{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FeqBw9SmQIng"
   },
   "source": [
    "# matplotlib入門"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PiblMOEKhCop"
   },
   "source": [
    "## 達成目標\n",
    "ひとまず (1) サンプルからの散布図、(2) 関数からの折れ線グラフを作図できるようになろう。それ以外についてはその都度調べながらでOK。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4KJtetXsheB8"
   },
   "source": [
    "## 参考サイト\n",
    "- [matplotlib公式チュートリアル](https://matplotlib.org/stable/tutorials/index.html)\n",
    "- [japanize_matplotlib](https://pypi.org/project/japanize-matplotlib/), グラフ内に日本語を使いたい場合はこれを使うと楽。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bbWlPn1_jTIt"
   },
   "source": [
    "## チュートリアル"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "E2VC9ACxQDxP"
   },
   "outputs": [],
   "source": [
    "# 適当なデータを用意\n",
    "import numpy as np\n",
    "datasets = np.array([[4,7], [8,10], [13,11], [17,14]])\n",
    "x = datasets[:,0]\n",
    "y = datasets[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "Fl6kS2uhQTLc",
    "outputId": "9b093a7c-a12e-4e8d-f85e-ee048abae81a"
   },
   "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": [
    "# サンプルを点で描画\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x, y, color=\"black\", label=\"dataset\")\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "S7YyK4j7Q2Ax",
    "outputId": "83f5b4ad-bc80-478c-9ff9-fa26ffaf0482"
   },
   "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": [
    "# 線グラフ描画するための適当な関数を用意\n",
    "def function(x):\n",
    "  return (x-10)**2\n",
    "\n",
    "x2 = np.linspace(4, 17, 5) #定義域[4,17]で5等分したサンプルを用意\n",
    "y2 = function(x2)           #サンプルにおけるyの値を準備\n",
    "plt.plot(x2, y2, color=\"blue\", linewidth=3, label=\"plot example\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "Zg2FbTzKQlhr",
    "outputId": "f223ad3c-5cd6-4905-921e-6cd27f39a7e0"
   },
   "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": [
    "#サンプル数を増やしてもう一度実行\n",
    "x2 = np.linspace(4, 17, 100)\n",
    "y2 = function(x2)\n",
    "plt.plot(x2, y2, color=\"blue\", linewidth=3, label=\"plot example\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "lhm_iFFRRnqL",
    "outputId": "9278330d-c575-4c98-987d-6ec6ba5e014a"
   },
   "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": [
    "#複数のグラフを1つに描画\n",
    "plt.scatter(x, y, color=\"black\", label=\"dataset\")\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.plot(x2, y2, color=\"blue\", linewidth=3, label=\"plot example\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nfYC2baFFC9f+7TcYOdLfeEQkdo0eDdnZrn3GGXDNNf7GU1FxnfSNCR7bnzgRtm3zLx4RiU27dgVveD5wYGxe5UOcJ31wY25nneXa+/drbF9Eyu/JJ+HXX127WTO47jp/46mMuE/6xsCQIV5fV/siUh67dsHYsV5/4ECoEsOZM4ZDL7srr9TVvohUzBNPeFf5p54KHTr4G09lJUTS19W+iFTEzp0wbpzXHzwYqlb1L55QSIikD0Wv9h9/3N94RCT6jRoVPJZ//fX+xhMKCZP0i7va//5738IRkSi3Y0fwjJ3Bg2N7LD8gDv4KZXfllcHz9lWBU0RKMnIk7N3r2qefDn/5i7/xhEpCJX1jXNnlgMmTYfNm/+IRkei0bVt8XuVDgiV9gHbt4LzzXDsnB4YN8zceEYk+jz7q1dg580y49lo/owmthEv6xsDDD3v955+Hb7/1Lx4RiS6bNwdX0hw2LH6u8iEBkz7AJZcE19t/6CFfwxGRKDJsWHC9/Pbt/Y0n1BIy6UPw1f706bB2rX+xiEh0WL8+eFes4cNjt8ZOSRI26V9wgds8HdxeugULs4lIYho6NHjv20su8TeecEjYpA/BN3H/8Q9Yvty/WETEX6tXu0/9AQVHA+JJ2JK+MaavMWapMWalMWaKMSbZGJNqjJmXf3yhMSYtXOcvi7PPDl5hN2CAf7GIiL8efBCsde327b2d9+JNWJK+MaY+UBs431rbHEgBrgGeA5621p4HjADGl/wukTF0qHdnfsECWLjQ13BExAfLlsFbb3n94cP9iyXcwpL0rbU7rbUDrLXWGFMTOAJYC5xsrX0n/zlzgWbGmORwxFBWp5wCXbt6/f79vd/2IhL/rHU/9wE33+zm5sersI7pG2NmABuAfwM/AzsKPeVHoF4xr8s0xiw3xizfsaPwS0Jv8GBIzv/Vs2wZvP122E8pIlHin//0PuFXqxa8aj8ehTXpW2s7AWlAK+AKiib4BsDOYl6XZa3NsNZmNGjQIJwhApCWBnfc4fX79XPz90UkvuXluZ/3gO7d4YQT/IsnEsI1pn+mMaYrgLU2G/gaN67/hTHmsvzntAHWWGsPhCOG8howAGrVcu0vv4Rp0/yNR0TC79VX4dNPXbt6dbcrVrwL15X+f4Hz8odoFgPpwGTgLuB+Y8wSoD9wd5jOX24NGkCfPl5/8GCv9oaIxJ+cnOAZe//v/8Exx/gWTsQYG+V3LTMyMuzyCE2g//VX99Fu+3bXHzEi+BeBiMSPceOgZ0/XPvJI+OYbqFPH15BCyhizwlqbUfh4Qi/OKqxmzeCVuY88Aj/95F88IhIev/wSfMN2wID4SviHoqRfyG23eTdyfv7ZJX4RiS8jR7r9b8FN5LjrLn/jiSQl/UKSkoJ31Bo3DjZs8C8eEQmtH36A0aO9/sMPw2GH+RdPpCnpF6NDB1dSFYre7BGR2DZoEGRnu/YZZ0CnTv7GE2lK+sUwBkaN8vozZ6oYm0g8WLUquHTyqFHxtUFKWSTYX7fszj8frrvO6/furfIMIrGub1/v5/jyy6FNG3/j8YOS/iE89hhUreraixapPINILJs/3z3AXd2PGOFvPH5R0j+Epk2DyzP06eNtoyYisSM3N3jNTffu0KyZf/H4SUm/FIMHQ+3arr1uHUyY4G88IlJ+U6bAF1+49uGHx39RtUNR0i9F/frBC7aGDtWCLZFY8r//Bc/Au/9+aNjQv3j8pqRfBnfdBccf79q7dyf2VYJIrHnkEQhUaD/2WDcpI5Ep6ZfBYYe5FXwBTz8NX33lXzwiUjbffANjxnj9xx+HGjV8CycqKOmX0bXXwkUXufbBg3Dvvb6GIyJl0LevN/miVSu46SZ/44kGSvplZIy7YjDG9d99F+bM8TUkETmEf/8bZs3y+gV/fhOZkn45nHmmK8gW0KuXpnCKRKODB72yyeBKLbRs6V880URJv5yGDQuewjlunL/xiEhRkybB6tWuffjhbixfnFKTvjHmFWNM6/CHEhsaNHBz9wOGDoVt2/yLR0SC7dwZvO3hgAGJsSNWWZXlSn8scLMxZqkxpqcxpna4g4p2d90FJ53k2r/8Ag884G88IuIZONBNrQY31bpXL3/jiTalJn1r7VJr7e3AlUBrYL0x5lljTGq4g4tWycnw1FNe/4UXYOlS/+IREWflSsjK8vqjR7sNz8VTluGdlsaYLGAO8C5wLPAC8HqYY4tqbdsGV+G8+25X30NE/JGX5z6F5+W5ftu2cNVV/sYUjcoyvDMEeN1ae661drK1dr+1djHwYHhDi34FryJWroTJk/2NRySRTZsGy5a5dlKSm2ShKZpFlWV453Jr7YJijhc5lmjS06FfP6/fv7+376aIRM7u3W4hVkDv3q5KrhSlKZuV1KcPNGni2rt366auiB8GDgyur6MtTkumpF9JNWoE39R97jlYssS/eEQSzaefwsSJXn/MGDc3X4qnpB8CV14J11zj9e+4w60IFJHwys2FHj2Cb94WnGAhRSnph8jYsZCS4tpffKGVuiKRMGkSfPKJax92GIwfr5u3pVHSD5G0tODNVgYNgi1b/ItHJN5t2xY8kaJfPzjxRP/iiRVK+iHUqxeceqpr//prcMEnEQmt++5zK+LBJfv77/c3nlihpB9CycnBN5TeeAPeesu/eETi1T//CS+95PUnTNDK27JS0g+xCy+E7t29/t13w549/sUjEm+ys93N24Cbb4Y2bfyLJ9Yo6YfBiBFw1FGuvWVLcMU/Eamchx6Cb7917Tp13Mp4KTsl/TA48kh48kmvP26cN8NARCrus8/giSe8/qhR0LChb+HEJCX9MLn5ZjdnGNwc4u7dtcuWSGXk5rqd6wKFDVu3hltv9TWkmBS2pG+MucEYs8wYs9gY86oxJsUYc4YxZpEx5kNjzDvGmLrhOr/fjIFnngmeuz9ihL8xicSyp56C5ctd+7DD3Bx9zckvv7AkfWPMkUBf4BJr7Z+ATcBtwMvAPdbaVrgyzUPDcf5o0aSJ214x4OGHYe1a/+IRiVXr1wfX03nwQRVUq6iwJH1r7U/ABdbaffmHqgH7gd3W2s/yjz0LXFHc640xmcaY5caY5TsCVZRiVM+ecM45rp2TA3/7m+rui5RHXp4b1tmXn01OP11z8isjbMM71tr9xpjqxpixQA1gNbCtwNdzcL8MinttlrU2w1qb0aBBg3CFGBFVq7oibElJrr9smUo0iJRHVhYsXOjaVavClCnez5OUXzjH9BsDbwDzrLU9cAn/qAJfPwxIiFubzZoVrbu/bp1/8YjEis2bXfnygD594Oyz/YsnHoRrTL86MBXItNa+C2Ct/QaoaYxplv+0zrhx/YQwYACcdppr79vnZh0EKgOKSFHWuuHQX391/ZNOgsGD/Y0pHhQ7vBICbYBTgBeNd3v9feAWYLIxJg/YBXQN0/mjTnIyTJ3qxvdzc+GDD1xFQNXnESne5Mmu3AK4WTpTpqjUQigYa63fMRxSRkaGXR6YpxUHBg70ZvTUqAGrVsEJJ/gbk0i02bjRfTIOXOXfd59biCVlZ4xZYa3NKHxci7MibODA4GGerl01m0ekoLw8N/wZSPgnn+ymO0toKOlHWGCYp1r+wNrSpbqCESno6afh3/927SpV3M9LjRq+hhRXlPR9cNZZwRuuDBzohnlEEt1XX0Hfvl6/b19o2dK/eOKRkr5P+vWDFi1c+8AB6NwZfvvN35hE/BT4Odi/3/VPOw2GDPE1pLikpO+TatVg2jRvNsKqVZqOJolt2DCvtk5yMkyf7mrsSGgp6fvo5JPh8ce9/ogR3spDkUTy8ccwfLjXHzbMlVuQ0FPS99ndd8Of/+za1rqPt7t3+xuTSCTt2QOdOnmz2C68EO6919+Y4pmSvs8CsxPq1XP9LVvgjjvcLwCRRNCzp6uiCVCrFrzwgquxI+GhpB8FGjVyqw8DXnkFXnzRv3hEIuXll91FT8Azz0B6ul/RJAYl/Shx3XWuzkjAnXfC11/7F49IuG3cGLzBeefO0LGjb+EkDCX9KPLkk97GEHv3wk03aRqnxKcDB9w4/v/+5/rHHedqUUn4KelHkZo13cfd5GTXX7kSHnjA35hEwmHwYLcaHdz4/YwZcMQR/saUKJT0o0zz5jBypNcfMwZmz/YtHJGQmz8fHn3U6w8bBq1a+RdPolHSj0J//ztcdZXX79IFNm3yLx6RUNm61Y3dB7RtG1x2QcJPST8KGQPPPw+NG7v+7t1w441uj12RWHXwoBvHD2x73bChm6VWRVkoovTtjlL16rmpm4FqnB99pM2gJbYNGuRVzzTGjeMfddShX5OIZsyYQXp6OlWqVCE9PZ0ZM2aE9P2V9KPYeefBY495/TFjYNYs38IRqbDZs4PH8QcPhksu8S+eaDVjxgwyMzPZtGkT1lo2bdpEZmZmSBO/ds6Kcta6OfxvveX6tWrBJ5+4/UJFYsGGDa6c+M8/u367djB3roZ1ipOens6mYm7gpaWlsXHjxnK9l3bOilGB8f0mTVx/zx74y1+8XYVEotm+fXD99V7CP/ZYVz1TCb94mzdvLtfxitC3PgbUrQv/+IdXhnntWujeXfV5JLpZ61bcfvqp6yclwWuvQf36/sYVzVJTU8t1vCKU9GNE8+auLknAq6+6Fbwi0erpp92eEQFjxmgXrNIMHz6clJSUoGMpKSkML1h3upKU9GNI166uAmdAnz7w3nv+xSNSksWLoVcvr3/LLcH/d6V4nTp1Iisri7S0NIwxpKWlkZWVRadOnUJ2Dt3IjTE5OdC6NSxb5vp167oNKE44wdewRH733XduK9Dt210/I8P9EggMT0pk6EZunEhOduP7jRq5/u7dcM017gaviN/27nX/HwMJv3794PtR4j8l/Rj0hz/AG294+4euXQt//Svk5fkblyS2vDw3jLNypetXqwavvw4hvAcpIaCkH6POOQeysrz+229Dv37+xSPy8MMuyQdMmAAXXeRfPFI8Jf0Y1qUL3Hef1x8xAqZM8S8eSVwzZ8KQIV7/73+H227zLRw5BCX9GPf448EVOW+/HRYu9C0cSUBLlrhhnYA2bWD0aN/CkVIo6ce4qlXhpZfgjDNc/+BBt2L3q6/8jUviQ2nFv775Bq691qsAe8opbgFWoFCgRB8l/ThQsya8844rVQtuRs/ll8O2bf7GJbGttOJfu3bBFVfAzp3u+Q0awJw5UKeOfzFL6ZT048Sxx7rEH1jMt3EjXHmlm0InUhEDBgwgOzs76Fh2djYDBgxg3z64+mr473/d8erV3WSCQI0oiV5K+nEkI8OVZwgUs1qxAm64wW1CLVJeJRX52rRpCx07envcGuPKLWjLw9gQlqRvjLneGPOqMWZzgWOpxph5xpilxpiFxpi0cJw70V1xhZsqFzB3rptFEYmF1+He/EEiq6QiXzVrTuHNN73+k0/C//1fZGKSELDWhvwBXATUB7YVOPZP4Kr8dnvgnbK819lnn22l/Pr3t9alevfo0ye855s+fbpNSUmxwO+PlJQUO3369PCeWMKmuH/TatWGRfT/lVQcsNwWl5+LOxiqRyDpAynAd4W+tgFILu09lPQrJi/P2u7dgxP/iBHhO19aWlpQcgg80tLSwndSCbvp06fbtLQ0a4yxdesOCfr/dPPN1ubm+h2hlKSkpB/WgmvGmG3W2obGmEbAbGvtWQW+9hFwrbV2azGvywQyAVJTU88ubicZKd3Bg+5jd8GP4pMmQWZm6M9VpUoVivu/ZIwhT/UhYt60aa7Ka8Bll7nd3JKT/YtJDs3vgms7gXqFjjXIP16EtTbLWpthrc1o0KBB2IOLV9WquTn8F17oHevRw+1cFGqR2PxB/DFrFtx6q9c/7zxXRE0JPzZFJOlba3OAL4wxlwEYY9oAa6y1mlcSZjVquKl0Gfm/7611qyffeCO054nE5g8SebNnw003QW6u659+ujtW6J9aYkgkp2zeBdxvjFkC9AfujuC5E1rt2jBvHjRr5vq5uXDjjW5ef6hEYvMHiawFC6BDB2/Kb9OmMH++28NBYpc2UUkg27a5oZ5161w/Kcl9dL/ySn/jkujzr3+5mk779rn+ccfBf/4Dxxzjb1xSdn6P6UsUaNjQ/TAfd5zrHzjgruTmzPE3LokuCxa4C4FAwj/2WHj/fSX8eKGkn2COPRb+/W9vuXxOjivQ9tZb/sYl0WHePFdeYf9+12/c2CX8NC2ljBtK+gkoNdWVXy6Y+Dt0gFde8TUs8dnbb7utDn/7zfVTU2HRIu2/HG+U9BNUIPEff7zr5+ZCx44wdaqfUYlfXnrJfeILlEhOT3cJPzAUKPEjLpO+asCUTWqquzl3yimun5cH3brBmDG+hiUR9swzbo/lwLTM4493FwTp6X5GJeESd0m/tBrgEqxRI3dFd+aZ3rFevaB//8gUaRP/WAvDhsEdd3j/1s2aweLFGsOPZ3E3ZTM9PZ3iyjakpaWxcePGEEYWX3bvdlP0lizxjv3tbzBxonZBike5uW4f24kTvWMtWrgbuUce6V9cEjoJM2WzpBrgJR0Xp25db6pewLPPuht7v/7qX1wSevv2uZpMBRP+JZe46bxK+PEv7pK+asBUXEqKW6xVsLDW3LluQdcPP/gXl4TO9u1w8cXBZThuvhnefRdq1fIvLomcuEv6qgFTOUlJMGUKDBjgHVu50u2KtGqVf3FJ5a1Z4/4dP/rIO3bffa4An4qnJY64S/qqAVN5Vaq4G3yTJ0PVqu7Yd9+56oqhLtQmkTFvnvv3C9zWqlIFxo6FUaO87TUlMcTdjVwJrfnz3fjvnj3esaFD4cEH3d6oEt2shREjoF8/b4ZOzZrw8stua02JXwlzI1dCq107+PBDbxEXwKBBbgXv//7nX1xSur17XVnkBx7wEn7jxvDBB0r4iUxJX0p16qluHPjii71jb7zhpvitXu1fXFKyL7+Eli3h1Ve9Y3/6EyxfDmec4V9c4j8lfSmTevXcUE/Pnt6xdetcYpk2zb+4pKiZM90v5DVrvGN33QXvvQdHH+1fXBIdlPSlzJKS3M2/l17ydk7KznZTPDt3Dh73l8jbuxduv93VUNq71x2rXt3Nxho/XjN0xFHSl3K7+Wb4+GM46STv2PTpcNZZ8Mkn/sWVyFauhLPPhqws79iJJ7phuW7d/ItLoo+SvlTIH//oxodvucU7tn49nHsuDBnibbEn4ZWb62bntGwJ//2vd/yGG9y/z+mn+xebRCclfamwmjXh+efdVX7Nmu5Ybi489JBL/gXHlCX0vvoKzj8f7r/f+yV7+OFuOOfll+GII/yNT6KTkr5UWqdO8PnncMEF3rEVK6B5c3fVH9iUQ0LjwAF4/HFXGbXg6toWLdwwT7duWkMhJVPSl5A47jhXg33ECO+G4YED7qr/rLOCq3dKxS1b5sbuH3jA+2WalATDh8PSpW4cX+RQlPQlZKpWhT594NNP3RhzwNq17lNA166wbZt/8cWyHTvczJzzz4cvvvCON2/uPlX1768S2FI2SvoScn/8o7uyHzvWjTEHTJvmZvw88YSGfMrqwAG3k9mJJ7qZOYGVtSkpMHKkG9457TRfQ5QYo6QvYVG1qlvItXat23s14JdfoHdvt0Xjyy9rd66SWAuvv+52surVK7jkRfv27iZ5795uaEekPJT0JaxSU+Ef/3CreZs29Y5v2ODm+2dkwJw5Sv4B1rqVsy1bukJ3X3/tfe2EE+Cdd2D2bO1fKxWnpC8R0batG4seMyZ4d6ZPP3W7dbVqldjJ31q3kcn558Of/xy8yK12bXeDfM0a973SzBypDCV9iZjkZLjnHvjmG+jb15UICPj4Y5fQTj/djf3n5PgXZyQdOODKWmRkuGGbZcu8rx12mBvC+eYbd4NcZRQkFJT0JeLq1HHzzL/91v0SOOww72urV7tZPunpbo5/vG7TuH07PPooNGni1jl8+qn3teRk6NHDFbQbOdIVuxMJFW2iIr77/ns3oycryysUFlC1Klx9tSv3cPnlsX3j8uBBt/n45Mnw1luuX1D16pCZ6a7qGzf2J0aJHyVtoqKkL1Fj926YOBHGjSt+Pn+DBm5TkOuvd2Pfga0co1lenhu6mjkTXnnFXeEXdvTRcPfd7uq+fv3IxyjxSUlfYkZODrz5Jjz9NPznP8U/5+ij3SeAyy+HSy+Nrjoze/bAokXw9ttutk1JC9IuuMBd2d9wQ/AQl0goKOlLTPryS3dj98UX3TBQcapVc1Mc//Qnl0jPPTd4hlC4/fSTWyS1bBm8/75rFx66CfjDH9ynlb/9ze1IJhIuUZP0jTE3AL2BqsBCa+19h3q+kr6Aq965cKFbsPTGG8UPkxSUnu5KFJx+ulsfcOKJbp/funUrNuXRWpfcN2xwN6DXrnU3nVetcjdcD6V+ffeppGNHaN06NoalJPZFRdI3xqQBC4BzgF+Al4FXrbX/KOk1SvpSWG6uu6p+912YNy945ktpqld3V9tHH+3mvx9xhCsLXa0aVMmfy7Z/v9sRbO9e2LnT1b358ceiN5kP5Ywz3Hz7a65xnzyU6CXSoiXp3w6kWWv75/cvAbpZazuX9BolfSnNjz/CBx+4x+LFrsxzJDdxSUpyZY5btXI3mC++GI46KnLnFylOSUk/0nX56gEFb2ttBYr8eBhjMoFMgNTU1MhEJjHrqKNcfZ9AjZ+cHHcvYOVK9+e6de6xYUP5rtYLq1nTzatPS4OTT3Z1cZo1cwXmCi40E4lmkU7624EmBfoN848FsdZmAVngrvQjE5rEi+RkN7xyxhlFv7ZnD2zd6j4d/PKLe/z6q5taGXjUqOEeKSluYVSDBu5Rp45KIEjsi3TSnwu8Z4x53Fq7B7gVeDPCMUgCq1XLPQoWfxNJJBFN+tbarcaYR4D/GGNygMWHuokrIiKhFfG9dqy1M4AZkT6viIio4JqISEJR0hcRSSBK+iIiCURJX0QkgSjpi4gkkKivsmmM2QFsquDL6wM7QxhOJCl2f8Rq7LEaNyj2cEmz1jYofDDqk35lGGOWF1d7IhYodn/EauyxGjco9kjT8I6ISAJR0hcRSSDxnvSz/A6gEhS7P2I19liNGxR7RMX1mL6IiASL9yt9EREpQElfRCSBxHXSN8YMNMYs9DuO8jDGpBpj3jTGvG+M+acx5nS/YyorY0x/Y8zHxpglxpjXjDG1/I6pJMaY640xrxpjNhc4lmqMmWeMWWqMWZi/p3PUKSH2xsaY+flxLzXGtPIzxpIUF3uBr51kjNlrjEn3IbRSlfB9r2KMGZ7/8/q+MaaXnzGWRdwmfWNMBsG7dMWKiUBfa+0lQEfge5/jKRNjzGnANcC51trzgS1AD3+jOqQdwJ1AcoFjzwFPW2vPA0YA4/0IrAyKi300MNxa2xq4DZjgQ1xlUVzsGGOqAWOBhT7EVFbFxd4J+C3/5/VS3EZRUS0uk74xpgbwJPCA37GUhzGmIZACZBpjFgMPAdn+RlVmO4Hf8PZoqAp85ls0pbDWLrLW/r6S0hiTApxsrX0n/+tzgWbGmOSS3sMvhWPP18Va+5/8djVgX4TDKpMSYgcYBLyKS6xRqYTYOwHbjTHvAQuAGpGPrHziMukDI4Gx1tof/Q6knFKB5sA0a+2fgJ+Afv6GVDbW2q24K+MJxph+wG7gPX+jKpc6FE04PwL1Ih9K+Vlr9wMYY64GxgG3+BpQOeQPRZ1urZ3idywVkAocba1tA/QCXjYmundSjrukb4xpB9S11r7udywV8DOwylq7Kr//CnC2f+GUnTHmYuBCa213a+2jwBrcJ5VYsZOiCb4B0VtXJYhxRgDnAW2ttev8jqksjDGHA2OI7qHAQ/kZmA5grV0N7MXV44lacZf0gSuBBvk3Q9/EfUSf5nNMZbUeSDHGHJ/fb0cUD5EUcjJwWIF+MnCiT7GUm7U2B/jCGHMZgDGmDbDGWnvA38jK7EHga2vtA4Gr/hjREjDAM/k/r5cAWfn35GLBHOD/APJv/Nckyi8U4n5xljFmYf7NrZiQP1tnDJAEbAO6W2t/8TWoMsi/YpsAnAIcwI0p/81au9HPuEpjjNlmrW2Y304DpuJ+Yf0GdLPWVrTCa9gVin078GWhp7TN/2UWdQrGXuj4VGBINP+/KfR9r44bTmuKu+jpba39wM/4ShP3SV9ERDzxOLwjIiIlUNIXEUkgSvoiIglESV9EJIEo6YuIJBAlfRGRBKKkLyKSQJT0RcrJGPOAMeah/PZYY0wnv2MSKSstzhIpJ2NMFVwJ3XeBM6213XwOSaTMlPRFKsAYcx7wAdAkmks1iBSmpC9STsaYJNxV/nTgKmttB59DEikzjemLlN9wYJa1diqw2RgTq2WBJQHpSl9EJIHoSl9EJIEo6YuIJBAlfRGRBKKkLyKSQJT0RUQSiJK+iEgCUdIXEUkg/x/p8jBuxeBn0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ラベル名等に日本語を使いたい場合は、japanize_matplotlib を使うと楽。\n",
    "# 参考: https://pypi.org/project/japanize-matplotlib/\n",
    "\n",
    "import japanize_matplotlib\n",
    "plt.scatter(x, y, color=\"black\", label=\"データセット\")\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.plot(x2, y2, color=\"blue\", linewidth=3, label=\"プロット例\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "intro_matplotlib.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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