4_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(chap:matplotlib)=\n",
    "# `Matplotlib`：図示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div name=\"html-admonition\" style=\"font-size: 0.8em\">\n",
    "<input type=\"button\" onclick=\"location.href='https://translate.google.com/translate?hl=&sl=ja&tl=en&u='+window.location;\" value=\"Google translation\" style=\"color:#ffffff;background-color:#008080; height:25px\" onmouseover=\"this.style.background='#99ccff'\" onmouseout=\"this.style.background='#008080'\"/> in English or the language of your choice.\n",
    "</div><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 説明"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 警告メッセージを非表示\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Matplotlib`（「マットプロットリブ」と読む）はプロットのための代表的なパッケージであり、ここではその使い方を解説する。プロットには`Matplotlib`のモジュールである`pyplot`を使うことになる。慣例に沿って`plt`としてインポートする。<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pyplot`モジュールを使ってプロットする場合、主に３つのコードの書き方がる。\n",
    "\n",
    "**書き方１**：（オブジェクト指向）\n",
    "```\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "ax.plot(...)\n",
    "```\n",
    "**書き方２**：（オブジェクト指向）\n",
    "```\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(...)\n",
    "```\n",
    "**書き方３**： `Matlab`という有料ソフトのコードに沿った書き方\n",
    "```\n",
    "plt.plot(...)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本サイトでは書き方２に沿って`Matplotlib`の使い方を説明する。一方で，その裏にある考え方を理解するには、書き方１から始めた方が良いと思うので，書き方１の簡単な説明をすることにする。{numref}`fig:4-figure_axes`は，`Matplotlib`が描画する図の階層的な構造を示している。ここで重要なのは「キャンバス」と「軸」の違いである。\n",
    "\n",
    "\n",
    "`Matplotlib`によるプロットは次の図のような構成となっている。\n",
    "\n",
    "```{figure} /images/figure_axes_mpl.png\n",
    "---\n",
    "scale: 40%\n",
    "name: fig:4-figure_axes\n",
    "---\n",
    "キャンバスと軸の関係\n",
    "```\n",
    "\n",
    "* キャンバスとは表示される**領域**であり，実際には表示されない透明のキャンバ」である。\n",
    "    * 書き方１と書き方２では変数`fig`がキャンバスを表している。\n",
    "    * `figure`や`fig`などの変数名や引数名があれば、キャンバスを指していると理解すれば良いだろう。\n",
    "* 軸とは１つの図を表示する**区域**である。\n",
    "    * 書き方１と書き方２では変数`ax`が軸を表している。\n",
    "    * `ax`や`axes`などの変数名や引数名があれば、軸を表していると理解すれば良いだろう。\n",
    "    * キャンバス上に複数のグラフを表示する場合は複数の区域を設定する必要がある。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "これを踏まえて，書き方１は次のような手順で描画していと解釈できる。\n",
    "1. キャンバスを用意する。\n",
    "    * それを行なっているのが１行目である。右辺の`plt.figure()`でキャンバスを作成し、それを変数`fig`に割り当てている。\n",
    "1. キャンバスに軸を追加する。\n",
    "    * これをおこなっているのが２行目である。右辺では、キャンバス`fig`のメソッド`add_subplot()`を使いキャンバスに軸を追加し、それを左辺の変数`ax`に割り当てている。実際に、書き方１の２行のコードを実行すると透明のキャンバス上に軸が表示される。（試してみよう。）\n",
    "    * 作成された軸`ax`にメソッド`plot()`などを使い直線・曲線・点など付け加えたり、タイトルなどの「飾り付け」をおこない作図することになる。\n",
    "    * １つのキャンバスに複数の軸を作成し，複数の図を表示することが可能となる。\n",
    "\n",
    "この説明でわかることは、`Matplotlib`で表示される図は透明のキャンバス上に描かれ装飾された軸ということである。以下の説明では、「図」はキャンバス全体に表示されたものを指す場合もあれば，ある一つの軸に表示されたものを指す場合もああるので，文脈で判断してほしい。\n",
    "\n",
    "さて書き方２に話を戻すと、既に察している読者もいるかも知れないが、１行目は書き方１の最初の２行を１行に省略したものである。右辺の`plt.subplots()`は透明のキャンバスと軸の２つをタプルとして返す。最初の要素がキャンバスでありそれを左辺の`fig`に、２つ目の要素が軸であり`ax`に割り当てている。実際、１行目だけを実行すると軸が表示される。後は書き方１同様、`ax`のメソッド`plot()`などを使い軸を飾り付けして作図することになる。\n",
    "\n",
    "ちなみに、書き方３は書き方２の３行を１行に省略した形と解釈して良いだろう。本来は`Matlab`（有料）ユーザーの`Python`への移行を促す為に用意された書き方である。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "次に`plot()`のデータに関する引数について説明するが，主に２つの設定方法がある。\n",
    "\n",
    "**データの引数の書き方１**：\n",
    "```\n",
    "plot(＜横軸のデータ＞, ＜縦軸のデータ＞)\n",
    "```\n",
    "**データの引数の書き方２**：変数のラベル名が使える場合（`Pandas`の`DataFrame`）\n",
    "```\n",
    "plot(＜横軸の変数名＞, ＜縦軸の変数名＞, data=＜データ名＞)\n",
    "```\n",
    "\n",
    "この２つの書き方の簡略形として，横軸の変数を省略することもできる。\n",
    "```\n",
    "plot(＜縦軸のデータ＞)\n",
    "\n",
    "plot(＜縦軸の変数名＞, data=＜データ名＞)\n",
    "```\n",
    "この場合，横軸には`縦軸のデータ`のインデックス番号が使われることになる。手っ取り早く図示したい場合は良いかも知れない。\n",
    "\n",
    "以下ではそれぞれの引数の書き方について解説する。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## データの引数の書き方１"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### `array`を使って"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "プロットするデータを生成しよう。変数`x`に横軸に使うデータを次のように割り当てる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.,  0.,  2.])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(-2, 2, 3)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "３つの要素からなる`array`である。縦軸のデータとして`y0`を用意する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4., 0., 4.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y0 = x**2\n",
    "y0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "同じように`y0`も３つの要素からなる`array`である。プロットしてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdc51cff040>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y0, marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "２行目に`marker='o'`が追加されているが，「●」を表示するために使っている。このような引数の使い方は後で詳しく説明するので，ここでは気にしないで読み進めて欲しい。\n",
    "\n",
    "「●」のマーカーがある点が`x`と`y0`の`array`要素の組み合わせとして表示されている。`plot()`はデフォルトでそれらの点を直線で結んでいる。この場合，データの組み合わせが３点しかないため$y=x^2$の形を明確に表示できていない。言い換えると，データの組み合わせの数を増やすことにより$y=x^2$をより正確に表示されることになる。もう一度，`x`と`y0`のデータを生成しなおす。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(-2, 2, 100)\n",
    "y0 = x**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`x`と`y0`にはそれぞれ100の要素があり，これらを使うことにより100のデータの組み合わせが成立することになる。プロットしてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdc51e72ec0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y0, marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "より$y=x^2$の図に近づいたことがわかる。\n",
    "\n",
    "（注意点）\n",
    "* 上の２つの図の上に文字が表示されているが，表示したくない場合は最後に`;`を加えるか次の行に`pass`と書くと表示されなくなる。\n",
    "* `x`を省いて`ax.plot(y0)`としても同じような図が表示されるが，横軸の値がデータの数に対応することになり，意図した図と異なることになる。\n",
    "\n",
    "次に縦軸のデータとして，２つを用意する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y1 = x+1\n",
    "y2 = np.exp(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`y0`，`y1`，`y2`を同じ軸にプロットしてみよう。コードは簡単で`ax.plot()`をリピートするだけである。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y0)           \n",
    "ax.plot(x, y1)\n",
    "ax.plot(x, y2)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "それぞれの`ax.plot()`を「軸`ax`にデータの組み合わせをプロットする」と読めば理解しやすいと思う。また同じようなコードが続いているので，次のように`for`ループを使うことより短いコードで同じ図を表示することができる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "for y in [y0, y1, y2]:\n",
    "    ax.plot(x, y)           \n",
    "\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### `DataFrame`を使って"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "まず`DataFrame`を作成しよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "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>X</th>\n",
       "      <th>Y0</th>\n",
       "      <th>Y1</th>\n",
       "      <th>Y2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.135335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.959596</td>\n",
       "      <td>3.840016</td>\n",
       "      <td>-0.959596</td>\n",
       "      <td>0.140915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.919192</td>\n",
       "      <td>3.683298</td>\n",
       "      <td>-0.919192</td>\n",
       "      <td>0.146725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.878788</td>\n",
       "      <td>3.529844</td>\n",
       "      <td>-0.878788</td>\n",
       "      <td>0.152775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.838384</td>\n",
       "      <td>3.379655</td>\n",
       "      <td>-0.838384</td>\n",
       "      <td>0.159074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          X        Y0        Y1        Y2\n",
       "0 -2.000000  4.000000 -1.000000  0.135335\n",
       "1 -1.959596  3.840016 -0.959596  0.140915\n",
       "2 -1.919192  3.683298 -0.919192  0.146725\n",
       "3 -1.878788  3.529844 -0.878788  0.152775\n",
       "4 -1.838384  3.379655 -0.838384  0.159074"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic = {'X':x, 'Y0':y0, 'Y1':y1, 'Y2':y2}  # 辞書の作成\n",
    "\n",
    "df0 = pd.DataFrame(dic)      # DataFrameの作成\n",
    "\n",
    "df0.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "横軸に`X`，縦軸に`Y0`をプロットしてみる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df0['X'],df0['Y0'])\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "横軸に`X`，縦軸に`Y0`，`Y1`，`Y2`をプロットしてみる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xr8uHUlpdzyNf7CTEx0MWxAhhB1sObuGO7+8g1DuUd6a843QhvSzpAE/9bzfnDezC3y4e5JCLcZx+jPq3PNxceOuakQyKCuD2D7eyKb3Y7JKE6NB+zv2ZW1ffSqRvJIunLna6kF6bUsB9/9nOmJ4hvGKnlqWnokMFNYCvpxuLbhhNdLA3N72XwK7cMrNLEqJDWp21mjvX3EnPwJ68O/VdInwizC6pVRIyS/jzB4nERfiz8PpReLm3cUGLtRFKs9qnuGN0uKAGCPXz5P05Ywnwduf6RZtJL6w0uyQhOpTPUz/nvnX3MSB0AAunLHSqKXgAuw+Uc+PiLUQFerNkzhgCvE5xxaSlBpJXwle3w7/6wrvTwAYzzzrMGPWxooK8eX/OGC5/awPXLNzEp7eOo1uQ86yMEsIRaa1ZtGsRL299mfHdxvPiWS/i427+gpDWyCiq4rpFm/HzdGPJnDGE+Xm27gmqiiH1G0hZCWlrwFINngEQdy70m24cWbu2b7QqW8w7HjVqlE5ISGj35z0Vu3LLmLVgI6G+HvznltOJCLDN5dyF6OgarY38K+FffLDnA6b1nMaz4591qt4dADml1Vzx1gZqG6z855bT6BPh37IfLEk3jpxTVsL+DaCt4B8F8dMh/nzocQa4tW2ZuVIqUWs9qtnvdfSgBkjMKuHadzYTHezNJ3NPJ9jXeS6iKYQjqGus45H1j7AqaxXX9L+GB0Y/4FRd8AAKymu54u0NlFTV89Hc0xgYdYJl7VrDgW2QvMII54Ldxv0RA41gjp8OXYdBO84Q6fRBDfDLviJuWLyFfl38WXrz2FMfkxKikymrK+OuNXextWAr94+6n+sHXm92Sa1WUlXPlfM3kFNawwc3jWVE92YupttQD5nrjWBOXgkVB0C5QPdxR8M5ONZmNZ4oqDvsGPWxxvUJ482rR3DL+4lcv2gz788Za7cLUwrhrLLKs7jj+zvIrczl+QnPM7XnVLNLarVD1fVcs3ATWcXVvHvD6N+HdM0h2LfaOHLetxrqysHdB3qfA/GPQ9/zwMf8E6WdKqkm9e/Ca1cN5/YPtzH73S0snj0aH49OtQuEaLGEgwncs/YeFIqFUxYyossIs0tqtfJaC9ct2sy+gkoWXD+KcX3CoCwHUr6G5OWQ+RNYG8A3HAbMgPgLoNdZ4O5YEw86zdDHb/0v6QB3f7yN03qFsuiG0W2fPylEB/Pfff/lqQ1PEe0XzRuT3iAmwPlaMlTWNXDtO5vYlXuIDy7wY2zdRkhZAXlJxgNC44zhjH7nQ/QocDE3B2To4xgXDo3C0mjlvk+TuOm9hPaZ7C5EB9BgbeCFhBf4YM8HjO06lhfOesGpekkfVllTy7z5i7mwaC1LA3fg820uoCB6NEx+0gjn8L5ml9linTKoAS4dEY1VwwOfSVgLAcZJwwfWPcCGvA1c3f9q7h91P24uThQRdZWQtgbL7uXoX1fytK6g0d0D124TIf5B6DsN/J2z/48T/Su0v5kjowEJayGSS5K554d7yK/O5+lxT3NJ3CVml9QyFfmQ+rUxSyN9LTTWUav8WN0wnB7jZzJi4kzw9DO7yjbr1EENRlhrrXnw8x3MXryFhdePkhOMolNZlraMpzc8TaBnIIunLmZo+FCzSzqxwlRjrDl5JeRsATQEdadu+A38bV8snxTE8PKsUYwY7FwNok5EEgm4fFQMri6K+z9N4oZFW1h042iZuic6vLrGOp7f8jyfpHzC6MjRPD/heUK9Q80u64+sVshNMGZpJK+E4r3G/V2HwsRHod90yvz7cu27m9lTWM6rVw1n6qCOE9IgQX3EpSOicXd14Z5PtnPdO5tYPLsNjVqEcHDZ5dnct+4+9pTs4caBN3LXiLscazzaUgPp64wj55RvoKoAXNwg9gwYewv0mwaBxtBlSZUxT3pfQSVvXTOSSf2dcxz6RBzoX8Z8Fw6Nwt1VcceH27h6wSbemz2GEFluLjqY7zK/44lfnsBFufDqOa9ydszZZpdkqC6B1G+NcN73vdHsyMPfaHYUfz70mQzeQb/7kYLyWq5euIn9JdUsuH4UZ/V1nms0toYE9TGmDurK/Otc+PMHW/nT2xtYetNYaeQkOoRqSzXztszj872fMyRsCM+f9TxRflHmFlWaebTZUdYvoBuNZkdDZxlznGPPBLfmu9vllFZz9cJNFFbU8e6NoxnXO8y+tdtRp1zw0hK/pBVx03sJhPt78sGcscSEOFcrRyF+K7kkmQd/fJDMskzmDJ7DbcNuw93FhKE9rSFv+9Fwzt9l3B8xwGgRGj8dug4HlxM3fEovrOSahZuorGtg8ewxzffucDLSlOkUbd1fyg2LNuPj4cb7c8YQ16WFLRGFcBCN1kYW/7qY17a/RohnCH8/8++M7TrWvkU01EPWT03h/DWU5zQ1Ozr9aDiH9Grx0+3KLeP6RZsBeH/OWAZEBdiqcruSoG6DPXnlXLdoM5ZGK4tvHMOwmCCzSxKiRXIqcnjsp8fYWrCVc3ucy+OnPU6QV5B9Nl5bDvtWGc2O9q4ymh25eUOfSUY4950Kvq2fYbIxvZib3ksg0Nud9+eMoVe488+RPkyCuo2yiqu45p1NFFfWM//aUZwR13HHwoTzs2orn6Z8yguJL+CqXHl07KNc0OsC219du/xAU4vQFZCxHqwW8AmDflObmh2d3aZmR6t253PHh1uJDvbmg5vG0jXQsRontZUEdTsoKK/lukWbSSus5IUrhnHRUJNPwgjRjNzKXJ74+Qk2HdzE6V1P56lxT9nuyuBaGw31k1caMzUObDPuD+nddOWTC4zeGu3Q7Ojjzft59MudDO4WyLs3dszZWG1qyqSUigGWAJGAFZivtX6lfUt0fBEBXnxyy+ncvCSBuz7aRmFFHXPO6Gl2WUIAxlj0R8kf8e9t/8ZFufDE6U9wWdxl7X8U3dgA2RuNcE5eDoearrrdbRRMetwI57C+7XblE601r63ZxwurUpnQN5w3rx6BbydcjNaSV9wA3Ke13qqU8gcSlVKrtNa7bVybwwn0dmfJ7DHc8/F2nlm+m4LyWh6aGo+Li43fUgpxAntL9/LkL0+yo2gHZ3Q7g8dPe7x9j6Lrq4yLuCavNC7qWlMCrh7GUMYZ9xqLT/wj2297TRoarTz5v1/5YON+Lh3ejedmDsHd1bku/9VeThrUWus8IK/pdoVSag/QDeh0QQ3g5e7K61eP4Mllv/L2j+nkHqrhX5cPlWZOwu6qLdW8veNtlvy6BH8Pf/555j+Z3nN6+xxFVxb+ptnRD9BQC16BxknAftONk4KetpsFVV3fwJ0fbuP75AJuOasXD53XuQ+IWvUeQikVCwwHNjXzvbnAXIDu3bu3R20Oy9VF8fSMgUQHe/OPr5PJL69lwXWjCPLpeONmwjGtzV7LPzb9gwNVB7i4z8X8ZeRfCPZq41zion1NzY5WQPZmQENgdxh5gxHOPcaBHa46XlhRx5z3trArt4xnZgzk2tNjbb5NR9fik4lKKT9gHfCs1vqLEz22I55MPJ7/JR3gvv8kER3szaIbRhMb5mt2SaIDyyrP4rnNz7E+dz19gvrw19P+ysguI0/tyaxWyE08Gs5Fqcb9kUOMJdv9pkPk4Ha90vbJpOZXcOO7WyipqufVWcOZPKDj9e04njZf4UUp5Q58Diw9WUh3NhcOjaJLgBe3vJ/AxW/8zPxrRzGmp/kXwxQdS5WligU7FrBk9xI8XD24f9T9XBV/Fe6tPcK11ELGOiOYU7+Bynyj2VGP8TD6JmO8Ocicd8TrUgu5Y+lWvDxc+eSW0xgSHWRKHY7opEfUyhjweg8o0Vrf05In7UxH1IdlFlUxe/EWskur+eelQ7is6aIEQrRFo7WRZWnL+Pe2f1NUU8SFvS7k3pH3Eu7TiuZD1SXGopPk5U3NjqrAw89ochR/vtH0yNvcJdjvb8ziyWW/Ehfhx6IbRhMV1LHmSLdEW4+oxwPXAjuVUtub7ntUa72ynerrEGLDfPnytvHcujSR+z5NIjW/ggenxuPaiU+AiLbZmLeRFxNeZE/JHoaED+GVia8wJHxIy364NOvo4pPDzY78ImHIFcYUup7Hb3ZkT5ZGK0//bzfvb8zinPgI/j1ruPSCb0ZLZn38BEjatECgjzvvzR7D0//bzds/prO3oJJXrhyGv/S1Fq2QUpLCS4kv8fOBn4nyjWLehHlMjZ164tkcWhtX1z4czoebHYXHw/i7jXCOOnmzI3sqrarntqVb2ZBezC0TesmBzQnIykQbOfxWrmeYL/OvHdmhehII28guz+b1pNdZmb4Sfw9/5g6Zy5XxV+Lpepwj30YLZP7UFM4rjzY7ijnNWBnYbzqE9rbvi2ih5IPlzF2SyMGyWv5+6eAj1y/tzGQJuUl+SSvijg+3YWm08sqVwzgnvvOcwRYtd7DqIPN3zOfLvV/i5uLGrP6zmDNoDoGegX98cG057FtthHPqd1BXZjQ76j3RGG/uOxV8HbsXzcqdedz/aRK+nm68dc1IRvZw/hal7UGC2kQ5pdXc8n4iu/PK+cvkvtw+sU+nnrgvjsqvymfhzoV8vvdzNJqZcTOZO2TuH08UlucZwZyyEjJ+hMZ68AmFvtOMI+deE8HD8fulN1o1L3yXwhtr0xjRPYg3rxlJF7koxxFtnp4nTl10sA+f/Xkcj3yxgxdWpZKUc4gXLh9GoI+MW3dWByoPsGjXIr7c+yVWbWVGnxncPORmuvl1Mx6gNRQmG2PNKSuNuc5g9GweM9c4co4Z2y7NjuyluLKOuz7exs/7ipk1pjtPXjQATzfnqd9sckRtJ1prlmzI4m8rdtM10Js3rxnBwKhm3tqKDiujLINFuxaxPG05KJjRewY3Db6JaP9osDZC9iYjnJNXQGmG8UNRI4xgjr8AwvvZdfFJe9m6v5Tbl26lpKqeZy4exBWjYswuySHJ0IcDScxq+qWtrufJCwcya0yM7fsEC1PtKNzBol2LWLN/DR6uHlwWdxk3DrqRSPcAo4/G4cUn1cVGs6OeE4wTgf2mQ4CNWpTagdaaRT9n8s+v9xAZ6MWbV49kUDc5ODkeCWoHU1RZx72fbGf93iIuGhrF3y8dLHNHO5hGayPrctaxZPcSEvMT8ffw58p+V3JVj6mEZW0yhjTSfoCGGvAMhL5TmpodTQYv57+0VFm1hfs/S2LV7nzOHdCFf80cKsN9JyFB7YCsVs2b69J44bsUeoT68uqs4XK00QFU1lfyVdpXfLjnQ/ZX7CfKN4qrepzHzForvntXG8Mb2goB0U1DGtON5dt2aHZkL4lZpdz10TYKKmp5ZFp/bhwfK+8aW0CC2oFtTC/mno+3U1JVz0PT4pktv9ROKaMsg4+TP+artK+oslQxJKA317mFM2l/Em6FKcaDugw+Or+561CnHG8+kUar5q11aby4KpWoIC/+feVwhneAq4PbiwS1gyutqufBz3ewanc+E/uFM2/mUML9zV/eK07MYrWwNnstn6R8wqa8TbgpV6a6h3NVfjaDD+WBcjVagx7uRBfcw+ySbSavrIa/fJLEhvRiLhwaxbOXDCJAVuS2igS1E9Ba8/7GLJ5dsQdfTzf+eelgpgxs/6tmiLbLrsjmi71f8N+9X1BUW0JX5cHMQ4e4tKyEMFefpittNzU78un4nRSXJR3gr1/upMGqefKigVw+MlreFZ4CmUftBJRSXHd6LKf3CuXuj7cz9/1E/jQqhv+7cICcaHQANQ01fL//e/675xM2FW3HBTizupaZ5RWc6RqAa78LjHDuOQHcO8cijrJqC48v28VX2w8wvHsQL10xTPqx24gkgIOJ6+LPf28fz0urU3lrXRo/pxUxb+YQxvV27GXBHZHWmm35W1m26z2+PfATldpCN0sDt1dWcrF7JJH9LjPCudtIh2p2ZA8/pBTw8Oc7KKqs5y/n9uW2s3vj1kmvZ2gPMvThwBKzSrjvP0lkFldzw7hYHpzaDx8P+dtqa+klqazcvoDluevItdbgbbVyblUNF/vEMDLuYlz6Xwhhfcwu0xQVtRaeXbGHj7dk07eLHy9cPozB0TJbqT3IGLUTq6lv5Llvkln8SyYxId7889IhjO8jR9ftLbc4lW+3vc3XeT+RbK1Gac3YOgsX+fZkUr+Z+PS/CPwizC7TVGuS83n0i10UVNQyd0Jv7j03TpaBtyMJ6g5gU3oxD3+xk4yiKq4cHcMj0/rLAoI2yj24ndXbF/Bt/mZ2UgvA4PpGpvn34bz+fyKi/8XgIWOuxZV1PLN8N//dfoB+Xfx5buYQhsUEmV1WhyNB3UHUWhp5efVeFqxPJ9jHg8cvHMCFQ7rKGfYW0lYraZlrWLNrCauLd7LHpQGA/g1wXlB/pgyYRUzfC8FVhpfAGKP/LDGHv6/cQ2VdA7ed3YfbJ/bBw03Gom1BgrqD2ZVbxqNf7mRHThln9Q3n6RkD6REqR37NaWioY9uvH7Fu73/5oTyN/U3v1IdY3Tg3dCiTBl9HTOzEDrf4pK32FVTy2Jc72ZRRwqgewfz90sH07eJvdlkdmgR1B9Ro1by/IZN/fZdKfaOVW8/qza1n98bLXcYMSyoO8HPSItbvX8NPdQVUuCjctGas8uWcyLGcNXQOXSKHml2mQ6qqa+DVNft456d0vN1deXR6f64YFSM91O1AgroDyy+v5e8r9/DV9gPEhHjz1/MHMGVAl041HGKxWtiZ/RO/7P6Ynwu38au1Gq0UIY1WJnhGcFb3czh92E34+jtvJzpb01rzvx15/GPlHvLKarliVDQPTY0n1E9WyNqLBHUnsCGtmMe/2sXegkrG9wnl/y4YQHyk83dha47WmrRDaWxKW8HGzFVsqdxPldK4aM3gBjgjMI4z+15M/wF/wqWTLD5pi505ZTz1v19JyCplYFQAT88YJJfHMoEEdSfR0Ghl6ab9vLQ6lfIaC1eO6c49k+OI8HfusNJak1meyZa8zWzJ/I4thTsothqzNGIsFsbiw/jIMYwZfA0B3cfLeHML5ZXV8K9vU/liWw6hvh7cP6Ufl4+KkSuBm0SCupM5VF3Py6v38sHGLDzcXJg7oRc3n9kLXydZit5obSS1NJWtBVvZejCBxLxNFFsqAAhvaGB0bT1jfaMZ22sa3QZfCcGx5hbsZMprLby1No13fspAAzeOi+WOc/rgL02UTCVB3UllFFUx75tkvt51kDA/T+6Y2JtZY7s73CKF8vpydhTuIKkwiaSCJHYUJlHVUA1AZIOVUbU1jLRYGdVlND3iL0H1m9opmh21t1pLI0s2ZPLG2jQOVVu4eFgU95/Xj+hgx78wbmcgQd3JJWaVMu+bZDZllNAtyJu7J8VxyYhuuJvQm6G+sZ7U0lR2Fe1iZ9FOdhTuILM8EwAXFH1xZ2hFKSNqahju4kfXuKlGP41eZ4G7t93r7QjqG6z8JyGbV9fsJb+8jgl9w3lgSj9Z+u1gJKgFWmvW7y3i+W9T2JlbRo9QH+6Y2IdLhnezWTOd+sZ69h3ax+7i3Uc+UktTsVgtAIR4BDDENYDB5UUMKcpicF09viF9mprrnw/Roztds6P2VN9g5dPEbF5fs48DZbWM6B7Eg1PjOa1XqNmliWZIUIsjtNZ8v6eAl79PZVduOTEh3vz5rN5cNiK6TXOwS2tLSS1NJaUkhZTSFJJLkkk/lE6DNlb/+bv70z8knoHuQQwqL2ZQ9la6luxHoYxAPhzO4X3b66V2WjX1jXy8ZT/zf0wnr6yW4d2DuHdyX86MC+tU0zadjQS1+IPDgf3qD/tIyj5EuL8nN53Rk1lju5/wyhzVlmrSy9LZd2gfaYfSSC1NZW/pXgprCo88JtQrlPjQePqH9Keffw8GVpQQnbERtfdbqD0Erp7Q62wjnPtOA/8utn/BnUBpVT1LN2Wx6OdMSqrqGdMzhDsm9pGAdhIS1OK4tNZsSCvmjbVp/LSvCD9PN/40OoZLRwVRpw6SUZZBRlkGaWVppB9K50DVgSM/6+HiQe+g3sQFxxEXFEffkL70C+5HaEMDpH4NySshfS001oF3MPSdalySqvc54Oln3ovuYDKLqlj0cwafJuRQY2nk7H7h3D6xD6Nj5YSrM5GgFs2qslSxv3w/WRVZ7C/fT9LBfWw/mEpZQx7KtfrI4zxdPYkNiKVXUC96BfYiLiiO3kG9ifaPxs2lacpfYSqkrDDCOWcLoCGox9HrBXY/XZodtSOrVbN+XxHv/ZLJDykFuLkoLh7WjZvO7EW/SOnJ4YzkUlydVKO1kcKaQnIqcsitzCWnMofsimyyK7LJqcihpLbkd4+P8I4gvkssYV5DOFjkz/Y0D8orQugSHM35PWO5dHj00daq1kbISTgazsV7jfu7DoOJjxrh3GWgLD5pZyVV9XyemMNHm/eTXlRFmJ8Hd07swzWn9SAiwLkXNonjkyNqJ1bbUEt+dT4Hqw6SV5VHXmUeB6oOHP1clUeDteHI412UC5E+kUT7RxPjH3Pko0dAD2L8Y/Bx//182lpLI8uSDrB0YxZJOWUEuDVwV89cLvTcTkTeGlRVIbi4QeyZTUfO0yAw2t67ocNrtGp+SSvi04Qcvtl1kPpGKyN7BHPtaT2YNjjS4ebFi1MjR9RORmtNeX05hdWFFNQUUFB99CO/Kv9IOJfWlf7hZ8O9w4nyi2JQ6CCm9JhCN/9udPPtRrR/NF19u+Lu2vLVZ17urlwxwJcr3LIp2/4VXvvX4pldQ4X25nu3kdT1vZuBEy4lNrpbe7580WRvfgVfbsvly2255JXVEuDlxlVjuzNrTHcZ3uhkJKjtqLahluLaYkpqSiiuLaaopojiGuNzUU0RhTWFxufqQuqt9X/4+SDPILr4dCHCJ4JBYYOI9I2ki08Xuvp2patfV7r4dMHD1aPthZZmGsMZKSsh6xfQjQT6d4URV1HXZyo/VMbxaVIBP+0sQu/YzpDoDC4aGsW0wV3pFiSLUtpif3E1K3bmsSzpAHvyynFRcFbfcP56/gAm9Y+QNradlAx9nCKrtlJRX0FZXRmldaXG59pSDtUdoqS2hNLaUkprSympM26X1JZQZalq9rkCPAII9w4nzDuMMJ8wwr3DjQ+fcCJ8IojwjiDcJxwvNxuNQWoNedshuWm8ueBX4/6IAcZYc/x06Dr8D4tPDpbVsnzHAb7afoCduWUADI0OZOqgrpw7oAu9w31lWthJaK1Jya9g1a/5fL3rILvzygEY3j2IGUOjOH9IFOH+0mq0M5BZH8ehtabKUkWlpZLy+nIq6iuoqK+gvL6c8rpyyuvLKasrM75uun34o7y+nEbd2Ozzurm4EeIZQrBXMMFewYR4hRDiFUKodyihXqFHvg73CSfEK6R9joJbq6EeMtcbR80pX0N5LigXY3bG4XAO6dXip8soquLrXXl8s+sgO3KM0I4N9WFS/y6c3S+c0bEhcjTYpLq+gU0ZJaxLKWT1nnxySmsAGNE9iGmDujJ1UCQxIdJ/o7PpcEFtabRQ3VBNlaWKKkvVkdvVluojwXvkc/3RryvqK458rrAYt63aesJt+bv7E+AZQIBHAAGeAQR5BhHoEUigZyBBnkEEewUT6BlIsGcwQV5BBHsG4+vuoEeStWWwd5URzntXQV05uHlDn0nGycC488C37cuLcw/VsGZPPqv3FLAhrZj6Riuebi6M7RXKuN6hnNYrlEFRATZbuu5oLI1WduSUsTG9mF/SitiSUXpkn5zRJ4xJ/bswqX8EXWTWRqfmNEH9z83/pLK+kpqGGmoba6lpqKHaUm18bqim2lJNdUP172YynIiHiwd+Hn74uPng7+GPn4cfvu6++Lsbt/3c/QjwCDjyvQCPgCNfB3oG4ufuh6uLkx8FluU2HTWvhIz1YLWATxj0mwrxFxgrBG3Y7Ojw0eP61CLW7y1kb0ElAH6ebgzvHsSI7sGM7BHM0OigDnNV9dKqerbnHGJrVilb95eybf8hquuNd1/xkf6cGRfGhL7yLkP8XpuDWik1FXgFcAUWaq3/eaLHn2pQT/lsChqNt5v3Hz583HzwcffBx80HbzdvfN198XX3xdvdG1833yNf+7j74OduBLIpQwpm0xoKdjedDFwBB7YZ94f0NoYz4i9oanZkTkAUVtSxKaOYjenFJGYdIuVgOdamX8HuIT4Mjg5kQNcA4iP96RfpT7cgb8d8d4Kx6CSntIaU/ApSDpbz64FyduaWHRnKcHVR9O/qz8juwZzeO5QxPUMJ8e2Ev5OiRdoU1EopVyAVOBfIAbYAs7TWu4/3M84yRt1hNDZA9samk4Er4FCWcX/06Kbx5vMhrK9DLj6pqLWQlF3GjtxD7Mwp+13QAfh4uBIb6kuvcF9iQ32JDvYmOtiHqCAvugR42fxiCJV1DeSX13LgUA05pTVkl1STVVxNelEVGUWV1FqODp31CPVhULdABncLZEh0IEOjg5zmYg3CfG2dRz0G2Ke1Tm96so+BGcBxg1rYQX0VpK0xjpxTv4aaUnD1MIYyzrjXWHziH2l2lSfl7+XOGXFhnBEXduS+8loLe/MrSD5Ywb6CSjKKqtiRU8bKnXlHjr4P8/N0I9zfkyAfd4J9PAjydsfX0w1fTzd8PFzxcHPBw9UFdzcXDv+Z0oClwUp9o5X6BitV9Q1U1TVQVdfIoep6SqstHKqup7Cijqr6358wdnVRxAR70zPMl3G9Q+kT4Ue/SH/6dvHHT0JZ2EhLfrO6Adm/+ToHGHvsg5RSc4G5AN27d2+X4sQxKgt/0+zoB2ioBa/Ao82O+kwCT+dfCBHg5c7IHiGM7PH7pkKWRisHy2rJKa3hwKEaCirqKKiopbCijkPVFvLLa0k5WEFVfQPVdY3UN574RPFhHq4u+Hq64uvpdiTwY0J8CPfzpEuAJxEBnkQFehMd4kMXf89OcxJUOI6WBHVz75f/MF6itZ4PzAdj6KONdYnDivYd7aeRvQnQEBgDI28wwrnHOGjFakNn5u7qQkyIT4unrlkarViajpqPDW1PV1fc3RTuri6mXOlGiNZoSVDnADG/+ToaOHCcx4q2slohN8EYa05ZCUWpxv2RQ+Dsh41wjhzskOPNjuZwCPvI+Tvh5FoS1FuAOKVUTyAXuBK4yqZVdTaWWsj4EZKXQ+o3UJlvNDvqMR5G32yMNwfFnPx5hBAd0kmDWmvdoJS6A/gWY3reIq31rzavrKOrLoG93xlHzmlroL4SPPygz2RjCl3cZKPZvhCi02vRaWqt9UpgpY1r6fhKs4zhjOQVR5od4RcJgy83ptD1nABu0tdBCPF7Mp/IlrSGvKSmcF4J+TuN+8PjYfzdxpFz1B+bHQkhxG9JULe3Rgtk/dx0MvBrKMs2mh3FjIVznzGOnEN7m12lEMKJSFC3h9py2Le6qdnRd0bzIzdv6D0RznrIOBnoG3by5xFCiGZIUJ+q8ryj480ZPzY1OwqF+AuNnhq9JoKHtKoUQrSdBHVLaQ2FyUf7aRzYatwf0gvG3mIMacSMNa3ZkRCi45KgPhFrI+zfePTIuTTDuL/bSDjn/4xwDo+XxSdCCJuSoD5WfbUxrzllpbH4pLrYaHbUcwKMu9NYGRjQ1ewqhRCdiAQ1QFWREcrJKyDtB2ioAc9A6HueMd7cZ3KHaHYkhHBOnTeoi9OODmlkbwJthYBoGHGdEc49xneaZkdCCMfWeYLaajVOAB5udlSYbNzfZTBMeMAY0ug6VMabhRAOp2MHdUNdU7OjpsUnlQdBuULseBh5ozG/ObiH2VUKIcQJdbygrik1rrCdvMJYhFJfCe6+RpOjfudD3LngE3Ly5xFCCAfRMYL6UPZvmh39DNYG8I2AwTONcO45Ady9zK5SCCFOiXMGtdZwcGdTOC83boNxAddxdxrh3G2kNDsSQnQIzhPUjRajNejhTnRl+wEFMWPg3KeNcA7rY3aVQgjR7hw7qOsqjHHm5JWw99umZkdeRh+Nsx4wLurqF2F2lUIIYVOOF9QVB48eNWesg8Z68A4xjpjjzzc60nn4ml2lEELYjeMEdX01vHehcWFXgOBY43qBh5sduTpOqUIIYU+Ok34ePkYnun5TjaPniP6y+EQIIXCkoAa4bIHZFQghhMOR+WtCCOHgJKiFEMLBSVALIYSDk6AWQggHJ0EthBAOToJaCCEcnAS1EEI4OAlqIYRwcEpr3f5PqlQhkHWKPx4GFLVjOe1F6modqat1pK7W6Yh19dBahzf3DZsEdVsopRK01qPMruNYUlfrSF2tI3W1TmerS4Y+hBDCwUlQCyGEg3PEoJ5vdgHHIXW1jtTVOlJX63SquhxujFoIIcTvOeIRtRBCiN+QoBZCCAdnelArpZ5XSiUrpXYopb5USgUd53FTlVIpSql9SqmH7VDX5UqpX5VSVqXUcafbKKUylVI7lVLblVIJDlSXvfdXiFJqlVJqb9Pn4OM8zi7762SvXxn+3fT9HUqpEbaqpZV1na2UKmvaP9uVUo/boaZFSqkCpdSu43zfrH11srrsvq+athujlPpBKbWn6f/i3c08pn33mdba1A9gCuDWdPs54LlmHuMKpAG9AA8gCRhg47r6A/2AtcCoEzwuEwiz4/46aV0m7a95wMNNtx9u7t/RXvurJa8fmA58DSjgNGCTHf7tWlLX2cBye/0+NW1zAjAC2HWc79t9X7WwLrvvq6btdgVGNN32B1Jt/ftl+hG11vo7rXVD05cbgehmHjYG2Ke1Ttda1wMfAzNsXNcerXWKLbdxKlpYl933V9Pzv9d0+z3gYhtv70Ra8vpnAEu0YSMQpJTq6gB12Z3W+keg5AQPMWNftaQuU2it87TWW5tuVwB7gG7HPKxd95npQX2M2Rh/hY7VDcj+zdc5/HHHmEUD3ymlEpVSc80upokZ+6uL1joPjF9kIOI4j7PH/mrJ6zdjH7V0m6crpZKUUl8rpQbauKaWcOT/f6buK6VULDAc2HTMt9p1n9nl4rZKqdVAZDPfekxr/VXTYx4DGoClzT1FM/e1eV5hS+pqgfFa6wNKqQhglVIquelIwMy67L6/WvE07b6/mtGS12+TfXQSLdnmVoyeD5VKqenAf4E4G9d1Mmbsq5YwdV8ppfyAz4F7tNblx367mR855X1ml6DWWk8+0feVUtcDFwCTdNMAzzFygJjffB0NHLB1XS18jgNNnwuUUl9ivL1tU/C0Q112319KqXylVFetdV7TW7yC4zxHu++vZrTk9dtkH7W1rt/+h9dar1RKvaGUCtNam9mAyIx9dVJm7iullDtGSC/VWn/RzEPadZ+ZPvShlJoKPARcpLWuPs7DtgBxSqmeSikP4Epgmb1qPB6llK9Syv/wbYwTo82eobYzM/bXMuD6ptvXA3848rfj/mrJ618GXNd0dv40oOzw0I0NnbQupVSkUko13R6D8X+02MZ1nYwZ++qkzNpXTdt8B9ijtX7xOA9r331m7zOmzZxB3YcxlrO96eOtpvujgJXHnEVNxThr/pgd6roE469iHZAPfHtsXRhn75OaPn51lLpM2l+hwPfA3qbPIWbur+ZeP/Bn4M9NtxXwetP3d3KCmT12ruuOpn2ThHFyfZwdavoIyAMsTb9bcxxkX52sLrvvq6btnoExjLHjN7k13Zb7TJaQCyGEgzN96EMIIcSJSVALIYSDk6AWQggHJ0EthBAOToJaCCEcnAS1EEI4OAlqIYRwcP8Pey+wjJp9Bp4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df0['X'],df0['Y0'])\n",
    "ax.plot(df0['X'],df0['Y1'])\n",
    "ax.plot(df0['X'],df0['Y2'])\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "引数の書き方１の簡略形として，横軸の変数を省略できることを紹介した。その場合はどうなるかを考えてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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c3TqyGx1DA3l2hfbSlfJUL6/OpKy6jvsnpVgdxelaNYYuIsnUb0e3vomPR4vINhH5RET6N/Pzs0UkXUTS8/PzW5/WYmHBAcwa150Ve/PYeaTY6jhKqVY6XVnDy2sOM7l/F/p06WB1HKdzuKCLSDjwLvCQMeZ0o483A92MMYOBZ4H3mvoOY8w8Y0yaMSYtJibmHCNba8aYZDqEBPDsigNWR1FKtdLC1Ycpqaz1yt45OFjQRSSQ+mL+mjFmaePPjTGnjTGl9ufLgEAR8cpdljuEBHLn2O58tusEe441/ndNKeWuSqtqeWl1Jhf3iWVAfITVcVzCkVkuArwE7DHGPNVMmy72dojICPv3FjgzqDuZOTaZsCB/ntMZL0p5jFfXZVFUXsOPL061OorLOLJ4wVjgdmCHiGy1v/crIAnAGDMXuB64R0RqgQrgZuPFd+BEhgYxY0wyc78+yIETJaR2bm91JKXUWZRX1/LiykOMT41mSGKk1XFcpsWCboz5Bjjriu/GmOeA55wVyhPcNb4HC9cc5ukvDvDcLcOsjqOUOovFa7MoKKvmoUu8t3cOeqfoOYsKC+KOMcl8vOMY+0+UWB1HKdWM8upa/mPvnXvq5s+O0oJ+Hu4a34PQQH+e+UJnvCjlrhatzaKwrJqHLulldRSX04J+HrSXrpR7K6uqZd7KQ0zoFcPwbh2tjuNyWtDP05le+tPaS1fK7fz/3rl3j52foQX9PHUMC+LOscks23GMvcd1XrpS7qK0qpZ5Kw8yoVcMw5K8v3cOWtCd4q7xPQgPCuCfy/dbHUUpZffyN5mcKq/h4Uu9f+z8DC3oThAZGsSs8fV3j+7I1TVelLJacXkN81Yd4pK+nRnsxfPOG9OC7iQzx3UnMjSQfyzfZ3UUpXzei6sOUVJZy099qHcOWtCdpkNIIHdP6MlX+/LZlKW7GilllYLSKhaszuSKQV3pF+d9KyqejRZ0J7pjTDeiw4P4x+c6lq6UVf6z8hCVNXX8xEdmtjSkBd2JQoMCuHdiCmsOFrA646TVcZTyOceLK1m45jDXDIknJdb31ljSgu5kt4xMIi4ihL99uhcvXp9MKbf0zIoD2IzhJz42dn6GFnQnCwn056FLe7Ett5jPdh23Oo5SPiPzZBlvbszhlhFJJEaFWh3HElrQXWDa0Hh6xoTx98/3U1tnszqOUj7hqeX7CfL34/5Jvjd2foYWdBcI8PfjZ5f1JiOvlKVbjlgdRymvt/NIMR9uO8rMccnEtA+2Oo5ltKC7yOQBXRiUEMG/lu+nsqbO6jhKebW/f76PiHaBzJ7Q0+oolnJkC7pEEflSRPaIyC4RebCJNiIiz4hIhohsFxGf3/FBRPjl5D4cLa7k1XVZVsdRymutPVjAV/vyuWdiTyLaBVodx1KO9NBrgYeNMX2BUcB9ItKvUZspQKr9MRt4wakpPdTYlGjGp0bz7IoMistrrI6jlNex2Qx/+WQPXSNCuHNMstVxLNdiQTfGHDPGbLY/LwH2APGNmk0FFpl664BIEenq9LQe6JEpfThdWcPzX+uG0ko528c7jrE9t5iHL+tNSKC/1XEs16oxdBFJBoYC6xt9FA/kNHidy/eLPiIyW0TSRSQ9Pz+/lVE9U/+4CK4dEs/Lqw9ztKjC6jhKeY3qWhtPfraPPl3ac+3Q75Ubn+RwQReRcOBd4CFjTOOFv5vaRPp7d9UYY+YZY9KMMWkxMTGtS+rBfnpZ/U0OT+nyuko5zevrs8guLOeXU/rg73fWfex9hkMFXUQCqS/mrxljljbRJBdIbPA6ATh6/vG8Q0LHUO4ck8y7m3PZc0w3wVDqfJ2urOGZFRmM7tGJib18p3PYEkdmuQjwErDHGPNUM80+AGbYZ7uMAoqNMcecmNPj3TcxhYh2gfzp4z26JIBS5+n5Lw9SWFbNry7vS32JUuBYD30scDswSUS22h+Xi8gcEZljb7MMOARkAC8C97omrueKCA3kgUmpfJNxkq/2+cb1A6VcIaewnAWrM5k2NJ6BCRFWx3ErAS01MMZ8Q9Nj5A3bGOA+Z4XyVreN6sbidVn8adkexqdGE+Cv93Up1Vp/+2wffgI/+0Fvq6O4Ha0obSgowI9HpvQhI6+UJRtzWv4BpdR3bMk+xYfbjjJ7fA/iIttZHcftaEFvY5f168yI7lH8a/l+TlfqzUZKOcoYwx8/3kNM+2DuvtC3b/Fvjhb0NiYi/OaKfhSUVfPvFXqzkVKO+mj7MTZlneLhS3sRFtziaLFP0oJugYEJEdwwPIEFqzPJPFlmdRyl3F5FdR1//WQv/eM6cENaYss/4KO0oFvk55N7E+Tvx58+3mN1FKXc3ryVhzhSVMHvruqvNxGdhRZ0i8S2D+H+San8b88JVh3QaYxKNedoUQUvfJ3BFYO6MqJ7lNVx3JoWdAvNHJdMt06hPPHhbt3ZSKlm/PWTvRgDj07pY3UUt6cF3ULBAf48dnlfDuSVsljXTFfqezYeLuSDbUe5+8KeJHT0zX1CW0MLusUu7deZ8anRPLV8PydLq6yOo5TbqK2z8Zv3dhIXEcKcC3tYHccjaEG3mIjw+6v7U1lTx/99stfqOEq5jdfWZ7P3eAm/ubIfoUE6TdERWtDdQM+YcGaO687bm3LZnH3K6jhKWe5kaRX/+Hwf41KimTygi9VxPIYWdDfxwKRUOncI5nfv76LOpqsxKt/25Kf7KK+u4/dX99PVFFtBC7qbCAsO4LEr+rHjSDFLNmRbHUcpy2zJPsWb6TnMGtedlNj2VsfxKFrQ3chVg7oypmcn/vbpXr1AqnxSbZ2Nx/67ky4dQvjxxalWx/E4WtDdiIjwxNQBVNTU8We9g1T5oIVrs9h97DS/u6of4bpeS6tpQXczKbHh3D2hJ0u3HGHNwZNWx1GqzRwvruSpz/cxsXeMXgg9R45sQbdARPJEZGczn08UkeIGuxn91vkxfcv9k1JIigrlN+/tpLpW7yBVvuEPH+2m1mZ44uoBeiH0HDnSQ38FmNxCm1XGmCH2xxPnH8u3hQT68/jU/hzML2PeyoNWx1HK5b7al8fHO47x40kpJHXSO0LPVYsF3RizEihsgyyqgYt6x3LFwK48syKDQ/mlVsdRymXKq2v59Xs7SYkN564Jekfo+XDWGPpoEdkmIp+ISP/mGonIbBFJF5H0/HxdYbAlv7uqH8EBfvzqvzuo37ZVKe/z1Of7yT1VwV+mDSQ4wN/qOB7NGQV9M9DNGDMYeBZ4r7mGxph5xpg0Y0xaTEyMEw7t3WI7hPCry/uy7lAhb6fnWh1HKafbkVvMgtWZ3DIyiQuSdWnc83XeBd0Yc9oYU2p/vgwIFJHo806mALgpLZERyVH8adke8kt0brryHrV1Nh5Zup3o8GB+OVmXxnWG8y7oItJF7JekRWSE/TsLzvd7VT0/P+HP0wZSUV3H7z/cZXUcpZxm/jeZ7Dp6msev7k9Eu0Cr43gFR6YtLgHWAr1FJFdEZonIHBGZY29yPbBTRLYBzwA3Gx3wdaqU2HAeuDiFj7cf49Odx62Oo9R5O5hfylPL93NZv84659yJWrwVyxgzvYXPnwOec1oi1aS7L+zJsh3H+c37OxnVI4rI0CCrIyl1Tupshl+8s512gf788Rqdc+5Meqeohwj09+PJGwZxqqyaJz7abXUcpc7ZorWH2ZR1it9e2Y/YDiFWx/EqWtA9SP+4CO6Z2JOlm4/w5d48q+Mo1WrZBeX87dP62/unDYu3Oo7X0YLuYe6flEJqbDiPLt1BcXmN1XGUcpjNZvj5O9vw9xP+fO1AHWpxAS3oHiY4wJ9/3DiY/NIqnfWiPMrLaw6zPrOQ317Vj7jIdlbH8Upa0D3QoIRI7rsohf9uOcKnO49ZHUepFmXklfK3T/dycZ9YbhieYHUcr6UF3UPdf1EK/eM68Nh/d+pmGMqt1dbZePjtbbQL8ucv03SoxZW0oHuooAA/nrpxCCWVtTyma70oN/bCVwfZllPEH68ZoLNaXEwLugfr3aU9D1/Wi892ndC1XpRb2pZTxNNfHOCqwXFcOSjO6jheTwu6h/vR+B6M6hHF7z/cRVZBmdVxlPpWeXUtD725ldj2wfxx6gCr4/gELegezt9PeOrGIQT4CQ+9uZXaOt3hSLmHP3y0h8MFZfzjxiFEhOpaLW1BC7oXiItsx5+uHciW7CKeXZFhdRylWL77BEs2ZDN7Qg9G9+xkdRyfoQXdS1w1OI5pQ+N5dsUBNmTqBlPKOseLK/nFO9vo17UDP720l9VxfIoWdC/yxDUDSIoK5cE3tlBUXm11HOWD6myGB9/YQlWtjWdvGao7ELUxLeheJDw4gGenD+NkaRU/f2e7TmVUbe65FRmszyzkiakD6BkTbnUcn6MF3csMTIjgl5P7sHz3CRavy7I6jvIhGzILefqL/Vw7NJ7rdOEtSziywcUCEckTkZ3NfC4i8oyIZIjIdhEZ5vyYqjVmjevOpD6x/PGjPezILbY6jvIBBaVVPLBkC0lRofxB1zi3jCM99FeAyWf5fAqQan/MBl44/1jqfIgIf79hMJ3Cg7j39U26KqNyqTqb4aE3t1JYXs2/bx1GeHCL++YoF2mxoBtjVgJnmzYxFVhk6q0DIkWkq7MCqnMTFRbEc7cM41hRJT97Z5uOpyuXeXbFAVYdOMnjV/enf1yE1XF8mjPG0OOBnAavc+3vKYsN79aRRy/vy/LdJ3hx1SGr4ygv9M2Bkzz9xQGmDY3n5gsSrY7j85xR0JsaLGuyOygis0UkXUTS8/PznXBo1ZKZY5OZMqAL//fpPtYeLLA6jvIiR4oqeOCNLaTEhPPHa3Xc3B04o6DnAg3/aU4AjjbV0BgzzxiTZoxJi4mJccKhVUtEhL9dP4jkTqHc//pmjhZVWB1JeYHKmjrmLN5ETa2NubcPJzRIx83dgTMK+gfADPtsl1FAsTFGd11wI+1DAvnP7WlU1dq459VNVNbUWR1JeTBjDL9+byc7jhTz1E1DdL65G3Fk2uISYC3QW0RyRWSWiMwRkTn2JsuAQ0AG8CJwr8vSqnOWEhvOP24czLbcYn77/k69SKrO2avrsnhnUy4PXJzKpf06Wx1HNdDi/5OMMdNb+NwA9zktkXKZH/Tvwo8npfDsigz6de3AnWO7Wx1JeZi1Bwt4/MPdTOoTy0MXp1odRzWid4r6mJ9c0otL+3XmiY92s3K/XphWjssuKOee1zaRHB3Gv24egp+fXgR1N1rQfYyfn/DPm4bQq3N77nt9MwfzS62OpDxASWUNsxZuBGD+jDQ6hOj65u5IC7oPCg8O4MUZaQT5+3HXwnRdmVGdVf0KilvJPFnG87cOIzk6zOpIqhla0H1UYlQoc28fTu6pCu5evImqWp35or7PGMPjH+5ixd48fn91f8b0jLY6kjoLLeg+7ILkKJ68YRDrMwt55N0dOvNFfc9L32SyaG0Wsyf04LZR3ayOo1qgdwP4uKlD4skpLOfvn+8nMSpUd5hR3/p053H+tGwPUwZ04ZHJfayOoxygBV1x30UpZBeW88wXB4iPDOGmC5KsjqQstimrkAff2MKQxEj+eZPOaPEUWtAVIsKfrh1IXkkVjy7dQcfQIC7r38XqWMoi+0+UMPOVdOIi2zF/RhohgbqNnKfQMXQFQKC/H8/fOoyBCZH8eMkW3WjaRx0pqmDGSxsIDvBj0cwRdAoPtjqSagUt6OpboUEBvHznBcR3bMeshRvZc+y01ZFUGyoorWLGS+spq6pl4cwRJEaFWh1JtZIWdPUdUWFBLJo5gvDgAG5/ab3eeOQjiitqmLFgA7mnKph/Rxp9u3awOpI6B1rQ1fckdAzl1R+NBOC2+evJKSy3OJFypbKqWma+spH9J0qYe/twRvboZHUkdY60oKsm9YwJZ/GskZRX13Hr/PUcL660OpJygcqaOmYvTmdrThHPTh/KRb1jrY6kzoMWdNWsvl07sHDmCArLqpn+4jot6l6msqaOuxals+ZgAU9eP4jJA3QrYE+nBV2d1ZDESBbOHEF+SZUWdS9ypph/k3GSv103iGnDEqyOpJxAC7pq0fBuHb8t6jfPW8uxYt3GzpNVVNfxo4X1xfzJ6wdzQ5pu7uwtHCroIjJZRPaJSIaIPNLE5xNFpFhEttofv3V+VGWl4d06smjWCApKq7lh7lqyCsqsjqTOQUllDXcs2MDqg/XF/Prh2jP3Jo5sQecP/BuYAvQDpotIvyaarjLGDLE/nnByTuUGhiV15PW7RlFWVcsNc9dy4ESJ1ZFUK5wqq+bW+evZnH2KZ24eqsXcCznSQx8BZBhjDhljqoE3gKmujaXc1cCECN68ezQAN/5nLdtzi6wNpBxy4nQlN81by97jJfzn9uFcNTjO6kjKBRwp6PFAToPXufb3GhstIttE5BMR6d/UF4nIbBFJF5H0/Hzd/sxT9ercnrfnjCYsOICb563jq315VkdSZ5GRV8K059eQe6qCV+68gIv76sbO3sqRgt7UMmuNF87eDHQzxgwGngXea+qLjDHzjDFpxpi0mJiYVgVV7qVbpzCW3jOG5E5h/GhhOu9syrU6kmpC+uFCrnthLVW1Nt6cPZoxKbpBhTdzpKDnAg0vgycARxs2MMacNsaU2p8vAwJFRP/keLnYDiG8efcoRvXoxM/e3sYzXxzQTTLcyCc7jnHr/PVEhQWx9J4xDEyIsDqScjFHCvpGIFVEuotIEHAz8EHDBiLSRUTE/nyE/XsLnB1WuZ/2IYEsuPMCpg2N56nl+3noza1U1uh2dlYyxvDcigPc89pm+sd14N17xpDUSRfa8gUtrodujKkVkfuBzwB/YIExZpeIzLF/Phe4HrhHRGqBCuBmo101nxEU4Mc/bhxMz9hwnvxsH9mF5cy7PY2Y9rr0alurrKnj0aU7+O+WI1wzJI6/XjdI1zP3IWJV3U1LSzPp6emWHFu5zic7jvGTt7bSMTSI528dxtCkjlZH8hlHiyq459VNbMst5meX9eK+i1Kw/8dZeRER2WSMSWvqM71TVDnVlIFdeWfOGPz9hJv+s44lG7KtjuQT1hw8yVXPfsPB/DLm3jac+yelajH3QVrQldMNiI/gw/vHMbJHFI8u3cHP395GeXWt1bG8ks1meOGrg9w2fz2RoYG8d99YJg/Q7QN9le4pqlyiY1gQr/xwBP9cvp9/f5XBlpwinrtlKH266MYJzpJfUsVP39rKqgMnuWJgV/7v+kGEB+tfaV+mPXTlMv5+ws9+0JvFM0dSVF7D1OdWs3hdlk5tdIKV+/OZ8vQqNmQW8udrB/LcLUO1mCst6Mr1xqVG88mD4xnZoxO/eW8nd7y8UZfhPUfl1bX8+r0dzFiwgY6hgbx//1huGZmk4+UK0IKu2khM+2BeufMC/jC1PxszC7nsn1/z3y252ltvhY2HC5ny9CpeW5/Nj8Z158Mfj9MhLPUdWtBVm/HzE24fncyyB8eT2rk9P3lzG3e8vJHsAt2z9GyKy2t4dOkObpi7ljqbYcldo/j1lf10frn6Hp2HrixRZzMsXnuYJz/bR50xPHhxL2aN605QgPYxzjDG8OH2Yzzx4W4Ky6qYNa47P7m0F6FBOlbuy842D10LurLU0aIKfvfBLpbvPkH36DAeu7wvF/eN9fkx4R25xTzx0S42Hj7FwPgI/jJtIAPidS0WpQVdeYAv9+Xxh492cyi/jPGp0fxych+fLGBHiir41/L9vLM5l6jQIH72g97cmJaIv59v/wOn/j8t6Moj1NTZWLQ2i2e+OEBxRQ1XDOzKTy/rRc+YcKujudzJ0iqe//Igr67LAmDG6G48cEkqHUICLU6m3I0WdOVRTlfWMH/lIeZ/k0llTR1XDIpjzoU96B/nfT32o0UVvLjqEG9syKGqto7rhyfw4CW9iI9sZ3U05aa0oCuPdLK0ihdXHeK1ddmUVtUysXcMM8d2Z1xKNH4ePgSx80gxr6w5zPtbj2AzMHVIHPdOTCEl1vv/N6LOjxZ05dGKy2tYvO4wr6w5zMnSarpHh3HbqG5MGxpPx7Agq+M5rKK6js92HWfR2sNszi6iXaA/N6YlcNeEHiR01PXKlWO0oCuvUFVbxyc7jrNw7WG2ZBcR6C9c1DuWacPimdg71i3nZdfZDBsyC/nvllyW7ThOaVUtyZ1CuX10MtcPTyCinY6Rq9Y5W0HXCa3KYwQH+HPN0HiuGRrP7qOnWbo5l/e2HuXz3ScIDfJnYu8YLuvXhQm9YoiysOdeXl3LukMFfLbzBP/bc4KCsmrCgvyZMrAr04bGM6pHJ48fMlLuyaEeuohMBp6mfsei+caYvzb6XOyfXw6UA3caYzaf7Tu1h66cobbOxpqDBXy26zif7z5BfkkVAP26dmBcajRp3ToyODGSzh1CXJahuLyG7UeK2JxVxOqDJ9mSfYqaOkN4cAAX9Ynlsn6dubhvrN4QpJzivIZcRMQf2A9cSv2G0RuB6caY3Q3aXA78mPqCPhJ42hgz8mzfqwVdOZvNZtiWW8TqjJOszihgU9YpqutsAHTpEEKfru1JiQmnZ2w4iR1D6RIRTOcOIbR3YGpgZU0dJ05Xcry4kiNFFRzML+VgXhn7TpSQebIMABEYEBfBmJROjO0ZzcgeUQQHuN8wkPJs5zvkMgLIMMYcsn/ZG8BUYHeDNlOBRfZ9RNeJSKSIdDXGHDvP7Eo5zM9PGJrUkaFJHbl/UiqVNXXsPnaardlFbMstYv+JUtYeLKCq1vadnwvy9yM8JICwYH+CA/w5MxhSU2ejtKqO0qoaKmu++zMBfkK3TqH06hzO9cMTGJIYycCECJ03rizlSEGPB3IavM6lvhfeUpt44DsFXURmA7MBkpKSWptVqVYJCfRnWFJHhjXY19RmMxwpquBIUcW3Pe5T5TWUVtVQVlVHVW3dt20D/PwICw6gfUgAHUIC6NwhhC4RIXSNaEe3TqEE+uu6M8q9OFLQm7p603icxpE2GGPmAfOgfsjFgWMr5VR+fkJiVCiJUTpNUHkfR7oYuUBig9cJwNFzaKOUUsqFHCnoG4FUEekuIkHAzcAHjdp8AMyQeqOAYh0/V0qpttXikIsxplZE7gc+o37a4gJjzC4RmWP/fC6wjPoZLhnUT1v8oesiK6WUaopDE2ONMcuoL9oN35vb4LkB7nNuNKWUUq2hl+mVUspLaEFXSikvoQVdKaW8hBZ0pZTyEpYtnysi+UDWOf54NHDSiXE8hS+ety+eM/jmefviOUPrz7ubMSamqQ8sK+jnQ0TSm1ucxpv54nn74jmDb563L54zOPe8dchFKaW8hBZ0pZTyEp5a0OdZHcAivnjevnjO4Jvn7YvnDE48b48cQ1dKKfV9ntpDV0op1YgWdKWU8hIeV9BFZLKI7BORDBF5xOo8riAiiSLypYjsEZFdIvKg/f0oEVkuIgfsv3Zs6bs8jYj4i8gWEfnI/toXzjlSRN4Rkb323/PRPnLeP7H/+d4pIktEJMTbzltEFohInojsbPBes+coIo/aa9s+EflBa4/nUQXdvmH1v4EpQD9guoj0szaVS9QCDxtj+gKjgPvs5/kI8IUxJhX4wv7a2zwI7Gnw2hfO+WngU2NMH2Aw9efv1ectIvHAA0CaMWYA9Utz34z3nfcrwORG7zV5jva/4zcD/e0/87y95jnMowo6DTasNsZUA2c2rPYqxphjxpjN9ucl1P8Fj6f+XBfamy0ErrEkoIuISAJwBTC/wdvefs4dgAnASwDGmGpjTBFeft52AUA7EQkAQqnf5cyrztsYsxIobPR2c+c4FXjDGFNljMmkfn+JEa05nqcV9OY2o/ZaIpIMDAXWA53P7ARl/zXWwmiu8C/gF4CtwXvefs49gHzgZftQ03wRCcPLz9sYcwT4O5BN/WbyxcaYz/Hy87Zr7hzPu755WkF3aDNqbyEi4cC7wEPGmNNW53ElEbkSyDPGbLI6SxsLAIYBLxhjhgJleP4wQ4vs48ZTge5AHBAmIrdZm8py513fPK2g+8xm1CISSH0xf80Ys9T+9gkR6Wr/vCuQZ1U+FxgLXC0ih6kfSpskIq/i3ecM9X+mc40x6+2v36G+wHv7eV8CZBpj8o0xNcBSYAzef97Q/Dmed33ztILuyIbVHk9EhPox1T3GmKcafPQBcIf9+R3A+22dzVWMMY8aYxKMMcnU/76uMMbchhefM4Ax5jiQIyK97W9dDOzGy8+b+qGWUSISav/zfjH114q8/byh+XP8ALhZRIJFpDuQCmxo1TcbYzzqQf1m1PuBg8BjVudx0TmOo/6/WtuBrfbH5UAn6q+KH7D/GmV1Vhed/0TgI/tzrz9nYAiQbv/9fg/o6CPn/TiwF9gJLAaCve28gSXUXyOoob4HPuts5wg8Zq9t+4AprT2e3vqvlFJewtOGXJRSSjVDC7pSSnkJLehKKeUltKArpZSX0IKulFJeQgu6Ukp5CS3oSinlJf4fAUkkdUlw0K0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df0['Y0'])\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "上の図との違いは横軸の値である。この場合，横軸に`df0`の行インデックスが自動的に使われている。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## データの引数の書き方２"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "次に引数`data`を使う書き方を考えてみる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 同じ`DataFrame`を使う"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "横軸に`X`，縦軸に`Y0`を指定してプロットしてみる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot('X', 'Y0', data=df0)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "変数`Y1`と`Y2`も同じ軸`ax`にプロットしてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot('X', 'Y0', data=df0)\n",
    "ax.plot('X', 'Y1', data=df0)\n",
    "ax.plot('X', 'Y2', data=df0)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "````{note}\n",
    "この場合，同じようなコードが続いているのでforループを使うと良いだろう。\n",
    "```\n",
    "fig, ax = plt.subplots()\n",
    "for v in ['Y0','Y1','Y2']:\n",
    "    ax.plot('X', v, data=df0)\n",
    "```\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "横軸の`X`を指定せずにプロットしてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot('Y0', data=df0)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "行インデックスが横軸の値として使われている。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 異なる`DataFrame`の変数を図示する"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "別の`DataFrame`を作成する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "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>X</th>\n",
       "      <th>Y3</th>\n",
       "      <th>Y4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.959596</td>\n",
       "      <td>0.959596</td>\n",
       "      <td>-3.840016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.919192</td>\n",
       "      <td>0.919192</td>\n",
       "      <td>-3.683298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.878788</td>\n",
       "      <td>0.878788</td>\n",
       "      <td>-3.529844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.838384</td>\n",
       "      <td>0.838384</td>\n",
       "      <td>-3.379655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          X        Y3        Y4\n",
       "0 -2.000000  1.000000 -4.000000\n",
       "1 -1.959596  0.959596 -3.840016\n",
       "2 -1.919192  0.919192 -3.683298\n",
       "3 -1.878788  0.878788 -3.529844\n",
       "4 -1.838384  0.838384 -3.379655"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic = {'X':x, 'Y3':-x-1, 'Y4':-x**2}\n",
    "df1 = pd.DataFrame(dic)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`X`を横軸として`df0`の`Y0`，`Y3`を横軸として`df1`の`Y3`を同じ図に表示してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot('X', 'Y0', data=df0)\n",
    "ax.plot('Y3', 'Y4', data=df1)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "tags": [
     "remove-cell"
    ]
   },
   "source": [
    "## その他の引数とメソッド"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "(sec:4-other_methods)=\n",
    "## その他の引数とメソッド"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "上で説明したコードでは単純なプロットしかできない。一方で様々な引数やメソッドが用意されており，それらを駆使することにより様々な「飾り付け」をすることが可能となる。ここでは主なものを紹介するが，他にも数多く用意されているので`Matplotlib`の[サイト](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html)を参考にしてほしい。また，これらの引数・メソッドは，上述の「データの引数の書き方１」と「データの引数の書き方２」の両方に有効であることも覚えておこう。\n",
    "\n",
    "引数・メソッドがもたらす違いを際立たせるために新たな`DataFrame`を使おう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    X     Y     Z\n",
       "0  10   5.0   7.0\n",
       "1  20  30.0   2.0\n",
       "2  30  15.0  10.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic = {'X':[10, 20, 30],\n",
    "       'Y':[5.0, 30.0, 15.0],\n",
    "       'Z':[7.0, 2.0, 10.0]}\n",
    "df2 = pd.DataFrame(dic)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "以下のプロットでは，横軸の変数を省略する簡略形を使うので，行インデックが横軸に使われるので注意しよう。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### `subplots()`の引数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`subplots()`はキャンバスと軸を設定する`plt`のメソッドとなるが、主に３つの引数を紹介する。\n",
    "* `figsize`：図の大きさ\n",
    "    * `figsize=(キャンバスの横幅、キャンバスの縦の長さ)`\n",
    "* `nrows`（デフォルトは`1`）：軸の数を指定するために使う引数（「図を並べる」のセクションで説明する）\n",
    "* `ncols`（デフォルトは`1`）：軸の数を指定するために使う引数（「図を並べる」のセクションで説明する）\n",
    "* `sharex`（デフォルトは`False`）：複数の軸がある場合に使う引数（「図を並べる」のセクションで説明する）\n",
    "* `sharey`（デフォルトは`False`）：複数の軸がある場合に使う引数（「図を並べる」のセクションで説明する）\n",
    "* `constrained_layout`（デフォルトは`False`）：複数の軸がある場合に使う引数であり，図の間隔を自動調整する（「図を並べる」のセクションで説明する）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fix, ax = plt.subplots(figsize=(8,4))\n",
    "ax.plot('X', data=df2)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### `plot()`の基本的な引数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`plot()`は軸`ax`にデータを描くメソッドだが、引数を使うことによりデータの表示方法を指定できる。詳しくは[このリンク](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html)を参照することにして，ここでは基本的な引数だけを紹介する。\n",
    "* `linestyle`：線のスタイル（リストにして列の順番で指定する;`-`，`--`，`-.`，`:`などがある）\n",
    "* `linewidth` or `lw`：線の幅\n",
    "* `color` or `c`：色（[参照サイト](https://matplotlib.org/stable/gallery/color/named_colors.html)）\n",
    "    * `r`は赤\n",
    "    * `k`は黒\n",
    "    * `g`はグリーン\n",
    "* `marker`：観測値のマーカー（`o`，`.`，`>`，`^`などがある; [参照サイト](https://matplotlib.org/stable/api/markers_api.html)）\n",
    "* `markersize`：マーカーの大きさ\n",
    "* `label`：以下で説明する`ax.legend()`がある場合に有効となる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fix, ax = plt.subplots()\n",
    "ax.plot('X'\n",
    "           , data=df2\n",
    "           , linestyle=':'\n",
    "           , linewidth=2\n",
    "           , color='red'\n",
    "           , marker='o'\n",
    "           , markersize=10\n",
    "           , label='X series')\n",
    "ax.plot('Y'\n",
    "           , data=df2\n",
    "           , linestyle='-'\n",
    "           , linewidth=2\n",
    "           , color='k'\n",
    "           , marker='^'\n",
    "           , markersize=10\n",
    "           , label='X series')\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "引数をいちいち書くのが面倒な場合、次の３つを簡略して一緒に指定できる。\n",
    "* `linestyle`\n",
    "* `color`\n",
    "* `marker`\n",
    "\n",
    "例えば、\n",
    "* `linestyle=':'`\n",
    "* `color='red'`\n",
    "* `marker='o'`\n",
    "\n",
    "の場合、`:ro`と書くことができる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fix, ax = plt.subplots()\n",
    "ax.plot('Y', ':ro', data=df2)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "（注意点）\n",
    "* `:ro`は文字列\n",
    "* `:`，`r`，`o`の順番を変えても良い。\n",
    "* `:`や`:o`のように１つもしくは２つだけを指定しても良い。\n",
    "* `:ro`は`=`を使う引数の前に置く。\n",
    "\n",
    "詳細は[参考サイト（英語）](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html)を参照。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### `ax`の基本的なメソッド"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`ax`は透明なキャンバス上にある軸を指しているが、そのメソッドを使うことにより、軸周辺を「飾り付け」することができる。\n",
    "* `.set_title()`：タイトルを設定する。\n",
    "    * 文字列で指定し、大きさは引数`size`で指定する。\n",
    "* `.set_xlabel()`：横軸ラベル\n",
    "    * 文字列で指定し、大きさは引数`size`で指定する。\n",
    "* `.set_ylabel()`：縦軸ラベル\n",
    "    * 文字列で指定し、大きさは引数`size`で指定する。\n",
    "* `.legend()`：凡例を表示する。\n",
    "    * `plot()`の引数`label`がなければ、データ・フレームの列ラベルが使われる。\n",
    "    * `plot()`の引数`label`があれば、それが使われる。\n",
    "* `.grid()`：グリッド線が表示される。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot('X', data=df2, label='X series')\n",
    "ax.set_title('A Large Title', size= 30)     # タイトルの設定\n",
    "ax.set_xlabel('Horizontal Axis', size=20)   # 横軸ラベルの設定\n",
    "ax.set_ylabel('Vertical Axis', size=20)     # 縦軸ラベルの設定\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 図を並べる"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "複数の図を並べたいとしよう。考え方としては、透明のキャンバスに複数の軸を設定し、それぞれの軸を使って作図していけば良いのである。キャンバスは四角なので偶数個の軸が設定できる。例として、次のように軸を配置したいとしよう。\n",
    "```\n",
    "+---+---+---+\n",
    "| 1 | 2 | 3 |\n",
    "+---+---+---+\n",
    "| 4 | 5 | 6 |\n",
    "+---+---+---+\n",
    "```\n",
    "２行・３列の配置になっており、左上から軸の番号が振られている。このような場合、`subplots()`の２つの引数が必須であり、別の２つの引数が有用である。\n",
    "```\n",
    "subplots(行の数, 列の数, sharex=False, sharey=False, constrained_layout=False)\n",
    "```\n",
    "* 行の数（デフォルトは`1`）：上の例では`2`\n",
    "* 列の数（デフォルトは`1`）：上の例では`3`\n",
    "* `sharex`（デフォルトは`False`）：`True`にすると、全ての図で横軸が同じになり、不要な横軸の数字などを非表示になる。\n",
    "* `sharey`（デフォルトは`False`）：`Ture`にすると、全ての図で縦軸が同じになり、不要な縦軸の数字などを非表示にする\n",
    "* `constrained_layout`（デフォルトは`False`）：`Ture`にすると、全ての図の間隔を調整して見やすくなる。\n",
    "\n",
    "上の例を使うと次のようになる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "６つの軸が表示されているが、それらは`ax`に割り当てられている。`ax`を表示してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>],\n",
       "       [<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`<AxesSubplot:>`が軸を示しており、それぞれの軸に対応している。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "軸は`(2,3)`の`array`に格納されているのが確認できる。従って、それぞれの軸は`array`の要素を抽出することによりアクセスできる。\n",
    "* `ax[0,0]`：軸１を抽出\n",
    "* `ax[0,1]`：軸２を抽出\n",
    "* `ax[0,2]`：軸３を抽出\n",
    "* `ax[1,0]`：軸４を抽出\n",
    "* `ax[1,1]`：軸５を抽出\n",
    "* `ax[1,2]`：軸６を抽出\n",
    "\n",
    "これを使い次のように６つのを図示できる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,3,figsize=(8,3))\n",
    "ax[0,0].plot('X', data=df2)\n",
    "ax[0,1].plot('Y', data=df2)\n",
    "ax[0,2].plot('Z', data=df2)\n",
    "ax[1,0].plot('X', data=df2)\n",
    "ax[1,1].plot('Y', data=df2)\n",
    "ax[1,2].plot('Z', data=df2)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "横軸と縦軸を共有するには次のようにする。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,3,figsize=(8,3),sharex=True,sharey=True)\n",
    "ax[0,0].plot('X', data=df2)\n",
    "ax[0,1].plot('Y', data=df2)\n",
    "ax[0,2].plot('Z', data=df2)\n",
    "ax[1,0].plot('X', data=df2)\n",
    "ax[1,1].plot('Y', data=df2)\n",
    "ax[1,2].plot('Z', data=df2)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`ax`のメソッド（例えば、軸のタイトル）はそれぞれ設定することができる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "次に、キャンバス全体のタイトルを設定したいとしよう。キャンバスは`fig`に割り当てられているので、そのメソッド`suptitle`を使い表示することができる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 3, figsize=(8,3),\n",
    "                       sharex=True, sharey=True,\n",
    "                       constrained_layout=True)\n",
    "ax[0,0].plot('X', data=df2)\n",
    "ax[0,0].set_title('ax[0,0]')\n",
    "\n",
    "ax[0,1].plot('Y', data=df2)\n",
    "ax[0,1].set_title('ax[0,1]')\n",
    "\n",
    "ax[0,2].plot('Z', data=df2)\n",
    "ax[0,2].set_title('ax[0,2]')\n",
    "\n",
    "ax[1,0].plot('X', data=df2)\n",
    "ax[1,0].set_title('ax[1,0]')\n",
    "\n",
    "ax[1,1].plot('Y', data=df2)\n",
    "ax[1,1].set_title('ax[1,1]')\n",
    "\n",
    "ax[1,2].plot('Z', data=df2)\n",
    "ax[1,2].set_title('ax[1,2]')\n",
    "\n",
    "fig.suptitle(\"Grand Title\", fontsize=20)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### ２軸グラフ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "複数のデータを表示する際、右の縦軸を使いデータを表示したい場合がある。例を使って説明することにする。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xY30gI7/YRVqmhW8f6sEH93Z2iaQBMPfPMP4KjeWVQe3p7H7FCz0uXUOtP5Nyv2mMOQO8D5wm52Yg3hizuUAHsnVFEakErAKeNsYkiMg84E3AWP/9AHjo8s3y2FWebWPGmAXAAshpqrI1LqWUa9h6JJYXV/sTGZ/KhF5NeL5/Wyq5UNmO7UfP8eFvRxjaxYtxPRs7O5yikGWM8bnSm9Yv90OAZkAc8KOIjDPGLLH3QDb9VxWRMuQkjaXGmNUAxpjoXO8vBDbksWkE0CjX64YU8NZIKeWa4lIyeGtjMCt9I2heuyIrHu3NdU1rODusf4iKT+XJHw7Sqk4l3im5xQtvA04YY2IBRGQ10Aco/MQhOZ/wV0CwMebDXMvrW59/AAwDAvLYfB/QSkSaAWeAUcAYe4NUSrmmn/2jeGVdIBdTMnji5hZMvcVxRQltlZFl4YmlB0jPzGbeuO5U8HSduyAHOw30EpEKQCpwK7C/IDuy5RPsC4wH/EXkkHXZS8BoEelCTtPTSeBRABHxAr40xgwwxmSJyBTgV6A0sMgYE1iQQJVSriMmMY3X1gXyc8BZOnhV4duHrqODl2OLEtrq3Z+DOXA6jrljutGidiVnh+M0xpg9IrISOABkAQexPh6wV77dcZ1Bu+Mq5ZqMMaz0jeCtjcGkZmbz9G2tmHh9c4cXJbTVT4cjmbrsIA/2bcprgzo4O5wilV933MJUYu/ZlFL2Cb+Qwktr/Nl29BzXNa3OzBHeLv0NPiwmp7xJt8bVePEuLV5YmDRxKKWuymIxLN51ktm/hiLAG0M6MK5nE0o5oSihrZLTc4oXli1TmrljtXhhYdPEoZS6orCYRKat8sf31EVuaF2bd4Z1pGF15xQltJUxhpfW+BMWm8R3D/WkflUtXljYNHEopf4lM9vCgq3H+eS3o1QoW5oP7unM8G4NikU31iW7T7HuUCTP3dGafq1qOTsct6SJQyn1DwFn4nlhpR9BUQnc3ak+MwZ3oHblss4OyyaHwuN4Y0MQt7Stw+M3lZzihY6miUMpBeQUJfzk96Ms2HqcGhU9+WJcd/p3rOfssGx2MTmDJ5YeoG6Vcnx4b2eXfgZT3GniUEqx98QFpq/y4/i5ZO71acjLA9pTtUIZZ4dlM4vF8PTynOKFKx/r7TK1sdyVJg6lSrCk9Cxm/RzCd7tP0bB6eZY83LNYPheY80cYfx+J5e1hHfFuWM3Z4bg9TRxKlVB/hsbw8mp/ohLSeLBvU567ow0VXagooa22Honl49+PMLxrA8b0cMvihS6n+J0lSqlrcjE5gzc3BLH64Bla1qnEysl96N4kr1kRXF9kXCpP/XCQ1nUq8/awElu80OE0cShVQhhj2OR/ltfWBxCXksmTt7TkiVtaUtbDtYoS2iojy8LjSw+QmW2YN64b5T2L599RHGniUKoEiE5I45W1AWwOiqZTg6osfqgn7b2qODusa/L2xiAOhcfx+dhuNHfh0ifuSBOHUm7MGMOK/eG8tTGYjCwLL97Vlof7NcPDRYsS2mr94Ui+3XWKh/s1Y4ALzGNe0mjiUMpNnT6fwotr/NgRdp4ezWowc3gnt/hmfjQ6kemr/PBpUp3pd7V1djglkiYOpdxMtsXwzc6TvP9rKKVLCW8N7ciYHo3dYkBccnoWjy09QAXP0nw2ppvLlnN3d7bMANgIWAzUAyzAAmPMJyLyHjAIyACOAQ8aY+Ly2P4kkAhkk8+cuEqpa3M0OpEXVvlx8HQcN7epzdvDOuFVzT2K/BljmL7an+OxSSx5uCf1qpZzdkjFioi0AZbnWtQceNUY87Hd+8pvIicRqQ/UN8YcEJHKgC8wlJz5w/+wzvI3C8AYMy2P7U8CPsaYc7YGpRM5KWWfjCwLX/x9jDl/HKVSWQ9eG9SBIV283Kp76rc7T/La+kCev7MNT9ysdaguZ89ETiJSmpzpvHsaY07Ze6x87zis84pHWX9PFJFgoIExZnOu1XYDI+09eGFb5RtB7xY13eYbllK2OBwex7RVfoScTWRQZy9eG9SeWpWKR1FCWx04fZG3NgZxa9s6PHZjC2eH4w5uBY4VJGmAnc84RKQp0BXYc9lbD/HPW6DcDLBZRAww3xiT5xy3IjIJmATg6Wl/nZmLyRm8/lMgnh6lmDeuO9c1rWH3PpQqTlIzsvn4tyMs3Hac2pXLsnCCD7e3r+vssArdheQMpiw9QL2q5fjw3i5u8aymiHiIyP5crxdc6XoLjAKWFfRANs85LiKVgL+Bt40xq3MtfxnwAYabPHYmIl7GmEgRqQNsAaYaY7Ze7VgFbaoKi0lk4mJfIi6m8PrgjozpqeUHlHvaffw801f5cfJ8CqN7NGL6Xe2oWr74FCW0VbbF8MDXe9lz4gKrH+tDxwZVnR2Sy7K1qUpEPIFIoIMxJrogx7KpS4KIlAFWAUsvSxr3AwOBsXklDQBjTKT13xhgDdCjIIHaomWdyqx9oi99WtTipTX+/HetPxlZlqI6nFIOl5iWyctr/Bm1YDcWA98/0pN3h3u7ZdIA+PT3o2w7eo7XB3fQpFF47gIOFDRpgG29qgT4Cgg2xnyYa3l/YBpwozEm5QrbVgRKWZ+NVATuAN4oaLC2qFq+DIseuI7Zv4Yw/+/jHIlO4vOx3dyuzVeVPH+ERPPymgCiE9J4pF8znr2jNRU83bdH/V+hMXz6x1FGdGvIqOsaOTscdzKaa2imAtt6VfUDtgH+5HTHBXgJ+BQoC5y3LtttjJksIl7Al8aYASLSnJy7DMhJUt8bY97OL6jC6lW17tAZXljpR61KZZk/vrt+Y1HF0vmkdN7YEMS6Q5G0rluJWSO86dq4eBYltFXExRQGztlOvSrlWPN4X61DZQNbmqpEpAIQDjQ3xsQX+Fi2PuNwpMLsjusfEc+k7/ZzMSWD2SM7M7izV6HsV6miZozhJ78oZqwPJDEtk8dvaskTN7fE08O9B72lZ2Vz7xe7OB6bzPqp/WhWy6YepiWePd1xr/lY7p44AGIT03l8qS/7Tl7ksZta8NwdbSitPTOUCzsbn8Z/1/rzW3AMnRtWZdZIb9rWK95FCW31ytoAvtt9ii/GdaN/R61DZStNHEUwADAjy8KMnwL5fs9pbm5Tm09Gd6VKOfd8oKiKL2MMP+wL552NwWRaLPzn9jY81K9Zifmis+7QGZ764RATr2/Gy3e3d3Y4xYomjiIcOb5k9ylmrA+kcc0KLJzgQws3KPqm3MPJc8m8uNqfXcfP06t5DWYO96ZpCWqmORKdyJDPdtCxQRW+n9hL61DZSRNHEZcc2XP8PI8vPUBGloVPR3fl5rZ1iuxYSuUn22JYtP0EH2wJpUypUrx0dztGXdfIrcqF5CcpPYvBn20nITWLjU/2o24VrUNlL00cDqhVdSYulUmL9xMUlcDzd7bhsRtblKj/UZVrCD2byAsrD3M4Ip7b2tXhraGdSlzxPmMMU5Yd5Gf/KJY+0oveLWo6O6RiSROHg4ocpmZk88IqP346HMlA7/q8N7KzdvtTDpGRZWHun2F8/lcYlcuVYcbgDgzyrl8iv7x8veMEr/8UxLT+bXnsJq1DVVCOTBzuO3rIBuU9S/PpqC60r1+F2b+GcOJcMvPHd6dh9QrODk25sUPhcbyw8jBHopMY0sWL1wZ1oEZF++uzuQPfUxd5e2Mwt7Wry+Qbmzs7HGWjEn3HkdufITE8+cNBypQuxbyx3ejZXG+XVeFKycjiw81HWLTjBHUql+PtYR25tZ37FSW01fmkdAbO2U6Z0qX4aWo/ty2b4ijaVOWk+TiOxSYxcfF+Tp9P4bXBHRjfq4nDY1DuaWfYOaav9uf0hRTG9mzMtLvaluju4NkWw/2L9rL3pBYvLCyaOJw4kVNCWiZPLTvIn6GxjO7RmNcHd3D7kbqq6MSnZvLupmB+2BdO05oVmDnCm156N8uHm0P59I8wZo3oxH3XaRXrwqCJw8kzAGZbDB9sDuXzv47h06Q688Z1p3ZlLZKo7LMlKJr/rvUnNjGdidc35+nbWmvnC3KahR/8Zh/3dG/Ie/d0dnY4bkMTh4tMHfvT4UieX3mY6hU8WTDeh04N9XZa5e9cUjoz1geywS+KtvUqM3ukN94Nqzk7LJcQfiGneKFXtfKsebwP5cpoIi0smjhcJHEABJyJ59HvfDmXlM7skd4M6dLA2SEpF2WMYd2hSF7/KZCk9Cym3tKKyTe20KZOq/SsbO75YhcnYpP5aWq/EjUq3hE0cbhQ4oCcb5CPLz3A3hMXePSG5rzQv22JqR2kbBMZl8rLa/z5MzSWLo2qMXukN63rVnZ2WC7l5TX+LN1zmvnju3Nnh3rODsftaOJwscQBkJlt4Y2fgvhu9ylubF2bT0d1pWqFktsrRuWwWAxL955m1s8hZFsMz93Zhgf6NNUvFpdZczCCZ5Yf5tEbmvPigHbODsctaeJwwcRxyfd7TvPa+gAaVq/AwgndaVlHv1WWVCfOJTNtlR97T1ygb8uavDvMm8Y1dfDo5ULPJjJk7na8G1bj+0d64qHFC4uEIxNHvv8FRaSRiPwpIsEiEigiT1mX1xCRLSJy1PpvnlOSiUh/EQkVkTARmV7Yf4CjjenZmO8n9iIxLZOhc3fyW1CBp+1VxVRWtoUv/j5G/4+3EhyVwOwR3ix5uKcmjTwkpmXy2BJfKpUtw2eju2rScDIRqSYiK0UkxHpN712g/dgwdWx9oL4x5oCIVAZ8gaHAA8AFY8xMa0KoboyZdtm2pYEjwO1ABLAPGG2MCbraMV35juOSyLhUHv3Ol4DIeJ67ow2P36RFEkuCoMgEpq3yw/9MPLe3r8tbQztqJdcrMMbwxPcH+DUwmu8f6anVGIqYjVPHfgtsM8Z8KSKeQAVjTJy9x8o3/RtjoowxB6y/JwLBQANgCPCtdbVvyUkml+sBhBljjhtjMoAfrNsVe17VyvPj5N4M7uzFe7+GMuX7g6RkZDk7LFVE0rOy+WBzKIM/205UfCpzx3RjwfjumjSuYtGOk2zyP8sLd7bRpOECRKQKcAPwFYAxJqMgSQPsLHIoIk2BrsAeoK4xJsoaQJSI5DWpRQNyJka/JALoeYV9TwImAXh6Fo+Cb+XKlObj+7rQwasKM38O4VhsEgsn+NCohjZZuBPfUxeZtsqPsJgkhnVtwKsD21O9hBYltNX+kxd4d1Mwd7Svy6QbtHihg3iIyP5crxcYYxbket0ciAW+FpHO5LQePWWMsbt5x+YGRxGpBKwCnjbGJNi6WR7L8mwbM8YsMMb4GGN8PDyKT9FeEWHSDS1Y9MB1nIlLZfBn29l17Lyzw1KFIDk9i9d/CmTkFztJSc/i6wev46P7umjSyMe5pHSe+P4ADaqX5717OmsTruNkXbqGWn8WXPa+B9ANmGeM6QokAwV67mxT4hCRMuQkjaXGmNXWxdHW5x+XnoPE5LFpBNAo1+uGQGRBAnV1N7Wpw7on+lKjoifjvtrDtztP4oo91pRtth2N5c6Pt/L1jpOM79WEzc/eyM1tdKbI/GRbDE/9cJC4lEzmje2uFW9dSwQQYYzZY329kpxEYjdbelUJOW1iwcaYD3O9tR643/r7/cC6PDbfB7QSkWbWBzGjrNu5pea1K7H2ib7c3KY2r60PZPoqf9Kzsp0dlrJDfEomL6w8zPiv9lKmdClWPNqbN4Z0pFLZ4nMX7EwfbTnCjrDzvDm0I+29qjg7HJWLMeYsEC4ibayLbgWu2lHpSmzpVdUP2Ab4Axbr4pfIec6xAmgMnAbuMcZcEBEv4EtjzADr9gOAj4HSwCJjzNv5BVUcelVdjcVi+Oi3I8z5I4xujavxxfju1KmsD1Fd3S8BZ3llXQAXkjOYdENznrq1ldZSssPvwdE8/O1+7vNpxKyR3s4Op8SxsVdVF+BLwBM4DjxojLlo97FcsTmluCeOSzb6RfHcj4epWr4M88d3p3Ojas4OSeUhNjGnKOFG/yja1a/C7BHeWtDSTuEXUrj70200rF6B1Vq80Cl05LibJA7I6fc/cfF+YpPSmTm8E8O7NXR2SMrKGMPqA2d4Y0MQqRnZPHVbKybd0JwyOkjNLmmZ2Yz8Yienzqewcer1OhDSSTRxuFHiALiQnMHjS33ZffwCE69vxrT+bXUErZNFXEzhpTUBbD0SS/cm1Zk1wpuWdSo5O6xi6cXV/izbe5qFE3y4vX3JnQrX2RyZOPSJnwPUqOjJdw/35K0NQSzcdoKQs4nMGd2VahW0W6ejWSyGJXtOMevnEAwwY1B7JvRuSiktSlggq3wjWLb3NJNvbKFJowTROw4HW77vNP9dG4BXtfIsnOCjpbcd6FhsEtNX+bHv5EWub1WLd4Z10sGa1yDkbAJD5+6gS6NqLHlYixc6mzZVuXHiAPA9dYFHvztAakYWH93XhTt0boIilZltYeG243z821HKeZTilYHtGdm9oQ5MuwYJaZkM+WwHyelZbHiyn/YadAGaONw8cQBExecUSfSLiOeZ21oz9ZaW2lxSBALOxDNtlR+BkQn071CPN4Z20IvcNTLG8NiSA2wJjmbZxF70aFbD2SEp9BlHiVC/anlWPNqbl1b789FvRwiOSuCDeztTUQeaFYq0zGw+/f0o87cep3oFT+aN7cZdneo7Oyy38NX2E/wSeJaXB7TTpFFC6R2Hkxlj+Gr7Cd7ZFEzrupVZMN5HuzNeo/0nL/DCKj+OxyYzsntD/nt3O+2IUEj2nbzAqAW7ub1dXeaN66bNfS5Em6pKUOK4ZNvRWKZ8fxARmDumG31b1nJ2SMVOUnoW7/0SwuLdp/CqWp53h3fihta1nR2W24hNTOfuT7dRwbM066f2o0o5rUPlSjRxlMDEAXDyXDITF+/n+Llk/nt3Ox7o01S/0dno7yOxvLTan8j4VO7v3ZTn72yjzX6FKCvbwriv9nAoPI41j/elXX2tQ+VqNHGU0MQBOd+an1l+iC1B0Yzs3pC3hnbU8g1XEZeSwZsbgll1IILmtSsye4Q3Pk213b2wzfolhHl/HeP9ezozsrtWP3BFmjhKcOKAnEFqH/9+lE9/P0qXRtWYrzPN5WmTfxSvrgvgYkomk29sztRbtChhUdgSFM3ExfsZ3aMR7w7X4oWuShNHCU8cl/wSEMWzKw5TqawH88d3p2vj6s4OySXEJKTx6rpAfgk8SwevKswe6U0HLy1KWBROn0/h7jnbaFKzAisna/FCV6aJQxPH/4SczSmSGB2fztvDOnKPT6P8N3JTxhh+9I3grQ1BpGVZeOa21ky8vpmOWC4iaZnZjJi3k/ALKWx88nodZe/iNHFo4viHi8kZPPH9AXYeO8+DfZvy8oB2Je5iGX4hhZfW+LPt6Dmua1qdmSO8aVFbixIWpemr/PhhXzhf3e/Dre20DpWr0wGA6h+qV/Rk8UM9eGdTCIt2nOBIdCKfje5WIua+zrYYFu86yXu/hiLAm0M6MLZnEx1lX8R+3B/OD/vCefymFpo01L/YMgPgImAgEGOM6Whdthy4NP1gNSDOGNMlj21PAolANtaJ1G0JSu84ruzH/eG8vCaAulXLsnCCD23ruW+3yLCYRF5Y6ceB03Hc2Lo2bw/rSMPq2lxS1IIiExj2+Q66Na7Odw/3KHF3t8WVjTMAnqQA1+R/7ceGxHEDkAQsvpQ4Lnv/AyDeGPPGFYL0McacsycoTRxXd+D0RSZ/50tSehYf3tuZ/h3dq5RGZraF+X8f49Pfw6hQtjSvDmzPsK4NdEyLAySkZTJ4znZSM7PZMPV6alcu6+yQlI3sSBx2X5Mvl+9XCWPMVuDCFYIQ4F5g2bUEoezTrXF1fpraj9Z1KzN5yQE+2nIEi8X1nlUVhH9EPIPmbOf9zUe4vUNdtjxzI8O7aSVbRzDG8NyKw0RcTGXumG6aNNQVXes96PVAtDHm6BXeN8BmEfEVkUlX25GITBKR/SKyPysr6xrDcn91q5Tjh0m9GNGtIZ/8fpTJS3LuQIqrtMxsZv4cwtDPd3A+OYP547vrxcvBFm47zuagaKbf1VYHURZPHpeuodafvK65Nl+Tr8amXlUi0hTYcHlTlYjMA8KMMR9cYTsvY0ykiNQBtgBTrXcwV6VNVbYzxvD1jpO8vSmYFrUrsnCCD01qOqRjRaHZc/w801f7c+JcMvf5NOKlAe2oWkHrIDnSnuPnGfPlHu7sUJe5Y7R4YXFkY1NVga7JlyvwHYeIeADDgeVXWscYE2n9NwZYA/Qo6PFU3kSEh/o1Y/FDPYhJTGfwZzvYdjTW2WHZJDEtk1fWBnDfgt1kZltY8nBPZo301qThYDEJaUxZdpAmNSowa4S3Jg03VljX5GtpqroNCDHGROT1pohUFJHKl34H7gACruF46ir6tqzF+if6Ua9KOe5ftJcvtx3HFcfoXPJnSAx3frSVJXtO8VDfZmx+5gb6tdKKwI6WlW1hyrKDJKZl8vm4blTWirduqzCvyfkmDhFZBuwC2ohIhIg8bH1rFJc9FBcRLxHZZH1ZF9guIoeBvcBGY8wvBQlS2aZxzQqsfrwPd7Svx1sbg/nPj4dJy8x2dlj/cCE5g2eWH+LBb/ZRsawHKyf34dVB7angqUOKnOG9zaHsPXGBd4Z1cuuu3QooxGuyjhx3QxaL4bM/w/hwyxE6N6zK/PE+1Kvq3CKJxhg2+kfx2rpA4lMzefymFjxxS0vKemjtI2fZHHiWSd/5MqZnY94Z1snZ4ahrpCVHNHEUil8Dz/Ls8kNUKOvBF+O6072Jc4okRiek8d+1AWwJiqZTg6rMHumt8zk42anzyQycs52mNSvy4+TeWrzQDWji0MRRaI5EJzJx8X6i4tJ4a2hH7r3OcUUSjTGs2B/OWxuDyciy8OztrXm4nxYldLa0zGyGfb6TyLhUNkztp8UL3YQmDk0chSouJYOpyw6y7eg5HujTlJfvbkeZIr54nz6fwvTVfuw8dp4ezWowa4Q3zWoVr27C7uqFlYdZsT+CRQ/4cEtbrUPlLrTIoSpU1Sp48vUD1zHz5xC+3H6C0LOJzB3bjRpFUCQx22L4escJPth8hNKlhLeGdmRMj8ZalNBFrNgXzor9EUy5uaUmDVVgesdRwqzyjeDFNf7UqVyWBeN9aO9VeM8ajkTnFCU8FB7HLW3r8NbQjnhVK19o+1fXJjAynuGf78SnaXUWP9ST0prM3Yo2VWniKFKHw+OY9N1+ElKz+ODezgzodG1FEjOyLMz76xif/XmUSmU9mDG4A4M7e+lAMhcSn5rJoDnbyciysPHJftSspKVc3I0mDk0cRS4mIY3JS3w5cDqOqbe05JnbWheoOelweBzTVvkRcjaRQZ29mDGovV6UXIwxhknf+fJnSAzLH+3ttN51qmjpMw5V5OpUKceySb14dW0gc/4IIzgqgY/u62LzyOHUjGw++u0IX247Tu3KOXOD3N5e28xd0fytx9kSFM2rA9tr0lCFQu84SjhjDIt3neKNDUE0q5VTJDG/3k+7jp3nxdV+nDyfwugejXhxQDuqaKkKl7Tr2HnGfrmbuzrV57PRXbX50I1pU5UmDofbeewcTyw9QLbFMGdMN25sXftf6ySkZTLz5xC+33OaxjUqMHN4J/q01PpSriomIY0Bn26nSnkP1k/pR6Wy2sDgzjRxaOJwivALKUxcvJ8j0YlMv6stE69v/r9vqL8HR/PymgBiEtN4uF8znr29DeU9dbSxq8rMtjB24R78z8SzbkpfWtet7OyQVBHTxKGJw2lSMrJ47sfDbPI/y9AuXjx/Zxtm/RLK+sORtKlbmVkjvenSqJqzw1T5eGdTMAu2Hufj+7owtGsDZ4ejHEAThyYOpzImp0jipUF8gmHKLa14/KaWeHpouRBX90vAWSYv8WVcr8a8NVSLF5YUjkwcehVQ/3I2IY1Dp+OAnCRSqVwZ+rWspUmjGDhxLpnnfzxM54ZVeWVge2eHo9yUXgnU/1gshu/3nOaOD7ey49g5/nt3O35+6gaqlS/D6IW7Wbb3tLNDVFeRmpHNY0t8KV1amDu2m5asV0XGlomcFolIjIgE5Fo2Q0TOiMgh68+AK2zbX0RCRSRMRKYXZuCqcJ08l8yYL3fz0hp/Ojaoyq9P38Aj1zenTb3KrHuiH71b1OLF1f68sjaAzGyLs8NVlzHG8Mq6AEKjE/novi40rK4Vb1XeRKS0iBwUkQ0F3Yct/fO+AT4DFl+2/CNjzPtXCw6YC9wORAD7RGS9MSaogLGqIpCVbWGRtSihZ+lSzBzeifuua/SP/v5VK5Th6weuY/YvIczfepzQ6ETmje2mI8RdyPJ94az0jeDJW1txc5s6zg5HubangGCgwIXq8r3jMMZsBS4UYN89gDBjzHFjTAbwAzCkAPtRRSTkbAIj5u3knU0hXN+qFluevZFRPRrnOUisdCnhxQHt+Pi+LhwOj2PwZzsIjIx3QtTqcgFn4nl1fSDXt6rFU7e2cnY4yoWJSEPgbuDLa9nPtTzjmCIiftamrLzqGDQAwnO9jrAuy5OITBKR/SKyPysr6xrCUvlJz8rmwy1HGPjpdiIupjJndFcWTrBtetmhXRvw4+TeWIxhxLyd/HQ40gERqyuJT8nksaW+1KzoySejumrF25LN49I11PozKY91PgZeAK6pvbmgiWMe0ALoAkQBH+SxTl5n8BX7/hpjFhhjfIwxPh4eOsK1qBw8fZFBc7bz6e9HGehdny3P3sggOyvZejesxropfenoVZWpyw4y+5cQsi2u163b3Vkshv/8eIiz8WlFNr+KKlayLl1DrT8Lcr8pIgOBGGOM77UeqEBXaGNMdK5gFgJ5PWSJAHLPU9oQ0K+nTpKSkcUHm4+waMcJ6lUpd82zv9WpXI7vJ/bitfUBfP7XMULOJvLxqC5as8qB5v19jN+CY5gxqD3dGmvxQpWvvsBga2emckAVEVlijBln745sGgAoIk2BDcaYjtbX9Y0xUdbfnwF6GmNGXbaNB3AEuBU4A+wDxhhjAvM7ng4ALFw7ws4xfbUf4RdSGduzMdPvamtzFdz8GGNYsuc0r68PpHHNCiyc4EOL2pUKZd/qynYeO8e4L/dwt7cXn47qosULlV0DAEXkJuA5Y8zAghzLlu64y4BdQBsRiRCRh4HZIuIvIn7AzcAz1nW9RGQTgDEmC5gC/ErOE/wVtiQNVXjiUzOZvsqPsV/uobQIP0zqxdvDOhVa0gAQEcb3asKSR3oSl5LJ0Lk7+DM0ptD2r/7tbHwaTy47SLNaFZk5vJMmDeVwWnLETW0OPMt/1wZwLimdiTc055nbWlOuTNEOCIu4mMKkxb4En03ghTvbMvnG5npRK2SZ2RZGL9hNUFQC657oSystXqistFaVJo4CO5eUzoz1gWzwi6JtvcrMHumNd8NqDjt+SkYWz6/0Y6NfFIM6ezF7hLdW0S1Eb20I4svtJ/hkVBeGdNHiher/6QyAym7GGNYeOsPrPwWRkp7Nf25vzaM3tnB4fakKnh58Nror7etX4f3NoRyPTWLBBB8aVCvv0Djc0c/+UXy5/QQTejfRpKGcSu843EBkXCovr/Hnz9BYujauxuwR3i7RhPFHSDRPLTuEp0cp5o3rTo9mNZwdUrF1PDaJwZ/toEWdSqx4tJfWoVL/ok1VmjhsYrEYlu49zcxNwVgMPH9nG+7v09SlBoGFxSQxafF+Tl9IYcbgDozr1cTZIRU7qRnZDPt8B9EJaWx48nq9e1N50sShiSNfx2OTmL7Kn70nL9CvZS3eHd6JRjVcs7BdfGomT/1wkL9CYxnTszEzBnXQEu02Msbwnx8Ps+bgGb55sEeeU/oqBfqMQ11FVraFL7ef4KMtR/D0KMXsEd7c49PQpXsvVS1fhq/uv473N4cy769jHI1O5POx3aldWYsk5mfZ3nBWHzjD07e10qShXIbecRQjQZEJvLDqMAFnErijfV3eHNqRulXyry/lStYdOsO0VX7UqODJggk+dGxQ1dkhuSz/iHhGzNtJrxY1+eaB6yjlQk2QyvVoU5Umjn9Iz8rmsz/CmPfXMapVKMPrgzsyoFM9l77LuJqAM/FMWryf88kZzB7prT2E8hCXksHAOduxWAwbnrxe61CpfGni0MTxP76nLjBtlT9hMUkM79aAV+5uT3U3uIicS0rn8SUH2HvyAo/e2JwX7mzrUg/1ncliMTz87T62h53jx8l96NKomrNDUsWAPuNQJKdn8d6voXy76yReVcvzzYPXcZMbTdBTq1JZljzSk9d/CmT+38cJiUrk09FdqVpeiyR+/lcYf4bG8saQDpo0lEvSOw4XtO1oLC+u9ifiYioTejfhhf5tqVTWfXP80j2neG1dII1q5BRJbFmn5BZJ3BF2jvFf7WGgtxefaPFCZQdtqiqhiSM+JZO3Ngbxo28EzWtVZOYI7xIzaG7viQs8tsSXjCwLH4/qwq3tCl7yvbg6G5/G3Z9uo3pFT9Y90ZeKbvxlQRU+TRwlMHH8EnCWV9YFcCE5g0k3NOepW1sVeVFCV3MmLpVHv9tPYGQCz93RhsdvalFivnFnZlsYtWA3wVEJrJ/Sl5Z1nD/yXxUvmjhKUOKISUxjxvpANvmfpX39Kswe6V2iu6imZmQzbZUf6w9Hcrd3fd4b6U0FT/f/5v3GT0Es2nGCOaO7Mqizl7PDUcWQPhwvAYwxrDpwhjc3BJGamc3zd7Zh0g3NKVO6ZI+oLu9Zmk9GdaGDVxVm/hLC8dhkFozv7rKj4gvDRr8oFu04wQN9mmrSUMWC3nE4QcTFFF5aE8DWI7F0b1KdWSO8S/QD4Sv5MzSGJ5cdpEzpUnw+thu9mtd0dkiF7lhsEoPnbKd1vcosn9RbS7GoAnOppioRWQRcmuT80tSx7wGDgAzgGPCgMSYuj21PAolANtaJ1G0Jyl0Th8Vi+G73KWb9EgLAtP5tGd+riY4IvorjsUlMXLyfU+dTeHVQe8b3auI2zz1SMrIYOncH55Iy2DC1H15avFBdg/wSh4iUA7YCZclpbVppjHmtQMeyIXHcACQBi3MljjuAP4wxWSIyC8AYMy2PbU8CPsaYc/YE5Y6J41hsEtNW+rH/1EWub1WLd4a5blFCV5OQlsnTPxzij5AYRl3XiNeHdCj2ZcWNMTy74jBrD51h8UM9uL6V1qFS18aGxCFARWNMkoiUAbYDTxljdtt7rHyfcRhjtopI08uWbc71cjcw0t4DlxSZ2RYWbD3OJ78fpXyZ0rx/T2dGdGvgNt+aHaFKuTIsnODDh1tCmfvnMY7GJDFvXDfqVC5edbpyW7rnNGsOnuHZ21tr0lAOYXLuEpKsL8tYfwr0rKIwGlQfAn6+wnsG2CwiviIy6Wo7EZFJIrJfRPZnZWUVQljOF3AmnqFzd/Der6Hc1q4OW569gZHdXbuSrasqXUp4/s62fDamK0GRCQyeswO/iDhnh1Ugh8PjeOOnIG5qU5spN7d0djjKfXhcuoZaf/51zRWR0iJyCIgBthhj9hTkQDY9HLfecWy41FSVa/nLgA8w3OSxIxHxMsZEikgdYAsw1RizNb/jFfemqrTMbD79/Sjztx6negVP3hragf4d6zs7LLcRGBnPpMW+xCalM2tEJ4Z1bejskGx2MTmneCHAhqn93KLumHIN9jwcF5FqwBpyrskB9h6rwHccInI/OQ/Nx+aVNACMMZHWf2OsQfYo6PGKi30nLzDgk218/tcxhndtwO/P3qhJo5B18KrK+il96dqoGs8sP8w7m4LJtrhe78DLWSyGZ1YcIjYxnc/HdtOkoZzG2pnpL6B/QbYvUOIQkf7ANGCwMSblCutUFJHKl34H7gDszmzFRVJ6Fq+uC+CeL3aRnmVh8UM9eO+ezlStoEX7ikJNa5HECb2bsGDrcR74ei/xKZnODuuqPvszjL9CY3llUHs6a/FC5WAiUtt6p4GIlAduA0IKtC8belUtA24CagHRwGvAi+R06TpvXW23MWayiHgBXxpjBohIc3LuMiDnIfz3xpi3bQmquDVV/X0klpdW+xMZn8r9vZvy/J1ttM6QA/2w9zSvrAugQbXyLJzgQ6u6rleuY9vRWCYs2suQzl58dJ8WL1SFz4ZeVd7At0Bpcm4aVhhj3ijQsXQAYMHFpWTwxoYgVh84Q4vaFZk1whufpiWjKKGr2X/yApOXHCA1I4uPR3Xl9vauUyQxMi6VgXO2U6uSJ2uf6FsiSqgox3OpAYDO4OqJwxjDzwFneXVdAHEpmUy+sQVTbmlZ4ooSupqo+FQe/c4Xv4h4nr29NVNubun0wZUZWRbuW7CLI2cTWT+1Hy1qa4UAVTQ0cbhw4ohJSOOVdQH8GhhNxwZVmDXCmw5eJbcooatJy8zmxdX+rDl4hrs61uP9ezo7tdlwxvpAvtl5krljunG3t3aSUEVHixy6IGMMP/pG8NaGINKyLEzr35aJ1zfDo4QXJXQ15cqU5sN7O9O+fhXe/TmYE+eSWTjBxymj9H86HMk3O0/yYN+mmjSUW9E7DhuEX0jhxdX+bA87R4+mNZg5ohPNtcnB5f19JJap3x+gdClh7phu9GlZy2HHDotJYshn22lTrzI/aPFC5QDaVOUiiSPbYli86ySzfwmllMD0u9oytqcWJSxOTp5L5pHF+zlxLplX7m7H/X2aFnmPpuT0nOKF55Mz2PhkP+pX1eKFquhp4nCBxBEWk8gLK/04cDqOG1vX5p3hnWig1UuLpcS0TJ5ZfpjfgqO516chbw7tWGRFEo0xPPXDITb4RfLdwz3p68C7HFWy6TMOJ8rMtvDFX8eY80cYFcqW5qP7OjO0ixYlLM4qlyvDgvHd+fi3I3z6RxhHY5KYP647daoUfpHE73afYv3hSJ67o7UmDeW29I4jF/+IeJ5feZiQs4nc7V2f1wd3oFalsg6PQxWdTf5R/GfFYaqU92D+eB+6FOII7oOnL3Lv/F1c36o2X07w0SZN5VDaVOXgxJGWmc1Hvx1h4dbj1KpUljeHduTODvUcdnzlWMFRCUxcvJ+YxHTeHdaJEd2vvUjiheQMBn66jVKlhA1T+1GtgtahUo6licOBiWPP8fNMX+3PiXPJ3OfTiJfubkfV8lpfyt1dSM7giaUH2HX8PA/1bcZLA9oWuGt1tsXw4Df72H3sPCsf6413w2qFG6xSNtBnHA6QmJbJrF9CWLL7NI1qlGfpI/ogsySpUdGTxQ/34O2NwSzacYLQ6AQ+G12wirVz/jjK1iOxvD2soyYNVSKUyDuOP0NieHmNP1EJaTzUtxn/uaO11g8qwVbsC+e/awOoV7UcCyf40Kae7UUS/z4SywNf72VYlwZ8cG9n7UShnEabqooocVxIzuDNDUGsOXiGVnUqMWukN90aVy/046jix/fURSYv8SU5PYsP7+1C/475P+M6E5fKwE+3UadyOdY+0ZfynlqrTDmPJo5CThzGGDb4RTFjfSDxqZk8fnNLnri5RZH15VfF09n4NB5d4svh8Dievq0VT97S6oo9ozKyLNw7fxdhMUmsn9JXKwkop9PEUYiJIzohjZfXBPBbcDTeDasya4Q37epXKZR9K/eTlpnNS2v8WX3gDHe0r8uH93WhUh5FEl9bF8C3u07x+dhuDOikdaiU82niKITEYYxh+b5w3t4UTEaWhf/c0ZqH+mpRQpU/YwyLdpzk7Y1BtKxTiYUTfGhS8///f1x/OJInlx3k4X7NeGVgeydGqtT/c2TiyPcqKiKLRCRGRAJyLashIltE5Kj13zwfFIhIfxEJFZEwEZlemIFfzenzKYz9cg/TV/vTvn4Vfn36Bibd0EKThrKJiPBwv2Ysfqgn0QnpDP5sB9uPngPgaHQi01f54dOkOtPvauvkSJWynYg0EpE/RSRYRAJF5KkC78uGqWNvAJKAxcaYjtZls4ELxpiZ1oRQ3Rgz7bLtSgNHgNuBCGAfMNoYE5RfUAW948i2GL7ecYL3N4fiUaoULw5oy+jrGusIXlVgp84nM3HxfsJiknj+zrasOhBBXEoGG6ZeT72qhV+yRKmCsmHq2PpAfWPMARGpDPgCQ225Jv9rX7Y0VYlIU2BDrsQRCtxkjImyBvOXMabNZdv0BmYYY+60vn4RwBjzbn7HK0jiiE/J5P6v93IoPI5b2tbh7WEdtSqpKhRJ6Vk8u/wQm4OiKSWw5JGe9GmhY36Ua7G3qUpE1gGfGWO22Husgg5eqGuMiQKwJo86eazTAAjP9ToC6HmlHYrIJGASgKen/YOwqpT3oEnNCjzYtymDO3tpf3pVaCqV9eCLcd35ZudJqlcso0lDuSoPEdmf6/UCY8yCvFa03gx0BfYU6EAF2chGeV25r3h7Y/0DF0DOHYfdBxPhk1Fd7d1MKZuUKiU81K+Zs8NQ6mqyjDE++a0kIpWAVcDTxpiEghyooE+Lo61NVJfazWLyWCcCaJTrdUMgsoDHU0opdY1EpAw5SWOpMWZ1QfdT0MSxHrjf+vv9wLo81tkHtBKRZiLiCYyybqeUUsrBJKf9/isg2Bjz4bXsy5buuMuAXUAbEYkQkYeBmcDtInKUnF5TM63reonIJgBjTBYwBfgVCAZWGGMCryVYpZRSBdYXGA/cIiKHrD8DCrIjtx0AqJRSJYlLDQBUSimlctPEoZRSyi6aOJRSStlFE4dSSim7uOTDcRGxAKkF3NwDyCrEcAqLxmUfjcs+Gpd93DGu8sYYh9wMuGTiuBYist+W0ZOOpnHZR+Oyj8ZlH43r2mhTlVJKKbto4lBKKWUXd0wceVaDdAEal300LvtoXPbRuK6B2z3jUEopVbTc8Y5DKaVUEdLEoZRSyi7FJnGISH8RCRWRMOs855e/LyLyqfV9PxHpZuu2RRzXWGs8fiKyU0Q653rvpIj4W6tU7r982yKO6yYRic9VJfNVW7ct4riezxVTgIhki0gN63tF+XktEpEYEQm4wvvOOr/yi8tZ51d+cTnr/MovLmedX41E5E8RCRaRQBF5Ko91nHKOFYgxxuV/gNLAMaA54AkcBtpfts4A4GdyZh7sBeyxddsijqsPUN36+12X4rK+PgnUctLndRM588jbvW1RxnXZ+oOAP4r687Lu+wagGxBwhfcdfn7ZGJfDzy8b43L4+WVLXE48v+oD3ay/VwaOuMI1rKA/xeWOowcQZow5bozJAH4Ahly2zhBgscmxG6gmObMT2rJtkcVljNlpjLlofbmbnJkQi9q1/M1O/bwuMxpYVkjHvipjzFbgwlVWccb5lW9cTjq/bPm8rsSpn9dlHHl+RRljDlh/TyRnjqIGl63mlHOsIIpL4mgAhOd6HcG/P/QrrWPLtkUZV24Pk/ON4hIDbBYRXxGZVEgx2RNXbxE5LCI/i0gHO7ctyrgQkQpAf3KmubykqD4vWzjj/LKXo84vWzn6/LKZM88vEWkKdAX2XPZWcTjHgJy6KMWB5LHs8n7EV1rHlm0LyuZ9i8jN5PyP3S/X4r7GmEgRqQNsEZEQ6zcmR8R1AGhijEmSnFnA1gKtbNy2KOO6ZBCwwxiT+9tjUX1etnDG+WUzB59ftnDG+WUPp5xfIlKJnGT1tDEm4fK389jEZc6x3IrLHUcE0CjX64ZApI3r2LJtUcaFiHgDXwJDjDHnLy03xkRa/40B1pBzS+qQuIwxCcaYJOvvm4AyIlLLlm2LMq5cRnFZM0IRfl62cMb5ZRMnnF/5ctL5ZQ+Hn18iUoacpLHUGLM6j1Vc9hz7F2c+YLH1h5w7o+NAM/7/4VCHy9a5m38+WNpr67ZFHFdjIAzoc9nyikDlXL/vBPo7MK56/P8A0B7Aaetn59TPy7peVXLaqSs64vPKdYymXPlhr8PPLxvjcvj5ZWNcDj+/bInLWeeX9W9fDHx8lXWcdo7Z+1MsmqqMMVkiMgX4lZweBouMMYEiMtn6/hfAJnJ6JYQBKcCDV9vWgXG9CtQEPhcRgCyTU/2yLrDGuswD+N4Y84sD4xoJPCYiWeSUsB9lcs5SZ39eAMOAzcaY3BPPF9nnBSAiy8jpCVRLRCKA14AyueJy+PllY1wOP79sjMvh55eNcYETzi+gLzAe8BeRQ9ZlL5GT+J16jhWElhxRSilll+LyjEMppZSL0MShlFLKLpo4lFJK2UUTh1JKKbto4lBKKWUXTRxKKaXsoolDKaWUXf4PlIzdnJxBLyAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()  # (1)\n",
    "\n",
    "ax2 = ax1.twinx()          # (2)\n",
    "\n",
    "ax1.plot('X', data=df2)    # (3)\n",
    "ax2.plot('Z', data=df2)    # (4)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "コードの説明\n",
    "\n",
    "> 1. キャンバスと軸を作成し、軸を`ax1`に割り当てる。\n",
    "> 2. `ax1`のメソッド`twinx()`を使い、右の縦軸を準備し`ax2`に割り当てる。\n",
    "> 3. `ax1`に`X`をプロットする。\n",
    "> 4. `ax2`に`Z`をプロットする。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "また上で説明した方法で様々な「飾り付け」をすることができる。ただ、凡例については少し追加的なコードが必要となるので、それについても簡単に以下で説明する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nggXPnw4LO3/6qqvOny5Z8vzp667z+rD9+vUjIiKCli1bZri8WrVqdO/enZ49e3L//fdTt25d4uPjiY+P/2sstbNnz1K6dOm/tnn00Ucv2M/x48dZvHgxLVqcGxH99OnTANSpU4c2bdrwyCOP0Lx5c69jN8Y4fvrpJ6Kjo4mPj+fxxx9n+PDhlCpV6uJ3GBEBc+f6LkA/EJGqOMmsFnAGmCUiM1R1i5txBTShqepuYLfn8zER2QiUATYAiPNz5hEgi75fQsOCBQv48ssvWblyZabr3HTTTaxYsYKZM2fSu3dv7r33Xh566CEiIiL+GrE6vSuuuLDaOyUlhWLFirF69eoLlo0ePZply5YxY8YMoqKiWL16NSVKlLjov8uYvCIxMZGXXnqJt956i9KlSzN9+nTuv//+i9/hkCFw+DAMGgQ+upfuR1WApaqaCCAi3wMPAUPcDMq1b01EKgLVgWVpZtcF9mSR5RWYIyIrRCQ2i33HisjPIvJzcnKyz2L2lUOHDvHUU08xfvx4imQx9MOuXbsICwvjiSeeoHv37qxcuZKbb76Zffv2/ZXQkpKSWL9+fZbHK1q0KJUqVWLKlCmA05R4zZo1gHNvrXbt2gwcOJCSJUuyc+dOH/2VxoSu+fPnU61aNd58803atWvH+vXrLy2ZqTqjTv/6KwTH858FUq+hnlf66208UE9ESohIGNAUKHfhbgLLlQerReRK4Eugi6oeTbPoMWBSFpvWUdVdInINMFdENqnqwvQrqWocEAdOK0cfhu4To0ePZu/evXTo0OG8+b179z6vynDdunX06NGDfPnyUbBgQd577z0KFSrEF198wbPPPsuRI0dITk6mS5cuREREZHnMiRMn0qFDBwYNGkRSUhItW7YkMjKSHj16sGXLFlSVhg0bEhkZ6Ze/2ZhQcOTIEV544QXi4uL429/+xvz582nQoMGl71gE3nvPaZQWHAktyzYKqrpRRN4A5gLHgTU4t5RcFfBm+yJSEPgamK2q/0kzvwDwB1BDVRO82E9/4LiqDstqvdzQbD+Y2XdljGP69Om0b9+eP//8k65duzJgwADCwsIubadJSfD889CjB1So4JtAfSCnzfZF5FUgQVXf9WNY2QpolaPnHtlYYGPaZObRCNiUWTITkSs8DUkQkSuAe3GKvcYY4zf79u3j8ccf54EHHqBEiRIsXbqUoUOHXnoyA9i4ET75BDK5Jx7MPDVliEh5oDlZ164FRKCrHOsArYF1IrLaM6+Pqs4EWpLuCxGR64ExqtoUuBaY6mkGWwD4VFVnBSpwY0zeoqpMmjSJZ599lqNHjzJgwAB69epFoUKFfHeQW2917puVLOm7fQbOlyJSAkgCOqnqIbcDCnQrx0U4zyxktKxNBvN24dxsRFV/A3x2g0dVrfPdbIR6LzLGZGbnzp106NCBGTNmULt2bcaOHZvtfeoc2bLFeYD6kUdyazJDVeu6HUN6Qd821B8KFy7MgQMH7IKdhdTx0AoXLux2KMYETEpKCu+//z4RERHMnz+ft956ix9//NG3yQycJvodOzrN9I3P5Mm+HJOSkkhISODUqVMuRZU7FC5cmLJly1LQxT7ljAmULVu20K5dO77//nsaNmxIXFwcN9xwg38OlpQEv/ziPEQdhHJrX455MqEZY0yq5ORkhg8fTr9+/bjssst48803adu2rX9uScyfD7VqQQYdIAST3JrQ8mSVozHGAKxdu5Y77riDHj160LhxYzZs2EB0dLR/ktnevdCsGfTs6ft9GyAPj1htjMm7Tp8+zeDBg3nttde4+uqrmTx5Mi1atPBvQ7FrroGvvoKoKP8dI4+zKkdjTJ6ydOlSoqOj2bBhA61bt+att97yb/+lKSlO0/zKlf13DB+zKkdjjAliJ06c4Pnnn+fOO+/k2LFjzJw5k/Hjx/u/M+4RI6BaNechauNXVuVojAl58+bNo127dvz+++907NiR1157jaJFiwbm4K1aOa0ab7klMMfLw6yEZowJWYcPHyYmJoZGjRpRoEABvv/+e0aNGhWYZHb0qNOLfqlSTl+N1pGD31lCM8aEpP/973+Eh4fz0Ucf0bNnT9asWUO9evUCc/CkJGjaFNq0CczxDGBVjsaYELNnzx46d+7MlClTiIyMZPr06dSoUSOwQRQoAM2bQ5kygT1uHmetHI0xIUFVmTBhAl26dOH48eO89NJLvPDCC4Hv6SYlJTeMOJ0la+VojDEu2bFjB82aNePJJ5/k5ptvZvXq1fTt2zfwyWzzZqhaFVauDOxxDWAJzRiTi6WkpPDuu+8SERHBwoULGTFiBD/88IN7g9KeOQNFizoNQUzAWZWjMSZX2rx5MzExMSxatIh77rmHuLg4Klas6HZYTsvGXN6i0aocjTEmAJKTk3n99deJjIwkPj6eDz/8kNmzZ7ubzOLiYMAA5/5ZLk9muVlAE5qIlBOR+SKyUUTWi8hznvn9ReQPEVnteTXNZPsmIrJZRLaKSK9Axm6Mcd/q1aupXbs2vXv3plmzZmzYsIE2bdq4P1jv8uWwdKm7MQSYiDzvuY7Hi8gkEXF98MSAVjmKSGmgtKquFJEiwArg/4BHgOOqOiyLbfMDvwD3AAnAcuAxVd2Q1TGtytGY3O/UqVO88sorvPHGG5QsWZJRo0bxr3/9y+2wzlGF06chRAbEza7KUUTKAIuAcFU9KSKfAzNV9aNAxZiRgJbQVHW3qq70fD4GbAS8fVCjFrBVVX9T1TPAZ8CD/onUGBMsFi9eTPXq1Xn11Vd54okn2LBhQ3Aks5QU6NMH/vjDqWYMkWSWAwWAy0WkABAG7HI5HvfuoYlIRaA6sMwz6xkRWSsi40Tk6gw2KQPsTDOdQCbJUERiReRnEfk5OTnZl2EbYwLk+PHjPPvss9x1110kJiYya9YsPvroI4oXL+52aI6NG2HkSJg50+1I/KFA6jXU84pNu1BV/wCGATuA3cARVZ3jRqBpuZLQRORK4Eugi6oeBd4D/gZE4Xw5b2a0WQbzMqwvVdU4Va2pqjULFLDOUIzJbebMmUPVqlV555136NSpE/Hx8TRu3NjtsM4XEeEktZgYtyPxh+TUa6jnFZd2oafQ8SBQCbgeuEJEnnAj0LQCntBEpCBOMpuoqv8FUNU9qnpWVVOAD3CqF9NLAMqlmS5LEBRxjTG+c/DgQZ566ikaN25M4cKFWbhwISNHjqRIkSJuh3ZOQgJMm+Z8Lls2r7ZqbAT8rqr7VDUJ+C9wp8sxBbyVowBjgY2q+p8080unWe0hID6DzZcDlUWkkogUAloCX/kzXmNM4Hz55ZeEh4fzySef0KdPH1avXs1dd93ldlgXGjwYWreG/fvdjsRNO4DbRSTMc11viNMmwlWBbuV4F/ADsA5I8czuAzyGU92owDbgaVXdLSLXA2NUtaln+6bAcCA/ME5VB2d3TGvlaExw+/PPP3nmmWf48ssvqV69OuPGjSMqKsrtsDJ3+jSsXQt//7vbkfiNNw9Wi8gA4FEgGVgFxKjq6UDEl2lM1lOIMcYNqsrHH39M165dSUxMpH///nTr1i3w/S9666ef4NZb80RrRuspxBhjvLRt2zYaN27MU089RUREBGvWrKFXr17Bm8wOHoRGjeC559yOxGTBmgAaYwImJSWFUaNG0bt3b0SEd955hw4dOpAv2IdbKV4cJk2CyEi3IzFZsCpHY0xAbNy4kZiYGBYvXkzjxo15//33qVChgtthZU0Vdu6E8uXdjiSgrMrRGGMykJSUxKuvvkpUVBSbNm3i448/5ptvvgn+ZAYwdixUqeI0AjFBz6ocjTF+s3LlSqKjo1m9ejUtWrRg5MiRXHvttW6H5b1mzWD7dmfQThP0rMrRGONzJ0+eZODAgQwdOpRSpUrx7rvv8tBDD7kdlvdOnnRaM+bNh6ZzbZWjldCMMT71ww8/EBMTwy+//ELbtm0ZNmwYV1+dUfesQersWXjoIbjuOvjoI7ejMTlg99CMMT5x7NgxOnXqRL169Thz5gxz585l7NixuSuZgVMqq18f7rjD7UhMDlmVozHmkn3zzTc8/fTTJCQk8OyzzzJo0CCuvPJKt8PKOdU8W82YVm6tcrQSmjHmoh04cIAnn3ySpk2bcuWVV/Ljjz8yfPjw3JnMdu6EWrVg9Wq3IzEXyRKaMSbHVJUpU6YQHh7OpEmT6NevH6tWreKO3FxNd/AgnDoFYWFuR2IuklU5GmNyZNeuXXTq1In//e9/1KhRg7FjxxIZKj1opKRAsPdaEgBW5WiMCWmqytixYwkPD2fWrFkMGTKEpUuX5v5kNnkyvP66JbMQYP96xphs/fbbb9xzzz3ExMQQGRnJmjVr6NGjByExIvzcufD1105zfZOrWZWjMSZTZ8+eZeTIkfTt25f8+fMzZMgQYmNjg78z4ZxQhePHIZhGxXaZVTl6QUTKich8EdkoIutF5DnP/KEisklE1orIVBEplsn220RknYisFpGfAxm7MXnNhg0buOuuu3j++edp0KAB69evp3379qGRzFSdasY//3Sa6VsyyxERudlzHU59HRWRLm7HFegzMxnopqpVgNuBTiISDswFqqrqrcAvQO8s9nG3qkapak3/h2tM3nPmzBleeeUVoqKi2LJlCxMmTODrr7+mXLlybofmO7/8AgMHwqefuh1JrqSqmz3X4SigBpAITHU3qgB3faWqu4Hdns/HRGQjUEZV56RZbSnwcCDjytTx45Abn6cx5iItX76c6Oho1q1bR8uWLXn77be55ppr3A7L926+Gdatg0qV3I4kFDQEflXV7W4H4lrdgYhUBKoDy9Itagt8k8lmCswRkRUiEpvFvmNF5GcR+Tk5OfniApw6FW68ETZsuLjtjclFEhMTeeGFF7j99ts5cOAA06ZNY9KkSaGXzPbvh1mznM9/+5u1asxcgdRrqOeV6fUWaAlMClRgWVLVgL+AK4EVQPN08/viFFslk+2u97xfA6wB6mV3rLCwML0oGzaotmqleurUxW1vTC6xYMECvfHGGxXQdu3a6aFDh9wOyX86d1YtXFh19263IwlqwAn17lpeCNgPXOvN+v5+BfzniYgUBL4EJqrqf9PM/zdwP9DK84VeQFV3ed734iS+Wn4LtEoVmDABLrvM6T1gyBA4c8ZvhzMm0I4ePUqHDh1o0KABKSkpzJs3j7i4OIoVK+Z2aP7zxhvwzTdOT/rGF+4DVqrqHrcDgRzcQ/NUET4B3AQUTr9cVR/xYh8CjAU2qup/0sxvAvQE6qtqYibbXgHkU+fe2xXAvcBAb+O/JNOnQ69eTj9vDRoE5JDG+NOMGTNo3749u3btomvXrgwcOJArrsh1rbS9Fx8PlSvD5Zfb/2HfeoxLrG4UkSLAg2SeW17wel+ZFIbSH7AG8D2w03PQtcBVQEUgAdiqqv/wYj93AT8A64AUz+w+wAjgMuCAZ95SVW0vItcDY1S1qYjcwLlWNAWAT1V1cHbH9NlzaOvXQ0SE8/nECQjl//wmZO3bt48uXbrw6aefEhERwdixY6ldu7bbYfnXkSPO/bIHHoBx49yOJlfw5jk0EQnDyQk3qOqRizzO34AfgTDgCmAfUBznGn8IOKKqN3i9Py8T2nc4gbcFkoCaqrpSRO7Eyc5Pq+qsHP4tAeHzB6vXroVGjZzqyHvv9d1+jfEjVWXy5Ml07tyZI0eO0KdPH/r06UOhQoXcDi0w/vtfqFbNKaWZbAXqwWoR+QqncWIL4ARQE6d9xKPAa8DDqrrc2/15ew8tCviUc6WqwgCquhgYALzu7QFzvWuvdaosqlVzOxJjvPLHH3/w4IMP8thjj1GpUiVWrFhB//7980Yy27vXeW/e3JJZcKoFjAZOe6YLqepZVf0UeBN4Oyc78zahKXDG01hjL1AhzbKdQN45U669Fj7/HEqXdnobGD7cqdIwJsioKh988AHh4eF8++23DBs2jCVLllAtr/wY++wzp6px1Sq3IzGZKwwcVdUU4CBwfZpl8UCOer72NqFtAP7m+bwEeF5EKotIBeAF4NecHDRkrF0LPXrAJ5+4HYkx59m6dSsNGzYkNjaW2267jbVr19KtWzfy58/vdmiBc9dd8NRTULWq25GYzP3CuQLSKqC9iBT2tIaPBnblZGfe3kNrDVRQ1UEiUgWYw7lMegKnnnNOpjtwkd87J46PdxqLiEBiog0OaFx19uxZhg8fTr9+/ShYsCDDhg0jJiYGp4FxHpGUBAUKOP8nzUUJ4D20rji9RXUTkduB2cDlOLe3CgBtVHWC1/vzJqFlEMSVwB2eAy/1PBcWlALW2/7+/U6z/h49oEMH/x/PmHTi4+Np27Yty5cv55///CfvvfceZcqUcTuswFKFli2haFGIi7OkdpHc6m1fRMoBTXByy3eqGp+T7b2qchSRJ0WkROq0qh5X1bmq+hWQLCJP5uSgISksDOrWhb//3e1ITB5z5swZ+vfvz2233cbvv//OpEmTmDZtWt5LZuAktFtucRqAWDILeiJSz1NAAkBVd6rqB6o6AtgmIvVytD8vqxzPAneo6k8ZLKsB/KSqQVk579p4aJ98AvXrQ/nygT+2yTN++ukn2rZty/r163n88cd5++23KVmypNthmVwugFWOPs0t3jYKyeqnTgngqLcHzBMOHYLnnoPB2T73bcxFOXHiBN26deOOO+7g8OHDTJ8+nYkTJ+bdZLZvH9x9t9NQy+QmWeWWK3GGpfFapl1ficiDON2RpOonIvvSrVYYqAt4/eBbnnD11bBsGVzvaTdz8qTT5Y4xPvDdd9/Rrl07fvvtN9q3b8/rr7/OVVdd5XZY7vrjD9i5E86edTsSkw1PNWKDNLNiPN0fplUYaIbTq5TXsurL8Rog7QMrfwPS9+h5BqfF46CcHDRPSH2IMykJmjSB6tWdZ9aMuUiHDx+mR48ejBkzhhtvvJEFCxZQv359t8MKDlFRsGmT07rRBLvaQGfPZ8XpJST9OF9ngE1Aj5zs2Nt7aPOBDqq6KSc7Dwau3UNLdfYs9OvnPAvz+OPuxWFyta+++ooOHTrw559/0q1bN/r370+YPSLi9Jy/aRN06WKNQHwogPfQfgf+T1XX+GR/F9NsPzdxPaGlN3cuFC8ONWq4HYnJBfbu3cuzzz7L5MmTqVatGuPGjaNmzZpuhxU82raFlSth6VIofEFH7eYiudVs/1J5ndB82cV/IAVVQktJcaoer7wSFi2yX5QmU6rKp59+ynPPPcfRo0fp168fPXv2zBv9L+aEKhw8CCVKZL+u8VogE5pnWLE6ZJ5b3vV6X15WOfq0i/9ACqqEBk5nqadPQ7lyzv21fPkgL3VHZLK1c+dO2rdvz8yZM6lduzZjx44lInXoIuN491145BHIq606/SyAVY7XAt8BVXDup6X+yv8rMfmj2f5bwM/AtZ4DNsV5kvsJ4DhOV//GG9dc4yQzgGefhX/+E5LT3w81eVFKSgrvvfceERERLFiwgLfeeosff/zRkll6v/0GXbvC6NFuR2Iu3ZvAYaAcTm6pjTPOZj9gC06pzWveNgmqBcSQrot/4FMRKYnTxf+dOTmwwal+LFnSWmYZtmzZQkxMDAsXLqRhw4bExcVxww1BWenhvhtugBUr4Oab3Y7EXLr6wHPAbs+0qOoO4FURyQe8CzT2dmfeltB80sW/iJQTkfkislFE1ovIc575xUVkrohs8bxfncn2TURks4hsFZFeXsYevGJj4ZVXnM+bNsHXX7sbjwmIiRMnUrFiRfLly0eFChVo2bIlt956K2vWrGHs2LHMnTvXkllGjh6F7793PkdE2A9Bl4lIMRH5QkQ2ea7pd1zEbooB+zy55SjO42KpFpPTgpKqZvsCfgJaez7PxXn2rDBQEJgIbPFyP6WB2zyfi+AMHRAODAF6eeb3At7IYNv8OMPU3AAUwhnVNDy7Y4aFhWmu0LKl6nXXqR4/7nYkxo8mTJigYWFhinOP4K/Xbbfdpn/88Yfb4QW37t1VCxVS3bnT7UhCHnBCs7+efwzEeD4XAoplt00G+1gLPOL5/CMwKc2yt4DtOdmft41CfNrFf5r9TgPe8bwaqOpuESkNLFDVm9OtewfQX1Ube6Z7A6jqa1kdI+gahWTm5Enn3kBEhNNy68wZuOwyt6MyPlaxYkW2b99+wfzy5ctnON+kceIELFgAzZq5HUnIy65RiIgUxSlU3KDeJJHM9/MaUEpVY0TkPmAaziDSSUB5oKeqDvN6fxcTi6eL//twSmk57uLfs4+KwEKgKrBDVYulWXZIVa9Ot/7DQBNVjfFMtwZqq+ozGew7FogFKFSoUI3Tp0+nXyW4vfcevP++88xaqVJuR2N8KF++fGT0f05ESElJcSGiXGDrVqhQAQoWdDuSPENEznB+t1NxqhqXZnkUEIcz+HMksAJ4TlUvqfQgIjWBh3AKTHNV9ZucbH9RldCquhPnj7konuECvgS6qOpRLwcfzGilDLOx54uPA6eEdrFxuuaGG5ySWvHibkdifOT48eO8+OKLGSYzcEpoJgPHjzujVjRsCOPHux1NXpKsqlk9wV8AuA3orKrLRORtnNtF/S7loKr6M06L+ouSVefEORqHRlUXerOeZ2jtL4GJqvpfz+w9IlI6TZVjRgOGJuA07UxVlhwOz51rNG7svMDpuX/aNPj3v+1B7Fxq7ty5xMbGsm3bNu655x4WLVrEyZMn/1oeFhbGYBuZIWNXXglDh0J4uNuRmPMlAAmquswz/QVOQsuWiOTo15s6rR69Xjmzm3UpwFnPe+rns2nmpZ0+6+UNQAHGA8PTzR/K+Y1ChmSwbQHgN6AS5xqFRGR3zFzTKCQzr7yiWrCg6ubNbkdicujgwYPatm1bBbRy5cq6cOFCVXUahlSoUEFFRCtUqKATJkxwOdIgdfiw2xHkWXjXKOQH4GbP5/7A0Oy20fNzi1cvb/aZ+sr0HpqIpH2aszQwDpgF/BenBHUN8C+cZwTaquq32SVPEbnL8yWs8/xRAH2AZcDnODcBdwAtVPWgiFwPjFHVpp7tmwLDcVo8jlPVbH/W5ppGIZlJSXGeuUkdCfvMGbDuj4Le1KlT6dixI/v27aNHjx689NJLXG5DCHlvxgxo3dq5j2z9ngacNz2FeO6jjcEpYPwGPKWqh7zYd9pWPUVxWrlv5MLccgvQQ1U/8zruzBJaugCmAetU9cUMlg0ColT1fm8PGki5PqGltWABPPWU87ya9R4RlPbs2UPnzp2ZMmUKkZGRjB07lhp2Qc6533+HgQOdLq7sh0DABbDrq4+Ak6raIYNlo4ErVLW1t/vz9sHqhsD3mSz7nvMHazP+cvXVUKXKua6zTNBQVcaPH0+VKlWYNm0agwcPZvny5ZbMciq1pWelSvDhh5bMQl9znJJZRr4EHsjJzrxNaAc5f/TqtB7yLDf+FhkJM2dC0aLOOGtxcdYPZBDYvn079913H//+97+pUqUKa9asoU+fPhS0ZuY517EjdOjgPItp8oKTwF2ZLKsLnMrJzrxttv868I7n2bGvOFfP+SDO82gXPAtm/Gz6dHj6abj+erg/KGt7Q15qZ8K9evVCVRkxYgSdOnUiXz5vfyea86g6tRBgLXrzjveAfiJSggtzy9NAjpr/5mQ8tAdxGnBUx0mEycBq4FVV/V9ODhpIIXUPLb3Fi+FOT1dnSUn24GkAbd68mZiYGBYtWsQ999xDXFwcFStWdDus0KBqCc1lAR4P7TngBZzGh6lDyPyJ09p9eE725fVPSVWdpqq1cXoHKQ0UVtVawZzMQl5qMvv9d7jpJpgzx9148oCkpCRef/11IiMjiY+P58MPP2T27NmWzC7F0aPwwAOwYYMzbcksT1HVt3GeMa6E0xlxJaBsTpMZXERPIer0irwnp9sZPypUyElolSq5HUlIW7VqFdHR0axatYrmzZszatQorrvuOrfDyv1++w1WroQDB9yOxLjEk1e2e14X7aL6csxNQrrKMTPjx0Pz5k4vC+aSnTp1ioEDBzJkyBBKlizJqFGj+Ne//uV2WKHl1CkoXNjtKIxHIKscfcnuXoeaTZucZ9VGjnQ7kpDw448/EhUVxWuvvUbr1q3ZsGGDJTNfWbTIec5M1ZKZ8QlLaKHmllvgxx+he3dnOinJ3XhyqWPHjtG5c2fq1q3LqVOnmD17Nh9++CHFrcNo3/nwQ3jrLUhMdDsSEyKsyjGUHT8O9epB+/bO6NjGK7NnzyY2NpadO3fyzDPP8Oqrr3KlVd/6XkoK7NkDpUu7HYlJJ+SqHEWkoTc7EJHLRGSA70IyPqPqDEVjjUW8cvDgQdq0aUOTJk24/PLL+eGHHxgxYoQlM1+bMMEZRSJfPktmeZCIPCki2V6URKSMiLyUk31nVeU4V0Q+FZFMm3GJyD9xOpXsmpODmgApUgS++ALuuceZ/vpr2BWaI+5cqi+++IIqVaowYcIE+vTpw+rVq6lTp47bYYWeHTsgOtoZEsbkVR8Bqz2DNGelLPByTnacVUJrgdMlySYReVZE/lpXRCqKyFc4w2WvAayn3GB37Bi0aQM9ergdSVDZvXs3//rXv2jRogVlypTh559/ZvDgwRS2Rgr+Ub48LFkCL+Xoh7cJPb8BH4nIRBEp6qudZprQVPVLoAowFhgG/Cwi9UXkZZxht6sAzVT1Ic3JAGzGHUWKOL31jxjhTOfxPiBVlQ8//JDw8HBmzJjB66+/zk8//URUVJTboYWmU6dg6VLn8223WatG8zTQAaeLq9UicocvdpplK0dVPaGq3YAaQDHgO5whtl/DGVzzG18EYQKkalUoUcK5Gf/II+daQuYx27Zto3HjxrRt25aqVauyZs0aevbsSYECOe5nwHhr8GCoWxe2bXM7EhMkVDUOJ7ccAr4XkZfS1gRejGw3FpHSQF+gIrDSM/seoPKlHNi4KCUFKlSAsmXdjiSgzp49y4gRI6hatSpLlixh1KhRfP/999x8881uhxb6evSATz4B6yLMpKGqm4HawFvASziJrfzF7i+rEavzAc8CA3CGh3lOVb8SkZo4PSRHAiOA/qp63KuDiYwD7gf2qmpVz7zJQOoVpRhwWFWjMth2G3AMZ1juZFWt6c0x83SzfW8tX+5UAVWr5nYkfrNx40aio6NZsmQJTZo0YfTo0VSoUMHtsEJfQgJcdx1Y6TdX8XLE6m1cxDVZRFKA21X1p3Tz7wbGA1fiVEf+DixW1fxeB66qGb5wetI/DbwKXJ5umQAdcRJdAvBIZvtJt1094DYgPpPlbwIvZbJsG1DSm+OkfYWFhanJQkqKao0aqpGRqmfPuh2Nz505c0YHDRqkhQoV0uLFi+v48eM1JSXF7bDyhsRE1RtvVH3iCbcjMTkEnNDsr+cXdU0GUoBamSwrBkzxJMkfgbM52XdWP5sOALeqUyRMnwQVeFdEpniS0CTgcy+S50LPmGoXEBEBHgH+kd1+jA+JwFdfOT2e58t3bsTgEBjTa8WKFbRt25a1a9fyyCOPMGLECK699lq3w8o7Lr8c+vaFynZ3wnhHVQ8DLUQkGqcaMkeyauXYMKNklm6dfar6JNAgpwfOQF1gj6puyexwwBwRWSEiWXZ7ISKxIvKziPycnMdb83nl+uudLrMABgxwOjY+c8bdmC7ByZMn6dWrF7Vr12bv3r1MnTqVyZMnWzILpNTurNq0AXueLzcqkHoN9bwyuuZ6fU1O526clvKZUtWxQBTQNgf7zfnwMZkc/Acf7OYxnJJeZuqo6i4RuQbnoe9Nqrowk3jigDhw7qH5ILa8o0QJuPbaXDtY6MKFC4mJiWHLli1ER0czdOhQrk4dBdkExsKFTivar7+Gml7dVjHBx5t7Yl5fk9NS1e+9CUBVf8N5Xs1rQVGvJCIFgObA5MzWUdVdnve9wFSgVmCiy2OefRZGj3aqIv/4A+bNczsirxw9epROnTpRv359kpKSmDt3LmPGjLFk5obSpeGuu5wx+kzICsZrclAkNKARsElVEzJaKCJXiEiR1M/AvUB8AOPLW1JHDO7TBx5+GI4ccTeebMycOZOqVavy3nvv0aVLF+Lj42nUqJHbYeU9qS2mK1d2ulwr6rMOIEyQCdZrckATmohMApYAN4tIgufGH0BL0lU3isj1IjLTM3ktsEhE1gA/ATNUdVag4s6zRo2CmTPhqquc6bNn3Y0nnf3799O6dWuaNWtGkSJF+PHHH3nrrbe44opc10l4aOjdG5577lzDIhPKgvKabMPHGO9MmQLDhsH06XDNNa6GoqpMmTKFZ555hkOHDtG7d2/69u3LZZdd5mpceZoqdOvmNAYZPdrtaMwlyq3Dx9jTjsY7l10GxYqdK625ZNeuXXTs2JFp06ZRo0YNvv32W2699VZXYzI41dT/+Y+VzoyrguUemgl2DzwAs2Y5ie3kSfjyy4AeXlUZO3Ys4eHhzJ49myFDhrB06VJLZm47eRKeeAK2eJ62CYHnF03uZWef8V5qY5F33oEWLSA+MPeAf/vtNxo1akRMTAyRkZGsXbuWHj16WGfCweCXX2DOHOfdGJfZPTSTc8nJMH/+uYFDU1L88ss8tTPhF198kfz58zNkyBBiY2PJZ6WA4HLsmDM8kQkZufUeml0ZTM4VKHAuma1eDbfeChs3+vQQ69evp06dOnTt2pW7776b9evX0759e0tmwWL1avjwQ+ezJTMTJOzqYC5NcrJzQSte3Ce7O3PmDAMHDqR69eps3bqViRMnMn36dMqVK+eT/RsfGTUK+vVz+gA1JkhYlaO5dKrO/TVVp3n/ww9fVBXk8uXLiY6OZt26dbRs2ZIRI0ZQqlQpPwRsLtnZs7BjB1Sq5HYkxg+sytHkXamNRWbNgkcfdXqJyIHExER69OjB7bffzoEDB5g2bRqTJk2yZBaMpk1zSmX581syM0HHSmjGd1Thm2/gvvucJOdFY5EFCxbQrl07tm7dSrt27Rg6dChXufysm8nE7t1OEuvY0XnmzIQsK6EZIwJNmzrve/fCbbdl2rnxkSNHaN++PXfffTcpKSnMmzePuLg4S2bBrHRp+O47Z4ghY4KQJTTjH6dOQeHCTu8i6Xz99ddERETwwQcf0K1bN9atW8c//mHjugatpCSnVSPAnXdaq0YTtCyhGf8oXx6WLIEaNZzpr79m344dPP744/zzn//k6quvZsmSJQwbNoywsDB3YzVZGzoUatU61xuIMUHKulow/uNpLKK//oo++CAfX3YZXyQn079/f3r37k2hQoVcDtB4pUMHKFXKGRbGmCBmCc34VUJCAh26dOFkSgpnwsNZ+dFHVI2IONcy0gSvffucEcyvvhratXM7GmOyZVWOxi9SUlKIi4sjIiKCefPm0fTNN5m/bBlVb7oJGjWCcePcDtFk5fRp59+pdWu3IzHGa4Ee4HOciOwVkfg08/qLyB8istrzaprJtk1EZLOIbBWRXoGL2uTU1q1badiwIU8//TQ1atRg3bp1dO3alfz58zuNRQoVAhuEM7gVKuQ0z3/iCbcjMUFMRPKLyCoR+drtWCDAz6GJSD3gODBeVat65vUHjqvqsCy2yw/8AtwDJADLgcdUdUN2x7Tn0AInOTmZ4cOH069fPwoVKsSbb75JdHQ0kr56MbVnEYAffoBbbnHu0ZjgcOaMk9BMnuXtc2gi0hWoCRRV1fv9H1nWAlpCU9WFwMGL2LQWsFVVf1PVM8BnwIM+Dc5cknXr1nHnnXfSo0cP7rnnHjZs2EBMTMyFyQzOJbOTJ51haGJjAxusydzKlU7jj+XL3Y7EBDkRKQs0A8a4HUuqYLmH9oyIrPVUSV6dwfIywM400wmeeRkSkVgR+VlEfk5OTvZ1rCaN06dP8/LLL3Pbbbexbds2PvvsM6ZNm0aZMpn+85xz+eXw9dfw9tvOdIj3WpMrhIU5JeaKFd2OxLirQOo11PPK6FfncOAFIGiGKQ+GVo7vAa8A6nl/E2ibbp2MmsRlevVT1TggDpwqR9+EadJbtmwZ0dHRrF+/nlatWjF8+HBKliyZs53UrOm8q8LTT0PJkjB4sLWCdMstt8Ds2W5HYdyXrKo1M1soIvcDe1V1hYg0CFhU2XC9hKaqe1T1rKqmAB/gVC+mlwCkHT+kLLArEPGZC504cYKuXbtyxx13cOTIEb7++msmTJiQ82SWVornR56IJTM3vPEGvPCC04u+MdmrAzwgIttwbgH9Q0QmuBtSEJTQRKS0qu72TD4ExGew2nKgsohUAv4AWgKPByhEk8a8efNo164dv//+O+3bt+eNN96gaNGil77j/Pnh/ffPTW/c6HRsfPPNl75vkzVVSEiA/fv9MvK4CT2q2hvoDeApoXVXVdebxAY0oYnIJKABUFJEEoCXgQYiEoVThbgNeNqz7vXAGFVtqqrJIvIMMBvID4xT1fWBjD2vO3z4MD169GDMmDHceOONLFiwgPr16/v2IKklM1WIiYEDB2D9eifZGf8RgZEjncFarXRscjEbPsZka9q0aXTo0IE9e/bQvXt3+vfvz+WXX+7fg27f7vTY//e/n2ssYhdb30pKgmefdaoabWwzk4YNH2NCzt69e2nZsiX/93//R8mSJVm2bBlvvPGG/5MZQIUKTjIDGDUKWrVyHso2vrNpE3z2Gfz0k9uRGOMTrt9DM8FHVZk4cSLPPfccx48f55VXXuGFF15wrzPhxETnmTV72Ne3qlWDrVud/hqNCQFW5WjOs3PnTtq3b8/MmTO5/fbbGTt2LOHh4W6HdW7064MHnZLFnXe6HVHu9csv8PPP8Li1qzIZsypHk6ulpKTw3nvvER4ezoIFCxg+fDiLFi0KjmQG51rf9e4N997rNBgxF+fNN517Z4cOuR2JMT5lJTTDL7/8QkxMDD/88AONGjUiLi6OSsHaSODwYVi2DBo3dqbT9gtpvJOc7AzWWaWK25GYIGUlNJPrJCcnM2TIECIjI1m7di1jx45lzpw5wZvMAIoVO5fMvvsOGjZ0np8y2Zs3D44fhwIFLJmZkGQJLY9as2YNtWvXpmfPnjRp0oQNGzbQtm3bjDsTDlYHD8KxY3DZZW5HEvz27YMHHoCePd2OxBi/sSrHPOb06dMMGjSI119/neLFi/POO+/w8MMP565EllZqY5HkZJg/H+65x+2Igtf8+U7LxkvposzkCVblaILe4sWLqV69OoMGDeLxxx9nw4YNtGjRIvcmMzjXWOS995zGIitWuBtPsElJgc2bnc93323JzIQ0S2h5wPHjx3nuuee46667OHHiBN988w0ff/wxJULp+aOnn4bPP4caNZzpEK958NrIkXDrrbAh27Fwjcn1LKGFuLlz51KtWjVGjBhBx44diY+Pp0mTJm6H5XuFCjmDhQL8+qvznFpqySQve+IJePVVawRi8gRLaCHq0KFDtG3blnvvvZdChQqxcOFC3nnnHYoUKeJ2aP63fz8cOQIFC7odiXuOHHFKqSVKQLdu9miDyRMsoYWgqVOnEh4ezvjx4+nVqxdr1qyhbt26bocVOLVrw7p1cMMNzvTcuXmrCjIpCZo2hdat3Y7EmICyhBZC/vzzT1q0aEHz5s257rrr+Omnn3jttdcoXLiw26EFXuqQM/PnO41FPvnE3XgCqUABePRRp5m+MXmINdsPAarK+PHjef7550lMTOTll1+me/fuFMzLVW6pVGHSJHjkEedCH+o9i5w9a+PHmUtmzfaNK7Zv3859991HmzZtqFKlCqtXr6Z3796WzFKJOJ3wFijgPIT9j3/AwoVuR+UfmzdDRITT8bAxeVBAE5qIjBORvSISn2beUBHZJCJrRWSqiBTLZNttIrJORFaLSJ7/H5uSksI777xDREQEixYtYuTIkfzwww/ccsstbocWvA4ccBqMJCW5HYl/JCc7jUCuvdbtSEyIE5HCIvKTiKwRkfUiMsDtmCDAVY4iUg84DoxX1aqeefcC36lqsoi8AaCqF/TPIyLbgJqqmqOO+0KxynHz5s1ER0fz448/cu+99/L+++9TsWJFt8PKHdJWyf3wg9OAJJTGWQv1KlUTENlVOYrTG8MVqnpcRAoCi4DnVHVpwILMQEBLaKq6EDiYbt4cVU32TC4FygYyptwkKSmJ1157jcjISDZs2MBHH33ErFmzLJnlRGoyS0iARo3gxRfdjccX3n8f+vVzkrUlMxMA6jjumSzoebneICPYRqxuC0zOZJkCc0REgfdVNS6znYhILBALuDfKso+tWrWK6OhoVq1axcMPP8zIkSO57rrr3A4r9ypbFiZPhnr13I7k0q1ZA9u2WTIzvlQg3a2duPTXXBHJD6wAbgRGqeqyQAaYkYC3chSRisDXqVWOaeb3BWoCzTWDoETkelXdJSLXAHOBzp4SX5Zye5XjqVOnGDhwIEOGDKFkyZK8++67NG/e3O2wQktKCjz2mNPMvVUrt6O5OKdP26gDxmdy0srR0+5hKs41OT6b1f0qKFo5isi/gfuBVhklMwBV3eV534vz5dUKXITuWLRoEZGRkbz22ms8+eSTbNy40ZKZPxw/Dnv2OMPR5BYpKc5QMDt3OtOWzIxLVPUwsABwvU891xOaiDQBegIPqGpiJutcISJFUj8D9wKu/hLwp2PHjvHMM89Qt25dTp8+zezZsxk3bhxXX32126GFpqJF4dtv4ZlnnOk1a+DQIXdjys6mTc4IA7NmuR2JyYNEpFRqi3QRuRxoBGxyNSgC38pxEtAAKAnsAV4GegOXAQc8qy1V1fYicj0wRlWbisgNOKUycO77faqqg705Zm6rcpw9ezaxsbHs3LmTzp07M3jwYK688kq3w8o7Tp+GypWd57m++cbtaLK2axeULm33zozPedHK8VbgYyA/TsHoc1UdGKj4MmM9hQSJgwcP8vzzzzN+/HhuueUWxowZQ506ddwOK29avBiKFYPwcLcjudDOnfDTT/Cvf7kdiQlhubWnEEtoLlNVvvzySzp16sTBgwfp2bMnL774Yt7sfzEY9evn3J/q2zc4SkKdOjn9Uv76K5Qq5XY0JkTl1oQWbM3285Tdu3fTqVMnpk6dym233cbs2bOJiopyOyyTKiUFtm8PrgYXb70FbdtaMjMmA1ZCc4Gq8tFHH9G1a1dOnjzJgAED6NatGwUK2O+LoKPqPLBcoIBT3ZecDJUqBT6OpUudkafDwgJ/bJPn5NYSmuutHPOa33//nXvvvZe2bdtSrVo11q5dS8+ePS2ZBSsRJ5mBUzJq2DDwfUEePAiNG8Ozzwb2uMbkMnYVDZCzZ88yatQoevfuTb58+Rg1ahTt27cnXz77TZFrvPee0yNHoEcyKF7c6dWkatXs1zUmD7MqxwDYuHEj0dHRLFmyhCZNmvD+++9Tvnx5V2Myl2jyZJgzB95913/32FSde3jWV6cJMKtyNBdISkpi0KBBREVFsXnzZj755BNmzpxpySwU/PorbNni35aP48ZBlSrOg97GmGxZCc1PVqxYQdu2bVm7di2PPPIII0eO5Jprrgl4HMaPkpKc6sfERGdwzerVfbv/PXucEuDLL4NVTZsAshKaAeDkyZP07NmTWrVqsW/fPqZOncrkyZMtmYWi1HtpL74IderA7t2+2W9iolPdeO21MGCAJTNjvGSNQnxo4cKFxMTEsGXLFqKjoxk2bBjFihVzOyzjb336wN//7nRDdalSUqB5c7jmGhg//tL3Z0weYj/9fODo0aN07NiR+vXrk5yczLfffsuYMWMsmeUVJUs6w88ArFzpDENzsT33iziPBtSt67v4jMkjrIR2iWbOnEn79u1JSEjg+eef55VXXuGKK3Jd1bPxlS1bYONG5wHsnFJ1ElqPHr6Py5g8wEpoF2n//v20bt2aZs2aUaRIERYvXsx//vMfS2Z53aOPwvr1TpWhKizzchDfHTugRg1YscK/8RkTwiyh5ZCqMnnyZMLDw/nss8946aWXWLlyJbfffrvboZlgUaiQ8/7RR3D77fDjj9lvc+SIc/+sSBG/hmZMKLMqxxzYtWsXHTp04KuvvqJmzZp8++233HrrrW6HZYLVY485TfvvvDP7datVg1WrgqNHf2NyKSuheUFVGTNmDOHh4cyZM4ehQ4eyZMkSS2Yma4ULQ2ysk6T27IGmTZ0HstP67DMYNMgpnVkyM+aSBDShicg4EdkrIvFp5hUXkbkissXzfnUm2zYRkc0islVEevkzzokTJ1KxYkXy5ctHmTJlqFq1Ku3atSMqKop169bRvXt360zY5Mxvv8G6dXD06Pnz58+H2bOdHv2NySVEpJyIzBeRjSKyXkSeczsmCHBPISJSDzgOjFfVqp55Q4CDqvq6J1Fdrao9022XH/gFuAdIAJYDj6nqhuyOmdOeQiZOnEhsbCyJiYnnzX/qqacYM2aMdSZsLt7p0+f6fVy+HGrWdD6fOAFXXuleXMakk11PISJSGiitqitFpAiwAvg/b67J/hTQq7OqLgTSP6DzIPCx5/PHwP9lsGktYKuq/qaqZ4DPPNv5XN++fS9IZgDfffedJTNzaVKT2bJlUKvWuXtmlsxMLqOqu1V1pefzMWAjUMbdqILjHtq1qrobnC8JyKiPqDLAzjTTCWTx5YlIrIj8LCI/J+fweaAdO3bkaL4xOfb3v8Pw4U41pDHBqUDqNdTzis1sRRGpCFQHvHxGxX9yy42gjO6WZ1pXqqpxQBw4VY45OVD58uXZvn17hvON8Yl8+eC5oLjlYExmklW1ZnYriciVwJdAF1U9mt36/hYMJbQ9nvrY1HrZvRmskwCUSzNdFtjlj2AGDx5MWLph7sPCwhg8eLA/DmeMMbmSiBTESWYTVfW/bscDwZHQvgL+7fn8b2BaBussByqLSCURKQS09Gznc61atSIuLo4KFSogIlSoUIG4uDhatWrlj8MZY0yuIyICjAU2qup/3I4nVaBbOU4CGgAlgT3Ay8D/gM+B8sAOoIWqHhSR64ExqtrUs21TYDiQHxinql4VmYJhxGpjjMlNvGjleBfwA7AOSPHM7qOqMwMRX2ZsgE9jjDHnsQE+jTHGGBdZQjPGGBMSLKEZY4wJCZbQjDHGhISQbxQiIinAyYvcvABwEUMP+53FlTMWV85YXDkTinFdrqq5rsAT8gntUojIz948LR9oFlfOWFw5Y3HljMUVPHJdBjbGGGMyYgnNGGNMSLCElrU4twPIhMWVMxZXzlhcOWNxBQm7h2aMMSYkWAnNGGNMSLCEZowxJiTkyYQmIk1EZLOIbBWRXhksFxEZ4Vm+VkRu83ZbP8fVyhPPWhFZLCKRaZZtE5F1IrJaRH4OcFwNROSI59irReQlb7f1c1w90sQULyJnRaS4Z5k/v69xIrJXROIzWe7W+ZVdXG6dX9nF5db5lV1cbp1f5URkvohsFJH1InLBaLFunWOuU9U89cIZfuZX4AagELAGCE+3TlPgG5yRsm8Hlnm7rZ/juhO42vP5vtS4PNPbgJIufV8NgK8vZlt/xpVu/X8C3/n7+/Lsux5wGxCfyfKAn19exhXw88vLuAJ+fnkTl4vnV2ngNs/nIsAvwXANC4ZXXiyh1QK2qupvqnoG+Ax4MN06DwLj1bEUKCbOaNrebOu3uFR1saoe8kwuxRm5298u5W929ftK5zFgko+OnSVVXQgczGIVN86vbONy6fzy5vvKjKvfVzqBPL92q+pKz+djwEagTLrVXDnH3JYXE1oZYGea6QQuPBkyW8ebbf0ZV1rROL/AUikwR0RWiEisj2LKSVx3iMgaEflGRCJyuK0/40JEwoAmOMPFp/LX9+UNN86vnArU+eWtQJ9fXnPz/BKRikB1YFm6RbnhHPO5Am4H4ALJYF76ZxcyW8ebbS+W1/sWkbtxLjh3pZldR1V3icg1wFwR2eT5hRmIuFYCFVT1uDgji/8PqOzltv6MK9U/gR9VNe2vbX99X95w4/zyWoDPL2+4cX7lhCvnl4hciZNEu6jq0fSLM9gkaM4xf8mLJbQEoFya6bLALi/X8WZbf8aFiNwKjAEeVNUDqfNVdZfnfS8wFadqISBxqepRVT3u+TwTKCgiJb3Z1p9xpdGSdNVBfvy+vOHG+eUVF86vbLl0fuVEwM8vESmIk8wmqup/M1glaM8xv3L7Jl6gXzil0t+ASpy7KRqRbp1mnH9D9Sdvt/VzXOWBrcCd6eZfARRJ83kx0CSAcV3HuYf0awE7PN+dq9+XZ72rcO6DXBGI7yvNMSqSeSOHgJ9fXsYV8PPLy7gCfn55E5db55fnbx8PDM9iHdfOMTdfea7KUVWTReQZYDZOi59xqrpeRNp7lo8GZuK0EtoKJAJPZbVtAON6CSgBvCsiAMnq9KZ9LTDVM68A8KmqzgpgXA8DHUQkGWeonpbq/O9x+/sCeAiYo6on0mzut+8LQEQm4bTMKykiCcDLQME0cQX8/PIyroCfX17GFfDzy8u4wIXzC6gDtAbWichqz7w+OD9IXD3H3GZdXxljjAkJefEemjHGmBBkCc0YY0xIsIRmjDEmJFhCM8YYExIsoRljjAkJltCMMcaEBEtoxhhjQsL/AxspuEZ3WIB+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "ax1.plot('X', 'k-o', data=df2, label='X series')\n",
    "ax2.plot('Z', 'r:', data=df2, label='Z series')\n",
    "\n",
    "ax1.set_title('Title', size=20)    # (1)\n",
    "ax1.set_ylabel('X data', size=15)  # (2)\n",
    "ax2.set_ylabel('Z data', size=15)  # (3)\n",
    "\n",
    "ax1.legend()                       # (4)\n",
    "ax2.legend(loc=(0.015,0.81))        # (5)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "コードの説明\n",
    "\n",
    "> 1. 軸`ax1`にタイトルを設定しているが、`ax2`でも同じ図となる。\n",
    "> 2. 軸`ax1`の縦軸（左）のラベルを設定する。\n",
    "> 3. 軸`ax2`の縦軸（右）のラベルを設定する。\n",
    "> 4. 軸`ax1`に描いた`X`の凡例を設定する。\n",
    ">    * `legend()`には引数がないので`Matplotlib`が自動で凡例の位置を決める。この場合は図の左上に表示されている。\n",
    "> 5. 軸`ax2`に描いた`Z`の凡例を設定する。\n",
    ">    * `legend(loc=(0.015,0.8))`には引数`loc`があり、凡例の位置を指定している。`(0.015,0.8)`の数字は図の原点を`(0,0)`とて左の数字はx軸、右の数字はy軸の位置を指定している。もし引数を設定しないと、`X`の凡例を上書きすることになる。`(0.015,0.8)`のような指定方法だけではなく、他の方法もあるので[参照サイト](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html)を参考にして欲しい。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 日本語"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "２つ方法を紹介するが、`japanize_matplotlib`を使う方法がより簡単であろう。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### `japanize_matplotlib`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "使い方は到って簡単で、`Pandas`と同様にインポートするだけである。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import japanize_matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fix, ax = plt.subplots()\n",
    "ax.plot('X', 'Y', 'r-o', data=df2)\n",
    "ax.set_title('縦横タイトル', size= 30)\n",
    "ax.set_xlabel('横軸', size=20)\n",
    "ax.set_ylabel('縦軸', size=20)\n",
    "ax.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### フォントを指定する"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "2つの方法：\n",
    "1. フォントはインストールせず、PC内にあるフォントを指定する。\n",
    "1. フォントをインストールする方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "方法１の場合、以下で説明に使う変数`jfont`にフォントを指定する。\n",
    "    * Macの場合、例えば`AppleGothic`\n",
    "    * Windowsの場合、例えば`Yu Gothic`\n",
    "    * この方法では一部の日本語が文字化けする場合がある。\n",
    "\n",
    "方法２の場合：\n",
    "* [このサイト](https://ipafont.ipa.go.jp/node193#jp)から次の内の１つをダウンロードする。\n",
    "    * 2書体パック(IPAex明朝(Ver.xxx)、IPAexゴシック(Ver.xxx))\n",
    "    * IPAex明朝 (Ver.xxx)\n",
    "    * IPAexゴシック(Ver.xxx)\n",
    "* [このサイト](https://ipafont.ipa.go.jp/node72#jp)に従ってインストールする。\n",
    "* 次の両方もしくは１つがPCにインストールされる\n",
    "    * IPAexMincho（IPAex明朝）\n",
    "    * IPAexGothic（IPAexゴシック）\n",
    "\n",
    "上の例を使い、設定方法の例を示す。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hidden": true
   },
   "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": [
    "jfont = 'IPAexGothic'    # (1)\n",
    "\n",
    "fix, ax = plt.subplots()\n",
    "ax.plot('X', 'Y', 'ro-', data=df2)\n",
    "ax.set_title('縦横タイトル', size= 30, fontname=jfont)   # (2)\n",
    "ax.set_xlabel('横軸', size=20, fontname=jfont)          # (3)\n",
    "ax.set_ylabel('縦軸', size=20, fontname=jfont)          # (4)\n",
    "ax.legend(prop={'family':jfont, 'size':17})            # (5)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    ">* (1) 使用するフォントを`jfont`に割り当てる。\n",
    ">* (2) 引数`fontname`で`jfont`を指定する。タイトルのフォントが変更される。\n",
    ">* (3) 引数`fontname`で`jfont`を指定する。横軸名のフォントが変更される。\n",
    ">* (4) 引数`fontname`で`jfont`を指定する。縦軸名のフォントが変更される。\n",
    ">* (5) `legend`は他と設定方法が異なる。\n",
    "    * `prop`はフォントのプロパティを設定する引数であり、辞書で指定する。\n",
    "    * キー`family`に値`jfont`を指定する。凡例のフォントが変更される。\n",
    "    * キー`size`に数値を設定してフォントの大きさが変更される。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "この例では個別にフォントを設定したが、一括で全てのフォントを変更する方法もあるが説明は割愛する。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## マクロ経済学の例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 投資関数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "実質利子率`r`によって投資がどのように変化するかを考えてみよう。まず投資関数を次のように仮定する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def investment(y):\n",
    "    return 100/(1+r)**50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* `100`：実質利子率が`0`の場合の投資\n",
    "\n",
    "実質利子率は次のリストで与えられるとする。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_list = np.arange(0.01, 0.11, 0.01)\n",
    "r_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "i_list = []   # 空のリスト\n",
    "\n",
    "for r in r_list:\n",
    "    inv = investment(r)   # 投資の計算\n",
    "    i_list.append(inv)    # リストに追加\n",
    "\n",
    "df_inv = pd.DataFrame({'investment':i_list,     # DataFrameの作成\n",
    "                       'interest_rate':r_list})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "最初の5行を表示する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "hidden": true
   },
   "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>investment</th>\n",
       "      <th>interest_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60.803882</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.152788</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.810708</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.071262</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.720373</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   investment  interest_rate\n",
       "0   60.803882           0.01\n",
       "1   37.152788           0.02\n",
       "2   22.810708           0.03\n",
       "3   14.071262           0.04\n",
       "4    8.720373           0.05"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_inv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "プロットしてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hhSJyeirwKBQYWjiBhsYWbn9aQwtF5PRU4FFqWF46P7k6MLTwvkUaWigip1KBR7HrSgbx0eICfvuqhhaKyKlU4FHMzPjFp8aRm57EbX/T0EIROZEKPMplpQbuWrj1QC0/e1F3LRSR41TgMWDy8L589eLhzF2xk3lr9ngdR0SihAo8RvzH5ecwfmAm3//7GvbU1HsdR0SigAo8RiTGB+5a2NjcwneeXEWzhhaK9Hoq8BhSlJvGT64ey9ItB7ht7jsca2z2OpKIeCiUhxpLFPnMBwZSXevnl/PXU1Fdz303lZDXJ8nrWCLiAR2Bxxgz4ysXD+feGz7A+srDXPPnxWzce8TrWCLiARV4jJpeXMBTX5mMv7mFa+9ZwsKNVV5HEpFupgKPYecNzOKFr5dSmJ3CzIdW8Niy7V5HEpFupAKPcQOyUnjmq1OYNjKXHz5fzp0vrdMIFZFeQgXeA6QnJXDfTSXcPKWIB97cylceXUltgz52L9LThVTgZnadmS01s0Vm9pSZpZrZeDN7w8yWmdmLZpYd6bByZgnxcfzk6rH89OqxvL5+L9f9danuJS7Sw3VY4GaWA3wX+JBz7oPAduDLwFzgNufcJGAe8LNIBpXQzJhSxAMzLmDb/lo+8ec3dRdDkR6swwJ3zh0EpjrnWj+/nQAcA6qdc6uC0+4HropMRDlbl47O55mvTiHejOv+upTX1u31OpKIREBIp1Ccc8fMLNnMfg+kAOVAZbv5fs7woSAzm2VmZWZWVlWloW7d5dz+GTz/9VJG5Kfz5UfLuH/RFpzTxU2RniTUc+ADgeeA+c65WwmUd367+UmA/3TrOudmO+dKnHMleXl5YYgsocrPSObJWZO5YkwB//2P9/jRC+U0Nbd4HUtEwiSUc+DJwEPALOfcPADn3L+BdDMrDi52I4Hz4BJlUnzx3HPD+dx68XAeW7aDmQ+t4PCxRq9jiUgYhHIvlMuAc4FHzax12uvAzcB9ZtYCHABmRCKgdF1cnPH9j45maG4qP3iunE/fu4QHZlzAoJxUr6OJSBdYd54XLSkpcWVlZd22PTnVks37ufWxlfgS4rjvphImDtboT5FoZ2YrnXMlJ0/XB3l6mSkjcvn710pJ9SXw2dnLeGn1bq8jiUgnqcB7oRH56Tz3tSmMK8zkG0+8w5//tVkjVERikAq8l+qbnsRjX7qIT0wYwG9e2cAdT6/G36QRKiKxRA906MWSE+O5+/oJDM1N4+7XNrGruo6/3vgBslJ9XkcTkRDoCLyXMzO+fdk53H39BN7ZcYhP3rOErftrvY4lIiFQgQsA10ws5PEvX0RNfSOfvGcxy7cc8DqSiHRABS5tLijK4bmvTSEnzccXHljOsyt3eR1JRN6HClxOMKRvGs99tZQLinK4/elV/O7VDbToAREiUUkFLqfITE3k4Vsu5PqSQfzx9c3cNvcdjjU2ex1LRE6iUShyWonxcfzy2nEMzUvjl/PWs/tQPbNvKiE3PcnraCISpCNwOSMz49aLh/OXL5zPuj2HuebPi9m094jXsUQkSAUuHZpe3J8nZ02moamFT92zhEWbdF93kWigApeQjB+UxfNfL6UwO4Wb56zgieU7vI4k0uupwCVkhVkpPH3rZD44Mpf/+9wa7nxpnS5uinhIBS5npU9yIvffVMKMyUN44M2tXPybf/Hosu26j4qIB1TgctYS4uP46SeKmTtrEoOyU/nR8+V8+K4FPLtyF80aMy7SbVTg0mmThvXl6VsnM2fmBWQkJ3L706uYfvdC5pfv0e1pRbqBCly6xMy4dFQ+L35jKvfccD4tznHrY29z9Z8W88bGKhW5SASpwCUs4uKMK8f155VvT+O3nxnPwVo/Mx58i+v/uoy3th70Op5Ij6RnYkpENDQ18+SKnfzx9c1UHWngklF53PGRURQXZnodTSTmnOmZmCpwiah6fzMPL93GX974N4fqGrlyXAHfufwcRuT38TqaSMxQgYunDh9r5P5FW3lg0RbqG5v55MSBfPuykQzKSfU6mkjUU4FLVDhY6+feBZt5ZOl2WpzjsxcM5hsfGkG/jGSvo4lELRW4RJXKmmP88fVNPLliJ/Fxxs1Tirj14uFkp+l5nCInU4FLVNpxoI67X9vIc+9WkOZL4EsfHMoXpw6lT3Ki19FEosaZCrzDYYRm9mkze8rMdrSbNt7M3jCzZWb2opllhzuw9A6D+6Zy1/UTeOXb05g6Ipe7X9vEtF//i9kL/637rIh0oMMjcDO7GFgLlDvnCszMgHXAZ51zq8zsa8C5zrlvdrQxHYFLR1bvOsRvX93Iwo1V5PdJ4psfHsn1JYPwJegjC9J7dfkUiplVBgt8FDDHOTclON0HrHfODevoZ6jAJVTLtxzgt69uYMW2agblpPDtD5/DNRMLiY8zr6OJdLtOn0I5jb5AZesL55yf93k0m5nNMrMyMyurqtKDACQ0Fw3ry1NfOfU+K/PW6D4rIq06U+B7gfzWF2aWBPjPtLBzbrZzrsQ5V5KXl9eJzUlvdbr7rHz18cB9VhZs2Kcil17vrAvcOfdvIN3MioOTbgTmhTWVSDut91l59T8u5refGU91nZ+b56zQfVak1zvrc+DB7ycA9wItwAFghnOuuqOfoXPgEg7+phaeXLGDPwTvszJ5WF8+Pn4Al4/pR16fJK/jiYSdxoFLj1Pvb+aRpdt4fPkOdhyswwwuKMph+tgCriguoDArxeuIImGhApceyznH+sojzC+v5JW1layvPALAeQMzuWJsAdOLCxiel+5xSpHOU4FLr7F1fy3zyyuZv7aSVTsPAXBOv/S2I/Mx/TMIfJxBJDaowKVX2n2onlfXBsr8ra0HaXEwKCeF6cEj84mDsonT2HKJcipw6fX2H23gtXV7mb+2ksWb99PY7Mjvk9R2muXCoTkkxusTnxJ9VOAi7Rw+1si/1u9jfnklCzZUUd/YTFZqIped24/pYwuYOjKX5MR4r2OKACpwkTOq9zezcFMV88sree29vRw51kSaL55LR+czvbiAS0blk550xg8bi0TcmQpcf5XS66X44rlibAFXjC3A39TC0i0HmF9eyT/XVfLS6j34EuKYNjKXK8YWcPmYfmSl6p7lEh10BC5yBs0tjpXbq9uGJ1Ycqic+zpg8rC9XFBdwxZh+5OtJQtINdApFpAucc6ypqAkMTyyvZMv+Wszg/MHZbSNa9HxPiRQVuEiYOOfYvO9o21jztbsPAzAsN43iwkzGFWZSXJjJ2MIMMvRkIQkDFbhIhOw8WMf88kre2naQ8ooa9tQca5tX1De1rdTHFWYydkAmmakqdTk7KnCRbrL/aANrKmpYW1HDmooayisOU3Govm3+4JzUtqP04sIMxhVm6sKovC+NQhHpJrnpSVw6Kp9LR7XdNp8DRxtYu/twsNBrWLXrEP9Ys6dt/sDslHalHjhaz0lTqcv7U4GLdIO+6UlMOyePaeccf6hJda3/hFJfU1HDvPK2h11RmJVCcWEGxQMyKR4YKPXcdN0uV45TgYt4JDvNx9SRuUwdmds2raaukbW7A2W+pqKGtbsP88ravW3z+2cmM3ZA8Jz6wAyKCzPJ76OhjL2VClwkimSmJjJlRC5TRhwv9cPHGllbcZjyihrKg+X+P+v30nr5Kr9PUtvpl3GFmQzPT2dAVjJJCboVQE+nAheJchnJiUwe3pfJw/u2TTva0NR2kbT1NMzrG/bRfkxCfp8kCrNTGJidSmFWCgOzj38VZqWS4lPBxzoVuEgMSk9K4KJhfblo2PFSr21o4r09h9l2oI6K6np2VddRcaieVTsPMb98D43NJ44465vmC5T5SSXf+lr3f4l++l9IpIdIS0qgpCiHkqKcU+Y1tzj2HTkWLPZ6Kg4FCn5XdT3r9xzhtff24W9qOWGdzJTEE47Yj5d7CgOzUslISdCDMTymAhfpBeLjjP6ZKfTPTKGk6NT5LS2O/bUNpxR8RXU9W6pqWbRpP3X+5hPW6ZOU0FbogaP31BNeZ6f69LCMCFOBiwhxcUZ+n2Ty+yQzcXD2KfOdc1TXNbadmjn5KH75loMcaWg68WcaZKX6yE5NJCfNR1aqj5xUH9lpgWnZaa2vE8lO9ZGT5iMjOVGlfxZU4CLSITMjJy1QsuMGZp52mZr6xraj9opD9Rys9XOw1s+hukYO1vrZebCO1bsOUV3biL+55bQ/o7X0s1ITT1/2wWk5aYltbwiZKb239FXgIhIWmSmJZKYE7vfyfpxz1PmbOVjrp7rOT3VdI9VtZe/nYJ2f6tpGqutaS9/fYelnpiQGy94XPJoPHNVnpfpIT04gzRdPqi+BtKR40pISSPMlkOqLJz0pgdSkeHzxcTF5Pl8FLiLdyswCJZqUEPIteNuX/qG6Rg7WBcu+1k91beBNIFD8fioO1VNeUcPBOv8pF2bPJCEumMkXT2rw37SkhJNKP/Am0Fr6J74JHF8nzReY3x3PV1WBi0jUO7H0Q1vHOUd9YzO1Dc3U+Zs42tBEnb+Z2oYmahuaqfU3UdfQRG1wWp2/ObhMU9s6FYfq217XNjRR39jc8YaDfAlxJxz5v/D1qWEfe9+lAjez64A7gHhggXPu9rCkEhHpIjMj1Rc4iobw3EOmuaX1TaHptKVfG3xDqGto4qi/ibrgG0VtQxO+hPAfkXe6wM1sCHAncCFwGJhrZtc6554NVzgRkWgSH2ekJyVEzYecuvKWMB141jlX4wI3Ff8rcE1YUomISIe6UuB9gcp2r/cA+ScvZGazzKzMzMqqqqq6sDkREWmvKwW+lxMLuyA47QTOudnOuRLnXEleXt7Js0VEpJO6UuAvA580sz7B17cAL3Q9koiIhKLTZ+Kdc3vM7P8BC83MDyzSBUwRke7TpUupzrnHgcfDlEVERM5C5D8qJCIiEaECFxGJUeac63ipcG3MrArY3m0bjIxcYL/XIaKE9sWJtD9OpP1xXFf3xRDn3CnD+Lq1wHsCMytzzpV4nSMaaF+cSPvjRNofx0VqX+gUiohIjFKBi4jEKBX42ZvtdYAoon1xIu2PE2l/HBeRfaFz4CIiMUpH4CIiMUoFLiISo1Tg7ZjZdWb2lpmtNLPfnWb+bWa2wszeNbM72k2/xMzeNLM3zGyemfXr3uTh19l90W7+TWa2tXvSRl4X/jayzewJM/tX8Osj3Zs8/LqwL24JrrfQzF4xs8LuTR4ZIeyPL5nZy2a2+KTpl5rZ0uC6j5qZ76w37pzTV+A6wBBgA5AJGPAkcG27+aXAUsAX/HoTKCHwOLl3gLzgcl8H7vL69/FiX7SbPxh4EXjD69/F6/0B3A98NPh9KjDY69/Hi30RXH4bkBpc7lvAn73+fSK9P4LLXA6cByxrNy09uD8Kg69/Ddx+ttvXEfhxHT1h6GPAHOec3znnBx4EPuGcawYmO+dan1aRANR3Y+5I6NS+ADCzOAJX3L8H9JQr5J3aH2ZmwAXAB8xsIXAfUNe90cOus38bDcA+jj+cMglY2W2pI6fDJ5M55/5J4LGT7ZUCS5xzFcHXfzl5vVCowI/r6AlDZ5zvnDtmZnFm9gNgGvDzCGeNtE7vC+A7wGvOuXURTdi9Ors/8oBRwNvOuWnAfOBPkY0acZ3aF865YwQegD7bzG4FhgKPRjhrdwjpyWRhXO8EKvDjOnrC0Bnnm1km8Ayw0zl3rXMu1o+yOrUvzGwcgSOSuyKesHt19m+jBjjgnHs5OP0ZAkfksayzfxujCJw2uc459xdgDvBAhLN2h5CeTBbG9U6gAj+uoycMvQDcZGaJZhYPzAD+f3DeI8BPnXOPdFvayOrsvriKwDWBv5vZ80CxmT1vZsndFz0iOrU/nHMNQLmZXRxc7jIC10tiWWf/NoYBaQTOE0Pg/PjI7okcUZ19Mtli4CIz6x98/cUQ1zuR1xcBoukLuIHAf2DLgd8Gpy0ACoLf3wG8DawgeMEBKAYOBJdr/brH69/Fi31xmp+xwOvfw+v9QeCC7svAQuAVYKDXv4sX+4LAtaG7gLUELmwuBiZ6/bt0x/4Ivi6i3UXM4LTLCFwHWELgINB3ttvWJzFFRGKUTqGIiMQoFbiISIxSgYuIxCgVuIhIjFKBi4jEKBW4iEiMUoGLiMSo/wV4+H81Nac0tgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fix, ax = plt.subplots()\n",
    "ax.plot('interest_rate', 'investment', data=df_inv)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "この図に次の「飾り付け」をしてみよう。\n",
    "* タイトル`投資関数`を追加\n",
    "* 横軸ラベル`実質利子率`を追加\n",
    "* 縦軸ラベル`投資`を追加"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 将来価値"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`x`万円を実質年率`r`%の利息を得る金融商品に投資し，`t`年間の将来価値（期首の値）をリストで示す関数は以下で与えられた。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def calculate_futre_value(x, r, t):\n",
    "    \n",
    "    value_list = [x]           # 初期値が入ったリスト\n",
    "    \n",
    "    for year in range(1,t+1):  # 1からtまでの期間\n",
    "        x = x*(1+r)            # 来期のxの値の計算\n",
    "        value_list.append(x)   # リストに追加\n",
    "    \n",
    "    return value_list          # リストを返す"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "これを使い，\n",
    "* `x`=`100`\n",
    "* `t`=`30`\n",
    "\n",
    "の下で実質利子率が次のリストで与えられる値を取る場合の将来価値を図示する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "r_list = [0.01, 0.03, 0.06]   # 実質利子率のリスト\n",
    "dic = {}                      # 空の辞書\n",
    "\n",
    "for r in r_list:\n",
    "    dic['r='+str(r)] = calculate_futre_value(100, r, 30)  # 辞書に追加\n",
    "\n",
    "df_future = pd.DataFrame(dic) # DataFrameの作成"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`dic['r='+str(r)]`の説明：\n",
    "* `str(r)`：`r_list`の要素が割り当てられる`r`は浮動小数点型なので関数`str()`を使って文字列型に変換する。\n",
    "* `'r='+str(r)`：文字列型の`r=`と文字列型の`str(r)`を`+`で結合する。\n",
    "* `dic['r='+str(r)]`：辞書`dic`にキー・値のペアを作成する。\n",
    "    * キー：`'r='+str(r)`（文字列）\n",
    "    * 値：`calculate_futre_value(100, r, 30)`の返り値"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "最初の5行を表示する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "hidden": true
   },
   "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>r=0.01</th>\n",
       "      <th>r=0.03</th>\n",
       "      <th>r=0.06</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>106.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102.010000</td>\n",
       "      <td>106.090000</td>\n",
       "      <td>112.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103.030100</td>\n",
       "      <td>109.272700</td>\n",
       "      <td>119.101600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104.060401</td>\n",
       "      <td>112.550881</td>\n",
       "      <td>126.247696</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       r=0.01      r=0.03      r=0.06\n",
       "0  100.000000  100.000000  100.000000\n",
       "1  101.000000  103.000000  106.000000\n",
       "2  102.010000  106.090000  112.360000\n",
       "3  103.030100  109.272700  119.101600\n",
       "4  104.060401  112.550881  126.247696"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_future.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "最後の5行を表示する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "hidden": true
   },
   "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>r=0.01</th>\n",
       "      <th>r=0.03</th>\n",
       "      <th>r=0.06</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>129.525631</td>\n",
       "      <td>215.659127</td>\n",
       "      <td>454.938296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>130.820888</td>\n",
       "      <td>222.128901</td>\n",
       "      <td>482.234594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>132.129097</td>\n",
       "      <td>228.792768</td>\n",
       "      <td>511.168670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>133.450388</td>\n",
       "      <td>235.656551</td>\n",
       "      <td>541.838790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>134.784892</td>\n",
       "      <td>242.726247</td>\n",
       "      <td>574.349117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        r=0.01      r=0.03      r=0.06\n",
       "26  129.525631  215.659127  454.938296\n",
       "27  130.820888  222.128901  482.234594\n",
       "28  132.129097  228.792768  511.168670\n",
       "29  133.450388  235.656551  541.838790\n",
       "30  134.784892  242.726247  574.349117"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_future.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fix, ax = plt.subplots()\n",
    "ax.plot('r=0.01', data=df_future)\n",
    "ax.plot('r=0.03', data=df_future)\n",
    "ax.plot('r=0.06', data=df_future)\n",
    "ax.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`ax.plot()`が続いているので、`for`ループを使う事を推奨する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fix, ax = plt.subplots()\n",
    "\n",
    "for col in df_future.columns:     # (1)\n",
    "    ax.plot(col, data=df_future)\n",
    "\n",
    "ax.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "（１）の`df_future.columns`について説明すると、`.columns`はデータ・フレーム`df_future`の属性であり列ラベルを返す。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## その他のプロット"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 種類"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "上で説明した図はライン・プロットと呼ばれる。`Matplotlib`にはその他にも様々なは種類の図を描くことができるが，以下ではライン・プロット以外に[散布図](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html)と[ヒストグラム](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html)について説明する。引数に関しては，上で説明した[ライン・プロットの引数](sec:4-other_methods)と共通のものが多いので別に覚える必要はないが，それぞれ独自の引数もあるので基本的なものだけを取り上げることにする。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "説明には次のコードで生成する`DataFrame`を使う。列`X`と`Y`には標準正規分布（平均`0`，標準偏差`1`）から生成した100個のランダム変数が含まれている。`Z`には正規分布（平均`2`，標準偏差`1`）から抽出した100個のランダム変数が格納されている。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df3 = pd.DataFrame({'X':np.random.normal(size=100),\n",
    "                    'Y':np.random.normal(size=100),\n",
    "                    'Z':np.random.normal(loc=2, size=100)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 散布図"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* 基本的には次の構文となる。\n",
    "    ```\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.scatter(＜横軸のデータ＞, ＜縦軸のデータ＞)\n",
    "    ```\n",
    "* `DataFrame`を使う場合は次のように書くことも可能である。\n",
    "    ```\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.scatter(＜横軸の列ラベル＞, ＜縦軸の列ラベル＞, data=＜DataFrame＞)\n",
    "    ```\n",
    "\n",
    "`df1`を使って列`X`と`Y`を使ってプロットしてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter('X', 'Y', data=df3)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**＜基本的な引数＞**\n",
    "\n",
    "様々な引数があり図に「飾り付け」をすることができるが，主な引数を紹介する。[詳細はこのサイト](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html)を参照。\n",
    "* `color`：色（リストにして列の順番で指定する; [参照サイト](https://matplotlib.org/3.1.0/gallery/color/named_colors.html)）\n",
    "    * `r`又は`red`：赤\n",
    "    * `k`又は`black`：黒\n",
    "    * `g`又は`green`：グリーン\n",
    "* `marker`：観測値のマーカー（`o`，`.`，`>`，`^`などがある; [参照サイト](https://matplotlib.org/3.2.2/api/markers_api.html)）\n",
    "* `s`：マーカーの大きさ（`markersize`ではない！）\n",
    "* `fontsize`：横軸・縦軸の数字のフォントサイズの設定\n",
    "* `label`：凡例の表現を指定\n",
    "    * `ax.legend()`が設定されている場合のみ有効\n",
    "\n",
    "実際に引数を指定してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter('X', 'Y', data=df3,\n",
    "            color = 'black',\n",
    "            marker = 'D',\n",
    "            s = 20,\n",
    "            label= 'Xの凡例')\n",
    "ax.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`X`と`Z`の散布図を加えてタイトルなども付け加えてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter('X', 'Y', data=df3,\n",
    "            color = 'black',\n",
    "            marker = 'D',\n",
    "            s = 20,\n",
    "            label= 'Xの凡例')\n",
    "ax.scatter('X', 'Z', data=df3,\n",
    "            color = 'red',\n",
    "            marker = '1',\n",
    "            s = 80,\n",
    "            label= 'Xの凡例')\n",
    "ax.legend()\n",
    "ax.set_title('散布図', size=20)\n",
    "ax.set_xlabel('Xの値', size=15)\n",
    "ax.set_ylabel('YとZの値', size=15)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### ヒストグラム"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "* 基本的には次の構文となる。\n",
    "    ```\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.hist(＜データ＞)\n",
    "    ```\n",
    "* `DataFrame`を使う場合のように書くことも可能である。\n",
    "    ```\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.scatter(＜列ラベル＞, data=＜DataFrame＞)\n",
    "    ```\n",
    "    \n",
    "`df3`の列`X`を使ってプロットしてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.hist('X', data=df3)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**＜基本的な引数＞**\n",
    "\n",
    "様々な引数があり図に「飾り付け」をすることができる。詳しくは[このリンク](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html)を参照することにして，ここでは基本的な引数だけを紹介する。\n",
    "* `bins`：柱の数\n",
    "* `linewidth`又は`lw`：柱の間隔（デフォルトは`1`）\n",
    "* `color`：色（リストにして列の順番で指定する; [参照サイト](https://matplotlib.org/3.1.0/gallery/color/named_colors.html)）\n",
    "    * `r`又は`red`：赤\n",
    "    * `k`又は`black`：黒\n",
    "    * `g`又は`green`：グリーン\n",
    "* `edgecolor`又は`ec`：柱の境界線の色\n",
    "* `alpha`：透明度（`0`から`1.0`; デフォルトは`1`）\n",
    "* `density`：縦軸を相対度数にする（デフォルトは`False`）\n",
    "    * 全ての柱の**面積**の合計が`1`になるように縦軸が調整される。１つの柱の高さが`1`よりも大きくなる場合もある。\n",
    "* `label`：凡例の表現を指定\n",
    "    * `ax.legend()`が設定されている場合のみ有効\n",
    "    \n",
    "上のヒストグラムに引数をしてしてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(df3['X'],\n",
    "        color='green',\n",
    "        bins = 20,\n",
    "        ec='white',\n",
    "        lw=3,\n",
    "        density=True)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "次に複数のデータを並べてプロットする場合を考えよう。方法は簡単で，異なるデータをリストとして指定すれば良い。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.hist([df3['X'],df3['Z']],\n",
    "        bins = 20,\n",
    "        color = ['black','red'],\n",
    "        ec='black',\n",
    "        label=['Xの凡例','Yの凡例'])\n",
    "ax.legend()\n",
    "ax.set_title('ヒストグラム', size=20)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "上のコードに引数`stacked=True`を加えると，柱を重ねて表示される。試してみよう。また`ax.hist()`を２回使うと，２つのヒストグラムを重ねて表示することができる。この場合，引数`alpha`の値を調整すると良いだろう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(df3['X'],\n",
    "        bins = 20,\n",
    "        color = 'black',\n",
    "        ec='black',\n",
    "        alpha=0.5,\n",
    "        label='Xの凡例')\n",
    "ax.hist(df3['Z'],\n",
    "        bins = 20,\n",
    "        color = 'red',\n",
    "        ec='black',\n",
    "        alpha=0.5,\n",
    "        label='Zの凡例')\n",
    "ax.legend()\n",
    "ax.set_title('ヒストグラム', size=20)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "濃い赤の部分が重なっている部分となる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "ヒストグラムは縦軸に度数，横軸に階級を取ったグラフだが，関連する手法にカーネル密度推定と呼ばれるものがある。考え方は簡単で，上のようなヒストグラムのデータに基づき面積が１になるようにスムーズな分布を推定する手法である。残念ながら`Matplotlib`にはカーネル密度推定プロットのメソッドは実装されていない。しかし`DataFrame`のメソッド`plot()`を使えば簡単に描くことが可能である。興味がある読者は[このセクション](sec:5-kernel)を参考にしてはどうだろう。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 縦線・横線"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "図に縦線や横線を追加したい場合がある。その場合は，他の図と同じように「軸」に追加していく事になる。次のような書き方となる。\n",
    "\n",
    "* 縦線の場合\n",
    "    ```\n",
    "    ax.axvline(＜横軸の値＞)\n",
    "    ```\n",
    "    ここで`axvline`の`ax`はAXis，`v`はVertical，`line`はLINEのことを表している。\n",
    "* 横線の場合\n",
    "    ```\n",
    "    ax.axhline(＜縦軸の値＞)\n",
    "    ```\n",
    "    ここで`axhline`の`ax`はAXis，`h`はHorizontal，`line`はLINEのことを表している。\n",
    "\n",
    "ここで`ax`は`.subplots()`で返された「軸」のことである。\n",
    "\n",
    "ヒストグラムを使ってプロットしてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(df3['X'], alpha=0.2)\n",
    "ax.axhline(10)\n",
    "ax.axvline(0)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**＜基本的な引数＞**\n",
    "\n",
    "様々な引数があり図に「飾り付け」をすることができる。詳しくは[このリンク](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axvline.html)と[このリンク](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axhline.html)を参照することにして，ここでは基本的な引数だけを紹介する。\n",
    "* `ymin`：`axvline`の縦軸における最小値（`0`~`1`の値; デフォルト`0`）\n",
    "* `ymax`：`axvline`の縦軸における最大値（`0`~`1`の値; デフォルト`1`）\n",
    "* `xmin`：`axhline`の横軸における最小値（`0`~`1`の値; デフォルト`0`）\n",
    "* `xmax`：`axhline`の横軸における最大値（`0`~`1`の値; デフォルト`1`）\n",
    "* `linestyle`：線のスタイル（リストにして列の順番で指定する;`-``--``-.``:`）\n",
    "* `linewidth` or `lw`：線の幅\n",
    "* `color`：色（リストにして列の順番で指定する; [参照サイト](https://matplotlib.org/3.1.0/gallery/color/named_colors.html)）\n",
    "    * `r`は赤\n",
    "    * `k`は黒\n",
    "    * `g`はグリーン\n",
    "* `alpha`：透明度（`0`から`1.0`; デフォルトは`1`）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L7RWSvirpcxFxV915ymIKZQ62V0v6qKTLDrfyxjSWgmiYySvvjZI6h0N5Sw2/Ap/HOklvlXS3Pf1Z3l9HxLfri4QqsRREI10s6TRJt8z4d70lIm6oL1I5PIkJAEkxhQIASVHgAJAUBQ4ASVHgAJAUBQ4ASVHgAJAUBQ4ASf0/uAJtA2GUAHUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.hist(df3['X'], alpha=0.2)\n",
    "ax.axvline(0,\n",
    "           ymin=0.3,\n",
    "           ymax=0.95,\n",
    "           linestyle=':',\n",
    "           linewidth=5,\n",
    "           color='g',\n",
    "           alpha=0.8)\n",
    "ax.axhline(10,\n",
    "           xmin=0.05,\n",
    "           xmax=0.7,\n",
    "           linestyle='-.',\n",
    "           linewidth=3,\n",
    "           color='k',\n",
    "           alpha=0.5)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### 棒グラフ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "まず次のコードでデータを準備しよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "hidden": true
   },
   "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>country</th>\n",
       "      <th>gdp</th>\n",
       "      <th>con</th>\n",
       "      <th>inv</th>\n",
       "      <th>gov</th>\n",
       "      <th>netex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>110</td>\n",
       "      <td>55</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country  gdp  con  inv  gov  netex\n",
       "0       A  100   50   15   10     25\n",
       "1       B   90   60   10    5     15\n",
       "2       C  110   55   20   30      5"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.DataFrame({'country':['A','B','C'],\n",
    "                    'gdp':[100,90,110],\n",
    "                    'con':[50,60,55],\n",
    "                    'inv':[15,10,20],\n",
    "                    'gov':[10,5,30],\n",
    "                    'netex':[25,15,5]})\n",
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "３国のGDPとその構成要素からなる`DataFrame`である。\n",
    "* `country`：国\n",
    "* `gdp`：GDP\n",
    "* `con`：消費\n",
    "* `inv`：投資\n",
    "* `gov`：政府支出\n",
    "* `netex`：純輸出\n",
    "\n",
    "この`DataFrame`を使って棒グラフの作成方法を説明するが，次の構文となる。\n",
    "    ```\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.scatter(＜横軸の列ラベル＞, ＜縦軸の列ラベル＞, data=＜DataFrame＞)\n",
    "    ```\n",
    "\n",
    "まずA国の`gdp`の棒グラフを表示してみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD3CAYAAAAJxX+sAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAALXUlEQVR4nO3dX4xc91mH8edLTeyCI5o4a0VQXDdBVSsZEtBe2BSqFhXxJxW0KkRBxXFqyFIqxeKKpkhVJSMQVUAKFQVhG9FCK0VVIoqS4ooLlKSiTem6xCFC5U8qglDidOMurYCUBfxysbMwddb27pxdz/rN87nxnrNn5rzSWI+PfztnJ1WFJKmPb5r2AJKkjWXYJakZwy5JzRh2SWrGsEtSM9umPQDAddddV3v37p32GJJ0RTl16tTzVTVz/v4tEfa9e/cyPz8/7TEk6YqS5OnV9rsUI0nNGHZJasawS1Izhl2SmjHsktSMYZekZgy7JDVj2CWpGcMuSc1siTtPJW1te+/+5LRHaOuffuOWDX9Or9glqRnDLknNGHZJasawS1Izhl2SmjHsktSMYZekZgy7JDVj2CWpGcMuSc0YdklqxrBLUjOGXZKaMeyS1Ixhl6RmLhn2JD+V5ONJ/nls301JHknyWJIHk1wz2v+KJA8k+UySzyW5eRNnlyStYi1X7AvAu4GrAJIEuA84UlX7gZPA0dGx9wAPV9X3A3cCH97ogSVJF3fJsFfVI1X1/Niu1wCLVXV6tH0CWPkIkB8fbVNVTwBfS3LjBs4rSbqEST4abxdwZmWjqpaSrDzPtqp6YezYZ4HdwFPnP0mSOWAOYM+ePROMscyP7No8m/GRXZI23yQ/PH2O5VgDkGQ7sDTafGG0veL60fEvUlXHqmq2qmZnZmYmGEOStJp1h72qngJ2Jtk32nWQ5XV2gIeAdwIkeR1wdVV9aSMGlSStzSRLMQB3AMeTnAPOAodG+98HfCTJIaCAw4MnlCSty5rDXlXXj339OHBglWMWgZ/YkMkkSRPxBiVJasawS1Izhl2SmjHsktSMYZekZgy7JDVj2CWpGcMuSc0YdklqxrBLUjOGXZKaMeyS1Ixhl6RmJv21vdLE/NSrzeOnXgm8Ypekdgy7JDVj2CWpGcMuSc0YdklqxrBLUjOGXZKaMeyS1Ixhl6RmDLskNWPYJakZwy5JzRh2SWrGsEtSM4Zdkpox7JLUzKAP2kjyK8Bbgf8CngEOAzcAHwS2AwvA7VW1OGxMSdJaTXzFnuS7gZ8EDlTV64F/Ad4F3Accqar9wEng6EYMKklamyFLMc8D/8n/X/W/DPgasFhVp0f7TgCrflZXkrkk80nmFxYWBowhSRo3cdir6lngd4DfTfJeYBF4EjgzdswSF1juqapjVTVbVbMzMzOTjiFJOs/Ea+xJ3gS8oap+brR9K8tLMbvHjtkOLA0dUpK0dkOWYl7L8g9IV1zF8j8UO5PsG+07yPI6uyTpMhnyrpg/AvYneRL4KvAC8PPAK4DjSc4BZ4FDQ4eUJK3dxGGvqn/nwtE+MOnzSpKG8QYlSWrGsEtSM4Zdkpox7JLUjGGXpGYMuyQ1Y9glqRnDLknNGHZJasawS1Izhl2SmjHsktSMYZekZgy7JDVj2CWpGcMuSc0YdklqxrBLUjOGXZKaMeyS1Ixhl6RmDLskNWPYJakZwy5JzRh2SWrGsEtSM4Zdkpox7JLUjGGXpGa2DXlwku8EPgRcDfw38B7gf4APAtuBBeD2qlocOKckaY0GhR04Brynqp5Ici3wMuBR4LaqOp3k3cBR4K6B55EkrdHESzFJrgd2AIeTPAr8GvDtwGJVnR4ddgK4ZfCUkqQ1G7LGvgf4XuAjVfUG4BngHuDMygFVtcQF/leQZC7JfJL5hYWFAWNIksYNCfu/An9bVX892r4f+GZg98oBSbYDS6s9uKqOVdVsVc3OzMwMGEOSNG5I2P8R2JHktaPtNwOngJ1J9o32HQRODjiHJGmdJv7haVWdS3IH8HtJtgFfBg4DHwWOJzkHnAUObcSgkqS1GfSumKp6AnjTebsfBw4MeV5J0uS8QUmSmjHsktSMYZekZgy7JDVj2CWpGcMuSc0YdklqxrBLUjOGXZKaMeyS1Ixhl6RmDLskNWPYJakZwy5JzRh2SWrGsEtSM4Zdkpox7JLUjGGXpGYMuyQ1Y9glqRnDLknNGHZJasawS1Izhl2SmjHsktSMYZekZgy7JDVj2CWpGcMuSc1sSNiTvC/Jw6Ovb0rySJLHkjyY5JqNOIckaW0Ghz3JLPDq0dcB7gOOVNV+4CRwdOg5JElrNyjsSV4O3AvcPdr1GmCxqk6Ptk8At1zgsXNJ5pPMLywsDBlDkjRm6BX7PcC9VfXl0fYu4MzKN6tqCdi22gOr6lhVzVbV7MzMzMAxJEkrJg57kh8Brqmq+8d2PwfsHjtmO7A0+XiSpPVa9Wp6jd4CzCT5xGh7H/B+YGeSfVX1JHCQ5XV2SdJlMnHYq+qu8e0kD1fV7UluBo4nOQecBQ4NG1GStB5Drti/QVW9cfTn48CBjXpeSdL6eIOSJDVj2CWpGcMuSc0YdklqxrBLUjOGXZKaMeyS1Ixhl6RmDLskNWPYJakZwy5JzRh2SWrGsEtSM4Zdkpox7JLUjGGXpGYMuyQ1Y9glqRnDLknNGHZJasawS1Izhl2SmjHsktSMYZekZgy7JDVj2CWpGcMuSc0YdklqxrBLUjODwp7k1iSfTfLpJB9P8i1JbkrySJLHkjyY5JqNGlaSdGkThz3JtcAvAz9UVT8IPA3cCdwHHKmq/cBJ4OhGDCpJWpuJw15VXwF+oKpeGO3aBnwdWKyq06N9J4Bbho0oSVqPQUsxVfX1JDuS/DbwcuBJ4MzY95dYDv6LJJlLMp9kfmFhYcgYkqQxQ9fYXwn8CfCpqnoXy1HfPfb97cDSao+tqmNVNVtVszMzM0PGkCSNGbLGvgP4MDBXVScBquopYGeSfaPDDrK8zi5JukxWXSZZozcDrwP+OMnKvr8A7gCOJzkHnAUODRlQkrQ+E4e9qh4CvuMC3z4w6fNKkobxBiVJasawS1Izhl2SmjHsktSMYZekZgy7JDVj2CWpGcMuSc0YdklqxrBLUjOGXZKaMeyS1Ixhl6RmDLskNWPYJakZwy5JzRh2SWrGsEtSM4Zdkpox7JLUjGGXpGYMuyQ1Y9glqRnDLknNGHZJasawS1Izhl2SmjHsktSMYZekZgy7JDWzKWFPcmuSv0pyKslvbcY5JEmr2/CwJ3kV8KvADwOzwCuTvH2jzyNJWt1mXLH/KPBAVX21qgr4feCtm3AeSdIqtm3Cc+4CzoxtPwvsPv+gJHPA3Gjz35L83SbMshVdBzw/7SHWIh+Y9gRbwhXzeoGv2chL6TV71Wo7NyPszwGvHtu+frTvG1TVMeDYJpx/S0syX1Wz055Da+PrdeXxNducpZg/A96W5OrR9mHgTzfhPJKkVWz4FXtVPZvk14FHkywBn66qBzb6PJKk1W3GUgxV9THgY5vx3A285JafrnC+Xleel/xrluU3rkiSuvDOU0lqxrBLUjOG/TJK8s4kfz7tOXRpSZaSPJzkL5M8muR7pj2TLi7JriR/OHrdPpvkD5J867TnmgbDfnn9DMs3Y9047UF0SV+pqjdW1euBDwBHpz2QLixJgPuBh0av2wHgb4A7pzvZdGzKu2L0YkluBp4BPsHyX7a7pzmP1mUP8MVpD6GL+j5gafyt1VV17/TGmS6v2C+fXwSOAw8BP5bkqinPo4u7dvRf+i8Bv8Dya6et6wb8x/f/GPbLYHQX7luAu4CPAlfhL0bb6laWYm4AjgCfnPZAuqinge+a9hBbhWG/PH4W+FBV3VZVtwFvZ/kqUFeGLwI7pj2ELurzwLcl+emVHUnekeS9U5xparxB6TJI8gXgbVX19Ni+U8BtVfUP05tMFzL6dRifYfniZzvw/qr61HSn0sUk2QX8JnAjcA74e+CXquo/pjrYFBh2SWrGpRhJasawS1Izhl2SmjHsktSMYZekZgy7JDVj2CWpmf8F6qlJBuGR9bgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.bar('country','gdp', data=df4)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**＜基本的な引数＞**\n",
    "\n",
    "詳しい引数についての説明は[このリンク](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html)を参照することにして，ここでは基本的な引数だけを紹介する。\n",
    "* `width`：棒の幅（デフォルトは`0.8`）\n",
    "* `bottom`：縦軸での棒の基点（デフォルトは`0`）\n",
    "* `color`：色（リストにして列の順番で指定する; [参照サイト](https://matplotlib.org/3.1.0/gallery/color/named_colors.html)）\n",
    "    * `r`又は`red`：赤\n",
    "    * `k`又は`black`：黒\n",
    "    * `g`又は`green`：グリーン\n",
    "* `edgecolor`又は`ec`：柱の境界線の色\n",
    "* `linewidth`又は`lw`：柱の境界線の幅\n",
    "* `alpha`：透明度（`0`から`1.0`; デフォルトは`1`）\n",
    "* `label`：凡例の表現を指定\n",
    "    * `ax.legend()`と一緒に使う。\n",
    "\n",
    "これらの引数を使いプロットしてみよう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.bar('country','gdp', data=df4,\n",
    "       width=0.5,\n",
    "       color='green',\n",
    "       ec='black',\n",
    "       linewidth=5,\n",
    "       alpha=0.5,\n",
    "       label='GDP')\n",
    "ax.legend()\n",
    "ax.tick_params(axis='both', labelsize=15)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "`ax.tick_params()`は縦軸と横軸の文字の大きさを調節している。\n",
    "\n",
    "次に複数の列データを表示する場合を考えてみよう。まず単に軸にデータを追加する場合を考えてみる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "hidden": true
   },
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.bar('country','con', data=df4, label='消費')\n",
    "ax.bar('country','inv', data=df4, label='投資')\n",
    "ax.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "縦軸を確認するとわかるが，まず消費（`con`）の棒がプロットされ，それに投資（`ivn`）の棒が重ねてプロットされている。これは引数`base`（縦軸における棒の基点）がデフォルトの`0`に設定されており，消費も投資も`0`から始まっているためである。この点を踏まえ，棒を積み上げる場合を考えよう。その場合，投資の`base`は消費の高さになる必要がある。また政府支出を積み上げる場合は，積み上がった消費の高さが政府支出の基点になる必要がある。純輸出を積み上げる場合も同様に考える必要がある。この点に注意して棒を積み上げるには次のようなコードとなる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "hidden": true
   },
   "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": [
    "base = df4.iloc[:,2:].cumsum(axis='columns') # (1)\n",
    "base = base - df4.iloc[:,2:]                 # (2)\n",
    "\n",
    "var_list = df4.columns[2:]                   # (3)\n",
    "leg_list = ['消費','投資','政府支出','純輸出']    # (4)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for v, l in zip(var_list, leg_list):\n",
    "    ax.bar('country', v, data=df4,\n",
    "           label=l,\n",
    "           bottom=base.loc[:,v])            # (5)\n",
    "\n",
    "ax.plot('gdp', data=df4,                    # (6)\n",
    "        color='black', \n",
    "        marker='o',\n",
    "        label='GDP')\n",
    "ax.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "(1)〜(2)で棒の基点となる値を`DataFrame`として計算し，それを(5)で使っている。(3)〜(4)行目は`for`ループに使うイタラブルを作成している。また(6)では列`gdp`のライン・プロットを追加している。コードを見るとわかるように，少々複雑である。よりシンプルなコードで同じ図を描きたい場合は，[ここで](sec:5-bar)説明している`DataFrame`のメソッド`plot()`を参考にすると良いだろう。その方法では(1)〜(2)を自動計算するため引数`base`の値を考える必要がない。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "次に，複数の棒（データ）を横に並べたい場合を考えてみよう。主に次の２つの点でコードが少々複雑になってしまう。\n",
    "* 引数`width`を使い，棒の横の位置を調整する必要がある。\n",
    "* 国名`A`，`B`，`C`の位置を調整する必要がある。\n",
    "\n",
    "この２点を考慮すると次のコードとなる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = df4.index                 # (1)\n",
    "wd = 1/5                        # (2)\n",
    "x_ticks = df4.loc[:,'country']  # (3)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "for i, (v, l) in enumerate(zip(var_list, leg_list)):\n",
    "    ax.bar(idx+wd*(i+1),        # (4)\n",
    "           v,\n",
    "           data=df4,\n",
    "           label=l,\n",
    "           width=wd)            # (5)\n",
    "    \n",
    "ax.legend()\n",
    "ax.set_xticks(idx+wd*2.5,       # (6)\n",
    "              x_ticks,          # (7)\n",
    "              fontsize=15)      # (8)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "簡単にコードを説明する。\n",
    "* (1) `df4`の行インデック（番号）を`idx`に割り当てる。これを図の横軸に使う。そのままでは国名が表示されないので，(7)で設定することになる。\n",
    "* (2) 棒の幅を`1/5`として`wd`に割り当てる。\n",
    "* (3) 国名を`x_ticks`に割り当てる。(7)で使うことになる。\n",
    "* (4) 棒の横軸の位置を指定する。ループが実行されるごとに表示される棒は`wd`だけ右にシフトすることになる。\n",
    "* (5) 棒の幅を`wd`にする。\n",
    "* (6) 横軸の目盛の位置を指定する。\n",
    "* (7) 横軸の目盛ラベルを国名`x_ticks`とする。\n",
    "* (8) 目盛ラベルのフォントの大きさを指定する。\n",
    "\n",
    "よりシンプルなコードで同じ図を書きたい場合は，[ここで](sec:5-bar)説明している`DataFrame`のメソッド`plot()`を使う方法を利用しても良いだろう。"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}