{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib-Beispiel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib stellt Daten in Figuren (`Figures`) dar, d.h. in Fenstern, Jupyter-Widgets usw., von denen jede eine oder mehre Achsen (`Axes`) enthalten kann. Die einfachste Möglichkeit, eine Figur mit Achsen zu erstellen, ist die Verwendung von `pyplot.subplots` um dann mit `Axes.plot` einige Daten anzugeben."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Figur mit Achsen erstellen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Einige Daten auf den Achsen zeichnen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11b315350>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot([1, 2, 3, 4], [1, 4, 2, 3])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.11 Kernel",
   "language": "python",
   "name": "python311"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}