GAB_AI_DATA_ANALYSIS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## imported simple json over json as it is faster\n",
    "import simplejson as json\n",
    "import pandas as pd\n",
    "import timeit\n",
    "import sys\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as plticker\n",
    "import matplotlib.dates as mdates\n",
    "from collections import Counter\n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from datetime import timezone,tzinfo\n",
    "from dateutil.parser import parse\n",
    "\n",
    "import pytz\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = [20,10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_file=\"gabai.0013.json\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Reading line by line as:\n",
    "* As the file has erros  \n",
    "* only valid json objects need to be added ot the list, \n",
    "* The file is large so it saves memmory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49999\n",
      "99999\n",
      "149999\n",
      "199999\n",
      "249999\n",
      "299999\n",
      "349999\n",
      "399999\n",
      "449999\n",
      "499999\n",
      "549999\n",
      "599999\n",
      "649999\n",
      "699999\n",
      "749999\n",
      "799999\n",
      "849999\n",
      "Error at line number 872991\n",
      "Error at line number 872992\n",
      "Error at line number 872993\n",
      "Error at line number 872994\n",
      "Error at line number 872995\n",
      "Error at line number 872996\n",
      "Error at line number 872997\n",
      "Error at line number 872998\n",
      "Error at line number 872999\n",
      "Error at line number 873000\n",
      "Error at line number 873001\n",
      "Error at line number 873002\n",
      "Error at line number 873003\n",
      "Error at line number 873004\n",
      "Error at line number 873005\n",
      "Error at line number 873006\n",
      "Error at line number 873007\n",
      "Error at line number 873008\n",
      "Error at line number 873009\n",
      "Error at line number 873010\n",
      "Error at line number 873011\n",
      "Error at line number 873012\n",
      "Error at line number 873013\n",
      "Error at line number 873014\n",
      "Error at line number 873015\n",
      "Error at line number 873016\n",
      "Error at line number 873017\n",
      "Error at line number 873018\n",
      "Error at line number 873019\n",
      "Error at line number 873020\n",
      "Error at line number 873021\n",
      "Error at line number 873022\n",
      "Error at line number 873023\n",
      "Error at line number 873024\n",
      "Error at line number 873025\n",
      "Error at line number 873026\n",
      "Error at line number 873027\n",
      "Error at line number 873028\n",
      "Error at line number 873029\n",
      "Error at line number 873030\n",
      "899999\n",
      "949999\n",
      "999999\n",
      "CPU times: user 1min 23s, sys: 2min 8s, total: 3min 31s\n",
      "Wall time: 4min 24s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ls=[]\n",
    "with open(input_file) as f:\n",
    "    for i,line in enumerate(f):\n",
    "        try:\n",
    "            c_line=json.loads(line)\n",
    "            ls.append(c_line)\n",
    "            if((i+1)%50000==0):\n",
    "                print(i)\n",
    "        except:\n",
    "            print(\"Error at line number\",i)       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Inspecting Lines where error occured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<br />\n",
      "\n",
      "<b>Warning</b>:  require(/home/forge/gab.ai/bootstrap/../vendor/autoload.php): failed to open stream: No such file or directory in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "<br />\n",
      "\n",
      "<b>Fatal error</b>:  require(): Failed opening required '/home/forge/gab.ai/bootstrap/../vendor/autoload.php' (include_path='.:/usr/share/php') in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "\n",
      "\n",
      "<br />\n",
      "\n",
      "<b>Warning</b>:  require(/home/forge/gab.ai/bootstrap/../vendor/autoload.php): failed to open stream: No such file or directory in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "<br />\n",
      "\n",
      "<b>Fatal error</b>:  require(): Failed opening required '/home/forge/gab.ai/bootstrap/../vendor/autoload.php' (include_path='.:/usr/share/php') in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "\n",
      "\n",
      "<br />\n",
      "\n",
      "<b>Warning</b>:  require(/home/forge/gab.ai/bootstrap/../vendor/autoload.php): failed to open stream: No such file or directory in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "<br />\n",
      "\n",
      "<b>Fatal error</b>:  require(): Failed opening required '/home/forge/gab.ai/bootstrap/../vendor/autoload.php' (include_path='.:/usr/share/php') in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "\n",
      "\n",
      "<br />\n",
      "\n",
      "<b>Warning</b>:  require(/home/forge/gab.ai/bootstrap/../vendor/autoload.php): failed to open stream: No such file or directory in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "<br />\n",
      "\n",
      "<b>Fatal error</b>:  require(): Failed opening required '/home/forge/gab.ai/bootstrap/../vendor/autoload.php' (include_path='.:/usr/share/php') in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "\n",
      "\n",
      "<br />\n",
      "\n",
      "<b>Warning</b>:  require(/home/forge/gab.ai/bootstrap/../vendor/autoload.php): failed to open stream: No such file or directory in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "<br />\n",
      "\n",
      "<b>Fatal error</b>:  require(): Failed opening required '/home/forge/gab.ai/bootstrap/../vendor/autoload.php' (include_path='.:/usr/share/php') in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "\n",
      "\n",
      "<br />\n",
      "\n",
      "<b>Warning</b>:  require(/home/forge/gab.ai/bootstrap/../vendor/autoload.php): failed to open stream: No such file or directory in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "<br />\n",
      "\n",
      "<b>Fatal error</b>:  require(): Failed opening required '/home/forge/gab.ai/bootstrap/../vendor/autoload.php' (include_path='.:/usr/share/php') in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "\n",
      "\n",
      "<br />\n",
      "\n",
      "<b>Warning</b>:  require(/home/forge/gab.ai/bootstrap/../vendor/autoload.php): failed to open stream: No such file or directory in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "<br />\n",
      "\n",
      "<b>Fatal error</b>:  require(): Failed opening required '/home/forge/gab.ai/bootstrap/../vendor/autoload.php' (include_path='.:/usr/share/php') in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "\n",
      "\n",
      "<br />\n",
      "\n",
      "<b>Warning</b>:  require(/home/forge/gab.ai/bootstrap/../vendor/autoload.php): failed to open stream: No such file or directory in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "<br />\n",
      "\n",
      "<b>Fatal error</b>:  require(): Failed opening required '/home/forge/gab.ai/bootstrap/../vendor/autoload.php' (include_path='.:/usr/share/php') in <b>/home/forge/gab.ai/bootstrap/autoload.php</b> on line <b>17</b><br />\n",
      "\n",
      "CPU times: user 3.69 s, sys: 4.71 s, total: 8.4 s\n",
      "Wall time: 10.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with open(input_file) as f:\n",
    "    lines = f.readlines()\n",
    "for i in range(872991,873030):\n",
    "    print(lines[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Line Analysis\n",
    "* Now, we have confirmed the fact that we have not missed any useful  message \n",
    "* Also tried  changing the range at random for more sanity checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16.8 s, sys: 37.8 s, total: 54.7 s\n",
      "Wall time: 1min 15s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "## loading the list into a dataframe now.\n",
    "df=pd.DataFrame(ls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  How many messages/gabs are in this dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of messages is 999992\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of messages is\", len(df.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 189400,\n",
       " 'is_donor': False,\n",
       " 'is_investor': False,\n",
       " 'is_premium': False,\n",
       " 'is_private': False,\n",
       " 'is_pro': False,\n",
       " 'name': 'HenPre13',\n",
       " 'picture_url': 'https://files.gab.ai/user/59392bee3268b.jpg',\n",
       " 'username': 'Zwitscher',\n",
       " 'verified': False}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Explpring the user, we need to create a  new user column with the information of the curtrent dict.\n",
    "df.loc[1].user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.93 s, sys: 25.6 s, total: 35.6 s\n",
      "Wall time: 47.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "##making a new data frame where we have expanded the user column and preffixxed the column name with user_\n",
    "user_df = pd.concat([df.drop('user', axis=1), pd.DataFrame(df['user'].tolist()).add_prefix('user_')], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many users authored messages are in this dataset?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of users is 15973\n"
     ]
    }
   ],
   "source": [
    "## number of users\n",
    "print(\"Number of users is\", len(user_df['user_id'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  What are the mean/median/max/min number of messages authored by a user?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " User  Message Stats:\n",
      " Mean: 62.60514618418581 \n",
      " Median: 4.0 \n",
      " Max: 13799 \n",
      " Min: 1\n"
     ]
    }
   ],
   "source": [
    "### mean/median/max/min number of messages authored by a user\n",
    "grouped_user_df = user_df.groupby(['user_id']).user_id.count()\n",
    "print(\" User  Message Stats:\\n Mean: {} \\n Median: {} \\n Max: {} \\n Min: {}\".format( \\\n",
    "      grouped_user_df.mean(), grouped_user_df.median(), \\\n",
    "     grouped_user_df.max() , grouped_user_df.min())) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List the 10 users with the most messages, and the number of messages each.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name\t\t\tUser_ID\tCount\n",
      "\n",
      "Constitutional Drunk     64690\t13799\n",
      "EyeAm                    69650\t5996\n",
      "Ron MacDonald            100406\t5471\n",
      "#NSFW LeeLee             102726\t4956\n",
      "Jay                      169891\t4842\n",
      "Tipol J News             167852\t4816\n",
      "OpenQuotes 👌🐸            98146\t4678\n",
      "Dani                     105504\t4594\n",
      "bobofkake-Bro:)          29492\t4404\n",
      "Lea Morabito             77796\t4230\n",
      "CPU times: user 16.6 ms, sys: 4.98 ms, total: 21.6 ms\n",
      "Wall time: 15.1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# top 10 users with messages\n",
    "top10user_df_series = grouped_user_df.nlargest(10)\n",
    "#search user id in user_df, \n",
    "print(\"Name\\t\\t\\tUser_ID\\tCount\\n\")\n",
    "for x,y in top10user_df_series.iteritems():\n",
    "    ### can be optimezed look into it\n",
    "    c_index = (user_df.user_id.values == x).argmax()\n",
    "    c_user_name = user_df.loc[c_index].user_name\n",
    "    c_user_id=user_df.loc[c_index].user_id\n",
    "    print('{:<25}{}\\t{}'.format(c_user_name,c_user_id,y))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## URL MINING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "the web url matching regex used by markdown\n",
    "http://daringfireball.net/2010/07/improved_regex_for_matching_urls\n",
    "https://gist.github.com/gruber/8891611\n",
    "\"\"\"\n",
    "URL_REGEX = r\"\"\"(?i)\\b((?:https?:(?:/{1,3}|[a-z0-9%])|[a-z0-9.\\-]+[.](?:com|net|org|edu|gov|mil|aero|asia|biz|cat|coop|info|int|jobs|mobi|museum|name|post|pro|tel|travel|xxx|ac|ad|ae|af|ag|ai|al|am|an|ao|aq|ar|as|at|au|aw|ax|az|ba|bb|bd|be|bf|bg|bh|bi|bj|bm|bn|bo|br|bs|bt|bv|bw|by|bz|ca|cc|cd|cf|cg|ch|ci|ck|cl|cm|cn|co|cr|cs|cu|cv|cx|cy|cz|dd|de|dj|dk|dm|do|dz|ec|ee|eg|eh|er|es|et|eu|fi|fj|fk|fm|fo|fr|ga|gb|gd|ge|gf|gg|gh|gi|gl|gm|gn|gp|gq|gr|gs|gt|gu|gw|gy|hk|hm|hn|hr|ht|hu|id|ie|il|im|in|io|iq|ir|is|it|je|jm|jo|jp|ke|kg|kh|ki|km|kn|kp|kr|kw|ky|kz|la|lb|lc|li|lk|lr|ls|lt|lu|lv|ly|ma|mc|md|me|mg|mh|mk|ml|mm|mn|mo|mp|mq|mr|ms|mt|mu|mv|mw|mx|my|mz|na|nc|ne|nf|ng|ni|nl|no|np|nr|nu|nz|om|pa|pe|pf|pg|ph|pk|pl|pm|pn|pr|ps|pt|pw|py|qa|re|ro|rs|ru|rw|sa|sb|sc|sd|se|sg|sh|si|sj|Ja|sk|sl|sm|sn|so|sr|ss|st|su|sv|sx|sy|sz|tc|td|tf|tg|th|tj|tk|tl|tm|tn|to|tp|tr|tt|tv|tw|tz|ua|ug|uk|us|uy|uz|va|vc|ve|vg|vi|vn|vu|wf|ws|ye|yt|yu|za|zm|zw)/)(?:[^\\s()<>{}\\[\\]]+|\\([^\\s()]*?\\([^\\s()]+\\)[^\\s()]*?\\)|\\([^\\s]+?\\))+(?:\\([^\\s()]*?\\([^\\s()]+\\)[^\\s()]*?\\)|\\([^\\s]+?\\)|[^\\s`!()\\[\\]{};:'\".,<>?«»“”‘’])|(?:(?<!@)[a-z0-9]+(?:[.\\-][a-z0-9]+)*[.](?:com|net|org|edu|gov|mil|aero|asia|biz|cat|coop|info|int|jobs|mobi|museum|name|post|pro|tel|travel|xxx|ac|ad|ae|af|ag|ai|al|am|an|ao|aq|ar|as|at|au|aw|ax|az|ba|bb|bd|be|bf|bg|bh|bi|bj|bm|bn|bo|br|bs|bt|bv|bw|by|bz|ca|cc|cd|cf|cg|ch|ci|ck|cl|cm|cn|co|cr|cs|cu|cv|cx|cy|cz|dd|de|dj|dk|dm|do|dz|ec|ee|eg|eh|er|es|et|eu|fi|fj|fk|fm|fo|fr|ga|gb|gd|ge|gf|gg|gh|gi|gl|gm|gn|gp|gq|gr|gs|gt|gu|gw|gy|hk|hm|hn|hr|ht|hu|id|ie|il|im|in|io|iq|ir|is|it|je|jm|jo|jp|ke|kg|kh|ki|km|kn|kp|kr|kw|ky|kz|la|lb|lc|li|lk|lr|ls|lt|lu|lv|ly|ma|mc|md|me|mg|mh|mk|ml|mm|mn|mo|mp|mq|mr|ms|mt|mu|mv|mw|mx|my|mz|na|nc|ne|nf|ng|ni|nl|no|np|nr|nu|nz|om|pa|pe|pf|pg|ph|pk|pl|pm|pn|pr|ps|pt|pw|py|qa|re|ro|rs|ru|rw|sa|sb|sc|sd|se|sg|sh|si|sj|Ja|sk|sl|sm|sn|so|sr|ss|st|su|sv|sx|sy|sz|tc|td|tf|tg|th|tj|tk|tl|tm|tn|to|tp|tr|tt|tv|tw|tz|ua|ug|uk|us|uy|uz|va|vc|ve|vg|vi|vn|vu|wf|ws|ye|yt|yu|za|zm|zw)\\b/?(?!@)))\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 40 s, sys: 1.46 s, total: 41.5 s\n",
      "Wall time: 43.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "message_list= user_df['body'].tolist()\n",
    "urls=[]\n",
    "for message in message_list:\n",
    "    a=re.findall(URL_REGEX,message.lower())\n",
    "    urls.extend(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 most common urls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "URL  Count\n",
      "gab.ai 880\n",
      "mewe.com/join/lovehaswon 874\n",
      "https://www.abgeordneten-check.de/kampagnen/stoppt-die-digitale-zensur/startseite/aktion/368383z13871/nc/1/ 441\n",
      "military.com 384\n",
      "kek.gg 323\n"
     ]
    }
   ],
   "source": [
    "url_counter=Counter(urls)\n",
    "top_5_urls = url_counter.most_common(5)\n",
    "print(\"URL  Count\")\n",
    "for url,count in top_5_urls:\n",
    "    print(url,count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text Mining for frequently occuring words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5min 8s, sys: 4.67 s, total: 5min 12s\n",
      "Wall time: 5min 22s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "all_words=[]\n",
    "\n",
    "stop_words = set(line.strip() for line in open('stop_words.txt'))\n",
    "stop_words.add(\"https\")\n",
    "stop_words.add(\"http\")\n",
    "\n",
    "for message in message_list:\n",
    "    message = message.lower()\n",
    "    tokens = word_tokenize(message)\n",
    "    filtered_words = [w for w in tokens if not w in stop_words]\n",
    "    \n",
    "    alnum_words = [w for w in filtered_words if w.isalnum()]\n",
    "    \n",
    "    all_words.extend(alnum_words)\n",
    "    \n",
    "all_words_counter = Counter(all_words)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10 most common  words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word\tFrequency\n",
      "die\t53278\n",
      "trump\t47422\n",
      "people\t41539\n",
      "der\t36623\n",
      "news\t35665\n",
      "und\t35473\n",
      "gabfam\t28652\n",
      "maga\t27990\n",
      "das\t24876\n",
      "ist\t22695\n"
     ]
    }
   ],
   "source": [
    "top_10_words = all_words_counter.most_common(10)\n",
    "print(\"Word\\tFrequency\")\n",
    "for word,frequency in top_10_words:\n",
    "    print (\"{}\\t{}\".format(word,frequency))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Date Message Plotting:\n",
    "* Convert to correct time zone   \n",
    "* Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 46s, sys: 807 ms, total: 1min 46s\n",
      "Wall time: 1min 47s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "### convert all the tweets to EST time zone as we have diff. time zones and then add dates\n",
    "EST_tz = pytz.timezone('EST')\n",
    "created_at_list= user_df['created_at'].tolist()\n",
    "date_list =[]\n",
    "for curr_dt_string in created_at_list:\n",
    "    curr_dt   =  parse(curr_dt_string)\n",
    "    \n",
    "    ###converts all message to est time zone\n",
    "    curr_date   = curr_dt.astimezone(EST_tz).date()\n",
    "    date_list.append(curr_dt.date())\n",
    "dates_counter = Counter(date_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d57e7d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### plots date in the correct format\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot_date(dates_counter.keys(),dates_counter.values(),'-')\n",
    "\n",
    "ax.xaxis.set_major_locator(mdates.DayLocator(interval=2))\n",
    "ax.xaxis.set_minor_locator(mdates.DayLocator(interval=1))\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%B %d'))\n",
    "ax.tick_params(labelsize=12,length=6, width=2,direction='inout')\n",
    "\n",
    "ax.grid(True)\n",
    "plt.xlabel('Dates(2017)', fontsize=20)\n",
    "plt.ylabel('Number of Messages',fontsize=30)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mark_ra",
   "language": "python",
   "name": "mark_ra"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}