{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5ead5284",
   "metadata": {},
   "source": [
    "# Vorkurs Tag 4 - Lösungen\n",
    "\n",
    "Author: Christian Reichhardt\n",
    "\n",
    "**Hinweis:** Es gibt meistens keine richtigen oder falschen Lösungen, möglicherweise sind andere Lösungen ebenso gut. Wir zeigen euch nur einen unserer Meinung nach guten Weg."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "142b1809",
   "metadata": {},
   "source": [
    "## Aufgabe 4.1\n",
    "\n",
    "### Aufgabe 4.1.1\n",
    "\n",
    "Schreibe eine einfache Funktion, die beim Aufruf einen String “Die Funktion wurde aufgerufen” auf der Konsole zeigt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c68faef6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Die Funktion wurde aufgerufen.\n"
     ]
    }
   ],
   "source": [
    "def aufrufanzeiger():\n",
    "    \"\"\"Gibt auf der Konsole aus, dass die Funktion aufgerufen wurde\"\"\"\n",
    "    print('Die Funktion wurde aufgerufen.')\n",
    "\n",
    "aufrufanzeiger()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ba180f9",
   "metadata": {},
   "source": [
    "### Aufgabe 4.1.2\n",
    "\n",
    "Erweitere die Funktion, sodass auch gezeit wird von wem die Funktion aufgerufen wurde. Es soll also einen Parameter, deinen Namen als String akzeptieren und so einen String auf der Konsole zeigen:\n",
    "\n",
    "*Die Funktion wurde von Max aufgerufen*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "16911d00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Die Funktion wurde von Max aufgerufen.\n"
     ]
    }
   ],
   "source": [
    "def aufrufanzeiger(name):\n",
    "    \"\"\"Gibt auf der Konsole aus, von wem die Funktion aufgerufen wurde\n",
    "    :param name: Name der Person\n",
    "    \"\"\"\n",
    "    print(f'Die Funktion wurde von {name} aufgerufen.')\n",
    "\n",
    "aufrufanzeiger('Max')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "569d1b24",
   "metadata": {},
   "source": [
    "## Aufgabe 4.2\n",
    "\n",
    "Schreibe eine Funktion, die Vorname, Nacname und das Alter einer Person als Parameter akzeptiert und einen String, z.B. *Max Mustermann ist 20 Jahre alt* zurückliefert, also returniert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "24e519ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Mustermann ist 20 Jahre alt.\n"
     ]
    }
   ],
   "source": [
    "def altersausgabe(vorname, nachname, alter):\n",
    "    \"\"\"Gibt das Alter der angegebenen Person aus.\n",
    "    :param vorname: Vorname der Person\n",
    "    :param nachname: Nachname der Person\n",
    "    :param alter: Alter der Person\n",
    "    :return: String mit Name und Alter\n",
    "    \"\"\"\n",
    "    return f'{vorname} {nachname} ist {alter} Jahre alt.'\n",
    "\n",
    "print(altersausgabe('Max', 'Mustermann', 20))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f194824f",
   "metadata": {},
   "source": [
    "## Aufgabe 4.3\n",
    "\n",
    "Schreibe eine Funktion die drei Zahlen (integers) als als Parmeter übergeben bekommt und die kleinste Zahl zurück gibt.\n",
    "\n",
    "Der Aufruf *get_min(7,4,8)* soll die Zahl 4 zurückgeben."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4586fb53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "2\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "def get_min(a, b, c):\n",
    "    \"\"\"Gibt das Minimum von drei Zahlen aus.\n",
    "    :param a: erste Zahl\n",
    "    :param b: zweite Zahl\n",
    "    :param c: dritte Zahl\n",
    "    :return: kleinste der drei Zahlen\n",
    "    \"\"\"\n",
    "    kleinste_zahl = a\n",
    "    if b < a:\n",
    "        kleinste_zahl = b\n",
    "    if c < kleinste_zahl:\n",
    "        kleinste_zahl = c\n",
    "    return kleinste_zahl\n",
    "\n",
    "print(get_min(7, 4, 8))\n",
    "print(get_min(4, 8, 2))\n",
    "print(get_min(4, 6, 7))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e7bdf3d",
   "metadata": {},
   "source": [
    "Eine (schönere) Alternative:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "25326f90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "2\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "def get_min(a, b, c):\n",
    "    \"\"\"Gibt das Minimum von drei Zahlen aus.\n",
    "    :param a: erste Zahl\n",
    "    :param b: zweite Zahl\n",
    "    :param c: dritte Zahl\n",
    "    :return: kleinste der drei Zahlen\n",
    "    \"\"\"\n",
    "    kleinste_zahl = a\n",
    "    for i in [b, c]:\n",
    "        if i < kleinste_zahl:\n",
    "            kleinste_zahl = i\n",
    "    return kleinste_zahl      \n",
    "\n",
    "print(get_min(5, 4, 3))\n",
    "print(get_min(4, 8, 2))\n",
    "print(get_min(4, 6, 7))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff03a7e2",
   "metadata": {},
   "source": [
    "## Aufgabe 4.4\n",
    "\n",
    "Die Funktion *get_rand_num* bekommt eine Zahl als Parameter und soll eine zufälligen Exponenten generieren und diese dann ausgerechnet zurückgeben. Achtung, wenn die Funktion keinen Integer bekommt, dann soll einen String “kein Integer wert” zurückgegeben werden. Verwende diesen Startcode:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a4787f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2147483648\n",
      "Kein Integer Wert!\n",
      "Kein Integer Wert!\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "def get_rand_num(num):\n",
    "    \"\"\"Generiert einen zufälligen Exponenten und \n",
    "    berechnet den Potenzwert durch die gegebene Basis.\n",
    "    :param num: Basis\n",
    "    :return: Potenzwert\n",
    "    \"\"\"\n",
    "    if isinstance(num, int):\n",
    "        rand_num = random.randint(1, 100)\n",
    "        return num ** rand_num\n",
    "    \n",
    "    return 'Kein Integer Wert!'\n",
    "\n",
    "print(get_rand_num(2))\n",
    "print(get_rand_num(2.))\n",
    "print(get_rand_num('Hallo'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f7a201",
   "metadata": {},
   "source": [
    "Eine andere Alternative:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "61f62e5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75557863725914323419136\n",
      "Kein Integer Wert!\n",
      "Kein Integer Wert!\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "def get_rand_num(num):\n",
    "    \"\"\"Generiert einen zufälligen Exponenten und \n",
    "    berechnet den Potenzwert durch die gegebene Basis.\n",
    "    :param num: Basis\n",
    "    :return: Potenzwert\n",
    "    \"\"\"\n",
    "    if not isinstance(num, int):\n",
    "        return 'Kein Integer Wert!'\n",
    "    rand_number = random.randint(1, 100)\n",
    "    \n",
    "    return pow(num, rand_number)\n",
    "\n",
    "print(get_rand_num(2))\n",
    "print(get_rand_num(2.))\n",
    "print(get_rand_num('Hallo'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ee53c4d",
   "metadata": {},
   "source": [
    "## Aufgabe 4.5\n",
    "\n",
    "Erstelle eine Klasse “Person” die im Konstruktor einen *namen* und ein *alter* übergeben bekommt. Nun erstelle zwei Objekte dieser Klasse mit beliebigen Namen und Parametern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "608e86c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Person:\n",
    "    def __init__(self, name, alter):\n",
    "        self.name = name\n",
    "        self.alter = alter\n",
    "        \n",
    "alex = Person('Alex', 20)\n",
    "alexandra = Person('Alexandra', 19)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "165b9f99",
   "metadata": {},
   "source": [
    "## Zusatzaufgaben"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "682ba344",
   "metadata": {},
   "source": [
    "### 4.Z1\n",
    "\n",
    "Schreibe eine Funktion *mult*, die beliebig viele Eingabewerte annimmt und diese miteinander multipliziert zurückgibt. Wenn kein Wert gegeben wird, soll 0 zurückgegeben werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c1e0a587",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "3\n",
      "4800\n"
     ]
    }
   ],
   "source": [
    "def mult(*args):\n",
    "    \"\"\"Multipliziert alle eingegeben Werte und gibt das Ergebnis zurück.\n",
    "    Bei keiner Eingabe, wird 0 zurückgegeben.\n",
    "    :param *args: zu multiplizierende Werte\n",
    "    :return :Ergebnis der Multiplikation\n",
    "    \"\"\"\n",
    "    \n",
    "    # Gib 0 zurück, falls die Länge der Eingabe 0 beträgt\n",
    "    if len(args) == 0:\n",
    "        return 0\n",
    "    \n",
    "    # Iteriere durch die eingegeben Werte und mutlipliziere mit dem Zwischenergebnis\n",
    "    ergebnis = args[0]\n",
    "    for arg in args[1:]:\n",
    "            ergebnis = ergebnis * arg\n",
    "    return ergebnis\n",
    "\n",
    "print(mult())\n",
    "print(mult(3))\n",
    "print(mult(2, 3, 8, 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e87298a",
   "metadata": {},
   "source": [
    "### 4.Z2\n",
    "\n",
    "Erstelle mit NumPy ein Array aus drei Einsen. Erstelle zusätzlich ein lineares Array von 2 bis 10 mit fünf Werten. Konkateniere beide Arrays zum Array *a*. Finde heraus, wie du mit NumPy alle Werte von *a* miteinander multiplizieren kannst. Rufe die Funktion von *4.Z1* außerdem mit den Werten von *a* auf und betrachte das Ergebnis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4bb72be7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  1.  1.  2.  4.  6.  8. 10.]\n",
      "3840.0\n",
      "3840.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Arrays erstellen\n",
    "ones_arr = np.ones(3)\n",
    "linear_arr = np.linspace(2, 10, 5)\n",
    "# konkatenieren\n",
    "a = np.concatenate((ones_arr, linear_arr))\n",
    "\n",
    "print(a)\n",
    "\n",
    "# Produkt bilden\n",
    "print(np.prod(a))\n",
    "print(mult(*a))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac3b3a52",
   "metadata": {},
   "source": [
    "### 4.Z3\n",
    "\n",
    "Level: 👽\n",
    "\n",
    "Erstelle ein Pandas-DataFrame mit dem Datensatz deiner Wahl und gib den Mittelwert der jeweiligen Spalten aus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f110c6d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "63198a8f",
   "metadata": {},
   "source": [
    "### 4.Z4\n",
    "\n",
    "Level: 👽\n",
    "\n",
    "Lade den Datensatz 'diamonds.csv' per Pandas und wende darauf `.describe()` an. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e9f094e8",
   "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>carat</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.797940</td>\n",
       "      <td>61.749405</td>\n",
       "      <td>57.457184</td>\n",
       "      <td>3932.799722</td>\n",
       "      <td>5.731157</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>3.538734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.474011</td>\n",
       "      <td>1.432621</td>\n",
       "      <td>2.234491</td>\n",
       "      <td>3989.439738</td>\n",
       "      <td>1.121761</td>\n",
       "      <td>1.142135</td>\n",
       "      <td>0.705699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>2.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.710000</td>\n",
       "      <td>3.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.040000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>5324.250000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>4.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.010000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>18823.000000</td>\n",
       "      <td>10.740000</td>\n",
       "      <td>58.900000</td>\n",
       "      <td>31.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              carat         depth         table         price        length  \\\n",
       "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
       "mean       0.797940     61.749405     57.457184   3932.799722      5.731157   \n",
       "std        0.474011      1.432621      2.234491   3989.439738      1.121761   \n",
       "min        0.200000     43.000000     43.000000    326.000000      0.000000   \n",
       "25%        0.400000     61.000000     56.000000    950.000000      4.710000   \n",
       "50%        0.700000     61.800000     57.000000   2401.000000      5.700000   \n",
       "75%        1.040000     62.500000     59.000000   5324.250000      6.540000   \n",
       "max        5.010000     79.000000     95.000000  18823.000000     10.740000   \n",
       "\n",
       "              width        height  \n",
       "count  53940.000000  53940.000000  \n",
       "mean       5.734526      3.538734  \n",
       "std        1.142135      0.705699  \n",
       "min        0.000000      0.000000  \n",
       "25%        4.720000      2.910000  \n",
       "50%        5.710000      3.530000  \n",
       "75%        6.540000      4.040000  \n",
       "max       58.900000     31.800000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "df = pd.read_csv('diamonds.csv', index_col=0)\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4b017c",
   "metadata": {},
   "source": [
    "### 4.Z5\n",
    "\n",
    "Level: 👽\n",
    "\n",
    "Erstelle einen Matplotlib-Graph mit dem obigen Datensatz. Gibt dabei eine Spalte deiner Wahl an (nur mit numerischen Werten möglich). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f0a7e95c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "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": [
    "df['carat'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27be41bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = df.sort_values(by='carat', axis=0)\n",
    "plt.hist(df['carat'], bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "411c33f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df['color'].value_counts(), labels=df['color'].value_counts().index.array)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b25c4bf9",
   "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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25999</th>\n",
       "      <td>4.01</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>I1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>15223</td>\n",
       "      <td>10.14</td>\n",
       "      <td>10.10</td>\n",
       "      <td>6.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26000</th>\n",
       "      <td>4.01</td>\n",
       "      <td>Premium</td>\n",
       "      <td>J</td>\n",
       "      <td>I1</td>\n",
       "      <td>62.5</td>\n",
       "      <td>62.0</td>\n",
       "      <td>15223</td>\n",
       "      <td>10.02</td>\n",
       "      <td>9.94</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat      cut color clarity  depth  table  price  length  width  \\\n",
       "25999   4.01  Premium     I      I1   61.0   61.0  15223   10.14  10.10   \n",
       "26000   4.01  Premium     J      I1   62.5   62.0  15223   10.02   9.94   \n",
       "\n",
       "       height  \n",
       "25999    6.17  \n",
       "26000    6.24  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['carat'] == 4.01]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81bf4da8",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "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": "3.9.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}