{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "#Please adjust to your file\n",
    "df=pd.read_excel(\"/Users/davidmika/Desktop/First.xlsx\",\"Sheet1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stacked Column Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAFACAYAAACyQbdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHOBJREFUeJzt3X+cVnWd9/HXOwTHnymKhIzKVKwEiThOU0bbIpa4Sup2\nG2LRzZ2WtQ9Wa8u6dXfd2orWfu52W3rHpkZbgqw/KcsyFMu2xEERxR9JijmkiKTmDyCgz/5xzuAF\nDnNdM3Ndczhf3s/HYx5zzvecc12fuR7y9lzf8z3nq4jAzMzS9aqiCzAzs8Zy0JuZJc5Bb2aWOAe9\nmVniHPRmZolz0JuZJc5Bb2aWOAe9mVniHPRmZonbregCAA488MAYNWpU0WWYmZXK0qVLn46IYdX2\n2ymCftSoUXR0dBRdhplZqUh6rJb93HVjZpY4B72ZWeIc9GZmidsp+ujNzAbSpk2b6OzsZMOGDUWX\nUpOmpiaam5sZPHhwn4530JvZLqezs5N99tmHUaNGIanocnoUEaxbt47Ozk5aWlr69BruujGzXc6G\nDRs44IADdvqQB5DEAQcc0K9vHw56M9sllSHku/S3Vge9mVniHPRmZrk1a9bw3ve+l9e+9rUcffTR\nHHPMMVx33XX9ft3FixczderUOlTYN8kGvVSOHzPbOUQEp556Km9/+9t55JFHWLp0KfPnz6ezs3PA\na9m8eXNdXy/ZoDcz641bbrmFIUOG8JGPfGRr22GHHcY555zDli1b+OQnP8mb3vQmxo8fz7e+9S0g\nO1OfNGkSp512GmPGjOF973sfEQHATTfdxJgxY2htbeXaa6/d+povvvgiZ555Ju3t7Rx11FHccMMN\nAHznO9/h5JNPZvLkyRx33HF1/ds8vNLMDFixYgWtra3dbrvssst49atfzZ133snGjRuZOHEixx9/\nPAB33303K1as4OCDD2bixIn88pe/pK2tjQ996EPccsstvP71r+f000/f+lqzZ89m8uTJXH755Tz7\n7LO0t7fzjne8A4C77rqL5cuXM3To0Lr+bTUFvaRVwPPAFmBzRLRJGgpcBYwCVgHTIuKZfP8LgLPy\n/c+NiJ/UtWozswabNWsWt99+O0OGDOGwww5j+fLlXH311QA899xzPPzwwwwZMoT29naam5sBmDBh\nAqtWrWLvvfempaWF0aNHAzBjxgzmzJkDwE9/+lMWLlzIV77yFSAb6vm73/0OgHe+8511D3no3Rn9\nsRHxdMX6+cCiiLhI0vn5+v+VNBaYDowDDgZ+JukvImJL3ao2M6uzcePGcc0112xd/+Y3v8nTTz9N\nW1sbhx56KBdffDFTpkzZ5pjFixez++67b10fNGhQ1f71iOCaa67h8MMP36b9jjvuYK+99qrDX/JK\n/emjPwWYmy/PBU6taJ8fERsj4lFgJdDej/cxM2u4yZMns2HDBi699NKtbS+99BIAU6ZM4dJLL2XT\npk0A/OY3v+HFF1/c4WuNGTOGVatW8dvf/haAefPmbd02ZcoULr744q19+XfffXfd/5bt1Rr0QXZm\nvlTS2Xnb8Ih4Il9+EhieL48EHq84tjNv24aksyV1SOpYu3ZtH0o3M6sfSVx//fXcdttttLS00N7e\nzsyZM/niF7/IBz/4QcaOHUtraytvfOMb+fCHP9zjmXtTUxNz5szhpJNOorW1lYMOOmjrtgsvvJBN\nmzYxfvx4xo0bx4UXXtj4v63r/yo97iSNjIjVkg4CbgbOARZGxH4V+zwTEftL+gbw64j4Xt5+GfDj\niLh6R6/f1tYW9Z54pCxDF2v4+M2szh544AHe8IY3FF1Gr3RXs6SlEdFW7diazugjYnX++yngOrKu\nmDWSRuRvNgJ4Kt99NXBIxeHNeZuZmRWgatBL2kvSPl3LwPHAfcBCYGa+20zghnx5ITBd0u6SWoDR\nwJJ6F25mZrWpZdTNcOC6/KE6uwFXRsRNku4EFkg6C3gMmAYQESskLQDuBzYDszzixsysOFWDPiIe\nAY7spn0d0O3tWxExG5jd7+rMzKzf/AgEM7PEOejNzBLnZ92YmdV7PHYN46YHDRrEEUccsXX9+uuv\nZ9SoUfWtI+egNzMrwB577MGyZcsG5L3cdWNmljif0ZuZFWD9+vVMmDABgJaWlrrMZLUjDnozswK4\n68bMzOrGQW9mljh33ZiZJf4YWZ/Rm5kV4IUXXhiw93LQm5klzkFvZpY4B72ZWeIc9GZmiXPQm5kl\nzkFvZpY4B72Z7fKk+v7U9p5ixowZW9c3b97MsGHDmDp1at3/Pge9mVkB9tprL+677z7Wr18PwM03\n38zIkSMb8l4OejOzgpx44onceOONAMybN48zzjijIe/joDczK8j06dOZP38+GzZsYPny5bz5zW9u\nyPs46M3MCjJ+/HhWrVrFvHnzOPHEExv2Pn6omZlZgU4++WTOO+88Fi9ezLp16xryHg56M7MCnXnm\nmey3334cccQRLF68uCHv4aA3s11ekU8pbm5u5txzz23oezjozcwK0N1jiidNmsSkSZPq/l6+GGtm\nljgHvZlZ4hz0ZrZLihJNH9jfWh30ZrbLaWpqYt26daUI+4hg3bp1NDU19fk1fDHWzHY5zc3NdHZ2\nsnbt2qJLqUlTUxPNzc19Pr7moJc0COgAVkfEVElDgauAUcAqYFpEPJPvewFwFrAFODciftLnCs3M\n6mzw4MG0tLQUXcaA6U3XzUeBByrWzwcWRcRoYFG+jqSxwHRgHHACcEn+PwkzMytATUEvqRk4Cfh2\nRfMpwNx8eS5wakX7/IjYGBGPAiuB9vqUa2ZmvVXrGf2/A58C/lzRNjwinsiXnwSG58sjgccr9uvM\n27Yh6WxJHZI6ytJPZmZWRlWDXtJU4KmIWLqjfSK7dN2ry9cRMSci2iKibdiwYb051MzMeqGWi7ET\ngZMlnQg0AftK+h6wRtKIiHhC0gjgqXz/1cAhFcc3521mZlaAqmf0EXFBRDRHxCiyi6y3RMQMYCEw\nM99tJnBDvrwQmC5pd0ktwGhgSd0rNzOzmvRnHP1FwAJJZwGPAdMAImKFpAXA/cBmYFZEbOl3pWZm\n1ifaGe4Ma2tri46Ojrq+Zq0zsRdtJ/j4zUptV/63LmlpRLRV28+PQDAzS5yD3swscQ56M7PEOejN\nzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56\nM7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD\n3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEVQ16SU2Slki6R9IKSf+Stw+VdLOkh/Pf+1cc\nc4GklZIekjSlkX+AmZn1rJYz+o3A5Ig4EpgAnCDpLcD5wKKIGA0syteRNBaYDowDTgAukTSoEcWb\nmVl1VYM+Mi/kq4PznwBOAebm7XOBU/PlU4D5EbExIh4FVgLtda3azMxqVlMfvaRBkpYBTwE3R8Qd\nwPCIeCLf5UlgeL48Eni84vDOvM3MzApQU9BHxJaImAA0A+2S3rjd9iA7y6+ZpLMldUjqWLt2bW8O\nNTOzXujVqJuIeBa4lazvfY2kEQD576fy3VYDh1Qc1py3bf9acyKiLSLahg0b1pfazcysBrWMuhkm\nab98eQ/gncCDwEJgZr7bTOCGfHkhMF3S7pJagNHAknoXbmZmtdmthn1GAHPzkTOvAhZExA8l/QpY\nIOks4DFgGkBErJC0ALgf2AzMiogtjSnfzMyqUda9Xqy2trbo6Oio62tKdX25htkJPn6zUtuV/61L\nWhoRbdX2852xZmaJc9CbmSXOQW9mljgHvZlZ4hz0ZmaJc9CbmSXOQW9mlrhabpgy26XHKpuVnc/o\nzcwS56A3M0ucg97MLHEOejOzxDnozcwS56A3M0ucg97MLHEOejOzxDnozcwS56A3M0ucg97MLHEO\nejOzxDnozcwS56A3M0ucg97MLHEOejOzxDnozcwS56A3M0ucg97MLHEOejOzxDnozcwS56A3M0uc\ng97MLHFVg17SIZJulXS/pBWSPpq3D5V0s6SH89/7VxxzgaSVkh6SNKWRf4CZmfWsljP6zcAnImIs\n8BZglqSxwPnAoogYDSzK18m3TQfGAScAl0ga1IjizcysuqpBHxFPRMRd+fLzwAPASOAUYG6+21zg\n1Hz5FGB+RGyMiEeBlUB7vQs3M7Pa9KqPXtIo4CjgDmB4RDyRb3oSGJ4vjwQerzisM28zM7MC1Bz0\nkvYGrgE+FhF/rNwWEQFEb95Y0tmSOiR1rF27tjeHmplZL9QU9JIGk4X89yPi2rx5jaQR+fYRwFN5\n+2rgkIrDm/O2bUTEnIhoi4i2YcOG9bV+MzOropZRNwIuAx6IiK9VbFoIzMyXZwI3VLRPl7S7pBZg\nNLCkfiWbmVlv7FbDPhOB9wP3SlqWt/0DcBGwQNJZwGPANICIWCFpAXA/2YidWRGxpe6Vm5lZTaoG\nfUTcDmgHm4/bwTGzgdn9qMvMzOrEd8aamSXOQW9mljgHvZlZ4hz0ZmaJc9CbmSXOQW9mljgHvZlZ\n4hz0ZmaJc9CbmSXOQW9mljgHvZlZ4hz0ZmaJc9CbmSXOQW9mljgHvZlZ4hz0ZmaJc9CbmSXOQW9m\nljgHvZlZ4mqZHLyUYofT3O5sougCzCxxPqM3M0tcsmf0Vl/+hmRWXj6jNzNLnIPezCxxDnozs8Q5\n6M3MEuegNzNLnIPezCxxHl5pZqXmob/VOejNiqCShFP4voQUuOvGzCxxVYNe0uWSnpJ0X0XbUEk3\nS3o4/71/xbYLJK2U9JCkKY0q3MzMalPLGf13gBO2azsfWBQRo4FF+TqSxgLTgXH5MZdIGlS3as3M\nrNeqBn1E/Bz4w3bNpwBz8+W5wKkV7fMjYmNEPAqsBNrrVKuZmfVBX/voh0fEE/nyk8DwfHkk8HjF\nfp15m5mZFaTfF2MjIujDuCFJZ0vqkNSxdu3a/pZhZmY70NegXyNpBED++6m8fTVwSMV+zXnbK0TE\nnIhoi4i2YcOG9bEMMzOrpq9BvxCYmS/PBG6oaJ8uaXdJLcBoYEn/SjQzs/6oesOUpHnAJOBASZ3A\np4GLgAWSzgIeA6YBRMQKSQuA+4HNwKyI2NKg2s3MrAZVgz4iztjBpuN2sP9sYHZ/ijIzs/rxnbFm\nZolz0JuZJc5Bb2aWOAe9mVniHPRmZolz0JuZJc5Bb2aWOAe9mVniHPRmZolz0JuZJc5Bb2aWOAe9\nmVniHPRmZolz0JuZJc5Bb2aWOAe9mVniHPRmZolz0JuZJc5Bb2aWOAe9mVniHPRmZolz0JuZJc5B\nb2aWOAe9mVniHPRmZolz0JuZJc5Bb2aWOAe9mVniHPRmZolz0JuZJc5Bb2aWuIYFvaQTJD0kaaWk\n8xv1PmZm1rOGBL2kQcA3gb8GxgJnSBrbiPcyM7OeNeqMvh1YGRGPRMSfgPnAKQ16LzMz60Gjgn4k\n8HjFemfeZmZmA2y3ot5Y0tnA2fnqC5IeKqqWXjgQeLquryjV9eVKxp9nffnzrJ+yfJaH1bJTo4J+\nNXBIxXpz3rZVRMwB5jTo/RtCUkdEtBVdRyr8edaXP8/6Se2zbFTXzZ3AaEktkoYA04GFDXovMzPr\nQUPO6CNis6S/A34CDAIuj4gVjXgvMzPrWcP66CPiR8CPGvX6BSlVV1MJ+POsL3+e9ZPUZ6mIKLoG\nMzNrID8CwcwscQ56M7PEOejNzBJX2A1TOztJ7+5pe0RcO1C1pELSV/EIrLqR9J6I+K9qbVYbSYsi\n4rhqbWXkoN+xd/WwLQAHfe89AMyRtBtwBTAvIp4ruKYyuwDYPtS7a7MeSGoC9gQOlLQ/0HUL674k\n8ugWj7qxASfpcOADwBnAL4H/iIhbi62qPCT9NXAiMA24qmLTvsDYiGgvpLCSkvRR4GPAwWR38HcF\n/R/J/tv8RlG11YuDvgaSTgLGAU1dbRHx2eIqKq/8EdZTyYL+EGAB8DbgxYiYXmRtZSHpSGAC8Fng\nnys2PQ/cGhHPFFJYyUk6JyIuLrqORnDQVyHp/5N9rTsW+DZwGrAkIs4qtLASkvRvZCF/C3BZRCyp\n2PZQRBxeWHElJGlwRGwquo6USHorMIqKbu2I+G5hBdWJg74KScsjYnzF772BH0fEXxZdW9lI+gCw\nICJe7Gbbq91f3zuSJgKfIXuC4W5kXQ4REa8tsq6ykvSfwOuAZcCWvDki4tziqqoPX4ytbn3++yVJ\nBwPrgBEF1lNmMyLiisqGrlENDvk+uQz4e2ApLweT9V0b2TWO5M5+HfTV/VDSfsCXgbvIRtx8u9iS\nymVXGNVQkOci4sdFF5GQ+4DXAE8UXUi9ueumFyTtDjT57LN3thvV8PuKTcmMahhIklrzxWlkT4e9\nFtjYtT0i7iqirrKS9AOyE7h9yC5yL2Hbz/PkgkqrGwd9FfkokZN45QWarxVVU1mlPKphIEnqaShq\nRMTkASsmAZL+qqftEXHbQNXSKA76KiT9CNgA3Av8uas9Iv6lsKJKRtLkiLhlR3cb+y5js8ZyH311\nzRExvugiSu6vyIZUdne3se8y7iNJH++m+TlgaUQsG+h6yk7S82T/PVZ6DugAPhERjwx8VfXhM/oq\nJH0RWBQRPy26FrNKkq4kGynyg7xpKrCcrJvxvyLiSwWVVkqSPgd0AleSDRiYTjbc8i7gbyNiUnHV\n9Y+DvgpJfwN8j+xJn5t4eazyvoUWVkL5RdkryO7g/A+gFTjf/xPtG0k/B06MiBfy9b2BG4ETyM7q\nxxZZX9lIuicijtyubVlETOhuW5n4McXVfQ04BtgzIvaNiH0c8n12ZkT8ETgeOAB4P3BRsSWV2kFU\njA4hOxEZHhHrt2u32rwkaZqkV+U/08iuz8Eru3RKxX301T0O3JfiTRQF6Bo/fyLw3YhYIUk9HWA9\n+j5wh6Qb8vV3AVdK2gu4v7iySut9wNeBS8iC/dfADEl7AH9XZGH95a6bKiR9B3gt8GO2HVvr4ZW9\nJOkKshukWoAjycaAL46IowstrMQktQET89VfRkRHkfXYzslBX4WkT3fX7uGVvSfpVWQ3pDwSEc9K\nOgAYGRHLCy6tVCTtGxF/lDS0u+0R8YeBrqnMJH0qIr4k6WK66aLxs24Sl98stU9EnFd0LYkIYCzZ\n6JDPAntR8ehnq9mVZJ/hUrLPVNv99kPNeueB/Hey34Z8Rl+FpF9FxDFF15ECSZeS3XQ2OSLekD/3\n5qcR8aaCSzPbStKeEfFS0XXUk0fdVLdM0kJJ75f07q6foosqqTdHxCzykQz5BBlDii2pvJSZIenC\nfP1QSZ5dqo8kHSPpfuDBfP1ISZcUXFZduOumuiayRxNXPj/Ed3P2zaa8OywAJA2j4rES1muXkH9D\nAj5Hdn/CNYC/IfXNvwNTgIUAEXGPpLcXW1J9OOiriIgPFF1DQv4fcB0wXNJsstm6/qnYkkrtzRHR\nKuluyL4hSfI3pH6IiMe3G/GbxHP+HfRVSGoGLublIWy/AD4aEZ3FVVVOEfF9SUuB4/KmUyPigZ6O\nsR75G1J9PZ5PJRiSBgMf5eULtaXmPvrqriD7Kndw/vODvM36Zk+y8fOvAvYouJay6/qGdFD+Del2\n4AvFllRqHwFmkd3rsZpsKPCsQiuqE4+6qaLrWRfV2qw6Sf8MvIesH1nAqWQP3/p8oYWVmKQxZN+Q\nRPbwvSTOQIsgqSkiNlTfs3wc9FVIWkR2Bj8vbzoD+EBEHLfjo6w7kh4Cjuz6x5TfWr4sIg4vtrJy\nyp+2+HPgv7ubcN16R9JKYA1Z9+wvgNtTmU3OXTfVnUk2ZduTZHNJngb4Am3f/J5tb5DanewrsvXN\nI2QnHh2Slkj6qqRTii6qrCLi9WSf571ks8rdIymJ5/r7jN4aruLW8kPJhv7dnG96B7AkInxfQj9I\neg3Zych5wP4RsU/BJZVSPvDiL8kmyjkS+APZWf2/FlpYHTjodyDvT96RiIjPDVgxJSdpZr64BzCY\nLPQ3A+sBImJuQaWVmqRvkz1Soqu74XbgrojYXGhhJSXpz8CdwBci4oZq+5eJh1fuWHd9nnsBZ5E9\nS91BX7srgdlk3WCPkV04PJTs2sc/FFhX2R1ANoLpWbKzz6cd8v1yFPA24L2SzgceBm6LiMuKLav/\nfEZfA0n7kI2pPQtYAHw1Ip4qtqrykPRvwN7AxyPi+bxtX+ArwEsR8bEi6ys7SW8gu6Pz74FBEdFc\ncEmllc/S9TayLpwZABFxWKFF1YGDvgf5Y2A/TjYhwVzg6/nzWawXJD0M/MX2k7fkN/s8GBGji6ms\n3CRNJQuktwP7kU2U8YuIuLzQwkpKUgfZAIH/Jh95ExGPFVtVfbjrZgckfRl4NzAHOKJrXk7rk+hu\nhq6I2CLJZxp9dwJZIH09In5fdDFlls+V8PWI+M+ia2kEn9HvQH5hZiPZRcPKD8mTg/eSpOuBayPi\nu9u1zwCmRcTJxVRWXvm3oZ9FxLFF15IKSR0R0VZ0HY3goLeGkzSS7Gmf68kmywBoIxuF8zcR4bH0\nfZDfzPfuVG7qKZqki4CngauoGIyRwoxdDnobMJImA+Py1fsjYlGR9ZRdPin4UWT3JVQGU+mnviuC\npEe7aY6IKP2MXQ56s5KquD9hG74vwbbnoDcrsfx5QYdGxENF11J2kvYkG2V3aEScLWk0cHhE/LDg\n0vrNz7oxKylJ7wKWATfl6xMkLSy2qlK7AvgT8NZ8fTWQxJNVHfRm5fUZoJ3szlgiYhlQ+v7kAr0u\nIr4EbALIJwhXz4eUg4PerLw2dTPixjNM9d2f8q6wrhm7Xkc2xLr0fMOUWXmtkPReYFDen3wu2V2d\n1jefJusGO0TS98mmD/0/hVZUJ74Ya1ZS+cXDfwSOz5t+Anw+1VmSBoKkA4C3kHXZ/Doini64pLpw\n0JuVlKTWiLir6DrKTlJrT9tT+Iwd9GYlJelW4DXA1cBVEXFfwSWVUv45Qjb7WRtwD9kZ/XigIyKO\nKaq2evHFWLOSyp9zcyywFviWpHsl/VPBZZVORBybf5ZPAK0R0RYRR5PddZzE4zl8Rm+WAElHAJ8C\nTo+IIUXXU0aSVkTEuGptZeRRN2YllU84cjrwv4B1ZJPifKLQospteT494/fy9fcBywusp258Rm9W\nUpJ+DdwILAbu9Gib/pHUBPwt2UQuAD8HLk3hc3XQm5WMpN2AL5DNwfu7vPkQslv4/zEiNhVVm+2c\nfDHWrHy+DAwFWiKiNSJagdeRTSf4lUIrKzFJEyXdLOk3kh7p+im6rnrwGb1ZyXgO3saQ9CDZBOtL\ngS1d7RGxrrCi6sQXY83Kx3PwNsZzEfHjootoBHfdmJXP/ZL+9/aN+Ry8DxZQTypulfRlScdIau36\nKbqoenDXjVnJeA7exqi4Q7YrFEX27WlyQSXVjYPerKQ8B299SPp412L+O8juNr49IrqbR7Z0HPRm\ntkuT9OlumocCU4DPRMT8AS6p7hz0ZmbdkDQU+Fk+fLXUfDHWzKwbEfEHPJWgmVm6JB0LPFN0HfXg\ncfRmtkuTdC8vj7TpMhT4PfCKYaxl5D56M9ulSTpsu6YA1kXEi0XU0wgOejOzxLmP3swscQ56M7PE\nOehtlydpi6Rlku6RdJekt+btoySFpM9X7HugpE2SvpGvf0bSeUXVblYLB70ZrI+ICRFxJHAB8K8V\n2x4FTqpYfw+wYiCLM+svB73ZtvZl27HTLwEPSGrL108nm5vVrDQ8jt4M9pC0DGgCRgDbP61wPjBd\n0hqyCSl+Dxw8sCWa9Z2D3izvugGQdAzwXUlvrNh+E/A5YA1wVQH1mfWLu27MKkTEr4ADgWEVbX8i\ne+77J4CrCyrNrM98Rm9WQdIYYBCwDtizYtNXgdsi4g9SEs+5sl2Ig97s5T56yJ5WODOff3XrDhGx\nAo+2sZLyIxDMzBLnPnozs8Q56M3MEuegNzNLnIPezCxxDnozs8Q56M3MEuegNzNLnIPezCxx/wPG\nWr4i1BBIaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11077b410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group sum of sales at gender and BMI level\n",
    "var = df.groupby(['BMI','Gender']).Sales.sum()\n",
    "var.unstack().plot(kind='bar',stacked=True, color=['red','blue'], grid=False)\n",
    "#Show stacked column chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}