meetup_script.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Python - jupyer notebooks advantages for analysts **\n",
    "\n",
    "* Vast amount of libraries\n",
    "* Broad range of functionalities\n",
    "* Can handle huge amount of data (vs Excel for example)\n",
    "* Easily reproductible\n",
    "* Debug is easy\n",
    "\n",
    "\n",
    "** Your firends **\n",
    "\n",
    "* Python documentation\n",
    "* Alt + tab : direct link to documentation from notebook\n",
    "* Stackoverflow: people most probably already had and solved your issue\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import re\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.dates import date2num\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "categories_df = pd.read_csv('data/categories.csv')\n",
    "groups_topics_df = pd.read_csv('data/groups_topics.csv', encoding='latin1')\n",
    "cities_df = pd.read_csv('data/cities.csv')\n",
    "events_df = pd.read_csv('data/events.csv')\n",
    "topics_df = pd.read_csv('data/topics.csv', encoding='latin1')\n",
    "venues_df = pd.read_csv('data/venues.csv', encoding='latin1')\n",
    "groups_df = pd.read_csv('data/groups.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time insights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most common day time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Convert event time to datetime\n",
    "events_df['event_time'] = events_df['event_time'].apply(lambda s: datetime.datetime.strptime(str(s),'%Y-%m-%d %H:%M:%S'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "events_df['event_hour'] = events_df['event_time'].apply(lambda d: d.hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 18 artists>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J56hu7e5nNnGcAAAACzVcnrp79zEXr1rn+r1J9m7CmAAAALYcX5ILAAAwQHkCAAAYoDwB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\nAMAA5QkAAGCA8gQAADBAeQIAABjwfwFK2jrahydWmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bc6e358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot evolution per hour\n",
    "events_per_hour = events_df[['event_hour','event_id']].groupby('event_hour').count()\n",
    "events_per_hour.rename(columns={'event_id':'count_event_hour'},inplace=True)\n",
    "events_per_hour.reset_index(inplace=True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (14.5,8.2))\n",
    "\n",
    "x_axis = events_per_hour['event_hour']\n",
    "y1 = events_per_hour['count_event_hour']\n",
    "\n",
    "# Make the y-axis label, ticks and tick labels match the line color.\n",
    "ax.set_xticks(x_axis)\n",
    "\n",
    "ax.bar(x_axis, y1, label='Number of events per hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -25200\n",
       "1   -25200\n",
       "2   -25200\n",
       "3   -25200\n",
       "4   -28800\n",
       "Name: utc_offset, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Glance at utc_offset\n",
    "events_df['utc_offset'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "events_df['event_hour_with_utcoffset'] = events_df.apply(lambda r: (r['event_time'] + datetime.timedelta(seconds=int(r['utc_offset']))).hour,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 17 artists>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1sAAAHgCAYAAABTrneaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHh9JREFUeJzt3X+QrXddH/D3p7maIkoFc6EhP0xoE8bEqdFsU6YVxGIl\nMA4BO2IYK1goAUEq1tYh2hFahxl/gExpa5hQMkAHA7GCpDNgCVTBzhjwhkaSQCIXCHKvIYnQGltt\nNMmnf5znmpPL3d3L7n73OXvzes2c2ed8z/Oc894zZ/fZ93me893q7gAAALCz/trcAQAAAE5EyhYA\nAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAA++YOsJlTTjmlzzrr\nrLljAAAAJEluuOGGP+7u/Zutt/Jl66yzzsqBAwfmjgEAAJAkqarPH896TiMEAAAYQNkCAAAYQNkC\nAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAY\nQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYYN/cAQAAjlfV3Ake1D13\nAmDVObIFAAAwgLIFAAAwgLIFAAAwgLIFAAAwgLIFAAAwgLIFAAAwgLIFAAAwgLIFAAAwgLIFAAAw\ngLIFAAAwgLIFAAAwgLIFAAAwgLIFAAAwgLIFAAAwwKZlq6quqqq7qurmpbF3VdWN0+X2qrpxGj+r\nqv586bY3LW1zYVXdVFUHq+qNVVVjviUAAID57TuOdd6a5D8kefuRge7+wSPLVfX6JH+ytP5nuvuC\nY9zPFUlenOSjSd6X5OIk7//qIwMAAKy+TY9sdfdHknz5WLdNR6eem+Tqje6jqk5N8qjuvr67O4vi\n9uyvPi4AAMDesN3PbD05yZ3d/emlsbOnUwg/XFVPnsZOS3JoaZ1D0xgAAMAJ6XhOI9zI8/LQo1p3\nJDmzu79UVRcm+Y2qOv+rvdOquizJZUly5plnbjMiAADA7tvyka2q2pfk+5O868hYd9/b3V+alm9I\n8pkk5yY5nOT0pc1Pn8aOqbuv7O617l7bv3//ViMCAADMZjunEX5Pklu7+69OD6yq/VV10rT8hCTn\nJPlsd9+R5J6qetL0Oa/nJ3nvNh4bAABgpR3P1O9XJ/ndJE+sqkNV9aLppkvzlRNjPCXJJ6ap4P9L\nkpd295HJNV6W5D8lOZjFES8zEQIAACesWkwOuLrW1tb6wIEDc8cAAFbAKv2XzhX/EwoYqKpu6O61\nzdbb7myEAAAAHIOyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAA\nMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICy\nBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAA\nMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICy\nBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAAMICyBQAA\nMICyBQAAMICyBQAAMMCmZauqrqqqu6rq5qWx11TV4aq6cbo8c+m2y6vqYFXdVlVPXxq/sKpumm57\nY1XVzn87AAAAq+F4jmy9NcnFxxh/Q3dfMF3elyRVdV6SS5OcP23zK1V10rT+FUlenOSc6XKs+wQA\nADghbFq2uvsjSb58nPd3SZJ3dve93f25JAeTXFRVpyZ5VHdf392d5O1Jnr3V0AAAAKtuO5/ZekVV\nfWI6zfDR09hpSb6wtM6haey0afnocQAAgBPSVsvWFUmekOSCJHckef2OJUpSVZdV1YGqOnD33Xfv\n5F0DAADsii2Vre6+s7vv7+4Hkrw5yUXTTYeTnLG06unT2OFp+ejx9e7/yu5e6+61/fv3byUiAADA\nrLZUtqbPYB3xnCRHZiq8NsmlVXVyVZ2dxUQYH+vuO5LcU1VPmmYhfH6S924jNwAAwErbt9kKVXV1\nkqcmOaWqDiV5dZKnVtUFSTrJ7UlekiTdfUtVXZPkk0nuS/Ly7r5/uquXZTGz4SOSvH+6AAAAnJBq\nMTng6lpbW+sDBw7MHQMAWAGr9F86V/xPKGCgqrqhu9c2W287sxECAACwDmULAABgAGULAABgAGUL\nAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABg\nAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGUL\nAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABg\nAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGUL\nAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABgAGULAABggE3LVlVdVVV3VdXN\nS2O/VFW3VtUnquo9VfWN0/hZVfXnVXXjdHnT0jYXVtVNVXWwqt5YVTXmWwIAAJjf8RzZemuSi48a\nuy7Jt3b330nyB0kuX7rtM919wXR56dL4FUlenOSc6XL0fQIAAJwwNi1b3f2RJF8+auwD3X3fdPX6\nJKdvdB9VdWqSR3X39d3dSd6e5NlbiwwAALD6duIzWy9M8v6l62dPpxB+uKqePI2dluTQ0jqHpjEA\nAIAT0r7tbFxVP5PkviTvmIbuSHJmd3+pqi5M8htVdf4W7veyJJclyZlnnrmdiAAAALPY8pGtqvqR\nJN+X5IemUwPT3fd295em5RuSfCbJuUkO56GnGp4+jR1Td1/Z3WvdvbZ///6tRgQAAJjNlspWVV2c\n5KeSPKu7/2xpfH9VnTQtPyGLiTA+2913JLmnqp40zUL4/CTv3XZ6AACAFbXpaYRVdXWSpyY5paoO\nJXl1FrMPnpzkumkG9+unmQefkuTfVtVfJnkgyUu7+8jkGi/LYmbDR2TxGa/lz3kBAACcUGo6A3Bl\nra2t9YEDB+aOAQCsgFX6L50r/icUMFBV3dDda5uttxOzEQIAAHAUZQsAAGAAZQsAAGAAZQsAAGAA\nZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsA\nAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAA\nZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsA\nAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAA\nZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGCATctWVV1VVXdV1c1LY4+p\nquuq6tPT10cv3XZ5VR2sqtuq6ulL4xdW1U3TbW+sqtr5bwcAAGA1HM+RrbcmufiosVcl+VB3n5Pk\nQ9P1VNV5SS5Ncv60za9U1UnTNlckeXGSc6bL0fcJAHBCqVqdC7D7Ni1b3f2RJF8+aviSJG+blt+W\n5NlL4+/s7nu7+3NJDia5qKpOTfKo7r6+uzvJ25e2AQAAOOFs9TNbj+vuO6blLyZ53LR8WpIvLK13\naBo7bVo+ehwAAOCEtO0JMqYjVb0DWf5KVV1WVQeq6sDdd9+9k3cNAACwK7Zatu6cTg3M9PWuafxw\nkjOW1jt9Gjs8LR89fkzdfWV3r3X32v79+7cYEQAAYD5bLVvXJnnBtPyCJO9dGr+0qk6uqrOzmAjj\nY9Mph/dU1ZOmWQifv7QNAADACWffZitU1dVJnprklKo6lOTVSX4+yTVV9aIkn0/y3CTp7luq6pok\nn0xyX5KXd/f90129LIuZDR+R5P3TBQAA4IRUi49cra61tbU+cODA3DEAgBWwSlOYH8+fUHstL3B8\nquqG7l7bbL1tT5ABAADAV1K2AAAABlC2AAAABlC2AAAABlC2AAAABlC2AAAABlC2AAAABlC2AAAA\nBlC2AAAABlC2AAAABlC2AAAABlC2AAAABlC2AAAABlC2AAAABlC2AAAABlC2AAAABlC2AAAABlC2\nAAAABtg3dwCAY6maO8GDuudOAADsRY5sAQAADKBsAQAADKBsAQAADKBsAQAADKBsAQAADKBsAQAA\nDKBsAQAADKBsAQAADKBsAQAADKBsAQAADKBsAQAADKBsAQAADKBsAQAADKBsAQAADKBsAQAADKBs\nAQAADKBsAQAADKBsAQAADKBsAQAADKBsAQAADKBsAQAADKBsAQAADKBsAQAADKBsAQAADKBsAQAA\nDKBsAQAADKBsAQAADKBsAQAADKBsAQAADKBsAQAADLDlslVVT6yqG5cu91TVK6vqNVV1eGn8mUvb\nXF5VB6vqtqp6+s58CwAAAKtn31Y37O7bklyQJFV1UpLDSd6T5J8meUN3v255/ao6L8mlSc5P8vgk\nH6yqc7v7/q1mAAAAWFU7dRrh05J8prs/v8E6lyR5Z3ff292fS3IwyUU79PgAAAArZafK1qVJrl66\n/oqq+kRVXVVVj57GTkvyhaV1Dk1jAAAAJ5xtl62q+tokz0rya9PQFUmekMUphnckef0W7vOyqjpQ\nVQfuvvvu7UYEAADYdTtxZOsZST7e3XcmSXff2d33d/cDSd6cB08VPJzkjKXtTp/GvkJ3X9nda929\ntn///h2ICAAAsLt2omw9L0unEFbVqUu3PSfJzdPytUkuraqTq+rsJOck+dgOPD4AAMDK2fJshElS\nVY9M8o+SvGRp+Ber6oIkneT2I7d19y1VdU2STya5L8nLzUQIAACcqLZVtrr7/yb5pqPGfniD9V+b\n5LXbeUwAAIC9YKdmIwQAAGCJsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUA\nADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCA\nsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUA\nADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCA\nsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUAADCAsgUA\nADCAsgUAADCAsgUAADCAsgUAADDAtspWVd1eVTdV1Y1VdWAae0xVXVdVn56+Pnpp/cur6mBV3VZV\nT99ueAAAgFW1E0e2vru7L+juten6q5J8qLvPSfKh6Xqq6rwklyY5P8nFSX6lqk7agccHAABYOSNO\nI7wkydum5bclefbS+Du7+97u/lySg0kuGvD4AAAAs9tu2eokH6yqG6rqsmnscd19x7T8xSSPm5ZP\nS/KFpW0PTWMAAAAnnH3b3P47u/twVT02yXVVdevyjd3dVdVf7Z1Oxe2yJDnzzDO3GREAAGD3bevI\nVncfnr7eleQ9WZwWeGdVnZok09e7ptUPJzljafPTp7Fj3e+V3b3W3Wv79+/fTkQAAIBZbLlsVdUj\nq+objiwn+d4kNye5NskLptVekOS90/K1SS6tqpOr6uwk5yT52FYfHwAAYJVt5zTCxyV5T1UduZ9f\n7e7frKrfS3JNVb0oyeeTPDdJuvuWqromySeT3Jfk5d19/7bSAwAArKgtl63u/mySbzvG+JeSPG2d\nbV6b5LVbfUwAAIC9YsTU7wAAAA97yhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAA\nyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYA\nAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAA\nyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAAyhYAAMAA++YO\nAADMp2ruBA/qnjsBwM5yZAsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGAA\nZQsAAGAAZQsAAGAAZQsAAGAAZQsAAGCAfXMHgL2sau4ED+qeOwEAAMsc2QIAABhgy2Wrqs6oqt+q\nqk9W1S1V9ePT+Guq6nBV3Thdnrm0zeVVdbCqbquqp+/ENwAAALCKtnMa4X1JfrK7P15V35Dkhqq6\nbrrtDd39uuWVq+q8JJcmOT/J45N8sKrO7e77t5EBAABgJW35yFZ339HdH5+W/zTJp5KctsEmlyR5\nZ3ff292fS3IwyUVbfXwAAIBVtiOf2aqqs5J8e5KPTkOvqKpPVNVVVfXoaey0JF9Y2uxQNi5nPAxV\nrc4FAAC2Y9tlq6q+PsmvJ3lld9+T5IokT0hyQZI7krx+C/d5WVUdqKoDd99993YjAgAA7Lptla2q\n+positY7uvvdSdLdd3b3/d39QJI358FTBQ8nOWNp89Onsa/Q3Vd291p3r+3fv387EQEAAGaxndkI\nK8lbknyqu395afzUpdWek+TmafnaJJdW1clVdXaSc5J8bKuPz/GZ+1Q8p+UBAPBwtZ3ZCP9Bkh9O\nclNV3TiN/XSS51XVBUk6ye1JXpIk3X1LVV2T5JNZzGT4cjMRAgAAJ6otl63u/h9JjnW84n0bbPPa\nJK/d6mMCAADsFTsyGyEAAAAPpWwBAAAMoGwBAAAMoGwBAAAMoGwBAAAMoGwBAAAMoGwBAAAMoGwB\nAAAMoGwBAAAMoGwBAAAMoGwBAAAMsG/uAMDuqZo7wYO6507w8Oa1AADjObIFAAAwgLIFAAAwgLIF\nAAAwgLIFAAAwgLIFAAAwgLIFAAAwgLIFAAAwgLIFAAAwgH9qDMDK80+YAdiLlC0AABjMm0YPT8oW\nAABJFALYaT6zBQAAMIAjWwA7wLvBAMDRHNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkC\nAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAY\nQNkCAAAYYN/cAfaaqrkTPFT33AkAAIBjcWQLAABgAGULAABgAGULAABgAGULAABgAGULAABgALMR\nAsAOW6WZa81aCzAfR7YAAAAG2PWyVVUXV9VtVXWwql61248PAACwG3a1bFXVSUn+Y5JnJDkvyfOq\n6rzdzAAAALAbdvvI1kVJDnb3Z7v7L5K8M8klu5wBAABguN2eIOO0JF9Yun4oyd/b5QwAAOxxJqJh\nL1jJ2Qir6rIkl01X/09V3TZnnkFOSfLH272TXfxFs+28u/xLcS/lfdi9FpK9lddrd10Pu9dCIu8G\n9tJrN9lbeR92r4Vkb+X12t3QjrweVtA3H89Ku122Dic5Y+n66dPYQ3T3lUmu3K1Qc6iqA929NneO\n4yXvOHspayLvaHsp717Kmsg7mrzj7KWsibyjybu37PZntn4vyTlVdXZVfW2SS5Ncu8sZAAAAhtvV\nI1vdfV9V/ViS/5bkpCRXdfctu5kBAABgN+z6Z7a6+31J3rfbj7uC9tppkvKOs5eyJvKOtpfy7qWs\nibyjyTvOXsqayDuavHtItelTAAAAdtxuf2YLAADgYUHZ2mVV9cSqunHpck9VvXLuXOupqp+oqluq\n6uaqurqq/vrcmTZSVT8+Zb1lFZ/Xqrqqqu6qqpuXxh5TVddV1aenr4+eM+OydfL+wPT8PlBVKzW7\n0Dp5f6mqbq2qT1TVe6rqG+fMeMQ6WX9uynljVX2gqh4/Z8Zlx8q7dNtPVlVX1SlzZDuWdZ7f11TV\n4aXfv8+cM+Oy9Z7fqnrF9Pq9pap+ca58R1vn+X3X0nN7e1XdOGfGI9bJekFVXT9lPVBVF82Zcdk6\neb+tqn63qm6qqv9aVY+aM+Oyqjqjqn6rqj45vU5/fBpfuX3bBllXcr+2Qd5V3a+tl3dl9227ortd\nZrpkMUnIF5N889xZ1sl3WpLPJXnEdP2aJD8yd64N8n5rkpuTfF0Wn0f8YJK/PXeuozI+Jcl3JLl5\naewXk7xqWn5Vkl+YO+cmeb8lyROT/HaStbkzHkfe702yb1r+hVV5ftfJ+qil5X+e5E1z59wo7zR+\nRhaTHn0+ySlz59zk+X1Nkn85d7avIu93T7/HTp6uP3bunJu9HpZuf32Sn5075wbP7QeSPGNafmaS\n35475yZ5fy/Jd03LL0zyc3PnXMp2apLvmJa/IckfJDlvFfdtG2Rdyf3aBnlXdb+2Xt6V3bftxsWR\nrXk9LclnuvvzcwfZwL4kj6iqfVmUmD+aOc9GviXJR7v7z7r7viQfTvL9M2d6iO7+SJIvHzV8SZK3\nTctvS/LsXQ21gWPl7e5PdfdK/qPxdfJ+YHo9JMn1Wfx/v9mtk/WepauPTLIyH6pd57WbJG9I8lNZ\noazJhnlX0jp5fzTJz3f3vdM6d+16sHVs9PxWVSV5bpKrdzXUOtbJ2kmOHB36G1mhfds6ec9N8pFp\n+bok/3hXQ22gu+/o7o9Py3+a5FNZvFm7cvu29bKu6n5tg7yrul9bL+/K7tt2g7I1r0uzIjujY+nu\nw0lel+QPk9yR5E+6+wPzptrQzUmeXFXfVFVfl8W7lWdsss0qeFx33zEtfzHJ4+YMc4J7YZL3zx1i\nI1X12qr6QpIfSvKzc+fZSFVdkuRwd//+3Fm+Cq+YTme5ahVOa9rEuVn8TvtoVX24qv7u3IGO05OT\n3Nndn547yAZemeSXpp+11yW5fOY8m7kli/KSJD+QFd23VdVZSb49yUez4vu2o7KuvA3yruR+7ei8\ne2nfttOUrZnU4p86PyvJr82dZT3THyKXJDk7yeOTPLKq/sm8qdbX3Z/K4nD6B5L8ZpIbk9w/a6iv\nUi+OsT+s3vHZLVX1M0nuS/KOubNspLt/prvPyCLnj82dZz3TGxo/nb2107wiyROSXJDFG0ivnzfO\npvYleUySJyX5V0mumY4arbrnZYXfSJz8aJKfmH7WfiLJW2bOs5kXJnlZVd2QxelZfzFznq9QVV+f\n5NeTvPKoIxkrt2/bKOsqWi/vqu7XjpV3r+zbRlC25vOMJB/v7jvnDrKB70nyue6+u7v/Msm7k/z9\nmTNtqLvf0t0XdvdTkvyvLM4XXnV3VtWpSTJ9XZlThU4UVfUjSb4vyQ9NO/294B1ZoVOFjuFvZfFG\nzO9X1e1ZnMby8ar6m7Om2kB339nd93f3A0nenGRlJkVYx6Ek7+6FjyV5IMnKTEJyLNMp59+f5F1z\nZ9nEC7LYpyWLNz1X+rXQ3bd29/d294VZFNnPzJ1pWVV9TRZ/XL+ju488ryu5b1sn68paL++q7teO\n4/ld9X3bjlO25rMX3vn7wyRPqqqvm95NfVoW59+urKp67PT1zCx2+L86b6Ljcm0WO/5MX987Y5YT\nTlVdnMVnip7V3X82d56NVNU5S1cvSXLrXFk20903dfdju/us7j4ri2LwHd39xZmjrevIH36T52Rx\n6vEq+40sJslIVZ2b5GuT/PGsiTb3PUlu7e5DcwfZxB8l+a5p+R8mWeVTHpf3bX8tyb9O8qZ5Ez1o\n+vvgLUk+1d2/vHTTyu3bNsi6ktbLu6r7tQ3y7pl92wj+qfEMquqRWRSZJ3T3n8ydZyNV9W+S/GAW\nh6n/Z5J/duTD2quoqn4nyTcl+csk/6K7PzRzpIeoqquTPDWLd6fvTPLqLP6guibJmVnM6Pbc7l6J\nD/avk/fLSf59kv1J/neSG7v76XNlXLZO3suTnJzkS9Nq13f3S2cJuGSdrM/MYkasB7J4Lbx0+uzk\n7I6Vt7vfsnT77VnM4rUSZWCd5/epWZxC2EluT/KSpc+UzGqdvP85yVVZZP6LLGZS/O9zZVy23uuh\nqt6axc/YKpWBYz23tyX5d1mcqvn/krysu2+YK+OydfJ+fZKXT6u8O8nlq3I0o6q+M8nvJLkpi99d\nyeIU449mxfZtG2Q9OSu4X9sg7xuzmvu19fK+KCu6b9sNyhYAAMAATiMEAAAYQNkCAAAYQNkCAAAY\nQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAYQNkCAAAY4P8DKCzLeRnBELMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f9c27f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot evolution per hour\n",
    "events_per_hour_offset = events_df[['event_hour_with_utcoffset','event_id']].groupby('event_hour_with_utcoffset').count()\n",
    "events_per_hour_offset.rename(columns={'event_id':'count_event_hour'},inplace=True)\n",
    "events_per_hour_offset.reset_index(inplace=True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (14.5,8.2))\n",
    "\n",
    "x_axis = events_per_hour_offset['event_hour_with_utcoffset']\n",
    "y1 = events_per_hour_offset['count_event_hour']\n",
    "\n",
    "# Make the y-axis label, ticks and tick labels match the line color.\n",
    "ax.set_xticks(x_axis)\n",
    "\n",
    "ax.bar(x_axis, y1,color='blue', label='Number of events per hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2018-10-25 23:00:00'), Timestamp('2017-10-24 11:00:00'))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Max and min time\n",
    "events_df['event_time'].max(),events_df['event_time'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Construct time features\n",
    "events_df['event_day'] = events_df['event_time'].apply(lambda d: d.replace(hour=0,minute=0,second=0))\n",
    "events_df['event_week'] = events_df['event_day'].apply(lambda d: d - datetime.timedelta(d.weekday()))\n",
    "events_df['event_weekday'] = events_df['event_day'].apply(lambda d: d.weekday())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x111b5e080>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fhCrvJeey104AAAAALWOkKs9SCVUvvijddpvtSzVx4oHfKyuT9uyRtm3LXhsBAAAAtI5Q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\n6sL/BxFMPuzo5TzGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110cbe668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot evolution per week\n",
    "events_per_week = events_df[['event_week','event_id']].groupby('event_week').count()\n",
    "events_per_week.rename(columns={'event_id':'count_event_week'},inplace=True)\n",
    "events_per_week.reset_index(inplace=True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (14.5,8.2))\n",
    "\n",
    "x_axis = np.array([date2num(date) for date in events_per_week['event_week']])\n",
    "y1 = events_per_week['count_event_week']\n",
    "\n",
    "# Make the y-axis label, ticks and tick labels match the line color.\n",
    "ax.set_xticks(x_axis)\n",
    "ax.set_xticklabels(\n",
    "        [date.strftime(\"%Y-%m-%d\") for date in events_per_week['event_week']]\n",
    "        ,rotation=90)\n",
    "\n",
    "ax.plot(x_axis, y1,color='blue', label='Number of events per week')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 7 artists>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b2DqT5Ex3f3i5/XPZiq9PV9WVSbJ8f2ZZfzbJVdv2P7aMAQAAHDg7jq3ufjrJk1X1tcvQ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\nAAP+H7abxFxJPiqJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110c974a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot evolution per day\n",
    "events_per_weekday = events_df[['event_weekday','event_id']].groupby('event_weekday').count()\n",
    "events_per_weekday.rename(columns={'event_id':'count_event_weekday'},inplace=True)\n",
    "events_per_weekday.reset_index(inplace=True)\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (14.5,8.2))\n",
    "\n",
    "x_axis = events_per_weekday['event_weekday']\n",
    "y1 = events_per_weekday['count_event_weekday']\n",
    "\n",
    "# Make the y-axis label, ticks and tick labels match the line color.\n",
    "ax.set_xticks(x_axis)\n",
    "ax.set_xticklabels(\n",
    "        ['M','T','W','T','F','S','S']\n",
    "        ,rotation=90)\n",
    "\n",
    "ax.bar(x_axis, y1,color='blue', label='Number of events per weekday')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Events topics popularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Select only one topic per group\n",
    "groups_topics_unique_df = groups_topics_df.drop_duplicates(subset=['group_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((31212, 4), (11876, 4))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compare shapes of the two dataframes\n",
    "groups_topics_df.shape, groups_topics_unique_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Join events and groups\n",
    "events_enriched_df = pd.merge(events_df[['event_id','group_id','yes_rsvp_count','waitlist_count','event_time']]\n",
    "                              ,groups_topics_unique_df[['group_id','topic_name','topic_id']]\n",
    "                              ,on ='group_id' , how='left' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count unique topic_names within dataframe\n",
    "events_enriched_df['topic_name'].drop_duplicates().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topic_id</th>\n",
       "      <th>topic_name</th>\n",
       "      <th>count_event_per_topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>183.0</td>\n",
       "      <td>Spanish Language</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>206.0</td>\n",
       "      <td>Digital Photography</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>223.0</td>\n",
       "      <td>Photography</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>225.0</td>\n",
       "      <td>Ceramic Arts</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>387.0</td>\n",
       "      <td>Theater Industry</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   topic_id           topic_name  count_event_per_topic\n",
       "0     183.0     Spanish Language                      5\n",
       "1     206.0  Digital Photography                     25\n",
       "2     223.0          Photography                    253\n",
       "3     225.0         Ceramic Arts                     13\n",
       "4     387.0     Theater Industry                     50"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top 10 topics\n",
    "top10_topics_1 = events_enriched_df[['event_id','topic_id','topic_name']].groupby(['topic_id','topic_name']).count()\n",
    "top10_topics_1.rename(columns={'event_id':'count_event_per_topic'}, inplace=True)\n",
    "top10_topics_1.reset_index(inplace=True)\n",
    "top10_topics_1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topic_id</th>\n",
       "      <th>topic_name</th>\n",
       "      <th>count_event_per_topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>15720.0</td>\n",
       "      <td>Professional Networking</td>\n",
       "      <td>1433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4422.0</td>\n",
       "      <td>Social Networking</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1502.0</td>\n",
       "      <td>Art</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>15405.0</td>\n",
       "      <td>Business Referral Networking</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>223.0</td>\n",
       "      <td>Photography</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>15321.0</td>\n",
       "      <td>Literature</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10581.0</td>\n",
       "      <td>Social</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>17558.0</td>\n",
       "      <td>Performing Arts</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9696.0</td>\n",
       "      <td>New Technology</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>16389.0</td>\n",
       "      <td>Fine Arts</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    topic_id                    topic_name  count_event_per_topic\n",
       "33   15720.0       Professional Networking                   1433\n",
       "12    4422.0             Social Networking                    543\n",
       "10    1502.0                           Art                    438\n",
       "31   15405.0  Business Referral Networking                    367\n",
       "2      223.0                   Photography                    253\n",
       "28   15321.0                    Literature                    217\n",
       "26   10581.0                        Social                    190\n",
       "42   17558.0               Performing Arts                    178\n",
       "15    9696.0                New Technology                    169\n",
       "36   16389.0                     Fine Arts                    116"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create list of top10 topic_id from dataframe\n",
    "top10_topics_2 = top10_topics_1.sort_values(by='count_event_per_topic', ascending=False)\n",
    "top10_topics_2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 15720.,   4422.,   1502.,  15405.,    223.,  15321.,  10581.,\n",
       "        17558.,   9696.,  16389.])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create list of top10 topics\n",
    "top10_topics_list = top10_topics_2.head(10)['topic_id'].values\n",
    "top10_topics_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5807, 3904)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter events which topic is in top10\n",
    "events_top10_df = events_enriched_df[events_enriched_df['topic_id'].isin(top10_topics_list)].copy()\n",
    "#Compare shapes\n",
    "events_enriched_df.shape[0], events_top10_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create an array\n",
    "x_axis = [i for i in range(10)]\n",
    "x_axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 10 artists>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C1q5Jbpbkxd190yTfztRlcLPu7iR9SX9xVR1RVZuqatO55567giICAAAsx0rC1plJzuzu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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111c24438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (14.5,8.2))\n",
    "\n",
    "x_axis_labels = top10_topics_2.head(10)['topic_name']\n",
    "x_axis = [i for i in range(10)]\n",
    "y1 = top10_topics_2.head(10)['count_event_per_topic']\n",
    "\n",
    "ax.set_xticks(x_axis)\n",
    "ax.set_xticklabels(x_axis_labels\n",
    "        ,rotation=45)\n",
    "\n",
    "ax.bar(x_axis, y1,color='blue', label='Number of events per weekday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5rSR/N877vCTfTvKyiddPTHKvJP/WWvvWvGn/McnDx2kfneTB4/34Cxrvj39Ahh8M/HKG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\n4Aq+mZmZmZmZWQdwBd/MzMzMzMysA/wfrZE0AleW/ycAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bce1588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Boxplot functions\n",
    "plot = events_top10_df.boxplot(column=['yes_rsvp_count']\\\n",
    "                           , by='topic_name', showfliers = False, showmeans = False, figsize =(17,8.2)\\\n",
    "                           ,whis=[20, 80])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtering and functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Other filtering examples\n",
    "events_filtered_1 = events_enriched_df[events_enriched_df['yes_rsvp_count']>50]\n",
    "events_filtered_2 = events_df[(events_df['yes_rsvp_count']>50) & (events_df['venue.city']=='Chicago')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Function to rate popularity\n",
    "def rate_popularity(r):\n",
    "    if (r['yes_rsvp_count'] + r['waitlist_count']) >= 50:\n",
    "        return 'High popularity'\n",
    "    elif (r['yes_rsvp_count'] + r['waitlist_count']) >= 20:\n",
    "        return 'Medium popularity'\n",
    "    else:\n",
    "        return 'Low popularity'\n",
    "events_top10_df['popularity']=events_top10_df.apply(rate_popularity,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Pivot table\n",
    "events_popularity_summary = events_top10_df[events_top10_df['yes_rsvp_count']>5][['event_id','popularity','topic_name']].pivot_table(\n",
    "                                index='topic_name', columns='popularity', aggfunc='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">event_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popularity</th>\n",
       "      <th>High popularity</th>\n",
       "      <th>Low popularity</th>\n",
       "      <th>Medium popularity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>topic_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Art</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Business Referral Networking</th>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fine Arts</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Literature</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Technology</th>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Performing Arts</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Photography</th>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professional Networking</th>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social</th>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social Networking</th>\n",
       "      <td>9.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    event_id                                 \n",
       "popularity                   High popularity Low popularity Medium popularity\n",
       "topic_name                                                                   \n",
       "Art                                      1.0           18.0               2.0\n",
       "Business Referral Networking             1.0           17.0              52.0\n",
       "Fine Arts                                NaN            2.0               NaN\n",
       "Literature                               NaN           12.0               3.0\n",
       "New Technology                           7.0           18.0               9.0\n",
       "Performing Arts                          NaN            1.0               NaN\n",
       "Photography                             20.0            9.0               1.0\n",
       "Professional Networking                  3.0           14.0               4.0\n",
       "Social                                   1.0           15.0               1.0\n",
       "Social Networking                        9.0           78.0              40.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_popularity_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">event_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popularity</th>\n",
       "      <th>High popularity</th>\n",
       "      <th>Low popularity</th>\n",
       "      <th>Medium popularity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>topic_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Art</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Business Referral Networking</th>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fine Arts</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Literature</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Technology</th>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Performing Arts</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Photography</th>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professional Networking</th>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social</th>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social Networking</th>\n",
       "      <td>9.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    event_id                                 \n",
       "popularity                   High popularity Low popularity Medium popularity\n",
       "topic_name                                                                   \n",
       "Art                                      1.0           18.0               2.0\n",
       "Business Referral Networking             1.0           17.0              52.0\n",
       "Fine Arts                                0.0            2.0               0.0\n",
       "Literature                               0.0           12.0               3.0\n",
       "New Technology                           7.0           18.0               9.0\n",
       "Performing Arts                          0.0            1.0               0.0\n",
       "Photography                             20.0            9.0               1.0\n",
       "Professional Networking                  3.0           14.0               4.0\n",
       "Social                                   1.0           15.0               1.0\n",
       "Social Networking                        9.0           78.0              40.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_popularity_summary.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Description insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import regex -> stackoverflow is your friend\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NOTE', 'This', 'event', 'is', 'EVERY', 'FRIDAY', 'Signup', 'is', 'a']\n"
     ]
    }
   ],
   "source": [
    "#Example\n",
    "strs = 'NOTE: This event is EVERY FRIDAY!! Signup is a'\n",
    "result = re.split(r'[^0-9A-Za-z]+',strs)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def count_words(s):\n",
    "    if s==s:\n",
    "        s_list = re.split(r'[^0-9A-Za-z]+',s)\n",
    "        return len(s_list)\n",
    "    else:\n",
    "        return 0\n",
    "events_df['words_count'] = events_df['description'].apply(count_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VjVt9bwAAgHW0HWfE3pPk0ZXHtyV5oLv3J3lgeZyquibJ4STXJrkpyZ1VddE2vD8AAMBa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\nDAAAYNj/A4auV68EmHYLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141f8da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram\n",
    "fig, ax = plt.subplots(figsize = (14.5,8.2))\n",
    "\n",
    "ax.hist(events_df['words_count'], 50, facecolor='green', alpha=0.75)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Venues insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>venue_id</th>\n",
       "      <th>address_1</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>distance</th>\n",
       "      <th>lat</th>\n",
       "      <th>localized_country_name</th>\n",
       "      <th>lon</th>\n",
       "      <th>venue_name</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>state</th>\n",
       "      <th>zip</th>\n",
       "      <th>normalised_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>107078</th>\n",
       "      <td>25594207</td>\n",
       "      <td>351 Oak Street, Chicago</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.900400</td>\n",
       "      <td>USA</td>\n",
       "      <td>-87.637780</td>\n",
       "      <td>Dastorkon</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107079</th>\n",
       "      <td>25594260</td>\n",
       "      <td>4321 South Cottage Grove Avenue</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.816143</td>\n",
       "      <td>USA</td>\n",
       "      <td>-87.606239</td>\n",
       "      <td>The Connection</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107080</th>\n",
       "      <td>25594276</td>\n",
       "      <td>45 west 46th street 5 floor</td>\n",
       "      <td>New York</td>\n",
       "      <td>us</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.756954</td>\n",
       "      <td>USA</td>\n",
       "      <td>-73.980720</td>\n",
       "      <td>Bench Marq Technologies / Affinity Trading</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NY</td>\n",
       "      <td>10036</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107081</th>\n",
       "      <td>25594369</td>\n",
       "      <td>480 Sutter Street</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.789402</td>\n",
       "      <td>USA</td>\n",
       "      <td>-122.408371</td>\n",
       "      <td>San Francisco Marriott Union Square</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107082</th>\n",
       "      <td>25594390</td>\n",
       "      <td>146 Essex Street</td>\n",
       "      <td>New York</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.720417</td>\n",
       "      <td>USA</td>\n",
       "      <td>-73.986885</td>\n",
       "      <td>Beauty &amp; Essex</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107083</th>\n",
       "      <td>25594408</td>\n",
       "      <td>1217 Sutter Street</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.787579</td>\n",
       "      <td>USA</td>\n",
       "      <td>-122.420532</td>\n",
       "      <td>Rusted Mule</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107084</th>\n",
       "      <td>25594524</td>\n",
       "      <td>401 7th Avenue</td>\n",
       "      <td>New York</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.749771</td>\n",
       "      <td>USA</td>\n",
       "      <td>-73.990623</td>\n",
       "      <td>Hotel Pennsylvania</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107085</th>\n",
       "      <td>25594556</td>\n",
       "      <td>625 Madison Avenue</td>\n",
       "      <td>New York</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.763302</td>\n",
       "      <td>USA</td>\n",
       "      <td>-73.971001</td>\n",
       "      <td>Juice Press</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107086</th>\n",
       "      <td>25594582</td>\n",
       "      <td>1314 Ocean Avenue</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.724239</td>\n",
       "      <td>USA</td>\n",
       "      <td>-122.456764</td>\n",
       "      <td>Ocean Ale House</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107087</th>\n",
       "      <td>25594628</td>\n",
       "      <td>625 Columbus Avenue</td>\n",
       "      <td>New York</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.789299</td>\n",
       "      <td>USA</td>\n",
       "      <td>-73.969772</td>\n",
       "      <td>Trattoria iL Gusto Wine Bar</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107088</th>\n",
       "      <td>25594677</td>\n",
       "      <td>6730 North Clark Street</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.004436</td>\n",
       "      <td>USA</td>\n",
       "      <td>-87.673256</td>\n",
       "      <td>Smack Dab</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107089</th>\n",
       "      <td>25594759</td>\n",
       "      <td>725 Park Avenue</td>\n",
       "      <td>New York</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.769840</td>\n",
       "      <td>USA</td>\n",
       "      <td>-73.964302</td>\n",
       "      <td>Asia Society and Museum</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107090</th>\n",
       "      <td>25594793</td>\n",
       "      <td>333 E 55th St, #11F</td>\n",
       "      <td>New York</td>\n",
       "      <td>us</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.757565</td>\n",
       "      <td>USA</td>\n",
       "      <td>-73.964592</td>\n",
       "      <td>Private</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NY</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107091</th>\n",
       "      <td>25594952</td>\n",
       "      <td>170 W. Ontario</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>us</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.893604</td>\n",
       "      <td>USA</td>\n",
       "      <td>-87.633331</td>\n",
       "      <td>Trophy Room</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>IL</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107092</th>\n",
       "      <td>25594988</td>\n",
       "      <td>Address</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>us</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.774929</td>\n",
       "      <td>USA</td>\n",
       "      <td>-122.419418</td>\n",
       "      <td>Ex. Joe's Pizzeria</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        venue_id                        address_1           city country  \\\n",
       "107078  25594207          351 Oak Street, Chicago        Chicago      US   \n",
       "107079  25594260  4321 South Cottage Grove Avenue        Chicago      US   \n",
       "107080  25594276      45 west 46th street 5 floor       New York      us   \n",
       "107081  25594369                480 Sutter Street  San Francisco      US   \n",
       "107082  25594390                 146 Essex Street       New York      US   \n",
       "107083  25594408               1217 Sutter Street  San Francisco      US   \n",
       "107084  25594524                   401 7th Avenue       New York      US   \n",
       "107085  25594556               625 Madison Avenue       New York      US   \n",
       "107086  25594582                1314 Ocean Avenue  San Francisco      US   \n",
       "107087  25594628              625 Columbus Avenue       New York      US   \n",
       "107088  25594677          6730 North Clark Street        Chicago      US   \n",
       "107089  25594759                  725 Park Avenue       New York      US   \n",
       "107090  25594793              333 E 55th St, #11F       New York      us   \n",
       "107091  25594952                   170 W. Ontario        Chicago      us   \n",
       "107092  25594988                          Address  San Francisco      us   \n",
       "\n",
       "        distance        lat localized_country_name         lon  \\\n",
       "107078       0.0  41.900400                    USA  -87.637780   \n",
       "107079       0.0  41.816143                    USA  -87.606239   \n",
       "107080       0.0  40.756954                    USA  -73.980720   \n",
       "107081       0.0  37.789402                    USA -122.408371   \n",
       "107082       0.0  40.720417                    USA  -73.986885   \n",
       "107083       0.0  37.787579                    USA -122.420532   \n",
       "107084       0.0  40.749771                    USA  -73.990623   \n",
       "107085       0.0  40.763302                    USA  -73.971001   \n",
       "107086       0.0  37.724239                    USA -122.456764   \n",
       "107087       0.0  40.789299                    USA  -73.969772   \n",
       "107088       0.0  42.004436                    USA  -87.673256   \n",
       "107089       0.0  40.769840                    USA  -73.964302   \n",
       "107090       0.0  40.757565                    USA  -73.964592   \n",
       "107091       0.0  41.893604                    USA  -87.633331   \n",
       "107092       0.0  37.774929                    USA -122.419418   \n",
       "\n",
       "                                        venue_name  rating  rating_count  \\\n",
       "107078                                   Dastorkon     0.0           0.0   \n",
       "107079                              The Connection     0.0           0.0   \n",
       "107080  Bench Marq Technologies / Affinity Trading     0.0           0.0   \n",
       "107081         San Francisco Marriott Union Square     0.0           0.0   \n",
       "107082                              Beauty & Essex     0.0           0.0   \n",
       "107083                                 Rusted Mule     0.0           0.0   \n",
       "107084                          Hotel Pennsylvania     0.0           0.0   \n",
       "107085                                 Juice Press     0.0           0.0   \n",
       "107086                             Ocean Ale House     0.0           0.0   \n",
       "107087                 Trattoria iL Gusto Wine Bar     0.0           0.0   \n",
       "107088                                   Smack Dab     0.0           0.0   \n",
       "107089                     Asia Society and Museum     0.0           0.0   \n",
       "107090                                     Private     0.0           0.0   \n",
       "107091                                 Trophy Room     0.0           0.0   \n",
       "107092                          Ex. Joe's Pizzeria     0.0           0.0   \n",
       "\n",
       "            state    zip  normalised_rating  \n",
       "107078  not_found     -1                0.0  \n",
       "107079  not_found     -1                0.0  \n",
       "107080         NY  10036                0.0  \n",
       "107081  not_found     -1                0.0  \n",
       "107082  not_found     -1                0.0  \n",
       "107083  not_found     -1                0.0  \n",
       "107084  not_found     -1                0.0  \n",
       "107085  not_found     -1                0.0  \n",
       "107086  not_found     -1                0.0  \n",
       "107087  not_found     -1                0.0  \n",
       "107088  not_found     -1                0.0  \n",
       "107089  not_found     -1                0.0  \n",
       "107090         NY     -1                0.0  \n",
       "107091         IL     -1                0.0  \n",
       "107092         CA      1                0.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues_df.tail(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Venues name normalizing\n",
    "def normalize_string(s):\n",
    "    s_new = str(s)\n",
    "    s_new = s_new.lower()\n",
    "    s_new = s_new.replace('ü', 'ue')\n",
    "    s_new = s_new.replace('ä', 'ae')\n",
    "    s_new = s_new.replace('ß', 'ss')\n",
    "    s_new = s_new.replace('ö', 'oe')\n",
    "    s_new = s_new.replace('ã', 'a')\n",
    "    s_new = re.sub('[^a-zA-Z0-9 \\n\\.]', '', s_new)\n",
    "    return s_new\n",
    "venues_df['venue_name_normalized']=venues_df['venue_name'].apply(normalize_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Rating of bar, restaurant, coffee, hotel, cinema\n",
    "def get_venue_type(s):\n",
    "    if 'hotel' in s:\n",
    "        return 'Hotel'\n",
    "    elif any( k in s for k in ['bar','pub']):\n",
    "        return 'Bar'\n",
    "    elif any( k in s for k in ['restaurant','pizzeria','sushi'] ):\n",
    "        return 'Restaurant'\n",
    "    else:\n",
    "        return 'Other'\n",
    "venues_df['venue_type']=venues_df['venue_name_normalized'].apply(get_venue_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>venue_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>venue_type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bar</th>\n",
       "      <td>6074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotel</th>\n",
       "      <td>1429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>97544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Restaurant</th>\n",
       "      <td>2046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            venue_id\n",
       "venue_type          \n",
       "Bar             6074\n",
       "Hotel           1429\n",
       "Other          97544\n",
       "Restaurant      2046"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues_df[['venue_id','venue_type']].groupby('venue_type').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def percentile(n):\n",
    "    def percentile_(x):\n",
    "        return np.percentile(x, n)\n",
    "    percentile_.__name__ = 'percentile_%s' % n\n",
    "    return percentile_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>venue_id</th>\n",
       "      <th colspan=\"4\" halign=\"left\">normalised_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>percentile_10</th>\n",
       "      <th>percentile_90</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>venue_type</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>Bar</th>\n",
       "      <td>3489</td>\n",
       "      <td>3.265067</td>\n",
       "      <td>3.40</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotel</th>\n",
       "      <td>806</td>\n",
       "      <td>3.344206</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.33</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>52460</td>\n",
       "      <td>3.314451</td>\n",
       "      <td>3.33</td>\n",
       "      <td>2.40</td>\n",
       "      <td>4.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Restaurant</th>\n",
       "      <td>1442</td>\n",
       "      <td>3.287670</td>\n",
       "      <td>3.40</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           venue_id normalised_rating                                   \n",
       "              count              mean median percentile_10 percentile_90\n",
       "venue_type                                                              \n",
       "Bar            3489          3.265067   3.40          2.00          4.23\n",
       "Hotel           806          3.344206   3.50          2.33          4.33\n",
       "Other         52460          3.314451   3.33          2.40          4.36\n",
       "Restaurant     1442          3.287670   3.40          2.00          4.22"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues_df[venues_df['rating_count']>0][['venue_id','venue_type','normalised_rating']].groupby('venue_type')\\\n",
    "            .agg({'venue_id':'count','normalised_rating':['mean','median',percentile(10),percentile(90)]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seasonality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ismailbenbrahim/anaconda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_id</th>\n",
       "      <th>created</th>\n",
       "      <th>description</th>\n",
       "      <th>duration</th>\n",
       "      <th>event_url</th>\n",
       "      <th>fee.accepts</th>\n",
       "      <th>fee.amount</th>\n",
       "      <th>fee.currency</th>\n",
       "      <th>fee.description</th>\n",
       "      <th>fee.label</th>\n",
       "      <th>...</th>\n",
       "      <th>visibility</th>\n",
       "      <th>waitlist_count</th>\n",
       "      <th>why</th>\n",
       "      <th>yes_rsvp_count</th>\n",
       "      <th>event_hour</th>\n",
       "      <th>event_hour_with_utcoffset</th>\n",
       "      <th>event_day</th>\n",
       "      <th>event_week</th>\n",
       "      <th>event_weekday</th>\n",
       "      <th>words_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>153868222</td>\n",
       "      <td>2013-12-03 21:24:29</td>\n",
       "      <td>Solve the murder case while eating a 3 course ...</td>\n",
       "      <td>20700</td>\n",
       "      <td>https://www.meetup.com/SanFranciscoStartupFun/...</td>\n",
       "      <td>others</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>per person</td>\n",
       "      <td>Price</td>\n",
       "      <td>...</td>\n",
       "      <td>public</td>\n",
       "      <td>0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>2017-10-30</td>\n",
       "      <td>2017-10-30</td>\n",
       "      <td>0</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>184167702</td>\n",
       "      <td>2014-05-20 18:52:00</td>\n",
       "      <td>NOTE: This event is EVERY FRIDAY!! Signup is a...</td>\n",
       "      <td>77400</td>\n",
       "      <td>https://www.meetup.com/hr-and-tech-sf/events/1...</td>\n",
       "      <td>others</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>per person</td>\n",
       "      <td>Price</td>\n",
       "      <td>...</td>\n",
       "      <td>public</td>\n",
       "      <td>0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>179</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>2017-10-28</td>\n",
       "      <td>2017-10-23</td>\n",
       "      <td>5</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>215200502</td>\n",
       "      <td>2014-10-23 16:18:44</td>\n",
       "      <td>We'll be heading out with a group of travelers...</td>\n",
       "      <td>37800</td>\n",
       "      <td>https://www.meetup.com/hr-and-tech-sf/events/2...</td>\n",
       "      <td>others</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>per person</td>\n",
       "      <td>Price</td>\n",
       "      <td>...</td>\n",
       "      <td>public</td>\n",
       "      <td>0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>2017-10-27</td>\n",
       "      <td>2017-10-23</td>\n",
       "      <td>4</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>220826782</td>\n",
       "      <td>2015-02-28 19:27:32</td>\n",
       "      <td>NOTE: This event is EVERY FRIDAY!! Signup is a...</td>\n",
       "      <td>113400</td>\n",
       "      <td>https://www.meetup.com/SanFranciscoStartupFun/...</td>\n",
       "      <td>others</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>per person</td>\n",
       "      <td>Price</td>\n",
       "      <td>...</td>\n",
       "      <td>public</td>\n",
       "      <td>0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>198</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>2017-10-28</td>\n",
       "      <td>2017-10-23</td>\n",
       "      <td>5</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>227948102</td>\n",
       "      <td>2016-01-08 21:35:40</td>\n",
       "      <td>Aussie Aussie Aussie, Oi Oi Oi! Join us for th...</td>\n",
       "      <td>36000</td>\n",
       "      <td>https://www.meetup.com/hr-and-tech-sf/events/2...</td>\n",
       "      <td>others</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>per person</td>\n",
       "      <td>Price</td>\n",
       "      <td>...</td>\n",
       "      <td>public</td>\n",
       "      <td>0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2018-01-26</td>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>4</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    event_id              created  \\\n",
       "0  153868222  2013-12-03 21:24:29   \n",
       "1  184167702  2014-05-20 18:52:00   \n",
       "2  215200502  2014-10-23 16:18:44   \n",
       "3  220826782  2015-02-28 19:27:32   \n",
       "4  227948102  2016-01-08 21:35:40   \n",
       "\n",
       "                                         description  duration  \\\n",
       "0  Solve the murder case while eating a 3 course ...     20700   \n",
       "1  NOTE: This event is EVERY FRIDAY!! Signup is a...     77400   \n",
       "2  We'll be heading out with a group of travelers...     37800   \n",
       "3  NOTE: This event is EVERY FRIDAY!! Signup is a...    113400   \n",
       "4  Aussie Aussie Aussie, Oi Oi Oi! Join us for th...     36000   \n",
       "\n",
       "                                           event_url fee.accepts  fee.amount  \\\n",
       "0  https://www.meetup.com/SanFranciscoStartupFun/...      others         0.0   \n",
       "1  https://www.meetup.com/hr-and-tech-sf/events/1...      others         0.0   \n",
       "2  https://www.meetup.com/hr-and-tech-sf/events/2...      others         0.0   \n",
       "3  https://www.meetup.com/SanFranciscoStartupFun/...      others         0.0   \n",
       "4  https://www.meetup.com/hr-and-tech-sf/events/2...      others         0.0   \n",
       "\n",
       "  fee.currency fee.description fee.label     ...       visibility  \\\n",
       "0    not_found      per person     Price     ...           public   \n",
       "1    not_found      per person     Price     ...           public   \n",
       "2    not_found      per person     Price     ...           public   \n",
       "3    not_found      per person     Price     ...           public   \n",
       "4    not_found      per person     Price     ...           public   \n",
       "\n",
       "  waitlist_count        why  yes_rsvp_count  event_hour  \\\n",
       "0              0  not_found              72           2   \n",
       "1              0  not_found             179           4   \n",
       "2              0  not_found              45           3   \n",
       "3              0  not_found             198           4   \n",
       "4              0  not_found              24           1   \n",
       "\n",
       "  event_hour_with_utcoffset  event_day event_week event_weekday  words_count  \n",
       "0                        19 2017-10-30 2017-10-30             0          149  \n",
       "1                        21 2017-10-28 2017-10-23             5          350  \n",
       "2                        20 2017-10-27 2017-10-23             4          351  \n",
       "3                        21 2017-10-28 2017-10-23             5          350  \n",
       "4                        17 2018-01-26 2018-01-22             4          324  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Events per day\n",
    "events_per_day = events_df[['event_day','event_id']].groupby('event_day').count()\n",
    "events_per_day.rename(columns={'event_id':'count_event_day'},inplace=True)\n",
    "events_per_day.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Seasonality calculation\n",
    "res = sm.tsa.seasonal_decompose(events_per_day['count_event_day'].values,freq=7,model='multiplicative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IsBN4AKgKfCwi+e48SERai0isiMQmJiY6aGilMu+Ph4qdPHmScePGcePGDWbN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\nF9JyV0opL6TlrpRSXkjLXSmlvND/Bx3h7D+4y85PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bce1f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "plt.plot(res.seasonal[:7],color = 'black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_id</th>\n",
       "      <th>created</th>\n",
       "      <th>description</th>\n",
       "      <th>duration</th>\n",
       "      <th>event_url</th>\n",
       "      <th>fee.accepts</th>\n",
       "      <th>fee.amount</th>\n",
       "      <th>fee.currency</th>\n",
       "      <th>fee.description</th>\n",
       "      <th>fee.label</th>\n",
       "      <th>...</th>\n",
       "      <th>visibility</th>\n",
       "      <th>waitlist_count</th>\n",
       "      <th>why</th>\n",
       "      <th>yes_rsvp_count</th>\n",
       "      <th>event_hour</th>\n",
       "      <th>event_hour_with_utcoffset</th>\n",
       "      <th>event_day</th>\n",
       "      <th>event_week</th>\n",
       "      <th>event_weekday</th>\n",
       "      <th>words_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>244370856</td>\n",
       "      <td>2017-10-20 08:34:17</td>\n",
       "      <td>https://youtu.be/4ox00JX04BY SMX EAST NYC STAR...</td>\n",
       "      <td>207900</td>\n",
       "      <td>https://www.meetup.com/buildTechNY/events/2443...</td>\n",
       "      <td>others</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>per person</td>\n",
       "      <td>Price</td>\n",
       "      <td>...</td>\n",
       "      <td>public</td>\n",
       "      <td>0</td>\n",
       "      <td>not_found</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>2017-10-24</td>\n",
       "      <td>2017-10-23</td>\n",
       "      <td>1</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      event_id              created  \\\n",
       "435  244370856  2017-10-20 08:34:17   \n",
       "\n",
       "                                           description  duration  \\\n",
       "435  https://youtu.be/4ox00JX04BY SMX EAST NYC STAR...    207900   \n",
       "\n",
       "                                             event_url fee.accepts  \\\n",
       "435  https://www.meetup.com/buildTechNY/events/2443...      others   \n",
       "\n",
       "     fee.amount fee.currency fee.description fee.label     ...       \\\n",
       "435         0.0    not_found      per person     Price     ...        \n",
       "\n",
       "     visibility waitlist_count        why  yes_rsvp_count  event_hour  \\\n",
       "435      public              0  not_found               1          11   \n",
       "\n",
       "    event_hour_with_utcoffset  event_day event_week event_weekday  words_count  \n",
       "435                         7 2017-10-24 2017-10-23             1          276  \n",
       "\n",
       "[1 rows x 54 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_df[events_df['event_day']==events_df['event_day'].min()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Plx47d+7kzjvvpHLlyqxcuTJgL41sl/xNg/Pnz9O4cWOOHj3K+++/b8Vu/FJQ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\nICt3Y4wJQP8Pmbbg6jStbNEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bd15cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "plt.plot(res.seasonal[6:13],color = 'black')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}