Grouped barplots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* https://seaborn.pydata.org/examples/grouped_barplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = sns.load_dataset(\"titanic\")\n",
    "titanic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432.85x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"class\", y=\"survived\", \n",
    "            data=titanic, hue=\"sex\", kind=\"bar\")\n",
    "sns.despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x125657588>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Kqofg7h+5+/xwgLsLCQapeyfSykREpFsl20PAzEYSjDNUDKwiGBZbRER6iaQCIRyN9ADg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\nFYEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"class\", y=\"survived\", data=titanic, hue=\"sex\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x125657898>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 504.85x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the example Titanic dataset\n",
    "titanic = sns.load_dataset(\"titanic\")\n",
    "\n",
    "# Draw a nested barplot to show survival for class and sex\n",
    "g = sns.catplot(x=\"class\", y=\"survived\", hue=\"sex\", data=titanic,\n",
    "                height=6, kind=\"bar\", palette=\"muted\")\n",
    "g.despine(left=True)\n",
    "g.set_ylabels(\"survival probability\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
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  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
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   "types_to_exclude": [
    "module",
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    "builtin_function_or_method",
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   "window_display": false
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 "nbformat": 4,
 "nbformat_minor": 2
}