ggb/Philosophie.ipynb

## Philosophie.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Irgendwann landet man immer bei der Philosophie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ein Kollege hat einen Abrisskalender mit interessanten und/oder lustigen Fakten. Wenn er der Meinung ist, dass einer der Sprüche des Kalenders für einen von uns interessant ist, hinterlässt er uns das entsprechende Kalenderblatt auf dem Schreibtisch. Auf diese Weise hat das Folgende den Weg zu mir gefunden:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Wenn man in einem beliebigen englischen Wikipedia-Artikel auf den ersten regulären Artikel klickt, beim folgenden Artikel auch, beim darauf folgenden ebenfalls und so weiter, landet man in etwa 95 Prozent der Fälle irgendwann beim Artikel \"Philosophie\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ist das wirklich so?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup \n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "start_url = \"https://en.wikipedia.org/wiki/Special:Random\"\n",
    "target_url = \"https://en.wikipedia.org/wiki/Philosophy\"\n",
    "\n",
    "threshold = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filter_phrases = [\"/wiki/Help:IPA\", \".ogg\", \"#cite_note-\", \".php\", \".svg\", \"wiktionary\", \"https\", \"http\", \":Media_help\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hop(url, count, phrases):\n",
    "    if url == target_url:\n",
    "        return count\n",
    "    if count > threshold:\n",
    "        return \"threshold\"\n",
    "        \n",
    "    html_response = requests.get(url)\n",
    "    soup = BeautifulSoup(html_response.text, \"html.parser\")\n",
    "    \n",
    "    link_list = [x[\"href\"] for x in soup.select(\"#mw-content-text p a\") if x.has_attr(\"href\")]\n",
    "    filtered_list = [x for x in link_list if not any([y in x for y in phrases])]\n",
    "        \n",
    "    next_url = \"https://en.wikipedia.org\" + filtered_list[0]\n",
    "    \n",
    "    # print(filtered_list[0])\n",
    "    phrases.append(filtered_list[0])\n",
    "    \n",
    "    return hop(next_url, count + 1, phrases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def many_hops(n):\n",
    "    result = []\n",
    "    for i in range(0, n):\n",
    "        print(\"# \" + str(i))\n",
    "        try:\n",
    "            result.append(hop(start_url, 0, filter_phrases[:]))\n",
    "        except:\n",
    "            result.append(\"error\")\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# 0\n",
      "# 1\n",
      "# 2\n",
      "# 3\n",
      "# ...",
      "# 497\n",
      "# 498\n",
      "# 499\n"
     ]
    }
   ],
   "source": [
    "all_hops = many_hops(500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Errors: 19\n",
      "Threshold: 27\n"
     ]
    }
   ],
   "source": [
    "t = all_hops\n",
    "\n",
    "errors = [x for x in t if x == \"error\"]\n",
    "print(\"Errors: \" + str(len(errors)))\n",
    "\n",
    "threshold = [x for x in t if x == \"threshold\"]\n",
    "print(\"Threshold: \" + str(len(threshold)))\n",
    "\n",
    "counts = [x for x in t if x != \"threshold\" and x != \"error\"]\n",
    "df = pd.DataFrame(data = counts, columns = [\"Counts\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>454.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>68.792952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.523598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Counts\n",
       "count  454.000000\n",
       "mean    68.792952\n",
       "std     22.523598\n",
       "min      3.000000\n",
       "25%     66.000000\n",
       "50%     71.000000\n",
       "75%     79.000000\n",
       "max    101.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f59234dd860>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qtFkAnW2z5ORcL6/X2/NNQy6XS0ePHqHNggGDUxOBMBw9ekT33fcLNTe36L77fqGjR49Y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\nOufN5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f59250f7eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f591bf9efd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7NiIikplHewIvvvgiIiIiMGLECHz44YcYP348cnJy5K6NiIhk5tGeQFBQEKZPn47p06d7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5920cf2208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94.38669438669439"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "without_errors = 500 - len(errors)\n",
    "\n",
    "(without_errors - len(threshold)) / without_errors * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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