friso/stuff

## stuff
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 14, 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import py2neo\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "neo = py2neo.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3708"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = neo.cypher.execute('''\n",
    "match (m:Member)-[j:JOINED]->(g:Group)\n",
    "where g.urlname in ['Docker-Randstad', 'Big-Data-Amsterdam', 'DevOpsAmsterdam']\n",
    "return g.name as group, j.created as ts, 1 as count\n",
    "order by ts\n",
    "''')\n",
    "\n",
    "len(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts</th>\n",
       "      <th>group</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-06-08 01:29:50</td>\n",
       "      <td>DevOps Amsterdam</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-06-11 08:41:28</td>\n",
       "      <td>DevOps Amsterdam</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-06-11 08:45:09</td>\n",
       "      <td>DevOps Amsterdam</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-06-11 09:03:33</td>\n",
       "      <td>DevOps Amsterdam</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-06-11 09:17:25</td>\n",
       "      <td>DevOps Amsterdam</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ts             group  count\n",
       "0 2012-06-08 01:29:50  DevOps Amsterdam      1\n",
       "1 2012-06-11 08:41:28  DevOps Amsterdam      1\n",
       "2 2012-06-11 08:45:09  DevOps Amsterdam      1\n",
       "3 2012-06-11 09:03:33  DevOps Amsterdam      1\n",
       "4 2012-06-11 09:17:25  DevOps Amsterdam      1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    [(datetime.fromtimestamp(r.ts / 1000), r.group, r.count) for r in result],\n",
    "    columns=['ts', 'group', 'count']\n",
    ")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10be0e240>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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D6xnczMzMzMzaznLAUzXdmTap2gc4johf5ZlUVzyyY23De+6bFZP7vlnxuN83\nXe3FDvycNA3uvPzS6Z5HdszMzMzMrFckBKwAPFnDzQNJozobEvFazql1ySM71jY8f9esmNz3zYrH\n/b6pFgdmAp6r6q40fe044Bkiah0Vqlq/GdmR1Dp7ZFvLSv3IzMzMzOpkJHB7RC+f7yh9kfTA0IWB\nd4At65daF833h+fsmJmZmZlZ80mMBW6O4Ixe3nAu8D5wLvA4ER/3PYfe1wwudszMzMzMbIYk5gf+\nCQyLYFIvb/o3sDkR/8wvDz9U1ArI83fNisl936x43O+bZnfgT1UUOnMC8wPP1jOpnvSbNTtmZmZm\nZtYcEjMB3wF2reK2FYEniJhcn6xmzCM71ja8575ZMbnvmxWP+31T7A68CtxXxT3rAA/VJ53ecbFj\nZmZmZmbdyp6tcxhwVK93YUu+DYytT1a942LH2obn75oVk/u+WfG43zfc+sAU4I5e3yGtBswLjKtP\nSr3jNTtmZmZmZtaTfYEzqhzVORg4Na/1OiprKHAQsE1V93nraTMzMzMz64rErMDrwBIRTOzlTYOz\ne5Ym4o0+51DWLMA9wGPA6YzhHm89bWZmZmZmfTUMeK3XhU6yMfCPnAqd5YCTgeeBPaIU91Zzv6ex\nWduQNMq7s5gVj/u+WfG43zfUMOCFXl8tzQWMAU6vpTGVNRNwIGnK2gLAu8DdwD5Rqn5KmosdMzMz\nMzPrzmLAizO8ShJwCHAEcClwTrUNqawBpGf57A7sDDwBfByl2tf9uNixtuF/4TErJvd9s+Jxv2+o\nGY/sSAOAE4CRwBpEPFdtIyprA+A8YE7ga1GKB6pPdXoudszMzMzMrDuLAbd2eUaaB5gH+AWwCDCS\niHeqbUBl/ZI0ojM6SnFD7alOzxsUWNvwnvtmxeS+b1Y87vcNNf3IjjQA6afAc8AtQABfq7bQUVkr\nqqy/k6asLZt3oQMe2TEzMzMzs+4No3LNTpqy1vFw0eWIeK3agNkmBNsCpwBHAldGKd7ue6pdtOXn\n7JiZmZmZWWcSMwEfAkMi+ARpEOkBo/sAq1FDIaGyZiMVS58Ch0cpbq8+r97XDB7ZMTMzMzOzriwE\nvJkVOjsDPwIGAYfWWOgMA8YCTwG71bKVdLW8ZsfahufvmhWT+75Z8bjfN0zadlpaljTl7ATSiM7N\n1QRRWXOrrG8DVwF/AXZvRKEDHtkxMzMzM7OuDVuL+94C7gGOJOKiagOorI2AU0nrfs4CTo9STMk3\nzR7a95pOW+b4AAAgAElEQVQdMzMzMzPrTOLw+1ljrzV48Ewifl31/WV9DTgX+H6U4or88up9zeBp\nbGZmZmZmNp3dOH/dZXj6C8BJNYY4GPhxnoVOtTyNzdqGpFF+orJZ8bjvmxWP+32dpe2lf3wSc212\nFnv/8ofx+497dVtZ3yBtJT1X9tJ8wA51yrJXXOyYmZmZmVkizQtcBgzegDteeIyVrv7hjG4pS8AB\nwBGkramfy05NjFJ8UL9kZ8xrdszMzMzMLJG+C2z2LMN3XJpnJwKLRvDOdJelAmc1YAHgcGB2YPso\nxb/rn6Kfs2NmZmZmZtXbFBi7NM/ODkzpXOhkRc4mwBXAf4F/AjcBv2zkLmu95WLH2obn75oVk/u+\nWfG439eBNBfwbWAU8F1gBeDpz0+XNSuwJWm62prANsDfGvW8nFq52DEzMzMzKzJpdtIDQ5cCvknE\nG4h9GfjRXSrPth2wIKnIeQm4EdioFUdxuuI1O2ZmZmZmRSV9Ezib9NDPURrDZ8C+vPjlI1jwoQHM\n/PHjwOPA/VGKc5uY6ee8ZsfMzMzMzHomzQ2cCGxFxJ0AlHUUnw7emgf3m5XVz9ggzrrroabm2Ece\n2bG24fm7ZsXkvm9WPO73OUjP0rkbuJuIQwFU1gjgVi7+8xU8vRURHNjUHLvhkR0zMzMzM+vJbgGa\n+adcOzk9DPTbwFeBQ3l6q28CFzQ3vXx4ZMfMzMzMrEikJSaLBzbblX/8bTgLAS8AfwX+GKV4T+JZ\nYMsInmpuol3zyI6ZmZmZmU1ntf204KVDuefcVZn5b8P5O3BMlOLDjvMSswELA882LckcudixtuH5\nu2bF5L5vVjzu99VTWXMu8B43nHwX63wwiFdOWIdvRCnu7+LS5YBnIvis0TnWg4sdMzMzM7M2prKW\nUXDGdRez6Kqvcv4A+MGHv4hJ3Vy+FBUPE+3vBjQ7AbO8+F94zIrJfd+seNzve09lzQfc/rNxvLDq\nq0waAN8hui10AJYEnmtQenXnYsfMzMzMrH3tP/hTrhtzO+sMgB8T8ekMrl8C+HcjEmsEFzvWNiSN\nanYOZtZ47vtmxeN+P2Mqa6jKuh74zkN/YCLwEmnHtRlZkjYqdrxmx8zMzMys/ey//3jmPPl6PhwA\newNr07tnzrRVsdPjc3YknQ1sAbwREStmrw0FLgOGAc8DO0bEO9m5I4C9gMnAQRFxU/b66sC5wGDg\n+og4uJv2/JwdMzMzM7M+UFmD1n6ZV+48G808hW2AB4j4eIb3iYHAB8CQCD6pe6I1qqZmmNE0tnOA\nzTq9djhwc0QsA9ySHSNpBLATMCK75zRJHUmcDuwdEUsDS0vqHNPMzMzMzPKx08H38vHMUziJiLt6\nU+hkFgVea+VCp1o9FjsRcSfwdqeXtwLOy34/D9gm+31r4JKI+DQinic9iGhtSQsCQyJifHbd+RX3\nmOXG83fNisl936x43O+7p7IWnP0TfrXdBIYAF1Z5e1tNYYPa1uzMHxGvZ7+/Dsyf/b4QcG/FdS+T\nnr76afZ7h1ey183MzMzMrI9U1mzA0cAGwGK//St3D5rCQCKqLVxc7FSKiJDUm4VOvSbpXNJaIIB3\ngEc69lLvqOJ97OOujjtea5V8fOxjHzfmOCLGtVI+Pvaxjxtz3KFV8mna8czaiO35DcvxDHDQiuew\n1vAXGANsUkO8JeH0kPYf1TLvLx2vAsxNsjhV6HGDgqyBxYFrY+oGBU8BoyLiNaUpardFxHKSDs8S\nOy677kagBLyQXbN89vrOwMiI+G4XbUV4gwIzMzMzsxlSWesBRwKzAJtGKSYjjQHmJeLAquOJu4Bf\nR3BNvpnmq5qaoZbn7FwD7J79vjtwdcXr35I0SNISwNLA+Ih4DXhX0tqSBOxWcY9Zbjr/S4+ZFYP7\nvlnxFLnfq6w5VNbZKmsiaS38dcCWWaEzCBhN2gW5urhiNWAx4Po88222HqexSboEGAnMK+kl4GfA\nccDlkvYm23oaICImSLocmAB8BuwfU4eN9id96LOStp6+Mf+3YmZmZmbWvlTWAcDxwEXASsBrUYrJ\n6aQE/A54AniwhvBbAJdH8Fk+2baGGU5jayRPYzMzMzMzm5bKWpS0AcHmwFeiFM9Me4G+QJplNRPw\ndSLeqroNcTNwUgTX9j3j+qr3NDYzMzMzM2uAbKe1PwNvAstNV+gkp5JGc9arsdAZCqwN3NWXXFuR\nix1rG0Wev2tWZO77ZsVTlH6vsoYCNwOPAD+OUnR+/iVISwMbA4cT2ZS26p0GnB0x3fM1+70+bT1t\nZmZmZmb5UllzAVuR1uecDRwRpS7WnqR1Or8BTiPiw5raEmsD6wHL1JxwC/OaHTMzMzOzFqGyZgfu\nB94HDohS3N/9xdoBOApYi4j/1dSeOBoggp/Wcn8zVFMzeGTHzMzMzKzJVNZ8wFrAT4C7oxT79nyD\nBgHHAvvVWuhkvgRc2of7W5rX7FjbKMr8XTOblvu+WfG0U79XWQurrIOBp4AfAFcC+3V/g+ZEOhH4\nC/AMEbf0MYUVSNtVtyWP7JiZmZmZNZjKWhL4OnAEMB7YKErxSC9uPR0I0gNFb+pTDmIw6UGiXe3w\n1ha8ZsfMzMzMrIFUlkhbRT8HnBylGNe7GzUr8CqwNBH/7XMe4iukZ+us1tdYjeQ1O2ZmZmZmLUhl\nHQtsBMwO7BClmFLF7XsCj+RR6GQ2pY+jQ63Oa3asbbTT/F0z6z33fbPi6Y/9XmUtobL+AGwNHAqM\nrKrQkVYFSsABOaa1Cek5Pm3LIztmZmZmZnWksr4KXAz8ERgVpXijhjDHAGUictlMQGJmYGXgvjzi\ntSqv2TEzMzMzq4Nsbc7OwAnA9lGKO2oLpJVI082G9XGb6YqQrAJcEsHyecRrJK/ZMTMzMzNroqzQ\nOQdYB9gkSvGP6oNoCHAbsAxwSF6FTmZ10iYJbc1rdqxt9Mf5u2bWd+77ZsXT6v1eZQ0FrgJWA1at\nqdBJysBjwPxE/DG3/NKW0wcD1+UVs1V5ZMfMzMzMLAcqawng18B6wCXAzlGKj2oLpqHAHsBKRI0x\nurcH8ApwWc5xW47X7JiZmZmZ1UBlzQ2sBSwHDAd2A34HXBWlmFB7YM0P3ADcTMRPcki1IjQDgSeA\n/SIYl2fsRvGaHTMzMzOzOlJZ8wJPA/8G7gEmAitHKV7qW2CtA1wIXAD8vI9pdmVP4DXg9jrEbjku\ndqxtSBoV0csnEJtZ23DfNyueFun33wOujlLslUs0aQBp6ttawM+IuCCXuNM0wZKkLaw3jaB1pnfV\nkYsdMzMzM7MqqKwVSQv8180x7C7A4sCyRHySY1wAJAYAFwHHRPBw3vFbldfsmJmZmZn1ksqaDbgf\n+E2U4tz8AusK4M/1GNFJ4Vkf+H/ACv19VMdrdszMzMzMcqSyhgB7AzsBjwDn5RdccwAbAd/PLeb0\n9gDO7++FTrX8nB1rG62+576Z1Yf7vlnxNKnf3wqsD5wEfCdKOU2PkpYHJgBjiXg1l5jTNcFqwNbA\nWfWI38o8smNmZmZm1gOVNQcwAvhylOLT/AJLwNnAcUScllvcaZpgH9KmBAdH8GY92mhlHtmxttEC\nu7KYWRO475sVTxP6/SrA4zkXOusD9wKfkdbS5E5iJPALYN0ILqpHG63OxY6ZmZmZWc9WBx7KLZq0\nFHAl8HtgFBFTcov9eROsDowFdo3gX3nH7y9c7Fjb8Lx9s2Jy3zcrnib0+5VJmxL0XdqM4Dbg/4i4\nlIjJucSdpglE2kDhoAj+lnf8/sTFjpmZmZlZz1YAHssp1u7AeCLOzCleV1YA5gAuq2Mb/YKLHWsb\nnrdvVkzu+2bF08h+r7IGkDYneCKnkLsAZ+QUqzvbAlcVbZvprng3NjMzMzOz7i0BvBWlmNTnSNJs\npClxd/U5VrdNMDOwD7BVvdroTzyyY23D8/bNisl936x4Gtzv85zCtg7wKBEf5BRvGhJzAH8Fnojg\n4Xq00d+42DEzMzMz694KwOM5xToAuCqnWJ+TGCjxHeBq4GVgy7zb6K88jc3ahuftmxWT+75Z8TS4\n368AXN/nKNI3gNWAXfsca3q/ADYCzgfOjOCzOrTRL7nYMTMzMzPr3orAr/sUQVoY+COwHREf5ZHU\n1NDsRVqfs2EEr+cZux14Gpu1Dc/bNysm932z4mlUv1dZg4DhwJO1B9FMwIXAKUTcnVNqlUYDh7nQ\n6ZqLHTMzMzOzri0DvBCl+LgPMfYEBgHH5pPSVBILAasCt+Qdu114Gpu1Dc/bNysm932z4mlgv89j\nc4LDgdFETM4hn87OAE6MINepce3ExY6ZmZmZWScqaziwI/Bo7UH0RWBe4J6c0qoIzWqkZ/Z8M+/Y\n7cTT2KxteN6+WTG575sVT737vcpaGLgfeB+4sg+hVgceIiJySSwjMTdpw4PfRfBpnrHbjUd2zMzM\nzMymdQBwYZTioD7GWR14MId8OiuRHnR6Yh1itxUXO9Y2PG/frJjc982KpwH9fhtgtxzi5POMngoS\nawLfBlaMINcRo3bkaWxmZmZmZhmVNR+wEPBIDuGWoy/bVnftGOAn3mq6d1zsWNvwvH2zYnLfNyue\nOvf7DYC7o9TH3dOkAaStq/+ZR1IpJPMAawOX5xWz3bnYMTMzMzOb6pvkM/VsUeBtIt7LIRYSAsYA\nN0XwQR4xi8BrdqxteN6+WTG575sVT736vcqaDdgC+GEO4VYCJuQQp8NywA6kdUDWSx7ZMTMzMzNL\ntgTGRyneyCHWV4HbcojT4evANRG8lWPMtudix9qG5+2bFZP7vlnx1LHffwu4pM9RpHlJI0Q39TnW\nVFsAf8kxXiG42DEzMzOzwlNZcwEbA1f1LZC+BzwN/BV4uO+ZgcRcwJrArXnEKxLl/EDXPpEUEaFm\n52FmZmZmxaGyFgF+CwyOUmxTeyAtD4wD1iXi3/lkBxI7AHtHsFleMfuzamoGb1BgZmZmZoWksmYG\nNgVOBa4BTupjyD2B83MudAYAhwO/yitmkbjYsbYhaZR3ZTIrHvd9s+LJsd8fRVqnc1SU4sJakwF+\nTyp03iZtTJCnLQABY3OOWwgudszMzMyscFTWgsCBwGpRihf6EOoI4MukraFfJ2JKHvlVOBA4PoLW\nWXvSj3jNjpmZmZkVhsraAtgPGA78JUpxWO3BtD1wLLARES/lk2FleAYCk4AFIsjl4aTtwGt2zMzM\nzMw6UVlrAecAPwDeBG6vPZhEKnS+W49CJ7M88LILndq52LG24Xn7ZsXkvm9WPLX0e5W1HOkZOvtH\nKa7IIY01gMlE3JJDrO6sDjxYx/htz8/ZMTMzM7O2prKGAHcBJ+VU6ABsA1ydU6zpZFPY9sHP1umT\nmosdSUdIekLSY5IuljSLpKGSbpb0tKSbJM3d6fpnJD0ladN80jebyv+ya1ZM7vtmxVNDvz8AuC1K\ncWKOaXwVuDHHeJ1tDgwCzq5jG22vpmJH0uLAvsBqEbEiMBNp277DgZsjYhngluwYSSOAnYARwGbA\naZI8qmRmZmZmdaWyfgB8D/i//IJqDmAF4L7cYk7va8AVEeS9u1uh1FpwvAt8CswmaSAwG/AfYCvg\nvOya80jDewBbA5dExKcR8TzwLLBWrUmbdUXSqGbnYGaN575vVjy96fcq66sq6wbSzmvrRymeyTGF\nDYCHifgox5ifkxCp2Lm5HvGLpKZiJyLeAn4HvEgqct6JiJuB+SPi9eyy14H5s98XAl6uCPEysHBN\nGZuZmZmZ9UBlLUvajOBKYL0oxYv5BZeAI4Ezc4s5vQ2BT4BH6thGIdS0G5uk4aQt+xYn7f09VtKu\nlddEREjq6SE+XZ6TdC7wfHb4DvBIx7zMjirexz7u6rjjtVbJx8c+9nFjjiNiXCvl42Mf+7gxxx26\nPL8jRzOCX0cpzpQ0SmOUW/ubwU6HwHKbwAX1e3+XHgk7nRJBtMrn3eTjVYCOvQAWpwo1PVRU0k7A\nJhGxT3a8G7AOsBGwYUS8JmlB4LaIWE7S4VnSx2XX3wiUIuK+TnEj/FBRMzMzM6uSypqFtI5mG2A0\nsGyU4uP8G9LOwPZEbJd7bEBiJuANYOWIaWZGWaaamqHWNTtPAetImlWSSLtRTACuBXbPrtmdqdvx\nXQN8S9IgSUsASwPja2zbrEud/6XHzIrBfd+seLrp95cBY4F5gG/VpdBJ1gXurVNsSM/vec2FTj5q\nmsYWEf+QdD7wADAFeAg4AxgCXC5pb9JUtB2z6ydIupxUEH0G7B+1DCmZmZmZmWWy5+fsk/0MBpaP\nUvyvPo1pLuCPpPU0o+rSRvJj4Pw6xi+Umqax1YunsZmZmZlZb6iskaSRnNuAE4FHoxTv169BnQcE\ncCBRn3YklgLuBhaPoC47vbWDamqGmkZ2zMzMzMwaRWWJND1tFGkZxhKkEZCdoxS31D8BzQZsCyxc\nr0InMxq41IVOflzsWNuQpu7EZmbF4b5v1r5U1kDS7lvnA6uTHlr/IY8yKyuxUZTi8QalsgHwEBHv\n1qsBiQ2B72ZtWU5c7JiZmZlZS8hGcL4IjATmA34IzAmcQHpeTkD2jxxXNqzQAdgFuL7ObfwQOCyC\np+rcTqF4zY6ZmZmZNZXKWp207mYo6WH0DwBPA3dFKS5uZm5II0jrgpYhYlJ9muALwL+BRSJ4rx5t\ntBOv2TEzMzOzfkFlDQJOBa4CbgUeilJMbm5W0/ghcEq9Cp3MJsAdLnTy52LH2obn7ZsVk/u+Wf+k\nsuYGfgRsBrwC/La3RU7D+r00jLQxwfJ1bmlT4KY6t1FItT5U1MzMzMysJiprQeB20q5qxwHbt9ho\nDkirAGcBpxLxRv2aQaSRHRc7deA1O2ZmZmbWECprALAFacOBs4FjOjYdaCnSksAjwBnA4UR8Vr+m\nWB64AVgigtb7LFqQ1+yYmZmZWUvIdljbHLiAtAHBQ8AhUYqrm5pYz3YALiLi0Aa0tSlwkwud+vA0\nNmsbkkY1Owczazz3fbOWdxRwEqmAmClKsXpfC50G9Psdgcvr3EYHr9epI4/smJmZmVluspGc7YD1\ngS8DCwBrRyn+09TEektaClgYuKP+TTEL6XPard5tFZVHdqxteDcms2Jy3zdrOYcCZWAicAiwTN6F\nTp37/WjgcqIhGyasCfwzgrca0FYheWTHzMzMzHKhsuYDDgfWiFI81+x8qibNA+wHjGxQi2sA9zeo\nrULyyI61Dc/bNysm932zljIauLrehU6u/V4S0kZIJwIPAOcR8VRu8Xu2OvBgg9oqJBc7ZmZmZtZn\nKmt2YG/g/Gbn0mvScqSRldOAV4G9iDisMU0zK2lNk4udOvJzdszMzMysT7Ln51wPvA7sGaWY0uSU\nZkyaFRhPet7PiURjc5Y4BZgP+FYErf95tRA/Z8fMzMzMGukQYA5gy35R6CTHAk8AJ9Dgf/2XELAV\nsIkLnfpysWNtQ9Io78pkVjzu+2bNk20zfSLpGTrrRCk+a0i7fe33afrat4HlGl3oZJYFAni6CW0X\nitfsmJmZmVnVVNaawD3AhsCyUYoXmpxS70ibAZcAZSImNimLVYH7Imid9SRtysWOtQ3/y65ZMbnv\nmzWeyhoGXAecDqwfpXi3ke3X3O+l2UiFzvnAqTmmVK3FgBeb2H5heBqbmZmZmc2QypqFtNvamsDW\nQClKcV5zs6raNsB4In7f5DwWxVPYGsIjO9Y2/KwNs2Jy3zerP5U1F/BXUpHzILBilOLkpuVTS7+X\nBgJHASflnU8NFgNeanYSReCRHTMzMzPrlsrajDRd7XrgoCjF5CanVKuvAu+T3kezLYqnsTWEn7Nj\nZmZmZtNRWdsA3wNWAb4dpfhbk1PqG+kPwLNE/Kb5qTARWD6CN5qdS39UTc3gYsfMzMzMPqeyZgX2\nAH4O/AC4LkoxqalJ9ZU0C2kkZT0inm1uKgwGJgGDvRtbbaqpGbxmx9qG5+2bFZP7vll+VNY3gOeB\nzUkPCL2oFQudGvr9t4CHm13oZOYH3nCh0xhes2NmZmZWcCpreeD3wArA1lGKe5ucUt62BVpl57j5\ngdeanURReGTH2oaftWFWTO77Zn2jstYFbgP+AgzvD4VOVf1eGgCsD9xer3yqtADwerOTKAqP7JiZ\nmZkVlMpaErga2CNKcUOz86mTVYGJRPyn2Ylk5sfFTsN4ZMfahuftmxWT+75ZbVTWSOABYEx/K3R6\n3e+lOYE/0BrP1ungaWwN5JEdMzMzswJRWTsD2wEbArtEKW5sckr1dB7pIainNDuRCgsATzc7iaLw\n1tNmZmZmBaGy1gauAg4D7olS/KvJKdWPtAxwFzCMiI+anU4HiSuAsRFc1uxc+qtqagaP7JiZmZkV\nx8+BUpTiwmYnUldpU4IzgN+0UqGTWQ74Z7OTKAqv2bG24Xn7ZsXkvm/WOyprBLAScH6zc+mrXvT7\n9YF5gePrn03vZQ8UHQ482excisLFjpmZmVkx7AJcEKX4X7MTaYDRwHlETG52Ip0sDzwbQRH+G7QE\nFzvWNvysDbNict8367VtSOt1+r0e+700HNia1nmIaKUVgMebnUSRuNgxMzMza3MqazVgTuC+ZufS\nAL8ETiDijWYn0oX5gFebnUSRuNixtuF5+2bF5L5v1j2VNUBlbQH8FjglSjGl2Tnloct+Lwnpq8BX\ngN83OqdeGgq81ewkisTFjpmZmVn72gz4f8CdtNazZvKTipytgVuAPwN7EvFBk7PqzlBgYrOTKBJv\nPW1tw/P2zYrJfd+sR7sBx0UpTm12Inn6vN9LXwROIy38vwjYpAU3Jaj0BTyy01AudszMzMzakMqa\nnzSyc2Czc6mjHwABrEvEe81Ophc8je3/t3fncZfP9f/HH88ZY4bJGMLYzVhKlOxblsFYUpYoRPhW\nKkOUlCj9jk99K9EmayJGiai+lsq+JAqDsRMKUfaMJbK+fn+8z2Wuma6ZuZZzzvtzPp/n/XZza85y\nfc5zOvO+rut13u/3691hXsZmleF1+2b15LFvNlufAM6NRlRu2ZSkiUjzAx8DvtklhQ642Ok4Fztm\nZmZm1bQlcF7uEC0nzfMZWBW4FLiKiNtyRxoAFzsdpojIneEtkiIilDuHmZmZWTdToXlJG+GXjkY8\nlztPS0gjgJOBnYGHSIXcEUT3dJiTeB5YJoJqvCeZDKRm8J4dMzMzs+rZEPhLhQqdeYCjgaWAFUt6\nhs4cSYwA5geez52lTryMzSrD6/bN6slj36xPewDn5A4xJNJiSMchnQfcB6wF7E7Ek1067hcHno6g\nPMuqasAzO2ZmZmYVokIrADsBq+XOMkRfIbVqPh14hIib88YZsvWBG3OHqBvv2TEzMzOrCBVaGvg1\n8ItoxDG58wyatCwwFdiIiPtzx2kFiR8Bj0ZwVO4s3c57dszMzMxqotmM4IfAZsBiwAXAj7KGGixp\nOeDTwGTgR1UpdJo2otpnHpWSix2rDEkTfZK6Wf147JvxUdKStd2Bu6IRr2bOM3DSqsAhwPuBG4B3\nEvHU7J/eXeNeYgzwTqDbl+J1HRc7ZmZmZl1KheYDvgwcHI2YljvPgEgCdgW2B7YALgSWI+LlrLna\nY33g5gheyR2kblzsWGV00yc8ZtY6HvtWRyr0GeBrpA38vwEuzptogKThwPHABsBPgIOJeKy/X96F\n434j4NrcIerIxY6ZmZlZF1Gh3YCDgW2BB4EXo1GijlOzI40GDgXWBTYHriI1IHgha67O2Aj4bu4Q\ndeRubFYZ3bZ+18xaw2Pf6kSFhgMPAHtFI/6YO8+ASFOAscBpwJXACwzyF9FuGvfNw0SfBZaJ4Nnc\neapgIDXDoA8VlTRW0q8k3SPpbknrSVpY0mWS7pN0qaSxvZ5/mKT7Jd0raavBvq6ZmZlZjW0C/KsL\nC51NgE1Jh4KeR8Tzgy10utCmwH0udPIYdLEDHAP8PiLeReoAci9pavKyiHgHcEXzNpJWIW1AWwXY\nBjhB0lBe2+y/dMsnPGbWWh77VjO7AufkDjEgqRHB0cBXifh3Ky7ZZeP+QODE3CHqalAFh6QFgY0j\n4qcAEfF6RDxH6qYxpfm0KcCOzT/vAJwVEa9FxEOk6dd1hxLczMzMrE5UaB5gJ+Dc3FkG6AvACOCs\n3EE6rbmEbQvgl7mz1NVgZ1cmAE9JOk3SLZJ+orTpbFxEPNF8zhPAuOaflwQe7fX1jwJLDfK1zfok\naWLuDGbWeR77VgfNQmc/4KFoxN9y5+k3aSfSQZo7EfFm6y7bNeP+XcDfI3g+d5C6GmyxMw+wJnBC\nRKwJ/JvmkrUekdZhzmktZl3WaZqZmZkNigptoUJXAs8BHwEOyBxpoBrAp0gre+poLeCW3CHqbLCt\npx8FHo2Iqc3bvwIOAx6XtHhEPC5pCeDJ5uP/AJbp9fVLN+/7L5JOBx5q3pwO3NqzLrOnivdt3+7r\nds99Zcnj277t2525HRFXlymPb/v2UG6rkLiAyYxhHBOZD9iNaziD2/l+PBW/zZ1vQLfhdmDCghDP\nt+Hnc4/S/H37vv1eOPZ56cCW//1rdnt1Uic/gPEMwKBbT0u6BtgnIu6TdAQwf/OhZyLiO5IOBcZG\nxKFKDQp+QdqnsxRwObBizPLicutpMzMzqykVEvBjYBIwFXgMODoa0ecHxKUn7QDsR8TWuaPkInEe\ncEYEv8mdpUoGUjMM5VDRA4AzJc0L/BX4ODAcOEfSJ0mzM7sARMTdks4B7gZeB/abtdAxGyqpe3ru\nm1nreOxbFajQCOBjpA+GV4tGvJg50tBI2wDfAL7fnst3zbhfBngkd4g6G3SxExG3Aev08dCk2Tz/\nW8C3Bvt6ZmZmZlWkQu9jRqeyPSpQ6KwM/Bz4NjO69NbVssDfc4eos0EvY2sHL2MzMzOzulChjYHj\ngSWAvaMRv88caeiktYBLgK8RUeuzZSTmB/4FzB9ByzrRWeeWsZmZmZnZAKnQe4EfkFbIfAY4Pxqt\nOWwzq3Rg/A+AQ4k4JXecElgaeNSFTl4udqwyumj9rpm1kMe+lV2z8cCmpNbRGwETSAdt7haNeHJO\nXwImWRsAACAASURBVNtlvkL63fL0dr9Ql4z7CXgJW3YudszMzMxaTIXGkPYxLwTsC4wBTiEd13FT\nNOKFjPFaSxoFHAHsCEwi4vW8gUpjTWBa7hB15z07ZmZmZi2kQocA+5DOFHwcOBu4MBpRreVMqSPv\nKaQuchcB/0PEU3lDlYfEb4BzIjg7d5aq8Z4dMzMzswxUaDvSPpxPA1dGo0SfKreSNBr4NfAqMJqI\nlzMnKqN1gS/lDlF3w3IHMGuVWU9UNrN68Ni3slCheYDvAftFI66ocKEj0mHxTwI75Sh0yj7uJZYA\n5gP+ljtL3Xlmx8zMzKw11gBejUZckjtI20jDgWOBccBHvD9nttYBpkZQzYK3i7jYscrogq4sZtYG\nHvtWIusD1+UO0TbpsNCTgDeBrYh4NVeULhj36wBTc4cwL2MzMzMza5X1getzh2gLaR3gauA3wNZE\nPJ83UOmtDdyUO4S52LEKKfv6XTNrD499KwMVWh3YCrgid5aWS3t0TgU+T8SPiHitBJEm5s4wF6sB\nt+YOYS52zMzMzIZEhTYlFTkHRiOqeIjke0nnBJ2TO0g3kFgEGI0PFC0FFztWGV2wftfM2sBj33JS\nobGkDfv7RSPOyp2n5aRJwPHAsUR5zgkq+bh/L3C7mxOUgxsUmJmZmQ1As8X0eGBzYH/SXpbqzXpI\newNfB04Hfpg3TFfZGrg2dwhLFCVqAT+Q01DNZiVpYsk/6TGzNvDYt1ZQoflJBcycrAhMJu3HCNIv\ntFcAp1TuTB1pDHAfqRnBbbnjzKqs415iOGn52pYR3J07T1UNpGbwzI6ZmZnVhgoJeA8wstfdCwHH\nkJb3vzGHL/8X8GPgc9GI+9oWshy+BFxcxkKn5DYHHnOhUx6e2TEzM7NKaS4z+zCwYR8PL0tqC/xY\nr/teBU6ORkzpQLxyk0YCBwIHA+sQ8UjmRF1F4gzg5giOyZ2lyjyzY2ZmZpWmQosBXwPmb961KvDO\n5p/nBW4G/o//nqm5F9g7GvFcJ3J2FWlN4Puk4m9TFzqDsj5wZO4QNoNndqwyyrp+18zay2O/+ppL\nz8YBk4BvAEsBAk4EepZZ/RO4gbSX5k0XMwMkHQx8AfgecAwRc1rOl10Zx73ESOA5YEwEr+bOU2We\n2TEzM7OupELDgQVIxcxEYA9gPdK+mitJe0kuACIa+Q+3rARpV9L/r+sSlTwnqFNWAB52oVMuntkx\nMzOz7JqzN/sBhwBjSTM09wInAbdEI27PGK+6pNWBy4DNibgjd5xuJrETsHcEO+TOUnWe2TEzM7NS\nUqFhwPKkzmc9zQJ6lqi9BOwcjbgpX8IakVYEzgY+50KnJVYF7skdwmbmYscqo4zrd82s/Tz2y0uF\nliQVMu8D9iY1ExhDWqL2Eml/w9XAdNL5NTdFI/6TJWzdSFsBZwJHE/GL3HEGqqTjfi2g6/6/rDoX\nO2ZmZtZSzdmbvYGjgUeAB4AvA08ArwH3V+4Qzm4hCdgC+BmwExF/zJyoStYitey2EvGeHTMzM2sZ\nFdoO+DypycD+0YipmSNZj1TonENabvVVIv4vc6LKkBgH/AVYKILy/HJdUd6zY2ZmZh3RbCywJKm5\nwHhgK+BwYIqXpJXOx0nv0epEuGNYa20FXO1Cp3w8s2OVUdL1u2bWZh77naNCo4G3k1pCTwTWBN4L\nvAj8FJgK3BONuDlTRJsdaUHgLtLStRtzxxmqso17id8A50cwJXeWOvDMjpmZmbWUCk0GfkgqbK4g\nHeA5BfhjNOLNnNlsLqR5gTOAC6pQ6JSNxCbABsAnc2ex/+aZHTMzM5ut5h6cw4BVgI2j4RbFXUOa\nBzgV2Bz4M7AX4aWFrSZxHmlW57TcWepiIDWDix0zMzPrkwqNBW4HDgVujkb8JXMk6y9pGHAcsBLp\nHJ27MyeqJImRwJPA8hE8kztPXXgZm9VS2dbvmllneOy3XrN19HrAScA50ei+c1hqTRoBnAYsB2xP\nxLOZE7Vcicb9JsBdLnTKa1juAGZmZlYeze5qBfBL4OfAl/Imsn6TVkb6OfA46fDWrapY6JTMB4Df\n5Q5hs+dlbGZmZgaACq0HnNy8uU004rGceWwApPlJ57ycSCpSH6FMv+RVlMR9wC4R3Jo7S514z46Z\nmZkNiAptBZwJ7A+cG40S/YJgcyaNAY4HRhKxS+44dSGxLHATMM7n63TWQGoGL2OzypA0MXcGM+s8\nj/2hU6EVSIXOh6IR57jQ6SLSx4AHgTdIh4bWQknG/UR8kGjpuUGBmZlZTanQKODDwBeAb0Qjrs0c\nyfpL2hg4hHS+y0TCLcEz2Ay4OncImzMvYzMzM6s4FRoHrAas2+vuBYG9gGnAlcB3PaPTJaQ1gYtJ\nzSP+SMTfMieqJYkHgW0juCd3lrpx62kzMzMDQIX2Bn4APABcBbzefOglYItoxF25stmgbQ/8lIgp\nuYPUlcR4YD7g3sxRbC5c7FhllKjnvpl1kMf+XB0EfDgacWXuINYymwDfzR0ipxKM+83wfp2u4AYF\nZmZmFaVCHwPG4n0F1SHNQ1qOeF3uKDU3kTRTaiXnPTtmZmYVpEKbA2cBm3upWoVIKwKXETEhd5S6\nkhDwMLBlBH/JnaeOvGfHzMyshlRoceCrwFrAssBeLnQqZ2W8TyS3JUn7de7LHcTmzsvYrDJK0nPf\nzDrMYz9RoR2B64HXgCOA5aMRl2QNZe3gYofs43414Fbv1+kOntkxMzPrQio0DHgX8HVgOWAR4NPR\niEuzBrN2Wxm4KXeImnsvcHvuENY/ntmxynA3JrN6quPYV6EVgNtIZ63cB+wPrOFCpxZWAP6aO0Ru\nuca9xGhgC9L4sy7gmR0zM7OSU6F5gJ2A9YHFgW2Aw4ETfRBo7SwD/D13iDqSWBP4CfAocGHmONZP\n7sZmlVGCnvtmlkEVx74KrQe8nXSWx9qkhgO3ARcALwDnRiOeyZfQspCGkQ6DXZiIl3LHyanT417i\no8APSfvhTvJ+nbzcjc3MzKwLqZCAPUgHRt4GTAWOBa6JRjydM5uVwqLAi3UvdDpNYgfgaGCLCO7M\nnccGxjM7ZmZmmajQAsBRpKVp85Nmch4GdotG3Jwzm5WQtDZwMhFr5o5SFxLzAw8CO0Rwfe48lnhm\nx8zMrKRUaFFgZ0DAJ4A7gDOAN4FdohHPZYxn5fYe4JHcIWpmT+DPLnS6l4sdq4wqrts3s7nrprGv\nQgsCJwFvA/5GKnKOc5MBmyvpx8B2wOTcUcqgE+NeYhTwFWD3dr6OtZeLHTMzszZRoeWBdUhnowwH\n9iW1it4qGt53Yf0kLQnsAixDxIu549SBxHDgZ6RZnety57HB854dMzOzFmg2F1gD2Br4GDAaGEU6\nfPB6IIAroxF/yBbSuo8k4MeAiPhU7jh1IXEYsBWwTQSv5M5jM/OeHTMzs877EbA9cCVpL84zwMPR\niNeyprJutzWwEfC+3EHqQkLAZ4DtXOh0Pxc7VhndtG7fzFqnDGNfhTYDdgBWjUa8kDOLVc5ewHFE\nPJs7SJm0edyvSPod2W2mK2BIxY6k4cBNwKMRsZ2khYFfAssBDwG7RMT05nMPI33S9QZwYERcOpTX\nNjMzy0mFRgALAicAE4FPutCxlpI2AjYHPps7Ss3sCFzsg0OrYdgQv/5zwN3w1j+GQ4HLIuIdwBXN\n20haBdgVWAXYBjhB6RRgs5bJ/cmumeWRY+yr0GHA88CjwBPA+GjEhZ3OYRUlrYd0LvAb4H+I+Ffu\nSGXTrnEvMQ+wH3ByO65vnTfogkPS0sC2wCmkswIgrVWe0vzzFFJlDGlq/6yIeC0iHgIeANYd7Gub\nmZll9ilg3WjEqGjEAe6sZi2TGhKcTmpqsSkRF+cNVDt7Aw9HcGPuINYaQ5ld+QHwJdIhaD3GRcQT\nzT8/AYxr/nlJ0qdfPR4FlhrCa5v9F0kTc2cws87r9NhXoYWBRYC7Ovm6VhvrA/MC3yfintxhyqod\n415iNFAAh7T62pbPoIodSR8EnoyIacyY1ZlJpJ7Wc1rr6HWQZmbWjdYEbo1GvDnXZ5oNhLQOaena\nYZTpbJD6OAz4k2d1qmWwDQo2BLaXtC3pDIExkn4GPCFp8Yh4XNISwJPN5/8DWKbX1y/dvO+/SDqd\n1NwAYDpwa8+6zJ4q3rd9u6/bPfeVJY9v+7Zvd+Z2RFzd4ddfl2k85u83vt3K28OAN+AnwIGCp/C/\nr7ne7tGa6+2+Apy5D7BGWf5+vj3T7dWBsSTjGYAhHyoqaVPgi5G6sR0FPBMR35F0KDA2Ig5ValDw\nC9I+naWAy4EVY5YXlw8VNTOzklOhi4EToxHn585iFSJtAJwGvIuh/nJmAyZxBnBnBEflzmJzN5Ca\noVUd0XoG5ZHAlpLuI7VKPBIgIu4GziF1brsI2G/WQsdsqGb9pMfM6qGTY1+F5gE2AK7t1GtabewF\nnOFCp39aOe4lDgE2Ic2sWcUM+VDRiPgD8Ifmn/8FTJrN874FfGuor2dmZpbRl4AboxHP5A5iFSGN\nIHW3/QiwVuY0tSOxH/BxYKMIfHBrBQ15GVsreRmbmZmVlQotB9wCrBGN+HvuPFYR0tHAZsDZRHw3\nd5w6kegZ0+tHcH/uPNZ/A6kZhjyzY2ZmVmUqNAz4PPBF4GgXOtZiWwGfJuKG3EFq6P8BJ7jQqbZW\n7dkxy857dszqqQNj/4fAh4FtohFHtvm1rE6khYHlSbMLNgBDHfcSbwN2Bo5tSSArLc/smJmZzYYK\nTQY+AKwZjXgudx6rEGlx0h6wS4l4LXecGjoM+GPEW8ekWEV5z46ZmVkfVGgt4LfA+6IRf8udxypC\neg+pEcGRwG3Ax4n4Z95Q9SKxNXACqSnBY7nz2MB5z46ZmdkQqJCAo4DChY4NmSRSa+kfAC8CNwG7\n0Tw00TpHYllSofNFFzr14D07Vhnes2NWT60e+yq0IGnj8lLAqa28ttWINBLpYKSrgDdITS4mAeOJ\n2MmFztAMYdyfDEyJ4P9aGMdKzDM7ZmZmTSo0L3ADMA3YIRreS2GDIC0G3Nv879vAB4h4KW8ok9gS\nWAHYPncW6xzv2TEzM2tSoS8Ck6IR2+TOYl1MOgRYmYhP5I5iM0jcAHw3gnNzZ7Gh8Z4dMzOzAVCh\nscABwIHA+pnjWDdL+3M+BeyZO4rNILECMB74TeYo1mHes2OV4T07ZvXUorE/BVgN2Cga8dcWXM/q\nayLwMmk5pLXJIMb9R4ELInijDXGsxDyzY2ZmtaNCK5FmcNYHhgMbAstFw/sqbMgOBE6iTPsEak5i\nA2B/YPPcWazzvGfHzMwqT4UWAjYCxpA6Yn0Q+BNwJfA6MC0a8ad8Ca0SpHGkpgRLE/Hv3HEskTgG\neDKCb+bOYq3hPTtmZmZNKvR24E7gDtIZJ1cB34lG3Js1mFXROsBNLnTKQ2JhYGfATUdqysWOVYak\nieFzC8xqZ05jX4XGAN8F/i8asV9Hg1kdrQXcnDtEHfTnZ77E+4BzgZ8Dd3Uil5WPix0zM6scFRLp\nlPS9gOuAj+RNZDWxHnBa7hAGEsOA44AvRXBm7jyWj/fsmJlZpajQqaTlRG+Suqu9mDmSVZ00P3Ai\nqeHFBkT8K3Oi2pP4GPBZYIMIyvPLrrWE9+yYmVktqdD6pNa/uwH3udCxDtkOWAFYj4jpucPUXXNW\npwHs40LHfM6OVYbP2TGrp1nG/uHAD6MRU6MRz2WKZPWzFfBLFzqdM5ef+bsAzwLXdCaNlZlndszM\nrLuNYZQKfZC0L2dlUucls86QhpM6fR2VO4qBxIbAj4DtPatj4D07ZmbWhVRoHmAy6TDQ1YDngd8C\nJ0bD+yWsg6RJwNFErJE7St1JHAEcCnwkggszx7E28p4dMzPreio0nDRLM3qWh1YiLRt6ETgF+Blw\nUTRK9OmdVZ80Gvg16ZDaz2ROY8nOwOYR+IBge4uLHasMn7NjVh0qtChwELA9MHWWh58mbT6+LBrx\nanPsu9CxzpBG0pw9AG4D5iPitbyh6mfWn/kSSwJLATdkC2Wl5GLHzMxKRYW2Bc4B/gRsE414NHMk\ns95+AYwkFePXuNApjZ2AiyJ4I3cQKxfv2TEzs2xUaHFgcWAEqYPSNsDSwJ7RiN/mzGY2E0nAJ0iz\nOqsS8WrmRNYkIdIM8OERXJw7j7Wf9+yYmVlpqdBE0rkkw4E9gH80H7oa+B9gejTirzmymfUpdVw7\nkdQQ42MudEpnPyCAy3MHsfLxzI5VhvfsmJWTCq0ELN+8uTBwDKk17MvAH6MRNw7p+h771i7SesAU\nYBxwE7ATES/kDWUwY9xLLAz8Bdgkgnty57LO8MyOmZllo0IbA4sBGwOLAlsC05oPvwp8PBrxu0zx\nzGZPmoe0lHJTYE1gFeBzpFnHp4l4M184m43jgLNc6NjseGbHzMyGTIVE6py2DfB+4BbgDuAB4KZo\nhH8RsXKT1ga+DbwduAS4EbiIiP9kzWWzJbEVcDywWgQv585jneOZHTMzaysVmh9YHVgf2II0kzMS\n+B2wTjTiqYzxzOYs7cHZltSqGNLBtNsD3weO856c8pPYATgV+IQLHZsTFztWGV63b9YZKjQvcB/w\nPPAH0p6GZ4HrohEvdTyPx74NRDon5zxgEeDm5r0vA6sT8XS2XNZvEsvD5VNg0jYRXJ87j5Wbix0z\nMxuobYG/RiM2zR3ErN+kntmbtUl7xzYg4vW8oWwgJIYBXwc+BXefGTHJhY7NlffsmJlZv6nQSODP\nwPeiEWfmzmM2V9J8wGeAw4CfAdOB44l4NmsuGxCJEcApwIrAZOCOCMrzS6x1lPfsmJlZS6nQ24D3\nAV8iLWH7Rd5EZnMhLUoqbjYkdVObSLhRRjeSWBA4ExCwZQQdXy5r3WtY7gBmrSJpYu4MZlWjQsup\n0K7AdaTlI1cAH4tGeZYFeOzbW6RJSKcinUMqyqcBE4jY3oVOd5JYgfRePgLs2FPoeNxbf3lmx8zM\nZqJC44D9gXVJ+xv+BPwAmFKmIsdsJtI44FzgcOAF4HNEPJY3lA1Wc3/OgcAXgW9EcFzmSNalvGfH\nzMxQoVHAaGAZ4DLSL42XA5dHI57Pmc2sX6QGsAQR++aOYkMncSiwMzA5gpty57FyGUjN4GLHzKxG\nVGgxYFTz5jBgY2AtYG/gTSCAw6IRP8mT0GwQUjvph4HNibg7dxwbPAkBHyCdobN2BI9kjmQl5AYF\nVks+a8NsZio0DPgasF3zrpGkmZveMzW3AzcBq0UjuvKXCo99A94P3O1Cp7tJbAF8B3gbsPucCh2P\ne+svFztmZhWiQgLeRepAdTDwFHAA8Bpp1ubOaMQr+RKatcWmwKW5Q9jgSWxK6rg2GTg/gjczR7KK\n8DI2M7MupUJLAyv0umsSsA/pwMRpwDHA1W4qYJUn3QwcQMSfckexgZOYn9QI5ZsRnJs7j5Wfl7GZ\nmVWACr0f+CpppqbPpwB3wlsH6z0FbAbcF43wp6JWD9LBwBjwJvZuI7EAMAXYEvg18Ku8iayKXOxY\nZXj9rlWJCr0dOJs0U3MV9HlS+L+jEf/paLAS8tivMWlTUmvitYl4NXcc659mW+mVSC3tnwaWB56O\n6PP73Gyu4XFv/eNix8ysnD5IavvsJR1mfZHGAD8DPk7EP3LHsbmTWAb4HPAp4BXgJODInoNCzdph\nWO4AZq3iT3isKlRoJ+Bo4Oe5s3QDj/0akkYA/wtcRcTFuePY3El8BbiN9LvneyJYLIL/N9hCx+Pe\n+sszO2ZmJaFC40ltVzcEPhCNmJo1kFkZSQLOAJYFPpI5jc2FxBLAHsAXgFUjeCxzJKsZFztWGV6/\na92iebDnkr3ueifpEL0PAOcCK3kvTv957NfOFsCawOpEvJw7jM2exCLAjaS24Hu2stDxuLf+crFj\nZtYCzYYCB5MO7pyTYcDuMNMP/SeBq4EvRCOebktAsyqQFgMawFEudMpNYkHgEuCMCL6aO4/Vl8/Z\nMTMbIhXahdQR6kHSp5hzc3004rr2pjKrmLR87Srgr8B+hA/HLavm0rVTgUeAfQfSZc2sP3zOjpnZ\nIKnQaGBzZv7+uDDphPZZv7GuCYwnzcx8HTgzGm5/a9YmuwKLApOIeD13GOubhEht8+8GDnKhY7l5\nZscqw+t3rT9UaD7SUrJlgcnA0sBqwArNp7xJWlL2fK8vewW4Aph1H82DwDTglWj4l69cPPYrTlqe\n9MvzKsBEInx4aElJjAVOAyYAa0fQtu+LHvf15pkdM7NZqNBCpA5OWwGvk4qZn5AKm0fc+cysRKT3\nAN8CxgCrAkcBR1OmT2htJhKLApcD1wC7t7PQMRsIz+yYWWWp0AjSzM2ipPXjVwKHRMNr/c1KKe3L\neTepe9e3gL8A1xPx/By/zrJqNiM4FngOONBL16zdPLNjZrWmQgsA+wCfJy1ZexE4DjghGiX6hMfM\nZpAWIC0XXZY0i3Ns5kQ2FxIfBt4P7AzcB2zlQsfKxjM7Vhlev1s/KjSMtN9mE+ATwOjmQ4sAlwFH\ne3la9XnsV4A0Cvgl8DSwj5erlZ/EKqRlwN8BfhbBk519fY/7Omv7zI6kZUhr3xcDAjg5In4kaWHS\nN6vlgIeAXSJievNrDiP9MvIGcGBEXDqY1zaz+lKh+YH1SLM1E4CDgHmBm4EDmXF2zXPRiI7+4DWz\nQZLGAOeRCp39XOiUm8TiwIakIudrEfw4cySzORrUzI6kxYHFI+JWSW8j/aKxI/Bx4OmIOErSl4GF\nIuJQSasAvwDWAZYibWB7R0S8Oct1PbNjZn1SoQ2Bc4GHgZeB6cBJwOVemmbWhaT5gCOBTwGnAwcQ\n8UbWTDZHzSYEfyJ9H/5JBL/MHMlqqu0zOxHxOPB4888vSrqHVMRsTzqLAmAKaXrzUGAH4KyIeA14\nSNIDwLrA9YN5fTOrBxUaDhwObEFqDb1fNOL8vKnMbFCkJUlLTgWMAArgFmACEU/kjGazJzEJ+Brw\nPuA14OgI/l/eVGb9N+QGBZLGA2sANwDjYsY3rCeAcc0/L8nMhc2jpOLIrGW8freSvghMIi2XuNQH\ndlpfPPZLShoOLE76sGIiaQXIVUDPOP4KEWflCWezI7EQ8CHScuF3kQ5z/SLNtv0RlGL2zePe+mtI\nxU5zCduvgc9FxAupY2QSESFpTktL+nxM0umk/T6Qlqnc2vOPWdLE5rV927f/6zawuqTS5PHtwd9W\noc24np1YlI+yAltEI26TNFFH+P31bd8u9W24E1j7JPjIBNh267Sn7uoT4JkTYe87Ii6c5fmUKn+t\nb28xDi5fGvg0/Ppp+M902ONOYH3QisAG5crL6qQVRGXJ49vtf7/HkoxnAAbdjU3SCOC3wEUR8cPm\nffcCEyPicUlLAFdFxMqSDm2GPrL5vIuBRkTcMMs1I7xnx6zWVGgX4GjgfOD+aLj9rFlppX03KwN7\nApsDE4B7gWuBc4nwcvUuILE38G3gIuD8CC7IHMlsjgZSMwy2QYFIe3KeiYiDet1/VPO+7zQLnLEx\nc4OCdZnRoGDFmOXFXeyY1ZsK7QicCOwejbgqdx4zm0VqUNTT7n0MsBvwDPA7UgORO4h4KV9AGyiJ\ng4DPAP8T4b3U1h06UexsBFwD3M6MWejDgBuBc0gHgj3EzK2nv0JqPf06adnbJUMJbjYryet3u5UK\nrUg6lO4AYK9oxJWZI1kX8dhvI2k94COkZe9LAFuSZm6uIB3Wex4Rf8kX0IZCYl3SKp11Ing4d56B\n8Livt4HUDIPtxnYtaeNaXybN5mu+BXxrMK9nZtWhQhOAr5C6NA5v3v06MBXYOBrxYK5sZrUijQY2\nYsY4nJfUTGA9UvdDAc8BpwHPA3cB+xLxbOfDWqtJrEqajZvcbYWO2UAMes9OO3hmx6waVEjAgqQ1\n/MNJy1fXaz68GXAycArw7+Z9z0cjXut0TrNaSEvPvwl8llTQvPUIqVNqzzgM4DpSO+ipzfum47Nv\nKkdiV+B44EsRnJY7j9lAtX0ZW7u42DHrfs3DP08HliO1mX2BtNzlStIMzv3RiJuyBTSrG+mzwH6k\nlRf/6vXIm4TbudeNxL6k4nfLCG7JncdsMFzsWC15/W5+KrQQcD/wSeCCaJToG4xVlsd+H6QtSId6\njwUWArYh4oG8oSw3CQEPAzt2e6HjcV9vbd+zY2Y2KxVaCjgKuCgacX7uPGa1lI6FOI50gOdBwD+A\nP3sGx5rWAP4DTMsdxKxTXOxYZfgTnnxUaHngbOAB4PDMcaxmPPabpHmB84A3gBWIeDFzIiufdYBr\nIvo+2L2beNxbf7nYMbNBU6F1gC+RGhEcDxzhpWtmHSJNACYzo5vaO0lNBj5ExOvZclmZTQD+ljuE\nWSfNrn20WdeRNDF3hrpQoZVU6CrgV8CfgQnRiIYLHcuhdmNfEtLpwB2kn+P/bP53ObCnCx2bgwlA\nJdr7127c26B5ZsfMZkuFViK1jN4cGAGMAzYk/YL1ZeAkt4w2a7PUOnpjYBFgE2BP0pLR5Yh4Jmc0\n6zoTSIe+m9WGu7GZ1Ujz/Jv5e931DlLHphGzPHU4MBF4DfgDqYX0c8DLpJPTX45G/Kfdec1qQ5qP\n9CGCgIOB9wKrkQ73BLiTVOD8BTgJeMTn39hASTwFvCeCx3NnMRsKt542s5mo0NrA+4C9gFVJG5gB\nXgW+Qd+f9N0Ujfh7RwKa1VGasZkIbAfsTzqHCtLBnj0FzdS+v9hsYCRGkT60GlWFBgVWb249bbXk\nnvszNM+72QUYA3wAWB64CDgC+F004s186cxaqyvHfjoHZx9gLeBiYDwRj+UNZRU3P/ByVQqdrhz3\nloWLHbMKUaE9SZ8QvwO4lHTGxsnAud5bY1YS0vbACcCpwKeJeCFzIquHkaQzdsxqxcvYzCpChT4L\nHEgqdm6PRjyROZKZzUpaELiFVORckTuO1YfEeOAPESyXO4vZUHkZm1nNqNBaQANYNxpRibaiZpWR\n9uasQVqydgjwOxc6lsFI4JXcIcw6zcWOVUbd1u+q0HDSxubtgW2BA1zoWB2VduxLuwFbA+8GK+qq\ntAAAECtJREFUFgamAfsCV+aMZbU1igotYyvtuLfScbFj1mVUaCRwDOnsm+nAFODYaMS0rMHMbAZp\naeB44CvAL4FLCTcGsaw8s2O15GLHKqNGn/AcQGpAsDdwfTRKtPHOLINSjf20J2cJ4HzgO0T8OHMi\nsx6VKnZKNe6t1FzsmHUBFXo3sAWwHPBRYNNoxH15U5nZW1KRcwYwiXT47jeIOCZvKLOZVKrYMesv\nFztWGVVbv9s8CHRrYDNgdeAc4BlSE4JHcmYzK5OSjP2TgaeAhYh4NXMWs754z47Vkosds5JQoQWB\nFUl7cT4DzAucC5wITI1G/D1jPDPrIc1H+gBiC2AB0gcSSwAru9CxEvPMjtWSix2rjG79hEeFdia1\njV4O+BvwJGmp2q0+CNRs7jo69qVFgd+TTqO/jDSb81XgJiL+3bEcZgNXqWKnW3/mW+e52DHrMBUa\nAewGfB4YD7xEajYwLRrxbMZoZtYXaVXgV8BiwHzAD4DDKdOp3GZzV6lix6y/XOxYZZR5/a4KCfgR\n8HHSD5xrgMOBG4EXoxH+AWQ2SG0b++kw0CWAC4FvAucBbxAxveWvZdZ+3rNjteRix6wzJgPrAhOA\n513cmJWMtADwXWDxXveuDSwIHEnEqVlymbWOZ3asllSmWXhJERHKncOslVRoIeAvwGbRiLty5zGr\nFentpGYfAO9q/gfpU+4tSL8AArwTuAj4ba+v/isRd3Yiplm7SXwZeHsEh+TOYjZUA6kZPLNj1kYq\nNIzUkvZsFzpmHSTtD+wDrAD0NA54HPgzEM3/TgP+1XzsMSLu7nRMsw4aSYWWsZn1l4sdq4wyrd9V\nofHAQcAHgUeAvXLmMauy/xr70u6k8fdJ4AYi/AueWZrNrEzHwDL9zLdyG5Y7gFnVqNB+wM2kT9A+\nDGwZjXg5byqzmkhn4PwQ2IWIP7jQMXuL9+xYLXlmxyoj9yc8KjQc+DrwKWCdaMTfcuYxq4tZxv6+\nwI1E3JIpjllZVarYyf0z37qHix2zIVCh9YAfAysCw4FrgTWjEY9mDWZWN9Jw4EDgYGDLzGnMysh7\ndqyWXOxYZbR7/a4KvQf4GjARWLR59wukfQEXNW//OxolanFoVnXSsO/C0V+EHUkNCDYm4sHcscxK\naBQVmtnxnh3rLxc7ZoAKjSH9IJiddYCfAkeSCp77eh5wcWOWibQbcPDKMBrYi4jrckcyK7FKLWMz\n6y8XO1YZA/mER4WWAsYAewCTgFWBOTUR+CewezTiiqFkNLNBkhYH5mveWgl4P7AzcMAH4VLCTUDM\n5qJSxY5nday/XOxYraiQgG8AnyWdr3ExcChwSzTi+ZzZzGwW0khgf9IenPWB6c1HngauBjYi4u95\nwpl1nVF4z47VkIsdq4x+rt/9JrAVsFI04qn2pzKzAUuzOPsDqwMLkpaQ7khEn59Ke+2+Wb9UambH\n4976y8WO1YYKbUw63HP1aMTTufOY2SykrYAdgO2B3wN/AE4l4tmsucyqoVLFjll/udixypjdJzwq\n9E7gEGAT4BAXOmYlI40jLSfdETgO+DARN/T3y/3prlm/VKr1tMe99dew3AHM2qm5R+d44CXgMODs\nvInMDADpcKSpSK8BfyP9IrYBEd8bSKFjZv1WqdbTZv3lmR2rjN7rd1Xo7cBk4IPAG8AXohGvZYxn\nZj2k8cDngd1JS9VeJ+KNwV/Oa/fN+qFSy9g87q2/XOxYdbyNeZsHf24BfBn4HfC/wJUudMxKQJof\neDtpWem5RFyaOZFZnVSq2DHrL0WJzkOUFBGh3DmsfFRoHLAbsC7wjtk8bQLwPHAlcE40/IuUWXbS\ne0gfOiwJLE9aUno/sCvhjohmnSLxFLBKBB531vUGUjO42LHSau632QvYjNSh6TzgFuAGoK9/uI9F\nIx7tXEIzmyNpFdJ5OAVwI/BPIv6RNZNZTUm8ACwVgc+Us67nYse6XrPQ+QGwOXAqcFY04sk5fo3X\n75qVhyRSgXMyET9p80t57JvNhcSrwAIR1VjK5nFfbwOpGbxnx8pqO2ASsFE0YvrcnmxmpbMp8DbS\nhxVmlpHEMGAE8GruLGad5pkdGzIVWh1YcYBfNgzYmLRZuS/rAl+LRpw1lGxmlkGa1bkSOJOIU3LH\nMas7iVHA9AhG5c5i1gqe2bGZqNAw0iesgzUBWGM2j80HfJ3UPnagbgP+PJvHzgEuHMQ1zSwnaQ/g\nM8BY4PS8YcysyWfsWG252GkDFXobsEA/nz6ctAF/TD+fP4q0j2XkACKtACxC35v6++NZUjHz5mwe\n/3g04reDvHbLeP2uWWbSfKS9dgcC5xPxemde1mPfbC4q13ba4976y8VOLyr0TtJekWED+LLVSTMf\nb10GWBl4eQDXmAo80s/nBnAG8MwArv90NOLWATzfzGwwdgZuJuLs3EHMbCYjgf/kDmGWQ+327KjQ\noqQlFiOad60NLEuaYVmEtHzqpQFc8mFSO+Te/jq3zmFmZpUj/Qr4HRGn5Y5iZjNIrAT8PoKVcmcx\nawXv2ZmFCi0AXAqMAxYindfyUPPhs0h7RyAVKQMpdMzMDEAaBmwN7Js7ipn9F+/ZsdqqXLHTPJ9l\nLVJR02N3UnGzB/CqD56sJq/fNctqYeBVIp7u9At77JvNlffsWG11TbGjQqNJ564MI+2LmV3b4lWB\nBYEHe933IPBlLy0zM2ubxYCncocwsz55z47VVmmLHRUaxYxGAfMCPwHGAz2zMncA0/r40p8Dl0cj\nZtc5zCrKn/CYZbUokOUDJY99s7mq3MyOx731V+mKneYytLOBDwE9bUsDuBjYJBoxkC5nZmbWGZ7Z\nMSsv79mx2ipdsQNsCrwXWCAa4YFp/eb1u2ZZZZvZ8dg3m6vKzex43Ft/DeQ8mSGTtI2keyXdL+nL\ns3nansBJLnRsEFbPHcCsxhYl38yOx77ZnFVxz47HvfVLx4odScOB44BtgFWAj0p6Vx9PXQe4tlO5\nrFLG5g5gVmOLkWlmB499s7mp4jI2j3vrl07O7KwLPBARD0XEa6R9OTv08bwVSc0HBkzSxMHH69w1\n23VdZ22PVmb1e9T663ZLznZeN8s1pXFI30M6FulYUrfMOc7s1HXct+N67bput+Rs1zXbdd0SXLPf\ny9hKkDWrbhhT3XLNdl13oNfs5J6dpYBHet1+FFhv1id9eNro5zY/f8+vnnjE5AG/wJqssOmJmvyH\nwUfszDXbdd26Z12ecdufqMktL+BbmbXu71E7rtstOdt53U5fczhvDNuRRT80nbFPPMCK9wMEuupw\n/le3iu1mf9UNPyqxQCtzJu/YSHN83cFoddZ2/d3rmrNd12zXdbNdc17gCNIHycf088ITgasHHatz\n1xzf4uv1mEhrs7b6et10zXZdd+JAnqyIaPHrz+aFpJ2BbSLiU83bHwPWi4gDej2nM2HMzMzMzKxr\nRYT687xOzuz8A1im1+1lmHFmDtD/0GZmZmZmZnPTyT07NwErSRovaV5gV+CCDr6+mZmZmZnVSMdm\ndiLidUmfBS4BhgOnRsQ9nXp9MzMzMzOrl47t2TEzMzMzM+ukjh4q2mqSXpzL41dLWqtTeaxz5vbe\nW7V57NeTx329edzXl8d+vQ117Hd1sQPMbVoq+vEc605+X+vNY7+e/J7Wm8d9ffl9rbchjf1uL3aQ\ntKmkC3vdPk7S3jkzWWdIGi3pckk3S7pd0vbN+8dLukfSyZLulHSJpFG581preezXk8d9vXnc15fH\nfr0NZex3fbHTB3+yUx8vAx+KiLWAzYHv9XpsReC4iHg3MB3YOUM+6yyP/XrwuLfePO7rw2Pfeuv3\n2O/kOTtmrTYM+LakjYE3gSUlLdZ87MGIuL3555tp30nLZtZZHvdm9eSxb4NShWLndWaeoZovVxDr\nuD2ARYA1I+INSQ8CPVPXr/R63hv430UVeezXk8d9vXnc15fHfr0NeuxXYRnbw8AqkuaVNJY0tWn1\nsCDwZPOb3mbAcrkDWUd57NeTx329edzXl8d+vQ167HftzI6keYBXIuJRSecAdwIPArfkTWbt1vPe\nA2cCF0q6HbgJ6H1I7azrOL2muyI89uvJ477ePO7ry2O/3lox9rv2UFFJ7wV+HBHr585ineX3vt78\n/teT3/d68/tfX37v660V739XLmOTtC/wC+Dw3Fmss/ze15vf/3ry+15vfv/ry+99vbXq/e/amR0z\nMzMzM7M56cqZHasHSctIukrSXc2Dwg5s3r+wpMsk3Sfp0uZGtZ77r5L0gqRjZ7nWxZJubV7rVEkj\ncvydzGzuWjn2e13zAkl3dPLvYWb91+Kf+VdLulfStOZ/i+T4O1k5uNixMnsNOCgiVgXWB/aX9C7g\nUOCyiHgHcEXzNsB/SFOdX+zjWh+OiNWb11oQ2LXt6c1ssFo59pG0E/AC3rRsVmatHPcB7B4RazT/\ne7r98a2sXOxYaUXE4xFxa/PPL5I6rywFbA9MaT5tCrBj8zkvRcR1zNxvn15fT3NGZ17A3/jMSqqV\nY1/S24CDgP8F1P70ZjYYrRz3TR7vBrjYsS4haTywBnADMC4inmg+9AQwbpan9/npraRLms9/OSIu\nbk9SM2ulFoz9bwDfBV5qU0Qza7FW/MwHpjSXsLm5Qc252LHSa34y+2vgcxHxQu/HInXY6NfSlIjY\nGlgCGClp75YHNbOWGurYl7Q6sHxEnI8/5TXrCi36mb9HRLwb2BjYWNKerU9q3cLFjpVac9nZr4Gf\nRcR5zbufkLR48/ElgCf7e72IeKV5vXVandXMWqdFY399YG1JDwJ/BN4h6cp2ZTazoWnVz/yI+Gfz\nf18ktS5etz2JrRu42LHSkiTgVODuiPhhr4cuAHpmZvYGzpv1S2e5zujmN8iek3g/CExrS2gzG7JW\njf2IOCkiloqICcBGwH0RsXmbYpvZELTwZ/7wnu5rzeJpO8CdGGvM5+xYaUnaCLgGuJ0Z09aHATcC\n5wDLAg8Bu0TE9ObXPAQsQGpCMB3YEvgX8FtgJOmb4iXAIeF//Gal1IKx/yywVUTc2+ua44ELImK1\nTvwdzGxgWvgz/+/N64wAhgOXAV/wz/z6crFjZmZmZmaV5GVsZmZmZmZWSS52zMzMzMysklzsmJmZ\nmZlZJbnYMTMzMzOzSnKxY2ZmZmZmleRix8zMzMzMKsnFjpmZlYakBSVNzp3DzMyqwcWOmZmVyULA\nfrlDmJlZNcyTO4CZmVkvRwIrSJoGTAXeCYwh/byaHBHX5gxnZmbdRRGRO4OZmRkAkpYDfhsR75H0\nBWBURHxLkoDREfFi5ohmZtZFPLNjZmZlol5/ngr8VNII4LyIuC1TJjMz61Les2NmZqUUEX8ENgb+\nAZwuac/MkczMrMu42DEzszJ5AVgA3lrS9lREnAKcAqyRM5iZmXUfL2MzM7PSiIhnJF0n6Q5gNPBv\nSa+RiqC98qYzM7Nu4wYFZmZmZmZWSV7GZmZmZmZmleRix8zMzMzMKsnFjpmZmZmZVZKLHTMzMzMz\nqyQXO2ZmZmZmVkkudszMzMzMrJJc7JiZmZmZWSW52DEzMzMzs0r6/8djuYsILIDkAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bcc9588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(\n",
    "df.pivot(\n",
    "        index='ts',\n",
    "        columns='group',\n",
    "        values='count')\n",
    "  .resample('D', how='sum')\n",
    "  .fillna(0)\n",
    "  .cumsum()\n",
    "  .plot()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = neo.cypher.execute('''\n",
    "match (m:Member)-[j:JOINED]->(g:Group)\n",
    "where g.urlname in ['Docker-Randstad', 'Big-Data-Amsterdam', 'DevOpsAmsterdam']\n",
    "with m as member, collect(g.name) as groups\n",
    "match (member)-[:JOINED]->(x:Group)\n",
    "where x.urlname in ['Docker-Randstad', 'Big-Data-Amsterdam', 'DevOpsAmsterdam']\n",
    "return x.name as group, groups, count(1) as count\n",
    "order by group\n",
    "''')\n",
    "\n",
    "len(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>groups</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Big Data, Amsterdam</td>\n",
       "      <td>Docker Randstad, Big Data, Amsterdam, DevOps A...</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Big Data, Amsterdam</td>\n",
       "      <td>Docker Randstad, Big Data, Amsterdam</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Big Data, Amsterdam</td>\n",
       "      <td>Big Data, Amsterdam</td>\n",
       "      <td>1137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Big Data, Amsterdam</td>\n",
       "      <td>Big Data, Amsterdam, DevOps Amsterdam</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DevOps Amsterdam</td>\n",
       "      <td>Docker Randstad, DevOps Amsterdam</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DevOps Amsterdam</td>\n",
       "      <td>Big Data, Amsterdam, DevOps Amsterdam</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DevOps Amsterdam</td>\n",
       "      <td>DevOps Amsterdam</td>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DevOps Amsterdam</td>\n",
       "      <td>Docker Randstad, Big Data, Amsterdam, DevOps A...</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Docker Randstad</td>\n",
       "      <td>Docker Randstad, Big Data, Amsterdam, DevOps A...</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Docker Randstad</td>\n",
       "      <td>Docker Randstad, Big Data, Amsterdam</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Docker Randstad</td>\n",
       "      <td>Docker Randstad</td>\n",
       "      <td>739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Docker Randstad</td>\n",
       "      <td>Docker Randstad, DevOps Amsterdam</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  group                                             groups  \\\n",
       "0   Big Data, Amsterdam  Docker Randstad, Big Data, Amsterdam, DevOps A...   \n",
       "1   Big Data, Amsterdam               Docker Randstad, Big Data, Amsterdam   \n",
       "2   Big Data, Amsterdam                                Big Data, Amsterdam   \n",
       "3   Big Data, Amsterdam              Big Data, Amsterdam, DevOps Amsterdam   \n",
       "4      DevOps Amsterdam                  Docker Randstad, DevOps Amsterdam   \n",
       "5      DevOps Amsterdam              Big Data, Amsterdam, DevOps Amsterdam   \n",
       "6      DevOps Amsterdam                                   DevOps Amsterdam   \n",
       "7      DevOps Amsterdam  Docker Randstad, Big Data, Amsterdam, DevOps A...   \n",
       "8       Docker Randstad  Docker Randstad, Big Data, Amsterdam, DevOps A...   \n",
       "9       Docker Randstad               Docker Randstad, Big Data, Amsterdam   \n",
       "10      Docker Randstad                                    Docker Randstad   \n",
       "11      Docker Randstad                  Docker Randstad, DevOps Amsterdam   \n",
       "\n",
       "    count  \n",
       "0      72  \n",
       "1     100  \n",
       "2    1137  \n",
       "3      59  \n",
       "4     314  \n",
       "5      59  \n",
       "6     670  \n",
       "7      72  \n",
       "8      72  \n",
       "9     100  \n",
       "10    739  \n",
       "11    314  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    [(r.group, ', '.join(r.groups), r.count) for r in result],\n",
    "    columns=['group', 'groups', 'count']\n",
    ")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>groups</th>\n",
       "      <th>Big Data, Amsterdam</th>\n",
       "      <th>Big Data, Amsterdam, DevOps Amsterdam</th>\n",
       "      <th>DevOps Amsterdam</th>\n",
       "      <th>Docker Randstad</th>\n",
       "      <th>Docker Randstad, Big Data, Amsterdam</th>\n",
       "      <th>Docker Randstad, Big Data, Amsterdam, DevOps Amsterdam</th>\n",
       "      <th>Docker Randstad, DevOps Amsterdam</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Big Data, Amsterdam</th>\n",
       "      <td>1137</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DevOps Amsterdam</th>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "      <td>670</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docker Randstad</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>739</td>\n",
       "      <td>100</td>\n",
       "      <td>72</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "groups               Big Data, Amsterdam  \\\n",
       "group                                      \n",
       "Big Data, Amsterdam                 1137   \n",
       "DevOps Amsterdam                     NaN   \n",
       "Docker Randstad                      NaN   \n",
       "\n",
       "groups               Big Data, Amsterdam, DevOps Amsterdam  DevOps Amsterdam  \\\n",
       "group                                                                          \n",
       "Big Data, Amsterdam                                     59               NaN   \n",
       "DevOps Amsterdam                                        59               670   \n",
       "Docker Randstad                                        NaN               NaN   \n",
       "\n",
       "groups               Docker Randstad  Docker Randstad, Big Data, Amsterdam  \\\n",
       "group                                                                        \n",
       "Big Data, Amsterdam              NaN                                   100   \n",
       "DevOps Amsterdam                 NaN                                   NaN   \n",
       "Docker Randstad                  739                                   100   \n",
       "\n",
       "groups               Docker Randstad, Big Data, Amsterdam, DevOps Amsterdam  \\\n",
       "group                                                                         \n",
       "Big Data, Amsterdam                                                 72        \n",
       "DevOps Amsterdam                                                    72        \n",
       "Docker Randstad                                                     72        \n",
       "\n",
       "groups               Docker Randstad, DevOps Amsterdam  \n",
       "group                                                   \n",
       "Big Data, Amsterdam                                NaN  \n",
       "DevOps Amsterdam                                   314  \n",
       "Docker Randstad                                    314  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = df.pivot(index='group', columns='groups', values='count')\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10bd5c080>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ShsgRbJIkSZIkSdIsmGDTAE2MOwBJkiRJkqQZM8EmSZIkSZIkzYI12AbGGmzqWINN1lvS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\noZi7XkluQCv2P61f4x8B/3fuHEEryr6m/2fAJaz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tpR4FvLuqfgVcQCsa3XCZ/d8Q+PHc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\nkiRJkqQVsMEmSZIkSZIkrcD/AYFj3FXVf1cPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be02e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(kind='bar', stacked=True).legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
   ]
  }
 ],
 "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}