Main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data Analysis of the Influencers:\n",
    "# 2. Sentiment Analysis of the Influencers: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  TM -Import the Dependencies\n",
    "import tweepy\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from datetime import datetime, timezone\n",
    "import seaborn as sns\n",
    "from os import path, makedirs # fetch path and makedirs function from os file\n",
    "import csv # fetch csv file\n",
    "from glob import glob # fetching glob function only from the glob lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  TM -Import keys from the config file\n",
    "from config import consumer_key, consumer_secret, access_token, access_token_secret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  TM - Twitter API Keys\n",
    "# consumer_key = 'Your Key'\n",
    "# consumer_secret = 'Your Key'\n",
    "# access_token = 'Your Key'\n",
    "# access_token_secret = 'Your Key'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TM - Import and Initialize Sentiment Analyzer\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "analyzer = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TM - Setup Tweepy API Authentication\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "api = tweepy.API(auth, parser=tweepy.parsers.JSONParser())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  TM - Load and read csv file containing the details of the Influencers \n",
    "influencer_data_load = \"RawData/SentimentInfluencerInputData.csv\"\n",
    "influencer_data_read = pd.read_csv(influencer_data_load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------Start extraction!!!-----------\n",
      "exception for @ilanusglazer: Not authorized.\n",
      "exception for @billieelish: Not authorized.\n",
      "----------- Extraction Complete !!!-----------\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "#  Team version merged.\n",
    "# TM - for index, row in Influencers_DF.iterrows():\n",
    "Data_Influencers_DF = influencer_data_read\n",
    "Not_Found = 0\n",
    "\n",
    "#  TM - Define variable for holding tweets for influencer\n",
    "\n",
    "all_tweet_listing = []\n",
    "\n",
    "print(\"-----------Start extraction!!!-----------\")\n",
    "\n",
    "for index, row in Data_Influencers_DF.iterrows():\n",
    "    target_user = row[\"Twitter_Handle\"]\n",
    "    Genre =  row[\"Genre\"]\n",
    "    \n",
    "    try:\n",
    "        \n",
    "        # TM -  retrieve top 200 tweets for influencer\n",
    "        \n",
    "        public_tweets = api.user_timeline(target_user, count=100, result_type=\"recent\")\n",
    "\n",
    "        # TM -  write to all_tweet_listing\n",
    "\n",
    "        for tweet in public_tweets:\n",
    "            \n",
    "            Date = datetime.strptime(tweet['created_at'],'%a %b %d %H:%M:%S %z %Y').strftime('%m/%d/%Y')\n",
    "\n",
    "            all_tweet_listing.append({\"Influencer\":target_user,\n",
    "                               \"Date\": Date,\n",
    "                               \"Genre\":Genre,\n",
    "                               \"Tweet\":tweet[\"text\"],\n",
    "                               \"Retweet_Count\":tweet[\"retweet_count\"]})\n",
    "    \n",
    "        user_account = api.get_user(target_user)\n",
    "        user_geo_enabled = user_account[\"geo_enabled\"]\n",
    "        if (user_geo_enabled == True):\n",
    "            Data_Influencers_DF.at[index, \"Loc\"] = user_account[\"location\"]\n",
    "        else:\n",
    "            Data_Influencers_DF.at[index, \"Loc\"] = 'NA'\n",
    "\n",
    "        if (user_account[\"lang\"] == 'en'):\n",
    "            Data_Influencers_DF.at[index, \"Lang\"] = 'Eng'\n",
    "        else:\n",
    "            Data_Influencers_DF.at[index, \"Lang\"] = 'NA'\n",
    "        \n",
    "        Data_Influencers_DF.at[index, \"Created On\"] = datetime.strptime(user_account['created_at'],'%a %b %d %H:%M:%S %z %Y').strftime('%m/%d/%Y')\n",
    "        \n",
    "        Data_Influencers_DF.at[index, \"Age Of Account\"] = (datetime.now(timezone.utc) - datetime.strptime(user_account['created_at'],'%a %b %d %H:%M:%S %z %Y')).days\n",
    "        \n",
    "        \n",
    "       #  TM -  Data_Influencers_DF.at[index, \"Real Name\"] = user_real_name\n",
    "        Data_Influencers_DF.at[index, \"Tweets\"] = user_account[\"statuses_count\"]\n",
    "        Data_Influencers_DF.at[index, \"Followers\"] = user_account[\"followers_count\"]\n",
    "        Data_Influencers_DF.at[index, \"Following\"] = user_account[\"friends_count\"]\n",
    "        Data_Influencers_DF.at[index, \"Favorites Count\"] = user_account[\"favourites_count\"]\n",
    "        \n",
    "              \n",
    "    \n",
    "    except tweepy.TweepError as e:\n",
    "        Not_Found = Not_Found + 1\n",
    "        print(f\"exception for {row['Twitter_Handle']}: {e}\")\n",
    "\n",
    "print(\"----------- Extraction Complete !!!-----------\")        \n",
    "print(Not_Found)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TM - translate dict to a dataframe\n",
    "tweet_listing_pd = pd.DataFrame.from_dict(all_tweet_listing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 68511 entries, 0 to 68510\n",
      "Data columns (total 5 columns):\n",
      "Date             68511 non-null object\n",
      "Genre            68511 non-null object\n",
      "Influencer       68511 non-null object\n",
      "Retweet_Count    68511 non-null int64\n",
      "Tweet            68511 non-null object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# TM - stats for tweet listing for influencers. PLEASE DON'T REMOVE. Required to quantify digital footprint!\n",
    "tweet_listing_pd.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TM - top 1000 key words from Influencer tweets#SS -to \n",
    "\n",
    "Top_1000 = pd.Series(' '.join(tweet_listing_pd['Tweet']).lower().split()).value_counts()[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  TM - Saving the out put into a csv file\n",
    "Top_1000.to_csv(\"RawData/Top_1000_keywords.csv\", index=True, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TM -  Write to CSV for analysis\n",
    "tweet_listing_pd.to_csv(\"RawData/TweetListings.csv\", index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SS - String type casting for Tweets column\n",
    "\n",
    "tweet_listing_pd.Tweet = tweet_listing_pd.Tweet.map(''.join).str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SS - define target tags for social and entertainment\n",
    "\n",
    "social_target_tags = [\"#FamiliesBelongTogetherMarch\",\"#gun\",\"gun\",\"shooting\",\"gun-control\",\"election\",\"#metoo\",\"metoo\",\"FamiliesBelongTogetherMarch\",\"PrideMonth\",\"#PrideMonth\",\"FamiliesBelongTogether\",\"ChildreninCages\",\"UniteTheFamilies\",\"WeCare\"]\n",
    "\n",
    "entertainment_target_tags = [\"#SocialMediaDay\",\"SocialMediaDay\",\"WorldCup\",\"#WorldCup\",\"#fifa\",\"fifa\", \"#worldcup2018russia\",\"#PostASongLyricYouLove\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SS - function to match tweet with tags\n",
    "\n",
    "def matcher_tweet(x):\n",
    "    for i in social_target_tags:\n",
    "        if i.lower() in x.lower():\n",
    "            return(\"Social\")\n",
    "        \n",
    "    for j in entertainment_target_tags:\n",
    "        if j.lower() in x.lower():\n",
    "            return(\"Entertainment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SS - Add new column to tweet_listing_pd to capture Category - Social vs Entertainment\n",
    "\n",
    "tweet_listing_pd['Tweet_Category'] = tweet_listing_pd['Tweet'].apply(matcher_tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Influencer</th>\n",
       "      <th>Retweet_Count</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Tweet_Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47045</th>\n",
       "      <td>08/13/2017</td>\n",
       "      <td>Politicians</td>\n",
       "      <td>@BarackObama</td>\n",
       "      <td>1696821</td>\n",
       "      <td>\"No one is born hating another person because ...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30446</th>\n",
       "      <td>09/01/2017</td>\n",
       "      <td>Sports_celeb</td>\n",
       "      <td>@gabrielledoug</td>\n",
       "      <td>1138633</td>\n",
       "      <td>RT @PennStateIFC: With the current devastation...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6835</th>\n",
       "      <td>01/11/2017</td>\n",
       "      <td>Actors</td>\n",
       "      <td>@TaySchilling</td>\n",
       "      <td>852889</td>\n",
       "      <td>RT @POTUS: Thank you for everything. My last a...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42878</th>\n",
       "      <td>09/23/2017</td>\n",
       "      <td>Sports_celeb</td>\n",
       "      <td>@rolopez42</td>\n",
       "      <td>644119</td>\n",
       "      <td>RT @KingJames: U bum @StephenCurry30 already s...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47016</th>\n",
       "      <td>11/20/2017</td>\n",
       "      <td>Politicians</td>\n",
       "      <td>@BarackObama</td>\n",
       "      <td>499481</td>\n",
       "      <td>ME:  Joe, about halfway through the speech, I’...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47044</th>\n",
       "      <td>08/13/2017</td>\n",
       "      <td>Politicians</td>\n",
       "      <td>@BarackObama</td>\n",
       "      <td>491151</td>\n",
       "      <td>\"People must learn to hate, and if they can le...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4881</th>\n",
       "      <td>08/13/2017</td>\n",
       "      <td>Actors</td>\n",
       "      <td>@EmilyVanCamp</td>\n",
       "      <td>491151</td>\n",
       "      <td>RT @BarackObama: \"People must learn to hate, a...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4819</th>\n",
       "      <td>02/24/2018</td>\n",
       "      <td>Actors</td>\n",
       "      <td>@EmilyVanCamp</td>\n",
       "      <td>449120</td>\n",
       "      <td>RT @BarackObama: Young people have helped lead...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46997</th>\n",
       "      <td>02/22/2018</td>\n",
       "      <td>Politicians</td>\n",
       "      <td>@BarackObama</td>\n",
       "      <td>449120</td>\n",
       "      <td>Young people have helped lead all our great mo...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5712</th>\n",
       "      <td>02/22/2018</td>\n",
       "      <td>Actors</td>\n",
       "      <td>@smashleybell</td>\n",
       "      <td>449120</td>\n",
       "      <td>RT @BarackObama: Young people have helped lead...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date         Genre      Influencer  Retweet_Count  \\\n",
       "47045  08/13/2017   Politicians    @BarackObama        1696821   \n",
       "30446  09/01/2017  Sports_celeb  @gabrielledoug        1138633   \n",
       "6835   01/11/2017        Actors   @TaySchilling         852889   \n",
       "42878  09/23/2017  Sports_celeb      @rolopez42         644119   \n",
       "47016  11/20/2017   Politicians    @BarackObama         499481   \n",
       "47044  08/13/2017   Politicians    @BarackObama         491151   \n",
       "4881   08/13/2017        Actors   @EmilyVanCamp         491151   \n",
       "4819   02/24/2018        Actors   @EmilyVanCamp         449120   \n",
       "46997  02/22/2018   Politicians    @BarackObama         449120   \n",
       "5712   02/22/2018        Actors   @smashleybell         449120   \n",
       "\n",
       "                                                   Tweet Tweet_Category  \n",
       "47045  \"No one is born hating another person because ...           None  \n",
       "30446  RT @PennStateIFC: With the current devastation...           None  \n",
       "6835   RT @POTUS: Thank you for everything. My last a...           None  \n",
       "42878  RT @KingJames: U bum @StephenCurry30 already s...           None  \n",
       "47016  ME:  Joe, about halfway through the speech, I’...           None  \n",
       "47044  \"People must learn to hate, and if they can le...           None  \n",
       "4881   RT @BarackObama: \"People must learn to hate, a...           None  \n",
       "4819   RT @BarackObama: Young people have helped lead...           None  \n",
       "46997  Young people have helped lead all our great mo...           None  \n",
       "5712   RT @BarackObama: Young people have helped lead...           None  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SS - Analysis of tweet_listing_pd\n",
    "tweet_listing_pd_sorted = tweet_listing_pd.sort_values(\"Retweet_Count\",ascending=False)\n",
    "\n",
    "tweet_listing_pd_sorted.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweet_Category</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Actors</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Comedians</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Economists</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Musicians</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Politicians</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Sports_celeb</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Social</td>\n",
       "      <td>Actors</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Social</td>\n",
       "      <td>Comedians</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Social</td>\n",
       "      <td>Economists</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Social</td>\n",
       "      <td>Musicians</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Social</td>\n",
       "      <td>Politicians</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Social</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Social</td>\n",
       "      <td>Sports_celeb</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Tweet_Category           Genre  Tweet\n",
       "0   Entertainment          Actors     14\n",
       "1   Entertainment       Comedians      2\n",
       "2   Entertainment      Economists     12\n",
       "3   Entertainment       Musicians     15\n",
       "4   Entertainment     Politicians      3\n",
       "5   Entertainment  Social_workers      1\n",
       "6   Entertainment    Sports_celeb     34\n",
       "7          Social          Actors    111\n",
       "8          Social       Comedians    101\n",
       "9          Social      Economists     42\n",
       "10         Social       Musicians     20\n",
       "11         Social     Politicians    305\n",
       "12         Social  Social_workers     36\n",
       "13         Social    Sports_celeb    133"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SS - Genre vs Catgegory\n",
    "tweet_listing_pd_sorted_Summary = tweet_listing_pd_sorted.groupby([\"Tweet_Category\",\"Genre\"],as_index=False).count()\n",
    "tweet_listing_DF = tweet_listing_pd_sorted_Summary[[\"Tweet_Category\",\"Genre\",\"Tweet\"]]\n",
    "tweet_listing_DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 392.25x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SS  - Plot Tweet Category vs Number of Tweets\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "sns.factorplot(x='Tweet_Category', y='Tweet', hue='Genre', data=tweet_listing_DF, kind='bar')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  TM - Remove the records of people who have not tweeted at all\n",
    "Data_Influencers_DF = Data_Influencers_DF[Data_Influencers_DF['Tweets']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  TM -  Remove missing values.\n",
    "Data_Influencers_DF.dropna()\n",
    "\n",
    "Data_Influencers_DF.to_csv('OutPut/Influencer_Analysis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Twitter_Handle</th>\n",
       "      <th>First_Name</th>\n",
       "      <th>Last_Name</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Loc</th>\n",
       "      <th>Lang</th>\n",
       "      <th>Created On</th>\n",
       "      <th>Age Of Account</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>Followers</th>\n",
       "      <th>Following</th>\n",
       "      <th>Favorites Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@_AnneHathaway</td>\n",
       "      <td>Anne</td>\n",
       "      <td>Hathaway</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>09/13/2012</td>\n",
       "      <td>2118.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@IAMannalynnemcc</td>\n",
       "      <td>AnnaLynne</td>\n",
       "      <td>McCord</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>01/16/2010</td>\n",
       "      <td>3090.0</td>\n",
       "      <td>5776.0</td>\n",
       "      <td>228657.0</td>\n",
       "      <td>502.0</td>\n",
       "      <td>2157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@EmmaWatson</td>\n",
       "      <td>Emma</td>\n",
       "      <td>Watson</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>07/14/2010</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>1642.0</td>\n",
       "      <td>29143216.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>991.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@LeoDiCaprio</td>\n",
       "      <td>Leonardo</td>\n",
       "      <td>DiCaprio</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>04/16/2010</td>\n",
       "      <td>2999.0</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>19327180.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>@EvaLongoria</td>\n",
       "      <td>Eva</td>\n",
       "      <td>Baston</td>\n",
       "      <td>Actors</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>02/02/2010</td>\n",
       "      <td>3072.0</td>\n",
       "      <td>15806.0</td>\n",
       "      <td>7691876.0</td>\n",
       "      <td>809.0</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>@TheRock</td>\n",
       "      <td>Dwayne</td>\n",
       "      <td>Johnson</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>02/11/2011</td>\n",
       "      <td>2698.0</td>\n",
       "      <td>21662.0</td>\n",
       "      <td>13044259.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>@jessicaalba</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>Alba</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>11/24/2009</td>\n",
       "      <td>3142.0</td>\n",
       "      <td>6244.0</td>\n",
       "      <td>9649191.0</td>\n",
       "      <td>1359.0</td>\n",
       "      <td>1442.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>@EllenPompeo</td>\n",
       "      <td>Ellen</td>\n",
       "      <td>Pompeo</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NY/LA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>03/23/2013</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>5211.0</td>\n",
       "      <td>1551708.0</td>\n",
       "      <td>541.0</td>\n",
       "      <td>6060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>@JessicaCapshaw</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>Capshaw</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>05/05/2011</td>\n",
       "      <td>2615.0</td>\n",
       "      <td>4017.0</td>\n",
       "      <td>935946.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>@sarahdrew</td>\n",
       "      <td>Sarah</td>\n",
       "      <td>Drew</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>11/14/2009</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>9611.0</td>\n",
       "      <td>822552.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>622.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>@IamSandraOh</td>\n",
       "      <td>Sandra</td>\n",
       "      <td>Oh</td>\n",
       "      <td>Actors</td>\n",
       "      <td></td>\n",
       "      <td>Eng</td>\n",
       "      <td>09/20/2013</td>\n",
       "      <td>1746.0</td>\n",
       "      <td>578.0</td>\n",
       "      <td>1005870.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>@SaraRamirez</td>\n",
       "      <td>Sara</td>\n",
       "      <td>Ramirez</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>07/30/2013</td>\n",
       "      <td>1798.0</td>\n",
       "      <td>8030.0</td>\n",
       "      <td>820544.0</td>\n",
       "      <td>802.0</td>\n",
       "      <td>6653.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>@iJesseWilliams</td>\n",
       "      <td>Jesse</td>\n",
       "      <td>Williams</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>04/12/2009</td>\n",
       "      <td>3369.0</td>\n",
       "      <td>19843.0</td>\n",
       "      <td>2400110.0</td>\n",
       "      <td>3531.0</td>\n",
       "      <td>5721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>@TheRealKMcKidd</td>\n",
       "      <td>Kevin</td>\n",
       "      <td>McKidd</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>04/05/2009</td>\n",
       "      <td>3376.0</td>\n",
       "      <td>7614.0</td>\n",
       "      <td>771095.0</td>\n",
       "      <td>502.0</td>\n",
       "      <td>386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>@KatieHeigl</td>\n",
       "      <td>Katherine</td>\n",
       "      <td>Heigl</td>\n",
       "      <td>Actors</td>\n",
       "      <td>Utah / Toronto</td>\n",
       "      <td>Eng</td>\n",
       "      <td>11/03/2009</td>\n",
       "      <td>3164.0</td>\n",
       "      <td>8477.0</td>\n",
       "      <td>1325714.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>3124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>@JasonWGeorge</td>\n",
       "      <td>Jason</td>\n",
       "      <td>George</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>09/19/2012</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>4306.0</td>\n",
       "      <td>236849.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>3019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>@chy_leigh</td>\n",
       "      <td>Chyler</td>\n",
       "      <td>Leigh</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>03/02/2014</td>\n",
       "      <td>1583.0</td>\n",
       "      <td>5811.0</td>\n",
       "      <td>302121.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>5544.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>@katewalsh</td>\n",
       "      <td>Kate</td>\n",
       "      <td>Walsh</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>08/30/2009</td>\n",
       "      <td>3228.0</td>\n",
       "      <td>18113.0</td>\n",
       "      <td>894869.0</td>\n",
       "      <td>828.0</td>\n",
       "      <td>3559.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>@KellyMcCreary</td>\n",
       "      <td>Kelly</td>\n",
       "      <td>McCreary</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>12/26/2011</td>\n",
       "      <td>2381.0</td>\n",
       "      <td>4048.0</td>\n",
       "      <td>256645.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>11772.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>@IWashington</td>\n",
       "      <td>Isaiah</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Actors</td>\n",
       "      <td>Everywhere I tweet...</td>\n",
       "      <td>Eng</td>\n",
       "      <td>02/06/2011</td>\n",
       "      <td>2704.0</td>\n",
       "      <td>29865.0</td>\n",
       "      <td>98709.0</td>\n",
       "      <td>7490.0</td>\n",
       "      <td>11410.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>@DohertyShannen</td>\n",
       "      <td>Shannen</td>\n",
       "      <td>Doherty</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>03/02/2010</td>\n",
       "      <td>3044.0</td>\n",
       "      <td>9347.0</td>\n",
       "      <td>584207.0</td>\n",
       "      <td>611.0</td>\n",
       "      <td>3618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>@shenaegrimes</td>\n",
       "      <td>Shenae</td>\n",
       "      <td>Grimes</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>03/13/2010</td>\n",
       "      <td>3034.0</td>\n",
       "      <td>5713.0</td>\n",
       "      <td>228850.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>@JessicaLStroup</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>Stroup</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>04/23/2010</td>\n",
       "      <td>2993.0</td>\n",
       "      <td>1682.0</td>\n",
       "      <td>199225.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>759.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>@MattLanter</td>\n",
       "      <td>Matt</td>\n",
       "      <td>Lanter</td>\n",
       "      <td>Actors</td>\n",
       "      <td>Everywhere</td>\n",
       "      <td>Eng</td>\n",
       "      <td>11/12/2009</td>\n",
       "      <td>3155.0</td>\n",
       "      <td>8770.0</td>\n",
       "      <td>458151.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>2392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>@MACKWILDS</td>\n",
       "      <td>Tristan</td>\n",
       "      <td>Wilds</td>\n",
       "      <td>Actors</td>\n",
       "      <td></td>\n",
       "      <td>Eng</td>\n",
       "      <td>03/03/2009</td>\n",
       "      <td>3409.0</td>\n",
       "      <td>42826.0</td>\n",
       "      <td>182106.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>1103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>@EmilyVanCamp</td>\n",
       "      <td>Emily</td>\n",
       "      <td>VanCamp</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>05/22/2011</td>\n",
       "      <td>2599.0</td>\n",
       "      <td>1592.0</td>\n",
       "      <td>555782.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>@Gabriel_Mann</td>\n",
       "      <td>Gabriel</td>\n",
       "      <td>Mann</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>05/11/2010</td>\n",
       "      <td>2975.0</td>\n",
       "      <td>14831.0</td>\n",
       "      <td>262308.0</td>\n",
       "      <td>996.0</td>\n",
       "      <td>24288.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>@Nick_Wechsler</td>\n",
       "      <td>Nick</td>\n",
       "      <td>Wechsler</td>\n",
       "      <td>Actors</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>06/17/2013</td>\n",
       "      <td>1842.0</td>\n",
       "      <td>4518.0</td>\n",
       "      <td>152101.0</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>10853.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>@christabrittany</td>\n",
       "      <td>Christa</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Actors</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Eng</td>\n",
       "      <td>08/18/2010</td>\n",
       "      <td>2876.0</td>\n",
       "      <td>1482.0</td>\n",
       "      <td>110154.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>617.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>@smashleybell</td>\n",
       "      <td>Ashley</td>\n",
       "      <td>Madekwe</td>\n",
       "      <td>Actors</td>\n",
       "      <td>London girl living in LA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>05/13/2009</td>\n",
       "      <td>3338.0</td>\n",
       "      <td>22425.0</td>\n",
       "      <td>119985.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>3123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>@Noahpinion</td>\n",
       "      <td>Noah</td>\n",
       "      <td>Smith</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>04/14/2011</td>\n",
       "      <td>2637.0</td>\n",
       "      <td>140980.0</td>\n",
       "      <td>102403.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>211850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>@Austan_Goolsbee</td>\n",
       "      <td>Austan</td>\n",
       "      <td>Goolsbee</td>\n",
       "      <td>Economists</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Eng</td>\n",
       "      <td>11/19/2010</td>\n",
       "      <td>2783.0</td>\n",
       "      <td>4771.0</td>\n",
       "      <td>76957.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1607.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>@BruceBartlett</td>\n",
       "      <td>Bruce</td>\n",
       "      <td>Bartlett</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>09/15/2011</td>\n",
       "      <td>2483.0</td>\n",
       "      <td>35432.0</td>\n",
       "      <td>61214.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>@mileskimball</td>\n",
       "      <td>Miles</td>\n",
       "      <td>kimball</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>10/05/2009</td>\n",
       "      <td>3193.0</td>\n",
       "      <td>39004.0</td>\n",
       "      <td>16225.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>10640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>@KingEconomist</td>\n",
       "      <td>Stephen</td>\n",
       "      <td>King</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>07/30/2012</td>\n",
       "      <td>2164.0</td>\n",
       "      <td>4223.0</td>\n",
       "      <td>20688.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>@jodiecongirl</td>\n",
       "      <td>Jodi</td>\n",
       "      <td>Beggs</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>04/14/2009</td>\n",
       "      <td>3367.0</td>\n",
       "      <td>38950.0</td>\n",
       "      <td>15136.0</td>\n",
       "      <td>2838.0</td>\n",
       "      <td>20235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>@kaushikcbasu</td>\n",
       "      <td>kaushik</td>\n",
       "      <td>Basu</td>\n",
       "      <td>Economists</td>\n",
       "      <td>New York</td>\n",
       "      <td>Eng</td>\n",
       "      <td>12/02/2008</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>5587.0</td>\n",
       "      <td>109940.0</td>\n",
       "      <td>643.0</td>\n",
       "      <td>2345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>@haroldpollack</td>\n",
       "      <td>Harold</td>\n",
       "      <td>Pollack</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>12/25/2009</td>\n",
       "      <td>3112.0</td>\n",
       "      <td>65253.0</td>\n",
       "      <td>20537.0</td>\n",
       "      <td>2427.0</td>\n",
       "      <td>7088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>@econoflove</td>\n",
       "      <td>Shoshana</td>\n",
       "      <td>Grossbard</td>\n",
       "      <td>Economists</td>\n",
       "      <td>San Diego USA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>07/06/2009</td>\n",
       "      <td>3283.0</td>\n",
       "      <td>17559.0</td>\n",
       "      <td>4155.0</td>\n",
       "      <td>2334.0</td>\n",
       "      <td>4091.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>@SBronars</td>\n",
       "      <td>Stephen</td>\n",
       "      <td>Bronars</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>07/11/2011</td>\n",
       "      <td>2549.0</td>\n",
       "      <td>4965.0</td>\n",
       "      <td>2950.0</td>\n",
       "      <td>606.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>@joebrusuelas</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>Brusuelas</td>\n",
       "      <td>Economists</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Eng</td>\n",
       "      <td>01/28/2010</td>\n",
       "      <td>3078.0</td>\n",
       "      <td>22003.0</td>\n",
       "      <td>9077.0</td>\n",
       "      <td>3129.0</td>\n",
       "      <td>25179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>@IanShepherdson</td>\n",
       "      <td>Ian</td>\n",
       "      <td>Shepherdson</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>07/06/2011</td>\n",
       "      <td>2554.0</td>\n",
       "      <td>4291.0</td>\n",
       "      <td>11642.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>@bill_easterly</td>\n",
       "      <td>William</td>\n",
       "      <td>Easterly</td>\n",
       "      <td>Economists</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Eng</td>\n",
       "      <td>04/27/2009</td>\n",
       "      <td>3354.0</td>\n",
       "      <td>13461.0</td>\n",
       "      <td>109922.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>443.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>@AllisonSchrager</td>\n",
       "      <td>Allison</td>\n",
       "      <td>Schrager</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>10/12/2009</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>3313.0</td>\n",
       "      <td>5813.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>923.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>@RBReich</td>\n",
       "      <td>Robert</td>\n",
       "      <td>Reich</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>05/26/2010</td>\n",
       "      <td>2959.0</td>\n",
       "      <td>8138.0</td>\n",
       "      <td>593715.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>592.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>@TimDuy</td>\n",
       "      <td>Tim</td>\n",
       "      <td>Duy</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>09/06/2011</td>\n",
       "      <td>2492.0</td>\n",
       "      <td>18467.0</td>\n",
       "      <td>8764.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>7606.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>@kyrstensinema</td>\n",
       "      <td>Kyrsten</td>\n",
       "      <td>Sinema</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Eng</td>\n",
       "      <td>02/13/2009</td>\n",
       "      <td>3427.0</td>\n",
       "      <td>14452.0</td>\n",
       "      <td>33398.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>@RepBarbaraLee</td>\n",
       "      <td>Barbara</td>\n",
       "      <td>Lee</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>Washington, DC and Oakland, CA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>02/07/2011</td>\n",
       "      <td>2703.0</td>\n",
       "      <td>7903.0</td>\n",
       "      <td>186324.0</td>\n",
       "      <td>16312.0</td>\n",
       "      <td>3387.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>@MikeLICSW</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Langlois</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>10/04/2010</td>\n",
       "      <td>2829.0</td>\n",
       "      <td>3996.0</td>\n",
       "      <td>5949.0</td>\n",
       "      <td>6197.0</td>\n",
       "      <td>323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>@njsmyth</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>Smyth</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>Buffalo, NY, USA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>11/18/2008</td>\n",
       "      <td>3514.0</td>\n",
       "      <td>24422.0</td>\n",
       "      <td>6618.0</td>\n",
       "      <td>4833.0</td>\n",
       "      <td>4160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>@socworkpodcast</td>\n",
       "      <td>Jonathan</td>\n",
       "      <td>Singer</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>Eng</td>\n",
       "      <td>09/09/2009</td>\n",
       "      <td>3219.0</td>\n",
       "      <td>45426.0</td>\n",
       "      <td>14252.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>22421.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>@johannagreeson</td>\n",
       "      <td>Johanna</td>\n",
       "      <td>Greeson</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>Philadelphia, PA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>04/04/2009</td>\n",
       "      <td>3377.0</td>\n",
       "      <td>7987.0</td>\n",
       "      <td>1858.0</td>\n",
       "      <td>2582.0</td>\n",
       "      <td>183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>@JimmySW</td>\n",
       "      <td>Jimmy</td>\n",
       "      <td>Young</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>10/23/2009</td>\n",
       "      <td>3175.0</td>\n",
       "      <td>10335.0</td>\n",
       "      <td>2545.0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>2278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>@tiziana_dearing</td>\n",
       "      <td>Tiziana</td>\n",
       "      <td>dearing</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>10/28/2008</td>\n",
       "      <td>3535.0</td>\n",
       "      <td>4350.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>430.0</td>\n",
       "      <td>1535.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>@ProfessorChic</td>\n",
       "      <td>Jamie</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>04/04/2011</td>\n",
       "      <td>2647.0</td>\n",
       "      <td>79769.0</td>\n",
       "      <td>1456.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>24906.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>@DrKumarVishwas</td>\n",
       "      <td>Kumar</td>\n",
       "      <td>Vishwas</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>Ghaziabad,New Delhi</td>\n",
       "      <td>Eng</td>\n",
       "      <td>05/08/2010</td>\n",
       "      <td>2978.0</td>\n",
       "      <td>26829.0</td>\n",
       "      <td>4808310.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>88196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>@drjoekort</td>\n",
       "      <td>Joe</td>\n",
       "      <td>Kort</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>ÜT: 42.439627,-82.892816</td>\n",
       "      <td>Eng</td>\n",
       "      <td>12/09/2008</td>\n",
       "      <td>3493.0</td>\n",
       "      <td>5528.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>2638.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>@BDUTT</td>\n",
       "      <td>Barkha</td>\n",
       "      <td>Dutt</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>India</td>\n",
       "      <td>Eng</td>\n",
       "      <td>02/02/2009</td>\n",
       "      <td>3437.0</td>\n",
       "      <td>118526.0</td>\n",
       "      <td>6818754.0</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>27906.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>@RaggiKotak</td>\n",
       "      <td>Raggi</td>\n",
       "      <td>Kotak</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>03/03/2016</td>\n",
       "      <td>852.0</td>\n",
       "      <td>943.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>1118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>@Martha7144</td>\n",
       "      <td>Martha</td>\n",
       "      <td>Campbell</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>Belfast, Northern Ireland</td>\n",
       "      <td>Eng</td>\n",
       "      <td>08/04/2015</td>\n",
       "      <td>1063.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>1172.0</td>\n",
       "      <td>1092.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>358 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Twitter_Handle First_Name    Last_Name           Genre  \\\n",
       "0      @_AnneHathaway       Anne     Hathaway          Actors   \n",
       "1    @IAMannalynnemcc  AnnaLynne       McCord          Actors   \n",
       "2         @EmmaWatson       Emma       Watson          Actors   \n",
       "4        @LeoDiCaprio   Leonardo     DiCaprio          Actors   \n",
       "5        @EvaLongoria        Eva       Baston          Actors   \n",
       "6            @TheRock     Dwayne      Johnson          Actors   \n",
       "7        @jessicaalba    Jessica         Alba          Actors   \n",
       "8        @EllenPompeo      Ellen       Pompeo          Actors   \n",
       "9     @JessicaCapshaw    Jessica      Capshaw          Actors   \n",
       "10         @sarahdrew      Sarah         Drew          Actors   \n",
       "11       @IamSandraOh     Sandra           Oh          Actors   \n",
       "12       @SaraRamirez       Sara      Ramirez          Actors   \n",
       "13    @iJesseWilliams      Jesse     Williams          Actors   \n",
       "14    @TheRealKMcKidd      Kevin       McKidd          Actors   \n",
       "15        @KatieHeigl  Katherine        Heigl          Actors   \n",
       "16      @JasonWGeorge      Jason       George          Actors   \n",
       "17         @chy_leigh     Chyler        Leigh          Actors   \n",
       "18         @katewalsh       Kate        Walsh          Actors   \n",
       "19     @KellyMcCreary      Kelly     McCreary          Actors   \n",
       "20       @IWashington     Isaiah   Washington          Actors   \n",
       "21    @DohertyShannen    Shannen      Doherty          Actors   \n",
       "22      @shenaegrimes     Shenae       Grimes          Actors   \n",
       "23    @JessicaLStroup    Jessica       Stroup          Actors   \n",
       "24        @MattLanter       Matt       Lanter          Actors   \n",
       "25         @MACKWILDS    Tristan        Wilds          Actors   \n",
       "26      @EmilyVanCamp      Emily      VanCamp          Actors   \n",
       "27      @Gabriel_Mann    Gabriel         Mann          Actors   \n",
       "28     @Nick_Wechsler       Nick     Wechsler          Actors   \n",
       "29   @christabrittany    Christa        Allen          Actors   \n",
       "30      @smashleybell     Ashley      Madekwe          Actors   \n",
       "..                ...        ...          ...             ...   \n",
       "332       @Noahpinion       Noah        Smith      Economists   \n",
       "333  @Austan_Goolsbee     Austan     Goolsbee      Economists   \n",
       "334    @BruceBartlett      Bruce     Bartlett      Economists   \n",
       "335     @mileskimball      Miles      kimball      Economists   \n",
       "336    @KingEconomist    Stephen         King      Economists   \n",
       "337     @jodiecongirl       Jodi        Beggs      Economists   \n",
       "338     @kaushikcbasu    kaushik         Basu      Economists   \n",
       "339    @haroldpollack     Harold      Pollack      Economists   \n",
       "340       @econoflove   Shoshana    Grossbard      Economists   \n",
       "341         @SBronars    Stephen      Bronars      Economists   \n",
       "342     @joebrusuelas     Joseph    Brusuelas      Economists   \n",
       "343   @IanShepherdson        Ian  Shepherdson      Economists   \n",
       "344    @bill_easterly    William     Easterly      Economists   \n",
       "345  @AllisonSchrager    Allison     Schrager      Economists   \n",
       "346          @RBReich     Robert        Reich      Economists   \n",
       "347           @TimDuy        Tim          Duy      Economists   \n",
       "348    @kyrstensinema    Kyrsten       Sinema  Social_workers   \n",
       "349    @RepBarbaraLee    Barbara          Lee  Social_workers   \n",
       "350        @MikeLICSW       Mike     Langlois  Social_workers   \n",
       "351          @njsmyth      Nancy        Smyth  Social_workers   \n",
       "352   @socworkpodcast   Jonathan       Singer  Social_workers   \n",
       "353   @johannagreeson    Johanna      Greeson  Social_workers   \n",
       "354          @JimmySW      Jimmy        Young  Social_workers   \n",
       "355  @tiziana_dearing    Tiziana      dearing  Social_workers   \n",
       "356    @ProfessorChic      Jamie     Mitchell  Social_workers   \n",
       "357   @DrKumarVishwas      Kumar      Vishwas  Social_workers   \n",
       "358        @drjoekort        Joe         Kort  Social_workers   \n",
       "359            @BDUTT     Barkha         Dutt  Social_workers   \n",
       "360       @RaggiKotak      Raggi        Kotak  Social_workers   \n",
       "361       @Martha7144     Martha     Campbell  Social_workers   \n",
       "\n",
       "                                Loc Lang  Created On  Age Of Account  \\\n",
       "0                                NA  Eng  09/13/2012          2118.0   \n",
       "1                                NA  Eng  01/16/2010          3090.0   \n",
       "2                                NA  Eng  07/14/2010          2910.0   \n",
       "4                                NA  Eng  04/16/2010          2999.0   \n",
       "5                   Los Angeles, CA  Eng  02/02/2010          3072.0   \n",
       "6                                NA  Eng  02/11/2011          2698.0   \n",
       "7                                NA  Eng  11/24/2009          3142.0   \n",
       "8                             NY/LA  Eng  03/23/2013          1928.0   \n",
       "9                                NA  Eng  05/05/2011          2615.0   \n",
       "10                               NA  Eng  11/14/2009          3153.0   \n",
       "11                                   Eng  09/20/2013          1746.0   \n",
       "12                               NA  Eng  07/30/2013          1798.0   \n",
       "13                               NA  Eng  04/12/2009          3369.0   \n",
       "14                               NA  Eng  04/05/2009          3376.0   \n",
       "15                   Utah / Toronto  Eng  11/03/2009          3164.0   \n",
       "16                               NA  Eng  09/19/2012          2112.0   \n",
       "17                               NA  Eng  03/02/2014          1583.0   \n",
       "18                               NA  Eng  08/30/2009          3228.0   \n",
       "19                               NA  Eng  12/26/2011          2381.0   \n",
       "20            Everywhere I tweet...  Eng  02/06/2011          2704.0   \n",
       "21                               NA  Eng  03/02/2010          3044.0   \n",
       "22                               NA  Eng  03/13/2010          3034.0   \n",
       "23                               NA  Eng  04/23/2010          2993.0   \n",
       "24                       Everywhere  Eng  11/12/2009          3155.0   \n",
       "25                                   Eng  03/03/2009          3409.0   \n",
       "26                               NA  Eng  05/22/2011          2599.0   \n",
       "27                               NA  Eng  05/11/2010          2975.0   \n",
       "28                               NA  Eng  06/17/2013          1842.0   \n",
       "29                      Los Angeles  Eng  08/18/2010          2876.0   \n",
       "30         London girl living in LA  Eng  05/13/2009          3338.0   \n",
       "..                              ...  ...         ...             ...   \n",
       "332                              NA  Eng  04/14/2011          2637.0   \n",
       "333                         Chicago  Eng  11/19/2010          2783.0   \n",
       "334                              NA  Eng  09/15/2011          2483.0   \n",
       "335                              NA  Eng  10/05/2009          3193.0   \n",
       "336                              NA  Eng  07/30/2012          2164.0   \n",
       "337                              NA  Eng  04/14/2009          3367.0   \n",
       "338                        New York  Eng  12/02/2008          3500.0   \n",
       "339                              NA  Eng  12/25/2009          3112.0   \n",
       "340                   San Diego USA  Eng  07/06/2009          3283.0   \n",
       "341                              NA  Eng  07/11/2011          2549.0   \n",
       "342                   New York City  Eng  01/28/2010          3078.0   \n",
       "343                              NA  Eng  07/06/2011          2554.0   \n",
       "344                   New York City  Eng  04/27/2009          3354.0   \n",
       "345                              NA  Eng  10/12/2009          3186.0   \n",
       "346                              NA  Eng  05/26/2010          2959.0   \n",
       "347                              NA  Eng  09/06/2011          2492.0   \n",
       "348                         Phoenix  Eng  02/13/2009          3427.0   \n",
       "349  Washington, DC and Oakland, CA  Eng  02/07/2011          2703.0   \n",
       "350                              NA  Eng  10/04/2010          2829.0   \n",
       "351                Buffalo, NY, USA  Eng  11/18/2008          3514.0   \n",
       "352                     Chicago, IL  Eng  09/09/2009          3219.0   \n",
       "353                Philadelphia, PA  Eng  04/04/2009          3377.0   \n",
       "354                              NA  Eng  10/23/2009          3175.0   \n",
       "355                              NA  Eng  10/28/2008          3535.0   \n",
       "356                              NA  Eng  04/04/2011          2647.0   \n",
       "357             Ghaziabad,New Delhi  Eng  05/08/2010          2978.0   \n",
       "358        ÜT: 42.439627,-82.892816  Eng  12/09/2008          3493.0   \n",
       "359                          India   Eng  02/02/2009          3437.0   \n",
       "360                              NA   NA  03/03/2016           852.0   \n",
       "361       Belfast, Northern Ireland  Eng  08/04/2015          1063.0   \n",
       "\n",
       "       Tweets   Followers  Following  Favorites Count  \n",
       "0         2.0       282.0        0.0              0.0  \n",
       "1      5776.0    228657.0      502.0           2157.0  \n",
       "2      1642.0  29143216.0      382.0            991.0  \n",
       "4      1285.0  19327180.0      145.0             40.0  \n",
       "5     15806.0   7691876.0      809.0            151.0  \n",
       "6     21662.0  13044259.0      242.0            142.0  \n",
       "7      6244.0   9649191.0     1359.0           1442.0  \n",
       "8      5211.0   1551708.0      541.0           6060.0  \n",
       "9      4017.0    935946.0      348.0             86.0  \n",
       "10     9611.0    822552.0      201.0            622.0  \n",
       "11      578.0   1005870.0       21.0             14.0  \n",
       "12     8030.0    820544.0      802.0           6653.0  \n",
       "13    19843.0   2400110.0     3531.0           5721.0  \n",
       "14     7614.0    771095.0      502.0            386.0  \n",
       "15     8477.0   1325714.0      117.0           3124.0  \n",
       "16     4306.0    236849.0      697.0           3019.0  \n",
       "17     5811.0    302121.0      427.0           5544.0  \n",
       "18    18113.0    894869.0      828.0           3559.0  \n",
       "19     4048.0    256645.0      526.0          11772.0  \n",
       "20    29865.0     98709.0     7490.0          11410.0  \n",
       "21     9347.0    584207.0      611.0           3618.0  \n",
       "22     5713.0    228850.0       85.0             97.0  \n",
       "23     1682.0    199225.0      330.0            759.0  \n",
       "24     8770.0    458151.0      403.0           2392.0  \n",
       "25    42826.0    182106.0     1999.0           1103.0  \n",
       "26     1592.0    555782.0      235.0            100.0  \n",
       "27    14831.0    262308.0      996.0          24288.0  \n",
       "28     4518.0    152101.0     1201.0          10853.0  \n",
       "29     1482.0    110154.0      455.0            617.0  \n",
       "30    22425.0    119985.0      580.0           3123.0  \n",
       "..        ...         ...        ...              ...  \n",
       "332  140980.0    102403.0      810.0         211850.0  \n",
       "333    4771.0     76957.0      456.0           1607.0  \n",
       "334   35432.0     61214.0      950.0             33.0  \n",
       "335   39004.0     16225.0      920.0          10640.0  \n",
       "336    4223.0     20688.0      281.0            170.0  \n",
       "337   38950.0     15136.0     2838.0          20235.0  \n",
       "338    5587.0    109940.0      643.0           2345.0  \n",
       "339   65253.0     20537.0     2427.0           7088.0  \n",
       "340   17559.0      4155.0     2334.0           4091.0  \n",
       "341    4965.0      2950.0      606.0             98.0  \n",
       "342   22003.0      9077.0     3129.0          25179.0  \n",
       "343    4291.0     11642.0      519.0            254.0  \n",
       "344   13461.0    109922.0     1990.0            443.0  \n",
       "345    3313.0      5813.0      800.0            923.0  \n",
       "346    8138.0    593715.0      220.0            592.0  \n",
       "347   18467.0      8764.0      329.0           7606.0  \n",
       "348   14452.0     33398.0      300.0            269.0  \n",
       "349    7903.0    186324.0    16312.0           3387.0  \n",
       "350    3996.0      5949.0     6197.0            323.0  \n",
       "351   24422.0      6618.0     4833.0           4160.0  \n",
       "352   45426.0     14252.0      793.0          22421.0  \n",
       "353    7987.0      1858.0     2582.0            183.0  \n",
       "354   10335.0      2545.0     1997.0           2278.0  \n",
       "355    4350.0      1460.0      430.0           1535.0  \n",
       "356   79769.0      1456.0      572.0          24906.0  \n",
       "357   26829.0   4808310.0       91.0          88196.0  \n",
       "358    5528.0      2500.0     1027.0           2638.0  \n",
       "359  118526.0   6818754.0     1574.0          27906.0  \n",
       "360     943.0       341.0      400.0           1118.0  \n",
       "361     445.0       337.0     1172.0           1092.0  \n",
       "\n",
       "[358 rows x 12 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  TM - Print the DF\n",
    "Data_Influencers_DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Inna\\AppData\\Local\\conda\\conda\\envs\\PythonData\\lib\\site-packages\\ipykernel_launcher.py:4: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\Inna\\AppData\\Local\\conda\\conda\\envs\\PythonData\\lib\\site-packages\\ipykernel_launcher.py:5: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Genre</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>Followers</th>\n",
       "      <th>Average Tweets</th>\n",
       "      <th>Age Of Account</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Musicians</th>\n",
       "      <td>Musicians</td>\n",
       "      <td>455085.0</td>\n",
       "      <td>919362961.0</td>\n",
       "      <td>10583.372093</td>\n",
       "      <td>3075.767442</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sports_celeb</th>\n",
       "      <td>Sports_celeb</td>\n",
       "      <td>896670.0</td>\n",
       "      <td>512195341.0</td>\n",
       "      <td>7598.898305</td>\n",
       "      <td>2794.949153</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Politicians</th>\n",
       "      <td>Politicians</td>\n",
       "      <td>575159.0</td>\n",
       "      <td>247625927.0</td>\n",
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       "      <td>2821.671233</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedians</th>\n",
       "      <td>Comedians</td>\n",
       "      <td>481405.0</td>\n",
       "      <td>210181493.0</td>\n",
       "      <td>11195.465116</td>\n",
       "      <td>3142.348837</td>\n",
       "      <td>43</td>\n",
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       "    <tr>\n",
       "      <th>Actors</th>\n",
       "      <td>Actors</td>\n",
       "      <td>387975.0</td>\n",
       "      <td>102835738.0</td>\n",
       "      <td>9462.804878</td>\n",
       "      <td>2690.048780</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social_workers</th>\n",
       "      <td>Social_workers</td>\n",
       "      <td>350911.0</td>\n",
       "      <td>11884102.0</td>\n",
       "      <td>25065.071429</td>\n",
       "      <td>2874.928571</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Economists</th>\n",
       "      <td>Economists</td>\n",
       "      <td>580014.0</td>\n",
       "      <td>2194922.0</td>\n",
       "      <td>22308.230769</td>\n",
       "      <td>2852.000000</td>\n",
       "      <td>26</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Genre    Tweets    Followers  Average Tweets  \\\n",
       "Genre                                                                   \n",
       "Musicians            Musicians  455085.0  919362961.0    10583.372093   \n",
       "Sports_celeb      Sports_celeb  896670.0  512195341.0     7598.898305   \n",
       "Politicians        Politicians  575159.0  247625927.0     7878.890411   \n",
       "Comedians            Comedians  481405.0  210181493.0    11195.465116   \n",
       "Actors                  Actors  387975.0  102835738.0     9462.804878   \n",
       "Social_workers  Social_workers  350911.0   11884102.0    25065.071429   \n",
       "Economists          Economists  580014.0    2194922.0    22308.230769   \n",
       "\n",
       "                Age Of Account  Count  \n",
       "Genre                                  \n",
       "Musicians          3075.767442     43  \n",
       "Sports_celeb       2794.949153    118  \n",
       "Politicians        2821.671233     73  \n",
       "Comedians          3142.348837     43  \n",
       "Actors             2690.048780     41  \n",
       "Social_workers     2874.928571     14  \n",
       "Economists         2852.000000     26  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#   TM - Grouped on Genre \n",
    "#   TM - Data_Influencers_DF.is_copy = False. The .copy() helps to get rid of warning.\n",
    "\n",
    "Data_Influencers_DF['Average Tweets'] = Data_Influencers_DF['Tweets'].copy()\n",
    "Data_Influencers_DF['Count'] = 0\n",
    "\n",
    "\n",
    "AggregatedGenre = Data_Influencers_DF.groupby([\"Genre\"]).agg({'Genre': 'min', 'Tweets': 'sum', 'Followers': 'sum','Average Tweets':'mean','Age Of Account':'mean','Count':'size'})\n",
    "\n",
    "AggregatedGenre.sort_values(['Followers'], ascending =False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2874.928571</td>\n",
       "      <td>14</td>\n",
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       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actors</th>\n",
       "      <td>Actors</td>\n",
       "      <td>387975.0</td>\n",
       "      <td>102835738.0</td>\n",
       "      <td>9462.804878</td>\n",
       "      <td>2690.048780</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Politicians</th>\n",
       "      <td>Politicians</td>\n",
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       "      <td>73</td>\n",
       "    </tr>\n",
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       "      <th>Sports_celeb</th>\n",
       "      <td>Sports_celeb</td>\n",
       "      <td>896670.0</td>\n",
       "      <td>512195341.0</td>\n",
       "      <td>7598.898305</td>\n",
       "      <td>2794.949153</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Genre    Tweets    Followers  Average Tweets  \\\n",
       "Genre                                                                   \n",
       "Social_workers  Social_workers  350911.0   11884102.0    25065.071429   \n",
       "Economists          Economists  580014.0    2194922.0    22308.230769   \n",
       "Comedians            Comedians  481405.0  210181493.0    11195.465116   \n",
       "Musicians            Musicians  455085.0  919362961.0    10583.372093   \n",
       "Actors                  Actors  387975.0  102835738.0     9462.804878   \n",
       "Politicians        Politicians  575159.0  247625927.0     7878.890411   \n",
       "Sports_celeb      Sports_celeb  896670.0  512195341.0     7598.898305   \n",
       "\n",
       "                Age Of Account  Count  \n",
       "Genre                                  \n",
       "Social_workers     2874.928571     14  \n",
       "Economists         2852.000000     26  \n",
       "Comedians          3142.348837     43  \n",
       "Musicians          3075.767442     43  \n",
       "Actors             2690.048780     41  \n",
       "Politicians        2821.671233     73  \n",
       "Sports_celeb       2794.949153    118  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#   TM - Sorting on Average Tweets and grouped on Genere\n",
    "AggregatedGenre.sort_values(['Average Tweets'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " ## TM - whole cell\n",
    "# Labels for the sections of our pie chart\n",
    "labels = AggregatedGenre['Genre']\n",
    "\n",
    "# The values of each section of the pie chart\n",
    "# sizes = Data_Influencers_DF.groupby([\"Genere\"],as_index=False).sum()[\"fare\"].tolist()\n",
    "sizes = AggregatedGenre['Followers']\n",
    "\n",
    "# The colors of each section of the pie chart\n",
    "colors = [\"gold\", \"lightskyblue\", \"lightcoral\",'red','pink','darkblue','purple']\n",
    "\n",
    "# Tells matplotlib to seperate the \"Python\" section from the others\n",
    "explode = (0,0,0,0,0,0,0.1)\n",
    "\n",
    "# Creates the pie chart based upon the values above\n",
    "# Automatically finds the percentages of each part of the pie chart\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct=\"%1.1f%%\", shadow=True, startangle=120)\n",
    "\n",
    "# Tells matplotlib that we want a pie chart with equal axes\n",
    "plt.axis(\"off\")\n",
    "\n",
    "  \n",
    "plt.title(\"% Followers per Genres\", fontsize=16)\n",
    "\n",
    "plt.savefig('Images/% Followers per Genres.png')\n",
    "# Prints our pie chart to the screen\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " ## TM - whole cell\n",
    "# Labels for the sections of our pie chart\n",
    "labels = AggregatedGenre['Genre']\n",
    "\n",
    "# The values of each section of the pie chart\n",
    "# sizes = Data_Influencers_DF.groupby([\"Genere\"],as_index=False).sum()[\"fare\"].tolist()\n",
    "sizes = AggregatedGenre['Tweets']\n",
    "\n",
    "# The colors of each section of the pie chart\n",
    "colors = [\"gold\", \"lightskyblue\", \"lightcoral\",'red','pink','darkblue','purple']\n",
    "\n",
    "# Tells matplotlib to seperate the \"Python\" section from the others\n",
    "explode = (0,0,0,0,0,0,0.1)\n",
    "\n",
    "# Creates the pie chart based upon the values above\n",
    "# Automatically finds the percentages of each part of the pie chart\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct=\"%1.1f%%\", shadow=True, startangle=120)\n",
    "\n",
    "# Tells matplotlib that we want a pie chart with equal axes\n",
    "plt.axis(\"off\")\n",
    "\n",
    "  \n",
    "plt.title(\"% Tweets per Genres\", fontsize=16)\n",
    "\n",
    "plt.savefig('Images/% Tweets per Genres.png')\n",
    "# Prints our pie chart to the screen\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Twitter_Handle</th>\n",
       "      <th>First_Name</th>\n",
       "      <th>Last_Name</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Loc</th>\n",
       "      <th>Lang</th>\n",
       "      <th>Created On</th>\n",
       "      <th>Age Of Account</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>Followers</th>\n",
       "      <th>Following</th>\n",
       "      <th>Favorites Count</th>\n",
       "      <th>Average Tweets</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>@Noahpinion</td>\n",
       "      <td>Noah</td>\n",
       "      <td>Smith</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>04/14/2011</td>\n",
       "      <td>2637.0</td>\n",
       "      <td>140980.0</td>\n",
       "      <td>102403.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>211850.0</td>\n",
       "      <td>140980.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>@BDUTT</td>\n",
       "      <td>Barkha</td>\n",
       "      <td>Dutt</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>India</td>\n",
       "      <td>Eng</td>\n",
       "      <td>02/02/2009</td>\n",
       "      <td>3437.0</td>\n",
       "      <td>118526.0</td>\n",
       "      <td>6818754.0</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>27906.0</td>\n",
       "      <td>118526.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>@ProfessorChic</td>\n",
       "      <td>Jamie</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>04/04/2011</td>\n",
       "      <td>2647.0</td>\n",
       "      <td>79769.0</td>\n",
       "      <td>1456.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>24906.0</td>\n",
       "      <td>79769.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>@haroldpollack</td>\n",
       "      <td>Harold</td>\n",
       "      <td>Pollack</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>12/25/2009</td>\n",
       "      <td>3112.0</td>\n",
       "      <td>65253.0</td>\n",
       "      <td>20537.0</td>\n",
       "      <td>2427.0</td>\n",
       "      <td>7088.0</td>\n",
       "      <td>65253.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>@CoryBooker</td>\n",
       "      <td>Cory</td>\n",
       "      <td>Booker</td>\n",
       "      <td>Politicians</td>\n",
       "      <td>Newark, NJ</td>\n",
       "      <td>Eng</td>\n",
       "      <td>08/11/2008</td>\n",
       "      <td>3613.0</td>\n",
       "      <td>61946.0</td>\n",
       "      <td>4121795.0</td>\n",
       "      <td>93080.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>61946.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>@MarkThoma</td>\n",
       "      <td>Mark</td>\n",
       "      <td>Thoma</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>10/18/2008</td>\n",
       "      <td>3545.0</td>\n",
       "      <td>60106.0</td>\n",
       "      <td>44683.0</td>\n",
       "      <td>417.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>60106.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>@iliza</td>\n",
       "      <td>Iliza</td>\n",
       "      <td>Shiesinger</td>\n",
       "      <td>Comedians</td>\n",
       "      <td>Hollywood, CA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>02/15/2009</td>\n",
       "      <td>3425.0</td>\n",
       "      <td>49175.0</td>\n",
       "      <td>170279.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>27103.0</td>\n",
       "      <td>49175.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>@IGGYAZALEA</td>\n",
       "      <td>Iggy</td>\n",
       "      <td>Azalea</td>\n",
       "      <td>Musicians</td>\n",
       "      <td>California, USA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>06/09/2010</td>\n",
       "      <td>2946.0</td>\n",
       "      <td>47711.0</td>\n",
       "      <td>8083765.0</td>\n",
       "      <td>6511.0</td>\n",
       "      <td>4224.0</td>\n",
       "      <td>47711.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>@criscyborg</td>\n",
       "      <td>Cris</td>\n",
       "      <td>Cyborg</td>\n",
       "      <td>Sports_celeb</td>\n",
       "      <td>cris.cyborg.llc@gmail.com</td>\n",
       "      <td>Eng</td>\n",
       "      <td>11/29/2009</td>\n",
       "      <td>3138.0</td>\n",
       "      <td>46479.0</td>\n",
       "      <td>191740.0</td>\n",
       "      <td>2134.0</td>\n",
       "      <td>12433.0</td>\n",
       "      <td>46479.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>@socworkpodcast</td>\n",
       "      <td>Jonathan</td>\n",
       "      <td>Singer</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>Eng</td>\n",
       "      <td>09/09/2009</td>\n",
       "      <td>3219.0</td>\n",
       "      <td>45426.0</td>\n",
       "      <td>14252.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>22421.0</td>\n",
       "      <td>45426.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Twitter_Handle First_Name   Last_Name           Genre  \\\n",
       "332      @Noahpinion       Noah       Smith      Economists   \n",
       "359           @BDUTT     Barkha        Dutt  Social_workers   \n",
       "356   @ProfessorChic      Jamie    Mitchell  Social_workers   \n",
       "339   @haroldpollack     Harold     Pollack      Economists   \n",
       "276      @CoryBooker       Cory      Booker     Politicians   \n",
       "325       @MarkThoma       Mark       Thoma      Economists   \n",
       "82            @iliza      Iliza  Shiesinger       Comedians   \n",
       "125      @IGGYAZALEA       Iggy      Azalea       Musicians   \n",
       "216      @criscyborg       Cris      Cyborg    Sports_celeb   \n",
       "352  @socworkpodcast   Jonathan      Singer  Social_workers   \n",
       "\n",
       "                            Loc Lang  Created On  Age Of Account    Tweets  \\\n",
       "332                          NA  Eng  04/14/2011          2637.0  140980.0   \n",
       "359                      India   Eng  02/02/2009          3437.0  118526.0   \n",
       "356                          NA  Eng  04/04/2011          2647.0   79769.0   \n",
       "339                          NA  Eng  12/25/2009          3112.0   65253.0   \n",
       "276                  Newark, NJ  Eng  08/11/2008          3613.0   61946.0   \n",
       "325                          NA  Eng  10/18/2008          3545.0   60106.0   \n",
       "82                Hollywood, CA  Eng  02/15/2009          3425.0   49175.0   \n",
       "125             California, USA  Eng  06/09/2010          2946.0   47711.0   \n",
       "216   cris.cyborg.llc@gmail.com  Eng  11/29/2009          3138.0   46479.0   \n",
       "352                 Chicago, IL  Eng  09/09/2009          3219.0   45426.0   \n",
       "\n",
       "     Followers  Following  Favorites Count  Average Tweets  Count  \n",
       "332   102403.0      810.0         211850.0        140980.0      0  \n",
       "359  6818754.0     1574.0          27906.0        118526.0      0  \n",
       "356     1456.0      572.0          24906.0         79769.0      0  \n",
       "339    20537.0     2427.0           7088.0         65253.0      0  \n",
       "276  4121795.0    93080.0             91.0         61946.0      0  \n",
       "325    44683.0      417.0              5.0         60106.0      0  \n",
       "82    170279.0      468.0          27103.0         49175.0      0  \n",
       "125  8083765.0     6511.0           4224.0         47711.0      0  \n",
       "216   191740.0     2134.0          12433.0         46479.0      0  \n",
       "352    14252.0      793.0          22421.0         45426.0      0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -TM - People who are top ten Influencer who tweet more\n",
    "top_ten_twitters = Data_Influencers_DF.sort_values(['Tweets'],ascending=False).head(10)\n",
    "top_ten_twitters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# AggregatedGenre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Twitter_Handle</th>\n",
       "      <th>First_Name</th>\n",
       "      <th>Last_Name</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Loc</th>\n",
       "      <th>Lang</th>\n",
       "      <th>Created On</th>\n",
       "      <th>Age Of Account</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>Followers</th>\n",
       "      <th>Following</th>\n",
       "      <th>Favorites Count</th>\n",
       "      <th>Average Tweets</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>@katyperry</td>\n",
       "      <td>Katy</td>\n",
       "      <td>Perry</td>\n",
       "      <td>Musicians</td>\n",
       "      <td></td>\n",
       "      <td>Eng</td>\n",
       "      <td>02/20/2009</td>\n",
       "      <td>3419.0</td>\n",
       "      <td>9220.0</td>\n",
       "      <td>109577958.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>6214.0</td>\n",
       "      <td>9220.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>@BarackObama</td>\n",
       "      <td>Barack</td>\n",
       "      <td>Obama</td>\n",
       "      <td>Politicians</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>03/05/2007</td>\n",
       "      <td>4137.0</td>\n",
       "      <td>15517.0</td>\n",
       "      <td>103468481.0</td>\n",
       "      <td>621131.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15517.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>@taylorswift13</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>Swift</td>\n",
       "      <td>Musicians</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>12/06/2008</td>\n",
       "      <td>3496.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>85542986.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>@ladygaga</td>\n",
       "      <td>Lady</td>\n",
       "      <td>Gaga</td>\n",
       "      <td>Musicians</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>03/26/2008</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>8764.0</td>\n",
       "      <td>78902605.0</td>\n",
       "      <td>127004.0</td>\n",
       "      <td>1915.0</td>\n",
       "      <td>8764.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>@TheEllenShow</td>\n",
       "      <td>Ellen</td>\n",
       "      <td>DeGeneres</td>\n",
       "      <td>Comedians</td>\n",
       "      <td>California</td>\n",
       "      <td>Eng</td>\n",
       "      <td>08/14/2008</td>\n",
       "      <td>3610.0</td>\n",
       "      <td>16970.0</td>\n",
       "      <td>78053544.0</td>\n",
       "      <td>35730.0</td>\n",
       "      <td>954.0</td>\n",
       "      <td>16970.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>@Cristiano</td>\n",
       "      <td>Cristiano</td>\n",
       "      <td>Ronaldo</td>\n",
       "      <td>Sports_celeb</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>NA</td>\n",
       "      <td>06/14/2010</td>\n",
       "      <td>2941.0</td>\n",
       "      <td>3212.0</td>\n",
       "      <td>74239840.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3212.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>@jtimberlake</td>\n",
       "      <td>Justin</td>\n",
       "      <td>Timberlake</td>\n",
       "      <td>Musicians</td>\n",
       "      <td>Memphis, TN</td>\n",
       "      <td>Eng</td>\n",
       "      <td>03/25/2009</td>\n",
       "      <td>3387.0</td>\n",
       "      <td>3976.0</td>\n",
       "      <td>66046523.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3976.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>@BritneySpears</td>\n",
       "      <td>Britney</td>\n",
       "      <td>Spears</td>\n",
       "      <td>Musicians</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>09/22/2008</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>5475.0</td>\n",
       "      <td>58238917.0</td>\n",
       "      <td>386044.0</td>\n",
       "      <td>2554.0</td>\n",
       "      <td>5475.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>@ArianaGrande</td>\n",
       "      <td>Ariana</td>\n",
       "      <td>Grande</td>\n",
       "      <td>Musicians</td>\n",
       "      <td>ǝʌɐ uooɯʎǝuoɥ</td>\n",
       "      <td>Eng</td>\n",
       "      <td>04/23/2009</td>\n",
       "      <td>3358.0</td>\n",
       "      <td>41436.0</td>\n",
       "      <td>57677354.0</td>\n",
       "      <td>64047.0</td>\n",
       "      <td>34191.0</td>\n",
       "      <td>41436.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>@ddlovato</td>\n",
       "      <td>Demi</td>\n",
       "      <td>Lovato</td>\n",
       "      <td>Musicians</td>\n",
       "      <td>DALLAS/LA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>02/17/2009</td>\n",
       "      <td>3423.0</td>\n",
       "      <td>17085.0</td>\n",
       "      <td>57283232.0</td>\n",
       "      <td>539.0</td>\n",
       "      <td>2098.0</td>\n",
       "      <td>17085.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Twitter_Handle First_Name   Last_Name         Genre            Loc Lang  \\\n",
       "91       @katyperry       Katy       Perry     Musicians                 Eng   \n",
       "251    @BarackObama     Barack       Obama   Politicians             NA  Eng   \n",
       "86   @taylorswift13     Taylor       Swift     Musicians             NA  Eng   \n",
       "95        @ladygaga       Lady        Gaga     Musicians             NA  Eng   \n",
       "66    @TheEllenShow      Ellen   DeGeneres     Comedians     California  Eng   \n",
       "139      @Cristiano  Cristiano     Ronaldo  Sports_celeb         Madrid   NA   \n",
       "105    @jtimberlake     Justin  Timberlake     Musicians    Memphis, TN  Eng   \n",
       "107  @BritneySpears    Britney      Spears     Musicians             NA  Eng   \n",
       "87    @ArianaGrande     Ariana      Grande     Musicians  ǝʌɐ uooɯʎǝuoɥ  Eng   \n",
       "122       @ddlovato       Demi      Lovato     Musicians      DALLAS/LA  Eng   \n",
       "\n",
       "     Created On  Age Of Account   Tweets    Followers  Following  \\\n",
       "91   02/20/2009          3419.0   9220.0  109577958.0      216.0   \n",
       "251  03/05/2007          4137.0  15517.0  103468481.0   621131.0   \n",
       "86   12/06/2008          3496.0    100.0   85542986.0        0.0   \n",
       "95   03/26/2008          3750.0   8764.0   78902605.0   127004.0   \n",
       "66   08/14/2008          3610.0  16970.0   78053544.0    35730.0   \n",
       "139  06/14/2010          2941.0   3212.0   74239840.0       99.0   \n",
       "105  03/25/2009          3387.0   3976.0   66046523.0      282.0   \n",
       "107  09/22/2008          3570.0   5475.0   58238917.0   386044.0   \n",
       "87   04/23/2009          3358.0  41436.0   57677354.0    64047.0   \n",
       "122  02/17/2009          3423.0  17085.0   57283232.0      539.0   \n",
       "\n",
       "     Favorites Count  Average Tweets  Count  \n",
       "91            6214.0          9220.0      0  \n",
       "251             11.0         15517.0      0  \n",
       "86             458.0           100.0      0  \n",
       "95            1915.0          8764.0      0  \n",
       "66             954.0         16970.0      0  \n",
       "139              0.0          3212.0      0  \n",
       "105            192.0          3976.0      0  \n",
       "107           2554.0          5475.0      0  \n",
       "87           34191.0         41436.0      0  \n",
       "122           2098.0         17085.0      0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  -TM - Top ten Influencer who who have more Followers\n",
    "top_ten_influencer = Data_Influencers_DF.sort_values(['Followers'],ascending=False).head(10)\n",
    "top_ten_influencer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Sentiment Analysis of the Influencers: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------Start extraction of the tweets posted by the Influencers!!!-----------\n",
      "Extracting tweets from @katyperry\n",
      "Extracting tweets from @BarackObama\n",
      "Extracting tweets from @taylorswift13\n",
      "Extracting tweets from @ladygaga\n",
      "Extracting tweets from @TheEllenShow\n",
      "Extracting tweets from @Cristiano\n",
      "Extracting tweets from @jtimberlake\n",
      "Extracting tweets from @BritneySpears\n",
      "Extracting tweets from @ArianaGrande\n",
      "Extracting tweets from @ddlovato\n",
      "-----------End of Extraction of Tweets !!!-----------\n"
     ]
    }
   ],
   "source": [
    "# # -TM - Extract the first 1000 tweets of the Influencers\n",
    "print(\"-----------Start extraction of the tweets posted by the Influencers!!!-----------\")\n",
    "Influencers = top_ten_influencer['Twitter_Handle']\n",
    "Sentiment_array = []\n",
    "\n",
    "for user in Influencers:\n",
    "    # Set the tweet count to 100\n",
    "    tweet_count = 1000\n",
    "    print(\"Extracting tweets from %s\"%user)\n",
    "    \n",
    "    # Extract tweets up to 5 pages\n",
    "    for x in range(10):\n",
    "        influencer_tweets = api.user_timeline(user,page = x)\n",
    "# influencer_tweets        \n",
    "        \n",
    "        # For each tweet in a bunch of public tweets\n",
    "        for tweet in influencer_tweets:\n",
    "            \n",
    "            #Calculate the compound, positive, negative and neutral values of each tweet\n",
    "            compound = analyzer.polarity_scores(tweet[\"text\"])[\"compound\"]\n",
    "            pos = analyzer.polarity_scores(tweet[\"text\"])[\"pos\"]\n",
    "            neu = analyzer.polarity_scores(tweet[\"text\"])[\"neu\"]\n",
    "            neg = analyzer.polarity_scores(tweet[\"text\"])[\"neg\"]\n",
    "            \n",
    "            age_of_tweet = (datetime.now(timezone.utc) - datetime.strptime(tweet['created_at'],'%a %b %d %H:%M:%S %z %Y')).days\n",
    "            tweetAge = 0 \n",
    "            if age_of_tweet < 180:\n",
    "                tweetAge = 1\n",
    "            elif age_of_tweet < 365:   \n",
    "                tweetAge = 2\n",
    "            else:\n",
    "                tweetAge = 3\n",
    "            \n",
    " \n",
    "            \n",
    "            # Save the Tweets in an array as a dictionery item \n",
    "            Sentiment_array.append({\"Influencers\" : user,\n",
    "                                    \"Tweet Text\" : tweet[\"text\"],\n",
    "                                    \"Compound\" : compound,\n",
    "                                    \"Positive\" : pos,\n",
    "                                    \"Negative\" : neg,\n",
    "                                    \"Neutral\" : neu,\n",
    "                                    \"Date\" : datetime.strptime(tweet[\"created_at\"],'%a %b %d %H:%M:%S %z %Y').strftime('%m/%d/%Y'),\n",
    "                                    \"AgeOfTweet\":age_of_tweet,\n",
    "                                    \"TweetAgeType\":tweetAge,\n",
    "                                    \"Tweets Ago\": tweet_count\n",
    "                                   })\n",
    "            \n",
    "            #Decrease count of tweet by 1 in the reverse order\n",
    "            tweet_count -= 1\n",
    "\n",
    "print(\"-----------End of Extraction of Tweets !!!-----------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Influencers</th>\n",
       "      <th>Date</th>\n",
       "      <th>Tweet Text</th>\n",
       "      <th>Compound</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Tweets Ago</th>\n",
       "      <th>AgeOfTweet</th>\n",
       "      <th>TweetAgeType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>07/03/2018</td>\n",
       "      <td>My ❤ was touched by Carter's story. If you're ...</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.679</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/23/2018</td>\n",
       "      <td>Was so proud to give the gift of #transcendent...</td>\n",
       "      <td>0.8805</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.638</td>\n",
       "      <td>999</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/22/2018</td>\n",
       "      <td>found my first skin tag today. I think I’m an ...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>998</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/21/2018</td>\n",
       "      <td>Never forget, everyone was once a child.</td>\n",
       "      <td>0.1695</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.750</td>\n",
       "      <td>997</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/20/2018</td>\n",
       "      <td>My Aussies 🐨 &amp;amp; Kiwis 🥝! For the FINALE of ...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>996</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/15/2018</td>\n",
       "      <td>I 👁 you, @RoarForJess, and I ❤ you, and I’m si...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>995</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/09/2018</td>\n",
       "      <td>Scandinavians have figured it out.</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>994</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/09/2018</td>\n",
       "      <td>Oi oi @JackGarratt remember when we locked our...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>993</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/09/2018</td>\n",
       "      <td>WITNESS IS 1⃣❗🎂 🎈 https://t.co/fz83TjrJUr http...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>992</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/06/2018</td>\n",
       "      <td>IM SCREAMINGOSBEJRMF$&amp;amp;2@!K https://t.co/Sn...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>991</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/05/2018</td>\n",
       "      <td>CALIFORNIANS! What’s important to you? Is it g...</td>\n",
       "      <td>-0.8821</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.445</td>\n",
       "      <td>0.483</td>\n",
       "      <td>990</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/02/2018</td>\n",
       "      <td>We just played our 80TH show for #WITNESSTHETO...</td>\n",
       "      <td>0.3400</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.870</td>\n",
       "      <td>989</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/02/2018</td>\n",
       "      <td>bout to do Yoga to new Kanye record</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>988</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/01/2018</td>\n",
       "      <td>Happy #PRIDE month. I see you and I love you 🌈...</td>\n",
       "      <td>0.8360</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.470</td>\n",
       "      <td>987</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/01/2018</td>\n",
       "      <td>Imma #believer ♥️ https://t.co/BV2ZK9OeAX</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>986</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/01/2018</td>\n",
       "      <td>Here’s something to believe in: @cynthialovely...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>985</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>05/30/2018</td>\n",
       "      <td>RT @AmericanIdol: Did you think we were 'going...</td>\n",
       "      <td>-0.2924</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.902</td>\n",
       "      <td>984</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>RT @repjohnlewis: Thank you, @ABCNetwork. You ...</td>\n",
       "      <td>-0.3818</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.752</td>\n",
       "      <td>983</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>05/22/2018</td>\n",
       "      <td>Before this weekend’s American Idol finale, I…...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>982</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>05/22/2018</td>\n",
       "      <td>Season 1 is \"Going, Going, Gone\"! Congratulati...</td>\n",
       "      <td>0.8398</td>\n",
       "      <td>0.363</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.637</td>\n",
       "      <td>981</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>07/03/2018</td>\n",
       "      <td>My ❤ was touched by Carter's story. If you're ...</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.679</td>\n",
       "      <td>980</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/23/2018</td>\n",
       "      <td>Was so proud to give the gift of #transcendent...</td>\n",
       "      <td>0.8805</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.638</td>\n",
       "      <td>979</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/22/2018</td>\n",
       "      <td>found my first skin tag today. I think I’m an ...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>978</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/21/2018</td>\n",
       "      <td>Never forget, everyone was once a child.</td>\n",
       "      <td>0.1695</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.750</td>\n",
       "      <td>977</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/20/2018</td>\n",
       "      <td>My Aussies 🐨 &amp;amp; Kiwis 🥝! For the FINALE of ...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>976</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/15/2018</td>\n",
       "      <td>I 👁 you, @RoarForJess, and I ❤ you, and I’m si...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>975</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/09/2018</td>\n",
       "      <td>Scandinavians have figured it out.</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>974</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/09/2018</td>\n",
       "      <td>Oi oi @JackGarratt remember when we locked our...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>973</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/09/2018</td>\n",
       "      <td>WITNESS IS 1⃣❗🎂 🎈 https://t.co/fz83TjrJUr http...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>972</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>06/06/2018</td>\n",
       "      <td>IM SCREAMINGOSBEJRMF$&amp;amp;2@!K https://t.co/Sn...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>971</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Be careful https://t.co/qkTJLloN2y</td>\n",
       "      <td>0.1531</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.556</td>\n",
       "      <td>830</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>It was to a really cute boy 😝😍 https://t.co/0K...</td>\n",
       "      <td>0.5095</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.680</td>\n",
       "      <td>829</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Nope https://t.co/wYlJGkAsv2</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>828</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>My babies Batman and Cinderella https://t.co/1...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>827</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Skittles https://t.co/82bgpLEe8E</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>826</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Pizza https://t.co/xGP4oOXMS5</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>825</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>🤢🤮 https://t.co/bXxwjkTdWU</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>824</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1895</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>NARS https://t.co/DbSUgaq03V</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>823</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Very VERY soon.... could be a matter of weeks ...</td>\n",
       "      <td>0.0258</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.891</td>\n",
       "      <td>822</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Barbecues with family and friends https://t.co...</td>\n",
       "      <td>0.4767</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.617</td>\n",
       "      <td>821</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Concealer!! https://t.co/4yKVpx8D7J</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>820</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Everything and more https://t.co/0DQFNPuCAZ</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>819</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>You have to be able to make me laugh... https:...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>818</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Right now, yes https://t.co/mYsVBGjrjL</td>\n",
       "      <td>0.4019</td>\n",
       "      <td>0.474</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.526</td>\n",
       "      <td>817</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>😩😩😩 https://t.co/FjfnARF7WC</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>816</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Speechless by Dan + Shay https://t.co/LNdXdrf1Jc</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>815</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>LAUREL https://t.co/23AwoXMCWA</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>814</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Dogs are better https://t.co/DTwmKlhxda</td>\n",
       "      <td>0.4404</td>\n",
       "      <td>0.492</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.508</td>\n",
       "      <td>813</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Miss Dior https://t.co/ReUhY0QnP5</td>\n",
       "      <td>-0.1531</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.556</td>\n",
       "      <td>812</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Nope https://t.co/vkWPtjBzhi</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>811</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>I’m the happiest I’ve ever been in my life... ...</td>\n",
       "      <td>0.6369</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.704</td>\n",
       "      <td>810</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1909</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>😉 https://t.co/CfWsxiIxvq</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>809</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Nearly peed my pants https://t.co/cZf9VFT6aV</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>808</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Yes!! I plan on getting back into acting this ...</td>\n",
       "      <td>0.5080</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.753</td>\n",
       "      <td>807</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Someday I hope!! I love her 💖☁️ https://t.co/4...</td>\n",
       "      <td>0.8264</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.342</td>\n",
       "      <td>806</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>I can skateboard but I don’t because I’m clums...</td>\n",
       "      <td>-0.6956</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.655</td>\n",
       "      <td>805</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Pretty much... I have so much new music to rel...</td>\n",
       "      <td>0.6219</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.746</td>\n",
       "      <td>804</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>@ConfidentGirl65 Single!</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>803</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>A brand new song called........ https://t.co/j...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>802</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>05/29/2018</td>\n",
       "      <td>Batman is with my mom!! I miss him soooooo muc...</td>\n",
       "      <td>-0.2924</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.834</td>\n",
       "      <td>801</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1918 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Influencers        Date  \\\n",
       "0      katyperry  07/03/2018   \n",
       "1      katyperry  06/23/2018   \n",
       "2      katyperry  06/22/2018   \n",
       "3      katyperry  06/21/2018   \n",
       "4      katyperry  06/20/2018   \n",
       "5      katyperry  06/15/2018   \n",
       "6      katyperry  06/09/2018   \n",
       "7      katyperry  06/09/2018   \n",
       "8      katyperry  06/09/2018   \n",
       "9      katyperry  06/06/2018   \n",
       "10     katyperry  06/05/2018   \n",
       "11     katyperry  06/02/2018   \n",
       "12     katyperry  06/02/2018   \n",
       "13     katyperry  06/01/2018   \n",
       "14     katyperry  06/01/2018   \n",
       "15     katyperry  06/01/2018   \n",
       "16     katyperry  05/30/2018   \n",
       "17     katyperry  05/29/2018   \n",
       "18     katyperry  05/22/2018   \n",
       "19     katyperry  05/22/2018   \n",
       "20     katyperry  07/03/2018   \n",
       "21     katyperry  06/23/2018   \n",
       "22     katyperry  06/22/2018   \n",
       "23     katyperry  06/21/2018   \n",
       "24     katyperry  06/20/2018   \n",
       "25     katyperry  06/15/2018   \n",
       "26     katyperry  06/09/2018   \n",
       "27     katyperry  06/09/2018   \n",
       "28     katyperry  06/09/2018   \n",
       "29     katyperry  06/06/2018   \n",
       "...          ...         ...   \n",
       "1888    ddlovato  05/29/2018   \n",
       "1889    ddlovato  05/29/2018   \n",
       "1890    ddlovato  05/29/2018   \n",
       "1891    ddlovato  05/29/2018   \n",
       "1892    ddlovato  05/29/2018   \n",
       "1893    ddlovato  05/29/2018   \n",
       "1894    ddlovato  05/29/2018   \n",
       "1895    ddlovato  05/29/2018   \n",
       "1896    ddlovato  05/29/2018   \n",
       "1897    ddlovato  05/29/2018   \n",
       "1898    ddlovato  05/29/2018   \n",
       "1899    ddlovato  05/29/2018   \n",
       "1900    ddlovato  05/29/2018   \n",
       "1901    ddlovato  05/29/2018   \n",
       "1902    ddlovato  05/29/2018   \n",
       "1903    ddlovato  05/29/2018   \n",
       "1904    ddlovato  05/29/2018   \n",
       "1905    ddlovato  05/29/2018   \n",
       "1906    ddlovato  05/29/2018   \n",
       "1907    ddlovato  05/29/2018   \n",
       "1908    ddlovato  05/29/2018   \n",
       "1909    ddlovato  05/29/2018   \n",
       "1910    ddlovato  05/29/2018   \n",
       "1911    ddlovato  05/29/2018   \n",
       "1912    ddlovato  05/29/2018   \n",
       "1913    ddlovato  05/29/2018   \n",
       "1914    ddlovato  05/29/2018   \n",
       "1915    ddlovato  05/29/2018   \n",
       "1916    ddlovato  05/29/2018   \n",
       "1917    ddlovato  05/29/2018   \n",
       "\n",
       "                                             Tweet Text  Compound  Positive  \\\n",
       "0     My ❤ was touched by Carter's story. If you're ...    0.2500     0.214   \n",
       "1     Was so proud to give the gift of #transcendent...    0.8805     0.362   \n",
       "2     found my first skin tag today. I think I’m an ...    0.0000     0.000   \n",
       "3              Never forget, everyone was once a child.    0.1695     0.250   \n",
       "4     My Aussies 🐨 &amp; Kiwis 🥝! For the FINALE of ...    0.0000     0.000   \n",
       "5     I 👁 you, @RoarForJess, and I ❤ you, and I’m si...    0.0000     0.000   \n",
       "6                    Scandinavians have figured it out.    0.0000     0.000   \n",
       "7     Oi oi @JackGarratt remember when we locked our...    0.0000     0.000   \n",
       "8     WITNESS IS 1⃣❗🎂 🎈 https://t.co/fz83TjrJUr http...    0.0000     0.000   \n",
       "9     IM SCREAMINGOSBEJRMF$&amp;2@!K https://t.co/Sn...    0.0000     0.000   \n",
       "10    CALIFORNIANS! What’s important to you? Is it g...   -0.8821     0.072   \n",
       "11    We just played our 80TH show for #WITNESSTHETO...    0.3400     0.130   \n",
       "12                  bout to do Yoga to new Kanye record    0.0000     0.000   \n",
       "13    Happy #PRIDE month. I see you and I love you 🌈...    0.8360     0.530   \n",
       "14            Imma #believer ♥️ https://t.co/BV2ZK9OeAX    0.0000     0.000   \n",
       "15    Here’s something to believe in: @cynthialovely...    0.0000     0.000   \n",
       "16    RT @AmericanIdol: Did you think we were 'going...   -0.2924     0.000   \n",
       "17    RT @repjohnlewis: Thank you, @ABCNetwork. You ...   -0.3818     0.094   \n",
       "18    Before this weekend’s American Idol finale, I…...    0.0000     0.000   \n",
       "19    Season 1 is \"Going, Going, Gone\"! Congratulati...    0.8398     0.363   \n",
       "20    My ❤ was touched by Carter's story. If you're ...    0.2500     0.214   \n",
       "21    Was so proud to give the gift of #transcendent...    0.8805     0.362   \n",
       "22    found my first skin tag today. I think I’m an ...    0.0000     0.000   \n",
       "23             Never forget, everyone was once a child.    0.1695     0.250   \n",
       "24    My Aussies 🐨 &amp; Kiwis 🥝! For the FINALE of ...    0.0000     0.000   \n",
       "25    I 👁 you, @RoarForJess, and I ❤ you, and I’m si...    0.0000     0.000   \n",
       "26                   Scandinavians have figured it out.    0.0000     0.000   \n",
       "27    Oi oi @JackGarratt remember when we locked our...    0.0000     0.000   \n",
       "28    WITNESS IS 1⃣❗🎂 🎈 https://t.co/fz83TjrJUr http...    0.0000     0.000   \n",
       "29    IM SCREAMINGOSBEJRMF$&amp;2@!K https://t.co/Sn...    0.0000     0.000   \n",
       "...                                                 ...       ...       ...   \n",
       "1888                 Be careful https://t.co/qkTJLloN2y    0.1531     0.444   \n",
       "1889  It was to a really cute boy 😝😍 https://t.co/0K...    0.5095     0.320   \n",
       "1890                       Nope https://t.co/wYlJGkAsv2    0.0000     0.000   \n",
       "1891  My babies Batman and Cinderella https://t.co/1...    0.0000     0.000   \n",
       "1892                   Skittles https://t.co/82bgpLEe8E    0.0000     0.000   \n",
       "1893                      Pizza https://t.co/xGP4oOXMS5    0.0000     0.000   \n",
       "1894                         🤢🤮 https://t.co/bXxwjkTdWU    0.0000     0.000   \n",
       "1895                       NARS https://t.co/DbSUgaq03V    0.0000     0.000   \n",
       "1896  Very VERY soon.... could be a matter of weeks ...    0.0258     0.109   \n",
       "1897  Barbecues with family and friends https://t.co...    0.4767     0.383   \n",
       "1898                Concealer!! https://t.co/4yKVpx8D7J    0.0000     0.000   \n",
       "1899        Everything and more https://t.co/0DQFNPuCAZ    0.0000     0.000   \n",
       "1900  You have to be able to make me laugh... https:...    0.0000     0.000   \n",
       "1901             Right now, yes https://t.co/mYsVBGjrjL    0.4019     0.474   \n",
       "1902                        😩😩😩 https://t.co/FjfnARF7WC    0.0000     0.000   \n",
       "1903   Speechless by Dan + Shay https://t.co/LNdXdrf1Jc    0.0000     0.000   \n",
       "1904                     LAUREL https://t.co/23AwoXMCWA    0.0000     0.000   \n",
       "1905            Dogs are better https://t.co/DTwmKlhxda    0.4404     0.492   \n",
       "1906                  Miss Dior https://t.co/ReUhY0QnP5   -0.1531     0.000   \n",
       "1907                       Nope https://t.co/vkWPtjBzhi    0.0000     0.000   \n",
       "1908  I’m the happiest I’ve ever been in my life... ...    0.6369     0.296   \n",
       "1909                          😉 https://t.co/CfWsxiIxvq    0.0000     0.000   \n",
       "1910       Nearly peed my pants https://t.co/cZf9VFT6aV    0.0000     0.000   \n",
       "1911  Yes!! I plan on getting back into acting this ...    0.5080     0.247   \n",
       "1912  Someday I hope!! I love her 💖☁️ https://t.co/4...    0.8264     0.658   \n",
       "1913  I can skateboard but I don’t because I’m clums...   -0.6956     0.000   \n",
       "1914  Pretty much... I have so much new music to rel...    0.6219     0.254   \n",
       "1915                           @ConfidentGirl65 Single!    0.0000     0.000   \n",
       "1916  A brand new song called........ https://t.co/j...    0.0000     0.000   \n",
       "1917  Batman is with my mom!! I miss him soooooo muc...   -0.2924     0.000   \n",
       "\n",
       "      Negative  Neutral  Tweets Ago  AgeOfTweet  TweetAgeType  \n",
       "0        0.107    0.679        1000           0             1  \n",
       "1        0.000    0.638         999          10             1  \n",
       "2        0.000    1.000         998          11             1  \n",
       "3        0.000    0.750         997          12             1  \n",
       "4        0.000    1.000         996          12             1  \n",
       "5        0.000    1.000         995          18             1  \n",
       "6        0.000    1.000         994          23             1  \n",
       "7        0.000    1.000         993          24             1  \n",
       "8        0.000    1.000         992          24             1  \n",
       "9        0.000    1.000         991          27             1  \n",
       "10       0.445    0.483         990          28             1  \n",
       "11       0.000    0.870         989          30             1  \n",
       "12       0.000    1.000         988          31             1  \n",
       "13       0.000    0.470         987          32             1  \n",
       "14       0.000    1.000         986          32             1  \n",
       "15       0.000    1.000         985          32             1  \n",
       "16       0.098    0.902         984          34             1  \n",
       "17       0.154    0.752         983          35             1  \n",
       "18       0.000    1.000         982          41             1  \n",
       "19       0.000    0.637         981          42             1  \n",
       "20       0.107    0.679         980           0             1  \n",
       "21       0.000    0.638         979          10             1  \n",
       "22       0.000    1.000         978          11             1  \n",
       "23       0.000    0.750         977          12             1  \n",
       "24       0.000    1.000         976          12             1  \n",
       "25       0.000    1.000         975          18             1  \n",
       "26       0.000    1.000         974          23             1  \n",
       "27       0.000    1.000         973          24             1  \n",
       "28       0.000    1.000         972          24             1  \n",
       "29       0.000    1.000         971          27             1  \n",
       "...        ...      ...         ...         ...           ...  \n",
       "1888     0.000    0.556         830          35             1  \n",
       "1889     0.000    0.680         829          35             1  \n",
       "1890     0.000    1.000         828          35             1  \n",
       "1891     0.000    1.000         827          35             1  \n",
       "1892     0.000    1.000         826          35             1  \n",
       "1893     0.000    1.000         825          35             1  \n",
       "1894     0.000    1.000         824          35             1  \n",
       "1895     0.000    1.000         823          35             1  \n",
       "1896     0.000    0.891         822          35             1  \n",
       "1897     0.000    0.617         821          35             1  \n",
       "1898     0.000    1.000         820          35             1  \n",
       "1899     0.000    1.000         819          35             1  \n",
       "1900     0.000    1.000         818          35             1  \n",
       "1901     0.000    0.526         817          35             1  \n",
       "1902     0.000    1.000         816          35             1  \n",
       "1903     0.000    1.000         815          35             1  \n",
       "1904     0.000    1.000         814          35             1  \n",
       "1905     0.000    0.508         813          35             1  \n",
       "1906     0.444    0.556         812          35             1  \n",
       "1907     0.000    1.000         811          35             1  \n",
       "1908     0.000    0.704         810          35             1  \n",
       "1909     0.000    1.000         809          35             1  \n",
       "1910     0.000    1.000         808          35             1  \n",
       "1911     0.000    0.753         807          35             1  \n",
       "1912     0.000    0.342         806          35             1  \n",
       "1913     0.345    0.655         805          35             1  \n",
       "1914     0.000    0.746         804          35             1  \n",
       "1915     0.000    1.000         803          35             1  \n",
       "1916     0.000    1.000         802          35             1  \n",
       "1917     0.166    0.834         801          35             1  \n",
       "\n",
       "[1918 rows x 10 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -TM - whole Cell\n",
    "# Create dataframe from the Dictionery item of the Sentiment Array\n",
    "Sentiment_DF = pd.DataFrame.from_dict(Sentiment_array)\n",
    "\n",
    "# -TM -Remove the '@' from the 'influence' column in the data frame\n",
    "Sentiment_DF[\"Influencers\"] = Sentiment_DF[\"Influencers\"].map(lambda x: x.lstrip('@'))\n",
    "\n",
    "# -TM - Re_arrang the columns and save into a CSV file\n",
    "Sentiment_DF = Sentiment_DF[[\"Influencers\", \"Date\", \"Tweet Text\"\n",
    "                             , \"Compound\", \"Positive\", \"Negative\"\n",
    "                             , \"Neutral\", \"Tweets Ago\",\"AgeOfTweet\",\"TweetAgeType\"\n",
    "                            ]]\n",
    "\n",
    "# -TM -Store output in a .CSV File\n",
    "Sentiment_DF.to_csv(\"OutPut/influencer_Sentiment_tweets_Analysis.csv\")\n",
    "\n",
    "Sentiment_DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['katyperry', 'BarackObama', 'taylorswift13', 'ladygaga',\n",
       "       'TheEllenShow', 'Cristiano', 'jtimberlake', 'BritneySpears',\n",
       "       'ArianaGrande', 'ddlovato'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -TM -Get the unique list of the influencers\n",
    "Influencers_array = Sentiment_DF[\"Influencers\"].unique()\n",
    "Influencers_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Influencers</th>\n",
       "      <th>Compound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ArianaGrande</td>\n",
       "      <td>0.200119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BarackObama</td>\n",
       "      <td>0.343343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BritneySpears</td>\n",
       "      <td>0.379596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cristiano</td>\n",
       "      <td>0.222748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TheEllenShow</td>\n",
       "      <td>0.364944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ddlovato</td>\n",
       "      <td>0.212090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>jtimberlake</td>\n",
       "      <td>0.210583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>katyperry</td>\n",
       "      <td>0.185715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ladygaga</td>\n",
       "      <td>0.347224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>taylorswift13</td>\n",
       "      <td>0.183432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Influencers  Compound\n",
       "0   ArianaGrande  0.200119\n",
       "1    BarackObama  0.343343\n",
       "2  BritneySpears  0.379596\n",
       "3      Cristiano  0.222748\n",
       "4   TheEllenShow  0.364944\n",
       "5       ddlovato  0.212090\n",
       "6    jtimberlake  0.210583\n",
       "7      katyperry  0.185715\n",
       "8       ladygaga  0.347224\n",
       "9  taylorswift13  0.183432"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -TM -Calculate the mean for each Influencers & store into a dataframe\n",
    "Influencers_Comp_Mean = Sentiment_DF.groupby(\"Influencers\").mean()[\"Compound\"].to_frame()\n",
    "\n",
    "#-TM -Reset the index \n",
    "Influencers_Comp_Mean.reset_index(inplace=True)\n",
    "\n",
    "Influencers_Comp_Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -TM - Whole cell\n",
    "# Set the values for x_axis & y_axis\n",
    "x_axis = Influencers_Comp_Mean.index.values\n",
    "y_axis = Influencers_Comp_Mean[\"Compound\"]\n",
    "X_Label = Influencers\n",
    "\n",
    "# Intialize the plots. \n",
    "fig,ax = plt.subplots()#  function that returns a tuple containing a figure and axes object(s)\n",
    "\n",
    "#Set the plot and assign the values like colors etc\n",
    "bars = ax.bar(x_axis,y_axis\n",
    "              , align = \"edge\"\n",
    "              , width = 1\n",
    "              , linewidth = 1\n",
    "              , edgecolor = 'black'\n",
    "              , color = [\"yellow\",\"lime\",\"red\",\"orange\",\"pink\"]\n",
    "             )\n",
    "\n",
    "# Set the tick(s) of the bar graph\n",
    "tick_locations = [value + 0.5 for value in range(len(x_axis))]\n",
    "plt.xticks(tick_locations,X_Label,rotation='vertical')\n",
    "\n",
    "# If value is positive then put True in the Summary else place False\n",
    "Influencers_Comp_Mean[\"Positive\"] = Influencers_Comp_Mean[\"Compound\"] > 0\n",
    "\n",
    "# Assign the height based on positive value after allocating True / false value\n",
    "height = Influencers_Comp_Mean.Positive.map({True: 0.03 , False: -0.03})\n",
    "\n",
    "# # Set the value on labels on the bars\n",
    "for bar in bars:\n",
    "    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + height[bars.index(bar)]\n",
    "            , round(Influencers_Comp_Mean[\"Compound\"][bars.index(bar)],3)\n",
    "            , ha = 'center'\n",
    "            , va = 'bottom'\n",
    "            )\n",
    "\n",
    "# Set the x_axis limits\n",
    "ax.set_xlim(0, len(x_axis))\n",
    "\n",
    "# Dynamically set the y_axis limits by finding the max & min value of y-axis\n",
    "ax.set_ylim(min(y_axis)-0.1, max(y_axis) + 0.1)\n",
    "\n",
    "# Set a horizontal line at y = 0\n",
    "plt.hlines(0,0,len(x_axis))\n",
    "\n",
    "# Title of the graph\n",
    "ax.set_title(\"Sentiments on Twitter of Influencers (%s)\" % (time.strftime(\"%x\")), fontsize=16)\n",
    "\n",
    "# Setting the y_axis label\n",
    "ax.set_ylabel(\"Polarity on Twitter \", fontsize=14)\n",
    "\n",
    "# # Setting the x_axis label\n",
    "ax.set_xlabel(\"The Influencers\", fontsize=14)\n",
    "  \n",
    "# Saving the graph\n",
    "plt.savefig(\"Images/The Influencer Twitter Sentiment.png\",bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "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>Twitter_Handle</th>\n",
       "      <th>First_Name</th>\n",
       "      <th>Last_Name</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Loc</th>\n",
       "      <th>Lang</th>\n",
       "      <th>Created On</th>\n",
       "      <th>Age Of Account</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>Followers</th>\n",
       "      <th>Following</th>\n",
       "      <th>Favorites Count</th>\n",
       "      <th>Average Tweets</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>@katyperry</td>\n",
       "      <td>Katy</td>\n",
       "      <td>Perry</td>\n",
       "      <td>Musicians</td>\n",
       "      <td></td>\n",
       "      <td>Eng</td>\n",
       "      <td>02/20/2009</td>\n",
       "      <td>3419.0</td>\n",
       "      <td>9220.0</td>\n",
       "      <td>109577958.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>6214.0</td>\n",
       "      <td>9220.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>@BarackObama</td>\n",
       "      <td>Barack</td>\n",
       "      <td>Obama</td>\n",
       "      <td>Politicians</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>03/05/2007</td>\n",
       "      <td>4137.0</td>\n",
       "      <td>15517.0</td>\n",
       "      <td>103468481.0</td>\n",
       "      <td>621131.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15517.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>@taylorswift13</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>Swift</td>\n",
       "      <td>Musicians</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>12/06/2008</td>\n",
       "      <td>3496.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>85542986.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>@ladygaga</td>\n",
       "      <td>Lady</td>\n",
       "      <td>Gaga</td>\n",
       "      <td>Musicians</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>03/26/2008</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>8764.0</td>\n",
       "      <td>78902605.0</td>\n",
       "      <td>127004.0</td>\n",
       "      <td>1915.0</td>\n",
       "      <td>8764.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>@TheEllenShow</td>\n",
       "      <td>Ellen</td>\n",
       "      <td>DeGeneres</td>\n",
       "      <td>Comedians</td>\n",
       "      <td>California</td>\n",
       "      <td>Eng</td>\n",
       "      <td>08/14/2008</td>\n",
       "      <td>3610.0</td>\n",
       "      <td>16970.0</td>\n",
       "      <td>78053544.0</td>\n",
       "      <td>35730.0</td>\n",
       "      <td>954.0</td>\n",
       "      <td>16970.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Twitter_Handle First_Name  Last_Name        Genre         Loc Lang  \\\n",
       "91       @katyperry       Katy      Perry    Musicians              Eng   \n",
       "251    @BarackObama     Barack      Obama  Politicians          NA  Eng   \n",
       "86   @taylorswift13     Taylor      Swift    Musicians          NA  Eng   \n",
       "95        @ladygaga       Lady       Gaga    Musicians          NA  Eng   \n",
       "66    @TheEllenShow      Ellen  DeGeneres    Comedians  California  Eng   \n",
       "\n",
       "     Created On  Age Of Account   Tweets    Followers  Following  \\\n",
       "91   02/20/2009          3419.0   9220.0  109577958.0      216.0   \n",
       "251  03/05/2007          4137.0  15517.0  103468481.0   621131.0   \n",
       "86   12/06/2008          3496.0    100.0   85542986.0        0.0   \n",
       "95   03/26/2008          3750.0   8764.0   78902605.0   127004.0   \n",
       "66   08/14/2008          3610.0  16970.0   78053544.0    35730.0   \n",
       "\n",
       "     Favorites Count  Average Tweets  Count  \n",
       "91            6214.0          9220.0      0  \n",
       "251             11.0         15517.0      0  \n",
       "86             458.0           100.0      0  \n",
       "95            1915.0          8764.0      0  \n",
       "66             954.0         16970.0      0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IB People who are top 5 Influencer, with the greatest number of followers\n",
    "top_5_influencer = Data_Influencers_DF.sort_values(['Followers'],ascending=False).head(5)\n",
    "top_5_influencer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------Start extraction of the tweets posted by the Influencers!!!-----------\n",
      "Extracting tweets from @katyperry\n",
      "Extracting tweets from @BarackObama\n",
      "Extracting tweets from @taylorswift13\n",
      "Extracting tweets from @ladygaga\n",
      "Extracting tweets from @TheEllenShow\n",
      "-----------End of Extraction of Tweets !!!-----------\n"
     ]
    }
   ],
   "source": [
    "#IB Working Module --  Extract the first 100 tweets of the top ten Influencers\n",
    "\n",
    "# Extract the first 100 tweets of the Influencers\n",
    "print(\"-----------Start extraction of the tweets posted by the Influencers!!!-----------\")\n",
    "Influencers = []\n",
    "Influencers = top_5_influencer[\"Twitter_Handle\"]\n",
    "Influencers\n",
    "\n",
    "# IB Influencer's overall sentiments\n",
    "Influencers_overall_results = []\n",
    "\n",
    "Sentiment_array_infl = []\n",
    "\n",
    "# IB loop through each top ten influencer\n",
    "for user in Influencers:\n",
    "    \n",
    "   # IB Variables for holding compound sentiments\n",
    "    compound_list = []\n",
    "    \n",
    "    # IB Variable for max_id\n",
    "    oldest_tweet = None\n",
    "    \n",
    "    # Set the tweet count to 100\n",
    "    tweet_count = 0\n",
    "    print(\"Extracting tweets from %s\"%user)\n",
    "    \n",
    "    # Extract tweets up to 5 pages\n",
    "    for x in range(5):\n",
    "        ### IB - it is working - print(f\"For page number: '{x}' \")\n",
    "        influencer_tweets = api.user_timeline(user,page = x,  max_id = oldest_tweet )       \n",
    "        \n",
    "        # For each tweet in a bunch of public tweets\n",
    "        for tweet in influencer_tweets:\n",
    "            \n",
    "            #Calculate the compound, positive, negative and neutral values of each tweet\n",
    "            compound = analyzer.polarity_scores(tweet[\"text\"])[\"compound\"]\n",
    "            pos = analyzer.polarity_scores(tweet[\"text\"])[\"pos\"]\n",
    "            neu = analyzer.polarity_scores(tweet[\"text\"])[\"neu\"]\n",
    "            neg = analyzer.polarity_scores(tweet[\"text\"])[\"neg\"]\n",
    "            \n",
    "            # Save the Tweets in an array as a dictionery item \n",
    "            Sentiment_array_infl.append({\"Influencers\" : user,\n",
    "                                    \"Tweet Text\" : tweet[\"text\"],\n",
    "                                    \"Compound\" : compound,\n",
    "                                    \"Positive\" : pos,\n",
    "                                    \"Negative\" : neg,\n",
    "                                    \"Neutral\" : neu,\n",
    "                                    \"Date\" : tweet[\"created_at\"],\n",
    "                                    \"Tweets Ago\" : tweet_count\n",
    "                                   })\n",
    "            \n",
    "            # IB Collect compund sentiments for each Infuencer\n",
    "            compound_list.append(compound)\n",
    "            \n",
    "            #Decrease count of tweet by 1 in the reverse order\n",
    "            tweet_count += 1\n",
    "            \n",
    "        # IB get Tweet ID, subtract 1,assign to oldest_tweet\n",
    "        oldest_tweet = tweet[\"id\"] - 1\n",
    "        \n",
    "    # IB List for dictionary of results for _overall_ sentiments for each user\n",
    "    Influencers_overall_results.append({\"Influencer\": user, \"Overall Sentiment\": np.mean(compound_list)})\n",
    "\n",
    "print(\"-----------End of Extraction of Tweets !!!-----------\")\n",
    "###Influencers_overall_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Compound</th>\n",
       "      <th>Date</th>\n",
       "      <th>Influencers</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Tweet Text</th>\n",
       "      <th>Tweets Ago</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.2500</td>\n",
       "      <td>Tue Jul 03 19:50:15 +0000 2018</td>\n",
       "      <td>@katyperry</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.679</td>\n",
       "      <td>0.214</td>\n",
       "      <td>My ❤ was touched by Carter's story. If you're ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.8805</td>\n",
       "      <td>Sat Jun 23 01:18:28 +0000 2018</td>\n",
       "      <td>@katyperry</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.362</td>\n",
       "      <td>Was so proud to give the gift of #transcendent...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>Fri Jun 22 19:27:57 +0000 2018</td>\n",
       "      <td>@katyperry</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>found my first skin tag today. I think I’m an ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.1695</td>\n",
       "      <td>Thu Jun 21 02:10:52 +0000 2018</td>\n",
       "      <td>@katyperry</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.250</td>\n",
       "      <td>Never forget, everyone was once a child.</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>Wed Jun 20 21:10:37 +0000 2018</td>\n",
       "      <td>@katyperry</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>My Aussies 🐨 &amp;amp; Kiwis 🥝! For the FINALE of ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>Fri Jun 15 19:50:07 +0000 2018</td>\n",
       "      <td>@katyperry</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>I 👁 you, @RoarForJess, and I ❤ you, and I’m si...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>Sat Jun 09 23:51:56 +0000 2018</td>\n",
       "      <td>@katyperry</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Scandinavians have figured it out.</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>Sat Jun 09 14:02:29 +0000 2018</td>\n",
       "      <td>@katyperry</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Oi oi @JackGarratt remember when we locked our...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>Sat Jun 09 04:08:27 +0000 2018</td>\n",
       "      <td>@katyperry</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>WITNESS IS 1⃣❗🎂 🎈 https://t.co/fz83TjrJUr http...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>Wed Jun 06 17:11:41 +0000 2018</td>\n",
       "      <td>@katyperry</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>IM SCREAMINGOSBEJRMF$&amp;amp;2@!K https://t.co/Sn...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Compound                            Date Influencers  Negative  Neutral  \\\n",
       "0    0.2500  Tue Jul 03 19:50:15 +0000 2018  @katyperry     0.107    0.679   \n",
       "1    0.8805  Sat Jun 23 01:18:28 +0000 2018  @katyperry     0.000    0.638   \n",
       "2    0.0000  Fri Jun 22 19:27:57 +0000 2018  @katyperry     0.000    1.000   \n",
       "3    0.1695  Thu Jun 21 02:10:52 +0000 2018  @katyperry     0.000    0.750   \n",
       "4    0.0000  Wed Jun 20 21:10:37 +0000 2018  @katyperry     0.000    1.000   \n",
       "5    0.0000  Fri Jun 15 19:50:07 +0000 2018  @katyperry     0.000    1.000   \n",
       "6    0.0000  Sat Jun 09 23:51:56 +0000 2018  @katyperry     0.000    1.000   \n",
       "7    0.0000  Sat Jun 09 14:02:29 +0000 2018  @katyperry     0.000    1.000   \n",
       "8    0.0000  Sat Jun 09 04:08:27 +0000 2018  @katyperry     0.000    1.000   \n",
       "9    0.0000  Wed Jun 06 17:11:41 +0000 2018  @katyperry     0.000    1.000   \n",
       "\n",
       "   Positive                                         Tweet Text  Tweets Ago  \n",
       "0     0.214  My ❤ was touched by Carter's story. If you're ...           0  \n",
       "1     0.362  Was so proud to give the gift of #transcendent...           1  \n",
       "2     0.000  found my first skin tag today. I think I’m an ...           2  \n",
       "3     0.250           Never forget, everyone was once a child.           3  \n",
       "4     0.000  My Aussies 🐨 &amp; Kiwis 🥝! For the FINALE of ...           4  \n",
       "5     0.000  I 👁 you, @RoarForJess, and I ❤ you, and I’m si...           5  \n",
       "6     0.000                 Scandinavians have figured it out.           6  \n",
       "7     0.000  Oi oi @JackGarratt remember when we locked our...           7  \n",
       "8     0.000  WITNESS IS 1⃣❗🎂 🎈 https://t.co/fz83TjrJUr http...           8  \n",
       "9     0.000  IM SCREAMINGOSBEJRMF$&amp;2@!K https://t.co/Sn...           9  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IB Prepare Results Dataframe for the scatter plot\n",
    "Influencers_overall_results_df = pd.DataFrame.from_dict(Sentiment_array_infl)\n",
    "Influencers_overall_results_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# IB Creating Sentiment Analysis for Top Ten Influences (07/02/2018) Scatter Plot\n",
    "\n",
    "# Separate media_user_results_df Dataset into 5 parts/ datasets by \"Media Source\"\n",
    "inf1_tweet_data_df = Influencers_overall_results_df[Influencers_overall_results_df['Influencers'] == \"@katyperry\"] \n",
    "inf2_tweet_data_df = Influencers_overall_results_df[Influencers_overall_results_df['Influencers'] == \"@BarackObama\"] \n",
    "# For each of media sources following the first one in the media dataset reset index to 0-99\n",
    "# in order to plot in the common area\n",
    "inf2_tweet_data_df = inf2_tweet_data_df.reset_index(drop=True)\n",
    "inf3_tweet_data_df = Influencers_overall_results_df[Influencers_overall_results_df['Influencers'] == \"@taylorswift13\"] \n",
    "inf3_tweet_data_df = inf3_tweet_data_df.reset_index(drop=True)\n",
    "inf4_tweet_data_df = Influencers_overall_results_df[Influencers_overall_results_df['Influencers'] == \"@ladygaga\"] \n",
    "inf4_tweet_data_df = inf4_tweet_data_df.reset_index(drop=True)\n",
    "inf5_tweet_data_df = Influencers_overall_results_df[Influencers_overall_results_df['Influencers'] == \"@TheEllenShow\"] \n",
    "inf5_tweet_data_df = inf5_tweet_data_df.reset_index(drop=True)\n",
    "\n",
    "# Get the values for each part of the scatter plot by \"Influencers\"\n",
    "# INF1\n",
    "# Get values for X axis\n",
    "inf1_tweet_ago_data = inf1_tweet_data_df.index\n",
    "# Get values for Y axis\n",
    "inf1_tweet_polarity_data = inf1_tweet_data_df[\"Compound\"] \n",
    "####################################################################\n",
    "# INF2\n",
    "# Get values for X axis\n",
    "inf2_tweet_ago_data = inf2_tweet_data_df.index \n",
    "# Get values for Y axis\n",
    "inf2_tweet_polarity_data = inf2_tweet_data_df[\"Compound\"] \n",
    "####################################################################\n",
    "# INF3\n",
    "# Get values for X axis\n",
    "inf3_tweet_ago_data = inf3_tweet_data_df.index\n",
    "# Get values for Y axis\n",
    "inf3_tweet_polarity_data = inf3_tweet_data_df[\"Compound\"] \n",
    "####################################################################\n",
    "# INF4\n",
    "# Get values for X axis\n",
    "inf4_tweet_ago_data = inf4_tweet_data_df.index\n",
    "# Get values for Y axis\n",
    "inf4_tweet_polarity_data = inf4_tweet_data_df[\"Compound\"] \n",
    "####################################################################\n",
    "# INF5\n",
    "# Get values for X axis\n",
    "inf5_tweet_ago_data = inf5_tweet_data_df.index\n",
    "# Get values for Y axis\n",
    "inf5_tweet_polarity_data = inf5_tweet_data_df[\"Compound\"] \n",
    "####################################################################\n",
    "\n",
    "# Organize the layout for the scatter plot\n",
    "plt.title(\"Sentimental Analysis of Top 5 Influencers 100 Tweets(07/03/2018)\", Fontsize= 10 )\n",
    "# Labels for the scatter plot circles for each Influencer\n",
    "labels = [\"KatyPerry\" \"BarackObama\" \"TaylorSwift\" \"LadyGaga\" \"TheEllenShow\"]\n",
    "# Colors for the scatter plot circles for each Influencer\n",
    "colors = [\"lightskyblue\",\"green\",\"red\", \"blue\", \"gold\"]\n",
    "\n",
    "# Define X and Y of the scatter plot\n",
    "plt.xlabel(\"Tweets Ago\")\n",
    "plt.ylabel(\"Tweet Polarity\")\n",
    "\n",
    "# Define X and Y limits\n",
    "plt.xlim(100, 0)\n",
    "plt.ylim( -1.0, 1.0 )\n",
    "\n",
    "# Writing data to the same Scatter Plot 5 times, for each Influencer \n",
    "#           (\"KatyPerry\" \"BarackObama\" \"TaylorSwift\" \"LadyGaga\" \"TheEllenShow\")\n",
    "plt.scatter( inf1_tweet_ago_data, inf1_tweet_polarity_data, c=\"lightskyblue\", edgecolor=\"black\", \n",
    "            linewidths=1, alpha =0.75, marker='o', label=\"KatyPerry\")\n",
    "plt.scatter( inf2_tweet_ago_data, inf2_tweet_polarity_data, c=\"green\", edgecolor=\"black\", \\\n",
    "            linewidths=1, alpha=0.75, marker='o', label=\"BarackObama\")\n",
    "plt.scatter( inf3_tweet_ago_data, inf3_tweet_polarity_data, c=\"red\", edgecolor=\"black\", \n",
    "            linewidths=1, alpha=0.75, marker='o', label=\"TaylorSwift\")\n",
    "plt.scatter( inf4_tweet_ago_data, inf4_tweet_polarity_data, c=\"blue\", edgecolor=\"black\", \n",
    "            linewidths=1, alpha=0.75, marker='o', label=\"LadyGaga\")\n",
    "plt.scatter( inf5_tweet_ago_data, inf5_tweet_polarity_data, c=\"gold\", edgecolor=\"black\", \n",
    "            linewidths=1, alpha=0.75, marker='o', label=\"TheEllenShow\")\n",
    " \n",
    "# Create the legend of the Scatter plot\n",
    "legend = plt.legend(fontsize=\"small\", loc=\"center left\", title=\"Top 5 Influencers\", bbox_to_anchor=(1, 0.5))\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "# Save the figure with the Scatter Plot\n",
    "plt.savefig(\"Images/InfluencersSentimentsTweeterPolarityScatterPlot.png\")\n",
    "\n",
    "# Show the Scatter Plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Twitter_Handle</th>\n",
       "      <th>First_Name</th>\n",
       "      <th>Last_Name</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Loc</th>\n",
       "      <th>Lang</th>\n",
       "      <th>Created On</th>\n",
       "      <th>Age Of Account</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>Followers</th>\n",
       "      <th>Following</th>\n",
       "      <th>Favorites Count</th>\n",
       "      <th>Average Tweets</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>@Noahpinion</td>\n",
       "      <td>Noah</td>\n",
       "      <td>Smith</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>04/14/2011</td>\n",
       "      <td>2637.0</td>\n",
       "      <td>140980.0</td>\n",
       "      <td>102403.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>211850.0</td>\n",
       "      <td>140980.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>@BDUTT</td>\n",
       "      <td>Barkha</td>\n",
       "      <td>Dutt</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>India</td>\n",
       "      <td>Eng</td>\n",
       "      <td>02/02/2009</td>\n",
       "      <td>3437.0</td>\n",
       "      <td>118526.0</td>\n",
       "      <td>6818754.0</td>\n",
       "      <td>1574.0</td>\n",
       "      <td>27906.0</td>\n",
       "      <td>118526.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>@ProfessorChic</td>\n",
       "      <td>Jamie</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>Social_workers</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>04/04/2011</td>\n",
       "      <td>2647.0</td>\n",
       "      <td>79769.0</td>\n",
       "      <td>1456.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>24906.0</td>\n",
       "      <td>79769.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>@haroldpollack</td>\n",
       "      <td>Harold</td>\n",
       "      <td>Pollack</td>\n",
       "      <td>Economists</td>\n",
       "      <td>NA</td>\n",
       "      <td>Eng</td>\n",
       "      <td>12/25/2009</td>\n",
       "      <td>3112.0</td>\n",
       "      <td>65253.0</td>\n",
       "      <td>20537.0</td>\n",
       "      <td>2427.0</td>\n",
       "      <td>7088.0</td>\n",
       "      <td>65253.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>@CoryBooker</td>\n",
       "      <td>Cory</td>\n",
       "      <td>Booker</td>\n",
       "      <td>Politicians</td>\n",
       "      <td>Newark, NJ</td>\n",
       "      <td>Eng</td>\n",
       "      <td>08/11/2008</td>\n",
       "      <td>3613.0</td>\n",
       "      <td>61946.0</td>\n",
       "      <td>4121795.0</td>\n",
       "      <td>93080.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>61946.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Twitter_Handle First_Name Last_Name           Genre         Loc Lang  \\\n",
       "332     @Noahpinion       Noah     Smith      Economists          NA  Eng   \n",
       "359          @BDUTT     Barkha      Dutt  Social_workers      India   Eng   \n",
       "356  @ProfessorChic      Jamie  Mitchell  Social_workers          NA  Eng   \n",
       "339  @haroldpollack     Harold   Pollack      Economists          NA  Eng   \n",
       "276     @CoryBooker       Cory    Booker     Politicians  Newark, NJ  Eng   \n",
       "\n",
       "     Created On  Age Of Account    Tweets  Followers  Following  \\\n",
       "332  04/14/2011          2637.0  140980.0   102403.0      810.0   \n",
       "359  02/02/2009          3437.0  118526.0  6818754.0     1574.0   \n",
       "356  04/04/2011          2647.0   79769.0     1456.0      572.0   \n",
       "339  12/25/2009          3112.0   65253.0    20537.0     2427.0   \n",
       "276  08/11/2008          3613.0   61946.0  4121795.0    93080.0   \n",
       "\n",
       "     Favorites Count  Average Tweets  Count  \n",
       "332         211850.0        140980.0      0  \n",
       "359          27906.0        118526.0      0  \n",
       "356          24906.0         79769.0      0  \n",
       "339           7088.0         65253.0      0  \n",
       "276             91.0         61946.0      0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IB Top 5 People who who have more Followers\n",
    "top_5_twitters = Data_Influencers_DF.sort_values(['Tweets'],ascending=False).head(5)\n",
    "top_5_twitters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------Start extraction of the tweets posted by the Influencers!!!-----------\n",
      "Extracting tweets from @Noahpinion\n",
      "Extracting tweets from @BDUTT\n",
      "Extracting tweets from @ProfessorChic\n",
      "Extracting tweets from @haroldpollack\n",
      "Extracting tweets from @CoryBooker\n",
      "-----------End of Extraction of Tweets !!!-----------\n"
     ]
    }
   ],
   "source": [
    "#IB Working Module --  Extract the first 100 tweets of the top Twitters\n",
    "\n",
    "# # Extract the first 100 tweets of the Influencers\n",
    "print(\"-----------Start extraction of the tweets posted by the Influencers!!!-----------\")\n",
    "Twitters = []\n",
    "Twitters = top_5_twitters[\"Twitter_Handle\"]\n",
    "Twitters\n",
    "\n",
    "# IB Twitters's overall sentiments\n",
    "Twitters_overall_results = []\n",
    "\n",
    "Sentiment_array_tw = []\n",
    "\n",
    "# IB loop through each top ten twitter\n",
    "for user in Twitters:\n",
    "    \n",
    "    ###print(f\"For Twitter: '{user}' \")\n",
    "    # IB Variables for holding compound sentiments\n",
    "    compound_list_tw = []\n",
    "    \n",
    "    # IB Variable for max_id\n",
    "    oldest_tweet = None\n",
    "    \n",
    "    # Set the tweet count to 100\n",
    "    tweet_count = 0\n",
    "    print(\"Extracting tweets from %s\"%user)\n",
    "    \n",
    "    # Extract tweets up to 5 pages\n",
    "    for x in range(5):\n",
    "        \n",
    "        ### IB - it is working - print(f\"For page number: '{x}' \")\n",
    "        twitters_tweets = api.user_timeline(user,page = x,  max_id = oldest_tweet )       \n",
    "        \n",
    "        # For each tweet in a bunch of public tweets\n",
    "        for tweet in twitters_tweets:\n",
    "            \n",
    "            #Calculate the compound, positive, negative and neutral values of each tweet\n",
    "            compound = analyzer.polarity_scores(tweet[\"text\"])[\"compound\"]\n",
    "            pos = analyzer.polarity_scores(tweet[\"text\"])[\"pos\"]\n",
    "            neu = analyzer.polarity_scores(tweet[\"text\"])[\"neu\"]\n",
    "            neg = analyzer.polarity_scores(tweet[\"text\"])[\"neg\"]\n",
    "            \n",
    "            # Save the Tweets in an array as a dictionery item \n",
    "            Sentiment_array_tw.append({\"Twitter\" : user,\n",
    "                                    \"Tweet Text\" : tweet[\"text\"],\n",
    "                                    \"Compound\" : compound,\n",
    "                                    \"Positive\" : pos,\n",
    "                                    \"Negative\" : neg,\n",
    "                                    \"Neutral\" : neu,\n",
    "                                    \"Date\" : tweet[\"created_at\"],\n",
    "                                    \"Tweets Ago\" : tweet_count\n",
    "                                   })\n",
    "            \n",
    "            # IB Collect compund sentiments for each Infuencer\n",
    "            compound_list_tw.append(compound)\n",
    "            \n",
    "            #Decrease count of tweet by 1 in the reverse order\n",
    "            tweet_count += 1\n",
    "            \n",
    "        # IB get Tweet ID, subtract 1,assign to oldest_tweet\n",
    "        oldest_tweet = tweet[\"id\"] - 1\n",
    "        \n",
    "    # IB List for dictionary of results for _overall_ sentiments for each user\n",
    "    Twitters_overall_results.append({\"Twitter\": user, \"Overall Sentiment\": np.mean(compound_list_tw)})\n",
    "\n",
    "print(\"-----------End of Extraction of Tweets !!!-----------\")\n",
    "###Twitters_overall_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Compound</th>\n",
       "      <th>Date</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Tweet Text</th>\n",
       "      <th>Tweets Ago</th>\n",
       "      <th>Twitter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>Tue Jul 03 19:37:57 +0000 2018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>@eean Imagine that!</td>\n",
       "      <td>0</td>\n",
       "      <td>@Noahpinion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>Tue Jul 03 19:34:44 +0000 2018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Salute your new pizza overlords https://t.co/j...</td>\n",
       "      <td>1</td>\n",
       "      <td>@Noahpinion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3182</td>\n",
       "      <td>Tue Jul 03 19:27:25 +0000 2018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.881</td>\n",
       "      <td>0.119</td>\n",
       "      <td>To be fair, tons of people probably noticed th...</td>\n",
       "      <td>2</td>\n",
       "      <td>@Noahpinion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0772</td>\n",
       "      <td>Tue Jul 03 19:25:32 +0000 2018</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.141</td>\n",
       "      <td>Someone finally noticed that Bernie Sanders is...</td>\n",
       "      <td>3</td>\n",
       "      <td>@Noahpinion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.2960</td>\n",
       "      <td>Tue Jul 03 18:13:24 +0000 2018</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.000</td>\n",
       "      <td>@LiglyCnsrvatari No idea...</td>\n",
       "      <td>4</td>\n",
       "      <td>@Noahpinion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.6705</td>\n",
       "      <td>Tue Jul 03 18:08:44 +0000 2018</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.000</td>\n",
       "      <td>RT @dreamingofpeace: “On immigration, trade an...</td>\n",
       "      <td>5</td>\n",
       "      <td>@Noahpinion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.4215</td>\n",
       "      <td>Tue Jul 03 17:54:42 +0000 2018</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.781</td>\n",
       "      <td>0.000</td>\n",
       "      <td>RT @meta_meg: @CathyReisenwitz @Noahpinion @ne...</td>\n",
       "      <td>6</td>\n",
       "      <td>@Noahpinion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>Tue Jul 03 17:54:27 +0000 2018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>RT @butleriano: All of this has been said a mi...</td>\n",
       "      <td>7</td>\n",
       "      <td>@Noahpinion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.3612</td>\n",
       "      <td>Tue Jul 03 17:46:03 +0000 2018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.094</td>\n",
       "      <td>RT @Noahpinion: Trump is governing like a grum...</td>\n",
       "      <td>8</td>\n",
       "      <td>@Noahpinion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.4417</td>\n",
       "      <td>Tue Jul 03 17:45:43 +0000 2018</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.000</td>\n",
       "      <td>@eean @oakwtf When you're a monopoly or quasi-...</td>\n",
       "      <td>9</td>\n",
       "      <td>@Noahpinion</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Compound                            Date  Negative  Neutral  Positive  \\\n",
       "0    0.0000  Tue Jul 03 19:37:57 +0000 2018     0.000    1.000     0.000   \n",
       "1    0.0000  Tue Jul 03 19:34:44 +0000 2018     0.000    1.000     0.000   \n",
       "2    0.3182  Tue Jul 03 19:27:25 +0000 2018     0.000    0.881     0.119   \n",
       "3    0.0772  Tue Jul 03 19:25:32 +0000 2018     0.126    0.733     0.141   \n",
       "4   -0.2960  Tue Jul 03 18:13:24 +0000 2018     0.524    0.476     0.000   \n",
       "5   -0.6705  Tue Jul 03 18:08:44 +0000 2018     0.244    0.756     0.000   \n",
       "6   -0.4215  Tue Jul 03 17:54:42 +0000 2018     0.219    0.781     0.000   \n",
       "7    0.0000  Tue Jul 03 17:54:27 +0000 2018     0.000    1.000     0.000   \n",
       "8    0.3612  Tue Jul 03 17:46:03 +0000 2018     0.000    0.906     0.094   \n",
       "9   -0.4417  Tue Jul 03 17:45:43 +0000 2018     0.133    0.867     0.000   \n",
       "\n",
       "                                          Tweet Text  Tweets Ago      Twitter  \n",
       "0                                @eean Imagine that!           0  @Noahpinion  \n",
       "1  Salute your new pizza overlords https://t.co/j...           1  @Noahpinion  \n",
       "2  To be fair, tons of people probably noticed th...           2  @Noahpinion  \n",
       "3  Someone finally noticed that Bernie Sanders is...           3  @Noahpinion  \n",
       "4                        @LiglyCnsrvatari No idea...           4  @Noahpinion  \n",
       "5  RT @dreamingofpeace: “On immigration, trade an...           5  @Noahpinion  \n",
       "6  RT @meta_meg: @CathyReisenwitz @Noahpinion @ne...           6  @Noahpinion  \n",
       "7  RT @butleriano: All of this has been said a mi...           7  @Noahpinion  \n",
       "8  RT @Noahpinion: Trump is governing like a grum...           8  @Noahpinion  \n",
       "9  @eean @oakwtf When you're a monopoly or quasi-...           9  @Noahpinion  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IB Prepare Results Dataframe for the scatter plot\n",
    "Twitters_overall_results_df = pd.DataFrame.from_dict(Sentiment_array_tw)\n",
    "Twitters_overall_results_df.head(10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# IB Creating Sentiment Analysis for Top Ten Influences Scatter Plot\n",
    "\n",
    "# Separate media_user_results_df Dataset into 5 parts/ datasets by \"Media Source\"\n",
    "tw1_tweet_data_df = Twitters_overall_results_df[Twitters_overall_results_df['Twitter'] == \"@Noahpinion\"] \n",
    "tw2_tweet_data_df = Twitters_overall_results_df[Twitters_overall_results_df['Twitter'] == \"@BDUTT\"] \n",
    "# For each of media sources following the first one in the media dataset reset index to 0-99\n",
    "# in order to plot in the common area\n",
    "tw2_tweet_data_df = tw2_tweet_data_df.reset_index(drop=True)\n",
    "tw3_tweet_data_df = Twitters_overall_results_df[Twitters_overall_results_df['Twitter'] == \"@ProfessorChic\"] \n",
    "tw3_tweet_data_df = tw3_tweet_data_df.reset_index(drop=True)\n",
    "tw4_tweet_data_df = Twitters_overall_results_df[Twitters_overall_results_df['Twitter'] == \"@haroldpollack\"] \n",
    "tw4_tweet_data_df = tw4_tweet_data_df.reset_index(drop=True)\n",
    "tw5_tweet_data_df = Twitters_overall_results_df[Twitters_overall_results_df['Twitter'] == \"@CoryBooker\"] \n",
    "tw5_tweet_data_df = tw5_tweet_data_df.reset_index(drop=True)\n",
    "\n",
    "# Get the values for each part of the scatter plot by \"Twitter\"\n",
    "# TW1\n",
    "# Get values for X axis\n",
    "tw1_tweet_ago_data = tw1_tweet_data_df.index\n",
    "# Get values for Y axis\n",
    "tw1_tweet_polarity_data = tw1_tweet_data_df[\"Compound\"] \n",
    "####################################################################\n",
    "# TW2\n",
    "# Get values for X axis\n",
    "tw2_tweet_ago_data = tw2_tweet_data_df.index \n",
    "# Get values for Y axis\n",
    "tw2_tweet_polarity_data = tw2_tweet_data_df[\"Compound\"] \n",
    "####################################################################\n",
    "# TW3\n",
    "# Get values for X axis\n",
    "tw3_tweet_ago_data = tw3_tweet_data_df.index\n",
    "# Get values for Y axis\n",
    "tw3_tweet_polarity_data = tw3_tweet_data_df[\"Compound\"] \n",
    "####################################################################\n",
    "# TW4\n",
    "# Get values for X axis\n",
    "tw4_tweet_ago_data = tw4_tweet_data_df.index\n",
    "# Get values for Y axis\n",
    "tw4_tweet_polarity_data = tw4_tweet_data_df[\"Compound\"] \n",
    "####################################################################\n",
    "# TW5\n",
    "# Get values for X axis\n",
    "tw5_tweet_ago_data = tw5_tweet_data_df.index\n",
    "# Get values for Y axis\n",
    "tw5_tweet_polarity_data = tw5_tweet_data_df[\"Compound\"] \n",
    "###################################################################\n",
    "\n",
    "# Organize the layout for the scatter plot\n",
    "plt.title(\"Sentimental Analysis of Top 5 Tweeters Tweets(07/03/2018)\", Fontsize= 10 )\n",
    "\n",
    "# Labels for the scatter plot circles for each Twitter\n",
    "labels = [\"Noahpinion\" \"BDUTT\" \"ProfessorChic\" \"HaroldPollack\" \"CoryBooker\"]\n",
    "# Colors for the scatter plot circles for each Twitter\n",
    "colors = [\"lightskyblue\",\"green\",\"red\", \"blue\", \"gold\"]\n",
    "\n",
    "# Define X and Y of the scatter plot\n",
    "plt.xlabel(\"Tweets Ago\")\n",
    "plt.ylabel(\"Tweet Polarity\")\n",
    "\n",
    "# Define X and Y limits\n",
    "plt.xlim(100, 0)\n",
    "plt.ylim( -1.0, 1.0 )\n",
    "\n",
    "# Writing data to the same Scatter Plot 5 times, for each Twitter \n",
    "#           ( \"Noahpinion\" \"BDUTT\" \"ProfessorChic\" \"HaroldPollack\" \"CoryBooker\")\n",
    "plt.scatter( tw1_tweet_ago_data, tw1_tweet_polarity_data, c=\"lightskyblue\", edgecolor=\"black\", \n",
    "            linewidths=1, alpha =0.75, marker='o', label=\"Noahpinion\")\n",
    "plt.scatter( tw2_tweet_ago_data, tw2_tweet_polarity_data, c=\"green\", edgecolor=\"black\", \\\n",
    "            linewidths=1, alpha=0.75, marker='o', label=\"BDUTT\")\n",
    "plt.scatter( tw3_tweet_ago_data, tw3_tweet_polarity_data, c=\"red\", edgecolor=\"black\", \n",
    "            linewidths=1, alpha=0.75, marker='o', label=\"ProfessorChic\")\n",
    "plt.scatter( tw4_tweet_ago_data, tw4_tweet_polarity_data, c=\"blue\", edgecolor=\"black\", \n",
    "            linewidths=1, alpha=0.75, marker='o', label=\"HaroldPollack\")\n",
    "plt.scatter( tw5_tweet_ago_data, tw5_tweet_polarity_data, c=\"gold\", edgecolor=\"black\", \n",
    "            linewidths=1, alpha=0.75, marker='o', label=\"CoryBooker\")\n",
    " \n",
    "# Create the legend of the Scatter plot\n",
    "legend = plt.legend(fontsize=\"small\", loc=\"center left\", title=\"Top 5 Influencers\", bbox_to_anchor=(1, 0.5))\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "# Save the figure with the Scatter Plot\n",
    "plt.savefig(\"Images/TweetersSentimentsTweeterPolarityScatterPlot.png\")\n",
    "\n",
    "# Show the Scatter Plot\n",
    "plt.show()"
   ]
  }
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