naoki-maeda/Untitled1.ipynb

## Untitled1.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "num = np.random.randn(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.42559566,  2.41714914, -0.11235299, -2.20919961, -0.36818768,\n",
       "        0.33940087,  0.76533743,  0.26359083, -0.51136158, -1.48288657])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.DataFrame(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.425596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.417149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.112353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.209200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.368188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.339401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.765337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.263591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.511362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.482887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0 -0.425596\n",
       "1  2.417149\n",
       "2 -0.112353\n",
       "3 -2.209200\n",
       "4 -0.368188\n",
       "5  0.339401\n",
       "6  0.765337\n",
       "7  0.263591\n",
       "8 -0.511362\n",
       "9 -1.482887"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10983d7b8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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dnFAdRpLRdtnFgmafPnbGwTnnwMqVoavKPilpCfPeP+C93817X9N7f7D3/t3y\nfH0iYfPWjRpFVaFszQ472FHRjzyi1J7pvvvOmgN794bTToP33oN27UJXJZmuRg34+99t6eGTT9om\nRZ99Frqq7BL7/vBFi6x5TXsLhNW3r50W+dJLoSuRqLz8si0ZnDwZxo+30YE6dUJXJdmkZ08712LV\nKtvG+J//DF1R9oh9GBg3zpaddO0aupLsdtRRtr/DkCGhK5FkW7MGLrvMurpbtLBzBfT9JqHsu681\nE/7pT3bQ0YAB1sgq0Yp9GEgk4IQTtJ45NOes+3fqVJtDlszw0Ue2nfD998Mdd9jIz847h65Ksl3d\nuvDUU7bd9W23wXHH2dHYEp1Yh4HPP7chI00RxEPnzrbl7M03h65EKst7uPdeO2Bo/Xp4+2249FJt\nLCXx4RxcfrlNE3/0kS0/nF6uBelSHrH+1i8shFq1dBRqXFSpYkN2kybpSNJ0tmiRnSh38cVw/vk2\nJLv//qGrEtm8I4+05Ye77Wb/f999amSOQmzDgPe2K1WXLpCXF7oa2aB7dzuJTKMD6WniRJuTfe89\n2xv+nnugZs3QVYls3U47wauvwkUX2ZbYPXrAjz+GriqzxDYMzJxp3euaIoiXnBw7qe7JJ+2gGkkP\nP/1kowAnnQSHHGJNgscfH7oqkbKrVg3uvNOayp991o5Znz07dFWZI7ZhIJGA+vVthyqJl7PPth3D\nhg4NXYmUxbvv2nzr6NG2w9szz9jeESLpqFs363Hx3lY4jR8fuqLMEMswUFJi/QKnnWZpUOIlN9eW\noo0aBfPnh65GtmT9elsKevDBsM02NjVw3nk6V0DSX4sWFgg6drRwcNll8PPPoatKb7EMAzNmwJdf\naoogzs4/3zakuf320JXI5sybB0cfDddcY8dPT58OzZqFrkokeWrXtjeN99xjK2OOOQYWLgxdVfqK\nZRh4/nnYdVd7RyPxVLu2daM/8ojW/8ZNImGrA+bNgylT7KAhjbBJJnLOGgqnTLGl6AccYHuhSPnF\nMgy8/DLk52vNc9xddJHtDnn33aErEYDvv7cu6x49bPh05kw44ojQVYlE79BDbflhy5a2c+Htt2v5\nYXnF8uV2xQpNEaSD7baDCy6wA0a+/z50NdnttddsNOBf/7IluWPGQL16oasSSZ0GDWwHzcsvhyuu\ngFNPtdcSKZtYhoE99rC10BJ/l14Kq1fDAw+EriR7zZtn5wrsuiu8/76CtGSvnBxb5fT00zbCfOCB\n8OGHoat/JnqwAAAZGElEQVRKD7EMA1r/nD4aNoReveCuu3QGeSjXXmujAJMmWSAQyXYnn2xLamvW\ntP0IEonQFcWfwoBUWv/+sGwZDBsWupLsM3Om7R/wt79ZU6eImKZN4Y037ATOHj2sx2nt2tBVxVcs\nw8BOO4WuQMqjSRMbmr7tNn2zpdqVV9oPvd69Q1ciEj95efDYY/Dgg/Dww9ZQu2BB6KriKZZhQNLP\nVVfZ3hCjR4euJHu88gq8+KKdE6GlgyKb5xz07WtNtgsX2m6cr7wSuqr4URiQpGjZ0g6VGjrUdr6T\naJWU2PTMwQfbn7uIbF3btlBUZGGgQwfbnbOkJHRV8aEwIEkzYAB8+qn2Ck+FJ56wddW33qrthUXK\nqn59a7S95hq4+mprNFy2LHRV8aAwIElz0EG2xG3IEG34EaU1a+wHWefOcNhhoasRSS9Vq8LgwfDc\nczBtmi0/nDMndFXhKQxIUg0caGvdJ00KXUnmeugh21vg5ptDVyKSvk480aYNcnKssTDbj2RXGJCk\nOvJIm8e+6SaNDkRh+XK44Qbb26Fly9DViKS3Jk3sXIO6dS0QfPxx6IrCURiQpHLORgfeeEMHhkTh\n1lttc6fBg0NXIpIZGja0QNCggb2ZmTkzdEVhKAxI0nXsCPvtZ70DkjxffWU7Pfbrp704RJJphx1g\n8mTbwfPoo236INsoDEjSOWcrC156ybYEleT4299sE5X+/UNXIpJ5tt/ezjPYay845hh4883QFaWW\nwoBEols32HNPNbkly8cfw6OP2jkEdeuGrkYkM9WrZ29i9tvPVka99lroilJHYUAiUbWq7Uo4YUJ2\nN+Uky4ABNoTZt2/oSkQyW5068PzztknR8cfb9EE2UBiQyJx5JjRqBLfcErqS9DZtGkycaCs0atQI\nXY1I5qtVC/71L1th0LGjbfud6RQGJDLVq8Pll8OYMTB3buhq0pP3cMUV0KYNnH566GpEskfNmvDM\nMzZd0LkzPPts6IqipTAgkerTB7bd1k40lPJ7+mlrZLr1Vqii71aRlKpRw7ZX79QJTjkFnnoqdEXR\n0Y8XiVStWnDJJdb8tnBh6GrSy88/W6/A8cfDn/4UuhqR7FS9Oowda03Rp58OhYWhK4qGwoBE7sIL\n7RvqrrtCV5Jehg+3g5/UcyESVk4OjBplfVA9esBjj4WuKPkUBiRy9epZIHjwQVi6NHQ16eHHH21f\ngTPPtGVOIhJW1aoW0AsK4Nxz4eGHQ1eUXAoDkhKXXALr1sH994euJD3ccQd8/72dQyAi8VClih0U\n9te/wvnnZ9bPM4UBSYkGDayZ8J577F2vbNnixdZwedFF0Lhx6GpEZGPO2c+xyy+379E77ghdUXIo\nDEjKXHEFrFiRecNryXb99VCtmjUPikj8OGcrfK6+2kLBTTeFrqjyckIXINmjcWObA7/9dush0AY6\nv/XppxaWhgyB7bYLXY2IbIlzcOON9nPsmmtgzRo7TdS50JVVjEYGJKWuvBIWLcrMbtxkGDjQjlS9\n6KLQlYhIWVx7ra34ueEG24Ld+9AVVYxGBiSl9toLTj3Vvnl69bIlO2Leess2OBk5EnJzQ1cjImXV\nv7+NEFxyiY0Q3HVX+o0QaGRAUm7AAJgzB8aNC11JfHhvP1D23Rd69gxdjYiU18UX2/Lpe+6Bv/wF\nSkpCV1Q+kb0vc84NBDoCrYA13nvNgAoABxwAJ5xg8+Ldu2ubXYDnnoOpU2HSJFvPLCLpp29fGyHo\n3RvWrrX+n3T5fo7yx3A1YBzwYIT3kDQ1cCB89FHmH/5RFuvX21zj0Ufb1sMikr7OPdd2Kxw5Es4+\n2/ZXSQeRjQx47wcDOOfOjuoekr4OOwwOP9xGBzp3Tr/5tWR67DELRiNHZvefg0im6NHDtmA/4wwb\nIRgzxpYLx5kGaCWYgQPh7bdh8uTQlYSzciVcd50dgHLggaGrEZFk6dbNGoKfecb+f82a0BVtncKA\nBHPccdY/MGRI6ErCufde+OabzNi0RER+7aST4J//hBdegC5dYNWq0BVtWbmmCZxzNwNXbuVTPNDC\ne/9JZYrq168fdevW/dVj+fn55OfnV+ayEjPO2ehAt27w5pvQrl3oilLru+9g6FBrOtpjj9DViEgU\nTjgB/vUvmw7t3NnCQV5e5a9bWFhI4SbnKS9fvrzC13O+HDskOOe2B7b/nU+b473/X8tEac/AXWVZ\nTeCcaw0UFRUV0bp16zLXJemrpAT23huaNbNvkmxy6aUwbBh8/jnssEPoakQkSv/5D3TsCG3aWDio\nUyf59yguLqZNmzYAbbz3xeX52nJNE3jvv/Pef/I7H2nSOylxUKWKddJPnAgffBC6mtT54gv4+99t\nR0YFAZHMd+SR8NJLMGOGTZFW4k18JCLrGXDO7eKc2x/YFajqnNu/9KNWVPeU9HTGGXZuwdChoStJ\nnWuuge23tx3LRCQ7HHIIvPwyzJoF7dvD0qWhK/pFlA2E1wPFwCCgdun/FwNtIrynpKFq1Wz3vbFj\nbcg80733ni01GjwYaikai2SVgw6CV1+FuXPhmGPg229DV2QiCwPe+3O991U38zE1qntK+urVC+rX\nt2NBM92VV0Lz5rY5iYhkn1atYMoUWLjQNhtbtCh0RVpaKDFRs6Y11I0cCV99Fbqa6Lz0Evz73zYl\nokOaRLLXPvtYIFi2DI46KvzPPYUBiY0LLrBQcMcdoSuJRkmJjQoceqgtMRKR7Na8ua0yWLnSGgzn\nzw9Xi8KAxMY228BFF8E//gFLloSuJvkSCeskvvVWbTssImbPPe2QspISOOIIO9E1BIUBiZWLL7b/\n3ntv2DqSbfVqW0HQpYt1FIuIbLDbbhYIqle3QPBJpbbtqxiFAYmV+vXhvPPgvvtgxYrQ1STPAw/A\nl1/CzTeHrkRE4qhRI5syqFvXpgw+/ji191cYkNi57DL46Sd46KHQlSTH99/b2QN9+sBee4WuRkTi\nqmFDW3b4hz9YU+H776fu3goDEjuNGsE558Cdd8b7YI+yGjrUpgkGDQpdiYjE3R/+YCe57rKLLTss\nKkrNfRUGJJb697fNOEaMCF1J5SxYAPfcY6MdDRuGrkZE0sH228Mrr0DTprYx0ZtvRn9PhQGJpT33\nhNNPt877n38OXU3FDRpkB5JccUXoSkQkndSrZ3uS7LsvHHssvPZatPdTGJDYuuoqmDcPNjmlM218\n+CE89hhcd100J5SJSGarUwdeeAHatoXjj7fpg6goDEhs7bcfdOpkHfglJaGrKb+rroImTWx1hIhI\nRdSqZUceH364HYH84ovR3EdhQGJt4ECYPRuefjp0JeXzn//Ac8/BkCG2dlhEpKJq1oRnnrHpgs6d\n4dlnk38PhQGJtXbtrKN2yBDwPnQ1ZeO9NUAedBB06xa6GhHJBLm5MH68jZaecgo89VRyr68wILE3\ncCAUF9shP+lg/Hh4+21tOywiyVW9uh313q2bNVgns59K56ZJ7B1zjL3LHjIEjjsudDVb9/PPFl46\ndrRNQ0REkiknB0aNsmDQsyesXQtnn52E61b+EiLRcs5eYLt0gWnT4LDDQle0ZQ8/bAeNpFuPg4ik\nj6pV4dFHLRCce64FgoKCyl1TYUDSQufOsPfetrLguedCV7N5P/wAgwdbSt9nn9DViEgmq1LFTnit\nUcNWLK1ZU7lD0BQGJC1UqQIDBtiw2IwZ0KpV6Ip+6/bbLRBcf33oSkQkGzhnJ7zWqGHHv19yScWv\npQZCSRunn27r9uN48t+iRXDHHXYEc6NGoasRkWzhHNx2G1x9Ndx9d8WvozAgaSMnB668Ep58Msx5\n31szeLCl86uuCl2JiGQb5+DGG6Fv34pfQ2FA0srZZ8OOO8Itt4Su5Bf//S888ogl83r1QlcjItmq\nMk2ECgOSVnJz7QTAxx+H+fNDV2MGDLCpgQsvDF2JiEjFKAxI2jn/fDvA4/bbQ1cC06fbMsIbb7Rp\nAhGRdKQwIGmndm1r1HvkEfjmm3B1bNh2uFUrOOOMcHWIiFSWwoCkpYsuso03KtM9W1kTJ8Lrr1v/\nQhV9J4lIGtOPMElL220HF1wAf/87fP996u+/bp2tHGjfHjp0SP39RUSSSWFA0tall9quWw88kPp7\njxhhRyvHaVWDiEhFKQxI2mrYEHr1grvugpUrU3ffn36CQYOsT6B169TdV0QkKgoDktauuAKWLYNh\nw1J3z7vvhu++sxUEIiKZQGFA0lqTJvYO/bbb7OSuqH37rU0N/OUvdm8RkUygMCBp76qr4MsvYfTo\n6O9144229efVV0d/LxGRVFEYkLTXsiV06QJDh8L69dHdZ84cePBB23Gwfv3o7iMikmoKA5IRBgyA\nTz+Fp56K7h5XXw1/+INteCQikkkUBiQjHHQQHHssDBliOwMm27vvwtixcP31ULNm8q8vIhKSwoBk\njIEDYeZMmDQpudf13o5O3ntvOzVRRCTTKAxIxjjySDj4YLjppuSODrz4IkyebD0JVasm77oiInGh\nMCAZwzkbHXjjDZg6NTnXXL/eRgWOOAI6dkzONUVE4kZhQDJKx46w337WO5AMY8bA++/Drbda2BAR\nyUQKA5JRNowOvPSSNf1VxurVcM01cOqp8Mc/Jqc+EZE4UhiQjHPqqbDnnnDzzZW7zn33wcKFyRtl\nEBGJq8jCgHNuV+fcMOfcHOfcSufcp865vznnqkV1TxGwJr+rroIJE+Djjyt2jaVLLQScdx40bZrc\n+kRE4ibKkYHmgAMKgJZAP6AvcFOE9xQB4MwzoVGjih8xfPPNsG4dXHddcusSEYmjyMKA9/5F731v\n7/0r3vsvvPf/Am4HTonqniIbVK8Ol19uDYBffFG+r50/36YILr8cGjSIpDwRkVhJdc9APWBpiu8p\nWapPH9h2WzvRsDyuvRbq1YPLLoumLhGRuElZGHDO7Qn8FXgoVfeU7FarFlxyCQwfDosWle1rZs6E\nUaNg0CCoXTva+kRE4qLcYcA5d7NzrmQrH+udc802+ZqdgeeBJ7z3jyareJHfc+GFNmVw551l+/yr\nrrKGwT59oq1LRCROcirwNbcDI37nc+Zs+B/n3E7AZGCa9/78stygX79+1K1b91eP5efnk5+fX85S\nJdvVq2eB4P777YV+u+22/LmTJ8MLL8D48VBNa15EJMYKCwspLCz81WPLly+v8PWcj+KItw0XtxGB\nycA7wJn+d27mnGsNFBUVFdG6devI6pLssngx7LabHXO8pdUBJSXQtq2FgOnTtdugiKSf4uJi2rRp\nA9DGe19cnq+Ncp+BhsAUYD7QH/iDc66Bc0792ZJSDRrYsP8998CPP27+c8aNg6IibTssItkpygbC\nDsDuwJ+ABcDXwMLS/4qk1BVXwIoV8PDDv/29tWvh6quhc2c4/PDU1yYiElqU+ww85r2vuslHFe+9\nDoGVlGvc2DYiuv12WLPm17/30EO2F0Flty8WEUlXOptAssaVV9oSw8ce++WxFSvghhugVy9o2TJc\nbSIiISkMSNbYay87xOiWW2yrYbAegZ9+gsGDw9YmIhKSwoBklQEDYM4caxj8+mvbf6BfP9hpp9CV\niYiEU5F9BkTS1gEHwAknWH/Aq69CXh707x+6KhGRsBQGJOsMHGirBj78EO6+GzbZ30pEJOsoDEjW\nOewwOPJIWLAA+vYNXY2ISHgKA5KVnn3WlhjWqBG6EhGR8BQGJCvVqWMfIiKi1QQiIiJZT2FAREQk\nyykMiIiIZDmFARERkSynMCAiIpLlFAZERESynMKAiIhIllMYEBERyXIKAyIiIllOYUBERCTLKQyI\niIhkOYUBERGRLKcwICIikuUUBkRERLKcwoCIiEiWUxgQERHJcgoDIiIiWU5hQEREJMspDIiIiGQ5\nhQEREZEspzAgIiKS5RQGREREspzCgIiISJZTGBAREclyCgMiIiJZTmFAREQkyykMiIiIZDmFARER\nkSynMCAiIpLlFAZERESynMKAiIhIllMYEBERyXIKAxErLCwMXULSZNJzAT2fOMuk5wJ6PnGWSc+l\nMiINA865fzrn5jnnVjnnvnbOPe6caxjlPeMmk/6hZdJzAT2fOMuk5wJ6PnGWSc+lMqIeGZgMdAOa\nAacAewBPRnxPERERKYecKC/uvb9no18ucM4NBZ52zlX13q+P8t4iIiJSNinrGXDObQf0AF5XEBAR\nEYmPSEcGAEpHA/4K5AFvAH/eyqfnAsyaNSvqslJm+fLlFBcXhy4jKTLpuYCeT5xl0nMBPZ84y6Tn\nstFrZ255v9Z578v3Bc7dDFy5lU/xQAvv/Seln78dsB2wKzAIWOG932wgcM6dAYwpV0EiIiKysR7e\n+0R5vqAiYWB7YPvf+bQ53vt1m/nanYEFwMHe+7e2cO3jgC+A1eUqTEREJLvlArsBL3rvvyvPF5Y7\nDFSGc64x9kJ/lPd+aspuLCIiIlsUWRhwzh0EtAWmAcuAPYHrgR2Afbz3P0dyYxERESmXKFcTrML2\nFngZmA08AszARgUUBERERGIipdMEIiIiEj86m0BERCTLKQyIiIhkuViFAefchc65uaUHG71Z2oSY\ndpxzhzvnJjrnvnLOlTjnOoeuqaKccwOcc28751Y45xY75552zjULXVdFOef6OudmOueWl35Md84d\nH7quZCj9uypxzt0ZupaKcM4NKq1/44+PQ9dVGc65nZxzo5xzS5xzK0v/7bUOXVd5lf5c3vTvpsQ5\nd1/o2irCOVfFOXeDc25O6d/LZ865a0LXVVHOudrOubudc1+UPp9pzrkDy3ON2IQB59zpwB3YxkQH\nADOBF51z9YMWVjG1sGbJC7FNmNLZ4cB9wB+B9kA14CXnXM2gVVXcAmzTrDalH5OBfzrnWgStqpJK\ng3MB9n2Tzj4EGgA7ln4cFracinPO1QNeB9Zg+6e0AC7DVlelmwP55e9kR+BY7GfbuJBFVcJVwPnA\nX4DmQH+gv3Pur0GrqrjhwDHYlv/7AP8GXi7PKcGxaSB0zr0JvOW9v7j01w77wX2v9/7WoMVVgnOu\nBDjZez8xdC3JUBrOvgGO8N5PC11PMjjnvgMu996PCF1LRTjnagNFwAXAtcB73vtLw1ZVfs65QcBJ\n3vu0e+e8OaVbsR/svT8ydC3J5py7GzjRe5+Wo4TOuWeBRd77go0eGw+s9N6fFa6y8nPO5QI/AJ28\n9y9s9Pi7wCTv/XVluU4sRgacc9Wwd2mvbHjMW0p5GTg4VF2yWfWwdwRLQxdSWaVDhd355dyMdPV3\n4Fnv/eTQhSRB09Lptc+dc6Odc7uELqgSOgHvOufGlU6xFTvn+oQuqrJKf173wN6NpqvpwDHOuaYA\nzrn9gUOBSUGrqpgcoCo2ArWxVZRjZC3yg4rKqD72ZBZv8vhiYK/UlyObUzpaczcwzXuftnO5zrl9\nsBf/DYm6i/d+dtiqKqY0zLTChnHT3ZvAOcB/gYbA34Cpzrl9vPc/BayronbHRmvuAG7Cptrudc6t\n9t6PDlpZ5XQB6gKPhS6kEoYC2wCznXPrsTfGV3vvx4Ytq/y89z86594ArnXOzcZeN8/A3kh/Wtbr\nxCUMbIkj/efcM8kDQEssQaez2cD+2ChHV+Bx59wR6RYInHONsHB2bCZs5OW9f3GjX37onHsbmAec\nBqTjFE4V4G3v/bWlv57pnNsbCwjpHAZ6Ac977xeFLqQSTsdeMLsDH2OB+h7n3Nfe+1FBK6uYnsCj\nwFfAOqAYSABlnnKLSxhYAqzHGoc29gd+O1ogATjn7gdOBA733i8MXU9llB6iNaf0l8XOubbAxdgP\n6XTSBtveu6h01AZshO2I0kaoGj4uTUEV4L1f7pz7BNvKPB0tBDY9j30WtjNrWio9X6Y9cHLoWirp\nVmCI9/7J0l9/5JzbDRgApF0Y8N7PBY4ubezexnu/2Dk3Fphb1mvEomeg9F1NEdYNCfxvSPoYbG5H\nAioNAicBR3vv54euJwJVgBqhi6iAl4F9sXc1+5d+vIu969w/nYMA/K8xcg/sRTUdvc5vpzn3wkY7\n0lUv7A1aOs6tbyyP3446lxCT18SK8t6vKg0C22IrWJ4p69fGZWQA4E7gMedcEfA20A/7CxsZsqiK\ncM7Vwt7NbHi3tntpg8pS7/2CcJWVn3PuASAf6Az85JzbMHqz3HufdsdMO+duAp7HVqrUwRqhjgQ6\nhKyrIkrn0X/Vu+Gc+wn4znu/6TvS2HPO3QY8i71Y7gwMxoY8C0PWVQl3Aa875wZgS/D+CPTBloCm\nndI3aOcAI733JYHLqaxngaudcwuAj7Dh9H7AsKBVVZBzrgP2evNfoCk28jGLcrx+xiYMeO/HlS5b\nux6bLpgBHOe9/zZsZRVyIPAqljw91kAE1nDTK1RRFdQXew5TNnn8XODxlFdTeQ2wuhsCy4H3gQ4Z\n0okP6d1j0wib59we+BY78bRdec9ljwvv/bvOuS5Ys9q12JDtxenYpFaqPbAL6dm/sam/AjdgK3H+\nAHwNPFj6WDqqC9yMheilwHjgGu/9+rJeIDb7DIiIiEgYaT0/IiIiIpWnMCAiIpLlFAZERESynMKA\niIhIllMYEBERyXIKAyIiIllOYUBERCTLKQyIiIhkOYUBERGRLKcwICIikuUUBkRERLLc/wPmIO60\n97iqjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1097adda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}