notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "f89812c4-a50a-4751-a215-526002978a3f",
    "_execution_state": "idle",
    "_uuid": "83fc22e4a32c787317cb50276a71b5be860f7983",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "#matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "76b3aacf-68e7-4307-b476-e94a0f2a7f00",
    "_execution_state": "idle",
    "_uuid": "278c2109b89b04502f0881263680394fb298f331"
   },
   "source": [
    "## Loading the data ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "88e71863-1911-44af-822f-c3058ef2edf9",
    "_execution_state": "idle",
    "_uuid": "a8a7d25eb7833fd7d9ccc575c403a4a6f844b884"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>favorited</th>\n",
       "      <th>favoriteCount</th>\n",
       "      <th>created</th>\n",
       "      <th>truncated</th>\n",
       "      <th>statusSource</th>\n",
       "      <th>screenName</th>\n",
       "      <th>retweetCount</th>\n",
       "      <th>isRetweet</th>\n",
       "      <th>retweeted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RT @rssurjewala: Critical question: Was PayTM informed about #Demonetization edict by PM? It's clearly fishy and requires full disclosure &amp;amp;</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-11-23 18:40:30</td>\n",
       "      <td>False</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" rel=\"nofollow\"&gt;Twitter for Android&lt;/a&gt;</td>\n",
       "      <td>HASHTAGFARZIWAL</td>\n",
       "      <td>331</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RT @Hemant_80: Did you vote on #Demonetization on Modi survey app?</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-11-23 18:40:29</td>\n",
       "      <td>False</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" rel=\"nofollow\"&gt;Twitter for Android&lt;/a&gt;</td>\n",
       "      <td>PRAMODKAUSHIK9</td>\n",
       "      <td>66</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RT @roshankar: Former FinSec, RBI Dy Governor, CBDT Chair + Harvard Professor lambaste #Demonetization.\\r\\n\\r\\nIf not for Aam Aadmi, listen to th</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-11-23 18:40:03</td>\n",
       "      <td>False</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" rel=\"nofollow\"&gt;Twitter for Android&lt;/a&gt;</td>\n",
       "      <td>rahulja13034944</td>\n",
       "      <td>12</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RT @ANI_news: Gurugram (Haryana): Post office employees provide cash exchange to patients in hospitals #demonetization https://t.co/uGMxUP9</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-11-23 18:39:59</td>\n",
       "      <td>False</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" rel=\"nofollow\"&gt;Twitter for Android&lt;/a&gt;</td>\n",
       "      <td>deeptiyvd</td>\n",
       "      <td>338</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RT @satishacharya: Reddy Wedding! @mail_today cartoon #demonetization #ReddyWedding https://t.co/u7gLNrq31F</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-11-23 18:39:39</td>\n",
       "      <td>False</td>\n",
       "      <td>&lt;a href=\"http://cpimharyana.com\" rel=\"nofollow\"&gt;CPIMBadli&lt;/a&gt;</td>\n",
       "      <td>CPIMBadli</td>\n",
       "      <td>120</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                 text  \\\n",
       "0  RT @rssurjewala: Critical question: Was PayTM informed about #Demonetization edict by PM? It's clearly fishy and requires full disclosure &amp;\n",
       "     \n",
       "1  RT @Hemant_80: Did you vote on #Demonetization on Modi survey app?                                                                                   \n",
       "2  RT @roshankar: Former FinSec, RBI Dy Governor, CBDT Chair + Harvard Professor lambaste #Demonetization.\\r\\n\\r\\nIf not for Aam Aadmi, listen to th\n",
       "   \n",
       "3  RT @ANI_news: Gurugram (Haryana): Post office employees provide cash exchange to patients in hospitals #demonetization https://t.co/uGMxUP9\n",
       "         \n",
       "4  RT @satishacharya: Reddy Wedding! @mail_today cartoon #demonetization #ReddyWedding https://t.co/u7gLNrq31F                                          \n",
       "\n",
       "   favorited  favoriteCount              created  truncated  \\\n",
       "0  False      0              2016-11-23 18:40:30  False       \n",
       "1  False      0              2016-11-23 18:40:29  False       \n",
       "2  False      0              2016-11-23 18:40:03  False       \n",
       "3  False      0              2016-11-23 18:39:59  False       \n",
       "4  False      0              2016-11-23 18:39:39  False       \n",
       "\n",
       "                                                                           statusSource  \\\n",
       "0  <a href=\"http://twitter.com/download/android\" rel=\"nofollow\">Twitter for Android</a>   \n",
       "1  <a href=\"http://twitter.com/download/android\" rel=\"nofollow\">Twitter for Android</a>   \n",
       "2  <a href=\"http://twitter.com/download/android\" rel=\"nofollow\">Twitter for Android</a>   \n",
       "3  <a href=\"http://twitter.com/download/android\" rel=\"nofollow\">Twitter for Android</a>   \n",
       "4  <a href=\"http://cpimharyana.com\" rel=\"nofollow\">CPIMBadli</a>                          \n",
       "\n",
       "        screenName  retweetCount  isRetweet  retweeted  \n",
       "0  HASHTAGFARZIWAL  331           True       False      \n",
       "1  PRAMODKAUSHIK9   66            True       False      \n",
       "2  rahulja13034944  12            True       False      \n",
       "3  deeptiyvd        338           True       False      \n",
       "4  CPIMBadli        120           True       False      "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets = pd.read_csv('../input/demonetization-tweets.csv', encoding='latin1') #Load the data.\n",
    "#data contains encoding, so encoding method is used or else the data wont be read by anaconda.\n",
    "pd.set_option('display.max_colwidth', -1) #Want to see the full sentences in text column.\n",
    "tweets = tweets.drop(['Unnamed: 0','X','replyToSN','replyToSID','id','replyToUID'],axis=1) #Remove unwanted column\n",
    "tweets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e5e85e04-8d79-49fb-9fab-85bb101e0de4",
    "_execution_state": "idle",
    "_uuid": "c53c4500d03b5bce2c0ee562c7ac9e34d152c40a"
   },
   "source": [
    "## Extracting the RT's ##\n",
    "Twitter tweets have @xyz, extracting them and setting them in a new column['Text_os].  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "87b2866b-2fc4-4b68-91f8-25c2607feaef",
    "_execution_state": "idle",
    "_uuid": "1076d0f1f6c14aa3d424f2c1b3b520dd22925ee3",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tweets['Text_os'] = '' \n",
    "tweets['Text_os'] = tweets['text'].str.extract('(@\\w{0,})',expand = False)# @xyz\n",
    "tweets['Text_os'] = tweets['Text_os'].str.strip('@')# Remove @ symbol -xyz\n",
    "tweets['Text_os'] = tweets['Text_os'].fillna(0) # Fill nan value as 0\n",
    "tweets['Text_os'] = tweets['Text_os'].replace('',0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3a425287-16d8-405c-bd97-3263a2148c37",
    "_execution_state": "idle",
    "_uuid": "591f8169ffbf71c0666cdf8a75d0a74b6a023af2"
   },
   "source": [
    "Converting True and False in few columns as 1(True) and 0(False), which will be used for latter analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "8a1096d3-b50f-4103-9c38-46453a9f26fb",
    "_execution_state": "idle",
    "_uuid": "05399580244fa904a3022bff43ac878e010411f0",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tweets['isRetweet'] = tweets['isRetweet']*1\n",
    "tweets['retweeted'] = tweets['retweeted']*1\n",
    "tweets['truncated'] = tweets['truncated']*1\n",
    "tweets['favorited'] = tweets['favorited']*1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "434fd7b9-6dd2-455f-813a-147a737b57f1",
    "_execution_state": "idle",
    "_uuid": "eb68c7845ef4c358d5d4b3701e48921132cb844a"
   },
   "source": [
    "Extracting the information related to device used. I believe Maximum people in India would have android phone.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "8fe36aaa-2635-4d99-a0ad-2a9d1886044e",
    "_execution_state": "idle",
    "_uuid": "ea9c22ded607fd3b56172e05960b7c531948c6aa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Android', 'CPIMBadli', 'Client', nan, 'iPhone', 'Phone', 'IFTTT',\n",
       "       'Facebook', 'Windows', 'TweetDeck', 'iPad', 'Hootsuite',\n",
       "       'RoundTeam', 'Google', 'BlackBerry', 'com', 'i', 'SocialNewsXYZ',\n",
       "       'Drivespark', 'Instagram', 'it', 'Social', 'Buffer', 'Mac', 'AI',\n",
       "       'Ads', 'HubSpot', 'SocialOomph', 'Twitter', 'Peregrine',\n",
       "       'NetCatNews', 'in', 'GrabInbox', 'MetroTwit', 'Widget',\n",
       "       'HeaderLabs', 'LinkedIn', 'cryptohawk', 'Update', 'bitcoinagile',\n",
       "       'iOS', 'IEFX', 'li', 'TwixxyBot', 'bot', 'Echofon', 'Nuzzel',\n",
       "       'Junction', 'Dabr', 'RealEstateBot', 'Studio', 'Singapore', 'Buzz',\n",
       "       'Lite', 'growth', 'Post', 'co', 'YoruFukurou', 'cmssocialservice',\n",
       "       'TwitterTrafficMachine', 'Klout', 'trump', 'Big', 'Twitterrific',\n",
       "       'App', 'SCBotBackend', 'CoSchedule', 'Integration', 'easypybot',\n",
       "       'PostBeyond', 'Sprinklr', 'agileminderbot', 'Publisher', 'Quora',\n",
       "       'IEHIAutoPost', 'Pluggio', 'OccuWorld', 'SocioAdvocacy', 'Edgar',\n",
       "       'InvestmentWatch', 'Countdown', 'Tweets', 'Willow', 'Customer',\n",
       "       'app', 'AgendaOfEvil', 'NGO', 'StockmarketStar', 'BotByROP',\n",
       "       'salutcavaouiettoibienoubien', 'BitcoinBtcNews', 'retweet',\n",
       "       'mounds', 'VoiceStorm', 'php', 'tweethunk', 'petyushin',\n",
       "       'Conversocial', 'TwitBot', 'Jr', 'IT', 'SocialFlow', 'News',\n",
       "       'ebooks'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets['Phone_use'] = tweets['statusSource'].str.extract('([A-Za-z]+\\<)', expand = False)\n",
    "tweets['Phone_use'] = tweets['Phone_use'].str.extract('([A-Za-z]+)', expand = True)\n",
    "tweets.Phone_use.unique() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "dd5182e3-63d2-40aa-a71c-bd66b4af1592",
    "_execution_state": "idle",
    "_uuid": "fba246a5621fa408a05e0afe14dcb6eea1bc5c2b"
   },
   "source": [
    "### Analyzing India's device choice ###\n",
    "Devices and systems used to send the tweets. \n",
    "I considered only main devices available in Indian Market. Android, Apple, Blackberry, Windows Phone , Twitter Website and via Other device and services....\n",
    "Note: iPhone, iPad, Mac, i has been coincided as Apple Phone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "2179adab-45fb-491d-8b81-bf7d710836a7",
    "_execution_state": "idle",
    "_uuid": "25d42dafc202fb03cf2d281e207a47f11c27aa87"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Android' 'Other' 'via_website' 'Apple_Phone' 'Windows_Phone'\n",
      " 'BlackBerry_Phone']\n"
     ]
    }
   ],
   "source": [
    "replace_words =['CPIMBadli','Facebook','SocialNewsXYZ','Instagram',\n",
    "'AI','NGO','Junction', 'Dabr', 'RealEstateBot', 'Studio', 'Singapore', 'Buzz',\n",
    "'TwitterTrafficMachine', 'Klout', 'trump', 'Big', 'Twitterrific','Lite','growth', 'Post', 'co', 'YoruFukurou', 'cmssocialservice','IFTTT', 'TweetDeck', 'Hootsuite', 'RoundTeam', 'Google','com', 'Drivespark', 'it', 'Social', 'Buffer','Ads', 'HubSpot','GrabInbox', 'MetroTwit', 'Widget', 'HeaderLabs', 'LinkedIn',\n",
    " 'cryptohawk', 'Update', 'bitcoinagile', 'iOS', 'IEFX', 'li','TwixxyBot', 'bot', 'Echofon', 'Nuzzel', 'App', 'SCBotBackend',\n",
    " 'easypybot', 'PostBeyond', 'Sprinklr', 'agileminderbot','Publisher', 'Quora', 'IEHIAutoPost', 'Pluggio', 'OccuWorld',\n",
    "'SocioAdvocacy', 'Edgar', 'InvestmentWatch', 'Countdown', 'Tweets','Willow', 'Customer', 'app', 'AgendaOfEvil', 'StockmarketStar',\n",
    "'BotByROP', 'salutcavaouiettoibienoubien', 'BitcoinBtcNews','retweet', 'mounds', 'VoiceStorm', 'php', 'tweethunk', 'petyushin',\n",
    "'Conversocial', 'TwitBot', 'Jr', 'IT', 'SocialFlow', 'News','Peregrine', 'NetCatNews', 'in', 'CoSchedule', 'Integration',\n",
    "'ebooks','SocialOomph','Twitter']\n",
    "\n",
    "tweets['Phone_use'] = tweets['Phone_use'].replace([replace_words],'Other')\n",
    "tweets['Phone_use'] = tweets['Phone_use'].replace(['Phone','Windows'],'Windows_Phone')\n",
    "tweets['Phone_use'] = tweets['Phone_use'].replace(['iPhone','iPad','Mac','i'],'Apple_Phone')\n",
    "tweets['Phone_use'] = tweets['Phone_use'].replace('BlackBerry','BlackBerry_Phone')\n",
    "tweets['Phone_use'] = tweets['Phone_use'].replace('Client','via_website')\n",
    "tweets['Phone_use'] = tweets['Phone_use'].fillna('Other')\n",
    "\n",
    "print (tweets.Phone_use.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "bbce0015-c8c0-498c-839a-fd5e49a4df3b",
    "_execution_state": "idle",
    "_uuid": "ee9d7be2c6df8dabe90567023a0620d7349c303a"
   },
   "source": [
    "## Count Plot and Pie chat analysis ##\n",
    ">> Android has more number of users in India"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "35312b85-9c4b-4e56-9563-f5c2f742ae50",
    "_execution_state": "idle",
    "_uuid": "8c446f8e186e1403127cdca5eeac92355878031e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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a/1td+R5J2fIGuCUzf1GD9ErKj+OnKb1Kf0fpTfvVMM+5F2WleR3lR2DXrV3w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\nJkmS1CKGM0mSpBYxnEmSJLXI/wcLU0XzAsWhqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea3563b898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,5])\n",
    "sns.countplot(x='Phone_use', data= tweets)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8973cc0e-b7b7-4e8c-ba73-d51c98e76b2e",
    "_execution_state": "idle",
    "_uuid": "4599fa04f9baad8f3aee5bc2960e8a1b47659214"
   },
   "source": [
    "From Pie Chat we see that 51% of twitter handler of this data set has android phone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "2079d9c6-f9b3-4480-8ba8-fc5488a4e575",
    "_execution_state": "idle",
    "_uuid": "e939cf702f0ab10c885b50dce882391d3e92152e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ws+n8C/apWTetX+tw588yOpBp4iavz786Be0c46KSNHVXXRTQIvbTXCSRUBO7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea36340ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phones = ['Android','BlackBerry_Phone','Other', 'via_website', 'Apple_Phone', 'Windows_Phone']\n",
    "count = []\n",
    "for i in phones:\n",
    "    k = tweets[tweets['Phone_use'] == i]\n",
    "    j = k.Phone_use.count()\n",
    "    count.append(j)\n",
    "\n",
    "perc = []\n",
    "numb = [0,1,2,3,4,5]\n",
    "for i in numb:\n",
    "    p = (count[i]/(count[0] +count[1]+count[2]+count[3]+count[4]+count[5]))*100\n",
    "    perc.append(p)\n",
    "    \n",
    "perc_new = []\n",
    "for i in perc:\n",
    "    k = \"%.2f\" % i\n",
    "    perc_new.append(k)\n",
    "#---------------------##------------------------------------###-----------------------    \n",
    "from pylab import *\n",
    "%matplotlib inline\n",
    "figure(1, figsize=(6,6))\n",
    "ax = axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "# The slices will be ordered and plotted counter-clockwise.\n",
    "labels = 'Android','BlackBerry_Phone','Other', 'via_website', 'Apple_Phone', 'Windows_Phone'\n",
    "fracs = perc_new\n",
    "explode=(0.2, 0, 0, 0,0 ,0)\n",
    "\n",
    "pie(fracs, explode=explode, labels=labels,\n",
    "                autopct='%1.1f%%', shadow=True, startangle=110)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c983b5e7-c8f7-4a80-bcb0-75f1780ff16d",
    "_execution_state": "idle",
    "_uuid": "e0a466d1aee3048ea942ad964413bd5d00004a01"
   },
   "source": [
    "## Analyzing the date pattern ##\n",
    "As we can see the date pattern in the dataset constitutes the month of November and April. \n",
    "November -2 days\n",
    "April - 11 days \n",
    "So instead on analyzing for 13 days, I have considered only 2 categorical value. i.e Nov and Apr.\n",
    "Also... Nov(which has just 2 days data) has maximum number of tweets than Apr(which has 11 days data). This was expected as demonetization happened on Nov and as time passed, the matter dried up.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "3754c825-6a66-4f20-962e-d9b89a6883d1",
    "_execution_state": "idle",
    "_uuid": "a1bf6115b52b4cba9eeb391d99c0a2f5c7a87097"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2016-11-23' '2016-11-22' '2017-04-21' '2017-04-20' '2017-04-19'\n",
      " '2017-04-18' '2017-04-17' '2017-04-16' '2017-04-15' '2017-04-14'\n",
      " '2017-04-13' '2017-04-12' '2017-04-11']\n",
      "0    Nov\n",
      "1    Nov\n",
      "Name: date, dtype: object\n",
      "Month: Nov  -> 8000\n",
      "Month: Apr  -> 6940\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea35d4fd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tweets['date'] = tweets['created'].str.extract('(....-..-..)', expand = False)\n",
    "print (tweets['date'].unique())\n",
    "tweets['date'] = tweets['created'].str.extract('(-..)', expand = False) # -11,-4\n",
    "tweets['date'] = tweets['date'].str.strip('-') #11,4\n",
    "tweets['date'] = tweets['date'].replace('11', 'Nov')# 11 to Nov\n",
    "tweets['date'] = tweets['date'].replace('04', 'Apr')# 04 to Apr\n",
    "print (tweets['date'].head(2))\n",
    "month = ['Nov','Apr']\n",
    "for i in month:\n",
    "    j = tweets[(tweets['date'] == i)]\n",
    "    c = j.date.count()\n",
    "    print ('Month:',i,' ->',c)\n",
    "    \n",
    "#---------------------------------------------------------------------------------\n",
    "sns.countplot(x= 'date', data = tweets)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c7c79741-d07a-431d-bd1d-f9ce8df0bacc",
    "_execution_state": "idle",
    "_uuid": "7b91d441a309e22bf44e97ccc3c0f6cf3d82ec0b"
   },
   "source": [
    "## Analyzing maximum RT's ##\n",
    "Since demonetization was done my Modi, so I guess he should his name maximum number of times in RT's.\n",
    "I found out the following Political personalities in the dataset. There can be many more as well. I have considered only the handful that I got.\n",
    "Following names I have taken under consideration: \n",
    "\n",
    " 1. Modi ('narendramodi)'- PM of India\n",
    " 2. Sushma Swaraj ('SushmaSwaraj') - Central Minister\n",
    " 3. Prime Minister Ofice('PMOIndia')\n",
    " 4. RBI\n",
    " 5. Arun Jaitley('arunjaitley')- FM of India\n",
    " 6. Rahul Gandhi ('OfficeOfRG')- Opposition\n",
    " 7. Arvind Kejriwal('ArvindKejriwal') - Leader Opposition\n",
    " 8. Mamta Banerjee('MamataOfficial')- Leader Opposition\n",
    " 9. Sitaram Yechury('SitaramYechury') -Leader Opposition\n",
    " 10. Finance Ministery of India ('FinMinIndia')\n",
    " 11. Shaktikant Das('DasShaktikanta') Finance Secretaryat the time of demoetization\n",
    " 12. Smriti Irani ('smritiirani')- Central Minister\n",
    " 13. Shashi Tharoor('ShashiTharoor')-Leader Opposition\n",
    "\n",
    "Surprisingly, Shashi Tharoor has max number of RT's with his name followed by Narendra Modi for November\n",
    "but for month of April, Modi has more number of tweets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "12d51292-44f2-4bff-ac91-74335c099eeb",
    "_execution_state": "idle",
    "_uuid": "ad5f0e2c58ac8b7b0f69064fad30c3f0ae5face6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============Month of November======================\n",
      "              Name  Total_tweets\n",
      "0   narendramodi    100         \n",
      "1   SushmaSwaraj    0           \n",
      "2   PMOIndia        19          \n",
      "3   RBI             5           \n",
      "4   arunjaitley     9           \n",
      "5   OfficeOfRG      5           \n",
      "6   ArvindKejriwal  29          \n",
      "7   MamataOfficial  6           \n",
      "8   SitaramYechury  2           \n",
      "9   FinMinIndia     1           \n",
      "10  DasShaktikanta  18          \n",
      "11  smritiirani     0           \n",
      "12  ShashiTharoor   147         \n"
     ]
    },
    {
     "data": {
      "image/png": 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qkF1JcEZiIr6zO7agnIakPIkl8KSzJXBrxAV4s6syTHQdEcqgl9PwyIQHaByi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\nRwL7WvkFDZ+m+XXZQzHza5Y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beyZwfubGVJSzcefxi/RdYBRlHG5e+Q1eintCUgyzM4hX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\nQRAEAaEMgiAIAkIZBEEQBMD/A1n3aRZuMfYeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea35b5eef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============Month of April=========================\n",
      "              Name  Total_tweets\n",
      "0   narendramodi    81          \n",
      "1   SushmaSwaraj    4           \n",
      "2   PMOIndia        16          \n",
      "3   RBI             2           \n",
      "4   arunjaitley     5           \n",
      "5   OfficeOfRG      7           \n",
      "6   ArvindKejriwal  8           \n",
      "7   MamataOfficial  0           \n",
      "8   SitaramYechury  3           \n",
      "9   FinMinIndia     2           \n",
      "10  DasShaktikanta  0           \n",
      "11  smritiirani     1           \n",
      "12  ShashiTharoor   73          \n"
     ]
    },
    {
     "data": {
      "image/png": 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CRwMvA3MC/8VbrjwJHGNmx+Zc73n4dX4tsCtwGD6XYwSwcc5dUPV5+twAzGxyTpn3AAsC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea35a7b5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name =['narendramodi','SushmaSwaraj','PMOIndia','RBI','arunjaitley','OfficeOfRG',\n",
    "       'ArvindKejriwal','MamataOfficial','SitaramYechury','FinMinIndia','DasShaktikanta',\n",
    "       'smritiirani','ShashiTharoor']\n",
    "Total_nov = []\n",
    "Total_apr = []\n",
    "# For november------------------------------------------------------------\n",
    "for i in name:\n",
    "    name_find_nov = tweets[(tweets['Text_os'] == i) & (tweets['date'] == 'Nov')]\n",
    "    name_find_total_nov = name_find_nov.Text_os.count()\n",
    "    Total_nov.append(name_find_total_nov)\n",
    "#For April-------------------------------------------------------------------\n",
    "for k in name:\n",
    "    name_find_apr = tweets[(tweets['Text_os'] == k) & (tweets['date'] == 'Apr')]\n",
    "    name_find_total_apr = name_find_apr.Text_os.count()\n",
    "    Total_apr.append(name_find_total_apr)\n",
    "##I dont know why I am not able to use 'name' and 'Total' directly. So I appended the same data in\n",
    "##new_name_nov(apr) and new_total_nov(apr), its working fine.\n",
    "new_name_nov = []\n",
    "new_total_nov = []\n",
    "new_name_apr = []\n",
    "new_total_apr = []\n",
    "#-----------------------------\n",
    "for i in name:\n",
    "    new_name_nov.append(i)\n",
    "for k in Total_nov:\n",
    "    new_total_nov.append(k)\n",
    "#-----------------------------\n",
    "for i in name:\n",
    "    new_name_apr.append(i)\n",
    "for k in Total_apr:\n",
    "    new_total_apr.append(k)\n",
    "#---------------Print the same----------------------------------\n",
    "print('=============Month of November======================')\n",
    "dn = {'Name' : pd.Series(new_name_nov),\n",
    "    'Total_tweets': pd.Series(new_total_nov)}\n",
    "dfn = pd.DataFrame(dn)\n",
    "print (dfn)\n",
    "#Ploting the graph-------------------------------------------------------\n",
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "new_na_nov = np.arange(len(new_name_nov))\n",
    "plt.bar(new_na_nov, new_total_nov)\n",
    "plt.xticks(new_na_nov, new_name_nov, rotation = 90,size=15)\n",
    "plt.xlabel('Name')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Month of November')\n",
    "plt.show()\n",
    "#------------------------------------------\n",
    "print('=============Month of April=========================')\n",
    "da = {'Name' : pd.Series(new_name_apr),\n",
    "    'Total_tweets': pd.Series(new_total_apr)}\n",
    "dfa = pd.DataFrame(da)\n",
    "print (dfa)\n",
    "#------------------------------------------------------------------\n",
    "plt.figure(2)\n",
    "plt.subplot(212)\n",
    "new_na_apr = np.arange(len(new_name_apr))\n",
    "plt.bar(new_na_apr, new_total_apr)\n",
    "plt.xticks(new_na_apr, new_name_apr, rotation = 90,size=15)\n",
    "plt.xlabel('Name')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Month of April')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e125a667-da2a-4da2-a5d6-53d4cc49f581",
    "_execution_state": "idle",
    "_uuid": "25a3af43a83c425313d81c81e68361c26af201f5"
   },
   "source": [
    "## Time analysis ##\n",
    "November: Since the matter was hot, so the tweets frequency was high. People have tweeted almost all the time, evening time tweets were pretty less. Afternoon to 5 pm, tweets frequencies were very high.\n",
    "\n",
    "\n",
    "April: By April the tweets should have a bit less, but we can see that there has been sudden spike in the tweets. May be some new incident related to demonetization have come up. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_cell_guid": "660429b8-c9d4-479a-8626-ef3ed18120d6",
    "_execution_state": "idle",
    "_uuid": "6d0519fd152e2ecc3d38b6d4fa24556fceb08d04"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Month of: Nov\n"
     ]
    },
    {
     "data": {
      "image/png": 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7dys3N1dlZWWyWq3y8PBQRESEJk+erDvuuEPh4eEmMwMAAABAs2B34RowYICq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\nrBY9KYqZGkoiJISgdN1/7BnuRrDmmpk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UKZ04caLc7V9++aVKS0vlcrlUp04d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\nAABgyP8D9Ebmg3h/wrcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea34040eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Month of: Apr\n"
     ]
    },
    {
     "data": {
      "image/png": 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+t6BQKITk5GTuMwiCIPz6178WQkJChOrqaiEkJEQICQmR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\nhcAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fea3434b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#from Tkinter import *\n",
    "#import Tkinter as tk\n",
    "#root = tk.Tk()\n",
    "#x = Label(text='Hours', font = (None,10))\n",
    "tweets['time'] = tweets['created'].str.extract('(..:)', expand = False)\n",
    "tweets['time'] = tweets['time'].str.strip(':')\n",
    "for i in month:\n",
    "    b = tweets[tweets['date'] == i]\n",
    "    print ('Month of:',i)\n",
    "    width = 14\n",
    "    height = 10\n",
    "    plt.figure(figsize = (width,height))\n",
    "    sns.set(font_scale=2)\n",
    "    sns.countplot(x= 'time', data = b)\n",
    "    plt.xlabel('Hours')\n",
    "    plt.ylabel('Number of Tweets')\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}