CovidTweetAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of Covid Tweets to identify the usage of the word 'test' and its frequency\n",
    "\n",
    "## Gather Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_name</th>\n",
       "      <th>user_location</th>\n",
       "      <th>user_description</th>\n",
       "      <th>user_created</th>\n",
       "      <th>user_followers</th>\n",
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       "      <th>user_verified</th>\n",
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       "      <th>hashtags</th>\n",
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       "      <th>is_retweet</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ᏉᎥ☻լꂅϮ</td>\n",
       "      <td>astroworld</td>\n",
       "      <td>wednesday addams as a disney princess keepin i...</td>\n",
       "      <td>5/26/2017 5:46</td>\n",
       "      <td>624</td>\n",
       "      <td>950</td>\n",
       "      <td>18775</td>\n",
       "      <td>False</td>\n",
       "      <td>7/25/2020 12:27</td>\n",
       "      <td>If I smelled the scent of hand sanitizers toda...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tom Basile 🇺🇸</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>Husband, Father, Columnist &amp; Commentator. Auth...</td>\n",
       "      <td>4/16/2009 20:06</td>\n",
       "      <td>2253</td>\n",
       "      <td>1677</td>\n",
       "      <td>24</td>\n",
       "      <td>True</td>\n",
       "      <td>7/25/2020 12:27</td>\n",
       "      <td>Hey @Yankees @YankeesPR and @MLB - wouldn't it...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Time4fisticuffs</td>\n",
       "      <td>Pewee Valley, KY</td>\n",
       "      <td>#Christian #Catholic #Conservative #Reagan #Re...</td>\n",
       "      <td>2/28/2009 18:57</td>\n",
       "      <td>9275</td>\n",
       "      <td>9525</td>\n",
       "      <td>7254</td>\n",
       "      <td>False</td>\n",
       "      <td>7/25/2020 12:27</td>\n",
       "      <td>@diane3443 @wdunlap @realDonaldTrump Trump nev...</td>\n",
       "      <td>['COVID19']</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ethel mertz</td>\n",
       "      <td>Stuck in the Middle</td>\n",
       "      <td>#Browns #Indians #ClevelandProud #[]_[] #Cavs ...</td>\n",
       "      <td>3/7/2019 1:45</td>\n",
       "      <td>197</td>\n",
       "      <td>987</td>\n",
       "      <td>1488</td>\n",
       "      <td>False</td>\n",
       "      <td>7/25/2020 12:27</td>\n",
       "      <td>@brookbanktv The one gift #COVID19 has give me...</td>\n",
       "      <td>['COVID19']</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DIPR-J&amp;K</td>\n",
       "      <td>Jammu and Kashmir</td>\n",
       "      <td>🖊️Official Twitter handle of Department of Inf...</td>\n",
       "      <td>2/12/2017 6:45</td>\n",
       "      <td>101009</td>\n",
       "      <td>168</td>\n",
       "      <td>101</td>\n",
       "      <td>False</td>\n",
       "      <td>7/25/2020 12:27</td>\n",
       "      <td>25 July : Media Bulletin on Novel #CoronaVirus...</td>\n",
       "      <td>['CoronaVirusUpdates', 'COVID19']</td>\n",
       "      <td>Twitter for Android</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         user_name         user_location  \\\n",
       "0           ᏉᎥ☻լꂅϮ            astroworld   \n",
       "1    Tom Basile 🇺🇸          New York, NY   \n",
       "2  Time4fisticuffs      Pewee Valley, KY   \n",
       "3      ethel mertz  Stuck in the Middle    \n",
       "4         DIPR-J&K     Jammu and Kashmir   \n",
       "\n",
       "                                    user_description     user_created  \\\n",
       "0  wednesday addams as a disney princess keepin i...   5/26/2017 5:46   \n",
       "1  Husband, Father, Columnist & Commentator. Auth...  4/16/2009 20:06   \n",
       "2  #Christian #Catholic #Conservative #Reagan #Re...  2/28/2009 18:57   \n",
       "3  #Browns #Indians #ClevelandProud #[]_[] #Cavs ...    3/7/2019 1:45   \n",
       "4  🖊️Official Twitter handle of Department of Inf...   2/12/2017 6:45   \n",
       "\n",
       "   user_followers  user_friends  user_favourites  user_verified  \\\n",
       "0             624           950            18775          False   \n",
       "1            2253          1677               24           True   \n",
       "2            9275          9525             7254          False   \n",
       "3             197           987             1488          False   \n",
       "4          101009           168              101          False   \n",
       "\n",
       "              date                                               text  \\\n",
       "0  7/25/2020 12:27  If I smelled the scent of hand sanitizers toda...   \n",
       "1  7/25/2020 12:27  Hey @Yankees @YankeesPR and @MLB - wouldn't it...   \n",
       "2  7/25/2020 12:27  @diane3443 @wdunlap @realDonaldTrump Trump nev...   \n",
       "3  7/25/2020 12:27  @brookbanktv The one gift #COVID19 has give me...   \n",
       "4  7/25/2020 12:27  25 July : Media Bulletin on Novel #CoronaVirus...   \n",
       "\n",
       "                            hashtags               source  is_retweet  \n",
       "0                                NaN   Twitter for iPhone       False  \n",
       "1                                NaN  Twitter for Android       False  \n",
       "2                        ['COVID19']  Twitter for Android       False  \n",
       "3                        ['COVID19']   Twitter for iPhone       False  \n",
       "4  ['CoronaVirusUpdates', 'COVID19']  Twitter for Android       False  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
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    }
   ],
   "source": [
    "# Import the libraries and dataset\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "covid_df = pd.read_csv('covid19_tweets.csv')\n",
    "covid_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 155101 entries, 0 to 155100\n",
      "Data columns (total 13 columns):\n",
      "user_name           155101 non-null object\n",
      "user_location       123729 non-null object\n",
      "user_description    146342 non-null object\n",
      "user_created        155101 non-null object\n",
      "user_followers      155101 non-null int64\n",
      "user_friends        155101 non-null int64\n",
      "user_favourites     155101 non-null int64\n",
      "user_verified       155101 non-null bool\n",
      "date                155101 non-null object\n",
      "text                155101 non-null object\n",
      "hashtags            110555 non-null object\n",
      "source              155048 non-null object\n",
      "is_retweet          155101 non-null bool\n",
      "dtypes: bool(2), int64(3), object(8)\n",
      "memory usage: 13.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# Explore the number of rows and columns, and its datatypes\n",
    "covid_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset has 155101 rows and 13 columns. However not all column details are important. Only the 'text' field data is being analyzed over the given period of time. So, need to clean the data.\n",
    "\n",
    "## Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "              date                                               text\n",
       "0  7/25/2020 12:27  If I smelled the scent of hand sanitizers toda...\n",
       "1  7/25/2020 12:27  Hey @Yankees @YankeesPR and @MLB - wouldn't it...\n",
       "2  7/25/2020 12:27  @diane3443 @wdunlap @realDonaldTrump Trump nev...\n",
       "3  7/25/2020 12:27  @brookbanktv The one gift #COVID19 has give me...\n",
       "4  7/25/2020 12:27  25 July : Media Bulletin on Novel #CoronaVirus..."
      ]
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     "metadata": {},
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   ],
   "source": [
    "# remove all the unnecessary data by dropping the irrelavant columns\n",
    "df = covid_df.copy()\n",
    "df.drop(['user_name','user_location','user_description','user_created','user_followers','user_friends','user_favourites','user_verified','hashtags','source','is_retweet'],axis = 1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 155101 entries, 0 to 155100\n",
      "Data columns (total 2 columns):\n",
      "date    155101 non-null object\n",
      "text    155101 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# refined dataset columns and rows\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets determine how many times, people have tweeted about 'test' in this period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10576"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of test related tweets\n",
    "df['text'].str.count(\"test\").sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, it is seen that the word 'test' was referred in tweets for about 10576 times.\n",
    "\n",
    "Get the dates on which 'test' phrase was tweeted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "               date                                               text\n",
       "16  7/25/2020 12:26  July 25 #COVID19 update\\n#TamilNadu - 6988\\nDi...\n",
       "31  7/25/2020 12:26  Actor @VishalKOfficial and his father Mr.G.K.R...\n",
       "47  7/25/2020 12:25  Coronavirus Testing Fiasco: St Mirren have ple...\n",
       "48  7/25/2020 12:25  Coronavirus Testing Fiasco: St Mirren have ple...\n",
       "50  7/25/2020 12:25  Coronavirus Testing Fiasco: St Mirren have ple..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "yes_test = df[df['text'].str.count('test') > 0]\n",
    "yes_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9672 entries, 16 to 155085\n",
      "Data columns (total 2 columns):\n",
      "date    9672 non-null object\n",
      "text    9672 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 226.7+ KB\n"
     ]
    }
   ],
   "source": [
    "yes_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thathrav\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:3643: 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",
      "  self[name] = value\n"
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      "text/plain": [
       "         date                                               text\n",
       "16  7/25/2020  July 25 #COVID19 update\\n#TamilNadu - 6988\\nDi...\n",
       "31  7/25/2020  Actor @VishalKOfficial and his father Mr.G.K.R...\n",
       "47  7/25/2020  Coronavirus Testing Fiasco: St Mirren have ple...\n",
       "48  7/25/2020  Coronavirus Testing Fiasco: St Mirren have ple...\n",
       "50  7/25/2020  Coronavirus Testing Fiasco: St Mirren have ple..."
      ]
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     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "# Extract only the date from the 'date' column by removing the time details\n",
    "yes_test.date = yes_test.date.apply(lambda x : x.rsplit(maxsplit = len(x.split())-1)[0])\n",
    "yes_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_test = df[df['text'].str.count('test') == 0]\n",
    "#no_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7/25/2020    1139\n",
       "8/8/2020      536\n",
       "8/2/2020      529\n",
       "8/1/2020      519\n",
       "8/4/2020      517\n",
       "8/9/2020      509\n",
       "7/27/2020     482\n",
       "8/6/2020      478\n",
       "7/28/2020     470\n",
       "8/17/2020     470\n",
       "8/13/2020     468\n",
       "7/31/2020     464\n",
       "7/26/2020     455\n",
       "8/18/2020     415\n",
       "8/14/2020     411\n",
       "8/12/2020     392\n",
       "8/16/2020     381\n",
       "8/11/2020     345\n",
       "8/10/2020     280\n",
       "7/29/2020     219\n",
       "7/30/2020     104\n",
       "8/7/2020       71\n",
       "7/24/2020      18\n",
       "Name: date, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yes_test['date'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determine the time period over which the dataset is collected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thathrav\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: 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",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Timestamp('2020-07-24 00:00:00'), Timestamp('2020-08-18 00:00:00'))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the start date and end date with 'test' tweets considered in the dataset\n",
    "yes_test['date'] = pd.to_datetime(yes_test['date'])\n",
    "yes_test['date'].min(),yes_test['date'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset has tweet results from July 24th, 2020 to August 18th, 2020 i.e., over 3+ weeks data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the bar graph of the 'test' tweets in the order of dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Tweet count')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar graph describing the distribution of tweets\n",
    "yes_test.date.value_counts().plot(kind='bar')\n",
    "plt.title('\"Test\" Tweets Per Day')\n",
    "plt.xlabel('Dates')\n",
    "plt.ylabel('Tweet count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thathrav\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: 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",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "# extract the date value excluding the time details\n",
    "yes_test['date'] = yes_test['date'].dt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thathrav\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:3643: 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",
      "  self[name] = value\n"
     ]
    },
    {
     "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>date</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>July 25 #COVID19 update\\n#TamilNadu - 6988\\nDi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>Actor @VishalKOfficial and his father Mr.G.K.R...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>Coronavirus Testing Fiasco: St Mirren have ple...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>Coronavirus Testing Fiasco: St Mirren have ple...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>Coronavirus Testing Fiasco: St Mirren have ple...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date                                               text\n",
       "16  2020-07-25  July 25 #COVID19 update\\n#TamilNadu - 6988\\nDi...\n",
       "31  2020-07-25  Actor @VishalKOfficial and his father Mr.G.K.R...\n",
       "47  2020-07-25  Coronavirus Testing Fiasco: St Mirren have ple...\n",
       "48  2020-07-25  Coronavirus Testing Fiasco: St Mirren have ple...\n",
       "50  2020-07-25  Coronavirus Testing Fiasco: St Mirren have ple..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract only the date from the 'date' column by removing the time details\n",
    "yes_test.date = yes_test.date.apply(lambda x : x.rsplit(maxsplit = len(x.split())-1)[0])\n",
    "yes_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Tweet count')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yes_test.date.value_counts().plot(kind='bar')\n",
    "plt.title('\"Test\" Tweets Per Day')\n",
    "plt.xlabel('Dates')\n",
    "plt.ylabel('Tweet count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis\n",
    "\n",
    "From the bar graph above, it is seen that the word 'test' with respect to covid19 is being made several times with maximum tweets on 25th July, 2020 and at a similar rate on Aug 1st, 2nd, 4th, 7th, 8th, 9th, 10th and so on, and similarly in July. \n",
    "\n",
    "However, there were many days like on 8/3, 8/5 and 8/15 when tweets related to 'test' was not at all made.\n",
    "\n",
    "This shows that people are actively to speaking about covid testing now.\n",
    "\n",
    "But on 25th July 2020, the tweets related to 'test' were very high comparatively which is an abnormal occurrance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1139"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test related tweet count on July 25th, 2020\n",
    "abnormal_date = yes_test[yes_test['date'] == '2020-07-25']\n",
    "abnormal_date.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1046"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt = abnormal_date[abnormal_date['text'].str.count('test') == 1]\n",
    "cnt.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt2 = abnormal_date[abnormal_date['text'].str.count('test') == 2]\n",
    "cnt2.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt3 = abnormal_date[abnormal_date['text'].str.count('test') == 3]\n",
    "cnt3.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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