probeklausurWS2022_23ml.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Probeklausur WT33 Wissenschaftliches Arbeiten und Statistische Datenanalyse\n",
    "## Teilbereich Statistische Datenanalyse\n",
    "\n",
    "### Freitag, 14.01.2022,  10:15 Uhr - 11:00 Uhr,  Dauer: 45 Minuten\n",
    "\n",
    "### 50 Punkte\n",
    "\n",
    "### Regeln\n",
    "- Alle Hilfsmittel sind erlaubt\n",
    "- Eine Kommunikation der Prüfungsteilnehmer untereinander ist während der Prüfung nicht erlaubt \n",
    "- Der Bearbeitungszeitraum muss selbständig eingehalten werden. Die Zeit startet sobald alle einführenden Hinweise gegeben wurden.\n",
    "\n",
    "- Diese Regeln müssen von den Prüfungsteilnehmern eingehalten werden und werden durch die Teilnahme an der Prüfung automatisch bestätigt.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Name:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matrikelnummer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hinweise\n",
    "\n",
    "### Während der Bearbeitung:\n",
    "- Lesen Sie die Angaben vollständig.\n",
    "- Geben Sie für alle notwendigen Lösungsschritte den geeigneten Python Code an. \n",
    "- Verwenden Sie beliebig viele Zwischenschritte oder Teillösungen.\n",
    "- Erzeugen Sie so viele Zellen im Notebook, wie Sie benötigen. Es gibt keine Obergrenze.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Bei Problemen:\n",
    "\n",
    "- Ihr Code ist nicht lauffähig bzw. Fehler lassen sich nicht beheben.\n",
    "    - Lösung: Lassen Sie den nicht-lauffähigen Code im Notebook und versuchen Sie Ihren Gedankengang zu Lösung des Problems schriftlich darzulegen. Benutzen Sie dazu entweder Kommentare (Code-Zeile beginnt mit #) oder eine zusätzliche Markdown-Zelle.\n",
    "\n",
    "\n",
    "- Eine Zelle lässt sich nicht ausführen:\n",
    "\n",
    "    - Kein Kernel ausgewählt\n",
    "        - Lösung: Python Kernel auswählen \n",
    "\n",
    "        ![Fehlermeldung](img/binder_error_kernel1.png)\n",
    "        \n",
    "        ![Fehlermeldung](img/binder_error_kernel2.png)\n",
    "\n",
    "\n",
    "    - Falscher Zelltyp gewählt\n",
    "        - Lösung: Den richtigen Zelltyp in der Menuleiste auswählen. Zellen mit Python-Code haben zwei rechteckige Klammern an der linken Seite: []\n",
    "\n",
    "        ![Fehlermeldung](img/binder_celltype1.png)\n",
    "        \n",
    "        ![Fehlermeldung](img/binder_celltype2.png)\n",
    "\n",
    "\n",
    "- Binder Session endet/Verbindung bricht ab (z.B. nach 10 Minuten ohne Aktivität)\n",
    "    Lösung:\n",
    "    1. Browser *NICHT* schließen.\n",
    "\n",
    "    2. Bestätigen Sie ggf. die Fehlermeldung mit \"Dismiss\"\n",
    "\n",
    "    ![Fehlermeldung](img/binder_error.png)\n",
    "    \n",
    "    3. Speichern Sie den ihr Notebook im Browser Speicher:\n",
    "\n",
    "    ![Fehlermeldung](img/binder_error2.png)\n",
    "\n",
    "    ![Fehlermeldung](img/binder_error3.png)\n",
    "\n",
    "    4. Öffnen Sie einen neuen Tab\n",
    "\n",
    "    5. Stellen Sie die Verbindung zum Notebook erneut her, indem Sie den Link aus Moodle zur Prüfung aufrufen bzw. in die Adresszeile des neuen Tabs einfügen.\n",
    "\n",
    "    6. Stellen Sie das ursprüngliche Notebook aus dem Browserspeicher wieder her:\n",
    "\n",
    "    ![Fehlermeldung](img/binder_error5.png)\n",
    "\n",
    "    ![Fehlermeldung](img/binder_error6.png)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Nach Ablauf der Bearbeitungszeit:\n",
    "- Laden Sie das Notebook mit dem Download-Button auf Ihre lokale Festplatte herunter.\n",
    "\n",
    "![Download](img/binder_Download.png)\n",
    "\n",
    "- Benennen Sie die Datei wie folgt: NACHNAME_MATRIKELNUMMER_WT33_PK_WS21.ipynb\n",
    "\n",
    "- Geben Sie das Notebook ab indem Sie die Datei (bzw. ggf. auch mehrere Dateien) in der Aufgabe in Moodle hochladen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aufgaben\n",
    "\n",
    "***Ab hier geht es los - Viel Erfolg!***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aufgabe 0 \n",
    "\n",
    "Vorbereitung [0 Punkte]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "nba = pd.read_csv(\"nba_all_elo.csv\")\n",
    "pd.set_option(\"display.max.columns\", None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aufgabe 1 \n",
    "\n",
    "[2 Punkte] Geben Sie die ersten 15 Zeilen des DataFrames aus.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>1295.3092</td>\n",
       "      <td>1277.9456</td>\n",
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       "      <td>Capitols</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>11/4/1946</td>\n",
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       "      <td>WSC</td>\n",
       "      <td>Capitols</td>\n",
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       "      <td>1320.3811</td>\n",
       "      <td>1337.7448</td>\n",
       "      <td>44.946995</td>\n",
       "      <td>PIT</td>\n",
       "      <td>Ironmen</td>\n",
       "      <td>56</td>\n",
       "      <td>1295.3092</td>\n",
       "      <td>1277.9456</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.393811</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8</td>\n",
       "      <td>194611050BOS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>11/5/1946</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>55</td>\n",
       "      <td>1294.8458</td>\n",
       "      <td>1288.4139</td>\n",
       "      <td>39.785030</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>57</td>\n",
       "      <td>1309.6521</td>\n",
       "      <td>1316.0840</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.620204</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gameorder       game_id lg_id  _iscopy  year_id  date_game  seasongame  \\\n",
       "0           1  194611010TRH   NBA        0     1947  11/1/1946           1   \n",
       "1           1  194611010TRH   NBA        1     1947  11/1/1946           1   \n",
       "2           2  194611020CHS   NBA        0     1947  11/2/1946           1   \n",
       "3           2  194611020CHS   NBA        1     1947  11/2/1946           2   \n",
       "4           3  194611020DTF   NBA        0     1947  11/2/1946           1   \n",
       "5           3  194611020DTF   NBA        1     1947  11/2/1946           1   \n",
       "6           4  194611020PRO   NBA        1     1947  11/2/1946           1   \n",
       "7           4  194611020PRO   NBA        0     1947  11/2/1946           1   \n",
       "8           5  194611020STB   NBA        1     1947  11/2/1946           1   \n",
       "9           5  194611020STB   NBA        0     1947  11/2/1946           1   \n",
       "10          6  194611030CLR   NBA        1     1947  11/3/1946           2   \n",
       "11          6  194611030CLR   NBA        0     1947  11/3/1946           1   \n",
       "12          7  194611040PIT   NBA        0     1947  11/4/1946           2   \n",
       "13          7  194611040PIT   NBA        1     1947  11/4/1946           2   \n",
       "14          8  194611050BOS   NBA        0     1947  11/5/1946           2   \n",
       "\n",
       "    is_playoffs team_id       fran_id  pts      elo_i      elo_n  win_equiv  \\\n",
       "0             0     TRH       Huskies   66  1300.0000  1293.2767  40.294830   \n",
       "1             0     NYK        Knicks   68  1300.0000  1306.7233  41.705170   \n",
       "2             0     CHS         Stags   63  1300.0000  1309.6521  42.012257   \n",
       "3             0     NYK        Knicks   47  1306.7233  1297.0712  40.692783   \n",
       "4             0     DTF       Falcons   33  1300.0000  1279.6189  38.864048   \n",
       "5             0     WSC      Capitols   50  1300.0000  1320.3811  43.135952   \n",
       "6             0     BOS       Celtics   53  1300.0000  1294.8458  40.459381   \n",
       "7             0     PRO  Steamrollers   59  1300.0000  1305.1542  41.540619   \n",
       "8             0     PIT       Ironmen   51  1300.0000  1295.3092  40.507980   \n",
       "9             0     STB       Bombers   56  1300.0000  1304.6908  41.492020   \n",
       "10            0     TRH       Huskies   60  1293.2767  1286.1534  39.548164   \n",
       "11            0     CLR        Rebels   71  1300.0000  1307.1233  41.747120   \n",
       "12            0     PIT       Ironmen   56  1295.3092  1277.9456  38.689037   \n",
       "13            0     WSC      Capitols   71  1320.3811  1337.7448  44.946995   \n",
       "14            0     BOS       Celtics   55  1294.8458  1288.4139  39.785030   \n",
       "\n",
       "   opp_id      opp_fran  opp_pts  opp_elo_i  opp_elo_n game_location  \\\n",
       "0     NYK        Knicks       68  1300.0000  1306.7233             H   \n",
       "1     TRH       Huskies       66  1300.0000  1293.2767             A   \n",
       "2     NYK        Knicks       47  1306.7233  1297.0712             H   \n",
       "3     CHS         Stags       63  1300.0000  1309.6521             A   \n",
       "4     WSC      Capitols       50  1300.0000  1320.3811             H   \n",
       "5     DTF       Falcons       33  1300.0000  1279.6189             A   \n",
       "6     PRO  Steamrollers       59  1300.0000  1305.1542             A   \n",
       "7     BOS       Celtics       53  1300.0000  1294.8458             H   \n",
       "8     STB       Bombers       56  1300.0000  1304.6908             A   \n",
       "9     PIT       Ironmen       51  1300.0000  1295.3092             H   \n",
       "10    CLR        Rebels       71  1300.0000  1307.1233             A   \n",
       "11    TRH       Huskies       60  1293.2767  1286.1534             H   \n",
       "12    WSC      Capitols       71  1320.3811  1337.7448             H   \n",
       "13    PIT       Ironmen       56  1295.3092  1277.9456             A   \n",
       "14    CHS         Stags       57  1309.6521  1316.0840             H   \n",
       "\n",
       "   game_result  forecast notes  \n",
       "0            L  0.640065   NaN  \n",
       "1            W  0.359935   NaN  \n",
       "2            W  0.631101   NaN  \n",
       "3            L  0.368899   NaN  \n",
       "4            L  0.640065   NaN  \n",
       "5            W  0.359935   NaN  \n",
       "6            L  0.359935   NaN  \n",
       "7            W  0.640065   NaN  \n",
       "8            L  0.359935   NaN  \n",
       "9            W  0.640065   NaN  \n",
       "10           L  0.351068   NaN  \n",
       "11           W  0.648932   NaN  \n",
       "12           L  0.606189   NaN  \n",
       "13           W  0.393811   NaN  \n",
       "14           L  0.620204   NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lösung\n",
    "nba.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aufgabe 2\n",
    "\n",
    "[8 Punkte] Erzeugen Sie einen Dataframe mit der Bezeichnung `celtics_playoffs`, der nur die Playoff-Spiele der Boston Celtics (`team_id` BOS) enthält. Hinweis: Benutzen Sie dazu das Flag in Spalte `is_playoffs`.\n",
    "\n",
    "Geben Sie außerdem einen Auszug des Dataframes aus (z.B. die ersten 10 Zeilen des DataFrames) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lösung\n",
    "celtics_playoffs = nba[(nba[\"team_id\"] == 'BOS') & (nba[\"is_playoffs\"] == 1) ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>gameorder</th>\n",
       "      <th>game_id</th>\n",
       "      <th>lg_id</th>\n",
       "      <th>_iscopy</th>\n",
       "      <th>year_id</th>\n",
       "      <th>date_game</th>\n",
       "      <th>seasongame</th>\n",
       "      <th>is_playoffs</th>\n",
       "      <th>team_id</th>\n",
       "      <th>fran_id</th>\n",
       "      <th>pts</th>\n",
       "      <th>elo_i</th>\n",
       "      <th>elo_n</th>\n",
       "      <th>win_equiv</th>\n",
       "      <th>opp_id</th>\n",
       "      <th>opp_fran</th>\n",
       "      <th>opp_pts</th>\n",
       "      <th>opp_elo_i</th>\n",
       "      <th>opp_elo_n</th>\n",
       "      <th>game_location</th>\n",
       "      <th>game_result</th>\n",
       "      <th>forecast</th>\n",
       "      <th>notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>549</td>\n",
       "      <td>194803280BOS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1948</td>\n",
       "      <td>3/28/1948</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>72</td>\n",
       "      <td>1312.5914</td>\n",
       "      <td>1304.9589</td>\n",
       "      <td>31.554913</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>79</td>\n",
       "      <td>1438.3618</td>\n",
       "      <td>1445.9944</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.462981</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>552</td>\n",
       "      <td>194803310BOS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1948</td>\n",
       "      <td>3/31/1948</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>81</td>\n",
       "      <td>1304.9589</td>\n",
       "      <td>1312.2666</td>\n",
       "      <td>32.283836</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>77</td>\n",
       "      <td>1445.9944</td>\n",
       "      <td>1438.6866</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.441218</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>555</td>\n",
       "      <td>194804020BOS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1948</td>\n",
       "      <td>4/2/1948</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>74</td>\n",
       "      <td>1312.2666</td>\n",
       "      <td>1304.6532</td>\n",
       "      <td>31.524557</td>\n",
       "      <td>CHS</td>\n",
       "      <td>Stags</td>\n",
       "      <td>81</td>\n",
       "      <td>1438.6866</td>\n",
       "      <td>1446.3000</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.462052</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3785</th>\n",
       "      <td>1893</td>\n",
       "      <td>195103200BOS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1951</td>\n",
       "      <td>3/20/1951</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>69</td>\n",
       "      <td>1506.6257</td>\n",
       "      <td>1488.9506</td>\n",
       "      <td>39.948803</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>83</td>\n",
       "      <td>1510.8330</td>\n",
       "      <td>1528.5082</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.634467</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3795</th>\n",
       "      <td>1898</td>\n",
       "      <td>195103220NYK</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1951</td>\n",
       "      <td>3/22/1951</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>78</td>\n",
       "      <td>1488.9506</td>\n",
       "      <td>1481.7932</td>\n",
       "      <td>39.198959</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>92</td>\n",
       "      <td>1528.5082</td>\n",
       "      <td>1535.6655</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.309308</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4498</th>\n",
       "      <td>2250</td>\n",
       "      <td>195203190BOS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1952</td>\n",
       "      <td>3/19/1952</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>105</td>\n",
       "      <td>1574.9653</td>\n",
       "      <td>1582.4563</td>\n",
       "      <td>48.504478</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>94</td>\n",
       "      <td>1579.5975</td>\n",
       "      <td>1572.1066</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.633899</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4508</th>\n",
       "      <td>2255</td>\n",
       "      <td>195203230NYK</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1952</td>\n",
       "      <td>3/23/1952</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>97</td>\n",
       "      <td>1582.4563</td>\n",
       "      <td>1578.0449</td>\n",
       "      <td>48.056339</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>101</td>\n",
       "      <td>1572.1066</td>\n",
       "      <td>1576.5179</td>\n",
       "      <td>A</td>\n",
       "      <td>L</td>\n",
       "      <td>0.373772</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4514</th>\n",
       "      <td>2258</td>\n",
       "      <td>195203260BOS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1952</td>\n",
       "      <td>3/26/1952</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>87</td>\n",
       "      <td>1578.0449</td>\n",
       "      <td>1572.3955</td>\n",
       "      <td>47.479900</td>\n",
       "      <td>NYK</td>\n",
       "      <td>Knicks</td>\n",
       "      <td>88</td>\n",
       "      <td>1576.5179</td>\n",
       "      <td>1582.1674</td>\n",
       "      <td>H</td>\n",
       "      <td>L</td>\n",
       "      <td>0.642088</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5251</th>\n",
       "      <td>2626</td>\n",
       "      <td>195303190SYR</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1</td>\n",
       "      <td>1953</td>\n",
       "      <td>3/19/1953</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>87</td>\n",
       "      <td>1584.0746</td>\n",
       "      <td>1596.8373</td>\n",
       "      <td>50.079281</td>\n",
       "      <td>SYR</td>\n",
       "      <td>Sixers</td>\n",
       "      <td>81</td>\n",
       "      <td>1646.4547</td>\n",
       "      <td>1633.6920</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>0.281965</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5257</th>\n",
       "      <td>2629</td>\n",
       "      <td>195303210BOS</td>\n",
       "      <td>NBA</td>\n",
       "      <td>0</td>\n",
       "      <td>1953</td>\n",
       "      <td>3/21/1953</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>111</td>\n",
       "      <td>1596.8373</td>\n",
       "      <td>1602.8749</td>\n",
       "      <td>50.679440</td>\n",
       "      <td>SYR</td>\n",
       "      <td>Sixers</td>\n",
       "      <td>105</td>\n",
       "      <td>1633.6920</td>\n",
       "      <td>1627.6544</td>\n",
       "      <td>H</td>\n",
       "      <td>W</td>\n",
       "      <td>0.589886</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gameorder       game_id lg_id  _iscopy  year_id  date_game  seasongame  \\\n",
       "1097        549  194803280BOS   NBA        0     1948  3/28/1948          49   \n",
       "1102        552  194803310BOS   NBA        0     1948  3/31/1948          50   \n",
       "1108        555  194804020BOS   NBA        0     1948   4/2/1948          51   \n",
       "3785       1893  195103200BOS   NBA        0     1951  3/20/1951          70   \n",
       "3795       1898  195103220NYK   NBA        1     1951  3/22/1951          71   \n",
       "4498       2250  195203190BOS   NBA        0     1952  3/19/1952          67   \n",
       "4508       2255  195203230NYK   NBA        1     1952  3/23/1952          68   \n",
       "4514       2258  195203260BOS   NBA        0     1952  3/26/1952          69   \n",
       "5251       2626  195303190SYR   NBA        1     1953  3/19/1953          72   \n",
       "5257       2629  195303210BOS   NBA        0     1953  3/21/1953          73   \n",
       "\n",
       "      is_playoffs team_id  fran_id  pts      elo_i      elo_n  win_equiv  \\\n",
       "1097            1     BOS  Celtics   72  1312.5914  1304.9589  31.554913   \n",
       "1102            1     BOS  Celtics   81  1304.9589  1312.2666  32.283836   \n",
       "1108            1     BOS  Celtics   74  1312.2666  1304.6532  31.524557   \n",
       "3785            1     BOS  Celtics   69  1506.6257  1488.9506  39.948803   \n",
       "3795            1     BOS  Celtics   78  1488.9506  1481.7932  39.198959   \n",
       "4498            1     BOS  Celtics  105  1574.9653  1582.4563  48.504478   \n",
       "4508            1     BOS  Celtics   97  1582.4563  1578.0449  48.056339   \n",
       "4514            1     BOS  Celtics   87  1578.0449  1572.3955  47.479900   \n",
       "5251            1     BOS  Celtics   87  1584.0746  1596.8373  50.079281   \n",
       "5257            1     BOS  Celtics  111  1596.8373  1602.8749  50.679440   \n",
       "\n",
       "     opp_id opp_fran  opp_pts  opp_elo_i  opp_elo_n game_location game_result  \\\n",
       "1097    CHS    Stags       79  1438.3618  1445.9944             H           L   \n",
       "1102    CHS    Stags       77  1445.9944  1438.6866             H           W   \n",
       "1108    CHS    Stags       81  1438.6866  1446.3000             H           L   \n",
       "3785    NYK   Knicks       83  1510.8330  1528.5082             H           L   \n",
       "3795    NYK   Knicks       92  1528.5082  1535.6655             A           L   \n",
       "4498    NYK   Knicks       94  1579.5975  1572.1066             H           W   \n",
       "4508    NYK   Knicks      101  1572.1066  1576.5179             A           L   \n",
       "4514    NYK   Knicks       88  1576.5179  1582.1674             H           L   \n",
       "5251    SYR   Sixers       81  1646.4547  1633.6920             A           W   \n",
       "5257    SYR   Sixers      105  1633.6920  1627.6544             H           W   \n",
       "\n",
       "      forecast notes  \n",
       "1097  0.462981   NaN  \n",
       "1102  0.441218   NaN  \n",
       "1108  0.462052   NaN  \n",
       "3785  0.634467   NaN  \n",
       "3795  0.309308   NaN  \n",
       "4498  0.633899   NaN  \n",
       "4508  0.373772   NaN  \n",
       "4514  0.642088   NaN  \n",
       "5251  0.281965   NaN  \n",
       "5257  0.589886   NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "celtics_playoffs.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hinweis**\n",
    "Sollten sie Aufgabe 2 nicht gelöst haben so führen Sie bitte folgendes Statment aus, um mit dem korrekten DataFrame weiterzuarbeiten."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "celtics_playoffs = pd.read_csv(\"celtics.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aufgabe 3\n",
    "\n",
    "[3 Punkte] Wie viele Spiele haben die Celtics insgesamt in den Playoffs absolviert?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "601"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lösung\n",
    "celtics_playoffs[\"game_id\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aufgabe 4\n",
    "\n",
    "[4 Punkte] Wie viele Spiele haben die Celtics insgesamt in den Playoffs **gewonnen**?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pts        37954\n",
       "opp_pts    33981\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lösung\n",
    "celtics_playoffs.loc[celtics_playoffs[\"game_result\"] == \"W\", \"game_id\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aufgabe 5\n",
    "\n",
    "[3 Punkte] Erzeugen Sie einen Boxplot, der die Häufigkeitsverteilung der erzielten Punkte der Celtics in diesen Spielen darstellt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lösung\n",
    "celtics_playoffs.pts.plot(kind=\"box\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aufgabe 6\n",
    "\n",
    "[6 Punkte] Welche Bedeutung hat der horizontale Strich in der Mitte der Box? Wie wird dieser Lageparameter genannt?\n",
    "\n",
    "Welche Bedeutung haben die beiden Punkte am oberen Ende der Darstellung? Warum werden sie so dargestellt?\n",
    "\n",
    "![Fehlermeldung](img/boxplot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antwort:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`#Lösung`\n",
    "Der Strich markiert den mittleren Wert der Datenreihe. 50% der Werte sind größer, 50% kleiner. Der Lageparameter heißt Median.\n",
    "\n",
    "Die Punkte heißen Ausreißer. Sie werden so dargestellt, da Sie mehr als das 1,5-fache des IQA vom 75% Quantil entfernt sind. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aufgabe 7\n",
    "\n",
    "[2 Punkte] Geben Sie den genauen Wert des Lageparameters aus Aufgabe 5 aus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lösung\n",
    "celtics_playoffs.pts.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "celtics_playoffs.pts.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aufgabe 8\n",
    "\n",
    "[6 Punkte] Gegeben sind die beiden u.g. Code-Fragmente. Was wird hier jeweils berechnet?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "celtics_playoffs[celtics_playoffs[\"game_result\"] == \"W\"].pts.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "celtics_playoffs[celtics_playoffs[\"game_result\"] == \"L\"].pts.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antwort: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`#Lösung`\n",
    "Es wird das arithmetische Mittel der erzielten Punkte für Boston jeweils für alle Siege und alle Niederlagen getrennt berechnet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aufgabe 9\n",
    "\n",
    "[6 Punkte] Geben Sie ein Statement an, dass die beiden Berechnungen aus Aufgabe 7 vereint und zwei Zeilen auf einmal ausgibt. \n",
    "\n",
    "Hinweis: Die Berechnung wird nun nicht mehr für explizit festgelegt Werte der Spalte `game_result` durchgeführt sondern für jeden gefundenen Wert gruppenweise erledigt.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "game_result\n",
       "L     97.743191\n",
       "W    110.331395\n",
       "Name: pts, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lösung\n",
    "celtics_playoffs.groupby(\"game_result\").pts.mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aufgabe 10\n",
    "\n",
    "[3 Punkte] Um die Verteilung der der Gegner in den verlorenen Spielen der Celtics zu analysieren wird Ihnen folgendes Statement vorgelegt. Wieso schlägt das Statement fehl?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "celtics_playoffs[celtics_playoffs[\"game_result\"] == \"L\"].opp_id.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antwort: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`#Lösung`\n",
    "Antwort: Die Spalte `opp_id` ist nominalskaliert. Hier sind Transformationen wie Addition und Division nicht erlaubt. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aufgabe 11\n",
    "\n",
    "[4 Punkte] Wie können Sie die Häufigkeitsverteilung der Gegner in den verlorenen Playoffspielen auf einfache Weise veranschaulichen? Geben Sie das passende Statement an.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lösung\n",
    "celtics_playoffs[celtics_playoffs[\"game_result\"] == \"L\"].opp_id.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aufgabe 12\n",
    "\n",
    "[3 Punkte] Ein ähnliches Statement wie in Aufgabe 9 sehen Sie nachfolgend. Es ist zwar lauffähig, jedoch statistisch ohne Aussagekraft. Warum?\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26809.272373540854"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "celtics_playoffs[celtics_playoffs[\"game_result\"] == \"L\"].gameorder.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antwort:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`#Lösung`\n",
    "Antwort: Die Spalte `gameorder` ist ordinalskaliert. Hier sind Transformationen wie Addition und Division nicht erlaubt. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language": "python",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11 (default, Jul 27 2021, 09:42:29) [MSC v.1916 64 bit (AMD64)]"
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  "vscode": {
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 "nbformat": 4,
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}