{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f90ea29",
   "metadata": {},
   "source": [
    "# Sortieren und Ranking\n",
    "\n",
    "Das Sortieren eines Datensatzes nach einem Kriterium ist eine weitere wichtige eingebaute Funktion. Um lexikografisch nach Zeilen- oder Spaltenindex zu sortieren, verwendet die Methoden [pandas.Series.sort_index](https://pandas.pydata.org/docs/reference/api/pandas.Series.sort_index.html) oder [pandas.DataFrame.sort_index](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_index.html), die ein neues, sortiertes Objekt zurückgibt. Mit `ascending=False` wird die Sortierreihenfolge umgekehrt:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "494c8d07",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2026-05-21T13:56:04.344028Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    0.849312\n",
       "5   -0.446785\n",
       "4   -1.180987\n",
       "3    1.739605\n",
       "2    1.535317\n",
       "1   -0.352107\n",
       "0    0.755917\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "rng = np.random.default_rng()\n",
    "s = pd.Series(rng.normal(size=7))\n",
    "\n",
    "s.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc386834",
   "metadata": {},
   "source": [
    "Um eine Serie nach ihren Werten zu sortieren, könnt ihr die `sort_values`-Methode verwenden:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fadf1cd3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T13:56:04.575215Z",
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     "shell.execute_reply.started": "2026-05-21T13:56:04.575206Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "4   -1.180987\n",
       "5   -0.446785\n",
       "1   -0.352107\n",
       "0    0.755917\n",
       "6    0.849312\n",
       "2    1.535317\n",
       "3    1.739605\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70aa0dfe",
   "metadata": {},
   "source": [
    "Alle fehlenden Werte werden standardmäßig an das Ende der Reihe sortiert:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac6aac28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T13:56:04.578894Z",
     "iopub.status.busy": "2026-05-21T13:56:04.578798Z",
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     "shell.execute_reply": "2026-05-21T13:56:04.582109Z",
     "shell.execute_reply.started": "2026-05-21T13:56:04.578886Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    0.334075\n",
       "6    1.507886\n",
       "0    1.549615\n",
       "1         NaN\n",
       "2         NaN\n",
       "3         NaN\n",
       "4         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(rng.normal(size=7))\n",
    "s[s < 0] = np.nan\n",
    "\n",
    "s.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "375693b4",
   "metadata": {},
   "source": [
    "Mit einem DataFrame können ihr auf beiden Achsen nach Index sortieren:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7c8c1daf",
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    "df = pd.DataFrame(rng.normal(size=(7, 3)))\n",
    "\n",
    "df"
   ]
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    "df.sort_index(ascending=False)"
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       "          2         1         0\n",
       "0 -0.492838 -0.749562  0.093003\n",
       "1 -2.079898 -0.402284 -0.628586\n",
       "2  0.970397 -0.879292  0.166077\n",
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       "5  1.187874  0.278702 -1.506027\n",
       "6  1.671595 -0.361845  0.461046"
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64442bea",
   "metadata": {},
   "source": [
    "Beim Sortieren eines DataFrame könnt ihr die Daten in einer oder mehreren Spalten als Sortierschlüssel verwenden. Dazu übergebt ihr eine oder mehrere Spaltennamen an die Option `by` von `sort_values`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a31b3904",
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     "shell.execute_reply.started": "2026-05-21T13:56:04.596215Z"
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   "outputs": [
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      "text/plain": [
       "          0         1         2\n",
       "1 -0.628586 -0.402284 -2.079898\n",
       "0  0.093003 -0.749562 -0.492838\n",
       "4 -1.555077  2.524556 -0.291195\n",
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       "5 -1.506027  0.278702  1.187874\n",
       "6  0.461046 -0.361845  1.671595"
      ]
     },
     "execution_count": 7,
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   ],
   "source": [
    "df.sort_values(by=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "853e1380",
   "metadata": {},
   "source": [
    "Um nach mehreren Spalten zu sortieren, könnt ihr eine Liste von Namen übergeben."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c41a18",
   "metadata": {},
   "source": [
    "Ranking weist Ränge von eins bis zur Anzahl der gültigen Datenpunkte in einem Array zu:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cfd396dd",
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     "iopub.execute_input": "2026-05-21T13:56:04.599978Z",
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     "shell.execute_reply": "2026-05-21T13:56:04.603320Z",
     "shell.execute_reply.started": "2026-05-21T13:56:04.599972Z"
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   "outputs": [
    {
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       "     0    1    2\n",
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    "df.rank()"
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  },
  {
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   "id": "6fe180d8",
   "metadata": {},
   "source": [
    "Wenn beim Ranking Gleichstände auftauchen, weist `rank` jeder Gruppe den mittleren Rang zu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0cec47c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-21T13:56:04.604017Z",
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     "shell.execute_reply": "2026-05-21T13:56:04.607231Z",
     "shell.execute_reply.started": "2026-05-21T13:56:04.604010Z"
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     "execution_count": 9,
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   "source": [
    "df.rank(method=\"max\")"
   ]
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  {
   "cell_type": "markdown",
   "id": "63fd4e17",
   "metadata": {},
   "source": [
    "## Weitere Verfahren mit `rank`\n",
    "\n",
    "Methode | Beschreibung\n",
    ":------ | :-----------\n",
    "`average` | Standard: jedem Eintrag in der gleichen Gruppe den durchschnittlichen Rang zuweisen\n",
    "`min` | verwendet den minimalen Rang für die gesamte Gruppe\n",
    "`max` | verwendet den maximalen Rang für die gesamte Gruppe\n",
    "`first` | weist die Ränge in der Reihenfolge zu, in der die Werte in den Daten erscheinen\n",
    "`dense` | wie `method='min'`, aber die Ränge erhöhen sich zwischen den Gruppen immer um 1 und nicht nach der Anzahl der gleichen Elemente in einer Gruppe"
   ]
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