{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第5章 Matplotlibを使いこなそう\n",
    "\n",
    "### 5-8: 描画スタイル"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-white',\n",
       " 'seaborn-whitegrid',\n",
       " 'seaborn',\n",
       " '_classic_test']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# リスト5.8.1：style一覧を出力する\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18ae4e80da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# リスト5.8.2：fivethirtyeightスタイルで描画\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "dat = [1, 3]\n",
    "ax.plot(dat)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}