9. python 시각화.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 필요한 모듈 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 주피터 노트북을 사용하는 경우에는 다음처럼 매직(magic) 명령으로 노트북 내부에 그림을 표시하도록 지정해야 한다.\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/예제실습/matplotlib인자.jpg\" alt=\" matplotlib\" style=\"width: 1200px;\"/>\n",
    "\n",
    "#### 색상: https://matplotlib.org/examples/color/named_colors.html\n",
    "#### 기타 스타일: https://matplotlib.org/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA 할 때 bar, scatter 자주 씀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. line 그리기\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1) plt.plot(data,'rs--')\n",
    "### - 색깔(color), 마커(marker), 선 종류(line style)의 순서로 지정한다. 만약 이 중 일부가 생략되면 디폴트값이 적용\n",
    "### - list, series 둘다 가능\n",
    "### - 기본적으로 해당 자료구조의 index가 x축 value가 y축으로 들어가게됨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     1\n",
       "b     2\n",
       "c     4\n",
       "d     8\n",
       "e    16\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr = pd.Series([1, 2, 4, 8, 16], index=['a', 'b', 'c', 'd', 'e'])\n",
    "sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22fbd3a3d88>]"
      ]
     },
     "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": [
    "# line을 그리는 함수\n",
    "plt.plot(sr)\n",
    "# jupyter notebook에서는 셀마다 실행하기 때문에 필요 없지만 다른 interpreter방식에서는 적어줘야함\n",
    "# series로 넣으면 알아서 index가 아래로, value가 y로 들어갑"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 실습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MFj4KdTUxDVfFP02WPhNaqL5zp9fR+NS6D2HmZXDQ8XDcP+0ocn8kBXpeA2csgPanwqyx8N+jYevs2MSrEoImS58RsWVEr70G2dleR+NDG2fAZz+HVgNg+HuQ1iz81zbvCMPesQl211qYXAiz74KaCvfiVQlDk6UPDRgAJ59sr+ucRIjS72HamZDdDU6YDOktI38PEehyPpyxELpfDgv+CJMGwoZpUQ9XJRZNlj529922/lIBZYth6khIbwUn/Bey2jTt/TLz4OgX4cQpYGrhkxFazK72S5Olj9XVwQsvwPffex2Jx3augU9PsaPCEz+G7M7Re+92J8OoedDn1mAxe/G70Xt/lTA0WfrYHXdAQQHccksS745XbrSJsrrMjihbutBePq05HP5np5i9AKaP1mJ29ROaLH0sNxceeMA2CU7KQvWqbTD1NDsZM+IDaD3Q3e3lF8LIb4PF7B/0heUvJfFfKhVKk6XPBQrV77oryX5na3bBtLNg+3w7g93m2Nhsd08x+xzI7Q9f/T87si1fEZvtK9/SZOlz6enw0kt2GWToApWEVlsFM86DzV/AMa9Ch9NiH0PLXnBykVPM/g180B8WPqbF7ElMk2UcGDoUeve21+vqvI3FdXW1MPNy+HESDHnWlvl4JbSYvd0pMOtWLWZPYpos40RNDZx3Htx7r9eRuMgY+PbXsOZNO+HS41deR2Q17wjH/+enxey1lV5HpmJIk2WcSEuDrCzbUX3tWq+jccmcO2HZc9D3TlvK4ycNFbN/OBA2Tvc6MhUjmizjyLhxdvB1991eR+KCBQ/bS8/rYOA4r6PZt9Bi9rpq+Hg4fH2tFrMnAU2WcaRrV7jxRnjllQQrVF/6rG3g2/UiKPy/+JjJancynDEPet9iGw9rMXvC02QZZ+680xaq//73XkcSJXtarZ0BQyfsu9WaH6VlwxGPwqlfBovZP7tAi9kTVBx9MxXYQvV//xtefNHrSKJgT6u1YXDcWwduteZX+UOcYvZxUPyeFrMnKE2WcWjYMMjPt2VEtbVeR9NIe7Vaez+yVmt+lJIO/e7SYvYEpskyTpWWwpFHwt//7nUkjVA6q+mt1vxqTzH732DL11rMnkBcS5YikiUiX4vIHBGZLyK/d+7vLiJfichSEXlTRDLciiGRtW4NLVrAffdBWZnX0USgbLFd7x2tVmt+JCnQ81o4c2FIMftQ2DrH68hUE7g5stwNnGiMGQgMAkaKyNHAw8ATxpiewFbgKhdjSFgi8OijsGkTPPyw19GEaa9Wa1Oi22rNj/YqZl9ji9nn3K3F7HHKtWRprHLnZrpzMcCJwL+c+ycAo92KIdEVFsIll8RJofperdY+sicSSwZ7FbNfCvMf0mL2OOXqMUsRSRWR2cBGYAqwHNhmjAkcwCkGOkZ9wxu20IOMJFhIHSxUf/JJryPZj6rttsv5nlZrg7yOKPYy8+Dol+oVs1+nxexxREwMyhtEpBXwDnAf8JIxpodzf2fgQ2PMYQ28ZgwwBqBt27aD33jjjbC3150McupgbkpVNML3vXnzcundu4z0dP+VqqTUVTKg9DZaVi3kh7xxlGYd6er2ysvLadGihavbaKqUugq673iJTjvfpioljyW5N7Cl2XFehxX3GvPZn3DCCd8ZYwrDerIxJiYX4H5gLLAZSHPuGwp8dKDXDh482ESqaOpUe6Wmxpj5y4wp3xXxe8SbXbuMqavzOooQNbuN+fR0Y14VY1a9GZNNTg187vFg89fGfDDAmFcxZsb5xuxa73VEca0xnz3wrQkzh7k5G97GGVEiIs2Ak4GFwFTgPOdpVwCurBHbM8baWQHbdkBNvBYkhmfRIujRw0cd1UNbrR35LHS9wOuI/Ocnxex9tJjdx9w8ZtkemCoic4FvgCnGmInA7cDNIrIMyAdecDEGaNkCjjoMcp3h+boNsGGLq5v0wiGH2FKisWOhutrjYEJbrQ16BHroKSr3qaFi9qmnajG7D7k5Gz7XGHO4MWaAMaa/MeYPzv0rjDFHGmN6GGPON8bsdiuGPVJTA0HBpq2wZZvrm4y19HR45BFYvBief97jYObcFWy11nesx8HEidBi9s1faTG7DyXXCh4RGNgLDu1mb++uguVrbWfdBHD22XD88XD//R4Wqi94BBb8yf+t1vxoTzH7AtvVSIvZfSW5kiXYhJnmjDRLy6BkE1QnxvFMEXjsMVuo/tprHgSw7DmYfXt8tVrzo+ad4Ph34dg3tZjdR5IvWYZqX2CPZzbLtLfXrofyXd7G1ESFhfDNN3DNNTHe8Oo3bRPcDqPir9WaH4nYSbEzFmgxu0/oNzrDaQtWXWOT5cZSb+OJgsJC+7u2PVb1ziWT4ItLoc1x8d1qzY8y820x+wn/hboqLWb3kCbLgPQ0GNIfura3t3fssrPmcVrGMWUKdOwYg47qG2fAjNBWa81d3mCSan8KnPED9L4Zlj8HH/Sz5UYqZjRZhkpPC86c/7jJTv7UxueSySOPtCc4u+UWF/P9nlZrXWyrtYxclzakAKcz+2NOZ/Z8mH4OfPYLqNjgdWRJQZPlvvTsAoN628kgY2x9ZhzNmufmwgMPQFERfPCBCxvYq9XalMRsteZXgWL2AQ9C8X9sMfuK8XG7FxQvIkqWIpIiIgnUqXU/RKB5lr2+YycsW2trNOPINdfAoYe6UKiebK3W/CglHfrfDafPgdx+8OUvtZjdZQdMliLymoi0FJFsYAGwWESSq9K4ZQsY3BfaFdjb23bExax5aKH69GhNoiZrqzW/yu0NJ0+DIU9rMbvLwhlZ9jXGlGH7Tn4IdAEuczUqP2rR3I6kjLHHMheviovdnrPPhgUL4KSTovBm2mrNnyTFLgL4STH7XK8jSyjhJMt0EUnHJst3jTHVhPSpSDoiMOBQ6N3dXq+rg02lvk2cItC7t72+cWMT3qhmF0w7C7b/AMP+DW2OjUp8Kor2FLO/ATtXw+TBWsweReEky2eBVUA2MF1EugLxdNaX6EtPg2znbIQbtsCCFVC209uYDuDZZ6F790Z2VK+tgs/Oh02fwdBXoMPIqMenokQEuv7Cnv8nUMw+aZAt8VJNcsBkaYx5yhjT0RgzymkBtxo4IQaxxYd2BXBYz2BXo21lvpw1P+00e9rce+6J8IV1tfDlFVDyodNq7ReuxKeiLLSYvXY3fHy8LWavTu5xTlOEM8HTVkReEJFJzu2+2D6UCuxf8jynvrCmFn5YDkvXeBtTA7p1gxtugFdeiaBQ3Rj49npY/QYMelhbrcWj+sXsE/tqMXsjhbMbPh74COjg3F4C3OhWQHEtLdUez+zmnFaoutpXs+Z33QV5eXDrrWEeYp1zFyx7FvreAX1vcz0+5ZJAMfspM+25gLSYvVHCSZYFxph/AnUAxp5sLDHa9LihZXawMcfqH2HWQh9047UChepffQXLlx/gyYFWaz2uhYEPxSI85baCI+E0LWZvrHCS5U4RyceZAXfO/a2r+MPRtQP0PtgWPIKdBPL4i3nNNbBsmT0FxT7tabV2obZaSzSpGVrM3kjhJMubgfeAQ0Tkc+Bl4LeuRpUo0tOgTWt7fWeFHWUWe7vrk54O7dvbnL2moUOre7VaexlSUmMeo4qBnxSzHwYLH7cTeqpB4cyGfw8MB44BrgH6GWO02jVSzbNsh/bAKqDKKk9PonbLLTBkSL2O6iWTtdVaMgkUs58xH9qeCLNu0WL2/QhnNvxy4GJgMHAEcJFzn4qEiG02nJ5mby9eaUeaHu2WX3yxLVJ/5BHnjo2fwYyfQavDtNVassnuDMPfc4rZVznF7PdoMXs94eyGDwm5DAMeAM52MabkcHAnO2seOB5Y4f5520IVFsIll9jTUBT/MN+2WmveWVutJavQYvZul8D8cVrMXk84u+G/DblcDRwOZLgfWoLLyQ4ez9yyDb6eB1tjWzA8bhwYU8c9v50H6S1tB6Gsg2Iag/KZzHwYOt42SYmXYvaynbB0teubaUw/y11Az2gHktRatoAu7YOrgKqqY7J73rVgLTeOeoapPxzLjiGf2Ca+SgG0P9UWs/e6KaSY/X2vowqq3G2XpAFUVsLGUprhbtVGOMcs3xeR95zLRGAx8K6rUSWb9DTo3hFSUmxjjtmLbVcjN1VuhKmncM+5D7Jw1hZyOurfP1VPWjYMftwWs2e0huln+6OYfWcFfDUveL6sgtYwdCAVLvf3SQvjOY+GXK8BVhtjil2KR4lA57aQ6RzpMMae2iItiiU8gVZrO9fQ4rT/wkGD2L0biovhkEOitxmVIAqOhJHfwcJH4If/gfVT4IgnoPvlsanBDbRFzMyAzu1sZckhnaG1c2w9JTYnfAjnmOW0kMvnmihdJgLt2wTXm6/bCN/8ALurovP+gVZr2+bBsLfhoOMAGD0azjzTlz1AlB+kZkD/e+D02ZDbF7680p5WpHylO9urqYFSZ+2LiP3+B34HRKBTW8iK7dTJPpOliOwQkbIGLjtExMdHexNMbgs4KC94yt7aJtRmhrZaO+Yf0OH0PQ9dey0sWgTPP9/EeFViy+0DJ093itm/dDqzR6mYPfQ4/cp1MH9ZsBa57yHQw9tj6vtMlsaYHGNMywYuOcaY5DgPjx/kZNtdDhF7bvOv5kHJpsjf5wCt1s4+G44/Hu6/v16hulL1uVHMvr0cvv4Bdjm1nZ3aBk8YCL5Ychv2zr6IHCQiXQIXN4NS+1HQ2jbrADsZFM6suTHw7W/222pNBB591BaqP/xwlGNWiakpxeyBMwwEmmY3y7SXwJ5Tsyw7UPCRcGbDzxaRpcBKYBq2a/okl+NSDUlPg0O72vMBAawohrlL7Bdvf+bcDcuegb6377fV2pAhdmXPjBkHfkulgHrF7BcfuJg9dInvktWw3tlLyki37Q19liBDhTOy/B/gaGCJMaY7cBLwuatRqfBkN7NfrsBsYEOjzAV/hgV/hB7XwMA/HvAtn3nGnms8RhOMKlFk5sPQCXsXs3/z672L2RettH/cwX7BDu8DPbt6E28jhPMrUW2M2QKkiEiKMWYqoKf184P2beyySbDHer6aB9t3BB9f9neYfRt0+QUU/jWs4z45OfZ7vHkzrNCuXSpS7U+FUfOg142w8mP49AlY6xSz57W0k5WBP+rNs3xxLDJc4dRZbhORFsAM4FUR2Yitt1R+Yoxz3CfL3l71T/j6Gmh/esSt1mpr4eijoUsX+OSTuPo+K6/troLUZjD4CWh9Iaypgpm/hNWvwuD/hWZtvY6w0cIZWU4HWgE3AJOB5cBZbgalGiG7GQzsZY/9lEyGRauh3YMw7F+2Ri4Cqan2fD1Tp8KHH7oUr0o8Fbvhy7n2jKcA3Y+EY4ZA3yuh+B2nM/sEzxtgN1Y4yVKw5+ApAloAbzq75cqPNn4GM863JRfdLw62WovwC3rttdCzJ4wdq4Xqaj9WFtvTp4Ddszmkc3BBhQhkZMW2mN1F4azg+b0xph9wPfakZdNE5GPXI1OR2zrbabXWAY67FLp3c+4vgzmLI1oFlJ5ue10uXKiF6ipETe3e3bEqdsPukPaCndoGz0EVKlDMXvhX2DzTFrMveiKuOrNHMue5EVgPbAG0j5fflC2BT09tuNVaTa2tBUoL5xB10Dnn2EL1udo4O7mF7pWsLoF5S4Mn4etzsD0DQDgkBQ79NZyxANqeAN/fHFed2cOps7xORIqAT4AC4GpjzAC3A1MR2LkWPj3FXj9xyk9brbVpbcs0UlPsF3/eUti89YBvKwKTJ8PTT7sQs4oPO3bCt/ODp3TueJA9Nh74w9uY2b/szrYb/zGvhxSz3+v7zuzhjCy7AjcaY/oZY+43xixwOygVgcpNMPUUqN5ma9xa9mr4eYEvdVW1HRXUhXcMs1kz++/ChVBSEoV4lb8ZA5u3QVm5vZ2VaY/J1NYFb+e2aHqJhAh0uzCkmP1BmHS4PebuU+Ecs7zDGDM7FsGoCO1ptbYahk+EvMMP/JrMDDvKDHRpX78Zlq3d75Kd7dvt6p677opS3Mp/Qhu0LF1tu12BXTU2qFewMXW0BYrZR0yG2gr4eBh8c70vO7PrOo14VVNhm7FumwvHvQ0HDQv/tSLBkcGuSijfud+RQm4uXH89vPwyzJrVxLiV/yxdDbMW2VGliF122O6nAq0AABiESURBVKtbbGPocBqM+sEWsy/9m/86s6PJMj7VVdtWaxtn2FZrHUc1/r0O7mR/OUTsRNCC5bYTdT133gl5efYUunFaJqcCdlXAsjXBvYlWOXuvrMlu5s161/QWtpj91NDO7Bfarv4+oMky3tTVwswroOQDOPKZn7Raa5TAL8bOCti2o8Gema1a2fZtWqgep6qqgwWzlVXw42Yod/4otsmz54DyS0OAgqNsZ/bD/mCL2Sf6o5jdJ/87Kix7Wq29DoP+BD3GRPf9c1vAUYfZE6gBFK8PrsYArrkG+veH5cuju1nlst1VdmXNj5vt7dYtYejAYKs/P0rNgMPudYrZ+zjF7CM9LWbXZBlP9mq1drs720h11pAbA5u2BVv7AxkZ9pjl737nzqZVFK0ugVVO+UJmhj3ckh+ysiaa53Ry055i9v+DzV94WszuWrIUkc4iMlVEForIfBG5wbk/T0SmiMhS59/WbsWQUCJstdZkInYWNNBCq3I3LF9LGvZLOmUK7Nixn9er2Kqt++nKmoqQusVObaF5s9jHFQ2SAode73kxu5sjyxrgFmNMH2w/zOtFpC9wB/CJMaYnttD9DhdjSAyNaLUWFaEjkK1l9nQWNTUsXgynnqod1X0hcBxv7Y+2V2RgSWuvbnZ1TSLZU8z+GuxcGfNidteSpTHmR2PM9871HcBCoCNwDjDBedoEYLRbMSSE1Y1vtRZV7dvY45lZmfTqBRefU8FjjxmK9Vyf3thZAd/NtytsANq1sVUNgRPbJWpfPRHodhGcEfti9pgcsxSRbsDhwFdAW2PMj2ATKrrOfN9KJsPMS6HNsY1qtRZ1gV/EqmrGXboCUwf33ONtSEnDGHv8ONDcOTPD/uHcs7Imw07cJGqSrC+r4CfF7D23PenqKDOyzgqN4DQOfhu7ZLJMwvwwRWQMMAagbdu2FBUVRbTd8vLyiF/jJy13z2Ng6Vh2pXZlTurt1Hz2tdch7SW9AH72s7W8/HJnTjn2ew7vWcFGH/SEjvfPvb4UILC26mias4M65hOSEOas9yIsH8kkNedvdONFmlcuomj6F/YYpxuMMa5dgHRsL8ybQ+5bDLR3rrcHFh/ofQYPHmwiNXXq1Ihf4xuls4z5Z64x7x1qTMUGr6PZp61bjenTx5j3nl5vzBezjamp8Tqk+P7c61u2xpiv5hlTV2dvl+8yprbW25h8rOjTjyN+DfCtCTOfuTayFDuEfAFYaIx5POSh94ArgD85/77rVgxxqWyJbY7aUKs1n2nVCn74AVLkIKjMtWVHxth1xe0K4qc8xS8qKmHdJujewf5f5rYIdooSsStr1D4Zcff75uYxy2OBy4ATRWS2cxmFTZKnOKfXPcW5rSDYas2Yhlut+VBKCtTUCq/+O8suECnbCcvXhtUCTmFX1QRW1uyuhpKNsMNph1bQGrp19M/KmiTn2sjSGPMZ9pQUDTnJre3GrdBWaycV7bvVmg9NmQKXXgrl5XDNNS1gcN/gKGhrmZ0Y0lHRT1XX2JU1XdpB1w52JDl0oO30o3xH/2T5QWNarfnIyJEwbBjcdx+UlQEtmtvdRmPsKHPxSs/X9fpG8QZYuc5eT0+D7h0hv5W9LaKJ0sc0WXqtKa3WfEIEHn0UNm605+3Z64GBh0Lv7vZ6bR1sKk2uxFlXB9tCVtbsrICdu4L/B53a2j8uyvc0WXoptNXa0Fea1mrNY0ceCRddBI89xt6F6unpwWV2GzbDghXBQupkULwB5iyxyw8BDu0K/XsmTz1kAtExv1dMHcy80rZaG/KMbbEf5x56CJYssSPMTp0aeEL7Nva0BIGuRlvLICc7sWbNKyph4Uq7e926JbTNh+zmtmgcNEnGMU2WXtjTau012xSj5zVeRxQV3brBN9/sJx+IBM8pXVML85fZGd/e3WMVYvQZA9ud89W0yrGtmYTgOY4yM+xFxT3dDffC3Hts6/w+t0G/xOojImLP2fPccwc4NJmWatcyd+tgb1dVN9ih3bdCz1m0ZBWs+dFeT02x5zgKtENTCUOTZawtfBTmP2Qb9w5KzBLTV1+1jYIP2FG9ZQu7Ww62/+L3C205jd+tKoFv5geLxfv1gH6HeB2Vcpkmy1ha9jzMGuu0Wns6YY9f/epX0LMnjB0brLc+oG4doE/3YOlM2U7/zJpXVsGK4uAPk9Pcnh0zMLrMbhZsmqwSlibLWFnzFnw9xvtWazGQkWF7XS5cCC+8EOaL0tPt8UuA8l0wa2HwdKxeqK0NjnKrqmDtetjuzOLnt7KdxzVBJhVNlrFQMhm+uMQ/rdZiYPToYKF6xB3Vs5vZEpt2+fZ2ZZWdEIqV2lq7smat09EnJ9uurNHjkElNk6XbNn0OM34Guf1sl+e05ChAFrE1l4MH2wmfiF/cvg2kObvki1bC7EXu7paXbIKVToFoaqpdflgQsrIm0MtTJS0tHXLT1tlQdAY072SblGa08jqimBoyJEqnzT24o50tDyyhrKyCZplNe89AyU+rHHt7Z4U9n3Zg0qZT26bHrRKKjizdUrbUabWWAyd+DM2S95dv5Up49tkmvEHLFsHjmVu2wTc/2PObN0XJRpizOFiu1KMzDOyVsJNuquk0WbphVzF8erJdpXNCfLRac9Mzz8B119nT6DZZbgvbpSfXWQVUVR3e7nllld2V3+IcE2iTZ8t9AiNUTZLqADRZRlvlJtuTsnobnPAR5Pb2OiLP3Xkn5OXBrbdG4bBjerrt8ShiS3dmL4Ilq3/6PGNs+VFgBJqRZu8zTrlPhjP7rr0iVZj0mxJN1WVQdDrsXGUnc/KO8DoiX2jVCu6/Hz79FCZNiuIbi0CndnBQnr1dV7f3Qfglq4Lt0FKclTUFepp61TiaLKOlpgKmnQVb5zit1o73OiJfueaaRhSqH4gIdGhjG1YArNvIULLtiFPEnjf7sB5R2phKdposoyGBWq25JSMD/vxnGD4cKt06W2luC1ZTtffKmjQt+FDRod+kpkrAVmtuOecce3FNyxasoZqDNUEqF+jIsikStNWa2z7/HF580esolIqMJsum2NNqbSz0vd3raOLGX/8Kv/lNvY7qSvmcJsvGCrRaO+RqGPSw1ulF4KGH7GHFe+/1OhKVCMrK4N//3rvFqBs0WTbG8hecVmsXwJC/aaKMULducMMNMGECzJ7tdTQqXj33HJx8MhQUwM9/DosW5bi6PU2WkVrzL6fV2kg7853ArdbcFNVCdZXwamvhs8/g8ceD9334Ifz4I9x8M8yYAb16NXEJ7AHotGEkSj6CLy6GgqEw7O2kaLXmllatbM/LLVvs7pO2hlT1lZXBRx/B++/bxLhli13AdfnldjT5+uvQrFnw+UVF7sajyTJcm74IabU2MWlarbnpqqu8jkD5zYoV0Lq1vbz1lu26n58Po0bBmWfCaadBrtNWNDRRxoImy3BsnQNFo6B5x6RsteYmY+wIISUFLtQS1aRTWwszZ9rR4/vv2+76Tz9tG6+MHg29esHQof7Y89BkeSBlS2HqqdpqzUV/+5s93/gZZ0COu8folQ8EWoaWl0P37rB5s11oNXw4jBljvwdgR5THHedtrKE0We6PtlpzXaCj+lFHwSOPwP/8j9cRKTesWBEcPebkwDvvQIsWdgTZv//eu9d+pbPh+xJotVa1FU6YrK3WXHTkkXDRRTZpaqF6YnnmGejXDw45BG68EUpKYMCA4ON/+ANccIH/EyVosmxYaKu1ERMhb7DXESW8hx6yx6/uucfrSFRjlZXZSZlf/hJ2OifC3L4d2reHJ56AZctgwQL4/e+9jbOxdDe8vpoKmHa2ndQ5/j/aai1GunWDP/4RuuiRjriyaRO89prdvZ4+Haqrbf3sb35jT1Z3++32kgg0WYaqq4bPLoCN0+GYV6HjGV5HlFRuvtnrCNSB1NbCl1/aOtl+/WDdOrt73acP3HSTLe8ZOjQxO+Ml4I/USHtarU20Sxi7XeR1RElp92549FEoLLQH/ZX3GioOv+oqeP55GDjQ7l4fcojXUbpPkyU4rdZ+67Raewh6Xut1RElLBMaPt7t2c+Yk5gglHmzdagvDAQ4/3M5m5+XtXRwO9vNKhkQJmiytuffC0qedVmt3eB1NUsvIsCVEP/sZvPCCPR2Fcl9g9zpQ3lNaanexU1LsSL+gIHF3r8OVxD+6Y+FjMH+ctlrzkdGjbTHyfffBxRdrobrbJkyAW26xu9dpaXD88XD11XayJjMTzj3X6wj9IblLh5a/ALNuhS7na6s1HxGxo5mNG+0oU0XPypXw1FNwyinw7bf2vs6d4fTT4Y037Oz2J5/YSZvMTG9j9ZvkHVnuabV2Ggz9h7Za85mjjoJx4+wvtWqarVtth6eJE2H+fHtfnz52VxvgxBPtRe1fciZLbbUWF+66y+sI4lNZGfz3v7b5xLnn2u48zzxj6x5/9Ss466zkmZSJpuRLloFWay37Oq3Wsr2OSO3H5s1wxx22yHnQIK+j8a+VK4OTM9Om2eONw4fbZJmVBevX239V4yXXMcutc6HoDNtq7YSPtNVaHEhLs00XtKP63mpr4bvvgrdvusmeqqO42B5vnD4dPv44+LgmyqZLnmS5p9VaCzhxirZaixOtWsH999tJh0mTvI7GW4G115dfDm3b2sL9tWvtYw8+CEuX2n6QjzwCw4Yld5mPG5IjWe4qhqmngKl1Wq119ToiFYFrr4UePWDsWKip8Tqa2AqcsfDDD22t4wUXwAcfBGev8/Ls4/372/8j5Z7ET5aVm+HTU2F3qbZai1MZGXY2d8ECW6ieyGpr4fPP7XHafv3g2Wft/UccEdy93rABXnkFfvELyNZD7jGT2AP16jIoGgk7V9pjlNpqLW6de65tDDxqlNeRuKO21s5UT5wY7Bx+/PHQoYN9vF07rTn1mmvJUkReBM4ENhpj+jv35QFvAt2AVcAFxpitbmw/xezWVmsJRCSxel0GZq83bbJ/BFJT7Yz1yJHBtdetdP7RV9zcDR8PjKx33x3AJ8aYnsAnzu3oq6umb+nvbau1oRO01VoCWbrUJpN167yOJHJz5tjzpffvDwcfbGevJ04MHpecNCm4e62J0n9cS5bGmOlAab27zwEmONcnAKOjv2Hbaq1g90wY8lfodnHUN6G8k54OU6bAvfd6HcmBlZXBv/4V7Br+3nt2GWfbtrZz+NKlMGuWbVah/C/WxyzbGmN+BDDG/CgiB+3riSIyBhgD0LZtW4rCPIN6fuXnHFb6GosyL2P9uj6wLrzXqfhx7rkHM358Z4455jt69Cjf67Hy8vKwvytuKCnJYubMfGbOzGfOnFbU1KTw4IPzOPbYLQwcmOacqMtO6RcX6zmHosntz16Mi5W+ItINmBhyzHKbMaZVyONbjTGtD/Q+hYWF5tvAqv9wbJhK0QIYccIJEces/G/bNrtc7/DD7SgztP9JUVERI0aMiFkstbWwY4fdbZ4/3+5iA/TubZcVnnWWtjaLlcZ89iLynTGmMJznxnoHYIOItAdw/t3oylbanqAdhBJYaKH6Rx/FfvuB3esrrrCz1Lfeau/v2xeeflqLwxNVrD/G94ArgD85/74b4+2rBHHttXb983HHxXa7V15pu7hXV9tO4qNG2f6bYP8+X3ddbONRseNm6dDrwAigQESKgfuxSfKfInIVsAY4363tq8SWkWEb1rol0Dl84kT46iu7zjolBQ491BaH6+518nHtozbG7OuMXye5tU2VfKZPtzPjEydGp6P6d9/Z5rgffhgsDh82zHYRb9NG28YlMy1aUHEtM9MmzMaublm5Ev7yF7uUEuxSwvfft0Xhgc7hn35qE6VKbroToeLaUUfBhRfCY4+Fd3Kzurq9T8wV6Bz+2GN2gubUU+3pLHT3WtWnI0sV9x56yB5j3FehellZcORYVWVPVfHoo3DQQfD443b2+uab7eNpaZooVcP0a6HiXvfudungo4/C8cdnM2KE3b2eONGOHouKbAefWbNsE9zJk+Gww3RJoYqMJkuVEO66yxaEd+y4k9/9zh6HBOjVyybSs84KPnfYMG9iVPFNk6VKCK1a2Q7iRUW2MW737rbhRs+eXkemEoUmS5VwTj/dXpSKJp3gUUqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMGiyVEqpMHiSLEVkpIgsFpFlInKHFzEopVQkYp4sRSQV+CtwOtAXuEhE+sY6DqWUioQXI8sjgWXGmBXGmCrgDeAcD+JQSqmweZEsOwJrQ24XO/cppZRvpXmwTWngPvOTJ4mMAcY4N8tFZHGE2ykANkf4GhX/9HNPXo357LuG+0QvkmUx0DnkdiegpP6TjDHPAc81diMi8q0xprCxr1fxST/35OX2Z+/Fbvg3QE8R6S4iGcCFwHsexKGUUmGL+cjSGFMjIr8BPgJSgReNMfNjHYdSSkXCi91wjDEfAh+6vJlG78KruKafe/Jy9bMXY34yt6KUUqoeXe6olFJhiItkKSJ3i8h8EZkrIrNF5KgovvcIEZkYrfdT0Scitc7n/oOIvCUizSN8/fO6Ssz/ovF7LiKjQz9rEblSRDqE3C4SkUbNmPs+WYrIUOBM4AhjzADgZPYualeJr8IYM8gY0x+oAq4N94UikmqM+ZUxZoF74ammiuLv+WjsMuqAK4EODT81Mr5PlkB7YLMxZjeAMWazMaZERAaLyDQR+U5EPhKR9gAiMkhEvnT+Or0jIq2d+/f8RRGRAhFZ5dUPpJpkBtADQET+43z+851FDDj3l4vIH0TkK2Bo4LMXkVQRGe+MUOeJyE1e/RDqJyL9Pb9aRL4RkTki8raINBeRY4CzgT87I9PbgULgVed2s9ANisipIjJTRL539lha7DdCY4yvL0ALYDawBHgaGA6kA18AbZzn/AJbggQwFxjuXP8D8KRzvQgodK4XAKuc6yOAiV7/nHrZ73eg3Pk3DXgXuM65nef82wz4Ach3bhvggpDXFzm/NIOBKSH3t/L6Z9PLns8i0t/z/JDXPgj81rk+Hjiv/mffwHehAJgOZDv33w7ct78YPSkdioQxplxEBgPDgBOAN7H/Of2BKSICtl7zRxHJxf4CTHNePgF4K/ZRqyhrJiKzneszgBec678TkXOd652BnsAWoBZ4u4H3WQEcLCJ/AT4A/uteyCoSkfyeOy/pLyIPAq2wifajCDd5NHZ3/XPnvTOAmft7ge+TJYAxphb7F6FIROYB1wPzjTFDQ5/nJMt9qSF42CHLjTiVayqMMYNC7xCREdjjWkONMbtEpIjg51rpfGf2YozZKiIDgdOw36ELgP/nZuAqfOH+njvGA6ONMXNE5ErsHmIkBLuXcVG4L/D9MUsR6SUiPUPuGgQsBNo4B4URkXQR6WeM2Q5sFZFhznMvAwKjzFXY3TCA89yPXLksF9jqJMre2JHCfolIAZBijHkbuBc4wuUYVZgi+T13Hs/B7k2mA5eEvG6H89i+bgd8CRwrIoHj381F5ND9xRgPI8sWwF9EpBV2dLgM243oOeApZzSZBjwJzAeuAJ5xyktWAL903udR4J8ichnwaWx/BOWCycC1IjIXWIz98h9IR+AlEQkMEu50KzgVsUh/z+8FvgJWA/MIJsQ3gL+LyO+wg6Lx2HxQAewZoRpjNjkj0tdFJNO5+x7sMdMG6QoepZQKg+93w5VSyg80WSqlVBg0WSqlVBg0WSqlVBg0WSqlVBg0Waq4JyLXisjlzvW9uswoFS1aOqQSirOS51ZjzLdex6ISiyZLFXecUeSt2IYZc4HlQDl2ldZ4YB1QAdwN/MoYc67zulOwTTh+FvuoVbzT3XAVV5zlbncDJxpjBgI3BB4zxvwL+Ba4xFlL/iHQR0TaOE/5JfBSjENWCUKTpYo3JwL/MsZsBjDGlO7ricbuNr0CXOosoxsKTIpJlCrhxMPacKVCCXb3O1wvAe8DlcBbxpgaV6JSCU9HlirefAJcICL5ACKSV+/xvbrMGGNKgBJsk4TxMYpRJSAdWaq4YoyZLyLjgGkiUgvMwk7sBIwnpMuMMaYCeBXbbVvPw6MaTWfDVcITkf8DZhljXjjgk5XaB02WKqGJyHfATuAU45wMS6nG0GSplFJh0AkepZQKgyZLpZQKgyZLpZQKgyZLpZQKgyZLpZQKgyZLpZQKw/8HfN0vPbFXnvYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 이미지 크기 변경\n",
    "plt.figure(figsize=(5,5))\n",
    "\n",
    "# plot 그리기\n",
    "plt.plot([\"Seoul\", \"Paris\", \"Seattle\"], [10, 35, 20], '-', color = \"Orange\")\n",
    "plt.plot([\"Seoul\", \"Paris\", \"Seattle\"], [30, 16, 25], ':', color = \"Pink\")\n",
    "plt.plot([\"Seoul\", \"Paris\", \"Seattle\"], [50, 8, 15], '--', color = \"Blue\")\n",
    "\n",
    "# x축 이름 지정\n",
    "plt.xlabel('city')\n",
    "# y축 이름 지정\n",
    "plt.ylabel('values')\n",
    "\n",
    "# 제목 지정\n",
    "plt.title('Example')\n",
    "\n",
    "# 범례 추가\n",
    "# upper, center, lower / left, center, right\n",
    "plt.legend(['a','b','c'], loc = 'upper left')\n",
    "\n",
    "# 축값 변경\n",
    "plt.ylim(0, 50)\n",
    "\n",
    "# grid 표사\n",
    "plt.grid(True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 이미지 크기 변경\n",
    "plt.figure(figsize=(7,7))\n",
    "\n",
    "# plot 그리기\n",
    "plt.plot([\"Seoul\", \"Paris\", \"Seattle\"], [10, 35, 20], 'r1--')\n",
    "plt.plot([\"Seoul\", \"Paris\", \"Seattle\"], [30, 16, 25], 'g2-')\n",
    "plt.plot([\"Seoul\", \"Paris\", \"Seattle\"], [50, 8, 15], 'b3:') # 한 번에 넣을 수 있음\n",
    "\n",
    "# x축 이름 지정\n",
    "plt.xlabel('city')\n",
    "\n",
    "# y축 이름 지정\n",
    "plt.ylabel('value')\n",
    "\n",
    "# 제목 지정\n",
    "plt.title('result')\n",
    "\n",
    "# 범례 추가, 항상 표시해주어야\n",
    "# upper, center, lower / left, center, right\n",
    "plt.legend(['a', 'b', 'c'], loc = 'upper center')\n",
    "\n",
    "# 축값 변경 ... 안 하면 최소 최댓값 default\n",
    "plt.ylim(5, 60)\n",
    "\n",
    "# grid 표사\n",
    "pplt.grid(True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (2) data.plot()\n",
    "## axes = data.plot()으로 지정후 시각화 변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "axes=sr.plot()\n",
    "# plt. 하고 사용 ... 여기서는 plt 없이도 축 설정 변경 가능 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# x축 이름 지정\n",
    "axes.set_xlabel('city')\n",
    "\n",
    "# # y축 이름 지정\n",
    "axes.set_ylabel('value')\n",
    "\n",
    "# # 제목 지정\n",
    "axes.set_title('result')\n",
    "\n",
    "# 범례 추가\n",
    "# upper, center, lower / left, center, right\n",
    "\n",
    "axes.legend('a', loc = 'upper left')\n",
    "\n",
    "# 축값 변경\n",
    "axes.set_ylim(0, 18)\n",
    "\n",
    "# grid 표사\n",
    "axes.grid(True)\n",
    "\n",
    "axes.figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. bar 그리기\n",
    "### plt.bar(x = 'x축 표시', height = 'y축 표시', width = '너비', color = '색깔')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    866\n",
       "b    468\n",
       "c    317\n",
       "d    394\n",
       "e    527\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "sr2 = pd.Series(random.sample(range(100, 1001), 5), index = list('abcde'))\n",
    "sr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'c')"
      ]
     },
     "execution_count": 31,
     "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": [
    "# bar 그리기\n",
    "plt.bar(x=sr2.index, height=sr2.values, width= 0.5, color = 'yellow' )\n",
    "\n",
    "# x축 지정\n",
    "plt.xlabel('a')\n",
    "\n",
    "# y축 지정\n",
    "plt.ylabel('b')\n",
    "\n",
    "# 제목\n",
    "plt.title('c')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22fbea89308>"
      ]
     },
     "execution_count": 38,
     "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": [
    "sr2.plot(kind='bar', rot=0) # default"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 여러개의 그래프를 하나로 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figsize 지정\n",
    "fig = plt.figure(figsize = (5,5))\n",
    "# ax1\n",
    "ax1 = fig.add_subplot(2,2,1) # 2행 2열 중 1st\n",
    "# 123\n",
    "# 456\n",
    "# 789 순서\n",
    "\n",
    "# ax2\n",
    "ax2 = fig.add_subplot(2,2,2)\n",
    "\n",
    "# ax3\n",
    "ax3 = fig.add_subplot(2,2,3)\n",
    "\n",
    "# ax4\n",
    "ax4 = fig.add_subplot(2,2,4)\n",
    "\n",
    "# histogram 그리기\n",
    "ax1.hist(random.sample(range(1,100), 50), bins = 10, color='y', alpha = 0.3) # alpha 가 낮을 수록 투명해짐\n",
    "\n",
    "# bar 그리기\n",
    "ax2.bar(sr2.index, height = sr2.values, color='b', alpha = 0.5) \n",
    "# 투명도로 하면 색이 겹쳐지고 다른 점에 가려져서 보이지 않는 경우 방지\n",
    "\n",
    "# scatter 그리기\n",
    "ax3.scatter(random.sample(range(1,100), 50), random.sample(range(1,100), 50), color = 'Pink')\n",
    "# 각각의 1열 값과 매칭이 됨 - y축과 x축의 관계를 파악하고자 할 때\n",
    "\n",
    "# ax4 위치에 y=x 그래프를 그려보기\n",
    "ax4.plot([x for x in range(10)] ) # x는 default : index 하나씩 증가\n",
    "\n",
    "plt.subplots_adjust(hspace=0.5 , wspace=0.3) # height, width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-white',\n",
       " 'seaborn-whitegrid',\n",
       " 'seaborn',\n",
       " 'Solarize_Light2',\n",
       " 'tableau-colorblind10',\n",
       " '_classic_test']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# style 변경\n",
    "# 사용가능한 style 출력\n",
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ggplot 사용\n",
    "plt.style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22fc0b0cc08>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot그려보기\n",
    "sr2.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22fc0a9af08>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dCl8IcsbdBoEAHDqIXfqS33FERESkgYsK9oR///d/58CBA5XHrutSVlbGQw89RLt27Xjvvfd45pln2LdvH61ateLWW2+lW7dulc/fu3cv06dPZ8eOHSQkJHD99dczfPjwuvlpwoRp1QZz9fXY3AXYJXOxfQdjmjbzO5aIiIg0UEEL3yOPPHLK8axZs1i/fj3t2rVj3759/OlPf+L73/8+ffr04e233+ZPf/oTf/7zn2nRogWu6/Lwww/TpUsX7r77bj777DMeeOABmjVrRp8+fershwoH5oaJ2LeXQ1Eh9qVnMbf/zO9IIiIi0kAFLXz/rKKiguXLlzNq1CgAVq5cSbt27ejfvz8A/fr1Y9myZaxcuZKMjAy2bt3KgQMHmDRpEo0aNaJdu3akp6ezbNmyGhe+wsJCCgsLAWjdujWBQKBGr29wks6hYuQkKl54Avv2G5jBI3AuvDjoy07OJezncxY0o6ppPsFpRlXTfILTjKqm+QRX09nUqPCtX7+e4uJiBgwYAEB+fj7t2rU75TmpqakUFBQAUFBQwHnnnUdsbGzl4+3ateO1116rUUiAxYsXk5OTA0B2djbJyck1/hoNjZ1wG3tXvUb5nnwCc5+m+cP/wBhTrddGwnzOlmZUNc0nOM2oappPcJpR1TSf2lOjwnfyzFxCQgIApaWlxMfHn/KchIQE9uzZA0BJSclpj8fHx1NSUlLjoEOHDqVv376VxwcPHqSioqLGX6fBGTsZHr2fYx9sZO+il3CuuKrKpwcCAZKTkyNnPmdAM6qa5hOcZlQ1zSc4zahqmk9wJ2dUXdUufHv37mXLli38/ve/r1yLjY2luLj4lOcVFRURFxcHQFxc3GmPFxcXVz5eE4mJiSQmJlYeV1RURMQvgf3e5dC5O2x9j/I5T+Jc2gMTHR30dZEyn7OhGVVN8wlOM6qa5hOcZlQ1zaf2VHtbltzcXFJSUrj44m+uI7vwwgvZvXv3Kc/Lz88nJSUFgJSUFL744gtKS0srH9+9ezcXXnjhWcaOHMYYnPFTwThwYC/2jYV+RxIREZEGplqFr7y8nBUrVjB48OBT1vv378+uXbtYs2YN5eXlrFmzho8//rjyGr/OnTvTvHlzZs2axfHjx8nPzyc3N5f09PTa/0nCmGmTgul3LQD21Wzska99TiQiIiINSbUKX15eHsePH6dfv36nrLdq1Yq77rqLefPmMXnyZObNm8ddd91FixYtvC/uOPziF7/g008/5fbbb+fBBx9k+PDhXHVV1dehyenMyEkQFw8lxdgFL/gdR0RERBqQal3Dd9VVV31nSevevTvdu3f/zte2atWK++6778zSSSWT1AQzLAM792nsqqXYq6/HtEnxO5aIiIg0ALq1WgNirhkByS3BurjZT2Kt9TuSiIiINAAqfA2IiY7GGTfFO9i6Eba842seERERaRhU+Bqay/vAxZ0BvLN85eU+BxIREZFQp8LXwFRu0wKwdw921RJ/A4mIiEjIU+FrgMyFF2N6Xw2AXTALW3TU30AiIiIS0lT4Gigz+laIaQRFhdhXZvsdR0REREKYCl8DZZo2wwwZA4Bd/ip23+c+JxIREZFQpcLXgJkho6FJM6gox82Z6XccERERCVEqfA2YaRSLGXOrd/BeHnb7+/4GEhERkZCkwtfAmV4DIOUiANzZM7Buhc+JREREJNSo8DVwxnFwJmR6B3t2467J9TeQiIiIhBwVvjBgLu6M6eHd67hi3jO4xUU+JxIREZFQosIXJszYyRAVBUe+5sicp/yOIyIiIiFEhS9MmOatMOkjASh86Xnswf0+JxIREZFQocIXRsywDEhqAmXHqdA2LSIiInKCCl8YMXHxBEbdDIC7bhV213afE4mIiEgoUOELM07/a4m+8OQ2LVlY1/U5kYiIiPhNhS/MGCdAk8yfewe7d2DXr/Y3kIiIiPhOhS8MxV7WC9MtDQA772nssWM+JxIRERE/qfCFqajxUyEQgK8OYpfN9zuOiIiI+EiFL0yZ887HDBwGgF0yF/v1lz4nEhEREb+o8IUxM3wixDeGY6XY+c/5HUdERER8osIXxkxColf6APvWG9iCXT4nEhERET+o8IU5M3AYtGwD1uJmz8Ba63ckERERqWcqfGHOREXhZNzmHezYAhvX+htIRERE6p0KXyTo2hM6dQPAzZmJLSvzOZCIiIjUJxW+CGCMwRl/OxgHDuzFLn/F70giIiJSj1T4IoQ5PxXTNx0A+0o2tvCwz4lERESkvqjwRRAz6iaIjYOSIuyCWX7HERERkXqiwhdBTFJTzLAMAOyqJdjPP/E5kYiIiNQHFb4IY9JHQLMW4Lq4c570O46IiIjUAxW+CGOiYzBjp3gHW97FbnnH1zwiIiJS91T4IpC54ipo3xEAN/tJbEWFz4lERESkLqnwRSBjDM6ETO/gi0+xq17zN5CIiIjUKRW+CGVSO2B6DQDALngeW3zU50QiIiJSV1T4IpgZcyvExMDRQuyr2X7HERERkTqiwhfBzLnNMdeOBsC+/gp2/+c+JxIREZG6oMIX4cyQMXDOuVBRjpvzlN9xREREpA6o8EU4ExuHGX2Ld7BxLfbDzf4GEhERkVqnwieY3ldD2/YAuNkzsK62aREREQknKnyCcRycCVO9g08+xr693N9AIiIiUqtU+AQA0+FSuLw3APalZ7GlJT4nEhERkdqiwieVnLFTICoKDh/CLpnrdxwRERGpJSp8Usm0OA8zaDgAdul87JcHfE4kIiIitUGFT05hrh8PjZOg7Dh23jN+xxEREZFaoMInpzDxCZiRkwCw61ZiP/7Q50QiIiJytlT45DSm3xBo3RY4sU2LtT4nEhERkbOhwienMYEATsbt3sGu7dj1q/0NJCIiImdFhU++lbn0cri0BwB27tPY48d8TiQiIiJnSoVPvpMz/nZwHPjqAHbZy37HERERkTOkwiffyZx3AWbAdQDYxXOxhw/5nEhERETOhAqfVMkMnwRxCXCsBDv/Ob/jiIiIyBlQ4ZMqmcQkzA0TALBv5mI/+djnRCIiIlJTKnwSlBl0PbQ4D6zVNi0iIiINUFR1n/j+++8ze/ZsPvnkE2JiYujduzeZmZkArFy5kpycHA4dOkTbtm3JzMykXbt2la/dtWsXWVlZfPrppzRt2pSMjAz69+9f+z+N1AkTFY0z7jbcvz0AH26GTXnQ/Uq/Y4mIiEg1VesM3wcffMAjjzzC8OHDefLJJ3n88ccZNGgQANu3bycrK4vMzExmzpxJr169ePDBBykuLgaguLiYBx54gF69ejFz5kymTZvG9OnT2bFjR939VFL7uveCS7oA4M6ZiS0v8zmQiIiIVFe1zvC98MILDB48mCuv/OaszskzeLm5uaSlpdGtWzcARowYwZIlS1i3bh0DBw4kLy+PmJgYRo4ciTGGrl27kpaWRm5uLh06dKh20MLCQgoLCwFo3bo1gUCg2q+NJCfnUhfzcW+cRvn9P4X9X2BWLiZw7eha/x71oS5nFA40n+A0o6ppPsFpRlXTfIKr6WyCFr7S0lJ27txJt27d+MUvfsHBgwe54IILuOWWW2jfvj0FBQUMHDiw8vnGGFJTUykoKACgoKCA1NRUjDGVz0lNTWX16prdvWHx4sXk5OQAkJ2dTXJyco1eH2nqZD4tW/JV+nCKli3AXTibliMnEkhqUvvfp57od6hqmk9wmlHVNJ/gNKOqaT61J2jhKyoqwlrL66+/zr333kubNm1YsGABDz74II8++iilpaXEx8ef8pr4+HhKSkoAKCkpOe3xhISEyrd8q2vo0KH07du38vjgwYNUVFTU6GtEgkAgQHJycp3Nxw7NgFVLsUWF7M36X6JuuqPWv0ddq+sZNXSaT3CaUdU0n+A0o6ppPsGdnFF1BS18cXFxAFx99dWkpKQAMHr0aBYuXMiHH35IbGzsaeWtuLiYli1bVr5+//79pzxeVFR0WgkMJjExkcTExMrjiooK/RJUoc7mk3gOZug47PzncJcvonzAUMx5F9T+96kH+h2qmuYTnGZUNc0nOM2oappP7Qn6oY34+HiaN2/+rY8ZY0hJSWH37t2Va9Za8vPzK8thSkoK+fn5p7zunx+XhscMHgnnNgfXxZ0z0+84IiIiEkS1PqU7ZMgQVqxYwZ49e6ioqGDBggVER0dzySWXkJ6eTl5eHps3b6a8vJyFCxdSVlZGWloaAGlpaRw7dowFCxZQXl7O5s2bycvLIz09vU5/MKk7JqYRZsyt3sHmDdgPNvobSERERKpUrU/pDh8+nJKSEu6//37KyspITU3l3nvvJT4+no4dO5KZmckTTzxRuQ/fPffcU/mWbUJCAvfccw8zZsxg9uzZNG3alGnTptXoE7oSekxaf+wbr8DHH+Jmz8C571GMPk0lIiISkoxtoLdN2Ldvn97X/xaBQICWLVvWy3zsru24D90NgLnpDpyBQ+v0+9WW+pxRQ6T5BKcZVU3zCU4zqprmE9zJGVWXbq0mZ8y074hJ8+6YYhe8gC0u8jmRiIiIfBsVPjkrZsxkiI6BwsPYRdl+xxEREZFvocInZ8U0a44ZPAoA+/pC7IG9PicSERGRf6XCJ2fNDB0L5zSF8nLcnKf8jiMiIiL/QoVPzpqJjcOMutk7ePct7I4t/gYSERGRU6jwSa0wfQbBBakAuNlPYl3X50QiIiJykgqf1ArjBHAmZHoHBTuxa5f7G0hEREQqqfBJrTGXdIHuVwJgX3oWe6zU50QiIiICKnxSy5yMKRCIgq+/wi6Z53ccERERQYVPaplp0Roz6HoA7NJ52K8O+pxIREREVPik1pkbJkDjRDh+HPvSM37HERERiXgqfFLrTHxjzIhJANi1K7C7d/icSEREJLKp8EmdMP2vg/MuAMDNnoG11udEIiIikUuFT+qECQRwMm73DnZuw254099AIiIiEUyFT+qM6dIDvncZAHbuU9iy4z4nEhERiUwqfFKnnIyp4Djw5X5s7gK/44iIiEQkFT6pU6ZNW0z/IQDYRXOwRw75nEhERCTyqPBJnTMjJkFcApSWYOc/73ccERGRiKPCJ3XOJJ6DuX48AHZNLnbPbp8TiYiIRBYVPqkXZtAN0LwVWBc3+0lt0yIiIlKPVPikXpjoaJxxU7yDbZvg/fW+5hEREYkkKnxSfy7rDR0uBcCdMxNbXu5zIBERkcigwif1xhiDM34qGAP7PsOuXOx3JBERkYigwif1yqS0x/QeBIBdMAtbVOhzIhERkfCnwif1zoy+GRrFQvFR7MIX/Y4jIiIS9lT4pN6ZJs0w140BwK5YhN27x+dEIiIi4U2FT3xhBo+GpslQUYGb85TfcURERMKaCp/4wjRqhBlzq3ewaR122yZ/A4mIiIQxFT7xjUnrD6kdAHCzZ2DdCp8TiYiIhCcVPvGNcRxvmxaAPfnYNbn+BhIREQlTKnziK3NRJ0zPfgDY+c9hS4p9TiQiIhJ+VPjEd2bsZIiKhsLD2MVz/I4jIiISdlT4xHemWQvM4JEA2GULsAf3+ZxIREQkvKjwSUgww8ZBUhMoL8POfdrvOCIiImFFhU9CgomNx4y6GQC7YQ1251afE4mIiIQPFT4JGeaqa+D8VADc2TOwrutzIhERkfCgwichwzgBnPG3ewf5H2HXrfQ3kIiISJhQ4ZOQYjp1g25pANh5z2KPHfM5kYiISMOnwichxxl3GwQCcOggdulLfscRERFp8FT4JOSYVm0wV18PgF0yF3voS58TiYiIhA771QHK//5wjV6jwichydwwERIS4fgx7EvP+h1HRETEd7bsOO6r2bi/+jfcdatq9FoVPglJJqExZviNANi338AW7PQ5kYiIiH/spvW4v/4Rdv5zcPwYNE6q0etV+CRkmQHXQavzAXBnZ2Gt9TmRiIhI/bL7Pqfisd/i/vV3cGAvGAdz9TCiH/hHjb5OVB3lEzlrJioKJ+M23L/8Dj7aCu++DT36+B1LRESkztnSEuyibOyyl6G83Fu8uDPOjT/AXJCKCQRq9PVU+CS0dbkCOneHre/hzn0Kp2tPTHS036lERETqhLUWu24VNucp+PrEhxabnIsZdxsmrT/GmDP6uip8EtKMMTjjp+Le/1M4sBf7xkLMkDF+xxIREal19iCPJ/4AACAASURBVNPduC/+A3Z84C0EojCDR2KuH4+JjTurr63CJyHPtEnB9LsWu2oJ9tVsbO9BmKQmfscSERGpFbaoEPvy89gVS8CeuK3opT1wJmRiWrWple+hwicNghk5Cbt+FZQUYxe8gLn53/yOJCIiclasW4Fds8zbfuxoobfYvBXOhEzo2vOM3779Nip80iCYpCaYYRnYuU9jVy3FXn09pk2K37FERETOiN21HXfWP+DktmMxjTDDMjDXjsJEx9T691PhkwbDXDMCu3IJHNyHm/0kzs9+U6t//YiIiNQ1e/gQdu5T2LeXV66ZK/piMm7DnNu8zr6vCp80GCY6GmfcFNy/PwxbN8KWd7xP8YqIiIQ4W16OfWMhduGLUFriLbZJwZk4DdOxa51/fxU+aVgu7wMXd4aPtnpn+Tp1x0Tp11hEREKX3boRd9Z02LvHW4hLwIychBk4rMb76Z0p/Z9SGpTKbVr+cCfs3YNdtQQz6Aa/Y4mIiJzGHtyHmz0DNq71FozBXJWOGX1Lve82ocInDY658GJM76uxby/HLpiF7TUQk9DY71giIiIA2OPHsEvmYpfMg7Lj3mJqB+8uGakX+5IpaOH7v//7P9asWUPUP71tdvPNNzNkyJDK45UrV5KTk8OhQ4do27YtmZmZtGvXrvLxXbt2kZWVxaeffkrTpk3JyMigf//+tfyjSCQxo2/FvvMWFBViX5mNmTDV70giIhLhrLWwca13Vu/L/d5i4jmYsZMxvQdhHMe3bNU6wzdgwAB++MMffutj27dvJysri7vuuovOnTuzaNEiHnzwQR599FHi4+MpLi7mgQceYPjw4fz2t79l27Zt/PGPf6RVq1Z06NChVn8YiRymaTPMkDHYhbOwy1/FDhyKadna71giIhKh7Bd7vLtkbH3PW3AczKAbMMNvxMQn+BsOOOuqmZubS1paGt26dSM6OpoRI0YQFRXFunXrAMjLyyMmJoaRI0cSHR1N165dSUtLIzc396zDS2QzQ0ZDk2ZQUY6bM9PvOCIiEoFsSTHunCdx7//xN2WvY1ec+x7z7pQRAmUPqnmGLy8vj7y8PJKSkrjiiivIyMggNjYWgIKCAgYOHFj5XGMMqampFBQUVD6empp6yn5pqamprF69ukZBCwsLKSz0dqFu3bo1gXr6VEtDc3IuETGf+AQqxk2hIuvP8F4eZscWnE7dgr4somZ0BjSf4DSjqmk+wWlGVWsI87Gui/v2cu+Ew+FD3uK5zYmamInpcVWd7xNb09kELXxDhw7lpptuIikpic8++4y//e1v/P3vf+dnP/sZAKWlpcTHx5/ymvj4eEpKvD1mSkpKTns8ISGB4uLiGgVdvHgxOTk5AGRnZ5OcnFyj10eaSJmPHTmBfSsXU/bRVkzOU7R49Nlqf8Q9UmZ0pjSf4DSjqmk+wWlGVQvV+RzfuZ1Dj/83Fdvf9xaiY0gaeyuJGVNwTpwQCzVBC98/f/jiggsuYPLkydx///2UlZURHR1NbGzsaeWtuLiYli1bAhAXF8f+/ftPebyoqOi0EhjM0KFD6du3b+XxwYMHqaioqNHXiASBQIDk5OSImo8dOwUeupuy3Tv4Yt7zBPoPqfL5kTijmtB8gtOMqqb5BKcZVS1U52MLD1Mx7xncVa+BtQCYy64kakImpS3Oo/TwYTh8uF6ynJxRddV4WxbnxCdM7IkfNCUlhd27d1c+bq0lPz+ftLS0ysdPXs93Un5+PikpNbsPamJiIomJiZXHFRUVIfVLEGoiaj7tO2J6XIV9500q5j2D7dEHExv8D4qImtEZ0HyC04yqpvkEpxlVLVTmYysqsKuWYOc/D8VHvcWWbXAmZmIu7YELEAI5qxL0QxtvvvkmRUVFAHzxxRc888wz9OjRg5gY78a+6enp5OXlsXnzZsrLy1m4cCFlZWWVhS8tLY1jx46xYMECysvL2bx5M3l5eaSnp9fhjyWRxoydDFFRcORr7OK5fscREZEwYXdswf39z7EvPOGVvUZxmHFTcH7zGObSHn7Hq7agZ/iWLVtGVlYW5eXlJCUlkZaWRkZGRuXjHTt2JDMzkyeeeKJyH7577rmn8i3bhIQE7rnnHmbMmMHs2bNp2rQp06ZN05YsUqtM81aY9JHeRpdL52P7D8E0a+F3LBERaaDsoS+xOTOx61ZVrpkrB3p76jVp5mOyM2PsyfdmG5h9+/aFxGneUBMIBGjZsmVEzseWFOP+1w+g8DCmZz+c7//Htz4vkmdUHZpPcJpR1TSf4DSjqvk5H1tWhs19GftqNhwr9RYvSMWZ9APMRZ3rNUtVTs6ounRrNQkbJi4eM+om7LN/w65fjb1mOKZ9R79jiYhIA2E3b8B9MQv2f+4tJCRiRt2M6X8txgndLWKqQ4VPworpOxj7xqvwWQHu7Cyc//xvX29lIyIioc/u/wJ3dha8v95bMAbTf4hX9hon+RuulqjwSVgxTgBn/FTc/7kPdu/Arl+N6TXA71giIhKC7LFS7KI52KUvQXm5t3hRJ5wbv49p297fcLVMhU/CjuncHbr2hPfXY+c9je1+JaZRI79jiYhIiLDWYjeswc6ZCYcOeovnnIsZNxnTa2Cd3yXDDyp8EpacjNtwP3gXvjqIXTYfc8MEvyOJiEgIsHvycV+cDh9u9hYCUZj04ZgbJlRrD9eGSoVPwpJpdT5m4DDs6wu9rVr6pjfIj9GLiEjtsMVHsS+/gF2xCFzXW/zeZTgTp2Fane9vuHqgwidhywyfiH17ORQfxc5/DjPlp35HEhGRemZdF/tmLvalZ6HwxG3PklviTJgK3XqF5du330aFT8KWSUj0St/sLOxbb2CvvgGTEl4X4YqIyHezu3fgvvAE5H/kLcTEYIaOw1w7GhMTWdd2q/BJWDMDh2FXLIZ9n+Fmz8C56w9+RxIRkTpmjxzCznsG++br3yz26IOTcXvE3oVJhU/CmomK8j7A8dffw44tsHEt9OzrdywREakDtrwcu+JV7IJZUFLsLZ53gbfNSqdu/obzmQqfhL+uPaFTN9i2CTdnJrZ7L78TiYhILbPbNnmfvv38E28hLh4z4kbMwOsxUao7ugWBhD1jDM7428E4cGAv7usL/Y4kIiK1xH55gIq/P4T7yK8qy5656hqc3z+Okz5SZe8ETUEigjk/FdM3Hbt6KRULX6Ri1ES/I4mIyFmwZcexr83DLs6B48e9xZSLcCb9ANPuEn/DhSAVPokYZtRN2PWroaSIr5/4E3bSHaD77IqINCjWWtiUhzt7Bhzc5y02TsKMuRVzVbrun/4dVPgkYpikpphhGdh5z1C88jXMzu2YKT/BXHix39FERKQa7N493nV6H2z0FhwHM3AYZsQkTEJjf8OFOBU+iSjm2tGY4qO4r72E/awA++B/eHsy3TABExXtdzwREfkWtrQY+0o2NncBVJR7ix0u9T59e/6FvmZrKFT4JKKYQIDA+Kk0uWYY+/90H+z7HPtqNva9PJzbfqaNmUVEQoi1FnftCmzOU3D4K2+xaTIm4zbMFX0j5i4ZtUGFTyJSo87dif7NXyif+wz29QXwWQHug3dhhmZgrs/Q2T4REZ8d3/Uh5X95APvRB95CVJT3Ls2wDEyjWH/DNUAqfBKxTKNYnAlTsZddifvUo3BgL/aVF0+c7fsppm07vyOKiEQce/QI5QteYN/KJeC63mLXnjgTpmJatPY3XAOmwicRz3T4Hs6vH8O+9Cz29YWwZzfuA3diho33/pLUHk4iInXOuhXYVUux85+DokJvsUVrnImZmC5X+BsuDOj/ZCJ4Z/vMxGnYy3rjPv2Yd7Zv4SzsphNn+85P9TuiiEjYsju34s76B3zysbfQKJZzJmZS3CcdV9us1ApNUeSfmEsuxfn1Y5irr/cWPvkY9/d34r4yG1te7m84EZEwY7/+CnfGI7gP/2dl2TNp/Yl+4AmSxk/BROt66tqiM3wi/8I0isVM+gH28t64Tz0GX+7Hvvz8N9f2tUnxO6KISINmy8uwry/ELpwNx0q8xfMv9LZZ6XApJhDwN2AYUuET+Q6mY1ec3/wFO/cp7IrFULAT9/c/xwy/ETNkjP6DJCJyBuyWd3FnT4e9n3kL8QmYUTdj+l+n/67WIRU+kSqY2DjMTXdgL+/jne376oD34Y6Na72zfa3b+h1RRKRBsAf24mbPgPfyvAVjMP2uxYy6BZOY5G+4CKDCJ1INplM372xfzkzsqtcg/yPc3/0MM+ImzLWj9FepiMh3sMeOYZfkYJfMg/Iyb7F9R+/t25SL/A0XQVT4RKrJxMVjbvl/2B59cJ/+C3x1EDvvaezGt72zfedd4HdEEZGQYa2Fd9/CzX4SvjrgLSY1wYydgrlyIEafvq1XKnwiNWQ6X4bzm79i5zyJXb0Udu/A/e3PMKNuwgweiXF0tk9EIpv9/BPcF6fDtk3eQiCAuWY45oaJmLh4f8NFKBU+kTNg4uIxt/7Iu7bvmb/CoYPYnKe8a/um/ATT6ny/I4qI1DtbXOTtYfrGK9/cJaNzd5yJ0/QuiM9U+ETOgrn0cu/avuwZ2DdzYdf2E2f7bsakD9fZPhGJCNZ1sW+/gZ37NBQe9habtcAZPxUuuxJjjL8BRYVP5GyZ+ATMlJ941/Y981f4+ivv7d6Nb+NM/gmmVRu/I4qI1Bmb/xHuC0/A7h3eQnQM5rqxmOvGYGIa+RtOKqnwidQS0+UK79q+2VnYt9+Andtwf/tTzJhbMIOG6wJlEQkrtvCwt03VmmVgrbd4eW+cjNsxyS39DSenUeETqUUmoTHm9p9he1yF++z/weGvsLNnYN9927u2r0VrvyOKiJwVW1GBXbEYu+B5KC7yFludj3PjNEzny/wNJ99JhU+kDphuPXEu+iv2xenYtcvho6249/8EM2YK5uphOtsnIg2S/XAL7qwn4LMCbyE2zrv70KAbMFGqFKFM/3RE6ohJaIyZ+nPv2r7n/gaHD2Ff/Af23be8s33NW/kdUUSkWuxXB72N59evrlwzva/29tQ7p6mPyaS6VPhE6pjp3gvnok7YWdOx61bCji3e2b6xkzEDhupsn4iELFtWhl36EnbRHDh+zFts2967S8ZFnfwNJzWiwidSD0zjJMy0O78521d4GPvCE9h33sKZ/GOd7RORkGM3rcedPR0O7PUWGidiRt+C6TtYW041QCp8IvXIXN4b5+LvYWc94b018uFm72zfuCmY/tfpbJ+I+M7u+xx3dhZs3uAtGAcz8DrMyJswCYn+hpMzpsInUs9MYhLm+//hfZL3+ce9s33P/937JO/kH2OatfA7oohEIFtagl00B7tsPpSXe4sXd8a58QeYC1L9DSdnTYVPxCemRx+cDt/z3trdsAa2bcL99Y8x42/D9BuinelFpF5Ya7HrV2PnzISvv/QWm5yLGXcbJq2//lsUJlT4RHxkEs/B/OBu79q+5/8OR49gn/2bd23frT/GNGvud0QRCWN2z27cWdNhxxZvIRCFuXYkZth4TGycv+GkVqnwiYQAc0VfnA6XeqXv3bdg63u4v/kRZvxU7wJp/YUtIrXIFh3Fvvw8dsVisK632OUKnAmZmJbaID4cqfCJhAiT1ATnh7/w3lp54QkoKsQ+81dv375bfoQ5N9nviCLSwFm3ArsmF/vSs3D0iLfYvBXOhGmYbj39DSd1SoVPJIQYYzBp/bEdu+A+9zhsXAtb3vXO9k3IxPS5Rmf7ROSM2F3bcWf9Awp2egsxjTDDMjDXjsJEx/gbTuqcCp9ICDJJTXHuuAe7bpV3tq/4KPapx7xr+275f5imzfyOKCINhD18CDv3aezbb1SumZ79vO2gztV1wpFChU8kRBljML0GYC/p4m3WvGkdbN6A++sfYSZmYnoP0tk+EflOtrwc+8Yr2IWzoLTEW2yT4t0l45Iu/oaTeqfCJxLiTJNzcf7ff2HzVmBn/QOKi7AzHz1xtu/fME10tk9ETmW3vof74nT44lNvIS4BM3ISZuAwTEB3yYhEKnwiDYAxBnPl1diOXXGf/Ru8vx7eX+/t23fjNEyvgTrbJyLYL/fjZs+Ad9/2FozBXJXu3RItqYm/4cRXKnwiDYhp0gznR7/Evv0G9sUs79q+Gf+D3fCmd23fOU39jigiPrDHj2GXzMMumQtlx73F1A7eXTJSL/Y3nIQEFT6RBsYYg+lzDbZTd9xn/gpb3oFN63B3bsPc+H3tjC8SQay1sHGtd1bvy/3eYuI5mLGTvet8dX9uOUGFT6SBMk2b4fzkPuxbr2NnZ3n79mX9GfvOmzg334FJ0tk+kXBmv9jjXae3daO34DiYQTdght+IiU/wN5yEHBU+kQbMnLg+x3bq5p3t+2Cj99f+Rx9gJv0Qc0Vfne0TCTO2pBj7yovY1xdCRYW32LErzsTvY9q09TechCwVPpEwYM5tjvPT32DXLMNmz4Cjhdh//NG7tu+mH+pibZEwYF0Xu3YFdt7TcPiQt3huc5zxt8PlffTHnVRJhU8kTBhjMP2uxXa+DPfpx2DbJnj3LdwdW7zSd0VfvyOKyBmyBbtwZz0Bu7Z7C1HRmOvGYK4bh2nUyN9w0iCo8ImEGdOsOc7Pf4td/Ro2eyYcPYL7xH9j3nkLM+kHmMRz/I4oItVkjx7BvvQcdvVrYK232L0XzvipmOat/A0nDUqNCp/rutx3333s2LGDxx9/nGbNvA1fV65cSU5ODocOHaJt27ZkZmbSrl27ytft2rWLrKwsPv30U5o2bUpGRgb9+/ev3Z9ERCoZYzD9r/PO9j3zV9i2CbthDfbDzTg33YHp0cfviCJSBetWYFe+hp3/HBQf9RZbtsGZOA1z6eX+hpMGqUaf13711Vdp9C+njrdv305WVhaZmZnMnDmTXr168eCDD1JcXAxAcXExDzzwAL169WLmzJlMmzaN6dOns2PHjtr7KUTkW5nkljg//y3mpjugUSwUHsb9+0O4//gj9ugRv+OJyLewOz7A/d2/Y1/4u1f2GsVhxk3B+c1jKntyxqp9hu/zzz9n6dKl3Hnnndx9992V67m5uaSlpdGtWzcARowYwZIlS1i3bh0DBw4kLy+PmJgYRo4ciTGGrl27kpaWRm5uLh06dKh20MLCQgoLCwFo3bo1Ad0a5ludnIvm890ickbX3IDtegXlMx/Fbn8fu3419sPNRN36I5zLe5/y1IicTw1pRlXTfIL7thnZQwepmDMTd+2KyjWn99UExt2GaRpZt1DU71BwNZ1NtQqf67o8/vjj3HzzzcTHx5/yWEFBAQMHDqw8NsaQmppKQUFB5eOpqamnfHooNTWV1atX1yjo4sWLycnJASA7O5vk5OQavT7SaD7BRdyMWrbE/jGLo4tyOPzkY9gjX1P+198TP3AoTX54F4F/ubYv4uZzBjSjqmk+wSUnJ2PLjlM4/wWOvDgDW1oCQHS7DjS9424ade7uc0J/6Xeo9lSr8C1atIgmTZrQq1cv9u/ff8pjpaWlp5XA+Ph4Skq8X9qSkpLTHk9ISKh8y7e6hg4dSt++33zK8ODBg1Sc3H9IKgUCAZKTkzWfKkT8jHoOICqlA+VP/i92xxaKVyymeONa72zfZVdqPtWgGVVN8wnu5Iz2vb6Y488/Dvs+9x5ISCQw5lYYMISvnQDs2+dvUJ/odyi4kzOqrqCFb+/evbzyyis89NBD3/p4bGzsaeWtuLiYli1bAhAXF3daSSwqKjqtBAaTmJhIYmJi5XFFRYV+Caqg+QQX0TNq1gJz5+9h+auVe3qV/+V3mCuvxt70A0hOjuz5VJNmVDXN57vZ/V9w4P/+wPF1J97tMg5mwBDMyJugcRKu5ZtNlSOYfodqT9DCt337do4cOcKdd94JeG/vAtx1111MnDiRlJQUdu/eXfl8ay35+fmkpaUBkJKSwrp16075mvn5+aSkpNTaDyEiNWccB3PNcGyXHrgzH4OdW7Frl1O2bRMlP/sVpFT/GlsRCc4WHsa+8xZ2wxoqdmyh7OQ2Kxd1wrnx+5i27f0NKGEtaOHr3bs3Xbp0qTz+8ssv+eUvf8kvf/lL2rRpQ0pKCn/4wx8YMGAAnTp1YtGiRZSVlVUWvrS0NJ577jkWLFjAsGHD2LZtG3l5efzqV7+qu59KRKrNtGiN8x8PYN9YiH3pWTj8FQfv/znOVdfA+KmY+MZ+RxRpsGzxUezGtdh1q2H7Jjhx0gTAOTcZM3YKtmc/3SVD6lzQwteoUaNTtmI5eWq1SZMmxMbG0rFjRzIzM3niiScq9+G75557Kt+yTUhI4J577mHGjBnMnj2bpk2bMm3atBp9QldE6pZxHEz6SOylV2Cffgy7cxvum6/DB+/h3PojTJcefkcUaTBsaTH2vXXYDWtgy7tQUf7NgzGNMN3SCKT1p1X6MPYf+lpvWUq9MNaePKfcsOzbt0//knyLQCBAy5YtNZ8qaEZVcwwkrH2Dr5/+G5QdB8BclY4ZPxUTn+BzutCg36GqReJ87LFjsGUD7rrVsHlD5b87AERFw6U9MGn9MF17YhrFRuSMakLzCe7kjKpLt1YTkVMYJ0Di6JspSu1E2YxH4OMPsW/mYreeONunjV9FALBlZfDBu96+lpvWwbHSbx4MBKDzZZgr+mK699IfS+I7FT4R+VbmvPNxfvEQdtnL2PnPw6GDuI/+BtPvWkzG7Zi4mn3SXiQc2PJy2L4Ju34NduNaKCn65kHjQMcumJ79MJddiWmc5F9QkX+hwici38k4AcyQMdiuPXFnPgq7d2BXL8V+8C7O5B9jOl/md0SROmfdCvhwi3c/6nffgqOF3zxoDFzUCdOzP6ZHb0xSU/+CilRBhU9EgjLnXYDzi4exS+djFzwPXx3E/Z9fY/pfh8mYgonV2T4JL9Z14ePt2HWrvZJ3+NCpT0jt4J3J63EV5lzdDUJCnwqfiFSLCQQwQ8eeONv3v1CwE7tqyTdn+zp18zuiyFmx1kL+TuyG1dj1a+DQwVOfcEGqdybviqswzVv5E1LkDKnwiUiNmDZtce75I3bJXOzCF+HL/biP/AozcBhm7GRMbJzfEUWqzVoLe/K9D15sWAMH9p76hPMu8D5de0U/TKs2/oQUqQUqfCJSYyYQwFw/Htstzbu275Nd2BWLsFvewZnyE8wlXYJ/EREf2S8+9Ure+jWwd8+pD7Y4zyt4af0wbXRXKAkPKnwicsbM+Rd+c7bvldlwcB/un/4Lc/X13tm+RrF+RxSpZA/sPVHyVsOe/FMfPLc5pmdfTM9+0La97nwhYUeFT0TOiomKwtwwAds9DffJ/4VPd2OXv/rN2b4Ol/odUSKY/eqA9+nadauhYOepD55zrnc9Xs9+0O4SlTwJayp8IlIrzPmpOPf+Gbs4B/vqbDiwF/eP92KuGY4ZfYvO9km9sYcPYTe8id2wGnZuO/XBxkmYHn0wPfvDxZ0wTsCfkCL1TIVPRGqNiYrCDJ944tq+//Uuhn99IXbzBpwpP8Vc3NnviBKmbOER7Ma3vDN5Oz4A637zYHwC5rLe3pm8jl0xAZU8iTwqfCJS60zbdjj/9Wfsq3Owi7Jh/xe4f7wHc80IzKibMY0a+R1RwoAtPordmOedydv6Hrj/VPIaxWEu64W5oh98rzsmKtq/oCIhQIVPROqEiYrGjJyE7d7LO9v3WQE292Xs++txbvsp5qJOfkeUBsiWlmA3rfM+ePHBu1Be/s2DMTGYLj0xaf3g0h6YGP1hIXKSCp+I1CmT0h7nvx7BvjIbuyQH9n+O+9//iRk8EjPyJv1PWYKyx4/B5ndw16+CzRvg+PFvHoyK8srdFX0x3dK0D6TId1DhE5E6Z6KjMaNvxl7Wy9u37/NPvNu0vb/eu7avfUe/I0qIsWVlsHWjt43Ke+vgWMk3DwYC0Km7t41K916Y+Mb+BRVpIFT4RKTemAsvxvnl/2AXzsIumQd7P8N9+D8x147CjJzE/2/v3qOrKtM7jn/fc0ICCZcAOeEShhgMGIyCXBJQEsSKU506UKtQ6xplrEFnDY46lqnFtXCYdgTXUlctqJgSilqqBaPO4AVURkcSwISrhAFhEMKgEkKuBHKBnLz9Y4eEUyEnUWDnHH6fv4B3n/CcZ7m2P56997tNl0i3SxQX2cZG+GKH82qzrZ9B3YnWReOBK65y3l87+lpM957uFSoSghT4ROSiMl26YP7uHuyo8c607/Ah7Advtd7blzTM7RLlIrJNfvjzLmxhHnbrBjh+LPCA5CudSd6YCZhevd0pUiQMKPCJiCtM0jA8c/8du+p17Advw+FDNC34Z8zNt2F+fBemi56qDFe2qQn273Eu125ZD9WVgQdcNtSZ5I2dgOnjc6dIkTCjwCcirjFdIjG3z3Ce5H15IZR8hV39JnZ7IZ57H8EkDXW7RDlPrLVwcB92U76zjUpFWeABg5Kcd9eOzcD4+rtTpEgYU+ATEdeZy1Oap32vYT/8nTPte+pXmJtvx9x6p6Z9Icpai/3qgBPyNuXB0ZLAA/oPciZ5aZmYAYPcKVLkEqHAJyKdgomMwtxxL/aa8c6078jX2PffwH7ePO1LvNztEqWd7OGvqP7D7zn18Wo4fChw0de/OeRlQMJlen+tyEWiwCcinYpJHo7nieewv1uOXbsKvj5I0/x/wvxoGuZvpuuNCZ2UPVqC3ZyPLczD/9UBTp252CfOuVSblgmJyQp5Ii5Q4BORTsdERmGm34cddS1NL/8HlB52Nm7eXuBM+wYPcbtEAWxFmRPyNufDgb0Ba57efWH0dTB2AgxJwXg8LlUpIqDAJyKdmBl6JZ4nFmLffhX78bvwVXHztG+6M/GL0CnsYrPVldgt67Gb8mHfrsDF7j0woyfgHTeR/pk3UlpWht/vd6dQEQmgs6WIdGomKgpz50zs6Gude/uOljgbN2//zJn2/SDJ7RLDnj1+DLt1o/PgxZ6dYJtaF7vFYEaNdy7XpozARETg8XoxXq97BYvItyjwiUhIMMOuwvPrhdi3XUDwXwAADaZJREFUmqd9hw7Q9OSjmFv/HnPzHZr2nWe29gR2+2fOJG/3djhzUhfVFTNyHCY9E64cpaeoRUKAzpAiEjJMVFfMP9zfOu0rO4L9/WvYbQXOWzoGXeZ2iSHN1tdhPy907snbuQUaG1sXu0TCiLF40jLhqrGYqCj3ChWRDlPgE5GQY664unna9wr2k/fhL1/S9NtHMT++09m7T5cT282ebICdW5xXmxVtgpMnWxe9EXDVaGcblZFpmK7R7hUqIt+LAp+IhCTTtRvmrp85T/K+sgjKS52tXLY139uXMNjtEjst23gK/rQduzkPu60AGupaFz0eGD4SkzYRM2ocJrq7e4WKyHmjwCciIc0MH4ln3kJs7svYT9fAwX00/fYRzJS7MD+8TdO+Ztbvhz07nEneto1Qe6J10RgYdpUzyRt9LaZHL/cKFZELQoFPREKe6RqN+cnPsaOvc6Z9FUedhzu2fYbnpw9hBl6a0z7b5Ic/78ZuWofduhFqqgMPuDzFmeSNuQ4T28edIkXkolDgE5GwYa68Bs+8RdjcZdh1H8CBvTT92y8xU+/C/PBvMZ7wn/ZZa2H/HuymPOzm9VBdEXhAYrIzyRubgenrc6dIEbnoFPhEJKyYbtGYu2c5075XF0FFGfbNV5qnfQ9jBgxyu8TzzloLf/myNeSVlwYeMOiyllebmfgB7hQpIq5S4BORsGRSR+H59SLsG/+Fzf8I9u+h6V8fxtz2E8zkKWEx7bNfH3TuyducB6WHAxf7JzgBLy0TM+AH7hQoIp2GAp+IhC0THYOZ8Yvmad/zUFWOfWMZdutGZ9rXP8HtEjvMlnztPF1bmAeHDwUuxvXDpGVg0iY6Uz1j3ClSRDodBT4RCXvm6jF4frMIu2IpdsMf4Msvmqd9d2NuvLXTT/ts2RHspnzspnVw6EDgYu84zNgJTsi7LFkhT0TOSoFPRC4JJro75t6HsWOuo+m/X4CqCuzKpc60796HMPED3S4xgK0sx27Od95fe2Bv4GLPWMyYCc77ay9PwXg87hQpIiFDgU9ELilmRBqeec9jVyzBbvwE9u2i6TcPYW67B/NXt7oanuyxKuyWDc4kb99usLZ1MaaHs0deWiZccVWnn0qKSOeiwCcilxwT0x3zj7/EjpngTPuqK7ErcrDbNuKZ8dBFfZLVnqjBbt3oTPK+KALb1LrYLRpzzXgn5A0fiYnQKVtEvhudPUTkkmVGpuNJHo793yXYz/4Ie//kTPtun4GZ9KMLNu2ztSew2wuwm/Nh1zbw+1sXo7piRqZj0jIgdTSmS+QFqUFELi0KfCJySTMxPTD3Pdp8b9+LcKwK+/p/Ovf2zfgFxtf/vPw9tqEe+3khdlM+7NwCjadaFyO6wIixeNIy4eo0TFTUefk7RUROU+ATEQHMNePxJF/phL3CdbCnyJn23fFTzMSbv9O0z546CUVbnA2Rd2yCkw2ti94ISB3lbKMychymW/R5/DYiIoEU+EREmpnuPTEzZzv39i1/EWqqsf/zEnbLBmfaF9cv6M+wjadg13ZnG5Xtn0F9XeuixwMpI52QN+paTEz3C/htRERaKfCJiPw/ZvS1eIamYl/Pbn6YYgdN8x7CTLsXM/Gvv3W89fthT5Ezydu6EWqPn/HDDAxNdd54MeY6TI9eF/GbiIg4FPhERM7C9OiJuf9Xzr19yxfD8WPY5S9it6zH3vsI1uejac9Omgr+iN2yAWqqA3/A5SmtIS+2rztfQkSkmQKfiEgbzJgJeIam0vTaS7BlA+z+nFNP/JzDMd3xlx8NPDgx2blcOzYD0zfenYJFRM5CgU9EJAjTMxbvz/6Fpk352NcWw/Ea/KfvzUtIdCZ5YzMw/TrX2zpERE5T4BMRaSdPWgb2ilRY/RbdffHUXjkK23+Q22WJiASlwCci0gGmZ2+8d91Pr379qD9yBP+ZmyaLiHRSeuO2iIiISJhT4BMREREJcwp8IiIiImFOgU9EREQkzCnwiYiIiIS5dj2l+/rrr5Ofn8/x48fp0qULw4cPZ8aMGcTFxQHw6aefkpubS2VlJYMHDyYrK4shQ4a0fP7LL78kJyeHQ4cO0bt3b6ZNm8bEiRMvzDcSERERkQDtmvBNnDiRp59+mldeeYUXXniBuLg4nnvuOQC++OILcnJyyMrKYtmyZYwbN44FCxZQW1sLQG1tLfPnz2fcuHEsW7aMmTNnsmTJEvbu3XvhvpWIiIiItGjXhC8hIaHl19ZajDF88803AKxdu5b09HRGjhwJwJQpU1izZg2FhYVMmjSJgoICIiMjmTp1KsYYRowYQXp6OmvXrmXYsGHtLrSmpoaamhoABg4ciNfrbfdnLyWn+6L+nJt61Db1Jzj1qG3qT3DqUdvUn+A62pt2b7ycn5/PkiVLqKurw+v1cs899wBw8OBBJk2a1HKcMYakpCQOHjzYsp6UlIQxpuWYpKQk8vLyOlTo6tWryc3NBWDlypUtl5Pl7NSf4NSjtqk/walHbVN/glOP2qb+nD/tDnwZGRlkZGRQVVXFxx9/zODBgwGor68nOjo64Njo6Gjq6pz3TNbV1X1rPSYmpuWSb3vdcsstZGRktPy+rKxMO9yfhdfrJS4uTv1pg3rUNvUnOPWobepPcOpR29Sf4E73qL06/Gq12NhYbrzxRh588EEWL15M165dvxXeamtr6devHwDdunWjtLQ0YP3EiRPfCoHB9OjRgx49erT83u/36z+CNqg/walHbVN/glOP2qb+BKcetU39OX++07Ysfr+fhoYGKioqSExM5MCBAy1r1lqKi4tJTEwEIDExkeLi4oDPn7kuIiIiIhdW0MDX1NTEmjVrqK6uBqC8vJylS5fi8/lISEhg8uTJFBQUUFRURGNjI++88w6nTp0iPT0dgPT0dBoaGli1ahWNjY0UFRVRUFDA5MmTL+w3ExERERGgnZd0t23bRm5uLg0NDURHR5OamsrcuXPxer2kpKSQlZVFdnZ2yz58c+bMablkGxMTw5w5c1i6dCkrVqygd+/ezJw5s0NP6IqIiIjIdxc08Hk8HubMmdPmMddffz3XX3/9OdeTk5NZsGBBx6sTERERke/NWGut20V0RE1NTcDDGyIiIiKXqvbmopB7l25NTQ3Tp09v2fhZAn3zzTfqTxDqUdvUn+DUo7apP8GpR21Tf4I73aPTL6UIJuQCn4iIiIh0jAKfiIiISJjzzps3b57bRXRUZGQkqampREVFuV1Kp6T+BKcetU39CU49apv6E5x61Db1J7iO9CjkHtoQERERkY7RJV0RERGRMKfAJyIiIhLmFPhEREREwpwCn4iIiEiYU+ATERERCXMKfCIiIiJhToFPREREJMwp8MklY9asWaxbt87tMkREApSXlzN9+nRKS0vdLkXCmAKfiIiISJhT4BMREREJcxFuF9Be77//Ph9++CEVFRXExMSQmZnJnXfeicejzCrtV1payty5cykuLiYhIYGsrCySk5PdLktCSH19PStXrqSwsJBjx44RFxfH/fffT0pKitulSYioqqoiOzubXbt2ERsby5QpU9wuSUJMQ0MDK1asoKCggNraWpKTk7nvvvvo37//OT8TMoGvT58+PP744/h8PoqLi5k/fz4+n4+bbrrJ7dIkhHz00Uc89thjDB48mHfffZcFCxawaNEioqOj3S5NQsTixYuprKzkiSeewOfzUVJSgjHG7bIkhCxcuJBu3bqxePFiTp48ybPPPut2SRJiXnrpJerq6njyySfp3r07b731Fk899RTPPPMMERFnj3YhMx4bP3488fHxGGNISkoiMzOToqIit8uSEHPDDTcwZMgQIiIimDp1KpGRkWzdutXtsiREVFdXs3HjRmbOnNlyPhowYECb/6oWOVNFRQU7d+7k7rvvJjo6mtjYWKZNm+Z2WRJCjh07xvr168nKyiI2NpaIiAjuuOMOKisr2bdv3zk/FzITvvz8fN577z2OHDmC3++nsbGRYcOGuV2WhJj4+PiWXxtjiIuLo7y83MWKJJQcPXoUgAEDBrhciYSq0+cbn8/X8mdnnpdEgjn9NPfs2bMD/tzv91NWVnbOz4VE4CsrK2PRokXMnj2bUaNGERERwauvvsr+/fvdLk1CzJnbHlhrKSsro2/fvi5WJKHk9P+kS0pKGDRokMvVSCjq06cP4Pzj4fRkWNuxSEecPg8tXLiQnj17tvtzIXFJt76+HmstPXv2xOv1snfvXvLy8twuS0LQJ598wv79+2lsbGTVqlWcPHmS0aNHu12WhIhevXoxfvx4cnJyKC0txVpLSUkJJSUlbpcmIaJv376kpqayfPlyamtrqaqq4s0333S7LAkhvXr1IiMjg5ycHCoqKgA4ceIEhYWF1NfXn/NzxlprL1aR30dubi6rV6+msbGR1NRU4uPjKS4uZt68eW6XJiFi1qxZTJo0iR07dlBcXMzAgQPJyspi6NChbpcmIaSuro4VK1ZQWFhITU0NPp9PT+lKh1RWVpKdnc3u3btbntLNzs7m+eef1+VdaZeGhgbefvttNmzYQFVVFTExMaSkpPDAAw/QtWvXs34mZAKfiIiIiHw3IXFJV0RERES+OwU+ERERkTCnwCciIiIS5hT4RERERMKcAp+IiIhImFPgExEREQlzCnwiIiIiYU6BT0RERCTMKfCJiIiIhLn/A/zt1nUsziDgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"seaborn-talk\")\n",
    "sr2.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. iris에서 sepal_length(꽃받침의 길이) 1행부터 20행까지의 데이터로 막대그래프 그리기\n",
    "### 단 sepal_length의 길이가 5가 넘으면 파란색 아니면 빨간색"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 실습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 불러오기\n",
    "import matplotlib\n",
    "\n",
    "matplotlib.rcParams['font.family'].insert(0, 'Malgun Gothic')\n",
    "# font.family : 한글을 추가해줘야 함, 주로 맑은 고딕"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Malgun Gothic', 'sans-serif']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.rcParams['font.family']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. iris에서 1행부터 5행까지의 데이터를 시각화해보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "iris[:5].plot(kind='bar', rot = 0)\n",
    "plt.ylim(0, 7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = iris.head(20)\n",
    "plt.xlabel('Data')\n",
    "plt.ylabel('sepal_length')\n",
    "plt.title('꽃받침의 길이 시각화')\n",
    "for x in range(len(data)):\n",
    "    if data['sepal_length'][x] >=5:\n",
    "        plt.bar(x=data.index, height=data['sepal_length'].values, width= 0.5, color = 'blue')\n",
    "    else:\n",
    "        plt.bar(x=data.index, height=data['sepal_length'].values, width= 0.5, color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x200b8f53788>"
      ]
     },
     "execution_count": 22,
     "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": [
    "iris['sepal_length'][:20].plot(kind = 'bar', rot=0, color=my_color2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_color=[]\n",
    "for i in iris['sepal_length'][:20]:\n",
    "    if i>=5:\n",
    "        my_color.append('blue')\n",
    "    else:\n",
    "        my_color.append('red')\n",
    "        \n",
    "my_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_color2 = ['blue' if x>5 else 'red' for x in iris['sepal_length'][:20]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. species별 평균 막대그래프 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": [
    "iris.groupby('species').mean().plot(kind = 'bar')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2009f0aeb08>"
      ]
     },
     "execution_count": 33,
     "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": [
    "iris.pivot_table(index = 'species', aggfunc = 'mean').plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 성별에따른 fare, age 평균 막대그래프 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x200b8aba408>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SXzQf888R8f6ImNa8gNL0ds8tvRW/pKNDQkR8CvhlZn4jIt7BwDcCL87MX0TEdxi4cNfrwE3Az4CezLy/fRNL++fKW4eKW4ETIuIm4GzgZGBtRPQwcCGlzszsBZ4ETjfcertz5a1DQkRMy8xdEXEUcC8DV2K8IDNfbV60a2dEnMjAyrsfuD0zf9LOmaX98cJUOlRcGRGfYODSurczcPGkjc1LmD4VEX8C/D3wKQYu+rU2Ii7KzJ1tm1jaD1feklSQ57wlqSDjLUkFGW9JKsh4S1JBxluSCjLeklSQ8Zakgv4PX4F0LlH4RI0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.groupby(titanic['sex'])[['fare', 'age']].mean().plot(kind = 'bar', rot = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. titanic에서\n",
    "## - fare와 age column에서 NaN값을 지우고\n",
    "## - 나이를 기준으로 오름차순 정렬 이후\n",
    "## - 나이에 따른 요금 선그래프 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data2 = titanic[['fare', 'age']].dropna().sort_values(by='age', ascending = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'fare')"
      ]
     },
     "execution_count": 57,
     "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": [
    "data2.plot(x='age', y='fare', color = 'Orange')\n",
    "plt.title('나이에 따른 요금')\n",
    "plt.ylabel('fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.dropna(subset=['fare', 'age'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = titanic.sort_values(by =,)\n",
    "plt.plot(temp.age, temp.fare)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 선실별 승객 수 비율 pie chart 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x200b913eb48>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = titanic.pclass.value_counts()\n",
    "temp.plot(kind = 'pie', autopct='%.1f%%') #소수 첫째자리까지 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>서울</th>\n",
       "      <th>경기</th>\n",
       "      <th>충청</th>\n",
       "      <th>경상</th>\n",
       "      <th>전라</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2015</td>\n",
       "      <td>1053.5</td>\n",
       "      <td>1023</td>\n",
       "      <td>512</td>\n",
       "      <td>897</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>1067</td>\n",
       "      <td>489</td>\n",
       "      <td>872</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017</td>\n",
       "      <td>987.0</td>\n",
       "      <td>1123</td>\n",
       "      <td>487</td>\n",
       "      <td>811</td>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          서울    경기   충청   경상   전라\n",
       "2015  1053.5  1023  512  897  451\n",
       "2016  1023.0  1067  489  872  421\n",
       "2017   987.0  1123  487  811  399"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "인구통계 = {'서울': [1053.5, 1023, 987],\n",
    "            '경기':[1023, 1067, 1123],\n",
    "            '충청':[512,489,487],\n",
    "            '경상':[897, 872, 811],\n",
    "            '전라':[451, 421, 399]\n",
    "            }\n",
    "\n",
    "df = pd.DataFrame(인구통계)\n",
    "df.index = [2015, 2016, 2017]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000200B8E99908>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000200B920B348>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000200B925E8C8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000200B92C0808>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000200B9320088>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 기본 그리기\n",
    "# https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html\n",
    "df.plot(kind = 'line',\n",
    "        title = '인구추이',\n",
    "        figsize = (7,7),\n",
    "        style = ['b.--', 'ko-', 'vg-', '^:', 'm<--'],\n",
    "        lw = 2, # 선두께\n",
    "        subplots = True, #False : 한 그래프에 표시\n",
    "        xticks = df.index # x축 표시 눈금\n",
    "       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "# 한글 깨짐 방지\n",
    "matplotlib.rcParams['font.family'].insert(0, 'Malgun Gothic')\n",
    "# Mac OS인 경우에는 AppleGothic 추가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000200B9379708>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000200B98B5CC8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000200AD250E88>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# 실습. 아래 그림처럼 연도별 지역별 인구수 그래프 그리기\n",
    "df.T.plot(kind = 'bar',\n",
    "        subplots = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/예제실습/그림의구조.jpg\" alt=\"그림의구조\" style=\"width: 1200px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 왕좌의 게임 데이터 분석 및 시각화 실습\n",
    "## 1. 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "bat = pd.read_csv('input/data-society-game-of-thrones/battles.csv')\n",
    "dth = pd.read_csv('input/data-society-game-of-thrones/character-deaths.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>name</th>\n",
       "      <th>year</th>\n",
       "      <th>battle_number</th>\n",
       "      <th>attacker_king</th>\n",
       "      <th>defender_king</th>\n",
       "      <th>attacker_1</th>\n",
       "      <th>attacker_2</th>\n",
       "      <th>attacker_3</th>\n",
       "      <th>attacker_4</th>\n",
       "      <th>defender_1</th>\n",
       "      <th>...</th>\n",
       "      <th>major_death</th>\n",
       "      <th>major_capture</th>\n",
       "      <th>attacker_size</th>\n",
       "      <th>defender_size</th>\n",
       "      <th>attacker_commander</th>\n",
       "      <th>defender_commander</th>\n",
       "      <th>summer</th>\n",
       "      <th>location</th>\n",
       "      <th>region</th>\n",
       "      <th>note</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Battle of the Golden Tooth</td>\n",
       "      <td>298</td>\n",
       "      <td>1</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tully</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Jaime Lannister</td>\n",
       "      <td>Clement Piper, Vance</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Golden Tooth</td>\n",
       "      <td>The Westerlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Battle at the Mummer's Ford</td>\n",
       "      <td>298</td>\n",
       "      <td>2</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Baratheon</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.0</td>\n",
       "      <td>Gregor Clegane</td>\n",
       "      <td>Beric Dondarrion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mummer's Ford</td>\n",
       "      <td>The Riverlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Battle of Riverrun</td>\n",
       "      <td>298</td>\n",
       "      <td>3</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tully</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>Jaime Lannister, Andros Brax</td>\n",
       "      <td>Edmure Tully, Tytos Blackwood</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Riverrun</td>\n",
       "      <td>The Riverlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Battle of the Green Fork</td>\n",
       "      <td>298</td>\n",
       "      <td>4</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Stark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>Roose Bolton, Wylis Manderly, Medger Cerwyn, H...</td>\n",
       "      <td>Tywin Lannister, Gregor Clegane, Kevan Lannist...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Green Fork</td>\n",
       "      <td>The Riverlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Battle of the Whispering Wood</td>\n",
       "      <td>298</td>\n",
       "      <td>5</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Stark</td>\n",
       "      <td>Tully</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>Robb Stark, Brynden Tully</td>\n",
       "      <td>Jaime Lannister</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Whispering Wood</td>\n",
       "      <td>The Riverlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            name  year  battle_number  \\\n",
       "0     Battle of the Golden Tooth   298              1   \n",
       "1    Battle at the Mummer's Ford   298              2   \n",
       "2             Battle of Riverrun   298              3   \n",
       "3       Battle of the Green Fork   298              4   \n",
       "4  Battle of the Whispering Wood   298              5   \n",
       "\n",
       "              attacker_king             defender_king attacker_1 attacker_2  \\\n",
       "0  Joffrey/Tommen Baratheon                Robb Stark  Lannister        NaN   \n",
       "1  Joffrey/Tommen Baratheon                Robb Stark  Lannister        NaN   \n",
       "2  Joffrey/Tommen Baratheon                Robb Stark  Lannister        NaN   \n",
       "3                Robb Stark  Joffrey/Tommen Baratheon      Stark        NaN   \n",
       "4                Robb Stark  Joffrey/Tommen Baratheon      Stark      Tully   \n",
       "\n",
       "  attacker_3 attacker_4 defender_1  ... major_death  major_capture  \\\n",
       "0        NaN        NaN      Tully  ...         1.0            0.0   \n",
       "1        NaN        NaN  Baratheon  ...         1.0            0.0   \n",
       "2        NaN        NaN      Tully  ...         0.0            1.0   \n",
       "3        NaN        NaN  Lannister  ...         1.0            1.0   \n",
       "4        NaN        NaN  Lannister  ...         1.0            1.0   \n",
       "\n",
       "   attacker_size defender_size  \\\n",
       "0        15000.0        4000.0   \n",
       "1            NaN         120.0   \n",
       "2        15000.0       10000.0   \n",
       "3        18000.0       20000.0   \n",
       "4         1875.0        6000.0   \n",
       "\n",
       "                                  attacker_commander  \\\n",
       "0                                    Jaime Lannister   \n",
       "1                                     Gregor Clegane   \n",
       "2                       Jaime Lannister, Andros Brax   \n",
       "3  Roose Bolton, Wylis Manderly, Medger Cerwyn, H...   \n",
       "4                          Robb Stark, Brynden Tully   \n",
       "\n",
       "                                  defender_commander  summer         location  \\\n",
       "0                               Clement Piper, Vance     1.0     Golden Tooth   \n",
       "1                                   Beric Dondarrion     1.0    Mummer's Ford   \n",
       "2                      Edmure Tully, Tytos Blackwood     1.0         Riverrun   \n",
       "3  Tywin Lannister, Gregor Clegane, Kevan Lannist...     1.0       Green Fork   \n",
       "4                                    Jaime Lannister     1.0  Whispering Wood   \n",
       "\n",
       "            region note  \n",
       "0  The Westerlands  NaN  \n",
       "1   The Riverlands  NaN  \n",
       "2   The Riverlands  NaN  \n",
       "3   The Riverlands  NaN  \n",
       "4   The Riverlands  NaN  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bat.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>Name</th>\n",
       "      <th>Allegiances</th>\n",
       "      <th>Death Year</th>\n",
       "      <th>Book of Death</th>\n",
       "      <th>Death Chapter</th>\n",
       "      <th>Book Intro Chapter</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Nobility</th>\n",
       "      <th>GoT</th>\n",
       "      <th>CoK</th>\n",
       "      <th>SoS</th>\n",
       "      <th>FfC</th>\n",
       "      <th>DwD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Addam Marbrand</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Aegon Frey (Jinglebell)</td>\n",
       "      <td>None</td>\n",
       "      <td>299.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Aegon Targaryen</td>\n",
       "      <td>House Targaryen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Adrack Humble</td>\n",
       "      <td>House Greyjoy</td>\n",
       "      <td>300.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Aemon Costayne</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name      Allegiances  Death Year  Book of Death  \\\n",
       "0           Addam Marbrand        Lannister         NaN            NaN   \n",
       "1  Aegon Frey (Jinglebell)             None       299.0            3.0   \n",
       "2          Aegon Targaryen  House Targaryen         NaN            NaN   \n",
       "3            Adrack Humble    House Greyjoy       300.0            5.0   \n",
       "4           Aemon Costayne        Lannister         NaN            NaN   \n",
       "\n",
       "   Death Chapter  Book Intro Chapter  Gender  Nobility  GoT  CoK  SoS  FfC  \\\n",
       "0            NaN                56.0       1         1    1    1    1    1   \n",
       "1           51.0                49.0       1         1    0    0    1    0   \n",
       "2            NaN                 5.0       1         1    0    0    0    0   \n",
       "3           20.0                20.0       1         1    0    0    0    0   \n",
       "4            NaN                 NaN       1         1    0    0    1    0   \n",
       "\n",
       "   DwD  \n",
       "0    0  \n",
       "1    0  \n",
       "2    1  \n",
       "3    1  \n",
       "4    0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dth.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 책의 챕터(Book of Death)별로 사망자 수 추이 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 6)"
      ]
     },
     "execution_count": 95,
     "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": [
    "dth['Book of Death'].value_counts().sort_index().plot()\n",
    "plt.grid(True)\n",
    "plt.xlim(0,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 대규모 전쟁의  공격진영과 수비 진영의 숫자 시각화\n",
    "### 대규모 전쟁 = 공격과 수비 모두 합쳐서 10,000명 이상 참가한 전쟁"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": false
   },
   "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>name</th>\n",
       "      <th>year</th>\n",
       "      <th>battle_number</th>\n",
       "      <th>attacker_king</th>\n",
       "      <th>defender_king</th>\n",
       "      <th>attacker_1</th>\n",
       "      <th>attacker_2</th>\n",
       "      <th>attacker_3</th>\n",
       "      <th>attacker_4</th>\n",
       "      <th>defender_1</th>\n",
       "      <th>...</th>\n",
       "      <th>major_death</th>\n",
       "      <th>major_capture</th>\n",
       "      <th>attacker_size</th>\n",
       "      <th>defender_size</th>\n",
       "      <th>attacker_commander</th>\n",
       "      <th>defender_commander</th>\n",
       "      <th>summer</th>\n",
       "      <th>location</th>\n",
       "      <th>region</th>\n",
       "      <th>note</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Battle of the Golden Tooth</td>\n",
       "      <td>298</td>\n",
       "      <td>1</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tully</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Jaime Lannister</td>\n",
       "      <td>Clement Piper, Vance</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Golden Tooth</td>\n",
       "      <td>The Westerlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Battle of Riverrun</td>\n",
       "      <td>298</td>\n",
       "      <td>3</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tully</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>Jaime Lannister, Andros Brax</td>\n",
       "      <td>Edmure Tully, Tytos Blackwood</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Riverrun</td>\n",
       "      <td>The Riverlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Battle of the Green Fork</td>\n",
       "      <td>298</td>\n",
       "      <td>4</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Stark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>Roose Bolton, Wylis Manderly, Medger Cerwyn, H...</td>\n",
       "      <td>Tywin Lannister, Gregor Clegane, Kevan Lannist...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Green Fork</td>\n",
       "      <td>The Riverlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Battle of the Camps</td>\n",
       "      <td>298</td>\n",
       "      <td>6</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Stark</td>\n",
       "      <td>Tully</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>12625.0</td>\n",
       "      <td>Robb Stark, Tytos Blackwood, Brynden Tully</td>\n",
       "      <td>Lord Andros Brax, Forley Prester</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Riverrun</td>\n",
       "      <td>The Riverlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Battle of Oxcross</td>\n",
       "      <td>299</td>\n",
       "      <td>15</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Stark</td>\n",
       "      <td>Tully</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>Robb Stark, Brynden Tully</td>\n",
       "      <td>Stafford Lannister, Roland Crakehall, Antario ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Oxcross</td>\n",
       "      <td>The Westerlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Siege of Storm's End</td>\n",
       "      <td>299</td>\n",
       "      <td>16</td>\n",
       "      <td>Stannis Baratheon</td>\n",
       "      <td>Renly Baratheon</td>\n",
       "      <td>Baratheon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Baratheon</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>Stannis Baratheon, Davos Seaworth</td>\n",
       "      <td>Renly Baratheon, Cortnay Penrose, Loras Tyrell...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Storm's End</td>\n",
       "      <td>The Stormlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Battle of the Fords</td>\n",
       "      <td>299</td>\n",
       "      <td>17</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Robb Stark</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tully</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>Tywin Lannister, Flement Brax, Gregor Clegane,...</td>\n",
       "      <td>Edmure Tully, Jason Mallister, Karyl Vance</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Red Fork</td>\n",
       "      <td>The Riverlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Battle of the Blackwater</td>\n",
       "      <td>299</td>\n",
       "      <td>20</td>\n",
       "      <td>Stannis Baratheon</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Baratheon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lannister</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>7250.0</td>\n",
       "      <td>Stannis Baratheon, Imry Florent, Guyard Morrig...</td>\n",
       "      <td>Tyrion Lannister, Jacelyn Bywater, Sandor Cleg...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>King's Landing</td>\n",
       "      <td>The Crownlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>Battle of Castle Black</td>\n",
       "      <td>300</td>\n",
       "      <td>28</td>\n",
       "      <td>Stannis Baratheon</td>\n",
       "      <td>Mance Rayder</td>\n",
       "      <td>Free folk</td>\n",
       "      <td>Thenns</td>\n",
       "      <td>Giants</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Night's Watch</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>Mance Rayder, Tormund Giantsbane, Harma Dogshe...</td>\n",
       "      <td>Stannis Baratheon, Jon Snow, Donal Noye, Cotte...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Castle Black</td>\n",
       "      <td>Beyond the Wall</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>Siege of Winterfell</td>\n",
       "      <td>300</td>\n",
       "      <td>38</td>\n",
       "      <td>Stannis Baratheon</td>\n",
       "      <td>Joffrey/Tommen Baratheon</td>\n",
       "      <td>Baratheon</td>\n",
       "      <td>Karstark</td>\n",
       "      <td>Mormont</td>\n",
       "      <td>Glover</td>\n",
       "      <td>Bolton</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>Stannis Baratheon</td>\n",
       "      <td>Roose Bolton</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Winterfell</td>\n",
       "      <td>The North</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name  year  battle_number             attacker_king  \\\n",
       "0   Battle of the Golden Tooth   298              1  Joffrey/Tommen Baratheon   \n",
       "2           Battle of Riverrun   298              3  Joffrey/Tommen Baratheon   \n",
       "3     Battle of the Green Fork   298              4                Robb Stark   \n",
       "5          Battle of the Camps   298              6                Robb Stark   \n",
       "14           Battle of Oxcross   299             15                Robb Stark   \n",
       "15        Siege of Storm's End   299             16         Stannis Baratheon   \n",
       "16         Battle of the Fords   299             17  Joffrey/Tommen Baratheon   \n",
       "19    Battle of the Blackwater   299             20         Stannis Baratheon   \n",
       "27      Battle of Castle Black   300             28         Stannis Baratheon   \n",
       "37         Siege of Winterfell   300             38         Stannis Baratheon   \n",
       "\n",
       "               defender_king attacker_1 attacker_2 attacker_3 attacker_4  \\\n",
       "0                 Robb Stark  Lannister        NaN        NaN        NaN   \n",
       "2                 Robb Stark  Lannister        NaN        NaN        NaN   \n",
       "3   Joffrey/Tommen Baratheon      Stark        NaN        NaN        NaN   \n",
       "5   Joffrey/Tommen Baratheon      Stark      Tully        NaN        NaN   \n",
       "14  Joffrey/Tommen Baratheon      Stark      Tully        NaN        NaN   \n",
       "15           Renly Baratheon  Baratheon        NaN        NaN        NaN   \n",
       "16                Robb Stark  Lannister        NaN        NaN        NaN   \n",
       "19  Joffrey/Tommen Baratheon  Baratheon        NaN        NaN        NaN   \n",
       "27              Mance Rayder  Free folk     Thenns     Giants        NaN   \n",
       "37  Joffrey/Tommen Baratheon  Baratheon   Karstark    Mormont     Glover   \n",
       "\n",
       "       defender_1  ... major_death  major_capture  attacker_size  \\\n",
       "0           Tully  ...         1.0            0.0        15000.0   \n",
       "2           Tully  ...         0.0            1.0        15000.0   \n",
       "3       Lannister  ...         1.0            1.0        18000.0   \n",
       "5       Lannister  ...         0.0            0.0         6000.0   \n",
       "14      Lannister  ...         1.0            1.0         6000.0   \n",
       "15      Baratheon  ...         1.0            0.0         5000.0   \n",
       "16          Tully  ...         0.0            0.0        20000.0   \n",
       "19      Lannister  ...         1.0            1.0        21000.0   \n",
       "27  Night's Watch  ...         1.0            1.0       100000.0   \n",
       "37         Bolton  ...         NaN            NaN         5000.0   \n",
       "\n",
       "   defender_size                                 attacker_commander  \\\n",
       "0         4000.0                                    Jaime Lannister   \n",
       "2        10000.0                       Jaime Lannister, Andros Brax   \n",
       "3        20000.0  Roose Bolton, Wylis Manderly, Medger Cerwyn, H...   \n",
       "5        12625.0         Robb Stark, Tytos Blackwood, Brynden Tully   \n",
       "14       10000.0                          Robb Stark, Brynden Tully   \n",
       "15       20000.0                  Stannis Baratheon, Davos Seaworth   \n",
       "16       10000.0  Tywin Lannister, Flement Brax, Gregor Clegane,...   \n",
       "19        7250.0  Stannis Baratheon, Imry Florent, Guyard Morrig...   \n",
       "27        1240.0  Mance Rayder, Tormund Giantsbane, Harma Dogshe...   \n",
       "37        8000.0                                  Stannis Baratheon   \n",
       "\n",
       "                                   defender_commander  summer        location  \\\n",
       "0                                Clement Piper, Vance     1.0    Golden Tooth   \n",
       "2                       Edmure Tully, Tytos Blackwood     1.0        Riverrun   \n",
       "3   Tywin Lannister, Gregor Clegane, Kevan Lannist...     1.0      Green Fork   \n",
       "5                    Lord Andros Brax, Forley Prester     1.0        Riverrun   \n",
       "14  Stafford Lannister, Roland Crakehall, Antario ...     1.0         Oxcross   \n",
       "15  Renly Baratheon, Cortnay Penrose, Loras Tyrell...     1.0     Storm's End   \n",
       "16         Edmure Tully, Jason Mallister, Karyl Vance     1.0        Red Fork   \n",
       "19  Tyrion Lannister, Jacelyn Bywater, Sandor Cleg...     1.0  King's Landing   \n",
       "27  Stannis Baratheon, Jon Snow, Donal Noye, Cotte...     0.0    Castle Black   \n",
       "37                                       Roose Bolton     0.0      Winterfell   \n",
       "\n",
       "             region note  \n",
       "0   The Westerlands  NaN  \n",
       "2    The Riverlands  NaN  \n",
       "3    The Riverlands  NaN  \n",
       "5    The Riverlands  NaN  \n",
       "14  The Westerlands  NaN  \n",
       "15   The Stormlands  NaN  \n",
       "16   The Riverlands  NaN  \n",
       "19   The Crownlands  NaN  \n",
       "27  Beyond the Wall  NaN  \n",
       "37        The North  NaN  \n",
       "\n",
       "[10 rows x 25 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. 대규모 전쟁만 선택\n",
    "big_bat = bat[bat['attacker_size'] + bat['defender_size'] >=10000]\n",
    "big_bat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>name</th>\n",
       "      <th>attacker_size</th>\n",
       "      <th>defender_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Battle of the Golden Tooth</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Battle of Riverrun</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Battle of the Green Fork</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Battle of the Camps</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>12625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Battle of Oxcross</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Siege of Storm's End</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Battle of the Fords</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Battle of the Blackwater</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>7250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>Battle of Castle Black</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>1240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>Siege of Winterfell</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name  attacker_size  defender_size\n",
       "0   Battle of the Golden Tooth        15000.0         4000.0\n",
       "2           Battle of Riverrun        15000.0        10000.0\n",
       "3     Battle of the Green Fork        18000.0        20000.0\n",
       "5          Battle of the Camps         6000.0        12625.0\n",
       "14           Battle of Oxcross         6000.0        10000.0\n",
       "15        Siege of Storm's End         5000.0        20000.0\n",
       "16         Battle of the Fords        20000.0        10000.0\n",
       "19    Battle of the Blackwater        21000.0         7250.0\n",
       "27      Battle of Castle Black       100000.0         1240.0\n",
       "37         Siege of Winterfell         5000.0         8000.0"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. 필요한 컬럼만 선택\n",
    "big_bat = big_bat[['name', 'attacker_size', 'defender_size']]\n",
    "big_bat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>attacker_size</th>\n",
       "      <th>defender_size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Battle of the Golden Tooth</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Battle of Riverrun</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Battle of the Green Fork</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Battle of the Camps</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>12625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Battle of Oxcross</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Siege of Storm's End</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Battle of the Fords</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Battle of the Blackwater</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>7250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Battle of Castle Black</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>1240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Siege of Winterfell</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            attacker_size  defender_size\n",
       "name                                                    \n",
       "Battle of the Golden Tooth        15000.0         4000.0\n",
       "Battle of Riverrun                15000.0        10000.0\n",
       "Battle of the Green Fork          18000.0        20000.0\n",
       "Battle of the Camps                6000.0        12625.0\n",
       "Battle of Oxcross                  6000.0        10000.0\n",
       "Siege of Storm's End               5000.0        20000.0\n",
       "Battle of the Fords               20000.0        10000.0\n",
       "Battle of the Blackwater          21000.0         7250.0\n",
       "Battle of Castle Black           100000.0         1240.0\n",
       "Siege of Winterfell                5000.0         8000.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_bat.set_index('name', inplace=True)\n",
    "big_bat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Rectangle' object has no property 'barmode'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-125-57425f234de5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# big_battles 시각화\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mbig_bat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'barh'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbarmode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'stack'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;31m# horizontal\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    792\u001b[0m                     \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlabel_name\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    793\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 794\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    795\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    796\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m     60\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ax\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"left_ax\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[0mplot_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 62\u001b[1;33m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     63\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    279\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    280\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 281\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    282\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    283\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_legend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36m_make_plot\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1393\u001b[0m                     \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1394\u001b[0m                     \u001b[0mlog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1395\u001b[1;33m                     \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1396\u001b[0m                 )\n\u001b[0;32m   1397\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_legend_handle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrect\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36m_plot\u001b[1;34m(cls, ax, x, y, w, start, log, **kwds)\u001b[0m\n\u001b[0;32m   1428\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1429\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1430\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbarh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1431\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1432\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_decorate_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mticklabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart_edge\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend_edge\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mbarh\u001b[1;34m(self, y, width, height, left, align, **kwargs)\u001b[0m\n\u001b[0;32m   2595\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'orientation'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'horizontal'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2596\u001b[0m         patches = self.bar(x=left, height=height, width=width, bottom=y,\n\u001b[1;32m-> 2597\u001b[1;33m                            align=align, **kwargs)\n\u001b[0m\u001b[0;32m   2598\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mpatches\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2599\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1599\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1600\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1601\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1602\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1603\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mbar\u001b[1;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[0;32m   2430\u001b[0m                 \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'_nolegend_'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2431\u001b[0m                 )\n\u001b[1;32m-> 2432\u001b[1;33m             \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2433\u001b[0m             \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_interpolation_steps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2434\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0morientation\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'vertical'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, props)\u001b[0m\n\u001b[0;32m    972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    973\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 974\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    975\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    976\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    973\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 974\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    975\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    976\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\anaconda\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36m_update_property\u001b[1;34m(self, k, v)\u001b[0m\n\u001b[0;32m    968\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    969\u001b[0m                     raise AttributeError('{!r} object has no property {!r}'\n\u001b[1;32m--> 970\u001b[1;33m                                          .format(type(self).__name__, k))\n\u001b[0m\u001b[0;32m    971\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Rectangle' object has no property 'barmode'"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD7CAYAAABgzo9kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAMNklEQVR4nO3bX4idd53H8fcnpNG1opvSSS4WK3sh203ECzs3tsSGFkotlF7YvVos66qTht0LXZD0pohkVw2t69X2IntRYS8iLbqy2oJ/tqQdSxWmNxqEhUUoRBQnVbJF2dZ0vnsxT3dOw2TmnJlzzpRv3y8I85znec6ZLz8m7zl5cp5UFZKkfvbt9QCSpNkw8JLUlIGXpKYMvCQ1ZeAlqSkDL0lN7d/uhCQLwGeBtap6eGT/u4F/Bf4M+C3wQFX9z6wGlSRNZpx38F8FXgWuu2r/54DvVNVHgR8AJ6c8myRpF7YNfFU9ADy3yaE7gCeH7W8CH5niXJKkXdr2Es0W3lFVfxy2XwYOXuvEJEvAEsD1119/y80337yLbytJby8vvvjipapamPR5uwn8WpJ9VbXGetxXr3ViVZ0FzgIsLi7WysrKLr6tJL29JHlpJ8/bzadofgLcN2x/HPjhLl5LkjRlEwc+yZkkB4AvA0tJzgO3AI9PeTZJ0i6MdYmmqs4D54ftU8PuS8DHZjKVJGnXvNFJkpoy8JLUlIGXpKYMvCQ1ZeAlqSkDL0lNGXhJasrAS1JTBl6SmjLwktSUgZekpgy8JDVl4CWpKQMvSU0ZeElqysBLUlMGXpKaMvCS1JSBl6SmDLwkNWXgJakpAy9JTRl4SWrKwEtSUwZekpoy8JLUlIGXpKYMvCQ1ZeAlqSkDL0lNGXhJasrAS1JTBl6SmjLwktSUgZekpgy8JDU1VuCTnE7ybJLnkxwd2X8gyeNJnknydJL3zm5USdIktg18kmPA4aq6HTgBPDJy+G7gl1V1B/At4NMzmVKSNLFx3sHfBZwDqKoLwA0jx14BDg7bNwKrU51OkrRj+8c45xBvDveVJPuqag34EfBwkp8DrwO3bvYCSZaAJYCbbrppdxNLksYyzjv4y2y8SwdYG+IO8CXg0ao6AnwCOLvZC1TV2aparKrFhYWFXQ0sSRrPOIFfBu4HSHIEuDhy7P3Ar4ft3wDvm+p0kqQdG+cSzVPAPUmWWb/mfiLJGeDh4c9jSfYB1wGfn9mkkqSJbBv44XLMyat2nxq+/hdw57SHkiTtnjc6SVJTBl6SmjLwktSUgZekpgy8JDVl4CWpKQMvSU0ZeElqysBLUlMGXpKaMvCS1JSBl6SmDLwkNWXgJakpAy9JTRl4SWrKwEtSUwZekpoy8JLUlIGXpKYMvCQ1ZeAlqSkDL0lNGXhJasrAS1JTBl6SmjLwktSUgZekpgy8JDVl4CWpKQMvSU0ZeElqysBLUlMGXpKaMvCS1NRYgU9yOsmzSZ5PcvSqY59M8uPh2J2zGVOSNKn9252Q5BhwuKpuT/JB4BHgnuHYUeAYcGtVrc10UknSRMZ5B38XcA6gqi4AN4wc+xTwEvBMkieS3Dj9ESVJOzFO4A8BqyOPryR543kfAC5V1XHgSeALm71AkqUkK0lWVldXNztFkjRl4wT+MnBw5PHayOWYK8DTw/Z3gSObvUBVna2qxapaXFhY2PGwkqTxjRP4ZeB+gCRHgIsjx15guB4PHAd+Os3hJEk7N07gnwIOJFkGHgVOJTmT5ADwGHA8yXngQeAfZzapJGki236KZrgcc/Kq3aeGr68BfzXtoSRJu+eNTpLUlIGXpKYMvCQ1ZeAlqSkDL0lNGXhJasrAS1JTBl6SmjLwktSUgZekpgy8JDVl4CWpKQMvSU0ZeElqysBLUlMGXpKaMvCS1JSBl6SmDLwkNWXgJakpAy9JTRl4SWrKwEtSUwZekpoy8JLUlIGXpKYMvCQ1ZeAlqSkDL0lNGXhJasrAS1JTBl6SmjLwktSUgZekpgy8JDVl4CWpqbECn+R0kmeTPJ/k6CbHDyf5Q5J3Tn9ESdJObBv4JMeAw1V1O3ACeGST0x4CLk15NknSLozzDv4u4BxAVV0Abhg9mOTDQAG/mPp0kqQdGyfwh4DVkcdXkuwDSPIu4CvAF7d6gSRLSVaSrKyurm51qiRpSsYJ/GXg4MjjtapaG7a/BpypqstbvUBVna2qxapaXFhY2OGokqRJjBP4ZeB+gCRHgIvD9iHgFuAzSb4BHAG+PpsxJUmT2j/GOU8B9yRZBl4BTiQ5AzxcVYtvnJTkPPA3sxhSkjS5bQM/XI45edXuU5ucd3xKM0mSpsAbnSSpKQMvSU0ZeElqysBLUlMGXpKaMvCS1JSBl6SmDLwkNWXgJakpAy9JTRl4SWrKwEtSUwZekpoy8JLUlIGXpKYMvCQ1ZeAlqSkDL0lNGXhJasrAS1JTBl6SmjLwktSUgZekpgy8JDVl4CWpKQMvSU0ZeElqysBLUlMGXpKaMvCS1JSBl6SmDLwkNWXgJakpAy9JTRl4SWpqrMAnOZ3k2STPJzk6sv9DSb6fZDnJE0kOzG5USdIktg18kmPA4aq6HTgBPDJyuIB7q+oY8BJw30ymlCRNbP8Y59wFnAOoqgtJbnjjQFX9bOS83wG/n+54kqSdGucSzSFgdeTxlSRvel6S24CjwPc2e4EkS0lWkqysrq5udookacrGCfxl4ODI47WqWgPIuoeAO4AHqur1zV6gqs5W1WJVLS4sLOx6aEnS9sYJ/DJwP0CSI8DFkWMPAr+qqtPXirskaW+ME/ingANJloFHgVNJzgyfmLkXOJHk/PDnH2Y5rCRpfNv+J+twOebkVbtPDV/vmfpEkqSp8EYnSWrKwEtSUwZekpoy8JLUlIGXpKYMvCQ1ZeAlqSkDL0lNGXhJasrAS1JTBl6SmjLwktSUgZekpgy8JDVl4CWpKQMvSU0ZeElqysBLUlMGXpKaMvCS1JSBl6SmDLwkNWXgJakpAy9JTRl4SWrKwEtSUwZekpoy8JLUlIGXpKYMvCQ1ZeAlqSkDL0lNGXhJasrAS1JTBl6SmjLwktTUWIFPcjrJs0meT3J0ZP+7k5xL8lySbyd5z+xGlSRNYtvAJzkGHK6q24ETwCMjhz8HfKeqPgr8ADg5kyklSRMb5x38XcA5gKq6ANwwcuwO4Mlh+5vAR6Y6nSRpx/aPcc4hYHXk8ZUk+6pqDXhHVf1x2P8ycHCzF0iyBCwND19NcmGnAzdzI3Bpr4d4C3AdNrgWG1yLDX+xkyeNE/jLvDnca0PcAdZGYn+QN/8i+H9VdRY4C5BkpaoWdzJsN67FOtdhg2uxwbXYkGRlJ88b5xLNMnD/8E2OABdHjv0EuG/Y/jjww50MIUmavnEC/xRwIMky8ChwKsmZJAeALwNLSc4DtwCPz2xSSdJEtr1EM1x+ufrTMaeGr5eAj034Pc9OeH5nrsU612GDa7HBtdiwo7VIVU17EEnSW4B3skpSUzMLvHe/bthiLT6U5PtJlpM8Mfy/RmvXWouR44eT/CHJO/divnnaai2SfDLJj4djd+7VjPOyxd+RA0keT/JMkqeTvHcv55yHJAtJ/inJ6av2T9zOmQTeu183bLMWBdxbVceAl9j4RFJL26zFGx7ibfDZ563WYgjcMeDWqrqtqv5zj8aci21+Lu4GfllVdwDfAj69ByPO21eBV4Hrrto/cTtn9Q7eu183XHMtqupnVfXq8PB3wO/nP95cbfVzQZIPs/5L7xfzH23utlqLT7H+C/+Z4V92N+7BfPO01Vq8wsZ9ODdyjXttOqmqB4DnNjk0cTtnFfhN734dtse6+7WRrdYCgCS3AUeB781zsD1wzbVI8i7gK8AX92KwPbDVz8UHgEtVdZz1v9BfmPNs87bVWvwI+MskPwf+Gvj3eQ/3FjJxO2cV+G3vfh22r3n3ayPXXIuse4j138wPVNXrezHgHG31c/E14ExVXZ7/WHtiq7W4Ajw9bH8XODLPwfbAVmvxJeDRqjoCfIK390cnJ27nrALv3a8btlqLB4FfVdXpt0Hc4RprkeQQ6zfKfSbJN1gP2tf3aMZ52ern4gXgnmH7OPDTuU42f1utxfuBXw/bvwHeN9/R3lImbudMPgc//Jb5F+CDrF9DOwH8PfAw8B7g34A/Af4b+LuR69DtbLMW3wb+FHhtOP0/quqf92LOedhqLarqtZHzzgN3V9X/7sWc87DNz8UB1u8KX2D93e3fVtXLezTqzG2zFn8OPMb6m9HrgM9X1Qt7NOrcJDnO+t+Bh5KcYYft9EYnSWrKG50kqSkDL0lNGXhJasrAS1JTBl6SmjLwktSUgZekpgy8JDX1f/q18zVn7PsyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# big_battles 시각화\n",
    "big_bat.plot(kind='barh', barmode = 'stack')\n",
    "# horizontal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 년도별로 사망자의 숫자와 전쟁이 벌어진 횟수 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Death Year\n",
       "297.0      3\n",
       "298.0     46\n",
       "299.0    156\n",
       "300.0    100\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = dth.groupby('Death Year')['Name'].count()\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "298     7\n",
       "299    20\n",
       "300    11\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp2 = bat.groupby('year')['name'].count() # groupby에서는 value count 불가하므로 기준 변수 세워야\n",
    "temp2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x200ba3a2848>"
      ]
     },
     "execution_count": 123,
     "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": [
    "temp3 = pd.concat([temp, temp2], axis = 1)\n",
    "temp3.columns = ['Death', 'battles']\n",
    "temp3.plot()"
   ]
  }
 ],
 "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": "3.7.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}