{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# seaborn-Beispiel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Importe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Stil definieren\n",
    "\n",
    "   [seaborn.set](https://seaborn.pydata.org/generated/seaborn.set.html) ist ein Alias für [seaborn.set_theme](https://seaborn.pydata.org/generated/seaborn.set_theme.html#seaborn.set_theme), mit dem sich unterschiedliche Stile, Paletten,Schriften und Farben definieren lassen, z.B.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"white\", color_codes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Erzeugen eines bivariaten Zufallsdatensatzes\n",
    "\n",
    "   Hierfür verwenden wir [numpy.random.RandomState.multivariate_normal](https://numpy.org/doc/stable/reference/random/generated/numpy.random.RandomState.multivariate_normal.html) aus [numpy.random.RandomState](https://numpy.org/doc/stable/reference/random/legacy.html?highlight=randomstate#numpy.random.RandomState), wobei ein *Seed* von `9`, `0` als Mittelwerte und 100 Proben eine nicht ganz symmetrische Kovarianzmatrix ergeben:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rs = np.random.RandomState(9)\n",
    "mean = [0, 0]\n",
    "cov = [(1, 0), (0, 2)]\n",
    "x, y = rs.multivariate_normal(mean, cov, 100).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. `JointGrid` für bivariate Plots\n",
    "\n",
    "   Mit [seaborn.JointGrid](https://seaborn.pydata.org/generated/seaborn.JointGrid.html) lassen sich bivariate Plots erzeugen. Wenn beide Funktionen unterschiedliche Schlüsselwortargumente übergeben, muss [JointGrid.plot_joint()](https://seaborn.pydata.org/generated/seaborn.JointGrid.plot_joint.html#seaborn.JointGrid.plot_joint) und [JointGrid.plot_marginals()](https://seaborn.pydata.org/generated/seaborn.JointGrid.plot_marginals.html#seaborn.JointGrid.plot_marginals) verwendet werden:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x154b964d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.JointGrid(x=x, y=y, space=0, height=6, ratio=10)\n",
    "grid.plot_joint(plt.scatter, color=\"g\")\n",
    "grid.plot_marginals(sns.rugplot, height=0.2, color=\"g\")"
   ]
  }
 ],
 "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
}