{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Haba Obstgarten"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Erste Analysen](#Erste-Analysen)\n",
    "* [Simulation](#Simulation)\n",
    "* [Ergebnisse](#Ergebnisse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hintergrund"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Hintergrund zu dem Spiel Obstgarten findet sich in meinem [Blog](http://datenspieler.com/Haba-Obstgarten/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Erste Analysen\n",
    "Die Liste `wuerfel` beschreibt die jeweiligen Seiten des Würfels. Mittels `random.choice(wuerfel)`, wird dann eine zufällige Aktion \"erwürfelt\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wuerfel = ('apfel', 'birne', 'kirsche', 'zwetschke', 'korb', 'rabe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'kirsche'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "aktion = random.choice(wuerfel)\n",
    "aktion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Im Python Dictionary `status` wird der Zustand des Spiels beschreiben, sprich wie viele Früchte noch auf den Bäumen sind und wie viele Rabenteile schon aufgelegt sind. Damit lässt sich dann einmal Würfeln und die Auswirkung davon, ausgenommen `Korb`, dazu komme ich gleich, leicht abbilden. Das `max(..., 0)` beim Update des Status dient dazu, dass nicht negative Zahlen entstehen, falls ein Obst gewürfelt wird, dass gar nicht auf einem Baum hängt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "status = {'apfel': 10, 'birne': 10, 'kirsche': 10, 'zwetschke': 10, 'rabe': 0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aktion: birne\n",
      "Stats: {'rabe': 0, 'apfel': 10, 'zwetschke': 10, 'kirsche': 10, 'birne': 9}\n"
     ]
    }
   ],
   "source": [
    "aktion = random.choice(wuerfel)\n",
    "print('Aktion:', aktion)\n",
    "if aktion in ('apfel', 'birne', 'kirsche', 'zwetschke'):\n",
    "    status[aktion] = max(status[aktion] - 1, 0)\n",
    "elif aktion == 'rabe':\n",
    "    status[aktion] += 1\n",
    "print('Stats:', status)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jetzt kommen wir zum spannenden Teil - der Korb-Seite des Würfels. Dass der Korb wichtig ist, zeigt folgende Überlegung. Angenommen, es gebe den Korb nicht: um den Raben fertig zu bauen, benötigt man 9 Teile, vom Obst gibt es 10 Teile. Somit wäre der Rabe im Schnitt also schneller zusammengebaut, als jedes einzelne Obst für sich genommen abgeerntet wäre. Bei 4 Obstsorten wäre der Rabe dann ganz klar im Vorteil und die Spieler würden fast immer verlieren. \n",
    "\n",
    "Aber zum Glück gibt es den Korb, der das Spiel zugunsten der Spieler beeinflusst. Zusätzlich ist der Korb der einzige Teil des Spiels, bei dem der Spieler eine Entscheidung treffen kann und muss: welches Obst soll geerntet werden? Die offensichtig optimale Strategie ist es, die Bäume abzuerntet, an denen noch die meisten Früchte hängen. Das kommt daher, dass der Würfel kein Gedächtnis hat, sprich bei den folgenden Würfen im Durchschnitt die 4 Obstsorten gleich häufig gewürfelt werden. Auch, wenn bei den vergangen Würfen die eine oder andere Sorte öfters gewürfelt wurde.\n",
    "\n",
    "Technisch erfolgt die Umsetzung via der `max` Funktion, der man via des Schlüsselworts `key` die Funktion `get` mitgeben kann. Damit wird das Maximum über die Werte des Dictionary `status` ermittelt. `max(status)` würde das alphabetisch \"größte\" Obst (=Zwetschke) liefern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "status['birne']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "status.get('birne')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'apfel'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(status, key=status.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: {'rabe': 0, 'apfel': 10, 'zwetschke': 10, 'kirsche': 10, 'birne': 9}\n",
      "Aktion: apfel\n",
      "Status: {'rabe': 0, 'apfel': 9, 'zwetschke': 10, 'kirsche': 10, 'birne': 9}\n"
     ]
    }
   ],
   "source": [
    "print('Status:', status)\n",
    "aktion = max(status, key=status.get)\n",
    "print('Aktion:', aktion)\n",
    "status[aktion] = max(status[aktion] - 1, 0)\n",
    "print('Status:', status)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aber nicht jeder Spieler folgt der optimalten Strategie. Ich habe zwei zusätzliche Strategien implementiert: einerseits eine Zufallsstrategie - hier wird einfach zufällig ein Obst geerntet, andererseits die schlechteste Strategie - man erntet immer das Obst, von dem es bereits am wenigsten gibt. In der Analyse wird sich dann die Auswirkung der Strategiewahl zeigen.\n",
    "\n",
    "Bei der technischen Umsetzung ist zu beachten, dass zuerst die Obstsorten, bei denen es gar keine Früchte mehr am Baum gibt, ausgeschlossen werden. Dazu lagere ich diese Obstsorten, zusammen mit dem Wert für den Raben temporär in andere Dictionaries aus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "status = {'apfel': 3, 'birne': 0, 'kirsche': 1, 'zwetschke': 4, 'rabe': 2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: {'rabe': 2, 'apfel': 3, 'zwetschke': 4, 'kirsche': 1, 'birne': 0}\n",
      "Status zu ernten: {'apfel': 3, 'zwetschke': 4, 'kirsche': 1}\n",
      "Aktion: kirsche\n",
      "Status zu ernten: {'apfel': 3, 'zwetschke': 4, 'kirsche': 0}\n",
      "Status: {'rabe': 2, 'apfel': 3, 'zwetschke': 4, 'kirsche': 0, 'birne': 0}\n"
     ]
    }
   ],
   "source": [
    "print('Status:', status)\n",
    "\n",
    "status_sicherung = {k:v for k,v in status.items() if (v == 0) | (k=='rabe')}\n",
    "status_zu_ernten = {k:v for k,v in status.items() if (v > 0) & (k!='rabe')}    \n",
    " \n",
    "print('Status zu ernten:', status_zu_ernten)\n",
    "aktion = min(status_zu_ernten, key=status_zu_ernten.get)\n",
    "print('Aktion:', aktion)\n",
    "status_zu_ernten[aktion] = max(status_zu_ernten[aktion] - 1, 0)\n",
    "print('Status zu ernten:', status_zu_ernten)\n",
    "\n",
    "status = {**status_sicherung, **status_zu_ernten}\n",
    "print('Status:', status)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulation \n",
    "\n",
    "Für eine Simulation müssen die obrigen Schritte nur mehr richtig in Funktionen verpackt werden und dann öfters ausgeführt werden. Konkret werden die folgenden Funktionen definiert:\n",
    "- `korb(status, strategie_max, strategie_min, strategie_random, detail_zuege=True, erster_durchlauf=True, strategie_zweiter_durchlauf=0)`: basierend auf der übergebenen Strategie wird die Aktivität Korb durchgeführt. `status` ist der aktuelle Status, sprich wieviele  Früchte sind auf welchen Bäumen. \n",
    "    - Die Variablen `strategie_max, strategie_min, strategie_random` geben jeweils an, wieviele Spieler (absolut oder prozentuell, funktioniert beides) die jeweilige Strategie verfolgen, d.h., entweder ein Obst vom Baum mit den meisten Früchten ernten, mit den wenigsten oder zufällig. \n",
    "    - `detail_zuege` gibt an, ob eine explizite Ausgabe erfolgen soll, welche Frucht ausgewählt wurde. \n",
    "    - Da bei Korb zwei Früchte geerntet werden, ruft sich die Funktion `korb` beim ersten Durchlauf, erkennbar an `erster_durchlauf` ein zweites Mal selber auf. Dabei wird mittels `strategie_zweiter_durchlauf` die konkrete Strategie mitgegeben. Die Annahme ist hier, dass die Strategie des Spielers für die erste Frucht gleich ist wie für die zweite Frucht und nicht nochmal zufällig ermittelt wird. \n",
    "    - In der Funktion werden zuerst der Rabe und die bereits abgeernteten Bäume entfernt, dann wird die Strategie festgelegt, basierend auf der Strategie eine (die Funktion muss also zweimal aufgerufen werden, da bei Korb ja zwei Früchte geerntet werden dürfen) Frucht abgeerntet und zuletzt Rabe sowie leere Bäume wieder hinzugefügt. Der aktualisierte `status` wird von der Funktion zurückgegeben.\n",
    "    \n",
    "- `spielen(zuege = 0, letz_status = False, strategie_max=1, strategie_min=0, strategie_random=0, status = {'apfel': 10, 'birne': 10, 'kirsche': 10, 'zwetschke': 10, 'rabe': 0})`: hier wird ein Spiel durchgeführt. \n",
    "    -`zuege`gibt an, ob die Zwischenschritte ausgegeben werden sollen (0 - keine Ausgabe, 1 - Ausgabe Einzelzüge, 2 - Ausgabe Einzelzüge und Details bzgl. Korb), \n",
    "    - `letzt_status` gibt an, ob das Ergebnis auch als Text ausgegeben werden soll, \n",
    "    - `strategie_max`, `strategie_min` und `strategie_random` bestimmen wie bei der Funktion `korb` beschrieben die Strategie bzgl. der Aktivität Korb. \n",
    "    - Mittels des Dictionaries `status` hat man die Möglichkeit alternative Ausgangssituationen zu übergeben, z.B. nur 8 Äpfel. \n",
    "    - Die Funktion gibt ein Dictionary mit den Ergebnissen zurück, konkret `gewonnen` (True oder False), `gespielte_runden` (Anzahl gespielter Runden), `raben_teile` (Anzahl ausgelegter Rabenteile, 9 falls der Rabe gewonnen hat), `fruechte` (Anzahl der Früchte, die noch nicht geerntet wurden) und `strategie_max_min_rand` (Information bzgl. der Strategie)\n",
    "\n",
    "- `simulation(anzahl, strategie_max=1, strategie_min=0, strategie_random=0)` : Simulation von `anzahl` Spielen. Zurückgegeben wird ein Dataframe,der pro Spiel die oben beschriebene Information (`gewonnen`,`gespielte_runden`, `raben_teile`, `fruechte`, `strategie_max_min_rand`) enthält."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def korb(status, strategie_max, strategie_min, strategie_random, detail_zuege=True, \n",
    "         erster_durchlauf=True, strategie_zweiter_durchlauf=0):\n",
    "    #rabe Fruechte mit Anzahl 0 entfernen in getrenntem dicstionary speichern\n",
    "    status_sicherung = {k:v for k,v in status.items() if (v == 0) | (k=='rabe')}\n",
    "    status_zu_ernten = {k:v for k,v in status.items() if (v > 0) & (k!='rabe')}    \n",
    "    \n",
    "    #Check ob noch Fruechte am Baum\n",
    "    if len(status_zu_ernten) > 0:\n",
    "\n",
    "        #Strategie bestimmen\n",
    "        if erster_durchlauf:\n",
    "            zufall_strategie = random.random() * (strategie_max + strategie_min + strategie_random)\n",
    "            strategie_zweiter_durchlauf = zufall_strategie\n",
    "        else:\n",
    "            zufall_strategie = strategie_zweiter_durchlauf\n",
    "\n",
    "        if zufall_strategie < strategie_max:\n",
    "            aktion = max(status_zu_ernten, key=status_zu_ernten.get)\n",
    "            status_zu_ernten[aktion] = max(0, status_zu_ernten[aktion] - 1)\n",
    "            if detail_zuege:\n",
    "                print('.korb-max {:<12}:'.format(aktion),status_zu_ernten)\n",
    "\n",
    "        elif zufall_strategie < strategie_max + strategie_min:\n",
    "            aktion = min(status_zu_ernten, key=status_zu_ernten.get)\n",
    "            status_zu_ernten[aktion] = max(0, status_zu_ernten[aktion] - 1)\n",
    "            if detail_zuege:\n",
    "                print('.korb-min {:<12}:'.format(aktion),status_zu_ernten)\n",
    "        else:\n",
    "            aktion = random.choice(list(status_zu_ernten.keys()))\n",
    "            status_zu_ernten[aktion] = max(0, status_zu_ernten[aktion] - 1)\n",
    "            if detail_zuege:\n",
    "                print('.korb-rnd {:<12}:'.format(aktion),status_zu_ernten)    \n",
    "            \n",
    "    # Fruechte mit Anzahl 0 und rabe wieder zu status zusammenfügen\n",
    "    # only works in Python 3.5 onwards\n",
    "    # status = {**status_sicherung, **status_zu_ernten}\n",
    "    \n",
    "    status = status_sicherung.copy()\n",
    "    status.update(status_zu_ernten)\n",
    "    \n",
    "    if erster_durchlauf:\n",
    "        status = korb(status, strategie_max, strategie_min, strategie_random, detail_zuege, \n",
    "                      erster_durchlauf=False, strategie_zweiter_durchlauf=strategie_zweiter_durchlauf)    \n",
    "    return status\n",
    "\n",
    "def spielen(zuege = 0, letzt_status = False, strategie_max=1, strategie_min=0, strategie_random=0,\n",
    "           status = {'apfel': 10, 'birne': 10, 'kirsche': 10, 'zwetschke': 10, 'rabe': 0}):\n",
    "# zuege = 0 - keine ausgabe einzelner Zuege\n",
    "# zuege = 1 - Ausgabe einzelner Zuege\n",
    "# zuege = 2 - Ausgabe einzelner Zuege + Detailzuege bzgl. Korb\n",
    "    status = {'apfel': 10, 'birne': 10, 'kirsche': 10, 'zwetschke': 10, 'rabe': 0}\n",
    "    runden = 0\n",
    "    \n",
    "    while ((status['apfel'] > 0) or (status['birne'] > 0) or (status['kirsche'] > 0) or (status['zwetschke'] > 0)) \\\n",
    "        and (status['rabe'] < 9): \n",
    "\n",
    "        aktion = random.choice(wuerfel)\n",
    " \n",
    "        if aktion in ('apfel', 'birne', 'kirsche', 'zwetschke'):\n",
    "            status[aktion] = max(status[aktion] - 1, 0)\n",
    "        elif aktion == 'rabe':\n",
    "            status[aktion] += 1\n",
    "        else:        \n",
    "            status = korb(status, strategie_max, strategie_min, strategie_random,\n",
    "                         detail_zuege = max(0, zuege - 1))\n",
    "    \n",
    "        if zuege:\n",
    "            print('{:<22}:'.format(aktion),status)\n",
    "                        \n",
    "        runden += 1\n",
    "            \n",
    "    if status['rabe'] == 9:\n",
    "        if letzt_status:\n",
    "            print('{:<22}:'.format('Verloren'),status)\n",
    "            print('Verloren, der Rabe ist fertig.')\n",
    "        return {'gewonnen': False, 'gespielte_runden': runden, \n",
    "                'raben_teile': status['rabe'], 'fruechte': sum(status[d] for d in status if d != 'rabe'),\n",
    "                'strategie_max_min_rand': (strategie_max, strategie_min, strategie_random)}\n",
    "    else:\n",
    "        if letzt_status:\n",
    "            print('{:<22}:'.format('Gewonnen'),status)\n",
    "            print('Gewonnen, alle Bäume abgeerntet.')\n",
    "        return {'gewonnen': True, 'gespielte_runden': runden, \n",
    "                'raben_teile': status['rabe'], 'fruechte': sum(status[d] for d in status if d != 'rabe'),\n",
    "                'strategie_max_min_rand': (strategie_max, strategie_min, strategie_random)}\n",
    "    \n",
    "def simulation(anzahl, strategie_max=1, strategie_min=0, strategie_random=0):\n",
    "    df = pd.DataFrame()\n",
    "    for i in range(anzahl):\n",
    "        df = df.append(pd.DataFrame([spielen(strategie_max=strategie_max,\n",
    "                                             strategie_min=strategie_min, \n",
    "                                             strategie_random=strategie_random)]))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def korb(status, strategie_max, strategie_min, strategie_random, detail_zuege=True, \n",
    "         erster_durchlauf=True, strategie_zweiter_durchlauf=0):\n",
    "    #rabe Fruechte mit Anzahl 0 entfernen in getrenntem dicstionary speichern\n",
    "    status_sicherung = {k:v for k,v in status.items() if (v == 0) | (k=='rabe')}\n",
    "    status_zu_ernten = {k:v for k,v in status.items() if (v > 0) & (k!='rabe')}    \n",
    "    \n",
    "    #Check ob noch Fruechte am Baum\n",
    "    if len(status_zu_ernten) > 0:\n",
    "\n",
    "        #Strategie bestimmen\n",
    "        if erster_durchlauf:\n",
    "            zufall_strategie = random.random() * (strategie_max + strategie_min + strategie_random)\n",
    "            strategie_zweiter_durchlauf = zufall_strategie\n",
    "        else:\n",
    "            zufall_strategie = strategie_zweiter_durchlauf\n",
    "\n",
    "        if zufall_strategie < strategie_max:\n",
    "            aktion = max(status_zu_ernten, key=status_zu_ernten.get)\n",
    "            status_zu_ernten[aktion] = max(0, status_zu_ernten[aktion] - 1)\n",
    "            if detail_zuege:\n",
    "                print('.korb-max {:<12}:'.format(aktion),status_zu_ernten)\n",
    "\n",
    "        elif zufall_strategie < strategie_max + strategie_min:\n",
    "            aktion = min(status_zu_ernten, key=status_zu_ernten.get)\n",
    "            status_zu_ernten[aktion] = max(0, status_zu_ernten[aktion] - 1)\n",
    "            if detail_zuege:\n",
    "                print('.korb-min {:<12}:'.format(aktion),status_zu_ernten)\n",
    "        else:\n",
    "            aktion = random.choice(list(status_zu_ernten.keys()))\n",
    "            status_zu_ernten[aktion] = max(0, status_zu_ernten[aktion] - 1)\n",
    "            if detail_zuege:\n",
    "                print('.korb-rnd {:<12}:'.format(aktion),status_zu_ernten)    \n",
    "            \n",
    "    # Fruechte mit Anzahl 0 und rabe wieder zu status zusammenfügen\n",
    "    # only works in Python 3.5 onwards\n",
    "    # status = {**status_sicherung, **status_zu_ernten}\n",
    "    \n",
    "    status = status_sicherung.copy()\n",
    "    status.update(status_zu_ernten)\n",
    "    \n",
    "    if erster_durchlauf:\n",
    "        status = korb(status, strategie_max, strategie_min, strategie_random, detail_zuege, \n",
    "                      erster_durchlauf=False, strategie_zweiter_durchlauf=strategie_zweiter_durchlauf)    \n",
    "    return status\n",
    "\n",
    "def spielen(zuege = 0, letzt_status = False, strategie_max=1, strategie_min=0, strategie_random=0,\n",
    "           status = {'apfel': 10, 'birne': 10, 'kirsche': 10, 'zwetschke': 10, 'rabe': 0}):\n",
    "# zuege = 0 - keine ausgabe einzelner Zuege\n",
    "# zuege = 1 - Ausgabe einzelner Zuege\n",
    "# zuege = 2 - Ausgabe einzelner Zuege + Detailzuege bzgl. Korb\n",
    "    status = {'apfel': 10, 'birne': 10, 'kirsche': 10, 'zwetschke': 10, 'rabe': 0}\n",
    "    runden = 0\n",
    "    \n",
    "    while ((status['apfel'] > 0) or (status['birne'] > 0) or (status['kirsche'] > 0) or (status['zwetschke'] > 0)) \\\n",
    "        and (status['rabe'] < 9): \n",
    "\n",
    "        aktion = random.choice(wuerfel)\n",
    " \n",
    "        if aktion in ('apfel', 'birne', 'kirsche', 'zwetschke'):\n",
    "            status[aktion] = max(status[aktion] - 1, 0)\n",
    "        elif aktion == 'rabe':\n",
    "            status[aktion] += 1\n",
    "        else:        \n",
    "            status = korb(status, strategie_max, strategie_min, strategie_random,\n",
    "                         detail_zuege = max(0, zuege - 1))\n",
    "    \n",
    "        if zuege:\n",
    "            print('{:<22}:'.format(aktion),status)\n",
    "                        \n",
    "        runden += 1\n",
    "            \n",
    "    if status['rabe'] == 9:\n",
    "        if letzt_status:\n",
    "            print('{:<22}:'.format('Verloren'),status)\n",
    "            print('Verloren, der Rabe ist fertig.')\n",
    "        return {'gewonnen': False, 'gespielte_runden': runden, \n",
    "                'raben_teile': status['rabe'], 'fruechte': sum(status[d] for d in status if d != 'rabe'),\n",
    "                'strategie_max': strategie_max,\n",
    "                'strategie_min': strategie_min,\n",
    "                'strategie_rnd': strategie_random}\n",
    "    else:\n",
    "        if letzt_status:\n",
    "            print('{:<22}:'.format('Gewonnen'),status)\n",
    "            print('Gewonnen, alle Bäume abgeerntet.')\n",
    "        return {'gewonnen': True, 'gespielte_runden': runden, \n",
    "                'raben_teile': status['rabe'], 'fruechte': sum(status[d] for d in status if d != 'rabe'),\n",
    "                'strategie_max': strategie_max,\n",
    "                'strategie_min': strategie_min,\n",
    "                'strategie_rnd': strategie_random}\n",
    "    \n",
    "def simulation(anzahl, strategie_max=1, strategie_min=0, strategie_random=0):\n",
    "    df = pd.DataFrame()\n",
    "    for i in range(anzahl):\n",
    "        df = df.append(pd.DataFrame([spielen(strategie_max=strategie_max,\n",
    "                                             strategie_min=strategie_min, \n",
    "                                             strategie_random=strategie_random)]))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fruechte': 8,\n",
       " 'gespielte_runden': 33,\n",
       " 'gewonnen': False,\n",
       " 'raben_teile': 9,\n",
       " 'strategie_max': 1,\n",
       " 'strategie_min': 0,\n",
       " 'strategie_rnd': 0}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spielen()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "birne                 : {'rabe': 0, 'apfel': 10, 'zwetschke': 10, 'kirsche': 10, 'birne': 9}\n",
      "zwetschke             : {'rabe': 0, 'apfel': 10, 'zwetschke': 9, 'kirsche': 10, 'birne': 9}\n",
      ".korb-rnd zwetschke   : {'apfel': 10, 'zwetschke': 8, 'kirsche': 10, 'birne': 9}\n",
      ".korb-rnd birne       : {'apfel': 10, 'zwetschke': 8, 'kirsche': 10, 'birne': 8}\n",
      "korb                  : {'rabe': 0, 'apfel': 10, 'zwetschke': 8, 'kirsche': 10, 'birne': 8}\n",
      ".korb-rnd kirsche     : {'apfel': 10, 'zwetschke': 8, 'kirsche': 9, 'birne': 8}\n",
      ".korb-rnd kirsche     : {'apfel': 10, 'zwetschke': 8, 'kirsche': 8, 'birne': 8}\n",
      "korb                  : {'rabe': 0, 'apfel': 10, 'zwetschke': 8, 'kirsche': 8, 'birne': 8}\n",
      "zwetschke             : {'rabe': 0, 'apfel': 10, 'zwetschke': 7, 'kirsche': 8, 'birne': 8}\n",
      "birne                 : {'rabe': 0, 'apfel': 10, 'zwetschke': 7, 'kirsche': 8, 'birne': 7}\n",
      "apfel                 : {'rabe': 0, 'apfel': 9, 'zwetschke': 7, 'kirsche': 8, 'birne': 7}\n",
      "apfel                 : {'rabe': 0, 'apfel': 8, 'zwetschke': 7, 'kirsche': 8, 'birne': 7}\n",
      ".korb-min zwetschke   : {'apfel': 8, 'zwetschke': 6, 'kirsche': 8, 'birne': 7}\n",
      ".korb-min zwetschke   : {'apfel': 8, 'zwetschke': 5, 'kirsche': 8, 'birne': 7}\n",
      "korb                  : {'rabe': 0, 'apfel': 8, 'zwetschke': 5, 'kirsche': 8, 'birne': 7}\n",
      "birne                 : {'rabe': 0, 'apfel': 8, 'zwetschke': 5, 'kirsche': 8, 'birne': 6}\n",
      "birne                 : {'rabe': 0, 'apfel': 8, 'zwetschke': 5, 'kirsche': 8, 'birne': 5}\n",
      "rabe                  : {'rabe': 1, 'apfel': 8, 'zwetschke': 5, 'kirsche': 8, 'birne': 5}\n",
      ".korb-min zwetschke   : {'apfel': 8, 'zwetschke': 4, 'kirsche': 8, 'birne': 5}\n",
      ".korb-min zwetschke   : {'apfel': 8, 'zwetschke': 3, 'kirsche': 8, 'birne': 5}\n",
      "korb                  : {'rabe': 1, 'apfel': 8, 'zwetschke': 3, 'kirsche': 8, 'birne': 5}\n",
      "kirsche               : {'rabe': 1, 'apfel': 8, 'zwetschke': 3, 'kirsche': 7, 'birne': 5}\n",
      "rabe                  : {'rabe': 2, 'apfel': 8, 'zwetschke': 3, 'kirsche': 7, 'birne': 5}\n",
      "apfel                 : {'rabe': 2, 'apfel': 7, 'zwetschke': 3, 'kirsche': 7, 'birne': 5}\n",
      "birne                 : {'rabe': 2, 'apfel': 7, 'zwetschke': 3, 'kirsche': 7, 'birne': 4}\n",
      "zwetschke             : {'rabe': 2, 'apfel': 7, 'zwetschke': 2, 'kirsche': 7, 'birne': 4}\n",
      "kirsche               : {'rabe': 2, 'apfel': 7, 'zwetschke': 2, 'kirsche': 6, 'birne': 4}\n",
      "apfel                 : {'rabe': 2, 'apfel': 6, 'zwetschke': 2, 'kirsche': 6, 'birne': 4}\n",
      "birne                 : {'rabe': 2, 'apfel': 6, 'zwetschke': 2, 'kirsche': 6, 'birne': 3}\n",
      "apfel                 : {'rabe': 2, 'apfel': 5, 'zwetschke': 2, 'kirsche': 6, 'birne': 3}\n",
      ".korb-max kirsche     : {'apfel': 5, 'zwetschke': 2, 'kirsche': 5, 'birne': 3}\n",
      ".korb-max apfel       : {'apfel': 4, 'zwetschke': 2, 'kirsche': 5, 'birne': 3}\n",
      "korb                  : {'rabe': 2, 'apfel': 4, 'zwetschke': 2, 'kirsche': 5, 'birne': 3}\n",
      "kirsche               : {'rabe': 2, 'apfel': 4, 'zwetschke': 2, 'kirsche': 4, 'birne': 3}\n",
      ".korb-min zwetschke   : {'apfel': 4, 'zwetschke': 1, 'kirsche': 4, 'birne': 3}\n",
      ".korb-min zwetschke   : {'apfel': 4, 'zwetschke': 0, 'kirsche': 4, 'birne': 3}\n",
      "korb                  : {'rabe': 2, 'apfel': 4, 'zwetschke': 0, 'kirsche': 4, 'birne': 3}\n",
      ".korb-max apfel       : {'apfel': 3, 'kirsche': 4, 'birne': 3}\n",
      ".korb-max kirsche     : {'apfel': 3, 'kirsche': 3, 'birne': 3}\n",
      "korb                  : {'rabe': 2, 'apfel': 3, 'zwetschke': 0, 'kirsche': 3, 'birne': 3}\n",
      "kirsche               : {'rabe': 2, 'apfel': 3, 'zwetschke': 0, 'kirsche': 2, 'birne': 3}\n",
      "rabe                  : {'rabe': 3, 'apfel': 3, 'zwetschke': 0, 'kirsche': 2, 'birne': 3}\n",
      "apfel                 : {'rabe': 3, 'apfel': 2, 'zwetschke': 0, 'kirsche': 2, 'birne': 3}\n",
      "apfel                 : {'rabe': 3, 'apfel': 1, 'zwetschke': 0, 'kirsche': 2, 'birne': 3}\n",
      "rabe                  : {'rabe': 4, 'apfel': 1, 'zwetschke': 0, 'kirsche': 2, 'birne': 3}\n",
      "birne                 : {'rabe': 4, 'apfel': 1, 'zwetschke': 0, 'kirsche': 2, 'birne': 2}\n",
      "zwetschke             : {'rabe': 4, 'apfel': 1, 'zwetschke': 0, 'kirsche': 2, 'birne': 2}\n",
      "kirsche               : {'rabe': 4, 'apfel': 1, 'zwetschke': 0, 'kirsche': 1, 'birne': 2}\n",
      "rabe                  : {'rabe': 5, 'apfel': 1, 'zwetschke': 0, 'kirsche': 1, 'birne': 2}\n",
      ".korb-max birne       : {'apfel': 1, 'kirsche': 1, 'birne': 1}\n",
      ".korb-max apfel       : {'apfel': 0, 'kirsche': 1, 'birne': 1}\n",
      "korb                  : {'rabe': 5, 'apfel': 0, 'zwetschke': 0, 'kirsche': 1, 'birne': 1}\n",
      "apfel                 : {'rabe': 5, 'apfel': 0, 'zwetschke': 0, 'kirsche': 1, 'birne': 1}\n",
      "rabe                  : {'rabe': 6, 'apfel': 0, 'zwetschke': 0, 'kirsche': 1, 'birne': 1}\n",
      "apfel                 : {'rabe': 6, 'apfel': 0, 'zwetschke': 0, 'kirsche': 1, 'birne': 1}\n",
      "zwetschke             : {'rabe': 6, 'apfel': 0, 'zwetschke': 0, 'kirsche': 1, 'birne': 1}\n",
      "apfel                 : {'rabe': 6, 'apfel': 0, 'zwetschke': 0, 'kirsche': 1, 'birne': 1}\n",
      "birne                 : {'rabe': 6, 'apfel': 0, 'zwetschke': 0, 'kirsche': 1, 'birne': 0}\n",
      "apfel                 : {'rabe': 6, 'apfel': 0, 'zwetschke': 0, 'kirsche': 1, 'birne': 0}\n",
      "rabe                  : {'rabe': 7, 'apfel': 0, 'zwetschke': 0, 'kirsche': 1, 'birne': 0}\n",
      "rabe                  : {'rabe': 8, 'apfel': 0, 'zwetschke': 0, 'kirsche': 1, 'birne': 0}\n",
      ".korb-rnd kirsche     : {'kirsche': 0}\n",
      "korb                  : {'rabe': 8, 'apfel': 0, 'zwetschke': 0, 'kirsche': 0, 'birne': 0}\n",
      "Gewonnen              : {'rabe': 8, 'apfel': 0, 'zwetschke': 0, 'kirsche': 0, 'birne': 0}\n",
      "Gewonnen, alle Bäume abgeerntet.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'fruechte': 0,\n",
       " 'gespielte_runden': 46,\n",
       " 'gewonnen': True,\n",
       " 'raben_teile': 8,\n",
       " 'strategie_max': 2,\n",
       " 'strategie_min': 1,\n",
       " 'strategie_rnd': 2}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spielen(zuege=2, letzt_status=True, strategie_max=2, strategie_min=1, strategie_random=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nun die Simulation, mit jeweils 100 000 Durchläufen, für jede der 3 Strategien. Achtung, das dauert recht lange. Bei mir z.B. 30 Minuten.\n",
    "Bitte insbesondere wenn du dieses Notebook via [mybinder](mybinder.org) ausführst, diese Zelle überspringen und mit der nächsten Zelle die 100 000 Simulationen laden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 34min 43s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "n = 100000\n",
    "df_max = simulation(n, strategie_max=1, strategie_min=0, strategie_random=0)\n",
    "df_min = simulation(n, strategie_max=0, strategie_min=1, strategie_random=0)\n",
    "df_rand = simulation(n, strategie_max=0, strategie_min=0, strategie_random=1)\n",
    "df = df_max\n",
    "df = df.append(df_min)\n",
    "df = df.append(df_rand)\n",
    "# Da das generieren der Daten doch etwas dauert, speichern wir die Daten zur Sicherheit ab.\n",
    "df.to_csv('df.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruechte</th>\n",
       "      <th>gespielte_runden</th>\n",
       "      <th>gewonnen</th>\n",
       "      <th>raben_teile</th>\n",
       "      <th>strategie_max</th>\n",
       "      <th>strategie_min</th>\n",
       "      <th>strategie_rnd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fruechte  gespielte_runden gewonnen  raben_teile  strategie_max  \\\n",
       "0         5                45    False            9              1   \n",
       "1         0                47     True            6              1   \n",
       "2         0                47     True            5              1   \n",
       "\n",
       "   strategie_min  strategie_rnd  \n",
       "0              0              0  \n",
       "1              0              0  \n",
       "2              0              0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 300000 entries, 0 to 299999\n",
      "Data columns (total 7 columns):\n",
      "fruechte            300000 non-null int64\n",
      "gespielte_runden    300000 non-null int64\n",
      "gewonnen            300000 non-null bool\n",
      "raben_teile         300000 non-null int64\n",
      "strategie_max       300000 non-null int64\n",
      "strategie_min       300000 non-null int64\n",
      "strategie_rnd       300000 non-null int64\n",
      "dtypes: bool(1), int64(6)\n",
      "memory usage: 16.3 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Damit man die Strategien leichter erkennt, übersetzen wir z.B. (1,0,0) in max."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'max'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_strategie = {(1, 0, 0): 'max', (0, 1, 0): 'min', (0, 0, 1): 'rnd'}\n",
    "dict_strategie.get((1, 0, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rnd    100000\n",
       "min    100000\n",
       "max    100000\n",
       "Name: strategie, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['strategie'] = df.apply(lambda x: dict_strategie.get((x['strategie_max'], \n",
    "                                                         x['strategie_min'], x['strategie_rnd'])), axis=1)\n",
    "df = df.drop(['strategie_max', 'strategie_min', 'strategie_rnd'], axis = 1)\n",
    "df.strategie.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Für die Strategie `max`, sprich die optimale Strategie, sieht man nun, dass man in rund 68% der Fälle gewinnt. Ein Spiel dauert im Durchschnitt 40 Runden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruechte</th>\n",
       "      <th>gespielte_runden</th>\n",
       "      <th>gewonnen</th>\n",
       "      <th>raben_teile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.00000</td>\n",
       "      <td>100000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.493880</td>\n",
       "      <td>40.12557</td>\n",
       "      <td>0.68352</td>\n",
       "      <td>6.689460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.110319</td>\n",
       "      <td>5.42102</td>\n",
       "      <td>0.465105</td>\n",
       "      <td>2.164281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.00000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>43.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>65.00000</td>\n",
       "      <td>True</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fruechte  gespielte_runden  gewonnen    raben_teile\n",
       "count  100000.000000      100000.00000    100000  100000.000000\n",
       "mean        2.493880          40.12557   0.68352       6.689460\n",
       "std         5.110319           5.42102  0.465105       2.164281\n",
       "min         0.000000          11.00000     False       0.000000\n",
       "25%         0.000000          37.00000         0       5.000000\n",
       "50%         0.000000          40.00000         1       7.000000\n",
       "75%         2.000000          43.00000         1       9.000000\n",
       "max        38.000000          65.00000      True       9.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.strategie == 'max'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Strategie `max` hat einen spürbaren Einfluss auf die Gewinnwahrscheinlichkeit. Bei der Strategie `min`, d.h. wenn man bei `Korb` immer die Obstsorte erntet, von der es sowieso schon am wenigsten gibt, sinkt die Gewinnwahrscheinlichkeit um mehr als 10%-Punkte auf 53%. Die zufällige Strategie, liegt mit 63% dazwischen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruechte</th>\n",
       "      <th>gespielte_runden</th>\n",
       "      <th>gewonnen</th>\n",
       "      <th>raben_teile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strategie</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.49388</td>\n",
       "      <td>40.12557</td>\n",
       "      <td>0.68352</td>\n",
       "      <td>6.68946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.74925</td>\n",
       "      <td>44.40649</td>\n",
       "      <td>0.53030</td>\n",
       "      <td>7.40862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rnd</th>\n",
       "      <td>2.81457</td>\n",
       "      <td>41.65798</td>\n",
       "      <td>0.63106</td>\n",
       "      <td>6.94978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           fruechte  gespielte_runden  gewonnen  raben_teile\n",
       "strategie                                                   \n",
       "max         2.49388          40.12557   0.68352      6.68946\n",
       "min         3.74925          44.40649   0.53030      7.40862\n",
       "rnd         2.81457          41.65798   0.63106      6.94978"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('strategie').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wenn man, zB. mit der Strategie `max` gewinnt, hat man im Durchschnitt 5.6 (bzw. Meadian 6) Rabenteile schon ausgelegt. Verliert man, dann sind im Durchschnitt noch 8 Früchte (Median 6) auf den Bäumen. Ein gewonnenes Spiel dauert 41 Züge, ein verlorenes ist mit 37 (Durchschnitt) bzw. 38 (Median) Zügen kürzer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">fruechte</th>\n",
       "      <th colspan=\"2\" halign=\"left\">gespielte_runden</th>\n",
       "      <th colspan=\"2\" halign=\"left\">raben_teile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strategie</th>\n",
       "      <th>gewonnen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">max</th>\n",
       "      <th>False</th>\n",
       "      <td>7.880056</td>\n",
       "      <td>6</td>\n",
       "      <td>37.248989</td>\n",
       "      <td>38</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>41.457470</td>\n",
       "      <td>41</td>\n",
       "      <td>5.619645</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">min</th>\n",
       "      <th>False</th>\n",
       "      <td>7.982223</td>\n",
       "      <td>7</td>\n",
       "      <td>41.047796</td>\n",
       "      <td>41</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>47.381369</td>\n",
       "      <td>47</td>\n",
       "      <td>5.999095</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">rnd</th>\n",
       "      <th>False</th>\n",
       "      <td>7.628801</td>\n",
       "      <td>6</td>\n",
       "      <td>38.695669</td>\n",
       "      <td>39</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>43.389852</td>\n",
       "      <td>43</td>\n",
       "      <td>5.751149</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    fruechte        gespielte_runden        raben_teile       \n",
       "                        mean median             mean median        mean median\n",
       "strategie gewonnen                                                            \n",
       "max       False     7.880056      6        37.248989     38    9.000000      9\n",
       "          True      0.000000      0        41.457470     41    5.619645      6\n",
       "min       False     7.982223      7        41.047796     41    9.000000      9\n",
       "          True      0.000000      0        47.381369     47    5.999095      6\n",
       "rnd       False     7.628801      6        38.695669     39    9.000000      9\n",
       "          True      0.000000      0        43.389852     43    5.751149      6"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['strategie', 'gewonnen']).agg(['mean', 'median'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Damit die Grafiken unten etwas leichter zu lesen sind, erzeuge ich statt der Spalte `gewonnen` mit Werten `False` und `True` eine Spalte `Ergebnis` mit `Gewonnen` oder `Verloren`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.loc[df.gewonnen == True, 'Ergebnis'] = 'Gewonnen'\n",
    "df.loc[df.gewonnen == False, 'Ergebnis'] = 'Verloren'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Für die Graphen verwende ich `matplotlib` und die Erweiterung `seaborn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "# Style der Graphiken\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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KFI3rk8lkyM3NRe3atZ85jqWlJd5//328//77AB6fMg4PD0dycjKWLl2qFvSepmlbq8g2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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cd38b6d080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Größe der Graphiken\n",
    "sns.set_context(\"talk\")\n",
    "ax = sns.barplot(x=\"strategie\", y=\"gewonnen\", data=df, order=['max', 'rnd', 'min'])\n",
    "sns.set(font_scale=1.5)\n",
    "sns.plt.title('Gewinnwahrscheinlichkeit je Strategie')\n",
    "sns.set(font_scale=1)\n",
    "sns.axlabel('Strategie', 'Gewinnwahrscheinlichkeit [%]')\n",
    "sns.plt.yticks(ax.get_yticks(), ax.get_yticks()*100)\n",
    "# Speichern der Graphik als svg File, um eine skalierbare Grapik im Blog zur Verfügung zu haben\n",
    "sns.plt.savefig('01_gewinnwahrscheinlichkeit.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MGDECvXv3tvq4N25kV2LVRERERM7P39+zzN/ZPfRVFoY+IiIicnV3C30cBUdE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GAAhD4AAAADIPQBAAAYAKEPAADAAAh9AAAABkDoAwAAMABCHwAAgAEQ+gAAAAyA0AcAAGAAhD4A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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cd38b60d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"talk\")\n",
    "sns.violinplot(x='strategie', y='gespielte_runden', data=df, order=['max', 'rnd', 'min'])\n",
    "sns.set(font_scale=1.5)\n",
    "sns.plt.title('Anzahl gespielter Runden je Strategie')\n",
    "sns.set(font_scale=1)\n",
    "sns.axlabel('Strategie', 'Gespielte Runden')\n",
    "sns.plt.savefig('02_gespielte_runden.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I+Pr64ubNm7h16xZatmyp0mndxsZG7ThIhGjDypUr0aBBA5w5cwanT5+GnZ0d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\noU9ERETEB/wHs4dx/DjLSqoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cd39ce57b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"talk\")\n",
    "sns.violinplot(x='strategie', y='gespielte_runden', hue='Ergebnis', data=df, order=['max', 'rnd', 'min'])\n",
    "sns.set(font_scale=1.5)\n",
    "sns.plt.title('Anzahl gespielter Runden je Strategie und Ergebnis')\n",
    "sns.set(font_scale=1)\n",
    "sns.axlabel('Strategie', 'Gespielte Runden')\n",
    "sns.plt.savefig('03_gespielte_runden_je_ergebnis.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um das Ergebnis noch etwas detailierter betrachten zu können, definiere ich noch eine Spalte `punkte`. \n",
    "- Im Fall, dass gewonnen wurde, ist diese die Anzahl der noch nicht befüllten Rabenteile, sprich zwischen 9 (noch kein Rabenteil im Spiel) und 1 (8 Rabenteile im Spiel, noch 1 Rabenfeld frei). \n",
    "- Im Fall, dass verloren wurde, wird die negative Anzahl der Früchte, die noch auf den Bäumen sind, angegeben, also zB. -5, falls noch 5 Früchte nicht geerntet wurden.\n",
    "\n",
    "Insgesamt ist `punkte` also eine Zahl zwischen -40 (wenn 9 mal hintereinander Rabe gewürfelt würde) und +9, wobei das Ergebnis des Spiels ums besser für die Spieler ist, je größer die Zahl ist. Zusätzlich erzeuge ich einen neuen Dataframe, der pro Strategie angebiet, wie häufig diese Endpunkte (in Prozent) auftreten."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>strategie</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>rnd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>punkte</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-38</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-37</th>\n",
       "      <td>0.001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-36</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-35</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-34</th>\n",
       "      <td>0.004</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.759</td>\n",
       "      <td>1.288</td>\n",
       "      <td>2.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.881</td>\n",
       "      <td>0.388</td>\n",
       "      <td>0.677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.173</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "strategie    max    min    rnd\n",
       "punkte                        \n",
       "-38        0.001  0.001  0.002\n",
       "-37        0.001    NaN    NaN\n",
       "-36        0.001  0.003    NaN\n",
       "-35          NaN  0.005  0.002\n",
       "-34        0.004  0.002  0.005\n",
       " 7         2.759  1.288  2.145\n",
       " 8         0.881  0.388  0.677\n",
       " 9         0.173  0.065  0.124"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['punkte'] = 9 - df.raben_teile - df.fruechte\n",
    "\n",
    "dfg = df.groupby(['punkte', 'strategie'])['punkte'].count().unstack('strategie')\n",
    "dfg = dfg / dfg.sum() * 100\n",
    "dfg.iloc[[0,1,2,3,4,-3,-2,-1],:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mit `seaborn` kann man gut Farbpaletten angeben. Hier erzeuge ich eine, die von tiefrot (der Rabe gewinnt mit großem Vorsprung) über hellrot (der Rabe gewinnt knapp) zu hellgrün (die Spieler gewinnen knapp) zu dunkelgrün (die Spieler gewinnen klar) reicht."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACk0AAABGCAYAAABrYUH3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAABX9JREFUeJzt3SGIVGscxuF35LIYDBebi7AWTRtki27aWzZpUBG1KAYt\nmwYETRvEpCBssmgQLSqiBk0WTatFbjBpuYKsbblRLOeGyW6Rz497/s8Dk4YD74QZON/8mJkMwzAE\nAAAAAAAAAAAAYOR29R4AAAAAAAAAAAAA8DuIJgEAAAAAAAAAAIASRJMAAAAAAAAAAABACaJJAAAA\nAAAAAAAAoATRJAAAAAAAAAAAAFCCaBIAAAAAAAAAAAAoYedo8vv33zQDAAAAAAAAAAAA4NdNrk9+\n+twfO165e3cy+fnF/3vDkMzN9V7Rzo8fyZ49vVe0MTeXbG8ne/f2XtLG9nayb1/vFe18+5YsLPRe\n0c6XL8nBg71XtPP5c7K42HtFOx8/JktLvVe08eFDcvRo7xXtvHuXrKz0XtHO27fJ6mrvFe28fp0c\nO9Z7RTuvXiUnT/Ze0cbz58nZs71XtPP4cXL+fO8V7Tx8mFy61HtFO/fuJWtrvVe0c+dOMp32XtHG\nxkZy9WrvFe3cupWsr/de0c6NG7PHWK2vJzdv9l7RzrVrs/fgGE2ns8/OsVpbS+7e7b2incuXkwcP\neq9o58KF5NGj3ivaOXcuefas94o2Tp1KXr7svaKd48dn9+xjtbo6O3MZq5WVZHOz94p2lpdnZ55j\ntLQ0O6seq8XF5NOn3ivaOXRo9l3RWC0sJFtbvVe0Mz+frYzz9c1nPu//fd97RjNH/jySp/887T2j\nmdMHTmfj75HeryeZHp7mypsrvWc0c/uv2znz4kzvGU08OfEky/eXe89oZvPiZvZv7O89o5mv0687\nhoVj5u+5AQAAAAAAAAAAgBJEkwAAAAAAAAAAAEAJokkAAAAAAAAAAACgBNEkAAAAAAAAAAAAUIJo\nEgAAAAAAAAAAAChBNAkAAAAAAAAAAACUIJoEAAAAAAAAAAAAShBNAgAAAAAAAAAAACWIJgEAAAAA\nAAAAAIASRJMAAAAAAAAAAABACaJJAAAAAAAAAAAAoATRJAAAAAAAAAAAAFCCaBIAAAAAAAAAAAAo\nQTQJAAAAAAAAAAAAlCCaBAAAAAAAAAAAAEoQTQIAAAAAAAAAAAAliCYBAAAAAAAAAACAEkSTAAAA\nAAAAAAAAQAmiSQAAAAAAAAAAAKAE0SQAAAAAAAAAAABQgmgSAAAAAAAAAAAAKEE0CQAAAAAAAAAA\nAJQgmgQAAAAAAAAAAABKEE0CAAAAAAAAAAAAJYgmAQAAAAAAAAAAgBJEkwAAAAAAAAAAAEAJokkA\nAAAAAAAAAACgBNEkAAAAAAAAAAAAUIJoEgAAAAAAAAAAAChBNAkAAAAAAAAAAACUIJoEAAAAAAAA\nAAAAShBNAgAAAAAAAAAAACWIJgEAAAAAAAAAAIASRJMAAAAAAAAAAABACaJJAAAAAAAAAAAAoATR\nJAAAAAAAAAAAAFCCaBIAAAAAAAAAAAAoQTQJAAAAAAAAAAAAlCCaBAAAAAAAAAAAAEoQTQIAAAAA\nAAAAAAAliCYBAAAAAAAAAACAEkSTAAAAAAAAAAAAQAmiSQAAAAAAAAAAAKAE0SQAAAAAAAAAAABQ\ngmgSAAAAAAAAAAAAKEE0CQAAAAAAAAAAAJQgmgQAAAAAAAAAAABKEE0CAAAAAAAAAAAAJYgmAQAA\nAAAAAAAAgBJEkwAAAAAAAAAAAEAJokkAAAAAAAAAAACgBNEkAAAAAAAAAAAAUIJoEgAAAAAAAAAA\nAChBNAkAAAAAAAAAAACUMBmGYeg9AgAAAAAAAAAAAKA1vzQJAAAAAAAAAAAAlCCaBAAAAAAAAAAA\nAEoQTQIAAAAAAAAAAAAliCYBAAAAAAAAAACAEkSTAAAAAAAAAAAAQAmiSQAAAAAAAAAAAKCE/wCn\nMm6L4JvzCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cd39e54518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anzahl_punkte_gewonnen = sum(df.punkte.unique() > 0)\n",
    "anzahl_punkte_verloren = sum(df.punkte.unique() < 0)\n",
    "\n",
    "pal = sns.light_palette(\"red\", n_colors=anzahl_punkte_verloren, reverse=True) + \\\n",
    "      sns.light_palette(\"green\", n_colors=anzahl_punkte_gewonnen)\n",
    "sns.palplot(pal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZuHFjY86cOXbvz5RtXrlyZePMmTO26d9//70RHBxsvPPOO4ZhpH6NXbp0yQgPDzdGjhxp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\nGGPMCnDTxxhjjDFmBf4f985W2UgAN2kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cd39e542b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"talk\")\n",
    "plot = sns.barplot(dfg.index, y = dfg['max'], palette=pal)\n",
    "for label in plot.get_xticklabels()[1::2]:\n",
    "   label.set_visible(False)\n",
    "sns.set(font_scale=1.5)\n",
    "sns.plt.title('Verteilung Punkte am Ende des Spiels bei Strategie \"max\"')\n",
    "sns.set(font_scale=1)\n",
    "sns.axlabel('Punkte', 'Häufigkeit [%]')\n",
    "sns.plt.savefig('04_verteilung_punkte_strategie_max.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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l0qFHjx6YM2cOPvnkE7Rq1QqvX79GREQE4uPjsXjxYlOnR0Q6/Pzzzzh79ixS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\nJ0mSFAGjT5IkKQJGnyRJUgQ+AZ2r7tXZoTXZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cd373a3828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(\"talk\")\n",
    "fig, axes = sns.plt.subplots(3, 1)\n",
    "for nr, strategie in enumerate(dfg.columns):\n",
    "    plot = sns.barplot(dfg.index, y = dfg[strategie], palette=pal, ax=axes[nr], label = strategie)\n",
    "    #axes[nr].set_title(strategie)\n",
    "    axes[nr].set_ylabel(strategie)\n",
    "    axes[nr].set_xlabel('Punkte')\n",
    "    for label in plot.get_xticklabels()[1::2]:\n",
    "        label.set_visible(False)\n",
    "fig.suptitle('Verteilung Punkte am Ende des Spiels je Strategie', fontsize=18)\n",
    "sns.plt.savefig('05_verteilung_punkte_je_strategie.svg')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}