sq_02_python_data_science/jupyter_book/05_pandas_visu.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1a44722c-9586-4418-ad7f-8a3c964a9db8",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "# Daten visualiseren"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a5292e13-c487-4e3a-8085-21d80469a734",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "d = pandas.read_csv('file:car.csv', sep='\\t', parse_dates=['Datum'])\n",
    "fuel_only = d[d[\"Typ\"] == \"Benzin\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac401f86-f53e-470f-b80a-02a86e3e5632",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Alles auf einmal\n",
    "Mittels `plot()` werden alle Zahlenspalten in einer Grafik dargestellt.\n",
    "\n",
    "**Hinweis:** auf https://jupyter.org/try-jupyter/lab/ wird nur dann eine Grafik angzeigt wenn man explizit `show()`aus `matplotlib.pyplot` aufruft."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f6af7955-e6ba-4f02-bfe0-e51f677778da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "d.plot();\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "531b9a96-967c-4a20-89c4-8ddc2dd6e4d8",
   "metadata": {},
   "source": [
    "Das ist in diesem Fall nicht besonders hilfreich weil jede Spalte eine andere Bedeutung hat.\n",
    "Bei Messwerten, bei dem jede Spalte ein anderes Experiment ist könnte dies aber nützlich sein."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73365e97-2317-496c-8747-9514c3f385da",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Zwei Spalten als X-Y-Koordinaten darstellen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9279c8d3-eada-4cc1-a62f-00738cd22dde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fuel_only.plot(kind=\"scatter\", x=\"km\", y=\"Preis\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c50a29cb-39c3-4b3e-8e16-0c56adc09278",
   "metadata": {},
   "source": [
    "Das sieht schon besser aus. Allerdings sind die Zahlen nicht wirklich vergleichbar. Besser wäre, den Preis / l darzustellen. Den müssen wir aber erst berechnen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f996d38c-c942-4f19-89ce-65ee49405419",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ppl = fuel_only[\"Preis\"] / fuel_only[\"Liter\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be50aaa8-1486-47a8-9f0c-bbdae50ef717",
   "metadata": {},
   "source": [
    "Jetzt haben wir eine Spalte `ppl` (Preis pro l) und die Tabelle `fuel_with_ppl`.\n",
    "Die Spalte `ppl` soll aber in die Tabelle eingefügt werden. Dazu benötigt sie zuerst einmal einen Namen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3092995f-b852-4db0-99aa-5bcf73cd0234",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ppl_with_name = ppl.rename(\"Preis_pro_l\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db096e70-02ec-4f11-bfea-f229f72e6b89",
   "metadata": {},
   "source": [
    "`rename()` benennt aber `ppl` nicht einfach um sondern erzeugt eine neue Spalte die den Namen enthält.\n",
    "Diese kann kann mit der Tabelle vereinigt werden wobei wiederum eine neue Tabelle entsteht."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "70eb9694-9866-42e8-b8b0-b22c6ec46377",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fuel_with_ppl = fuel_only.join(ppl_with_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd233836-e76e-4aed-b1e9-3b6f7c5439ef",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "Diese drei Befehle kann man auch in einem Zusammenfassen. Damit spart man sich neue Variablennamen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3df419ef-aae2-43fd-871c-d09985fad63f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "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>Datum</th>\n",
       "      <th>Typ</th>\n",
       "      <th>Beschreibung</th>\n",
       "      <th>Preis</th>\n",
       "      <th>km</th>\n",
       "      <th>Liter</th>\n",
       "      <th>Preis_pro_l</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-07-10</td>\n",
       "      <td>Benzin</td>\n",
       "      <td>ESSO</td>\n",
       "      <td>57.01</td>\n",
       "      <td>199</td>\n",
       "      <td>34.89</td>\n",
       "      <td>1.633993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-07-23</td>\n",
       "      <td>Benzin</td>\n",
       "      <td>Kaufland</td>\n",
       "      <td>55.03</td>\n",
       "      <td>828</td>\n",
       "      <td>34.20</td>\n",
       "      <td>1.609064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Datum     Typ Beschreibung  Preis   km  Liter  Preis_pro_l\n",
       "1 2012-07-10  Benzin         ESSO  57.01  199  34.89     1.633993\n",
       "3 2012-07-23  Benzin     Kaufland  55.03  828  34.20     1.609064"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_with_ppl = fuel_only.join((fuel_only[\"Preis\"] / fuel_only[\"Liter\"]).rename(\"Preis_pro_l\"))\n",
    "fuel_with_ppl.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b728a171-9484-4731-b933-628ed01f2f9c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "Damit können wir jetzt die Treibstoffkosten in Abhängigkeit vom km-Stand darstellen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4732e828-26e7-4563-826a-c1e2589c6f7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fuel_with_ppl.plot(kind=\"scatter\", x=\"km\", y=\"Preis_pro_l\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c63e68-eedd-485b-a247-66191d549f17",
   "metadata": {},
   "source": [
    "Interessant ist jetz noch der Verbrauch des Fahrzeugs, d.h. wieviele Liter pro 100 km es verbraucht hat.\n",
    "Dazu muss man die Differenz zwischen jeweils zwei km-Ständen berechnen.\n",
    "Dies geschieht mit der Funktion `diff()` die man auf eine Spalte der Daten anwendet."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21350aab-508b-4305-b817-6854bc80df37",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Aufgabe\n",
    "1. Was macht die Funktion `diff()` genau?\n",
    "1. Bei welchen Daten kann man die noch verwenden?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd0919aa-bf37-4437-af2f-00b9a8f121b2",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Lösung\n",
    "Schauen wir uns das mal genauer an. Zuerst die Daten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8bae2c0a-cab5-4c78-b9aa-8c065b69d152",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     199\n",
       "3     828\n",
       "4    1444\n",
       "6    2061\n",
       "7    2710\n",
       "Name: km, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_with_ppl[\"km\"].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bd5eec5b-d958-428a-a164-eab324e9930d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      NaN\n",
       "3    629.0\n",
       "4    616.0\n",
       "6    617.0\n",
       "7    649.0\n",
       "Name: km, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_with_ppl[\"km\"].diff().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f246d4-bead-4393-a478-dd53e2719e86",
   "metadata": {},
   "source": [
    "`diff()` berechnet die Differnz von zwei aufeinanderfolgenden Zeilen.\n",
    "Den Abstand kann man aber auch selbst festlegen. Möchte man die Differenz von jeweils den übernächsten Zeilen muss man den Parameter `periods` verwenden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "785605be-c477-4397-a7ac-bba042e94984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       NaN\n",
       "3       NaN\n",
       "4    1245.0\n",
       "6    1233.0\n",
       "7    1266.0\n",
       "Name: km, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_with_ppl[\"km\"].diff(periods=2).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b322a18f-8d5b-44a1-b7a3-02daae8caff7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Weiter mit der Verbrauchsberechung\n",
    "Wir erzeugen jetzt eine neue **Series** in unserem **Data Frame** die gefahrenen km zwischen zwei Tankstopps enthält.\n",
    "Mit der Funktion `diff()` geht das ohne Umwege:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b2173099-01c9-47d4-ba6d-177654600e51",
   "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>Datum</th>\n",
       "      <th>Typ</th>\n",
       "      <th>Beschreibung</th>\n",
       "      <th>Preis</th>\n",
       "      <th>km</th>\n",
       "      <th>Liter</th>\n",
       "      <th>Preis_pro_l</th>\n",
       "      <th>km_driven</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-07-10</td>\n",
       "      <td>Benzin</td>\n",
       "      <td>ESSO</td>\n",
       "      <td>57.01</td>\n",
       "      <td>199</td>\n",
       "      <td>34.89</td>\n",
       "      <td>1.633993</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-07-23</td>\n",
       "      <td>Benzin</td>\n",
       "      <td>Kaufland</td>\n",
       "      <td>55.03</td>\n",
       "      <td>828</td>\n",
       "      <td>34.20</td>\n",
       "      <td>1.609064</td>\n",
       "      <td>629.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Datum     Typ Beschreibung  Preis   km  Liter  Preis_pro_l  km_driven\n",
       "1 2012-07-10  Benzin         ESSO  57.01  199  34.89     1.633993        NaN\n",
       "3 2012-07-23  Benzin     Kaufland  55.03  828  34.20     1.609064      629.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_with_ppl[\"km_driven\"] = fuel_with_ppl[\"km\"].diff()\n",
    "fuel_with_ppl.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15ad8aed-e928-46dc-aa6e-25ced6032236",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "Nun können wir den Verbrauch berechnen ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "346095e8-ebeb-434c-81cd-ffa968176a74",
   "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>Datum</th>\n",
       "      <th>Typ</th>\n",
       "      <th>Beschreibung</th>\n",
       "      <th>Preis</th>\n",
       "      <th>km</th>\n",
       "      <th>Liter</th>\n",
       "      <th>Preis_pro_l</th>\n",
       "      <th>km_driven</th>\n",
       "      <th>l/100km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-07-10</td>\n",
       "      <td>Benzin</td>\n",
       "      <td>ESSO</td>\n",
       "      <td>57.01</td>\n",
       "      <td>199</td>\n",
       "      <td>34.89</td>\n",
       "      <td>1.633993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-07-23</td>\n",
       "      <td>Benzin</td>\n",
       "      <td>Kaufland</td>\n",
       "      <td>55.03</td>\n",
       "      <td>828</td>\n",
       "      <td>34.20</td>\n",
       "      <td>1.609064</td>\n",
       "      <td>629.0</td>\n",
       "      <td>5.437202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Datum     Typ Beschreibung  Preis   km  Liter  Preis_pro_l  km_driven  \\\n",
       "1 2012-07-10  Benzin         ESSO  57.01  199  34.89     1.633993        NaN   \n",
       "3 2012-07-23  Benzin     Kaufland  55.03  828  34.20     1.609064      629.0   \n",
       "\n",
       "    l/100km  \n",
       "1       NaN  \n",
       "3  5.437202  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_all = fuel_with_ppl.join((fuel_with_ppl[\"Liter\"] * 100 / fuel_with_ppl[\"km_driven\"]).rename(\"l/100km\"))\n",
    "fuel_all.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d09559-c563-43ba-8e0b-52c993bb36b1",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "... und den Verbrauch in der Grafik darstellen.\n",
    "Dabei ändern wir gleich noch ein paar Parameter um zu zeigen, was es alles so für Möglichkeiten gibt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a9da9245-86f4-4cdb-9dce-3129bb204dc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fuel_all.plot(x=\"Datum\", y=\"l/100km\", figsize=(15,4), title=\"Verbrauch meines Autos\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49454f16-873e-4802-9535-ad331f698742",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Box-and-Whisker-Plots\n",
    "Auf einen Blick sehen, wie die Daten verteilt sind.\n",
    "Wer erinnert sich noch an `describe()`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "95c4dddf-de31-425b-9bee-7221a0c9b7d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fuel_all.plot(kind=\"box\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "117cde42-698c-4d2d-841d-816772f6a422",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "Wenn sich die Werte in den Spalten voneinander unterscheiden ist es besser, wenn man die Daten einzeln visualisert:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "395dd8c4-5c88-4bcf-b77f-06db4f8f92ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fuel_all[[\"Liter\", \"Preis_pro_l\", \"km_driven\", \"l/100km\"]].plot(kind=\"box\", subplots=True, figsize=(15,4));\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "215661fd-30c1-4fa3-98e4-fbbd991f0402",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Bar-Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7f8176ab-6239-49e3-bf03-831448411301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d.groupby(\"Typ\")[\"Preis\"].sum().plot(kind=\"bar\");\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d3bdb98-87cb-4990-9038-1f26a9bc49f9",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Pie-Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c30096be-6783-4f26-aa70-1bcf6e4c9d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d.groupby(\"Typ\")[\"Preis\"].sum().plot(kind=\"pie\");\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}