Chloropleth.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-06-15 15:09:39--  https://raw.githubusercontent.com/tushar1210/Emotional-Anlaysis-BERT/master/final.csv?token=AFCBJT5FQN5TQ74GCFH2Z2S66B6DW\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 64:ff9b::c7e8:1485, 199.232.20.133\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|64:ff9b::c7e8:1485|:443... failed: Operation timed out.\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|199.232.20.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 7668756 (7.3M) [text/plain]\n",
      "Saving to: ‘final.csv’\n",
      "\n",
      "final.csv           100%[===================>]   7.31M  1.56MB/s    in 5.6s    \n",
      "\n",
      "2020-06-15 15:11:02 (1.30 MB/s) - ‘final.csv’ saved [7668756/7668756]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O final.csv https://raw.githubusercontent.com/tushar1210/Emotional-Anlaysis-BERT/master/final.csv?token=AFCBJT5FQN5TQ74GCFH2Z2S66B6DW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from collections import defaultdict\n",
    "from textwrap import wrap\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Country</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Emotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>un airbnb lejos con piscina y BBQ 🔥</td>\n",
       "      <td>anger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>@Airbnb @AirbnbHelp Just got off the phone - I...</td>\n",
       "      <td>anger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>@juulianarosa ver no airbnb prima</td>\n",
       "      <td>anger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Vacation real-estate markets are ‘toast’ becau...</td>\n",
       "      <td>anger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Airbnb???jummm 🤔🤔</td>\n",
       "      <td>joy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Country                                              Tweet  \\\n",
       "0           0  Brazil                un airbnb lejos con piscina y BBQ 🔥   \n",
       "1           1  Brazil  @Airbnb @AirbnbHelp Just got off the phone - I...   \n",
       "2           2  Brazil                  @juulianarosa ver no airbnb prima   \n",
       "3           3  Brazil  Vacation real-estate markets are ‘toast’ becau...   \n",
       "4           4  Brazil                                  Airbnb???jummm 🤔🤔   \n",
       "\n",
       "  Emotion  \n",
       "0   anger  \n",
       "1   anger  \n",
       "2   anger  \n",
       "3   anger  \n",
       "4     joy  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('final.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting plotly\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/70/56/eabdc7b7187cdb9d6121f6de2831ad5b85f7d002fa4bfe0476dbdb554bf6/plotly-4.8.1-py2.py3-none-any.whl (11.5MB)\n",
      "\u001b[K     |████████████████████████████████| 11.5MB 486kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/site-packages (from plotly) (1.14.0)\n",
      "Collecting retrying>=1.3.3\n",
      "  Downloading https://files.pythonhosted.org/packages/44/ef/beae4b4ef80902f22e3af073397f079c96969c69b2c7d52a57ea9ae61c9d/retrying-1.3.3.tar.gz\n",
      "Building wheels for collected packages: retrying\n",
      "  Building wheel for retrying (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for retrying: filename=retrying-1.3.3-cp37-none-any.whl size=11429 sha256=33ea794501cb068ff86e31f2be08b82c9062e6485eade8c7853134cea331064e\n",
      "  Stored in directory: /Users/tushar/Library/Caches/pip/wheels/d7/a9/33/acc7b709e2a35caa7d4cae442f6fe6fbf2c43f80823d46460c\n",
      "Successfully built retrying\n",
      "Installing collected packages: retrying, plotly\n",
      "Successfully installed plotly-4.8.1 retrying-1.3.3\n"
     ]
    }
   ],
   "source": [
    "!pip3 install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Country</th>\n",
       "      <th>Emotion</th>\n",
       "      <th>Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>anger</td>\n",
       "      <td>BRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>anger</td>\n",
       "      <td>BRA</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>anger</td>\n",
       "      <td>BRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>sadness</td>\n",
       "      <td>BRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>joy</td>\n",
       "      <td>BRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>USA</td>\n",
       "      <td>fear</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>USA</td>\n",
       "      <td>sadness</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>USA</td>\n",
       "      <td>sadness</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>USA</td>\n",
       "      <td>anger</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>USA</td>\n",
       "      <td>anger</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country  Emotion Code\n",
       "30   Brazil    anger  BRA\n",
       "31   Brazil    anger  BRA\n",
       "32   Brazil    anger  BRA\n",
       "33   Brazil  sadness  BRA\n",
       "34   Brazil      joy  BRA\n",
       "..      ...      ...  ...\n",
       "217     USA     fear  USA\n",
       "218     USA  sadness  USA\n",
       "219     USA  sadness  USA\n",
       "220     USA    anger  USA\n",
       "221     USA    anger  USA\n",
       "\n",
       "[192 rows x 3 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.drop(['Unnamed: 0','Tweet'],axis=1)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algeria  :  ALG\n",
      "Argentina  :  ARG\n",
      "Australia  :  AUS\n",
      "Austria  :  AUS\n",
      "Brazil  :  BRA\n",
      "Canada  :  CAN\n",
      "Chile  :  CHI\n",
      "China  :  CHI\n",
      "Colombia  :  COL\n",
      "Cuba  :  CUB\n",
      "Egypt  :  EGY\n",
      "France  :  FRA\n",
      "Germany  :  GER\n",
      "Greece  :  GRE\n",
      "Hong-kong  :  HON\n",
      "India  :  IND\n",
      "Italy  :  ITA\n",
      "Japan  :  JAP\n",
      "Macau  :  MAC\n",
      "Malaysia  :  MAL\n",
      "Mexico  :  MEX\n",
      "Morocoo  :  MOR\n",
      "Morooco  :  MOR\n",
      "Netherland  :  NET\n",
      "Peru  :  PER\n",
      "Portugal  :  POR\n",
      "Saudi-arabia  :  SAU\n",
      "SaudiArabia  :  SAU\n",
      "Singapore  :  SIN\n",
      "South-Africa  :  SOU\n",
      "South-Korea  :  SOU\n",
      "South-korea  :  SOU\n",
      "Spain  :  SPA\n",
      "Switzerland  :  SWI\n",
      "Thailand  :  THA\n",
      "Tunisia  :  TUN\n",
      "Turkey  :  TUR\n",
      "UAE  :  UAE\n",
      "UK  :  UK\n",
      "USA  :  USA\n",
      "Vietnam  :  VIE\n",
      "Zimbawe  :  ZIM\n"
     ]
    }
   ],
   "source": [
    "country_dict={}\n",
    "for c in df.Country:\n",
    "    country_dict[str(c)]=str(c).upper()[0:3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algeria  :  DZA\n",
      "Argentina  :  ARG\n",
      "Australia  :  AUS\n",
      "Austria  :  AUS\n",
      "Brazil  :  BRA\n",
      "Canada  :  CAN\n",
      "Chile  :  CHL\n",
      "China  :  CHN\n",
      "Colombia  :  COL\n",
      "Cuba  :  CUB\n",
      "Egypt  :  EGY\n",
      "France  :  FRA\n",
      "Germany  :  DEU\n",
      "Greece  :  GRC\n",
      "Hong-kong  :  HKG\n",
      "India  :  IND\n",
      "Italy  :  ITA\n",
      "Japan  :  JPN\n",
      "Macau  :  MAC\n",
      "Malaysia  :  MYS\n",
      "Mexico  :  MEX\n",
      "Morocoo  :  MAR\n",
      "Morooco  :  MAR\n",
      "Netherland  :  NLD\n",
      "Peru  :  PER\n",
      "Portugal  :  PRT\n",
      "Saudi-arabia  :  SAU\n",
      "SaudiArabia  :  SAU\n",
      "Singapore  :  SGP\n",
      "South-Africa  :  ZAF\n",
      "South-Korea  :  KOR\n",
      "South-korea  :  KOR\n",
      "Spain  :  ESP\n",
      "Switzerland  :  CHE\n",
      "Thailand  :  THA\n",
      "Tunisia  :  TUN\n",
      "Turkey  :  TUR\n",
      "UAE  :  ARE\n",
      "UK  :  GBR\n",
      "USA  :  USA\n",
      "Vietnam  :  VNM\n",
      "Zimbawe  :  ZWE\n"
     ]
    }
   ],
   "source": [
    "#add country code\n",
    "country_dict['Algeria']='DZA'\n",
    "country_dict['Chile']='CHL'\n",
    "country_dict['China']='CHN'\n",
    "country_dict['Germany']='DEU'\n",
    "country_dict['Greece']='GRC'\n",
    "country_dict['Hong-kong']='HKG'\n",
    "country_dict['Japan']='JPN'\n",
    "country_dict['Malaysia']='MYS'\n",
    "country_dict['Morocoo']='MAR'\n",
    "country_dict['Morooco']='MAR'\n",
    "country_dict['Netherland']='NLD'\n",
    "country_dict['Portugal']='PRT'\n",
    "country_dict['Singapore']='SGP'\n",
    "country_dict['South-Africa']='ZAF'\n",
    "country_dict['South-korea']='KOR'\n",
    "country_dict['South-Korea']='KOR'\n",
    "country_dict['Spain']='ESP'\n",
    "country_dict['Switzerland']='CHE'\n",
    "country_dict['UAE']='ARE'\n",
    "country_dict['UK']='GBR'\n",
    "country_dict['Zimbawe']='ZWE'\n",
    "country_dict['Vietnam']='VNM'\n",
    "\n",
    "\n",
    "for i in sorted(country_dict.keys()):\n",
    "    print(i,\" : \", country_dict[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "code=[]\n",
    "for c in df.Country:\n",
    "    code.append(country_dict[str(c)])    \n",
    "code[0:10]\n",
    "df['Code']=code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'BRA': 62, 'USA': 4722, 'DZA': 144, 'ARG': 30, 'AUS': 366, 'CAN': 2856, 'CHL': 13, 'CHN': 47, 'COL': 164, 'CUB': 618, 'EGY': 38, 'FRA': 899, 'DEU': 379, 'GRC': 86, 'HKG': 66, 'IND': 345, 'ITA': 390, 'JPN': 35, 'MAC': 68, 'MYS': 130, 'MEX': 223, 'MAR': 80, 'NLD': 1497, 'PER': 19, 'PRT': 124, 'SAU': 36, 'SGP': 146, 'ZAF': 155, 'KOR': 41, 'ESP': 237, 'CHE': 1089, 'THA': 125, 'TUN': 120, 'TUR': 95, 'ARE': 40, 'GBR': 1548, 'VNM': 84, 'ZWE': 92} \n",
      " {'BRA': 39, 'USA': 3562, 'DZA': 156, 'ARG': 19, 'AUS': 222, 'CAN': 2089, 'CHL': 8, 'CHN': 73, 'COL': 145, 'CUB': 569, 'EGY': 39, 'FRA': 666, 'DEU': 247, 'GRC': 78, 'HKG': 65, 'IND': 456, 'ITA': 332, 'JPN': 114, 'MAC': 65, 'MYS': 124, 'MEX': 167, 'MAR': 79, 'NLD': 1089, 'PER': 10, 'PRT': 107, 'SAU': 54, 'SGP': 132, 'ZAF': 165, 'KOR': 123, 'ESP': 200, 'CHE': 789, 'THA': 149, 'TUN': 128, 'TUR': 55, 'ARE': 47, 'GBR': 1100, 'VNM': 98, 'ZWE': 86} \n",
      " {'BRA': 26, 'USA': 2470, 'DZA': 77, 'ARG': 12, 'AUS': 191, 'CAN': 1757, 'CHL': 3, 'CHN': 32, 'COL': 69, 'CUB': 315, 'EGY': 30, 'FRA': 414, 'DEU': 186, 'GRC': 59, 'HKG': 47, 'IND': 309, 'ITA': 213, 'JPN': 30, 'MAC': 47, 'MYS': 92, 'MEX': 115, 'MAR': 44, 'NLD': 757, 'PER': 7, 'PRT': 67, 'SAU': 44, 'SGP': 100, 'ZAF': 104, 'KOR': 35, 'ESP': 110, 'CHE': 583, 'THA': 117, 'TUN': 71, 'TUR': 47, 'ARE': 40, 'GBR': 775, 'VNM': 68, 'ZWE': 58} \n",
      " {'BRA': 5, 'USA': 769, 'DZA': 21, 'ARG': 3, 'AUS': 57, 'CAN': 491, 'CHL': 2, 'CHN': 7, 'COL': 29, 'CUB': 85, 'EGY': 7, 'FRA': 135, 'DEU': 60, 'GRC': 15, 'HKG': 9, 'IND': 79, 'ITA': 64, 'JPN': 4, 'MAC': 7, 'MYS': 21, 'MEX': 36, 'MAR': 9, 'NLD': 210, 'PER': 2, 'PRT': 14, 'SAU': 14, 'SGP': 24, 'ZAF': 38, 'KOR': 3, 'ESP': 27, 'CHE': 159, 'THA': 27, 'TUN': 25, 'TUR': 13, 'ARE': 11, 'GBR': 216, 'VNM': 15, 'ZWE': 13}\n"
     ]
    }
   ],
   "source": [
    "#anger\n",
    "anger={}\n",
    "joy={}\n",
    "sadness={}\n",
    "fear={}\n",
    "\n",
    "for c in code:\n",
    "    anger[c]=0\n",
    "    joy[c]=0\n",
    "    sadness[c]=0\n",
    "    fear[c]=0\n",
    "for i in range(len(df.Country)):\n",
    "    if str(df.Emotion[i])=='anger':\n",
    "        anger[df.Code[i]]+=1\n",
    "    if str(df.Emotion[i])=='joy':\n",
    "        joy[df.Code[i]]+=1\n",
    "    if str(df.Emotion[i])=='sadness':\n",
    "        sadness[df.Code[i]]+=1\n",
    "    if str(df.Emotion[i])=='fear':\n",
    "        fear[df.Code[i]]+=1\n",
    "\n",
    "print(anger,'\\n',joy,'\\n',sadness,'\\n',fear)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a df\n",
    "anger_df=pd.DataFrame(anger.items(), columns=['Code', 'Emotion'])\n",
    "sad_df=pd.DataFrame(sadness.items(), columns=['Code', 'Emotion'])\n",
    "joy_df=pd.DataFrame(joy.items(), columns=['Code', 'Emotion'])\n",
    "fear_df=pd.DataFrame(fear.items(), columns=['Code', 'Emotion'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['Brazil', 'USA', 'Algeria', 'Argentina', 'Australia', 'Austria', 'Canada', 'Chile', 'China', 'Colombia', 'Cuba', 'Egypt', 'France', 'Germany', 'Greece', 'Hong-kong', 'India', 'Italy', 'Japan', 'Macau', 'Malaysia', 'Mexico', 'Morocoo', 'Morooco', 'Netherland', 'Peru', 'Portugal', 'Saudi-arabia', 'SaudiArabia', 'Singapore', 'South-Africa', 'South-Korea', 'South-korea', 'Spain', 'Switzerland', 'Thailand', 'Tunisia', 'Turkey', 'UAE', 'UK', 'Vietnam', 'Zimbawe']) \n",
      "\n",
      "\n",
      "\n",
      "['BRA', 'USA', 'DZA', 'ARG', 'AUS', 'CAN', 'CHL', 'CHN', 'COL', 'CUB', 'EGY', 'FRA', 'DEU', 'GRC', 'HKG', 'IND', 'ITA', 'JPN', 'MAC', 'MYS', 'MEX', 'MAR', 'NLD', 'PER', 'PRT', 'SAU', 'SGP', 'ZAF', 'KOR', 'ESP', 'CHE', 'THA', 'TUN', 'TUR', 'ARE', 'GBR', 'VNM', 'ZWE']\n"
     ]
    }
   ],
   "source": [
    "print(country_dict.keys(),'\\n\\n\\n')\n",
    "print(list(anger.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "def plot(df):\n",
    "    fig = go.Figure(data=go.Choropleth(\n",
    "        locations = df['Code'],\n",
    "        z = df['Emotion'],\n",
    "        text = df['Code'],\n",
    "        colorscale = 'Blues',\n",
    "        autocolorscale=False,\n",
    "        reversescale=True,\n",
    "        marker_line_color='darkgray',\n",
    "        marker_line_width=0.5,\n",
    "        colorbar_tickprefix = '',\n",
    "        colorbar_title = 'Emotion',\n",
    "    ))\n",
    "\n",
    "    fig.update_layout(\n",
    "        title_text='Airbnb Tweet Analysis [Fear]',\n",
    "        geo=dict(\n",
    "            showframe=False,\n",
    "            showcoastlines=False,\n",
    "            projection_type='equirectangular'\n",
    "        ),\n",
    "        annotations = [dict(\n",
    "            x=0.55,\n",
    "            y=0.1,\n",
    "            xref='paper',\n",
    "            yref='paper',\n",
    "            text='',\n",
    "            showarrow = False\n",
    "        )]\n",
    "    )\n",
    "\n",
    "    fig.show()"
   ]
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       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(fear_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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