougjohnson/', 'https://arstechnica.com/science/2022/01/ships-from-1581-ports-may-go-to-antarctica-bringing-unwanted-guests/?comments=1', 'https://arstechnica.com/cars/2022/01/teen-hacker-finds-bug-that-lets-him-control-25-teslas-remotely/', 'https://arstechnica.com/cars/2022/01/teen-hacker-finds-bug-that-lets-him-control-25-teslas-remotely/', 'https://arstechnica.com/cars/2022/01/teen-hacker-finds-bug-that-lets-him-control-25-teslas-remotely/', 'https://arstechnica.com/author/jonathan-m-gitlin/', 'https://arstechnica.com/cars/2022/01/teen-hacker-finds-bug-that-lets-him-control-25-teslas-remotely/?comments=1', 'https://arstechnica.com/science/2022/01/archaeologists-unearth-ancient-roman-town-along-uk-high-speed-rail-route/', 'https://arstechnica.com/science/2022/01/archaeologists-unearth-ancient-roman-town-along-uk-high-speed-rail-route/', 'https://arstechnica.com/science/2022/01/archaeologists-unearth-ancient-roman-town-along-uk-high-speed-rail-route/', 'https://arstechnica.com/author/ksmith/', 'https://arstechnica.com/science/2022/01/archaeologists-unearth-ancient-roman-town-along-uk-high-speed-rail-route/?comments=1', 'https://arstechnica.com/gaming/2022/01/god-of-war-on-pc-delivers-nearly-everything-wed-hoped-for/', 'https://arstechnica.com/gaming/2022/01/god-of-war-on-pc-delivers-nearly-everything-wed-hoped-for/', 'https://arstechnica.com/gaming/2022/01/god-of-war-on-pc-delivers-nearly-everything-wed-hoped-for/', 'https://arstechnica.com/author/samred/', 'https://arstechnica.com/gaming/2022/01/god-of-war-on-pc-delivers-nearly-everything-wed-hoped-for/?comments=1', 'https://arstechnica.com/staff/2022/01/todays-best-deals-apple-watch-se-bluetooth-speakers-switch-online-bundle-and-more/', 'https://arstechnica.com/staff/2022/01/todays-best-deals-apple-watch-se-bluetooth-speakers-switch-online-bundle-and-more/', 'https://arstechnica.com/staff/2022/01/todays-best-deals-apple-watch-se-bluetooth-speakers-switch-online-bundle-and-more/', 'https://arstechnica.com/author/ars-staff/', 'https://arstechnica.com/staff/2022/01/todays-best-deals-apple-watch-se-bluetooth-speakers-switch-online-bundle-and-more/?comments=1', 'https://arstechnica.com/science/2022/01/new-images-of-the-international-space-station-reveal-that-it-is-still-a-jewel/', 'https://arstechnica.com/science/2022/01/new-images-of-the-international-space-station-reveal-that-it-is-still-a-jewel/', 'https://arstechnica.com/science/2022/01/new-images-of-the-international-space-station-reveal-that-it-is-still-a-jewel/', 'https://arstechnica.com/author/ericberger/', 'https://arstechnica.com/science/2022/01/new-images-of-the-international-space-station-reveal-that-it-is-still-a-jewel/?comments=1', 'https://arstechnica.com/gadgets/2022/01/samsung-no-showed-on-its-major-exynos-2200-launch-and-wont-say-why/', 'https://arstechnica.com/gadgets/2022/01/samsung-no-showed-on-its-major-exynos-2200-launch-and-wont-say-why/', 'https://arstechnica.com/gadgets/2022/01/samsung-no-showed-on-its-major-exynos-2200-launch-and-wont-say-why/', 'https://arstechnica.com/author/ronamadeo/', 'https://arstechnica.com/gadgets/2022/01/samsung-no-showed-on-its-major-exynos-2200-launch-and-wont-say-why/?comments=1', 'https://arstechnica.com/cars/2022/01/with-qualcomm-and-android-the-polestar-3-is-more-smartphone-like-than-ever/', 'https://arstechnica.com/cars/2022/01/with-qualcomm-and-android-the-polestar-3-is-more-smartphone-like-than-ever/', 'https://arstechnica.com/cars/2022/01/with-qualcomm-and-android-the-polestar-3-is-more-smartphone-like-than-ever/', 'https://arstechnica.com/author/ronamadeo/', 'https://arstechnica.com/cars/2022/01/with-qualcomm-and-android-the-polestar-3-is-more-smartphone-like-than-ever/?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": [
    "### 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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "url3 = '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(url3)"
   ]
  },
  {
   "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": 17,
   "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/tt15097216/\">Jai Bhim</a>\n",
       " <span class=\"lister-item-year text-muted unbold\">(2021)</span>\n",
       " </h3>,\n",
       " <h3 class=\"lister-item-header\">\n",
       " <span class=\"lister-item-index unbold text-primary\">2.</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\">3.</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\">4.</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\">5.</span>\n",
       " <a href=\"/title/tt0468569/\">The Dark Knight</a>\n",
       " <span class=\"lister-item-year text-muted unbold\">(2008)</span>\n",
       " </h3>]"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Jai Bhim',\n",
       " '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\"]"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['(2021)',\n",
       " '(1994)',\n",
       " '(1972)',\n",
       " '(2020)',\n",
       " '(2008)',\n",
       " '(1974)',\n",
       " '(1957)',\n",
       " '(2003)',\n",
       " '(1994)',\n",
       " '(1993)']"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'9.3'"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['166955', '2524608', '28,341,469', '1738105', '134,966,411', '105969', '2474861', '534,858,444', '1206022', '57,300,000', '745533', '4,360,000', '1742556', '377,845,905', '1945178', '107,928,762', '1290110', '96,898,818', '2219102', '292,576,195', '381254', '34422', '1985641', '37,030,102', '1763818', '315,544,750', '1948453', '330,252,182', '729757', '6,100,000', '80169', '111744', '1574783', '342,551,365', '1819851', '171,479,930', '1092313', '46,836,394', '1226372', '290,475,067', '968073', '112,000,000', '704737', '53,367,844', '33253', '1670884', '188,020,017', '731227', '7,563,397', '711808', '10,055,859', '1318236', '216,540,909', '1228259', '136,801,374', '664080', '57,598,247', '1549651', '100,125,643', '1357805', '130,742,922', '1297940', '322,740,140', '51376', '333835', '269,061', '437425', '27144', '784073', '13,092,000', '813495', '13,182,281', '1268744', '53,089,891', '1262628', '132,384,315', '787132', '32,572,577', '1426161', '187,705,427', '1081233', '6,719,864', '1044282', '23,341,568', '1103636', '19,501,238', '1002551', '422,783,777', '1050193', '204,843,350', '249906', '11,990,401', '259173', '1136081', '210,609,762', '318833', '5,321,508', '642033', '32,000,000', '473008', '36,764,313', '550923', '1,024,560', '232696', '163,245', '178698', '19,181', '80529', '80141', '1,661,096', '1126129', '335,451,311', '37320', '37822', '236671', '5,017,246', '176504', '12,391,761', '460477', '190,241,310', '993360', '858,373,000', '966948', '678,815,482', '453653', '209,726,015', '1459854', '162,805,434', '1607516', '448,139,099', '375858', '6,532,908', '184301', '1,223,869', '1061740', '223,808,164', '377926', '11,286,112', '551409', '707,481', '1187303', '25,544,867', '372970', '2,375,308', '930103', '248,159,971', '964675', '44,017,374', '640271', '83,471,511', '838483', '78,900,000', '41116', '473614', '275,902', '119338', '8,175,000', '190087', '30035', '536,364', '214280', '216460', '288,475', '46927', '898,575', '522938', '159,227,644', '54677', '4,186,168', '39268', '52258', '35364', '39058', '311464', '687,185', '235572', '7,098,492', '167495', '6,857,096']\n"
     ]
    }
   ],
   "source": [
    "votes_earnings = soup.findAll('span', {'name':'nv'})\n",
    "print([ve['data-value'] for ve in votes_earnings])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['166955', '28,341,469', '134,966,411', '2474861', '1206022']\n",
      "['2524608', '1738105', '105969', '534,858,444', '57,300,000']\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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      "<a href=\"/chart/top/?ref_=ats_top1000serp_to_top250chart\">IMDb Top 250 Movies chart</a>\n",
      "IMDb Top 250 Movies chart\n",
      "---\n",
      "<a href=\"/name/nm4377096/\">T.J. Gnanavel</a>\n",
      "T.J. Gnanavel\n",
      "<a href=\"/name/nm1421814/\">Suriya</a>\n",
      "Suriya\n",
      "<a href=\"/name/nm7934649/\">Lijo Mol Jose</a>\n",
      "Lijo Mol Jose\n",
      "<a href=\"/name/nm8904193/\">Manikandan</a>\n",
      "Manikandan\n",
      "<a href=\"/name/nm8135195/\">Rajisha Vijayan</a>\n",
      "Rajisha Vijayan\n",
      "---\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"
     ]
    }
   ],
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 24,
     "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": 25,
   "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": 26,
   "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>Release</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>Jai Bhim</td>\n",
       "      <td>(2021)</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>164 min</td>\n",
       "      <td>\\nCrime, Drama</td>\n",
       "      <td>\\n\\n9.3\\n</td>\n",
       "      <td>166,955</td>\n",
       "      <td>166,955</td>\n",
       "      <td>T.J. Gnanavel</td>\n",
       "      <td>[Suriya, Lijo Mol Jose, Manikandan, Rajisha Vi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2,524,608</td>\n",
       "      <td>2,524,608</td>\n",
       "      <td>Frank Darabont</td>\n",
       "      <td>[Tim Robbins, Morgan Freeman, Bob Gunton, Will...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>1,738,105</td>\n",
       "      <td>1,738,105</td>\n",
       "      <td>Francis Ford Coppola</td>\n",
       "      <td>[Marlon Brando, Al Pacino, James Caan, Diane K...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>105,969</td>\n",
       "      <td>105,969</td>\n",
       "      <td>Sudha Kongara</td>\n",
       "      <td>[Suriya, Paresh Rawal, Urvashi, Aparna Balamur...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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>2,474,861</td>\n",
       "      <td>2,474,861</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>[Christian Bale, Heath Ledger, Aaron Eckhart, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Title Release Audience Rating  Runtime  \\\n",
       "0                  Jai Bhim  (2021)           TV-MA  164 min   \n",
       "1  The Shawshank Redemption  (1994)               R  142 min   \n",
       "2             The Godfather  (1972)               R  175 min   \n",
       "3           Soorarai Pottru  (2020)           TV-MA  153 min   \n",
       "4           The Dark Knight  (2008)           PG-13  152 min   \n",
       "\n",
       "                                Genre IMDB Rating      Votes  \\\n",
       "0          \\nCrime, Drama               \\n\\n9.3\\n    166,955   \n",
       "1                 \\nDrama               \\n\\n9.3\\n  2,524,608   \n",
       "2          \\nCrime, Drama               \\n\\n9.2\\n  1,738,105   \n",
       "3                 \\nDrama               \\n\\n9.1\\n    105,969   \n",
       "4  \\nAction, Crime, Drama               \\n\\n9.0\\n  2,474,861   \n",
       "\n",
       "  Box Office Earnings              Director  \\\n",
       "0             166,955         T.J. Gnanavel   \n",
       "1           2,524,608        Frank Darabont   \n",
       "2           1,738,105  Francis Ford Coppola   \n",
       "3             105,969         Sudha Kongara   \n",
       "4           2,474,861     Christopher Nolan   \n",
       "\n",
       "                                              Actors  \n",
       "0  [Suriya, Lijo Mol Jose, Manikandan, Rajisha Vi...  \n",
       "1  [Tim Robbins, Morgan Freeman, Bob Gunton, Will...  \n",
       "2  [Marlon Brando, Al Pacino, James Caan, Diane K...  \n",
       "3  [Suriya, Paresh Rawal, Urvashi, Aparna Balamur...  \n",
       "4  [Christian Bale, Heath Ledger, Aaron Eckhart, ...  "
      ]
     },
     "execution_count": 26,
     "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, 'Release': 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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "url4 = \"http://www.hubertiming.com/results/2017GPTR10K\"\n",
    "html = urllib.request.urlopen(url4)\n",
    "soup = bs(html, 'lxml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the text\n",
    "text = soup.get_text()\n",
    "text[0:200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup.find_all('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "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>Chip Time</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>36:21</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>36:42</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>37:44</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": 33,
   "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>1:41:18</td>, <td>1:42:10</td>, <td></td>]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "bs4.element.ResultSet"
      ]
     },
     "execution_count": 33,
     "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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[577, 443, \n",
      "\n",
      "                    LIBBY B MITCHELL\n",
      "\n",
      "                , F, HILLSBORO, 1:41:18, 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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[577, 443, \n",
      "\n",
      "                    LIBBY B MITCHELL\n",
      "\n",
      "                , F, HILLSBORO, 1:41:18, 1:42:10, ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 35,
     "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 36,
     "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": 37,
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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>36:21</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>36:42</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>37:44</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>38:34</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>39:21</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  \\\n",
       "0  None        None    None    None   \n",
       "1  None        None    None    None   \n",
       "2  None        None    None    None   \n",
       "3  None        None    None    None   \n",
       "4  None        None    None    None   \n",
       "5     M      TIGARD   36:21   36:24   \n",
       "6     M    PORTLAND   36:42   36:45   \n",
       "7     M    PORTLAND   37:44   37:48   \n",
       "8     M   BEAVERTON   38:34   38:37   \n",
       "9     M   HILLSBORO   39:21   39:24   \n",
       "\n",
       "                                                   7  \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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df['raw'].str.split(',', expand=True)\n",
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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",
       "    }\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>7</th>\n",
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       "      <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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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>36:21</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>36:42</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>37:44</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>38:34</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>39:21</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  \\\n",
       "0        None    None    None   \n",
       "1        None    None    None   \n",
       "2        None    None    None   \n",
       "3        None    None    None   \n",
       "4        None    None    None   \n",
       "5      TIGARD   36:21   36:24   \n",
       "6    PORTLAND   36:42   36:45   \n",
       "7    PORTLAND   37:44   37:48   \n",
       "8   BEAVERTON   38:34   38:37   \n",
       "9   HILLSBORO   39:21   39:24   \n",
       "\n",
       "                                                   7  \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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[0] = df1[0].str.strip('[')\n",
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_labels = soup.find_all('th')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[Place, Bib, Name, Gender, City, Chip Time, 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": 41,
   "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",
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       "\n",
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       "</style>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "                                                   0\n",
       "0  [Place, Bib, Name, Gender, City, Chip Time, Gu..."
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     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(all_header)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>[Place</td>\n",
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       "      <td>Name</td>\n",
       "      <td>Gender</td>\n",
       "      <td>City</td>\n",
       "      <td>Chip Time</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   Chip Time   Gun Time   Team]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2[0].str.split(',', expand=True)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>]</td>\n",
       "      <td>None</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",
       "    </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>36:21</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>36:42</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>37:44</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>38:34</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  \\\n",
       "0   Gender        City   Chip Time   Gun Time   \n",
       "0     None        None        None       None   \n",
       "1     None        None        None       None   \n",
       "2     None        None        None       None   \n",
       "3     None        None        None       None   \n",
       "4     None        None        None       None   \n",
       "5        M      TIGARD       36:21      36:24   \n",
       "6        M    PORTLAND       36:42      36:45   \n",
       "7        M    PORTLAND       37:44      37:48   \n",
       "8        M   BEAVERTON       38:34      38:37   \n",
       "\n",
       "                                                   7  \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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [df3, df1]\n",
    "\n",
    "df4 = pd.concat(frames)\n",
    "df4.head(10)"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>Gender</th>\n",
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       "      <td>Finishers:</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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       [Place    Bib   Name   Gender   City   Chip Time   Gun Time   Team]\n",
       "0      [Place    Bib   Name   Gender   City   Chip Time   Gun Time   Team]\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"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df4.rename(columns=df4.iloc[0])\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 583 entries, 0 to 581\n",
      "Data columns (total 8 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    Chip Time  578 non-null    object\n",
      " 6    Gun Time   578 non-null    object\n",
      " 7    Team]      578 non-null    object\n",
      "dtypes: object(8)\n",
      "memory usage: 41.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(583, 8)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.info()\n",
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df6 = df5.dropna(axis=0, how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>[Place</th>\n",
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       "      <th>Gender</th>\n",
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       "      <th>5</th>\n",
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       "      <td>814</td>\n",
       "      <td>\\r\\n\\r\\n                    JARED WILSON\\r\\n\\...</td>\n",
       "      <td>M</td>\n",
       "      <td>TIGARD</td>\n",
       "      <td>36:21</td>\n",
       "      <td>36:24</td>\n",
       "      <td>]</td>\n",
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       "      <th>6</th>\n",
       "      <td>2</td>\n",
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       "      <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>37:44</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>38:34</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>39:21</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  Chip Time  Gun Time  \\\n",
       "5      TIGARD      36:21     36:24   \n",
       "6    PORTLAND      36:42     36:45   \n",
       "7    PORTLAND      37:44     37:48   \n",
       "8   BEAVERTON      38:34     38:37   \n",
       "9   HILLSBORO      39:21     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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7 = df6.drop(df6.index[0])\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>Place</th>\n",
       "      <th>Bib</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Chip Time</th>\n",
       "      <th>Gun Time</th>\n",
       "      <th>Team</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>36:21</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>36:42</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>37:44</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>38:34</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>39:21</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  Chip Time  Gun Time  \\\n",
       "5      TIGARD      36:21     36:24   \n",
       "6    PORTLAND      36:42     36:45   \n",
       "7    PORTLAND      37:44     37:48   \n",
       "8   BEAVERTON      38:34     38:37   \n",
       "9   HILLSBORO      39:21     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": 48,
     "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": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>Bib</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Chip Time</th>\n",
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       "      <th>Team</th>\n",
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       "      <td>36:21</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>36:42</td>\n",
       "      <td>36:45</td>\n",
       "      <td>\\n\\r\\n                            INTEL TEAM ...</td>\n",
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       "      <td>3</td>\n",
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       "      <td>\\r\\n\\r\\n                    FRANCISCO MAYA\\r\\...</td>\n",
       "      <td>M</td>\n",
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       "      <td>37:44</td>\n",
       "      <td>37:48</td>\n",
       "      <td></td>\n",
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       "    <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>38:34</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>39:21</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  Chip Time  Gun Time  \\\n",
       "5      TIGARD      36:21     36:24   \n",
       "6    PORTLAND      36:42     36:45   \n",
       "7    PORTLAND      37:44     37:48   \n",
       "8   BEAVERTON      38:34     38:37   \n",
       "9   HILLSBORO      39:21     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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7['Team'] = df7['Team'].str.strip(']')\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 51,
   "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",
       "    }\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>Place</th>\n",
       "      <th>Bib</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Chip Time</th>\n",
       "      <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>36:21</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>36:42</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>37:44</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>38:34</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>39:21</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  Chip Time  Gun Time  \\\n",
       "5      TIGARD      36:21     36:24   \n",
       "6    PORTLAND      36:42     36:45   \n",
       "7    PORTLAND      37:44     37:48   \n",
       "8   BEAVERTON      38:34     38:37   \n",
       "9   HILLSBORO      39:21     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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7['Runner_mins'] = time_mins\n",
    "df7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.describe(include=[np.number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f5b79fe5a60>], [Text(1, 0, 'Runners')])"
      ]
     },
     "execution_count": 54,
     "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": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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19NiQigeLkqRoQ1TZs7Pq+tddyp6VVcPZDcqemVWscXY/WpFERIllCSWWnThglYfKuu/p+3TtRddq9NlRjTw9otf+7LXgh4Ok1Ckp5bbk1HRxk1quaFH61DStagCWtCO9E8r5sipDleA2X1FltKLKWCXosTBeVWWsog/3f1g7n9p5NJC9SRY3RZIRWTK4ff/Q+7Xvc/sUbYwq2jBpaowq1hT0qojmogQ6YIkgmAEnML5rXP339mvgvgEN3D9Qaw1LrUup+YpmNV3cpKaLm5Q9IyuLzr8dZiwX0+i6Ua369VW1ZZWxioafHNbQj4Y09PiQBu8fVPeXuyVJyVVJtVzVoparWtR8ZbOSy5K+ig5gkarHxaFnMkhFtVRVqbukNf1rNHD/gEp9JZV6Syr3lVXqK6k6Xn39iyLBAbhIOji3N5KOKN4W1zN6RlvO3BK0gh2Z4iaLmsxMzjnJSaoGLWiqSq7kVC1WVS1U5QpO1UJVxbGiKqMVFQ8XVRmpyBVOEPIiCrq8t8SD84rb40p0JhTvjCvRRe8HYDEhmAEKBugYuH9A/d/uV/+9/ZrYNSFJSq5JqvODnWq5qkVNlzYpuWLhBpZoJqrmS5vVfGmzpODHwvgr4xq8f1AD3x9Q7929OvTFQ5Kk7JlZtf6rVrW/v125Lbl5GT4BLCz1uDj0mxmkojJeUXF/UYWDBZUOlVQ8VFTxUFGlvpLkpKt1tXpe7JElTfG2uOJtcaU3phVrjSmWiynaFLRWxZpiimQiJ2ytevT2R3XDh26Y0d8iSZ+7/XP67C2frd2vFqtBC91wReXBYICoWrf1gbImdk1oeNtwMFBU6Bdjv6i9B/YqsTyhxMqEkiuTSq5IKtoUpScEsMAQzLBkFQ8X1fftPvXe3auBewdUHa8q2hBV8xXNWvWfVqn1mlalNy3eLn5mpszGjDIbM1rxH1bIVZxGto8EAfW7/dr35/v02mdfU6wtprbr29T2/ja1XtuqWI6vDQD+OedU6i2psLegwv6CCvuCqdxXrj3H4qZEV0KpdSk1bmlUoiuhv/vJ3+k3PvobimRPfq6vD5FERJFERPGWuLTmxM9xleDvLnYXVTpc0sMPP6zzU+dr9LlRDf1o6Oi6shElVySVXJ1Uam1KyTVJJZYl6BYJzGP8wsKSMrFvQj3/1KOeO3s09PiQ5IJWseUfW66297ep+bJmRZJL87rrFjU1nt+oxvMbtea316g0WNLA9wbUd0+f+r7dp8NfOiyLm1qvbVXHBzvUdkOb4s1x38UGsESUh8ta3b9avXf3amL3hCZ2T6g6GjYdRaREV0LpDWklL00qsSqh5PKkYq2x1wWR3hd6FW2IevgL6sOiQdhMdCWkd0iP7X9MN95yoySpMlIJQur+QtBquL+g/A/zGnxgMHht0pRaHYS01IaU0qekFW/lexyYLwhmWPSKh4vqubNH3Xd0K/9IcH5Dw7kNWveZdWq/oV3Zs7Lz7qjpfBBvjqvzpk513tSparmqoceH1PuNXvXc2aO+e/pkcVPLNS3q/GCn2m4kpAGon2qxqsLegsZfHdfEq0EIK/eVdY2uUf/P+pVYmVDjuY210W4TKxKKxJfmQbXJog1RZU7LKHNaprbMVZyKh4qa2Duhwu6CJvZOHBPWYi0xXR69XAMPDih9SlrJVUla1QBPCGZYlEp9JfXc1aPur3Zr8MFBqRqcN7X+D9er40MdymzKvOE6cFQkFlHzJc1qvqRZp/zZKRr+ybC6v9atnq/16MVvv0hIAzAj5cGyxl4a0/jL45rYNaHCgULtPKpYWyy45Mh7Uvrii1/Up2791JLt2TAdFrXgvLOVSemdwTJXcSrsK2j8lXGN7xzXsmeWqeeOHklSJBVRan3QmpbamFJ6fVqRFO83MBcIZlg0yvmyer/Zq+47ujVw34Bc2Sm9Ka21n16rzps6lT0j67uIi4KZBddEuyinU/50ipB2dYs6Ptih9hvbg3MlAGCSUn9J4y+N18JYqbskKQwFG1JqPbs1CGPrUsec13r4wGFCWR1Y1JRam1JqbUotV7Toc1/8nP7wfX8YBLVw6vt2XzCypAUj96ZPTStzekbpTWlF0wu3KygwnxHMsKBVRivq/VYQxvq/0y9XdEquTWrVb65S502dajingW6Ks+hkIa3/X/r1UvwltV7Xqs6bO9V2Q5tiDXzlAEuNc04TuyfU8tMWHXrwkMZeGqsN0BHJRJTelFbzu5uVPjWt5Gq60XlhUrw9rnh7XLmLcpKC0S0ndk3UWtXyD+c1eP+gZFJqbUrp09PKnJZRemNakQRhGagHfiVhwamMV9T/nX51f7Vbfd/qU3W8qsSKhFb+ykp13typxgsbCWMevC6kPTGs7q921+opko6o7f1t6ry5U63vbVU0xRFXYDFyzml857gGfzCo/A/yGvzBoAqvFbRaqzXaMKr0prTSV6WV2ZRRYiWjBM5X0XRU2TOytd4m1VJVE7smNPazMY29OKaBewc08N0BKSql16fVtaxLgw8PKndRjlZNYJoIZlgQqsWqBu4bUPcd3er9Zq8qwxXFO+Ja9kvL1HlTp5ouaWLnPo+YmXIX5pS7MAhp+Ufz6r4jaEnr+aceRXNRtf98uzpv7lTLlS2ctA8sYM45jb04dkwQKx4sSpLinXE1X9as5t9p1j3b79H7zn8f39ULVCQeOTqwyA1SdaKq8Z3jQVD72Zg6H+nU9su2K5KOqOniJjVf0ayWK1vUeH4j18IE3iSCGeatarmqwQcH1f3VbvXe1avyQFmxlpg6PtShzps71Xx5syIxftDPdxax2oWtN35uowYfGAxC2l09Onz7YcXb4+rYGtRp06UEbGC+c1Wn0edGjwaxhwdr54glViTUfHmzmi9rVtNlTcqclqn1YCh8usDnexGJpCLKnplV9sygRe0bL35DN112kwYeGNDgA4N69b+8qlf1qqJNUTVfHoS0litblHlbhl4twBQIZphXXMVp8IeDtWuNlXpKijZG1f5z7eq8qVMtV7fQl30Bi8Qiar2mVa3XtOrUvzlV/d/tV/cd3Tr0j4d04G8PKLEiEQzRf3OnGi+gSyowH7iq08iOkVpr2ODDg7VzxJJrkmq9trUWxNKnpPncLlGVdEXtN7ar/cZ2ScGlagYeGNDA/QMavH9Qfd/skyQllidqrWktV7YotSbls9jAvEIwg3fVclWDDw2q584e9d7Vq1JP6djzka5rZQSoRSiSjNR24pMHcdn/+f3a95f7lNqQUufNQUhreEeD7+ICS0a1XNXI9qNBLP/DvMqDQRBLrU+p/f3tarqsSc2XNyu9Lu25tJivEl0JdX24S10f7pIkje8a18D9QVAbuHdA3V/uliSlN6bVfGUQ1Jrf06xEe8JnsQGvCGbwolqsauCBgSCM/XOvyn1lRbIRtb2vTR1bO9T23jZFs4SxpSKajarr5i513dyl0mBJvf/cq+6vdGvvZ/dq7//Yq8zbM+r4hQ51/EIHFwQH6qxaqmrkqZGgNeyhQeUfyasyXJEkpTel1bG1IwhilzUrtZrWDUxPekNa6Q1prfj4iqA77LOjtaDW/eVuHfy7g5JJDWc31IJa06VNjOaLJYX/dsyZaqGqge8PqPtr3er7Zp/Kg2VFG6NquyEIY63X0jIGKd4c1/KPLtfyjy5Xsbuonq8HA4bs+aM92vPf9yh1SqoW0ujuCLx15ZGyhn88rPyjeeUfySv/WF7V0eBqzpm3ZdT1b7qCIPbuZiVXJD2XFouRRUwNZzWo4awGrf5Pq1UtVTX8xHAtqO3/X/u178/3yeLBaL9HglruohynM2BRI5hhVpWHyur/Xr/67u5T7929qgxVFG2Kqv3GdnVs7VDL1S0Mm44pJToTWvnLK7Xyl1eq2F1U7zd71fP1Hu37i3167U9eU3JVUm03tKn9hvZgMBiGaAZep3CwcDSEPZLXyPYRqSLJpOxZWS376LJgwI53NyvRSTcyzL1IPKKmdzWp6V1NWvdf16kyVlH+kXwtqO35gz3a85k9imQjwWBSVwYDzDSc28AgYFhUCGaou/Fd4+q7p0993+rT4A8G5UpOsdZY0MqxtUMtVzGAB966RGdCKz6+Qis+vkKlgZL6vtWn3m/06tAXD+nAXx9QtCGqlmtb1H5Du1qvb+U8BSxJrhoMXZ9/JF8LYxO7JiRJkXREuYtyWvt7a9V0SZNyW3KKNfEzAPNPNBOtDRQlSaX+kgYfGgxGfLx/ULv+8y5JUiQbUW5LTs2XNtf+pzkNAgsZ38iYscp4Rfkf5tV/b7/6v9OvsefHJEmZ0zNa9alVant/m3LvzHFUC3UTb4lr2UeWadlHlqkyUdHgA4Pqvbs3aJn9eq9kUuMFjWq9rlWt17Wq8YJG/v+wKBX2FzT0xJCGnxjW8E+GNbxtuDZQR7wzrqaLm7TyEyvVdElT0LrANQOxAMVb4+r4QIc6PtAhSSocKCj/w+DAw+APB7X7M7slJykqNZ7XqKZLmtR0aZOaLm6iFRgLCsEMb5lzTmPPj6n/e/3q/16/8g/nVZ2oyhKmpkubtPzjy9X2vjZlNmZ8FxVLQDQVVdv1bWq7vk3ur52GnxpW3z19GvjegPb84R7t+YM9ijXH1HJ1i1quaVHLFS1KrU9xbhoWnNJAScPbhjX8xLCGfhKEseKB4ELOFjNl35FVx4c6lNuSU9MlTUpvZOh6LE7JFcng0io3dUqSyvmy8j/K18La/r8ORveVpPRp6aA17aKcGi9oVPaMLAcoMG8RzPCGnHMae2FMgw8FI3YN/uDoxUQzb8toxX9coZZrWtR8WbOiGboQwB+LmHKbc8ptzmn9769Xqb+kge8PBAcRvtuvnq/1SAquvdT8nma1vCcYnpnr6GC+KXYXNbJjRKM7RjX8ZBDGxl8erz2ePjWt5vc0K3dh8GOz4ZyGEw6edPvnb1f+QH7G5Xnx5Rd1+qbTZ7SOHdt2aOvarTMuC3C8WFNMbde1qe26NknBYGPDTw4HXXp/mFfvXb069A+HJAUXxm44p0GNFzSqcXOjGi9oDC6EzsXPMQ8QzPA61UJVw08Pa+hHQxp6bCgIYj1BEEuuDi8menmzWq5uYehkzGvx1rg6P9Spzg91Hj3A8OCgBh4cUN89fTp8+2FJUmpdSrmLc2p6V5Ny78ope2aWro+YE5WJisZeGNPojtEgiD0T3JYOl2rPSaxIKHdhTst+aVntx2S8Of6m1p8/kK9LGPqdh35HW6+a2XqeeOiJGZcDeDMiyaODiei3gwPM46+MB11+w1bng184qP3/a78kKdoYVcN5DcpdkKuFNXpWwAeC2RLnnNPEqxMa3jasoceHNPSjIQ0/NSxXdJKk5NqkWt8bBLHmy5uVWscXFRYmM1P27Vll357Vyk+srF1HZ/DBQQ0+PKjB+wdrFzyNNkTVeFGjcltyajy/UY3nNyq5Osn/PqatWqhq/JVxjb88rtHnjoawsZ+NBSMkKjiSnzkjo7br25Q9K6uGsxqUfUdWiQ7OkQFmwsyU2ZhRZmOmdsFrVwkGyhneNlw7T3PfX+2r/f6JNkSVOSOj7BnZYDozuE2sSLAvwKwhmC0h1VJV4y+Pa+TpEQ0/NayRp0Y0/PSwKvngV0EkFVHj5kat+uQq5bbklHtnTsnlXMMGi9Pk6+is+uSq4CDFngkNPTak/GN5DT02pL1/vLf2ozneHlfDeQ1qPL9RDec1KHtmVumNaVrWUOMqwf/Q2EtjGn95XOMvjdfmJ/ZMSNWjz02tSyl7VlYdH+iohbD0xrQsyg8+YC5Y1Gqha9ktyyRJ1WJVo8+OanjbsEafHdXoc6Pqu6dPh75wqPa6WHPsaGALw1r2jKzinXECG2aMYLYIVYtVje8a19hzYxp9brQ2jb80LlcKjgRFUhFlz8qq68NdajivQQ3nBj9QGcYeS5WZKb0urfS6tLr+dXBEtTJW0ciOEY08GRzMGH5yWK/96Wty5eBzZAlT5m2ZY3bO6U1ppTekuabaIuSqTsXuogp7C5rYO6HCawUV9hY0visIYeOvHP2OlYLuUelT08ptyanrI13KbMoofWpamdMyiuXY/QLzTSQRUeN5jWo8r/GY5cWeokafGw1+V4WBredrPTp428Hac6K5qNIb0kptSB29PSW4Ta1J8fsKbwp7hgWqnC9rYu+EJnZPaHzneDC9HNy+7sjs+pSyZ2TV9r42Zc/IquHcBmVOz3CkH3gD0UxUTVua1LSlqbasMlE5etDj2WDKP5yvdYOUJEWk1JpUENJOTSu9Ma3U2lRtirXGOLI6z1TLVZV6Syp1l1TYX1DhtTB87Z00v69Q6+Z0RCQbUWpdSpm3ZdR2Y5syp2aU3pRW5tQMR9CBRSLRkVDi8oRaLm+pLXPOqXjoaGAbfyU4ODP2/Jj6vt0nV5j0XREJztFPb0gHYW19SslVSSVWJJRcEdzGmtgvYIbBzMyuk/Q5SVFJf++c++O6lGoJc1WnUk9JhYMFFQ8WVTxYVOFAQcUDxdqPhIm9E7Xuh0dEm6LKbMood1FOXb/YpfTGtDJvzyj7tiwXWwTqKJqK1s47m6ycL2vsxTGNvTwWtJ68PK6xl8c09KUhVYaO/bxGspEgpK1JKbEiocTyhJLLk7X5xLKE4q1xRRuj7KinqVqqqpwvqzxYrgWu4uFi7bbYXTxmWamvFFwHabKIlFyZVHJNUrmLckpuTSq1JqXkmqSSq4P5WAs/poClyMyUXJ5UcnlSrVe1HvOYqzoVDxaDsLZrXBO7JoLbVybUe3dvbWTrySKpyDFBLbH86HxyeVLx9rhibTHFW+OKpCN87yxS0w5mZhaV9HlJV0vaJ+kJM7vbOfd8vQq3UDnnVJ2oqjJaUWWkonJ/WaX+ksp9wW2prxQsm3Rbe3ygVDunZbJYS0zJNUml1qfUfFlzML82+IGQ3phWvI0js4BPsaaYchfllLsod8xy55xKfSUV9hQ0sWeiNhX2FDTx2oRGdoyoeKh4TCv3ERYzxVqDHfEJb9uC21hTTNFsVJFMRNFM9Jj5SDqyIIaBds6pWqiqOlpVZayiymjl2Pmxau22PBwErpNN1dETvKGhaC6qRFdC8c64MqdllHh3MJ/oTCjeFVdiWUKptSkllie89iyox1D3DFEPzD2LWHBQZ2VSze9uft3jldFK7aB74WB4eyA8IH+gqJHtIyp+p6jK8Al+EEqypCneFj92n9B27D4imosq2hBVrDGmaGMwX7vNRhfEfmEpmkmL2YWSdjrndkmSmd0h6UZJCyaYlUfKyj+Slys4VYtVVQtVuaI75rZarB77eMEFgSsMXbUfDyPHLjvRj6zJog3RY35YNaxqqM3Xjp4vTwRHTZYlFE3R6gUsRGamRHtCifbE61rZjnAVp2JPsEMuHiwGrTj9paMHdcLbwr6CRnaMqNxfVmXkxDvsE4mkI8eGtlREFrOpp/ix9xVR0JoUTs65k9+vBN+ZruSC79JS+J061bKCU2Xsjb83j/2jgpPwJ0+Z0zLHLmsJbuMt8SBwdSUU74gvmO/Tegx1zxD1wPwTzQa9nDKbMid9Xnm4XOs9VeqbdGD/uIP84zvHVfpJsOyYLpQnEclGgtAWBrZIOqJIMqJI6k1Mycjr9hMWM1n0JPuVyfuUqI7en6pRYarcONXyatBSeeTWVYL59Ka0Yo0L58ytmZR0paTXJt3fJ+mimRVnbhX3F/XMe5954ydacE0MS1jwT5uJ1I44RBuiwZGJSfcj2dc/PjmExVviDAwAoMaipuSypJLL3vwoqNViVeWBMLgNloMWpbGjLUu1+bGjLU+1++NVuYqTKx+dqqPVY+4fM1WcZEHIlKk2TXXfohZ8XyaCnXckFVGkMXLMMkuYIvGj36u1Vr5Jt9FM+H06uRUwe/TIL70EACxmscaYYo0xZU49eYA7wjmn6nhVpb5S0FAwXDnmtjwcHNQ70WPViaqqE8G5tkfmj58mD260UJx9/9lquaLljZ84T5hz03uTzWyrpOucc/8+vP9vJV3knPvV4553q6Rbw7unSfpZON8uqXdaG8dsoD7mF+pjfqE+5g/qYn6hPuYX6mN+oT7mj/lWF2udcx3HL5xJi9l+Sasn3V8VLjuGc+42Sbcdv9zMtjnnNs9g+6gj6mN+oT7mF+pj/qAu5hfqY36hPuYX6mP+WCh1MZP+dE9I2mRm680sIelmSXfXp1gAAAAAsHRMu8XMOVc2s1+V9D0Fw+V/wTn3XN1KBgAAAABLxIyGKXHO/Yukf5nmy1/XvRFeUR/zC/Uxv1Af8wd1Mb9QH/ML9TG/UB/zx4Koi2kP/gEAAAAAqA/GbAcAAAAAz2YUzMzsg2b2nJlVzWzzcY/9npntNLOfmdm1k5ZfFy7baWa/O2n5ejP7cbj8q+GAIjKzZHh/Z/j4ujfaBiQz+30z229m28Pp+kmPzXrdYHqmqgPUh5ntNrNnws/EtnBZq5ndZ2Yvh7ct4XIzs78K62KHmZ03aT23hM9/2cxumbT8/HD9O8PXcqGtSczsC2bWbWbPTlo26+//VNtYyqaoC/YbnpjZajN70Myet+B31SfD5Xw+PDhJffAZmWNmljKzn5jZT8O6+Ey4/C2/f/Wqo1nlnJv2JOltCq5N9pCkzZOWv13STyUlJa2X9IqCAUKi4fwGSYnwOW8PX/NPkm4O5/9W0i+H878i6W/D+ZslffVk25jJ37OYJkm/L+m3TrB81uuGadp1NmUdMNXtPd4tqf24ZX8i6XfD+d+V9Nlw/npJ31Fw+eQtkn4cLm+VtCu8bQnnW8LHfhI+18LXvtf33zyfJknvlnSepGfn8v2fahtLeZqiLthv+KuP5ZLOC+cbJb0Uvu98PuZXffAZmfu6MEkN4Xxc0o/D/+O65Ibp1NFsTjNqMXPOveCc+9kJHrpR0h3OuYJz7lVJOyVdGE47nXO7nHNFSXdIujE8anOFpDvD198u6ecmrev2cP5OSVeGz59qGzi5uagbTM8J68BzmZaCyf/Hx/9//6MLPC6p2cyWS7pW0n3OuX7n3ICk+yRdFz6Wc8497oJv8X+ctC5Ics49LKn/uMVz8f5PtY0la4q6mAr7jVnmnDvonHsqnB+W9IKkleLz4cVJ6mMqfEZmSfg/PhLejYeTU/1yw3TqaNbM1jlmKyW9Nun+vnDZVMvbJA0658rHLT9mXeHj+fD5U60LR/1q2MXhC5O6JsxF3WB6+J+efU7SvWb2pJndGi7rcs4dDOcPSeoK59/qZ2VlOH/8cpzcXLz/U20Dr8d+w7Ow69W5CloG+Hx4dlx9SHxG5pyZRc1su6RuBQcbXlH9csN06mjWvGEwM7Pvm9mzJ5g4ku/ZG9TN30g6RdI5kg5K+nOfZQXmiUucc+dJeq+kT5jZuyc/GB5JZqhaT+bi/aeOT4r9hmdm1iDp65I+5ZwbmvwYn4+5d4L64DPigXOu4pw7R9IqBS1cp/st0ex5w+uYOeeumsZ690taPen+qnCZpljep6AZPhYm08nPP7KufWYWk9QUPv9k21gS3mzdmNn/kXRPeHcu6gbTs+T/p2ebc25/eNttZt9Q8AV/2MyWO+cOht19usOnT1Uf+yVdftzyh8Llq07wfJzcXLz/U20DkzjnDh+ZZ78x98wsriAEfNk5d1e4mM+HJyeqDz4jfjnnBs3sQUnvVH1zw1uto1kzW10Z75Z0czgyynpJmxScdPqEpE3hKCcJBSfl3R0eoXlQ0tbw9bdI+uakdR0ZVWirpAfC50+1DUgKv1yP+HlJR0bemou6wfScsA48l2nRMLOsmTUemZd0jYLPxeT/4+P/vz9igS2S8mF3n+9JusbMWsJuLNdI+l742JCZbQn7pn9k0rowtbl4/6faBiZhv+FP+D/7D5JecM79xaSH+Hx4MFV98BmZe2bWYWbN4Xxa0tUKzvmrV26YTh3NHjezkVJ+XkGfy4Kkwwo+/Ece+7SCPqA/06SRyRSMJPRS+NinJy3fEL5BOyV9TVIyXJ4K7+8MH9/wRttgcpL0JUnPSNqh4J9x+VzWDdO06+2EdcBUl/d2g4LRln4q6bkj76+CfuT3S3pZ0vcltYbLTdLnw7p4RseOPPvvwv/7nZJ+adLyzQp21K9I+t+SzPffPZ8mSV9R0P2nFO47PjYX7/9U21jK0xR1wX7DX31coqAL4Q5J28Ppej4f864++IzMfV2cJenp8D1/VtJ/m+77V686ms3pyIcSAAAAAODJbHVlBAAAAAC8SQQzAAAAAPCMYAYAAAAAnhHMAAAAAMAzghkAAAAAeEYwAwAAAADPCGYAgDllZhUz225mz5rZt45cPHShMbM/MLOrfJcDALA4cB0zAMCcMrMR51xDOH+7pJecc3/koRwx51x5rrcLAMCJ0GIGAPDpR5JWSpKZPWRmm8P5djPbHc5/1MzuMrPvmtnLZvYnR15sZiNm9kdm9lMze9zMusLlHWb2dTN7IpwuDpf/vpl9ycwelfSlExUo3N4/m9l9ZrbbzH7VzH7DzJ4Ot9EaPu+LZrY1nN9tZp8xs6fM7BkzOz1cflnYOrg9fH3jLL2PAIAFjmAGAPDCzKKSrpR095t4+jmSbpL0Dkk3mdnqcHlW0uPOubMlPSzp4+Hyz0n6S+fcBZJ+QdLfT1rX2yVd5Zz78Em2d6akD0i6QNIfSRpzzp2rIEh+ZIrX9DrnzpP0N5J+K1z2W5I+4Zw7R9KlksbfxN8KAFiCCGYAgLmWNrPtkg5J6pJ035t4zf3OubxzbkLS85LWhsuLku4J55+UtC6cv0rS/w63c7eknJk1hI/d7Zx7o4D0oHNu2DnXIykv6Vvh8mcmbeN4d52gHI9K+gsz+3VJzXSdBABMhWAGAJhr42EL0lpJJukT4fKyju6XUse9pjBpviIpFs6X3NGTpScvj0ja4pw7J5xWOudGwsdG30QZJ2+vOul+ddI2pnpNrRzOuT+W9O8lpSU9eqSLIwAAxyOYAQC8cM6NSfp1Sb9pZjFJuyWdHz68dYarv1fSrx25Y2bnzHB902JmpzjnnnHOfVbSE5IIZgCAEyKYAQC8cc49LWmHpA9L+jNJv2xmT0tqn+Gqf13SZjPbYWbPS/qPM1zfdH0qvCzADkklSd/xVA4AwDzHcPkAAAAA4BktZgAAAADg2VQnMAMAsKiZ2bWSPnvc4ledcz/vozwAgKWNrowAAAAA4BldGQEAAADAM4IZAAAAAHhGMAMAAAAAzwhmAAAAAOAZwQwAAAAAPPv/+X7ANrZ9gJUAAAAASUVORK5CYII=\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": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5b77e8ca90>"
      ]
     },
     "execution_count": 56,
     "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": 57,
   "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": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, '')"
      ]
     },
     "execution_count": 58,
     "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": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "url5 = 'https://www.4icu.org/at/vienna/'\n",
    "url6 = 'https://www.opec.org/opec_web/en/press_room/307.htm'"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}