matplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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": [
    "y = [10, 14, 19, 20, 25]\n",
    "plt.plot(y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\user\\anaconda3\\envs\\abangues\\lib\\site-packages (3.3.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\user\\anaconda3\\envs\\abangues\\lib\\site-packages (from matplotlib) (1.3.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\user\\anaconda3\\envs\\abangues\\lib\\site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in c:\\users\\user\\anaconda3\\envs\\abangues\\lib\\site-packages (from matplotlib) (2020.6.20)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\user\\anaconda3\\envs\\abangues\\lib\\site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\user\\anaconda3\\envs\\abangues\\lib\\site-packages (from matplotlib) (8.0.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\user\\anaconda3\\envs\\abangues\\lib\\site-packages (from matplotlib) (2.8.1)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\users\\user\\anaconda3\\envs\\abangues\\lib\\site-packages (from matplotlib) (1.17.0)\n",
      "Requirement already satisfied: six in c:\\users\\user\\anaconda3\\envs\\abangues\\lib\\site-packages (from cycler>=0.10->matplotlib) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v5cfzG/ULVEr1VMYYFm88QXxoIDf20OFlrWnPClMviUiBiBxo8dgfROSIiOwTkbdEpE8rr82yrUS1R0TSOjG3U/Hx8mDh1fFszyhmb845q+MopTpge0Yx+0/17OFlrWnPEf3LwMyLHvsQGG2MuQI4Bjx1iddPN8aMM8YkdixizzA3KZpgPy+WbNKjeqV6ouc3ZhAa5MOcCa63glybRW+M2QSUXPTYBtuasACpQM8d69ZJgv28WTA5lvcP5JNVpMPOlOpJDuWVsfFYIfdNie/Rw8ta0xnn6O8H3m/lOQNsEJFdIrLoUm8iIotEJE1E0goLe+aHmjrsTKmeacmmEwT6eLIguWcPL2uNQ0UvIj8GGoCVrWwyxRgzAbgZeEREprX2XsaYJcaYRGNMYlhYmCOxLBMe7MedEyN5Y1cuheU67EypniD3bBXv7csnJSmG3gHeVsfpEh0uehG5B7gVmG9auYDcGJNn+70AeAtI6uj+eooHpw6iobGJ5zfqwiRK9QR//+QEHgL3X93zh5e1pkNFLyIzgR8CtxljqlrZJlBEgs/fBm4EDtjb1pUMCgtizoQoXknN5tS5aqvjKKUu4URhBavTcpifHMtAFxhe1pr2XF65CtgOJIhIrogsBP4GBAMf2i6dXGzbdqCIrLO9NALYIiJ7gc+AtcaYD7rkv8LJfGfGMDDwzH+OWR1FKXUJf9pwDF8vDx69bojVUbqUV1sbGGNS7Dz8Yivb5gGzbLczgLEOpeuhIvv4c/eVsSzbmsmDUwcxNCLY6khKqYvsyz3H2v35fPv6oYQG+Vodp0vpN2O7yCPThxDg48X/bThqdRSllB2//+AoIYE+POgCC4u0RYu+i4QE+rBo2iDWHzzD7pNnrY6jlGphy/EitqQX8cj0IQT7ueaVNi1p0XehhVfHExrkw+/eP6KTLZVyEsYYfvfBESL7+LNgcozVcbqFFn0XCvT14lvXDWVHZgmbjhdZHUcpBazbf5r9p0r5zoxh+Hq53rdg7dGi72IpSTFEh/jzu/eP0NSkR/VKWam+sYn/23CUYRFB3DHe9WbatEaLvov5eHnwvRkJHMov49/7dRUqpaz0RloumUWV/OCm4S43ofJStOi7wW1jBzK8fzB/3HCUugZdW1YpK1TXNfLMR8eYGNuXG0aEWx2nW2nRdwMPD+GHM4eTXVzF62k5VsdRyi0t357FmbJafjhzuEutHtUeWvTd5NqEMJLiQnj2o+NU1TW0/QKlVKcprarnuU/SmZ4QRpILrQXbXlr03URE+OHNCRSW17Jsa5bVcZRyK4s3naC8toEnZg63OooltOi70cTYEG4YEcHiT09wtrLO6jhKuYUzZTUs25rJ7LEDGTGgl9VxLKFF381+cFMCFXUN/EPHGCvVLZ756DiNTYbvzkiwOopltOi7WUL/YOaMj+LlbVnkl+oYY6W6UkZhBa/vzGFeUgwx/QKsjmMZLXoLfGfGUDDwlw+PWx1FKZf2xw/PjyEeanUUS2nRWyCqbwALJsfyxq4c0gsqrI6jlEvan1vK2n35PHB1PGHBrj2GuC3tWXjkJREpEJEDLR4LEZEPReS47fe+rbx2pogcFZF0EXmyM4P3dI9MH9w8xni9jjFWqiv8fv0R+gZ48+C0QVZHsVx7juhfBmZe9NiTwEfGmKHAR7b7FxART+DvNC8MPhJIEZGRDqV1If2CfHlw6iA+OHiaPTnnrI6jlEvZml7E5uPuM4a4LW0WvTFmE1By0cOzgeW228uB2+28NAlIN8ZkGGPqgNdsr1M2C6fG0y9Qxxgr1ZkuHEMca3Ucp9DRc/QRxph8ANvv9gZHRAItv++fa3vMLhFZJCJpIpJWWFjYwVg9S5CvF49eN4TtGcVs1jHGSnWK9w+cZl9uKY/fMBQ/b/cYQ9yWrvww1t4wiVYPW40xS4wxicaYxLCwsC6M5VzmJccQ1def36/XMcZKOaqhsYn/W3+UoeFBzJkQZXUcp9HRoj8jIgMAbL8X2NkmF4hucT8KyOvg/lyWr5cn350xjAOnylirY4yVcsi/duWSUVTJD25KcKsxxG3paNG/C9xju30P8I6dbXYCQ0UkXkR8gLm216mLzB4X+cUY4/pGHWOsVEfU1Dfyl/8cZ0JMH2aMjLA6jlNpz+WVq4DtQIKI5IrIQuC3wAwROQ7MsN1HRAaKyDoAY0wD8CiwHjgMrDbGHOya/4yezdND+MFNCWQVV/H6Th1jrFRHLN+WxemyGrccQ9wWr7Y2MMaktPLU9Xa2zQNmtbi/DljX4XRu5Lrh4UyK68szHx3nzglR+Pvoh0hKtVdpdT3PfXqCaxPCSB7Uz+o4Tke/GeskRIQnZg5vHmO8LdPqOEr1KM9vPEFpdT1P3OSeY4jbokXvRCbFhXD98HD+8ekJzlXpGGOl2uNMWQ0vbc1k9riBjBzonmOI26JF72R+MDOBilodY6xUez370XEaGg3fc+MxxG3Roncyw/v34o5xkby8NYu8czrGWKlLySis4LWdOcxLdu8xxG3RondC371xGCLwi3f1IiWlWmOM4SdvHyDA25NvufkY4rZo0TuhqL4BPHb9MDYcOsP6g6etjqOUU1qz+xTbThTzxM3D3X4McVu06J3UA1PjGd4/mJ+/c5Dymnqr4yjlVEoq6/h/aw8xMbYv85NirI7j9LTonZS3pwe/vfMKzpTX8McNx6yOo5RTeXrtYcprGvjNHWPw0FEHbdKid2LjovvwjcmxLN+execnz1odRymnsC29iDd35/LQNYNI6B9sdZweQYveyX3/pgQigv14as1+nYOj3F5NfSM/ems/cf0C9APYy6BF7+SC/bz55exRHDldzotb9Buzyr397eN0soqrePqOMTpr/jJo0fcAN43qz40jI/jLf45xsrjK6jhKWeLYmXIWbzzBnAmRTBkSanWcHkWLvof45exReHl48OO39+uyg8rtNDUZnlqzn2A/L35yiy49fbm06HuIAb39+cFNCWw+XsS7e3X9FuVeXv3sJLuyz/LjW0YSEuhjdZweR4u+B1kwOZZx0X341XuHdOiZchsFZTX87oMjXDW4H3dOaHXZaXUJWvQ9iKeH8L9zxnCuup7frDtsdRylusUv3ztEbUMTT98xRhcU6aAOF72IJIjInha/ykTk8Yu2uVZESlts8zOHE7u5EQN68eDUQaxOyyU1o9jqOEp1qY8On2Ht/ny+fd0Q4kMDrY7TY3W46I0xR40x44wx44CJQBXwlp1NN5/fzhjzq47uT/3XY9cPJTrEnx+9tZ/ahkar4yjVJSprG/jZOwcZFhHEommDrY7To3XWqZvrgRPGmOxOej91Cf4+njx9+xgyCit57hOdW69c058+PMapc9X875wx+HjpWWZHdNZPby6wqpXnrhSRvSLyvoiMau0NRGSRiKSJSFphYWEnxXJd04aFMXvcQJ77NJ30gnKr4yjVqfbnlrJsaybzk2OYGBtidZwez+GiFxEf4DbgDTtP7wZijTFjgb8Cb7f2PsaYJcaYRGNMYlhYmKOx3MJPbx1JgI8XP1pzgKYmvbZeuYaGxiaeemsf/YJ8eWKmrgHbGTrjiP5mYLcx5szFTxhjyowxFbbb6wBvEdGvtHWS0CBffjxrBJ9llbA6LcfqOEp1ipe3ZXHgVBm/+Mooevt7Wx3HJXRG0afQymkbEekvtuuhRCTJtj+9VKQTfS0xiuT4EH6z7jCF5bVWx1HKIblnq/jjhmNcNzycWWP6Wx3HZThU9CISAMwA1rR47GERedh296vAARHZCzwLzDX6/f1OJSL8Zs4Yauqb+NW/D1kdR6kOM8bws3cOIgK/mj1Kr5nvRA4VvTGmyhjTzxhT2uKxxcaYxbbbfzPGjDLGjDXGTDbGbHM0sPqywWFBPDJ9CO/tzePTowVWx1GqQ9btP83HRwr47oxhRPXVhb47k16z5CIevnYQg8MC+cnbB6iqa7A6jlKXpbS6nl+8d5DRkb2496o4q+O4HC16F+Hr5cn/zrmC3LPV/OU/x62Oo9Rl+d0HRyiuqOW3c67Ay1NrqbPpT9SFJMWHkJIUzYtbMjmYV9r2C5RyAmlZJby64yT3T4lndGRvq+O4JC16F/PkzBH0DfDhqTX7adRr65WTq2to4qk1+4ns4893ZgyzOo7L0qJ3Mb0DvPnZV0ayL7eUV7ZnWR1HqUtasukExwsq+PXtowj09bI6jsvSondBX7liANcMC+MP64/q0oPKaR0/U86zH6dzy5gBXDc8wuo4Lk2L3gWJCE/fMRovD+GbK3dRU68TLpVzqaht4OEVu+jl58XPb9OlAbuaFr2LiuobwJ+/Po6DeWX84t2DVsdR6gvGGJ58cx+ZRZX8NWUC4cF+VkdyeVr0Luz6ERH8z7WDeW1nDm/oLBzlJJZvy+Lf+/L5/k0JXDm4n9Vx3IIWvYv77oxhXDmoHz95+wCH8sqsjqPc3O6TZ3l63WFuGBHOw7qYSLfRondxXp4ePJsynt7+3vzPyl2U1dRbHUm5qeKKWh5ZuZv+vf3449fG4eGhs2y6ixa9GwgL9uXv8yeQc7aa76/ei86VU92tscnw+Ot7KK6s4x/zJ9I7QMcPdyctejcxKS6Ep24ezoZDZ1i6OcPqOMrNPPPRcTYfL+JXt43Sb79aQIvejSy8Op5ZY/rzuw+OsiNDlwVQ3ePTowX89ePjfHViFF+fFG11HLekRe9GRITf3XkFsSEBPLrqcwrKa6yOpFxc7tkqHn99DwkRwfx69midMW8RRxceyRKR/SKyR0TS7DwvIvKsiKSLyD4RmeDI/pTjgv28eW7BBMpr6vnWq5/T0NhkdSTlomobGnlk5W4aGw2LF0zE38fT6khuqzOO6KcbY8YZYxLtPHczMNT2axHwj07Yn3LQ8P69+M0dY9iRWcIfNhy1Oo5yUb/+9yH25pbyh6+NJS400Oo4bq2rT93MBl4xzVKBPiIyoIv3qdphzoQo5iXH8PzGDDYcPG11HOVi3v78FCtST7Jo2iBmjta1X63maNEbYIOI7BKRRXaejwRafiUz1/bYl4jIIhFJE5G0wsJCB2Op9vjZrSMZE9mb772xl+ziSqvjKBdx7Ew5T63ZT1JcCE/clGB1HIXjRT/FGDOB5lM0j4jItIuet/fJi92LuI0xS4wxicaYxLCwMAdjqfbw8/bkufkT8BDh4RW7dfiZctj5YWWBvl78bd54XS3KSTi6OHie7fcC4C0g6aJNcoGW11NFAXmO7FN1ruiQAP789bEczi/jZ+8csDqO6sGMMfzwX/vILq7ib/PGE95Lh5U5iw4XvYgEikjw+dvAjcDFTfEu8A3b1TeTgVJjTH6H06oucd3wCB6dPoTVabms3qnDz1THLNuaxdr9+fzgpgQmD9JhZc7EkSVdIoC3bNfFegGvGmM+EJGHAYwxi4F1wCwgHagC7nMsruoq35kxjM9zzvLTdw4wKrIXowbqtxdV++3KLuE36w4zY2QED00bZHUcdRFxxrkniYmJJi3tS5flqy5WVFHLrc9uwcfLg/e+dTW9/XUeiWrb+T83vt4evPuo/rmxiojsauUyd/1mrPqv0CBf/j5/PHnnqvn+Gzr8TLWtscnw2Gufc7aqjufmT9CSd1Ja9OoCE2ND+NGsEXx46AzPb9LhZ+rS/vKfY2xNL+bXs0fr6T4npkWvvuS+KXHccsUAfv/BEbaf0OFnyr6Pj5zhrx+nc1diFHfpsDKnpkWvvuT88LO40EC+tepzsor0y1TqQsfOlPOd1/cyckAvfjV7tNVxVBu06JVdQb5eLLl7Io1NTaQsTdVvzqovHD9Tzrylqfh6ebB4wUT8vHVYmbPToletGhIezMoHJlNd30jKklROFldZHUlZLL2ggpSlOxARVi2aTEy/AKsjqXbQoleXNHJgL1YsTKayrpGUpanklGjZu6sThRWkLE0FYNWDyQwOC7I4kWovLXrVptGRvVn5QDLlNfWkLE0l96yWvbvJLKokZUkqxhhWPZjMkPBgqyOpy6BFr9pldGRvVjyQTGl1c9nnnau2OpLqJlm2km9sMqx8YDJDI7TkexotetVuV0T1YcXCZM5V1jN3SSr5pVr2ri67uJKUpanUNTax8sFkEvpryfdEWvTqsoyN7sMrC5MoqawjZUkqp0t13VlXlVNSRcqSVKrrG1mxMJnh/XtZHUl1kBa9umzjY/qy/P4kCstrmbc0lTNlWvauJqekirlLUqmsa2TlA8mMHKgl35Np0asOmRjbXPany2pIWZpKgZa9y8g9W0XK0lTKa+pZ+UCyjjZwAVr0qsMS40J4+b4kTpc2l31hea3VkZSD8s5Vk7I0lbLqelY+MJnRkVryrkCLXjkkKT6EZfdOIu9cDfOWplJUoWXfU+WXNpf8uap6/rkwmTFRWvKuwpEVpqJF5BMROSwiB0XkMTvbXCsipSKyx/brZ47FVc4oeVA/Xrp3Ejlnq5i3NJViLfse53RpDSlLUimpqOOV+5MYG93H6kiqEzlyRN8AfM8YMwKYTPPi4CPtbLfZGDPO9utXDuxPObErB/fjpXsmkV1cxfwXdlBSWWd1JNVOBWXn/zVWx/KFSYyP6Wt1JNXJOlz0xph8Y8xu2+1y4DAQ2VnBVM9z1ZBQXrxnEplFlcx/YQdnteydXkF5DXNtV04tv38SE7TkXVKnnKMXkThgPLDDztNXisheEXlfREZ1xv6U87p6aChLv5HIicIK5r+wg3NVWvbOqrC89ovvQrx8fxITY0OsjqS6iMNFLyJBwJvA48aYsoue3g3EGmPGAn8F3r7E+ywSkTQRSSssLHQ0lrLQtGFhLLl7IukFFSx4cQelVfVWR1IXKapo/g5E3rkalt07iUlxWvKuzKGiFxFvmkt+pTFmzcXPG2PKjDEVttvrAG8RCbX3XsaYJcaYRGNMYlhYmCOxlBO4NiGc5++eyLHTFcx7QadeOpMThRWkLEkl92w1y+6bRPKgflZHUl3MkatuBHgROGyM+VMr2/S3bYeIJNn2p2vTuYnpw8N5/hsTOVlcxaxnNvPOnlNWR3JrxhhW78zh1me3UFRRy0v3TmKylrxb8HLgtVOAu4H9IrLH9tiPgBgAY8xi4KvAN0WkAagG5hpjjAP7VD3M9IRw1j02lcdf38Njr+1h07Eifjl7FEG+jvzRU5ertLqeH721n7X78rlqcD/+dNc4+vf2szqW6ibijL2bmJho0tLSrI6hOlFDYxPPfpzO3z4+TkxIAM/MHa/XaneTnVklPP7aHs6U1fC9GxN4aNogPDzE6liqk4nILmNMor3n9Juxqlt4eXrw3RnDWPXgZOoamrjzH9tYvPEETU3Od6DhKhoam/jzh8f4+vPb8fIU/vXNq/jmtYO15N2QFr3qVsmD+vH+Y9OYMTKC375/hLtf2qHTL7tA7tnm6ZPPfHSc28dHsvbbUxmn/4JyW1r0qtv1DvDmufkT+O2cMezOPsfNz2zmP4fOWB3LZazdl8/Nz2zmyOlynpk7jj/dNU4/E3FzWvTKEiLC3KQY3vvW1fTv5ccDr6Tx83cOUFPfaHW0HquqroEn/rWXR17dzeCwINZ9eyqzx+mX1ZVjV90o5bAh4UG89chV/P6Do7y4JZPUjBL+Om88w3Rd0sty4FQp3171OZnFlTw6fQiP3TAUb089jlPN9E+Cspyvlyc/vXUkL983ieLKWr7y1y38MzUbZ7wizNk0NRmWbsrgjue2Ul3fyKoHJ/P9mxK05NUF9E+DchrXJoTz/mPTmDyoHz99+wCL/rlLB6NdQkF5Dfcs+4yn1x3muuHhvP/YVP0ClLJLi145lbBgX5bdO4mf3DKCT48WMPOZTWw7UWR1LKfzydECZj2zmZ1ZJTx9x2gWL5hInwAfq2MpJ6VFr5yOh4fwwNRBvPU/Uwj09WL+Czv49b8P6WWYNF82+ZO393Pfsp2EBvny3qNXMz85FtukEaXs0m/GKqdWVdfAr/99mNd2nsRThFljBnDvlDi3mptujGFn1lmWbc1k/cHTiAjfuDKWH84cjp+3p9XxlJO41DdjtehVj3CyuIrl27NYvTOH8toGxkb34f4pcdw8egA+Xq75D9Oa+kbe25vHy9uyOJhXRm9/b1KSYrj7ylgi+/hbHU85GS165TIqaht4c1cuy7dlkVFUSXiwLwsmxzIvOYbQIF+r43WKgrIaVqRms3LHSYor6xgWEcS9V8Vzx/hI/H30CF7Zp0WvXE5Tk2Hj8UKWbc1i07FCfLw8uG3sQO6bEseogb2tjtche3LOsWxrJmv35dNoDNcPD+e+KfFcNbifnoNXbbpU0esXplSP5OEhTE8IZ3pCOOkF5by8LYs3d53iX7tySYoP4b6r4pgxMgIvJ7+evL6xifcPnGbZ1kw+P3mOIF8v7r4ylnuujCMuNNDqeMpF6BG9chmlVfWsTsth+fYscs9WE9nHn29cGcvcSTH0DvC2Ot4FiitqWfXZSf6Zms2Zslri+gVwz1VxfHViFMF+zpVV9Qx66ka5lcYmw4eHzrBsayY7Mkvw9/ZkzoRIbh8fyaDQQEICfbr9VIgxhuLKOtILKlizO5e39+RR19DE1KGh3DcljmuHhev4YOWQLit6EZkJPAN4Ai8YY3570fNie34WUAXca4zZ3db7atGrznIwr5SXt2bxzt7mYgXo5edFXGggcf0CiQsNJD40gLh+gcSHBjr8paOzlXVkFleSVdT8K7O46ovb5bUNAPh5ezBnQhT3XRXHUJ3pozpJlxS9iHgCx4AZQC6wE0gxxhxqsc0s4Fs0F30y8IwxJrmt99aiV52tpLKOz0+eJbOokqziSrKKqsgsqiSvtJqW/wv0CfD+ovSb/yIIaL4dGkgv2ymV0ur65vIurmx+vxaFXlpd/8V7eQhE9vW/4P3iQwOZENPX6U4lqZ6vqz6MTQLSjTEZtp28BswGDrXYZjbwim2d2FQR6SMiA4wx+Q7sV6nLFhLow/UjIr70eE19IzklVV/8BZBZ1FzYqRnFvPX5hYuZ9wv0wdD8l8Z5IjCwtz9xoQHcesWAFn9BBBId4o+vl14OqaznSNFHAjkt7ufSfNTe1jaRwJeKXkQWAYsAYmJiHIilVPv5eXsyNCLY7imU6rpGsktsp2FsR+witDjlE0hMSIB+O1U5PUeK3t4nRxefB2rPNs0PGrMEWALNp24cyKVUp/D38WR4/14M79/L6ihKOcSRi4xzgegW96OAvA5so5RSqgs5UvQ7gaEiEi8iPsBc4N2LtnkX+IY0mwyU6vl5pZTqXh0+dWOMaRCRR4H1NF9e+ZIx5qCIPGx7fjGwjuYrbtJpvrzyPscjK6WUuhwOjUAwxqyjucxbPra4xW0DPOLIPpRSSjnGuQeBKKWUcpgWvVJKuTgteqWUcnFa9Eop5eKccnqliBQC2RZGCAWKLNy/M9GfxYX053Eh/Xn8l9U/i1hjTJi9J5yy6K0mImmtDQdyN/qzuJD+PC6kP4//cuafhZ66UUopF6dFr5RSLk6L3r4lVgdwIvqzuJD+PC6kP4//ctqfhZ6jV0opF6dH9Eop5eK06JVSysVp0V+CiHxfRIyIhFqdxUoi8gcROSIi+0TkLRHpY3Wm7iYiM0XkqIiki8iTVuexkohEi8gnInJYRA6KyGNWZ7KaiHiKyOci8m+rs9ijRd8KEYmmeeHzk1ZncQIfAqONMVfQvCD8Uxbn6VYi4gn8HbgZGAmkiMhIa1NZqgH4njFmBDAZeMTNfx4AjwGHrQ7RGi361v0ZeIJWlj50J8aYDcaYBtvdVJpXCnMnSUC6MSbDGFMHvEbzwvduyRiTb4zZbbtdTnPBRVqbyjoiEgXcArxgdZbWaNHbISK3AaeMMXutzuKE7gfetzpEN2ttkXu3JyJxwHhgh8VRrPQXmg8KmyzO0SqHFh7pyUTkP0B/O0/9GPgRcGP3JrLWpX4exph3bNv8mOZ/tq/szmxOoN2L3LsTEQkC3gQeN8aUWZ3HCiJyK1BgjNklItdaHKdVblv0xpgb7D0uImOAeGCviEDzaYrdIpJkjDndjRG7VWs/j/NE5B7gVuB6435fvtBF7i8iIt40l/xKY8waq/NYaApwm4jMAvyAXiKywhizwOJcF9AvTLVBRLKARGOM207oE5GZwJ+Aa4wxhVbn6W4i4kXzh9DXA6eAncA8Y8xBS4NZRJqPgJYDJcaYxy2O4zRsR/TfN8bcanGUL9Fz9Ko9/gYEAx+KyB4RWdzWC1yJ7YPoR4H1NH/wuNpdS95mCnA3cJ3tz8Me2xGtclJ6RK+UUi5Oj+iVUsrFadErpZSL06JXSikXp0WvlFIuToteKaVcnBa9Ukq5OC16pZRycf8fxiooGzoMguwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-4.5, 5, 0.5)\n",
    "y = x**2\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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": [
    "x = np.arange(-4.5, 5, 0.5)\n",
    "\n",
    "y1 = x**2\n",
    "y2 = x+30\n",
    "y3 = x**2 + 10\n",
    "\n",
    "plt.plot(x, y1)\n",
    "plt.plot(x, y2)\n",
    "plt.plot(x, y3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EvAP8uOsv5h9ezcc//ADIevXdd8O338r9V18NL78M+fLJuGNHaYN62WUyLlxYDiAwbi84K6UCLJ54+tCHGtQImqq21MIigZ85I/0+PLXUu3fL7sQPP5Rx3Zw1qNruFyruawJIhccvv0CrVnL/5ZdLjxA9nVwp5ZFAAl3pynGOM5Wp5CKX65D+JWQS+Jkz3iWN5GS48UbvaeRRUdI175NPZFyypKxLe2qpY2Phu1W5aXmdZTSj+fqixZQtq0sgSqnz+5zPWc5y3uVdalDDdThpCpkerFWqQKNGMHmyJN6TJ70XGWNiZLNL6dIyjoqShk3/dJrTvMd7HOAA61hHGcr8+0FKKQW0ox0b2EAtgrdncrpzUGNMKWPMUmPMFmPMD8aYB1NuH2qM2WeM2ZDy3tafgQ4ZArfe6h0vWACPPOIdly+f/u7E3ORmBjNIIIHOdOYUp/wTrFIqZG1m89kNgMGcvCEDG3mMMbFArLV2vTHmEmAd0BHoCpyw1r6c0RcL5EaeC/mMz2hPe27ndsYz3vl2WKVU8GhNa7aylW1sIyc5XYcDZGMjj7X2gLV2fcrnfwJbgBK+DzFwbuAGnuZpJjCBcYxzHY5SKohMZzqzmR00yftCMnUZzxhTFqgDrE25qb8xZpMx5n1jTJrHMBtj+hpj4owxcYc8TaqDwBCG0IY23M/9rGGN63CUUo7NYx5nOEMBCgT90olHhhO4MSYv8CnwkLX2OPAWUB6oDRwAXknr66y146y19ay19YoUKZL9iH0kmmimMIWSlKQLXYgn3nVISilH5jOfG7iBl3jJdSiZkqEEboyJQZL3VGvtDABrbby1Nslamwy8C1zpvzD9oyAFmclMDnOYe7jHdThKKQd2sINbuIVa1OJRHnUdTqakW0ZojDHAeGCLtXZUqttjrbUHUoadgO/9E6J/1aIWH/MxVanqOhSlVICd5CSd6YzBMIMZ5CGP65AyJSN14A2BnsBmY8yGlNueAHoYY2oDFvgVuNsP8QVEO9oBYLFsZStXcIXjiJRS/max9KUvm9nMfOZTjnKuQ8q0dBO4tXYFpFlnN9/34bj1HM/xEi/xAz9QlrKuw1FK+dFYxjKVqQxjGK1p7TqcLAmZnZiB0Je+FKKQ7tBUKsytYAWP8Ajtac8TpLFtO0RoN5BUYomlP/0xGPayl2SSXYeklPKxAxzgJm6iHOWYxCSiQjgNhm7kfvQrv1Kd6oxkpOtQlFI+loc8NKMZM5lJPvK5DidbNIGnoQxlaEc7nuEZFrDAdThKKR9JIIF85GMqU6lGNdfhZJsm8DQYDOMYR01qcgu3sJOdrkNSSmXTZCZzFVdxkIOuQ/EZTeDnkYc8fMqnWCyd6cxJTroOSSmVDQUpSBnKZOiM3FChCfwCylOeaUxjE5u4h3uwXLhzo1Iq+Hj+3V7P9cxkJjnCqPhOE3g62tCGoQxlMpN5gzdch6OUyoQkkmhPe0Yz2nUofqEJPAOe4ina0Y6HeZiVrHQdjlIqg4YylLnMJTe5XYfiF5rAMyCKKCYzmQpUYAc7XIejlMqAiUxkOMO5gzu4i7tch+MX4bMY5Gf5yc93fMdFXARAMskhvQFAqXA2j3n0oQ8taMGbvBm2p25pBsoET/Key1wa0ICjHHUbkFLqX1axipu4idrUZgYzyEUu1yH5jSbwLMhFLnKQgwQSXIeilErlR36kHe0oQQnmM59LuMR1SH6lSyhZ0JKWtKAFBkMCCRhMWJUmKRWK9rCHVrQiF7lYxCKKUtR1SH6nM/As8iTvG7iB+7hPa8SVcuw3fiOGGBayMCR7e2eFJvBsiCGGutTlXd5lCENch6NUREoiCYD61OcnfgqZA4l9QRN4Ng1nOH3owzCGMZaxrsNRKqIkkEAHOjCCEYBMqiKJLtxmk8HwNm9ziEM8wAMUpShd6eo6LKUiRiEKUZjCrsNwQhO4D+QgBx/yIddxHbdyK4UoRHOauw5LqbB2ghPkJS8f8EHY1nmnJ90lFGNMKWPMUmPMFmPMD8aYB1NuL2iMWWyM+TnlYwH/hxu8cpObOcyhMpXpSEfWs951SEqFrVd4hTrUIZ74iE3ekLE18ETgUWvtFcDVQD9jTFVgMPCFtbYi8EXK2H/mz4eE4K67LkABFrKQQhSiDW10271SfjCZyQxgAHWoExpLJ/v3w48/+uWp003g1toD1tr1KZ//CWwBSgAdgIkpD5sIdPRLhABr1sD118Pw4X57CV8pQQk+53Ma0YhCFHIdjlJhZQELuIM7aEYzJjOZaKJdh5S+2FhJ4GfO+PypjbUZr182xpQFlgPVgd3W2vyp7jtirf3XMooxpi/QF6B06dJ1d+3albVIe/WCadNg5Uq46qqsPYcDpzhFAglcyqWuQ1EqpK1lLc1oRmUq8xVfhca/qWPHIF/2z900xqyz1tb75+0ZLiM0xuQFPgUestYez+jXWWvHWWvrWWvrFSlSJKNf9m9jxkCJEtCzJ/z1V9afJ4Aslna0oxOddKOPUtmwhS20pS2xxLKABaGRvNeuhdKl4Ysv/PYSGUrgxpgYJHlPtdbOSLk53hgTm3J/LPj5oLl8+WDiRNi+HQYO9OtL+YrB0Ic+3M3dEX2hRans2MteWtGKGGL4nM8pRjHXIaXvr79kspk/P9T718TZZ9ItIzTGGGA8sMVaOyrVXXOA3sALKR9n+yXC1Jo0gaFDoUoVv7+Ur9zMzWc//4EfqEpVTeZKZdAxjtGa1hzlKMtYRnnKuw4pYwYOlMnml1/6ZAnlfDIyA28I9ASaGWM2pLy3RRJ3S2PMz0DLlLH/PfMMdE3ZKJOJ9XvXvuM7alOb53jOdShKhYy85KUlLZnFLOpQx3U4GbNgAbz1FjzyiEw6/SjdGbi1dgWcd8robrfKmDGyxjR5Mpjgn9HWpjY96clQhlKQgtzP/a5DUiponeY08cRThjK8yquuw8mcb76BGjUCUjUXur1QTp2CqVMlgYcAg2Ec4+hABx7gAUYyUi9sKnUed3AHjWnMSU66DiXzhgyR0ueLLvL7S4VuAn/0UbjmGrj/fshqaWKA5SAHn/AJt3ALT/Ikj/GYJnGl0jCQgQxnOHnI4zqUjJs9W1YFAPIEJu7QTeDR0VKVkpwMvXvLxxAQQwyTmEQ/+vEyL3MXd51th6lUJNvPfsYwBoA61OFWbnUcUSbs3i17VQYNCui1udBN4ADlysHrr8Py5bBihetoMiyKKMYwhqd5mvGMpxvdOIPvd2kpFSp2sINGNOIJnmA3u12HkzmpJ5Hvvx/Qa3Kh343wttukzrJGDdeRZIrB8BzPUYACDGIQ61hHAxq4DkupgNvMZq7jOv7mb77gC0pT2nVImfPaa/DVVzB+PFx+eUBfOlNb6bOrXr16Ni4uzn8vsGYN1KkDuULrFOpd7KIMZQBIJFHP11QRYw1raEtbcpObxSymKlVdh5Q5P/0ENWtC69Ywa5bfZt/Z3kof9LZuhQYNZKNPiPEk74/5mPrUJ554xxEp5X9LWEILWlCQgqxkZeglb4Dy5WHECHj3XSflzOGTwKtUgTvvhBdfDKn18NQKUpBYYrmES1yHopRfzWAG13M9l3M5K1hBWcq6DinzTp+GHDlgwAAoWtRJCOGTwAFGjZILmz17wvEM99sKGi1owTzmkYc8HOMYW9nqOiSlfG4607mJm6hLXZaxjOIUdx1S5q1YIevd690e3BJeCTxvXpg0SUp6Hn7YdTRZ4umTcjd305CGfMM3jiNSyrdqU5tudGMxiylACB7k9eefUjKYOzdUrOg0lPBK4AANG8pOqOrVQ6pXyj+NZCT5yU9zmvMlX7oOR6lssVjmMheL5QquYBrTuJiLXYeVNQ8/LJsHJ02CS9wud4ZfAgdpePXwwyHRI+V8Uq8NtqUtswPQ7FEpf5nNbG7ghtD/OZ49W8oFBw2SyaJj4ZnAPT79VOrEQ3QmHkssy1hGbWpzIzcyiUmuQ1IqSzrQgU/4hA50cB1K9sybB7VrB021W3gn8D17ZLv9+PGuI8myghRkCUtoSlN605vXed11SEplyClO0Yc+bGc7BkMXuoR+L/x33pEe3zlzuo4ECPcE/sAD0KwZPPQQ7AjdE+Lzkpe5zKUznXmQB3mWZ7UJlgpqxzlOG9owgQmsZrXrcLLvs8/kgAZjoEDwXHgN7wQeFQUffCC1mr16QVLoNo3KRS4+4iNu4zZWsYpEEl2HpFSadrCDa7mWlaxkKlPpSU/XIWXPjh3Qo4cc0BBkwjuBA5QqBW+8AatWwZw5rqPJlhzkYDzjmc1sYohhL3vZznbXYSl11kxmUpe67GY3n/EZPejhOqTsSUyURlU5ckgeCTLhn8ABbr5ZOhZ26uQ6kmyLIoqLkEbx93IvTWiinQyVcwkk8CiP0pnOVKQi61lPa1q7Div7nnwSVq6U5F2qlOto/iUyuiYZI4c/AGzaBAULQsmSbmPygbGMZStbyUUuLJYkkrQRlgq4feyjG91YyUr60Y9XeIVchFZDuTQtWAAvvQT33gu33OI6mjRFxgzc49QpuO46uPFGOBP6s9YylKEVrQB4m7e5lmvZwx7HUalIM5/5bGQjH/IhYxkbHskb5EDiESOkXWyQSjeBG2PeN8YcNMZ8n+q2ocaYff84pT745c4Nb74ph44++KDraHyqCEXYzGbqUIfP+dx1OCrMJZHEZjYDcCd3soUtdKOb46h85Phxec+dG554ImhKBtOSkRn4B5DmYtar1traKe/zfRuWH3XuDIMHSz3n+++7jsZnutCFdazjMi6jDW14hmf0qDblN4/zOA1owH72YzCUJPSXJAHv6ToNGsDff7uOJl3pJnBr7XLgcABiCZzhw6FFC7jvPvj++/QfHyIqUYk1rKE3vRnGMFrRioMcdB2WCiOe/QcP8ACv8zqxxDqOyMdefFEOZujTJ6hn3h7ZWQPvb4zZlLLEct7KdmNMX2NMnDEm7tChQ9l4OR+Kjobp02UmXrmy62h8Kg95mMAExjOelaykNrX5mq9dh6VCnMXyMi/TgQ4kk0xJSnI7t4f+zsrUFi2Cp56C7t1l818IyGoCfwsoD9QGDgCvnO+B1tpx1tp61tp6RYoUyeLL+UHhwtLPICYGDh8O6U0+abmDO1jDGi7mYprSVPuoqCw7ylE605mBDCQHOTjNadch+d6vv8pmnapV4b33QqYRXpYSuLU23lqbZK1NBt4FrvRtWAH0xx9yjuZTT7mOxOdqUYs44riFW7iaq12Ho0LQetZTl7rMZS6jGMWnfEoe8rgOy/diYqB+fZgxAy4OnTa3WUrgxpjUC1+dgNBdSC5UCNq0gRdekL+8MJOPfExkIpWohMUykIGsY53rsFSQs1je4R0a0IC/+ZtlLONhHg6vJROQTqXJyVCiBCxc6PyAhszKSBnhdGA1UNkYs9cY0wd4yRiz2RizCWgKhObxNx6jR8NVV8nV563he4xZPPF8xEcsZrHrUFQQ+4u/6EUv7uEemtCE7/iOBjRwHZZ/vPMOXH89nDjhOpIsMTaAvbLr1atn4+LiAvZ6mbJ3L/znPzIjX7sWLr3UdUR+cYQj5CMfUUSxhCWUpSwVqOA6LBUkdrGL5jRnJzt5lmd5kieJCtf9fqtXQ+PG0LKldBuMCt4/pzFmnbW23j9vD96IA61kSfj4YyhTBhISXEfjNwUoQBRRJJPMvdxLNarxJE/yF3+5Dk055LkwWZKS1Kc+X/IlT/N0+Cbv+Hjo0kX6m0yZEtTJ+0JCM2p/adJE+h8UKhSyp/hkVBRRLGc53ejGSEZShSp8wifaZzwCTWQiFajAYQ4TTTTTmU4TmrgOy38SEqBrVzhyRK57BVF/78zSBP5PxsChQ3IQxJIlrqPxq1himcQkVrCCwhSmK11pTnN+4AfXoSk/s1hOcQqAOtShKU0jZ+fu7t1yOMO770KtWq6jyRZN4GnJnVuSePfucvp0mGtIQ+KI403eZAMbqEUtHuERjnHMdWjKD37kR1rSkju5E4Ca1GQykylCEO3T8Kfy5aVYIUg7DGaGJvC05M0rv1olJEjnwtNhuHHhH6KJ5l7uZRvb6EMfXuM1utLVdVjKh45znAEMoBa1WMc6GtAgspbMvv9emlMlJsIll7iOxic0gZ9PpUoweTKsWwf9+oX9mrhHYQrzDu/wLd8ykpGAVK6sZ73jyFRWWSyTmUxlKjOKUdzO7WxjG/3oF3513edz9Kgc6DJhguy8DhOawC+kfXvZofnVV2H1l54RdVPeAF7gBa7mavaz33FUKrM2sIFruIZe9KI0pVnLWsYxLnKWS0A26vTsKdvl//c/KFrUdUQ+owk8PUOHwvr1UpkSoR7ncaYxjcu4DIAv+CJyLniFKIvlIR6iLnXZxjbe531Ws5r61HcdWuCNGAFz58Krr0LDhq6j8SlN4OmJjoZ8+eQEn8cfl/rRCJOf/HShCyAzuha04EquZDWrHUem/smzpm0w5CQn/enPNrZxO7eHb033hfz2Gzz/vMzA+/VzHY3PReDfaBZt3y5HK914oxzNFqFqUYsP+ZB44mlAA9rQhnnMI5lk16FFvG1soypV+YqvAHiRFxnNaPKT32lcThUvLjsu3347ZDoMZoYm8IyqVg0mTYJVq6S8MDHRdUROGAzd6MZWtjKMYWxiE+1oR0UqMopRHOGI6xAjyla2sgTZr1CGMpSjHH8jJ8lEzAXKtOzeLUUIILXeecKwgyKAtTZg73Xr1rUhb8wYa8HaPn2sTU52HY1zf9u/7Uf2I9vINrJYbB6bxw6yg1yHFdYSbaKdbWfbFraFxWIr28o22erP4lmHDllbpYq1+fJZe/Cg62h8AoizaeRUnYFnVv/+Upny8cewY4fraJyLIYaudOVrvuY7vqMHPbiIiwBIJpk5zCGRyPxtxdf+4A/+j/+jAhXoQAe2sIXhDGc5yyN7tp3aX39Bu3bwyy8wZw4E0yEyfqDdCLPCWtmhWbas60iC2iIW0YpWfMzH3MRNrsMJWRvYwFjGMpWpnOY0jWlMf/rTkY7kIIfr8IJHQgJ06ACffw6ffgodO7qOyGfO141Q//azwhhv8n71VYiNlXVxdY7mNOczPuM6rgNgFKP4ju+4n/u5MoQPcQqk1aymAQ3ITW560Yt+9KMmNV2HFZwWLpRmdOPGhVXyvhBN4NmRkAAzZ8KaNVIn3rKl64iCSjTRtKPd2fFJTjKLWUxhCvWpT3/6041u5CKXwyiDz/M8TxRRDGIQV3EVb/M2XelKAUK3a15A3HADfPcd1K7tOpKA0TXw7IiJkXW2KlWgc2cIh+UhP3qKp9jHPsYwhuMcpze9KUlJbuVWxjOeneyMrN4cQCKJfMu3vMVbZ2/bwAY2shGQtr93c7cm7wsZOxZWrpTPIyh5g66B+8b+/dCgAZw8KT9IIXaungsWyxKWMIEJfMEXHOQgIEl+GMNIJJH97Kc0pR1H6lvJJLORjXzJlyxlKV/zNcc5DsA+9nEZl5FIoq5tZ9SkSXIUYq9eMHGi62j8RtfA/emyy2DRIrjmGk3gGWQwtEx5s1i2sIWlLKUe8jO6jnVczdXMYhYd6MAf/MEZzpzdzh9K9rKXGcxgKUtZxrKztfIVqUh3utOUpjShCcUpDqDJO6Pmz4c77oDmzWXdOwKl+5NijHkfaAcctNZWT7mtIPARUBb4FehqrY3sHRyVKsG2bbLtXmWKwVA15c2jNKV5jddoiPSumMhEHuVRKlGJZjQ7m/SKEnyNiY5znClMoTGNqUY1NrOZB3mQcpSjE51omvJWghKuQw1da9bATTfJJp0ZMyBXZF5HSXcJxRhzLXACmJQqgb8EHLbWvmCMGQwUsNYOSu/FwnYJ5Z8WL5aT7v/3P7joItfRhIVtbOMzPmMpS1nOcv7kTwCqUY1ruIZSlKIoRelDHwyGP/iDGGK4FN8eTp1EEoc5zCEOcTDlzfP5f/gPHejAEY5QiEK8yIsMZCCnOMVBDlKGMj6NJaL17Qtffim/8RYr5joavzvfEkqG1sCNMWWBuakS+E9AE2vtAWNMLPCVtbZyes8TMQl8+nS4+WbpP/zJJ9IQS/lMIomsYx1LU97WspZjHCMf+TjKUQC60531rGcb2wC4h3vYxS6KUISiKW+pP89LXiz27G8BYxhDQQpyC3JqSz3qsYc9/M7vafZ9MRi6051pTANgD3soSUndYOMvSUlyalbx4q4jCQhfr4EXs9YeAEhJ4sH3e6xLPXrAwYPw0ENw331h20jHlRzk4KqUt8EMBuRU9dR9WO7gDtrT/uw4mmh+53e2sIV44s+ewp5aferzDd8A8AEfUJ7yZxN4LWpRl7ppJv+iFKUQhYjG+x91KUr55c8e0Q4flpn3a69ByZIRk7wvxO9XS4wxfYG+AKVLh1dFwQU9+KC0nn3+eflBe/ZZ1xGFtYu4iFhiz449m4c83uCNs59bLH/x1znLIH/y5zlJ9xu+OSchj2e8H6NX6Tp5UrbIr1sHDzwgCVxlOYHHG2NiUy2hHDzfA62144BxIEsoWXy90DRihCTx3btl+73OwoOCwZA35a0c5dJ8TOrkrRxLSICuXeXC5SefwLXXuo4oaGQ1gc8BegMvpHyc7bOIwokxUt4UFSWfJyZCDi0RUyrDrIW77oJ582Qp8sYbXUcUVNLdiWmMmQ6sBiobY/YaY/ogibulMeZnoGXKWKUlOlqS9y+/QM2acuVcKZUxx47J9vhnn4W773YdTdBJdzpore1xnrua+ziW8JY/v8zEO3aEpUuhbl3XESkV3JKT5d/NqlXheyBDNmkvlEApUEDaXBYoAC1ayA+lUiptL74orWETEuDii/X60XloAg+kEiVg+XIoXFg6F3oa8CilhLUwaBAMHgyXXOI6mqCnCTzQypSBFSvkYky1aq6jUSp4JCXBPffASy/BvffClCnS8VOdlyZwF4oVky5q+fPLCfdz5riOSCn3+veXqq0nnoA33pBrRuqC9Dvk2iuvyFrfyy+7jkQpt/r0kROuRozQNe8M0gTu2mOPySaFgQNl5hHA/uxKOXf0KLz/vnxer560n1AZprtKXMuZE6ZNk+WU55+HI0fkhBFtgKXCXXw8tGoFP/4ouysrVHAdUcjRGXgwiI6WXWaDBkkL2n37XEeklH/t2gWNGsHPP8PcuZq8s0gTeLAwBl54ATZvhtKlZSnlzBnXUSnle1u2QMOG8PvvsGQJXHdd+l+j0qQJPNh4WmQOGSK14seOuY1HKV/buFF2WS5bBv/9r+toQpom8GBVvbp0X2vaVHqLKxXqjh6Vj927y/GDNWs6DSccaAIPVl27Sn341q1yWPLu3a4jUirrPvsMypaVncgAefM6DSdcaAIPZq1by2n38fHQpAmc/vcpMkoFvSlT5HjBSpWgatX0H68yTMsIg12jRvDVV/DTT3pAsgo9Y8fC/ffLUuDs2drfxMc0gYeC2rXlHWDWLChYUE8lUcFv0SJJ3h06wIcf6gTED3QJJZQkJUlj+1atYMYM19EodWEtWsB778neBk3efqEJPJRER8uspmZN6WY4YID0S1YqWOzbB+3bywlUUVHS30SPEfQbTeChpkgRuZJ/333SCKtpU93wo4LDkiVQp44cG7h1q+toIoIm8FCUK5e025w+XdbCc+VyHZGKZMnJ8NxzsqOySBH49lto08Z1VBFBE3go694dRo6Uz7/5Rv4RJSW5jUlFnldflZ3Dt9wiP4dXXOE6ooiRrcUpY8yvwJ9AEpBora3ni6BUFsycKb1UVq6UutsiRVxHpMJdYqKsb99zD8TGQo8e2sc7wHwxA29qra2tyduxkSPh3Xelv0SdOnrepvIfa2XWXb8+/PWXHDp8882avB3QJZRwYQzceSesXi0lW02aaBJXvnfsGHTpAo88IlvjExNdRxTRspvALbDIGLPOGNM3rQcYY/oaY+KMMXGHDh3K5supdNWpA+vWwTPPwNVXy216yo/yhQ0boG5d2VH58suyFyFfPtdRRTRjs/GP2xhzmbV2vzGmKLAYuN9au/x8j69Xr56Ni4vL8uupLDhwAG66CcaMkeSuVFZYKxVPv/wCH30k/bxVwBhj1qW1TJ2tGbi1dn/Kx4PATODK7Dyf8oP4eDn95L//lRO/dTauMuPkSVk2MQamToX16zV5B5EsJ3BjzMXGmEs8nwPXAd/7KjDlI7Vryz+6xo3h7ruhVy+58KRUerZuhSuvhDvukHHp0lC0qNuY1DmyMwMvBqwwxmwEvgHmWWsX+iYs5VNFisD8+dJHZepUeOIJ1xGpYPfhh1JlEh8vZYIqKGW5DtxauxOo5cNYlD9FR8uFzWuu8XY2/O03Se7R0U5DU0Hk6FEYOFCaUDVoIOvdJUu6jkqdh5YRRpqmTaFAAdn+3L69zLJWrXIdlQoWyclyes5jj0kfek3eQU0TeKQyRroZHjwoF6V695YZuYo8q1fDbbdJG4aCBWH7dnjxRYiJcR2ZSocm8EhljJy7uXUrPP64NMaqVEl6WajI8NtvkrgbNJBOgr/8IrfreZUhQxN4pMubV7bhf/+9NMeqlXJZ4/Bht3Ep/0lMhNdeg8qVYdo0GDxY/iOvUMF1ZCqTNIErUamS1InnyiW1v3XqyAx9927XkSlfS06Gd96Rmff338Pzz+usO0RpAlf/FhUFd90Fc+dKa9ARI+D0addRqezYs0fKAf/8E3LmhBUrpLS0UiXXkals0ASu/u2ii+Cpp2DLFmnM/9RTUL067N/vOjKVWWfOyBJZlSowcSKsWSO3Fyqk3QPDgCZwdX5lysiBtIsWSXfD2Fi5XXdyhob58+U/3ieflIOwt2yBli1dR6V8SBO4Sl/LlrKxwxg5tLZsWXj6aVkrV8HJWrlQGR0Nn38unQPLlnUdlfIxTeAqc3LkgNatYfhwWR9/4w1ZV1Xu7dwptf27d8t/tpMnw6ZNclalCkuawFXmFCsmieHrr2VJpX9/aXJ0/LjryCJTcrLMsNu1kzLA0aNh3jy5r1gxuWCpwla2zsRUEaxRI7kg9s03spPv0kvl9meegXr14PrrtceKvyUlyQELGzdKsn76aejbF0qUcB2ZCpBsHeiQWXqgQ5g7cQKqVpWStbJl4b77pBVpoUKuIwsfP/4ovUoGDZLxiy9Kv5IuXaSGX4UlvxzooNQ58uaFHTvgk09kWeWxxyS5zJ/vOrLQlpQEs2ZB8+ZQrRoMGSKHdIAk8ltu0eQdoTSBK9+KiZHZ4LJl8qt9797S8RDgiy+kPWlCgtsYQ8mGDXD55dCpE/z8s+ya3LNHSjxVxNMErvynZk14+23pOQ6yVb97d0k+zz2n3Q/PZ/16+c8OoGJF6U8zY4ZUmQwe7P1+qoinCVwFzrRpsj2/Zk1ZBihdWnYJRjpr4YcfYOxY6U9St64kaoCLL4Y5c2QGnkNrDtS5NIGrwImOluqUhQvhp5/g3nshf3657/Bh2TXYr5/s/vz9d6eh+pW1cq3A48475c9+//3wxx/w6quweLG7+FTI0CoUFRy2b5cE9vXX3q36NWrAm29KyWIos1Z6bS9d6n3fv1823JQqJcslv/4KzZpBuXKuo1VB6HxVKPo7mQoOFSrAggVygTMuzpvoPOu9U6bAqFFyJFyzZnK2p6f2PBjt3i1VOQULSlVOt25ye7Fi0lemaVNvC9fmzZ2FqUJbtmbgxpjWwGggGnjPWvvChR6vM3CVZXPmyNLC6tXSYS86WtaKly6FPHlg7165rXDhwB8FlpQkFx63bfP+x7NzJ4wZIztV4+NlWahpU2k/oF0AVSadbwae5QRujIkGtgEtgb3At0APa+2P5/saTeAq206dkh2gS5dKWd306XJ7+/aywQXk0OaiReVi6ccfy22TJ8t2/yJF5L6iRaF4cZkhp/b333DokJwVmiuXbEwC6ej3229yu+f+Ll3gpZfkazx12PnzQ+PGkqxvuEFKAJXKJn8soVwJbLfW7kx5gQ+BDsB5E7hS2ZY7tyTHpk3Pvf3hh6V3eeoEW6CA9/7Ro2HdunO/pkEDWLlSPm/bFlatgmPHvPd36iTlewAffCAfixaV/wQqVJALjyD9RubNky3s1atrCwEVMNlJ4CWAPanGe4Gr/vkgY0xfoC9A6dKls/FySl1AWkk9tTVrpMLDk9wPHjz3GLHLL5ek7JmdFykC5ct779+798JLH23bZv/PoFQmZSeBp/XT/K/1GGvtOGAcyBJKNl5PqazLkUMuIBYrlvb9Y8de+Ot13VoFoezUge8FSqUalwT0zC2llAqQ7CTwb4GKxphyxpicQHdgjm/CUkoplZ4sL6FYaxONMf2Bz5EywvettT/4LDKllFIXlK2NPNba+YD2ClVKKQe0F4pSSoUoTeBKKRWiNIErpVSI0gSulFIhKqDtZI0xh4BdAXvBfysMhHGj6UzT74eXfi/Opd+Pc7n+fpSx1v7rKKaAJnDXjDFxaTWEiVT6/fDS78W59PtxrmD9fugSilJKhShN4EopFaIiLYGPcx1AkNHvh5d+L86l349zBeX3I6LWwJVSKpxE2gxcKaXChiZwpZQKURGbwI0xA4wx1hhT2HUsrhhj/s8Ys9UYs8kYM9MYk991TC4YY1obY34yxmw3xgx2HY9LxphSxpilxpgtxpgfjDEPuo7JNWNMtDHmO2PMXNex/FNEJnBjTCnkMObdrmNxbDFQ3VpbEzmg+nHH8QRcyuHcbwBtgKpAD2NMVbdROZUIPGqtvQK4GugX4d8PgAeBLa6DSEtEJnDgVeAx0jgCLpJYaxdZaxNThmuQU5UizdnDua21fwOew7kjkrX2gLV2fcrnfyKJq4TbqNwxxpQErgfecx1LWiIugRtj2gP7rLUbXccSZO4AFrgOwoG0DueO2ISVmjGmLFAHWOs4FJdeQyZ7yY7jSFO2DnQIVsaYJUDxNO56EngCuC6wEblzoe+FtXZ2ymOeRH51nhrI2IJEhg7njjTGmLzAp8BD1trjruNxwRjTDjhorV1njGniOJw0hWUCt9a2SOt2Y0wNoByw0cgp4yWB9caYK621vwUwxIA53/fCwxjTG2gHNLeRuSlAD+f+B2NMDJK8p1prZ7iOx6GGQHtjTFvgIuBSY8wUa+2tjuM6K6I38hhjfgXqWWsjsuuaMaY1MApobK095DoeF4wxOZALuM2Bfchh3TdH6vmuRmY2E4HD1tqHHIcTNFJm4AOste0ch3KOiFsDV+cYC1wCLDbGbDDGvO06oEBLuYjrOZx7C/BxpCbvFA2BnkCzlJ+JDSkzUBWEInoGrpRSoUxn4EopFaI0gSulVIjSBK6UUiFKE7hSSoUoTeBKKRWiNIErpVSI0gSulFIh6v8Bf/drSYayo60AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-4.5, 5, 0.5)\n",
    "\n",
    "y1 = x**2\n",
    "y2 = x+30\n",
    "y3 = x**2 + 10\n",
    "\n",
    "plt.plot(x, y1, color='red', linestyle='dashed')\n",
    "plt.plot(x, y2, 'b:')\n",
    "plt.plot(x, y3, color='#00FF00', linestyle='-.')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fdc7d75248>]"
      ]
     },
     "execution_count": 10,
     "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": [
    "x = np.linspace(0, 10, 100)\n",
    "y = x**2\n",
    "\n",
    "plt.plot(x, y, '--r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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": [
    "plt.plot(x, y, '--r')\n",
    "plt.xlim(-20, 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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": [
    "plt.plot(x, y, '--r')\n",
    "plt.ylim(-20, 120)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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": [
    "plt.plot(x, y, '--r')\n",
    "plt.axis([-20, 30, -20, 120])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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": [
    "x = np.linspace(0, 10, 100)\n",
    "y1 = x**2\n",
    "y2 = x**3\n",
    "\n",
    "plt.plot(x, y1, 'r', label='라인1')\n",
    "plt.plot(x, y2, 'g', label='라인2')\n",
    "\n",
    "plt.xlabel('x 라벨')\n",
    "plt.ylabel('y 라벨')\n",
    "\n",
    "# plt.legend(loc=2)\n",
    "plt.legend(bbox_to_anchor=(1.04, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('font', family='Malgun Gothic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(131)\n",
    "plt.plot([1, 2], color='r')\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.plot([1, 2], color='g')\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.plot([1, 2], color='b')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "plt.plot([1, 2], color='r')\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.plot([1, 2], color='g')\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.plot([1, 2], color='b')\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.plot([1, 2], color='y')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "grid = plt.GridSpec(2, 3)\n",
    "\n",
    "plt.subplot(grid[0, 0])\n",
    "plt.plot([1, 2], color='r')\n",
    "\n",
    "plt.subplot(grid[0, 1])\n",
    "plt.plot([1, 2], color='g')\n",
    "\n",
    "plt.subplot(grid[0, 2])\n",
    "plt.plot([1, 2], color='b')\n",
    "\n",
    "plt.subplot(grid[1, :2])\n",
    "plt.plot([1, 2], color='y')\n",
    "\n",
    "plt.subplot(grid[1, 2])\n",
    "plt.plot([1, 2], color='c')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "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": [
    "x = np.random.rand(30)\n",
    "y = np.random.rand(30)\n",
    "a = np.random.rand(30)\n",
    "b = np.random.rand(30)\n",
    "e = np.random.rand(30)\n",
    "f = np.random.rand(30)\n",
    "color = np.random.rand(30)\n",
    "\n",
    "size = 100*np.random.rand(30)\n",
    "\n",
    "\n",
    "plt.scatter(x, y, s=size, c=color, alpha=0.5, marker='o')\n",
    "plt.scatter(a, b, s=size, c=color, alpha=0.5, marker='^')\n",
    "plt.scatter(e, f, s=size, c=color, alpha=0.5, marker='+')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "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": [
    "data = [np.random.randint(1, 7) for i in range(10)]\n",
    "plt.hist(data, bins=6, alpha=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "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": [
    "plt.bar(['10대', '20대', '30대', '40대', '50대'], [np.random.randint(1, 50) for i in range(5)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "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": [
    "plt.barh(['10대', '20대', '30대', '40대', '50대'], [np.random.randint(1, 50) for i in range(5)], color='g')\n",
    "plt.title('제목')\n",
    "plt.xlabel('나이')\n",
    "plt.ylabel('값')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('titanic_train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'].fillna(df['Age'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def changeAge(x):\n",
    "    if x // 10 == 0:\n",
    "        return '유아'\n",
    "    elif x // 10 == 1:\n",
    "        return '10대'\n",
    "    elif x // 10 == 2:\n",
    "        return '20대'\n",
    "    elif x // 10 == 3:\n",
    "        return '30대'\n",
    "    elif x // 10 == 4:\n",
    "        return '40대'\n",
    "    elif x // 10 == 5:\n",
    "        return '50대'\n",
    "    elif x // 10 == 6:\n",
    "        return '60대'\n",
    "    else:\n",
    "        return '노인'\n",
    "    \n",
    "df['AgeRange'] = df['Age'].apply(changeAge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgeRange\n",
       "10대     41\n",
       "20대    129\n",
       "30대     73\n",
       "40대     34\n",
       "50대     20\n",
       "60대      6\n",
       "노인       1\n",
       "유아      38\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survived_by_ages = df.groupby('AgeRange')['Survived'].sum()\n",
    "survived_by_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['유아', '10대', '20대', '30대', '40대', '50대', '60대', '노인'], dtype='object', name='AgeRange')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survived_by_ages = survived_by_ages.reindex(['유아', '10대', '20대', '30대', '40대', '50대', '60대', '노인'])\n",
    "survived_by_ages.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IUs8ZBJLUcwaBJPWcQSBJPWcQSFLPGQSS1HMGgST1nEEgST1nEEhSzxkEktRzBoEk9ZxBIEk9ZxBIUs8ZBJLUcwaBJPWcQSBJPWcQSFLPGQSS1HMGgST1nEEgST1nEEhSzxkEktRzYwmCJF9LclCSPZJcnmR1kjPHUYsk9V3nQZDkcOBR7eQ5wNFVtR+wLMk+XdcjSX23TZcHS7Ij8Abg/PbYS6pqbbv4YmBf4Joua5oUOSVjPX6dVGM9vqTx6bpF8OfAacAmYEdg/cCy9cDSmTZKsiLJmiRr1q1bN/oqJalHOguCJK8Dbqmqa9tZtwM7DayyFJjxLF9VK6tqeVUtn5qaGmmdktQ3XXYNvRa4K8nHgWcCBwB7JNmtqr4PHAac0mE9kiQ6DIKqesn06yQnA1+h6Q66KMkG4JKqunGUNdgPL0kP1Olg8bSqOnlgct9x1CBJanhDmST1nEEgST1nEEhSzxkEktRzBoEk9dxYrhqS+sDLlfVQYYtAknrOIJCknjMIJKnnDAJJ6jmDQJJ6ziCQpJ4zCCSp5wwCSeo5g0CSes4gkKSeMwgkqecMAknqOYNAknrOIJCknjMIJKnnDAJJ6jmDQJJ6ziCQpJ7r9KMqk+wEfBB4LE0IHQlsB3wAWAJcXVUndFmTJPVd159ZvANwfFXdluQlwNuBJwFHV9XaJBcm2aeqrum4LknqrU67hqrqtqq6rZ38KbABWFJVa9t5FwP7dlmTJPVd1y0CAJLsRtMaOA74s4FF64GnzbD+CmAFwO67795FidLDWk7JWI9fJ9VYj6/763ywOMmhwLuBNwM/AXYaWLwUWDe8TVWtrKrlVbV8amqqkzolqS86DYIkzwJeWlXHVNX6qrob2L5tIQAcBlzeZU2S1Hdddw0dBOyfZFU7fQtwPHBRkg3AJVV1Y8c1SVKvdRoEVXUGcMYMixwglqQx8YYySeo5g0CSes4gkKSeMwgkqecMAknqOYNAknrOIJCknjMIJKnnDAJJ6jmDQJJ6ziCQpJ4zCCSp5wwCSeo5g0CSes4gkKSeMwgkqecMAknqOYNAknqu688slqQ55ZSM9fh1Uo31+ONgi0CSes4gkKSeMwgkqecMAknqOYNAknpuIoIgyalJrkiyOskzxl2PJPXJ2IMgyf7ArlX1QuAY4MwxlyRJvTIJ9xEcCFwAUFU3JNl5zPVI0owervc4pGq8N08kORd4f1Xd0E5/CXhBVW0aWGcFsKKd3AO4qfNCG7sAPx7TsTfH2hbH2hbH2hZnnLU9saqmZlowCS2CnwFLB6Y3DYYAQFWtBFZ2WtUMkqypquXjrmMm1rY41rY41rY4k1rb2McIgKuAwwGSPB24dbzlSFK/TEKL4DPAIUmuAu6gGTCWJHVk7EHQdgMdO+465mns3VNzsLbFsbbFsbbFmcjaxj5YLEkar0kYI3hYSPKycdcwmySPSPI7465jJta2ONa2OJNc2zgZBEOSvCDJ5wa+Pp/ktweWTyX5dJLLk3wsyQ7touPGVPKvJLksyRfar09Oz6O5KusN1mZt1jb+2maT5HlJjh/Hscc+RjCBzgZeVFU/A0jyKOCLSZ5XVfcCfwq8r6quSHIk8F+A946yoCRTwNtoLq09MckewAeAJcDVVXXC9LpV9aJR1jJU107AB4HH0rypOBLYbkJq2w64GNgRCPBa4NcnobahOr8G/BHw3UmpLck3gfXt5ErgugmqbW/gLGBr4NPt10TUNlDjZVV10BzLtwbOAZ4GbAt8qKo+BmwPPLKTIocYBA90F7B3ktVAAc8DftGGAMATquqK9vXfAv/QQU1nAzcD062Pc4Cjq2ptkguT7FNV13RQx7AdgOOr6rYkLwHeDjxpQmrbCLy6qu5K8nqakNp/QmoDIMnhwKPayXMmqLYfDp5Ek1w6CbUl2RZ4N/CyqvrpJNW2QL8HfKeqjmtD4cL2RtqxsWvogV5Nc8L4O5pHX7wQeOXA8l/d7NaGw/Q951sluSjJUVu6oKo6ArgSIMk2wJKqWtsuvhjYd47NnwN8aEvX1NZ1W1Xd1k7+FNgwQbVtqqq72smnAt+clNoAkuxI00VxPs0bsompjYG/8Un6ewMOBr4HXNB2ze49QbUtxLNpLpufPod8ARjrwzYNgiFV9YOqendVvbz9OrGq/m1gla3a/xwk2YXm3gdoum0Or6rzRlziFPc122lfL51lXYDrGfG9GUl2o2kNnM0E1ZbkhCTfApYDX5uk2oA/B06jOenuyITUluQRwJOTXNn2rT9uUmqjCfSdgUOBo4FPTFBtg34zyaoZvp7SLr+O5hlrJAnwAuAbHdQ1K7uGWkleDPxxO/ls4P+0r59F826ygNNpWgl/kmQlcBJwbsel3g7sNDC9FFg3PdH+sW0NPAL4Dk3d9zIiSQ4FXgq8maZbbWJqq6ozgTOTHAy8b1JqS/I64JaqurbtUrt9Umqrqp8DT26P+2Im6PdG0933+araCKxN8hPuf+If69/btOnn+bRdktvM8Obwb4DTk1xMM0bw0aq6Jcnuo65tNgZBq6r+EfhHgCRfmO4jba80OLT946Od91LgjTSDPFfMtL8R1nl3ku2T7FZV3wcOA05pF/89zc15G2haKuePspYkzwJeWlXHDMyblNp2BO6s5kaZW2hOCBNRG83A9V1JPg48EzgA2GMSakuy9cB42Dqak+ek/N6+TNPy/EiSXdtjbjchtc1bVW1K8j+Auwe6L8fKIFiEqvoHuhkkns3xwEVJNgCXVNWNbV0PaJ00Lc+ROQjYP8mqdvqWCaptT+Ccto67gbfSPPlx7LVV1UsGjnMy8BWabo2x1wY8JcmHgXvar2OBR09CbVX11SQ3tRdybKT5W9tqEmpbhGOBLwGrpmdU1ZfaeZ0zCB4iqmoV7R9NVV3L3INinaiqM4AzZlg0CbVdC+w3NPu7TEBtg6rq5IHJsddWVTfxwN/bd5iA2gCq6kTgxKHZE1HbUPfy4PyjBiZPr6rLOitqnnzEhCR1LMm7aJ66fPvQouur6m2d12MQSFK/efmoJPWcQSBJPWcQSA9Seznx5tbZLcnEfUShBAaBNG9JvjDLorcMrLNDkr9t78z9VHv3OTR3xc76IDJpnAwCacv6z8A/VdULuO8xEtJEMwikLeu3aB4hQFX9E/AfxluOtHneUCY9eFNJLgI+CdTAIxqgg2fbSA+WLQLpwVvXPnn2k8CdSXaGXz0/3/9jmni2CKQFSPJRmkdHb0Xz5MizhlZZCZyV5D00z5O5oNsKpYUzCKT5O5jmKaabgHunu4AGH2ZWVava6WOBL1fVp7ovU1oYg0Cap6r6JfDLeay3ioGnSkqTzv5LSeo5HzonST1ni0CSes4gkKSeMwgkqecMAknqOYNAknrOIJCknjMIJKnn/j8thiObHQfaHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(survived_by_ages.index, survived_by_ages.values, color='g')\n",
    "plt.title('나이대별 타이타닉 생존자')\n",
    "plt.xlabel('나이')\n",
    "plt.ylabel('생존자')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    233\n",
       "male      109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survived_by_sex = df.groupby('Sex')['Survived'].sum()\n",
    "survived_by_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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": [
    "plt.bar(survived_by_sex.index, survived_by_sex.values, color='g')\n",
    "plt.title(\"성별 타이타닉 생존자\")\n",
    "plt.xlabel('성별')\n",
    "plt.ylabel('생존자')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "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>date</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>death</th>\n",
       "      <th>released</th>\n",
       "      <th>candidate</th>\n",
       "      <th>negative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20200121</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20200122</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20200123</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20200124</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20200125</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  confirmed  death  released  candidate  negative\n",
       "0  20200121          1      0         0          0         0\n",
       "1  20200122          1      0         0          0         0\n",
       "2  20200123          1      0         0          0         0\n",
       "3  20200124          2      0         0          0         0\n",
       "4  20200125          2      0         0          0         0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona = pd.read_csv('./kr_daily.csv')\n",
    "corona.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 133 entries, 0 to 132\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype\n",
      "---  ------     --------------  -----\n",
      " 0   date       133 non-null    int64\n",
      " 1   confirmed  133 non-null    int64\n",
      " 2   death      133 non-null    int64\n",
      " 3   released   133 non-null    int64\n",
      " 4   candidate  133 non-null    int64\n",
      " 5   negative   133 non-null    int64\n",
      "dtypes: int64(6)\n",
      "memory usage: 6.4 KB\n"
     ]
    }
   ],
   "source": [
    "corona.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_date(x):\n",
    "    x = str(x)\n",
    "    return x[:4] + '-' + x[4:6] + '-' + x[6:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-01-25'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "change_date('20200125')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "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>date</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>death</th>\n",
       "      <th>released</th>\n",
       "      <th>candidate</th>\n",
       "      <th>negative</th>\n",
       "      <th>date_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20200121</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20200122</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20200123</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20200124</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20200125</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  confirmed  death  released  candidate  negative   date_text\n",
       "0  20200121          1      0         0          0         0  2020-01-21\n",
       "1  20200122          1      0         0          0         0  2020-01-22\n",
       "2  20200123          1      0         0          0         0  2020-01-23\n",
       "3  20200124          2      0         0          0         0  2020-01-24\n",
       "4  20200125          2      0         0          0         0  2020-01-25"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona['date_text'] = corona['date'].apply(change_date)\n",
    "corona.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "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>date</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>death</th>\n",
       "      <th>released</th>\n",
       "      <th>candidate</th>\n",
       "      <th>negative</th>\n",
       "      <th>date_text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_text</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-28</th>\n",
       "      <td>20200528</td>\n",
       "      <td>11344</td>\n",
       "      <td>269</td>\n",
       "      <td>10340</td>\n",
       "      <td>857322</td>\n",
       "      <td>834952</td>\n",
       "      <td>2020-05-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-29</th>\n",
       "      <td>20200529</td>\n",
       "      <td>11402</td>\n",
       "      <td>269</td>\n",
       "      <td>10363</td>\n",
       "      <td>873718</td>\n",
       "      <td>849161</td>\n",
       "      <td>2020-05-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-30</th>\n",
       "      <td>20200530</td>\n",
       "      <td>11441</td>\n",
       "      <td>269</td>\n",
       "      <td>10398</td>\n",
       "      <td>891460</td>\n",
       "      <td>865162</td>\n",
       "      <td>2020-05-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-31</th>\n",
       "      <td>20200531</td>\n",
       "      <td>11468</td>\n",
       "      <td>270</td>\n",
       "      <td>10405</td>\n",
       "      <td>899354</td>\n",
       "      <td>876060</td>\n",
       "      <td>2020-05-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>20200601</td>\n",
       "      <td>11503</td>\n",
       "      <td>271</td>\n",
       "      <td>10422</td>\n",
       "      <td>909888</td>\n",
       "      <td>885830</td>\n",
       "      <td>2020-06-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                date  confirmed  death  released  candidate  negative  \\\n",
       "date_text                                                               \n",
       "2020-05-28  20200528      11344    269     10340     857322    834952   \n",
       "2020-05-29  20200529      11402    269     10363     873718    849161   \n",
       "2020-05-30  20200530      11441    269     10398     891460    865162   \n",
       "2020-05-31  20200531      11468    270     10405     899354    876060   \n",
       "2020-06-01  20200601      11503    271     10422     909888    885830   \n",
       "\n",
       "             date_text  \n",
       "date_text               \n",
       "2020-05-28  2020-05-28  \n",
       "2020-05-29  2020-05-29  \n",
       "2020-05-30  2020-05-30  \n",
       "2020-05-31  2020-05-31  \n",
       "2020-06-01  2020-06-01  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona.index = corona['date_text']\n",
    "corona.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.title('확진자')\n",
    "plt.xticks(rotation=70)\n",
    "plt.plot(corona['2020-05-18':].index, corona['2020-05-18':]['confirmed'].values, color='red', linestyle='dashed')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title('사망자')\n",
    "plt.xticks(rotation=70)\n",
    "plt.bar(corona['2020-05-18':].index, corona['2020-05-18':]['death'].values, color='red', linestyle='dashed')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}