Analyzing the data of users who shorten links ending with .gov or .mil. .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysing the data of  users who shorten links ending with .gov or .mil. \n",
    "\n",
    "In this data analysis project, I followed the steps outlined in chapter 14 of the [Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662)\n",
    "book.\n",
    "\n",
    "` Data sources`\n",
    "---\n",
    "\n",
    "In 2011, URL shortening service Bitly partnered with the US government website\n",
    "USA.gov to provide a feed of anonymous data gathered from users who shorten links ending with .gov or .mil. This is one of the preserved text files from that activity. \n",
    "\n",
    "The file contains web data called JSON "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as pyplot\n",
    "import seaborn as sns\n",
    "import sklearn as sk\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{ \"a\": \"Mozilla\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\/535.11 (KHTML, like Gecko) Chrome\\/17.0.963.78 Safari\\/535.11\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\/New_York\", \"gr\": \"MA\", \"g\": \"A6qOVH\", \"h\": \"wfLQtf\", \"l\": \"orofrog\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\/\\/www.facebook.com\\/l\\/7AQEFzjSi\\/1.usa.gov\\/wfLQtf\", \"u\": \"http:\\/\\/www.ncbi.nlm.nih.gov\\/pubmed\\/22415991\", \"t\": 1331923247, \"hc\": 1331822918, \"cy\": \"Danvers\", \"ll\": [ 42.576698, -70.954903 ] }\n",
      "{ \"a\": \"GoogleMaps\\/RochesterNY\", \"c\": \"US\", \"nk\": 0, \"tz\": \"America\\/Denver\", \"gr\": \"UT\", \"g\": \"mwszkS\", \"h\": \"mwszkS\", \"l\": \"bitly\", \"hh\": \"j.mp\", \"r\": \"http:\\/\\/www.AwareMap.com\\/\", \"u\": \"http:\\/\\/www.monroecounty.gov\\/etc\\/911\\/rss.php\", \"t\": 1331923249, \"hc\": 1308262393, \"cy\": \"Provo\", \"ll\": [ 40.218102, -111.613297 ] }\n",
      "{ \"a\": \"Mozilla\\/4.0 (compatible; MSIE 8.0; Windows NT 6.1; WOW64; Trident\\/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E; InfoPath.3)\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\/New_York\", \"gr\": \"DC\", \"g\": \"xxr3Qb\", \"h\": \"xxr3Qb\", \"l\": \"bitly\", \"al\": \"en-US\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\/\\/t.co\\/03elZC4Q\", \"u\": \"http:\\/\\/boxer.senate.gov\\/en\\/press\\/releases\\/031612.cfm\", \"t\": 1331923250, \"hc\": 1331919941, \"cy\": \"Washington\", \"ll\": [ 38.900700, -77.043098 ] }\n",
      "{ \"a\": \"Mozilla\\/5.0 (Macintosh; Intel Mac OS X 10_6_8) AppleWebKit\\/534.52.7 (KHTML, like Gecko) Version\\/5.1.2 Safari\\/534.52.7\", \"c\": \"BR\", \"nk\": 0, \"tz\": \"America\\/Sao_Paulo\", \"gr\": \"27\", \"g\": \"zCaLwp\", \"h\": \"zUtuOu\", \"l\": \"alelex88\", \"al\": \"pt-br\", \"hh\": \"1.usa.gov\", \"r\": \"direct\", \"u\": \"http:\\/\\/apod.nasa.gov\\/apod\\/ap120312.html\", \"t\": 1331923249, \"hc\": 1331923068, \"cy\": \"Braz\", \"ll\": [ -23.549999, -46.616699 ] }\n",
      "{ \"a\": \"Mozilla\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\/535.11 (KHTML, like Gecko) Chrome\\/17.0.963.79 Safari\\/535.11\", \"c\": \"US\", \"nk\": 0, \"tz\": \"America\\/New_York\", \"gr\": \"MA\", \"g\": \"9b6kNl\", \"h\": \"9b6kNl\", \"l\": \"bitly\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"bit.ly\", \"r\": \"http:\\/\\/www.shrewsbury-ma.gov\\/selco\\/\", \"u\": \"http:\\/\\/www.shrewsbury-ma.gov\\/egov\\/gallery\\/134127368672998.png\", \"t\": 1331923251, \"hc\": 1273672411, \"cy\": \"Shrewsbury\", \"ll\": [ 42.286499, -71.714699 ] }\n",
      "{ \"a\": \"Mozilla\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\/535.11 (KHTML, like Gecko) Chrome\\/17.0.963.79 Safari\\/535.11\", \"c\": \"US\", \"nk\": 0, \"tz\": \"America\\/New_York\", \"gr\": \"MA\", \"g\": \"axNK8c\", \"h\": \"axNK8c\", \"l\": \"bitly\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"bit.ly\", \"r\": \"http:\\/\\/www.shrewsbury-ma.gov\\/selco\\/\", \"u\": \"http:\\/\\/www.shrewsbury-ma.gov\\/egov\\/gallery\\/134127368675393.png\", \"t\": 1331923252, \"hc\": 1273672506, \"cy\": \"Shrewsbury\", \"ll\": [ 42.286499, -71.714699 ] }\n",
      "{ \"a\": \"Mozilla\\/5.0 (Windows NT 5.1) AppleWebKit\\/535.11 (KHTML, like Gecko) Chrome\\/17.0.963.79 Safari\\/535.11\", \"c\": \"PL\", \"nk\": 0, \"tz\": \"Europe\\/Warsaw\", \"gr\": \"77\", \"g\": \"wcndER\", \"h\": \"zkpJBR\", \"l\": \"bnjacobs\", \"al\": \"pl-PL,pl;q=0.8,en-US;q=0.6,en;q=0.4\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\/\\/plus.url.google.com\\/url?sa=z&n=1331923253510&url=http%3A%2F%2F1.usa.gov%2FzkpJBR&usg=TF5iGSqK1_GSADtHqTqjuUZyMIY.\", \"u\": \"http:\\/\\/www.nasa.gov\\/mission_pages\\/nustar\\/main\\/index.html\", \"t\": 1331923255, \"hc\": 1331922854, \"cy\": \"Luban\", \"ll\": [ 51.116699, 15.283300 ] }\n",
      "{ \"a\": \"Mozilla\\/5.0 (Windows NT 6.1; rv:2.0.1) Gecko\\/20100101 Firefox\\/4.0.1\", \"c\": null, \"nk\": 0, \"tz\": \"\", \"g\": \"wcndER\", \"h\": \"zkpJBR\", \"l\": \"bnjacobs\", \"al\": \"bg,en-us;q=0.7,en;q=0.3\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\/\\/www.facebook.com\\/\", \"u\": \"http:\\/\\/www.nasa.gov\\/mission_pages\\/nustar\\/main\\/index.html\", \"t\": 1331923255, \"hc\": 1331922854 }\n",
      "{ \"a\": \"Opera\\/9.80 (X11; Linux zbov; U; en) Presto\\/2.10.254 Version\\/12.00\", \"c\": null, \"nk\": 0, \"tz\": \"\", \"g\": \"wcndER\", \"h\": \"zkpJBR\", \"l\": \"bnjacobs\", \"al\": \"en-US, en\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\/\\/www.facebook.com\\/l.php?u=http%3A%2F%2F1.usa.gov%2FzkpJBR&h=fAQG5ntSGAQHqKPlWzuJKUA9LYeckHZCUxvjQipJDd7Rmmw\", \"u\": \"http:\\/\\/www.nasa.gov\\/mission_pages\\/nustar\\/main\\/index.html\", \"t\": 1331923254, \"hc\": 1331922854 }\n",
      "{ \"a\": \"Mozilla\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\/535.11 (KHTML, like Gecko) Chrome\\/17.0.963.79 Safari\\/535.11\", \"c\": null, \"nk\": 0, \"tz\": \"\", \"g\": \"zCaLwp\", \"h\": \"zUtuOu\", \"l\": \"alelex88\", \"al\": \"pt-BR,pt;q=0.8,en-US;q=0.6,en;q=0.4\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\/\\/t.co\\/o1Pd0WeV\", \"u\": \"http:\\/\\/apod.nasa.gov\\/apod\\/ap120312.html\", \"t\": 1331923255, \"hc\": 1331923068 }\n",
      "{ \"a\": \"Mozilla\\/5.0 (Windows NT 6.1; WOW64; rv:10.0.2) Gecko\\/20100101 Firefox\\/10.0.2\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\/Los_Angeles\", \"gr\": \"WA\", \"g\": \"vNJS4H\", \"h\": \"u0uD9q\", \"l\": \"o_4us71ccioa\", \"al\": \"en-us,en;q=0.5\", \"hh\": \"1.usa.gov\", \"r\": \"direct\", \"u\": \"https:\\/\\/www.nysdot.gov\\/rexdesign\\/design\\/community.gif\", \"t\": 1331923258, \"hc\": 1319563556, \"cy\": \"Seattle\", \"ll\": [ 47.595100, -122.332603 ] }\n",
      "{ \"a\": \"Mozilla\\/5.0 (Macintosh; U; Intel Mac OS X 10.4; en-US; rv:1.9.2.27) Gecko\\/20120216 Firefox\\/3.6.27\", \"c\": \"US\", \"nk\": 0, \"tz\": \"America\\/New_York\", \"gr\": \"DC\", \"g\": \"wG7OIH\", \"h\": \"A0nRz4\", \"l\": \"darrellissa\", \"al\": \"en-us,en;q=0.5\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\/\\/t.co\\/ND7SoPyo\", \"u\": \"http:\\/\\/oversight.house.gov\\/wp-content\\/uploads\\/2012\\/03\\/2012-3-15-FOIA-Management-Scorecard.pdf\", \"t\": 1331923259, \"hc\": 1331815838, \"cy\": \"Washington\", \"ll\": [ 38.937599, -77.092796 ] }\n",
      "{ \"a\": \"Mozilla\\/5.0 (Windows NT 6.1; WOW64; rv:10.0.2) Gecko\\/20100101 Firefox\\/10.0.2\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\/New_York\", \"gr\": \"VA\", \"g\": \"vNJS4H\", \"h\": \"u0uD9q\", \"l\": \"o_4us71ccioa\", \"al\": \"en-us,en;q=0.5\", \"hh\": \"1.usa.gov\", \"r\": \"direct\", \"u\": \"https:\\/\\/www.nysdot.gov\\/rexdesign\\/design\\/community.gif\", \"t\": 1331923259, \"hc\": 1319563556, \"cy\": \"Alexandria\", \"ll\": [ 38.790901, -77.094704 ] }\n",
      "{\"_heartbeat_\":1331923261}\n",
      "{ \"a\": \"Mozilla\\/5.0 (Windows; U; Windows NT 6.1; en-US; rv:1.9.2.17) Gecko\\/20110420 Firefox\\/3.6.17\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\/New_York\", \"gr\": \"GA\", \"g\": \"2rOUYc\", \"h\": \"2rOUYc\", \"l\": \"bitly\", \"al\": \"en-us,en;q=0.5\", \"hh\": \"1.usa.gov\", \"r\": \"direct\", \"u\": \"http:\\/\\/toxtown.nlm.nih.gov\\/index.php\", \"t\": 1331923262, \"hc\": 1255769846, \"cy\": \"Marietta\", \"ll\": [ 33.953201, -84.517700 ] }\n",
      "{ \"a\": \"Mozilla\\/5.0 (Windows NT 6.1) AppleWebKit\\/535.11 (KHTML, like Gecko) Chrome\\/17.0.963.79 Safari\\/535.11\", \"c\": \"HK\", \"nk\": 1, \"tz\": \"Asia\\/Hong_Kong\", \"gr\": \"00\", \"g\": \"nQvgJp\", \"h\": \"rtrrth\", \"l\": \"walkeryuen\", \"al\": \"zh-TW,zh;q=0.8,en-US;q=0.6,en;q=0.4\", \"hh\": \"j.mp\", \"r\": \"http:\\/\\/forum2.hkgolden.com\\/view.aspx?type=BW&message=3602284\", \"u\": \"http:\\/\\/www.ssd.noaa.gov\\/PS\\/TROP\\/TCFP\\/data\\/current\\/mtloopfpr17.png\", \"t\": 1331923263, \"hc\": 1317318030, \"cy\": \"Central District\", \"ll\": [ 22.283300, 114.150002 ] }\n",
      "{ \"a\": \"Mozilla\\/5.0 (Windows NT 6.1) AppleWebKit\\/535.11 (KHTML, like Gecko) Chrome\\/17.0.963.79 Safari\\/535.11\", \"c\": \"HK\", \"nk\": 1, \"tz\": \"Asia\\/Hong_Kong\", \"gr\": \"00\", \"g\": \"XdUNr\", \"h\": \"qWkgbq\", \"l\": \"walkeryuen\", \"al\": \"zh-TW,zh;q=0.8,en-US;q=0.6,en;q=0.4\", \"hh\": \"j.mp\", \"r\": \"http:\\/\\/forum2.hkgolden.com\\/view.aspx?type=BW&message=3602284\", \"u\": \"http:\\/\\/www.usno.navy.mil\\/NOOC\\/nmfc-ph\\/RSS\\/jtwc\\/ab\\/abpwsair.jpg\", \"t\": 1331923263, \"hc\": 1317318039, \"cy\": \"Central District\", \"ll\": [ 22.283300, 114.150002 ] }\n",
      "{ \"a\": \"Mozilla\\/5.0 (Macintosh; Intel Mac OS X 10.5; rv:10.0.2) Gecko\\/20100101 Firefox\\/10.0.2\", \"c\": \"US\", \"nk\": 0, \"tz\": \"America\\/New_York\", \"gr\": \"ME\", \"g\": \"zH1BFf\", \"h\": \"x3jOIv\", \"l\": \"andyzieminski\", \"al\": \"en-us,en;q=0.5\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\/\\/t.co\\/6Cx4ROLs\", \"u\": \"http:\\/\\/www.usda.gov\\/wps\\/portal\\/usda\\/usdahome?contentidonly=true&contentid=KYF_Compass_Careers_in_Agriculture.html\", \"t\": 1331923264, \"hc\": 1331839576, \"cy\": \"Buckfield\", \"ll\": [ 44.299702, -70.369797 ] }"
     ]
    }
   ],
   "source": [
    "!type \"D:\\MyLib.Doc\\Programming.Doc\\Python.allstuffs\\Python for DataAnalysis\\example.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"D:\\MyLib.Doc\\Programming.Doc\\Python.allstuffs\\Python for DataAnalysis\\example.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{ \"a\": \"Mozilla\\\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\\\/535.11 (KHTML, like Gecko) Chrome\\\\/17.0.963.78 Safari\\\\/535.11\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\\\/New_York\", \"gr\": \"MA\", \"g\": \"A6qOVH\", \"h\": \"wfLQtf\", \"l\": \"orofrog\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\\\/\\\\/www.facebook.com\\\\/l\\\\/7AQEFzjSi\\\\/1.usa.gov\\\\/wfLQtf\", \"u\": \"http:\\\\/\\\\/www.ncbi.nlm.nih.gov\\\\/pubmed\\\\/22415991\", \"t\": 1331923247, \"hc\": 1331822918, \"cy\": \"Danvers\", \"ll\": [ 42.576698, -70.954903 ] }\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the first line of the file\n",
    "\n",
    "open(path).readline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using python's third party libarary for converting JSON string to python dictionary object.**\n",
    "\n",
    "In the code below, we import the library to do the convertion. Then, for every line in the JSON format data file we convert it to python dictionary. Finally, we print the first record."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.78 Safari/535.11',\n",
       " 'c': 'US',\n",
       " 'nk': 1,\n",
       " 'tz': 'America/New_York',\n",
       " 'gr': 'MA',\n",
       " 'g': 'A6qOVH',\n",
       " 'h': 'wfLQtf',\n",
       " 'l': 'orofrog',\n",
       " 'al': 'en-US,en;q=0.8',\n",
       " 'hh': '1.usa.gov',\n",
       " 'r': 'http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/wfLQtf',\n",
       " 'u': 'http://www.ncbi.nlm.nih.gov/pubmed/22415991',\n",
       " 't': 1331923247,\n",
       " 'hc': 1331822918,\n",
       " 'cy': 'Danvers',\n",
       " 'll': [42.576698, -70.954903]}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "records = [json.loads(line) for line in open(path)]\n",
    "records[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**Finding out the most frequent occuring time zones in the dataset**\n",
    "\n",
    "we extract a list of time zones from very record that has a time zone field.  (the if condition handles the error that arises as some records lack a time zone fielD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['America/New_York',\n",
       " 'America/Denver',\n",
       " 'America/New_York',\n",
       " 'America/Sao_Paulo',\n",
       " 'America/New_York',\n",
       " 'America/New_York',\n",
       " 'Europe/Warsaw',\n",
       " '',\n",
       " '',\n",
       " '']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_zones = [rec['tz'] for rec in records if 'tz' in rec]\n",
    "time_zones[:10] #Accessing the first 10 time zones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#the loop format of the list comprehension above.\n",
    "timezones = []\n",
    "for rec in records:\n",
    "    if \"tz\" in rec:\n",
    "        timezones.append(rec['tz'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['America/New_York',\n",
       " 'America/Denver',\n",
       " 'America/New_York',\n",
       " 'America/Sao_Paulo',\n",
       " 'America/New_York',\n",
       " 'America/New_York',\n",
       " 'Europe/Warsaw',\n",
       " '',\n",
       " '',\n",
       " '']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timezones[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3440"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#there exists 3440 time zone records \n",
    "len(time_zones)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "**This function will be used to produce counts by timezones.**\n",
    "there exist two approaches to do the count\n",
    "> Using a dictionary to store counts while we iterate through the timezones.\n",
    "initialize an empty dictionary. For any record in the sequence, if its in the counts add one.\n",
    "\n",
    "> Using python library defaultdict.\n",
    "\n",
    "\n",
    "using a function allows for reusability.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_count(sequence):\n",
    "    counts = {}\n",
    "    for x in sequence:\n",
    "        if x in counts:\n",
    "            counts[x] +=1\n",
    "        else:\n",
    "            counts[x] = 1\n",
    "        return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def get_count(sequence):\n",
    "    counts = defaultdict(int)\n",
    "    for x in sequence:\n",
    "        counts[x] += 1\n",
    "    return counts\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the count of time zones and store them in a variable we call counts.\n",
    "counts = get_count(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1251"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extracting how many records have New York timezone\n",
    "counts['America/New_York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(int,\n",
       "            {'America/New_York': 1251,\n",
       "             'America/Denver': 191,\n",
       "             'America/Sao_Paulo': 33,\n",
       "             'Europe/Warsaw': 16,\n",
       "             '': 521,\n",
       "             'America/Los_Angeles': 382,\n",
       "             'Asia/Hong_Kong': 10,\n",
       "             'Europe/Rome': 27,\n",
       "             'Africa/Ceuta': 2,\n",
       "             'Europe/Madrid': 35,\n",
       "             'Asia/Kuala_Lumpur': 3,\n",
       "             'Asia/Nicosia': 1,\n",
       "             'Europe/London': 74,\n",
       "             'Pacific/Honolulu': 36,\n",
       "             'America/Chicago': 400,\n",
       "             'Europe/Malta': 2,\n",
       "             'Europe/Lisbon': 8,\n",
       "             'Europe/Paris': 14,\n",
       "             'Europe/Copenhagen': 5,\n",
       "             'America/Mazatlan': 1,\n",
       "             'Europe/Dublin': 3,\n",
       "             'Europe/Brussels': 4,\n",
       "             'America/Vancouver': 12,\n",
       "             'Europe/Amsterdam': 22,\n",
       "             'Europe/Prague': 10,\n",
       "             'Europe/Stockholm': 14,\n",
       "             'America/Anchorage': 5,\n",
       "             'Asia/Bangkok': 6,\n",
       "             'Europe/Berlin': 28,\n",
       "             'America/Rainy_River': 25,\n",
       "             'Europe/Budapest': 5,\n",
       "             'Asia/Tokyo': 37,\n",
       "             'Europe/Vienna': 6,\n",
       "             'America/Phoenix': 20,\n",
       "             'Asia/Jerusalem': 3,\n",
       "             'Asia/Karachi': 3,\n",
       "             'America/Bogota': 3,\n",
       "             'America/Indianapolis': 20,\n",
       "             'America/Montreal': 9,\n",
       "             'Asia/Calcutta': 9,\n",
       "             'Europe/Skopje': 1,\n",
       "             'Asia/Beirut': 4,\n",
       "             'Australia/NSW': 6,\n",
       "             'Chile/Continental': 6,\n",
       "             'America/Halifax': 4,\n",
       "             'America/Edmonton': 6,\n",
       "             'Europe/Bratislava': 3,\n",
       "             'America/Recife': 2,\n",
       "             'Africa/Cairo': 3,\n",
       "             'Asia/Istanbul': 9,\n",
       "             'Asia/Novosibirsk': 1,\n",
       "             'Europe/Moscow': 10,\n",
       "             'Europe/Sofia': 1,\n",
       "             'Europe/Ljubljana': 1,\n",
       "             'America/Mexico_City': 15,\n",
       "             'Europe/Helsinki': 10,\n",
       "             'Europe/Bucharest': 4,\n",
       "             'Europe/Zurich': 4,\n",
       "             'America/Puerto_Rico': 10,\n",
       "             'America/Monterrey': 1,\n",
       "             'Europe/Athens': 6,\n",
       "             'America/Winnipeg': 4,\n",
       "             'Europe/Riga': 2,\n",
       "             'America/Argentina/Buenos_Aires': 1,\n",
       "             'Asia/Dubai': 4,\n",
       "             'Europe/Oslo': 10,\n",
       "             'Asia/Yekaterinburg': 1,\n",
       "             'Asia/Manila': 1,\n",
       "             'America/Caracas': 1,\n",
       "             'Asia/Riyadh': 1,\n",
       "             'America/Montevideo': 1,\n",
       "             'America/Argentina/Mendoza': 1,\n",
       "             'Asia/Seoul': 5,\n",
       "             'Europe/Uzhgorod': 1,\n",
       "             'Australia/Queensland': 1,\n",
       "             'Europe/Belgrade': 2,\n",
       "             'America/Costa_Rica': 1,\n",
       "             'America/Lima': 1,\n",
       "             'Asia/Pontianak': 1,\n",
       "             'America/Chihuahua': 2,\n",
       "             'Europe/Vilnius': 2,\n",
       "             'America/Managua': 3,\n",
       "             'Africa/Lusaka': 1,\n",
       "             'America/Guayaquil': 2,\n",
       "             'Asia/Harbin': 3,\n",
       "             'Asia/Amman': 2,\n",
       "             'Africa/Johannesburg': 1,\n",
       "             'America/St_Kitts': 1,\n",
       "             'Pacific/Auckland': 11,\n",
       "             'America/Santo_Domingo': 1,\n",
       "             'America/Argentina/Cordoba': 1,\n",
       "             'Asia/Kuching': 1,\n",
       "             'Europe/Volgograd': 1,\n",
       "             'America/La_Paz': 1,\n",
       "             'Africa/Casablanca': 1,\n",
       "             'Asia/Jakarta': 3,\n",
       "             'America/Tegucigalpa': 1})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#every timezone and the number of times it occurs.\n",
    "counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To get the top five timezones, we can use the function below.**\n",
    "\n",
    "For every timezone and count in the dictionary, append them as a tuple in a list. Then sort them in ascending order and finally return the last five values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_count(count_dict, n=5):\n",
    "    value_key_pairs = [(count, tz) for tz,count in count_dict.items()]\n",
    "    value_key_pairs.sort()\n",
    "    return value_key_pairs[-n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(191, 'America/Denver'),\n",
       " (382, 'America/Los_Angeles'),\n",
       " (400, 'America/Chicago'),\n",
       " (521, ''),\n",
       " (1251, 'America/New_York')]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_count(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using python's more advanced library**\n",
    "\n",
    "Using Counter class we can do the timezone count much easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using python standard library to find collections\n",
    "\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = Counter(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('America/New_York', 1251),\n",
       " ('', 521),\n",
       " ('America/Chicago', 400),\n",
       " ('America/Los_Angeles', 382),\n",
       " ('America/Denver', 191)]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.most_common(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Counting the time zones with pandas**\n",
    "\n",
    "We create a dataframe for the records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a dataframe from the original records\n",
    "frame =  pd.DataFrame(records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>c</th>\n",
       "      <th>nk</th>\n",
       "      <th>tz</th>\n",
       "      <th>gr</th>\n",
       "      <th>g</th>\n",
       "      <th>h</th>\n",
       "      <th>l</th>\n",
       "      <th>al</th>\n",
       "      <th>hh</th>\n",
       "      <th>r</th>\n",
       "      <th>u</th>\n",
       "      <th>t</th>\n",
       "      <th>hc</th>\n",
       "      <th>cy</th>\n",
       "      <th>ll</th>\n",
       "      <th>_heartbeat_</th>\n",
       "      <th>kw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>US</td>\n",
       "      <td>1.0</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>MA</td>\n",
       "      <td>A6qOVH</td>\n",
       "      <td>wfLQtf</td>\n",
       "      <td>orofrog</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...</td>\n",
       "      <td>http://www.ncbi.nlm.nih.gov/pubmed/22415991</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.331823e+09</td>\n",
       "      <td>Danvers</td>\n",
       "      <td>[42.576698, -70.954903]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GoogleMaps/RochesterNY</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>UT</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>bitly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>http://www.AwareMap.com/</td>\n",
       "      <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.308262e+09</td>\n",
       "      <td>Provo</td>\n",
       "      <td>[40.218102, -111.613297]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>US</td>\n",
       "      <td>1.0</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>DC</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>bitly</td>\n",
       "      <td>en-US</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>http://t.co/03elZC4Q</td>\n",
       "      <td>http://boxer.senate.gov/en/press/releases/0316...</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.331920e+09</td>\n",
       "      <td>Washington</td>\n",
       "      <td>[38.9007, -77.043098]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n",
       "      <td>BR</td>\n",
       "      <td>0.0</td>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>27</td>\n",
       "      <td>zCaLwp</td>\n",
       "      <td>zUtuOu</td>\n",
       "      <td>alelex88</td>\n",
       "      <td>pt-br</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>direct</td>\n",
       "      <td>http://apod.nasa.gov/apod/ap120312.html</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>Braz</td>\n",
       "      <td>[-23.549999, -46.616699]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>MA</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>bitly</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/selco/</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.273672e+09</td>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>[42.286499, -71.714699]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   a   c   nk  \\\n",
       "0  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...  US  1.0   \n",
       "1                             GoogleMaps/RochesterNY  US  0.0   \n",
       "2  Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...  US  1.0   \n",
       "3  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...  BR  0.0   \n",
       "4  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...  US  0.0   \n",
       "\n",
       "                  tz  gr       g       h         l              al         hh  \\\n",
       "0   America/New_York  MA  A6qOVH  wfLQtf   orofrog  en-US,en;q=0.8  1.usa.gov   \n",
       "1     America/Denver  UT  mwszkS  mwszkS     bitly             NaN       j.mp   \n",
       "2   America/New_York  DC  xxr3Qb  xxr3Qb     bitly           en-US  1.usa.gov   \n",
       "3  America/Sao_Paulo  27  zCaLwp  zUtuOu  alelex88           pt-br  1.usa.gov   \n",
       "4   America/New_York  MA  9b6kNl  9b6kNl     bitly  en-US,en;q=0.8     bit.ly   \n",
       "\n",
       "                                                   r  \\\n",
       "0  http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...   \n",
       "1                           http://www.AwareMap.com/   \n",
       "2                               http://t.co/03elZC4Q   \n",
       "3                                             direct   \n",
       "4                http://www.shrewsbury-ma.gov/selco/   \n",
       "\n",
       "                                                   u             t  \\\n",
       "0        http://www.ncbi.nlm.nih.gov/pubmed/22415991  1.331923e+09   \n",
       "1        http://www.monroecounty.gov/etc/911/rss.php  1.331923e+09   \n",
       "2  http://boxer.senate.gov/en/press/releases/0316...  1.331923e+09   \n",
       "3            http://apod.nasa.gov/apod/ap120312.html  1.331923e+09   \n",
       "4  http://www.shrewsbury-ma.gov/egov/gallery/1341...  1.331923e+09   \n",
       "\n",
       "             hc          cy                        ll  _heartbeat_   kw  \n",
       "0  1.331823e+09     Danvers   [42.576698, -70.954903]          NaN  NaN  \n",
       "1  1.308262e+09       Provo  [40.218102, -111.613297]          NaN  NaN  \n",
       "2  1.331920e+09  Washington     [38.9007, -77.043098]          NaN  NaN  \n",
       "3  1.331923e+09        Braz  [-23.549999, -46.616699]          NaN  NaN  \n",
       "4  1.273672e+09  Shrewsbury   [42.286499, -71.714699]          NaN  NaN  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3560 entries, 0 to 3559\n",
      "Data columns (total 18 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   a            3440 non-null   object \n",
      " 1   c            2919 non-null   object \n",
      " 2   nk           3440 non-null   float64\n",
      " 3   tz           3440 non-null   object \n",
      " 4   gr           2919 non-null   object \n",
      " 5   g            3440 non-null   object \n",
      " 6   h            3440 non-null   object \n",
      " 7   l            3440 non-null   object \n",
      " 8   al           3094 non-null   object \n",
      " 9   hh           3440 non-null   object \n",
      " 10  r            3440 non-null   object \n",
      " 11  u            3440 non-null   object \n",
      " 12  t            3440 non-null   float64\n",
      " 13  hc           3440 non-null   float64\n",
      " 14  cy           2919 non-null   object \n",
      " 15  ll           2919 non-null   object \n",
      " 16  _heartbeat_  120 non-null    float64\n",
      " 17  kw           93 non-null     object \n",
      "dtypes: float64(4), object(14)\n",
      "memory usage: 500.8+ KB\n"
     ]
    }
   ],
   "source": [
    "frame.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3550       America/New_York\n",
       "3551                       \n",
       "3552        America/Chicago\n",
       "3553       America/New_York\n",
       "3554       America/New_York\n",
       "3555       America/New_York\n",
       "3556        America/Chicago\n",
       "3557         America/Denver\n",
       "3558    America/Los_Angeles\n",
       "3559       America/New_York\n",
       "Name: tz, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the last 10 time zones\n",
    "frame['tz'][-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     America/New_York\n",
       "1       America/Denver\n",
       "2     America/New_York\n",
       "3    America/Sao_Paulo\n",
       "4     America/New_York\n",
       "Name: tz, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the top five time zones\n",
    "frame['tz'][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts = frame['tz'].value_counts()\n",
    "type(tz_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "America/Caracas          1\n",
       "Asia/Kuching             1\n",
       "Europe/Skopje            1\n",
       "Asia/Pontianak           1\n",
       "Asia/Novosibirsk         1\n",
       "America/Santo_Domingo    1\n",
       "America/La_Paz           1\n",
       "America/Montevideo       1\n",
       "America/Costa_Rica       1\n",
       "America/Monterrey        1\n",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extracting the last 10 values\n",
    "tz_counts[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "America/New_York       1251\n",
       "                        521\n",
       "America/Chicago         400\n",
       "America/Los_Angeles     382\n",
       "America/Denver          191\n",
       "Europe/London            74\n",
       "Asia/Tokyo               37\n",
       "Pacific/Honolulu         36\n",
       "Europe/Madrid            35\n",
       "America/Sao_Paulo        33\n",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extract the top 10 values\n",
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization\n",
    " \n",
    " Using matplotlib library\n",
    " \n",
    " \n",
    "\n",
    " We clean the timezone by substituting unknown value and missing time zone data in the records.\n",
    " using boolean array indexing for empty strings.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_tz = frame['tz'].fillna('missing')\n",
    "type(clean_tz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3560"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(clean_tz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3440"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_tz[clean_tz == ' '] = 'unknown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts = clean_tz.value_counts()\n",
    "type(tz_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "America/New_York       1251\n",
       "                        521\n",
       "America/Chicago         400\n",
       "America/Los_Angeles     382\n",
       "America/Denver          191\n",
       "missing                 120\n",
       "Europe/London            74\n",
       "Asia/Tokyo               37\n",
       "Pacific/Honolulu         36\n",
       "Europe/Madrid            35\n",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using seaborn package to make a horizontal bar plot**\n",
    "\n",
    "Extract the top 10 timezone and create a barplot for that data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "America/New_York       1251\n",
       "                        521\n",
       "America/Chicago         400\n",
       "America/Los_Angeles     382\n",
       "America/Denver          191\n",
       "missing                 120\n",
       "Europe/London            74\n",
       "Asia/Tokyo               37\n",
       "Pacific/Honolulu         36\n",
       "Europe/Madrid            35\n",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset = tz_counts[:10]\n",
    "subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y=subset.index, x=subset.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**using the information about the browser, device or application to perform URL shortening**\n",
    "\n",
    "We use the data frame that contain all the records\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/20100101 Firefox/10.0.2'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][50]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mozilla/5.0 (Linux; U; Android 2.2.2; en-us; LG-P9'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][51][:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3560"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(frame.a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3440"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(frame.a.dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Split the first token in the string and make another summary of the user behavior.**\n",
    "\n",
    "we create a pandas series that contains the browser capability.\n",
    "The first token of the string corresponds to the browser capability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "results = pd.Series([x.split()[0] for x in frame.a.dropna()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               Mozilla/5.0\n",
       "1    GoogleMaps/RochesterNY\n",
       "2               Mozilla/4.0\n",
       "3               Mozilla/5.0\n",
       "4               Mozilla/5.0\n",
       "dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mozilla/5.0                 2594\n",
       "Mozilla/4.0                  601\n",
       "GoogleMaps/RochesterNY       121\n",
       "Opera/9.80                    34\n",
       "TEST_INTERNET_AGENT           24\n",
       "GoogleProducer                21\n",
       "Mozilla/6.0                    5\n",
       "BlackBerry8520/5.0.0.681       4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the top 8 browsers \n",
    "#according to their frequency of appearance in the dataframe\n",
    "\n",
    "results.value_counts()[:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Decomposing the top time zones into windows and non-windows users.**\n",
    "\n",
    "We can do this by removing the data points missing agents('a') string information. Then we use where function in numpy to categorize this data into windows and non-windows users\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>c</th>\n",
       "      <th>nk</th>\n",
       "      <th>tz</th>\n",
       "      <th>gr</th>\n",
       "      <th>g</th>\n",
       "      <th>h</th>\n",
       "      <th>l</th>\n",
       "      <th>al</th>\n",
       "      <th>hh</th>\n",
       "      <th>r</th>\n",
       "      <th>u</th>\n",
       "      <th>t</th>\n",
       "      <th>hc</th>\n",
       "      <th>cy</th>\n",
       "      <th>ll</th>\n",
       "      <th>_heartbeat_</th>\n",
       "      <th>kw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>US</td>\n",
       "      <td>1.0</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>MA</td>\n",
       "      <td>A6qOVH</td>\n",
       "      <td>wfLQtf</td>\n",
       "      <td>orofrog</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...</td>\n",
       "      <td>http://www.ncbi.nlm.nih.gov/pubmed/22415991</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.331823e+09</td>\n",
       "      <td>Danvers</td>\n",
       "      <td>[42.576698, -70.954903]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GoogleMaps/RochesterNY</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>UT</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>bitly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>http://www.AwareMap.com/</td>\n",
       "      <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.308262e+09</td>\n",
       "      <td>Provo</td>\n",
       "      <td>[40.218102, -111.613297]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>US</td>\n",
       "      <td>1.0</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>DC</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>bitly</td>\n",
       "      <td>en-US</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>http://t.co/03elZC4Q</td>\n",
       "      <td>http://boxer.senate.gov/en/press/releases/0316...</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.331920e+09</td>\n",
       "      <td>Washington</td>\n",
       "      <td>[38.9007, -77.043098]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n",
       "      <td>BR</td>\n",
       "      <td>0.0</td>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>27</td>\n",
       "      <td>zCaLwp</td>\n",
       "      <td>zUtuOu</td>\n",
       "      <td>alelex88</td>\n",
       "      <td>pt-br</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>direct</td>\n",
       "      <td>http://apod.nasa.gov/apod/ap120312.html</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>Braz</td>\n",
       "      <td>[-23.549999, -46.616699]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>US</td>\n",
       "      <td>0.0</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>MA</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>bitly</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/selco/</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n",
       "      <td>1.331923e+09</td>\n",
       "      <td>1.273672e+09</td>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>[42.286499, -71.714699]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   a   c   nk  \\\n",
       "0  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...  US  1.0   \n",
       "1                             GoogleMaps/RochesterNY  US  0.0   \n",
       "2  Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...  US  1.0   \n",
       "3  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...  BR  0.0   \n",
       "4  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...  US  0.0   \n",
       "\n",
       "                  tz  gr       g       h         l              al         hh  \\\n",
       "0   America/New_York  MA  A6qOVH  wfLQtf   orofrog  en-US,en;q=0.8  1.usa.gov   \n",
       "1     America/Denver  UT  mwszkS  mwszkS     bitly             NaN       j.mp   \n",
       "2   America/New_York  DC  xxr3Qb  xxr3Qb     bitly           en-US  1.usa.gov   \n",
       "3  America/Sao_Paulo  27  zCaLwp  zUtuOu  alelex88           pt-br  1.usa.gov   \n",
       "4   America/New_York  MA  9b6kNl  9b6kNl     bitly  en-US,en;q=0.8     bit.ly   \n",
       "\n",
       "                                                   r  \\\n",
       "0  http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...   \n",
       "1                           http://www.AwareMap.com/   \n",
       "2                               http://t.co/03elZC4Q   \n",
       "3                                             direct   \n",
       "4                http://www.shrewsbury-ma.gov/selco/   \n",
       "\n",
       "                                                   u             t  \\\n",
       "0        http://www.ncbi.nlm.nih.gov/pubmed/22415991  1.331923e+09   \n",
       "1        http://www.monroecounty.gov/etc/911/rss.php  1.331923e+09   \n",
       "2  http://boxer.senate.gov/en/press/releases/0316...  1.331923e+09   \n",
       "3            http://apod.nasa.gov/apod/ap120312.html  1.331923e+09   \n",
       "4  http://www.shrewsbury-ma.gov/egov/gallery/1341...  1.331923e+09   \n",
       "\n",
       "             hc          cy                        ll  _heartbeat_   kw  \n",
       "0  1.331823e+09     Danvers   [42.576698, -70.954903]          NaN  NaN  \n",
       "1  1.308262e+09       Provo  [40.218102, -111.613297]          NaN  NaN  \n",
       "2  1.331920e+09  Washington     [38.9007, -77.043098]          NaN  NaN  \n",
       "3  1.331923e+09        Braz  [-23.549999, -46.616699]          NaN  NaN  \n",
       "4  1.273672e+09  Shrewsbury   [42.286499, -71.714699]          NaN  NaN  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cframe =frame[frame.a.notnull()]\n",
    "cframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "cframe.index.name='col_list'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-80-c0ab94f5417d>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cframe['os'] = np.where(cframe['a'].str.contains('Windows'),\n"
     ]
    }
   ],
   "source": [
    "#compute whether each row is windows or not\n",
    "cframe['os'] = np.where(cframe['a'].str.contains('Windows'),\n",
    "                       'Windows','Not Windows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Windows\n",
       "1    Not Windows\n",
       "2        Windows\n",
       "3    Not Windows\n",
       "4        Windows\n",
       "Name: os, dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cframe['os'][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Grouping the data by time zone column and os**\n",
    "\n",
    "We create a variable that contains data grouped by timezone and operting sytem in use. We then use the size function to count the group and the unstack function ti reshape the data into tables finally will fill in the empty values with 0.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000019739EEF5B0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_tz_os = cframe.groupby(['tz','os'])\n",
    "by_tz_os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_count = by_tz_os.size().unstack().fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>os</th>\n",
       "      <th>Not Windows</th>\n",
       "      <th>Windows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Cairo</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Casablanca</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Ceuta</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Johannesburg</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Lusaka</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Anchorage</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Buenos_Aires</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Cordoba</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Mendoza</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Bogota</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Caracas</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chicago</th>\n",
       "      <td>115.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chihuahua</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Costa_Rica</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Denver</th>\n",
       "      <td>132.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Edmonton</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Guayaquil</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Halifax</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Indianapolis</th>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/La_Paz</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Lima</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Los_Angeles</th>\n",
       "      <td>130.0</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Managua</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Mazatlan</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Mexico_City</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Monterrey</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Montevideo</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Montreal</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/New_York</th>\n",
       "      <td>339.0</td>\n",
       "      <td>912.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Phoenix</th>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Puerto_Rico</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Rainy_River</th>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Recife</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Santo_Domingo</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Sao_Paulo</th>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/St_Kitts</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Tegucigalpa</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Vancouver</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Winnipeg</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Amman</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Bangkok</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Beirut</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Calcutta</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Dubai</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Harbin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Hong_Kong</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Istanbul</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Jakarta</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Jerusalem</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "os                              Not Windows  Windows\n",
       "tz                                                  \n",
       "                                      245.0    276.0\n",
       "Africa/Cairo                            0.0      3.0\n",
       "Africa/Casablanca                       0.0      1.0\n",
       "Africa/Ceuta                            0.0      2.0\n",
       "Africa/Johannesburg                     0.0      1.0\n",
       "Africa/Lusaka                           0.0      1.0\n",
       "America/Anchorage                       4.0      1.0\n",
       "America/Argentina/Buenos_Aires          1.0      0.0\n",
       "America/Argentina/Cordoba               0.0      1.0\n",
       "America/Argentina/Mendoza               0.0      1.0\n",
       "America/Bogota                          1.0      2.0\n",
       "America/Caracas                         0.0      1.0\n",
       "America/Chicago                       115.0    285.0\n",
       "America/Chihuahua                       1.0      1.0\n",
       "America/Costa_Rica                      0.0      1.0\n",
       "America/Denver                        132.0     59.0\n",
       "America/Edmonton                        2.0      4.0\n",
       "America/Guayaquil                       2.0      0.0\n",
       "America/Halifax                         1.0      3.0\n",
       "America/Indianapolis                    8.0     12.0\n",
       "America/La_Paz                          0.0      1.0\n",
       "America/Lima                            0.0      1.0\n",
       "America/Los_Angeles                   130.0    252.0\n",
       "America/Managua                         0.0      3.0\n",
       "America/Mazatlan                        1.0      0.0\n",
       "America/Mexico_City                     7.0      8.0\n",
       "America/Monterrey                       1.0      0.0\n",
       "America/Montevideo                      0.0      1.0\n",
       "America/Montreal                        3.0      6.0\n",
       "America/New_York                      339.0    912.0\n",
       "America/Phoenix                         3.0     17.0\n",
       "America/Puerto_Rico                     9.0      1.0\n",
       "America/Rainy_River                    10.0     15.0\n",
       "America/Recife                          0.0      2.0\n",
       "America/Santo_Domingo                   1.0      0.0\n",
       "America/Sao_Paulo                      13.0     20.0\n",
       "America/St_Kitts                        1.0      0.0\n",
       "America/Tegucigalpa                     0.0      1.0\n",
       "America/Vancouver                       6.0      6.0\n",
       "America/Winnipeg                        3.0      1.0\n",
       "Asia/Amman                              0.0      2.0\n",
       "Asia/Bangkok                            0.0      6.0\n",
       "Asia/Beirut                             1.0      3.0\n",
       "Asia/Calcutta                           2.0      7.0\n",
       "Asia/Dubai                              2.0      2.0\n",
       "Asia/Harbin                             0.0      3.0\n",
       "Asia/Hong_Kong                          4.0      6.0\n",
       "Asia/Istanbul                           0.0      9.0\n",
       "Asia/Jakarta                            2.0      1.0\n",
       "Asia/Jerusalem                          2.0      1.0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_count[:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Finally**\n",
    "\n",
    "We select the top overrall timezones.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "                                  24\n",
       "Africa/Cairo                      20\n",
       "Africa/Casablanca                 21\n",
       "Africa/Ceuta                      92\n",
       "Africa/Johannesburg               87\n",
       "Africa/Lusaka                     53\n",
       "America/Anchorage                 54\n",
       "America/Argentina/Buenos_Aires    57\n",
       "America/Argentina/Cordoba         26\n",
       "America/Argentina/Mendoza         55\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexer = agg_count.sum(1).argsort()\n",
    "indexer[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>os</th>\n",
       "      <th>Not Windows</th>\n",
       "      <th>Windows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>America/Sao_Paulo</th>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/Madrid</th>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific/Honolulu</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Tokyo</th>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/London</th>\n",
       "      <td>43.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Denver</th>\n",
       "      <td>132.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Los_Angeles</th>\n",
       "      <td>130.0</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chicago</th>\n",
       "      <td>115.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/New_York</th>\n",
       "      <td>339.0</td>\n",
       "      <td>912.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "os                   Not Windows  Windows\n",
       "tz                                       \n",
       "America/Sao_Paulo           13.0     20.0\n",
       "Europe/Madrid               16.0     19.0\n",
       "Pacific/Honolulu             0.0     36.0\n",
       "Asia/Tokyo                   2.0     35.0\n",
       "Europe/London               43.0     31.0\n",
       "America/Denver             132.0     59.0\n",
       "America/Los_Angeles        130.0    252.0\n",
       "America/Chicago            115.0    285.0\n",
       "                           245.0    276.0\n",
       "America/New_York           339.0    912.0"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#take function will slice off the rows from the data.\n",
    "count_subset = agg_count.take(indexer[-10:])\n",
    "count_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "America/New_York       1251.0\n",
       "                        521.0\n",
       "America/Chicago         400.0\n",
       "America/Los_Angeles     382.0\n",
       "America/Denver          191.0\n",
       "Europe/London            74.0\n",
       "Asia/Tokyo               37.0\n",
       "Pacific/Honolulu         36.0\n",
       "Europe/Madrid            35.0\n",
       "America/Sao_Paulo        33.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#nlargest method will sort the data from largest to smallest.\n",
    "agg_count.sum(1).nlargest(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**plot a bar graph for these data**\n",
    "\n",
    "We rearranged the data for plotting using the stack function. Then we create a new column called total. we reindex the data and finally print the top 10 rows.\n",
    "\n",
    "With that done, we use seaborn to plot a bargraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "count_subset = count_subset.stack() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_subset.name = 'total'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_subset = count_subset.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tz</th>\n",
       "      <th>os</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>Windows</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>Windows</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pacific/Honolulu</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pacific/Honolulu</td>\n",
       "      <td>Windows</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Windows</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Europe/London</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Europe/London</td>\n",
       "      <td>Windows</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  tz           os  total\n",
       "0  America/Sao_Paulo  Not Windows   13.0\n",
       "1  America/Sao_Paulo      Windows   20.0\n",
       "2      Europe/Madrid  Not Windows   16.0\n",
       "3      Europe/Madrid      Windows   19.0\n",
       "4   Pacific/Honolulu  Not Windows    0.0\n",
       "5   Pacific/Honolulu      Windows   36.0\n",
       "6         Asia/Tokyo  Not Windows    2.0\n",
       "7         Asia/Tokyo      Windows   35.0\n",
       "8      Europe/London  Not Windows   43.0\n",
       "9      Europe/London      Windows   31.0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_subset[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='total', ylabel='tz'>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='total', y='tz', hue='os', data=count_subset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To see the relative percentage of windows** users in the smaller groups is not possible with the plot above,\n",
    "\n",
    "We normalize the group pecentages to a sum to 1\n",
    "\n",
    "we create a function, that creates a new column - normed total that computes the average then return the group(the passed dataframe).\n",
    "The average helps us to plot a plot that contains percentages of even the smallest values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalized_total(group):\n",
    "    group['normed_total'] = group.total/ group.total.sum()\n",
    "    return group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = count_subset.groupby('tz').apply(normalized_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tz</th>\n",
       "      <th>os</th>\n",
       "      <th>total</th>\n",
       "      <th>normed_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.393939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>Windows</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.606061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.457143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>Windows</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.542857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pacific/Honolulu</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pacific/Honolulu</td>\n",
       "      <td>Windows</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.054054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Windows</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.945946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Europe/London</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.581081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Europe/London</td>\n",
       "      <td>Windows</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.418919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>America/Denver</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.691099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>America/Denver</td>\n",
       "      <td>Windows</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.308901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.340314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>Windows</td>\n",
       "      <td>252.0</td>\n",
       "      <td>0.659686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.287500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>Windows</td>\n",
       "      <td>285.0</td>\n",
       "      <td>0.712500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td></td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>245.0</td>\n",
       "      <td>0.470250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td></td>\n",
       "      <td>Windows</td>\n",
       "      <td>276.0</td>\n",
       "      <td>0.529750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>America/New_York</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>339.0</td>\n",
       "      <td>0.270983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>America/New_York</td>\n",
       "      <td>Windows</td>\n",
       "      <td>912.0</td>\n",
       "      <td>0.729017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     tz           os  total  normed_total\n",
       "0     America/Sao_Paulo  Not Windows   13.0      0.393939\n",
       "1     America/Sao_Paulo      Windows   20.0      0.606061\n",
       "2         Europe/Madrid  Not Windows   16.0      0.457143\n",
       "3         Europe/Madrid      Windows   19.0      0.542857\n",
       "4      Pacific/Honolulu  Not Windows    0.0      0.000000\n",
       "5      Pacific/Honolulu      Windows   36.0      1.000000\n",
       "6            Asia/Tokyo  Not Windows    2.0      0.054054\n",
       "7            Asia/Tokyo      Windows   35.0      0.945946\n",
       "8         Europe/London  Not Windows   43.0      0.581081\n",
       "9         Europe/London      Windows   31.0      0.418919\n",
       "10       America/Denver  Not Windows  132.0      0.691099\n",
       "11       America/Denver      Windows   59.0      0.308901\n",
       "12  America/Los_Angeles  Not Windows  130.0      0.340314\n",
       "13  America/Los_Angeles      Windows  252.0      0.659686\n",
       "14      America/Chicago  Not Windows  115.0      0.287500\n",
       "15      America/Chicago      Windows  285.0      0.712500\n",
       "16                       Not Windows  245.0      0.470250\n",
       "17                           Windows  276.0      0.529750\n",
       "18     America/New_York  Not Windows  339.0      0.270983\n",
       "19     America/New_York      Windows  912.0      0.729017"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='normed_total', ylabel='tz'>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='normed_total', y='tz', hue = 'os', data=results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}