Jakka Jawanas chat analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d22bcda0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import emoji"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd63fe6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rawToDf(file, key):\n",
    "    split_formats = {\n",
    "        '12hr' : '\\d{1,2}/\\d{1,2}/\\d{2,4},\\s\\d{1,2}:\\d{2}\\s[APap][mM]\\s-\\s',\n",
    "        '24hr' : '\\d{1,2}/\\d{1,2}/\\d{2,4},\\s\\d{1,2}:\\d{2}\\s-\\s',\n",
    "        'custom' : ''\n",
    "    }\n",
    "    datetime_formats = {\n",
    "        '12hr' : '%d/%m/%Y, %I:%M %p -',\n",
    "        '24hr' : '%d/%m/%Y, %H:%M -',\n",
    "        'custom': ''\n",
    "    }\n",
    "    \n",
    "    with open(file,'r',encoding=\"utf8\") as raw_data:\n",
    "        # converting the list split by newline char as one whole string as there can be multi-line messages\n",
    "        raw_string = ' '.join(raw_data.read().split('\\n')) \n",
    "        # splits at all the date-time pattern, resulting in list of all the messages with user names\n",
    "        user_msg = re.split(split_formats[key],raw_string)[1:]\n",
    "        # finds all the date-time patterns\n",
    "        date_time = re.findall(split_formats[key],raw_string)\n",
    "        \n",
    "        #Exporting to dataframe\n",
    "        df = pd.DataFrame({'date_time':date_time, 'user_msg':user_msg})\n",
    "        \n",
    "        \n",
    "    # converting date-time pattern which is of type String to type datetime,\n",
    "    # format is to be specified for the whole string where the placeholders are extracted by the method \n",
    "    df['date_time'] = pd.to_datetime(df['date_time'], format=datetime_formats[key],errors='ignore')\n",
    "    \n",
    "    \n",
    "    #split user and messages\n",
    "    usernames = []\n",
    "    msgs = []\n",
    "    for i in df['user_msg']:\n",
    "         # lazy pattern match to first {user_name}: pattern and spliting it aka each msg from a user\n",
    "        a = re.split('([\\w\\W]+?):\\s', i)\n",
    "        if(a[1:]): #User typed message\n",
    "            usernames.append(a[1])\n",
    "            msgs.append(a[2])\n",
    "        else:  # other notifications in the group(eg: someone was added, some left ...)\n",
    "            usernames.append(\"grp_notif\")\n",
    "            msgs.append(a[0])\n",
    "            \n",
    "        \n",
    "    # creating new columns         \n",
    "    df['user'] = usernames\n",
    "    df['msg'] = msgs\n",
    "\n",
    "    # dropping the old user_msg col.\n",
    "    df.drop('user_msg', axis=1, inplace=True)\n",
    "    \n",
    "    return df\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c96ab81d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = rawToDf('chats.txt', '12hr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b6e11ba",
   "metadata": {
    "scrolled": true
   },
   "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>date_time</th>\n",
       "      <th>user</th>\n",
       "      <th>msg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10/08/19, 9:23 pm -</td>\n",
       "      <td>grp_notif</td>\n",
       "      <td>Pruthvi PG BMSCE created group \"😎Jakka🎓Jawana'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/08/19, 9:23 pm -</td>\n",
       "      <td>grp_notif</td>\n",
       "      <td>You were added</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16/03/20, 6:49 pm -</td>\n",
       "      <td>Anant BMS</td>\n",
       "      <td>1.4k ivattu mado javabdari nandu.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16/03/20, 6:49 pm -</td>\n",
       "      <td>Anant BMS</td>\n",
       "      <td>Agta ide nodu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16/03/20, 6:58 pm -</td>\n",
       "      <td>Anant BMS</td>\n",
       "      <td>1.4k aytu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34919</th>\n",
       "      <td>07/07/21, 2:38 pm -</td>\n",
       "      <td>Pradeep</td>\n",
       "      <td>We have ADA lab exam at 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34920</th>\n",
       "      <td>07/07/21, 2:38 pm -</td>\n",
       "      <td>Omkar PG BMSCE</td>\n",
       "      <td>Ha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34921</th>\n",
       "      <td>07/07/21, 2:38 pm -</td>\n",
       "      <td>Omkar PG BMSCE</td>\n",
       "      <td>&lt;Media omitted&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34922</th>\n",
       "      <td>07/07/21, 2:38 pm -</td>\n",
       "      <td>VSM</td>\n",
       "      <td>&lt;Media omitted&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34923</th>\n",
       "      <td>07/07/21, 2:42 pm -</td>\n",
       "      <td>Nikshith</td>\n",
       "      <td>&lt;Media omitted&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34924 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date_time            user  \\\n",
       "0      10/08/19, 9:23 pm -        grp_notif   \n",
       "1      10/08/19, 9:23 pm -        grp_notif   \n",
       "2      16/03/20, 6:49 pm -        Anant BMS   \n",
       "3      16/03/20, 6:49 pm -        Anant BMS   \n",
       "4      16/03/20, 6:58 pm -        Anant BMS   \n",
       "...                     ...             ...   \n",
       "34919  07/07/21, 2:38 pm -          Pradeep   \n",
       "34920  07/07/21, 2:38 pm -   Omkar PG BMSCE   \n",
       "34921  07/07/21, 2:38 pm -   Omkar PG BMSCE   \n",
       "34922  07/07/21, 2:38 pm -              VSM   \n",
       "34923  07/07/21, 2:42 pm -         Nikshith   \n",
       "\n",
       "                                                     msg  \n",
       "0      Pruthvi PG BMSCE created group \"😎Jakka🎓Jawana'...  \n",
       "1                                        You were added   \n",
       "2                     1.4k ivattu mado javabdari nandu.   \n",
       "3                                         Agta ide nodu   \n",
       "4                                             1.4k aytu   \n",
       "...                                                  ...  \n",
       "34919                         We have ADA lab exam at 5   \n",
       "34920                                                Ha   \n",
       "34921                                   <Media omitted>   \n",
       "34922                                   <Media omitted>   \n",
       "34923                                   <Media omitted>   \n",
       "\n",
       "[34924 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f17da7f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "me = \"VSM\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6f75e72",
   "metadata": {},
   "source": [
    "### Data cleaning (omitting stickers, links and images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f328531e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13413, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images = df[df['msg']==\"<Media omitted> \"] #no. of images,stickers, images are represented by <media omitted>\n",
    "images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3f9a2fd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(141, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp_notif = df[df['user']==\"grp_notif\"] #no. of grp notifications\n",
    "grp_notif.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f072e8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(images.index, inplace=True) #removing images\n",
    "df.drop(grp_notif.index, inplace=True) #removing grp_notif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fe6139f3",
   "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>date_time</th>\n",
       "      <th>user</th>\n",
       "      <th>msg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16/03/20, 6:49 pm -</td>\n",
       "      <td>Anant BMS</td>\n",
       "      <td>1.4k ivattu mado javabdari nandu.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16/03/20, 6:49 pm -</td>\n",
       "      <td>Anant BMS</td>\n",
       "      <td>Agta ide nodu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16/03/20, 6:58 pm -</td>\n",
       "      <td>Anant BMS</td>\n",
       "      <td>1.4k aytu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16/03/20, 6:59 pm -</td>\n",
       "      <td>Anant BMS</td>\n",
       "      <td>We will make it more success once the clg star...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16/03/20, 7:00 pm -</td>\n",
       "      <td>Anant BMS</td>\n",
       "      <td>Released in wrong time. Should have released a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21365</th>\n",
       "      <td>07/07/21, 2:37 pm -</td>\n",
       "      <td>Omkar PG BMSCE</td>\n",
       "      <td>Ha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21366</th>\n",
       "      <td>07/07/21, 2:37 pm -</td>\n",
       "      <td>Omkar PG BMSCE</td>\n",
       "      <td>Pubg is lighter than COD now i guess</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21367</th>\n",
       "      <td>07/07/21, 2:37 pm -</td>\n",
       "      <td>VSM</td>\n",
       "      <td>@919108542860 olle phone itkondidane barode illa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21368</th>\n",
       "      <td>07/07/21, 2:38 pm -</td>\n",
       "      <td>Pradeep</td>\n",
       "      <td>We have ADA lab exam at 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21369</th>\n",
       "      <td>07/07/21, 2:38 pm -</td>\n",
       "      <td>Omkar PG BMSCE</td>\n",
       "      <td>Ha</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21370 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date_time            user  \\\n",
       "0      16/03/20, 6:49 pm -        Anant BMS   \n",
       "1      16/03/20, 6:49 pm -        Anant BMS   \n",
       "2      16/03/20, 6:58 pm -        Anant BMS   \n",
       "3      16/03/20, 6:59 pm -        Anant BMS   \n",
       "4      16/03/20, 7:00 pm -        Anant BMS   \n",
       "...                     ...             ...   \n",
       "21365  07/07/21, 2:37 pm -   Omkar PG BMSCE   \n",
       "21366  07/07/21, 2:37 pm -   Omkar PG BMSCE   \n",
       "21367  07/07/21, 2:37 pm -              VSM   \n",
       "21368  07/07/21, 2:38 pm -          Pradeep   \n",
       "21369  07/07/21, 2:38 pm -   Omkar PG BMSCE   \n",
       "\n",
       "                                                     msg  \n",
       "0                     1.4k ivattu mado javabdari nandu.   \n",
       "1                                         Agta ide nodu   \n",
       "2                                             1.4k aytu   \n",
       "3      We will make it more success once the clg star...  \n",
       "4      Released in wrong time. Should have released a...  \n",
       "...                                                  ...  \n",
       "21365                                                Ha   \n",
       "21366              Pubg is lighter than COD now i guess   \n",
       "21367  @919108542860 olle phone itkondidane barode illa   \n",
       "21368                         We have ADA lab exam at 5   \n",
       "21369                                                Ha   \n",
       "\n",
       "[21370 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(inplace=True, drop=True)\n",
    "df.shape\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1685c11c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Anant BMS', 'Pruthvi PG BMSCE', 'Vineet PG BMSCE', 'Nikshith',\n",
       "       'Shubham PG BMSCE', 'Natesh', 'Omkar PG BMSCE', 'Pradeep', 'VSM',\n",
       "       '+91 90350 84354', '+91 93806 49169', 'Vineeth',\n",
       "       'Karthik Sirsi PG'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users = df['user'].unique()\n",
    "users"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71dd3103",
   "metadata": {},
   "source": [
    "## Q1:  Who is the most active member of the group. Who is the least active?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "61cb8fe3",
   "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>msg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Anant BMS</th>\n",
       "      <td>4474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pruthvi PG BMSCE</th>\n",
       "      <td>3503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vineet PG BMSCE</th>\n",
       "      <td>3497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omkar PG BMSCE</th>\n",
       "      <td>2596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nikshith</th>\n",
       "      <td>2312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pradeep</th>\n",
       "      <td>1667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vineeth</th>\n",
       "      <td>1041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VSM</th>\n",
       "      <td>874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shubham PG BMSCE</th>\n",
       "      <td>607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karthik Sirsi PG</th>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Natesh</th>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>+91 93806 49169</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>+91 90350 84354</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   msg\n",
       "user                  \n",
       "Anant BMS         4474\n",
       "Pruthvi PG BMSCE  3503\n",
       "Vineet PG BMSCE   3497\n",
       "Omkar PG BMSCE    2596\n",
       "Nikshith          2312\n",
       "Pradeep           1667\n",
       "Vineeth           1041\n",
       "VSM                874\n",
       "Shubham PG BMSCE   607\n",
       "Karthik Sirsi PG   604\n",
       "Natesh             190\n",
       "+91 93806 49169      3\n",
       "+91 90350 84354      2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.DataFrame(df.groupby(\"user\")[\"msg\"].count().sort_values(ascending=False))\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7fc6dc3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = temp.reset_index()\n",
    "x = temp['user']\n",
    "y = temp['msg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "85adcfae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,30))\n",
    "plt.bar(x,y,align='center') # A bar chart\n",
    "plt.title('Number of messages from each user',fontsize=80)\n",
    "plt.xlabel('users',fontsize=50)\n",
    "plt.ylabel('Number of messages',fontsize=50)\n",
    "plt.rcParams[\"font.size\"] = \"10\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "286d259c",
   "metadata": {},
   "source": [
    "## Q2: Most used words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3e3cd4ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def word_count(str):\n",
    "    counts = dict()\n",
    "    words = str.split()\n",
    "\n",
    "    for word in words:\n",
    "        if word in counts:\n",
    "            counts[word] += 1\n",
    "        else:\n",
    "            counts[word] = 1\n",
    "\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fd6c9013",
   "metadata": {},
   "outputs": [],
   "source": [
    "msgs = ''\n",
    "for msg in df['msg']:\n",
    "    msgs = msgs + msg\n",
    "msgs_word_count = word_count(msgs)\n",
    "# msgs_word_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5c590f3c",
   "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>words</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.4k</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ivattu</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mado</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>javabdari</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nandu.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25849</th>\n",
       "      <td>madidnante</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25850</th>\n",
       "      <td>😫😢</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25851</th>\n",
       "      <td>squad</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25852</th>\n",
       "      <td>lighter</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25853</th>\n",
       "      <td>itkondidane</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25854 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             words  count\n",
       "0             1.4k      3\n",
       "1           ivattu      2\n",
       "2             mado      5\n",
       "3        javabdari      1\n",
       "4           nandu.      1\n",
       "...            ...    ...\n",
       "25849   madidnante      1\n",
       "25850           😫😢      1\n",
       "25851        squad      1\n",
       "25852      lighter      1\n",
       "25853  itkondidane      1\n",
       "\n",
       "[25854 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msgs_word_count_df = pd.DataFrame(msgs_word_count.items())\n",
    "msgs_word_count_df = msgs_word_count_df.rename(columns={0: \"words\", 1: \"count\"})\n",
    "msgs_word_count_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f3c15a73",
   "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>words</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23922</th>\n",
       "      <td>:)</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>😂</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>to</td>\n",
       "      <td>808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>I</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>is</td>\n",
       "      <td>767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>the</td>\n",
       "      <td>731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>in</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>and</td>\n",
       "      <td>503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>of</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>😂😂</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>will</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>for</td>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>a</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>was</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>it</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>ge</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>u</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>This</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>not</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>this</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>alli</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>that</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>have</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>are</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>message</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>deleted</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>😂😂😂</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7686</th>\n",
       "      <td>Iam</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>we</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>on</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         words  count\n",
       "23922       :)   1989\n",
       "90           😂    840\n",
       "45          to    808\n",
       "279          I    800\n",
       "25          is    767\n",
       "16         the    731\n",
       "22          in    535\n",
       "102        and    503\n",
       "88          of    475\n",
       "161         😂😂    390\n",
       "10        will    387\n",
       "321        for    378\n",
       "61           a    366\n",
       "201        was    362\n",
       "12          it    333\n",
       "469         ge    332\n",
       "73           u    332\n",
       "199       This    319\n",
       "550        not    307\n",
       "213       this    305\n",
       "974       alli    299\n",
       "116       that    277\n",
       "31        have    269\n",
       "84         are    265\n",
       "200    message    252\n",
       "202    deleted    234\n",
       "306        😂😂😂    230\n",
       "7686       Iam    220\n",
       "337         we    210\n",
       "55          on    205"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get top 20 most used words\n",
    "msgs_word_count_df.sort_values(by=['count'],ascending=False).head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "919d072f",
   "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>words</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3525</th>\n",
       "      <td>prathvi</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>oma</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>padda</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4440</th>\n",
       "      <td>praneetha</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3809</th>\n",
       "      <td>VSM</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3020</th>\n",
       "      <td>nikshith</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>vineeth</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>hippo</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15863</th>\n",
       "      <td>karthik</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9137</th>\n",
       "      <td>shubham</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8994</th>\n",
       "      <td>ananta</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           words  count\n",
       "3525     prathvi     58\n",
       "340          oma     54\n",
       "1009       padda     32\n",
       "4440   praneetha     29\n",
       "3809         VSM     25\n",
       "3020    nikshith     24\n",
       "41       vineeth     24\n",
       "381        hippo     20\n",
       "15863    karthik      6\n",
       "9137     shubham      5\n",
       "8994      ananta      3"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of following words\n",
    "words_to_search = ['hippo','oma','praneetha','prathvi','ananta','VSM','padda','nikshith','vineeth','shubham','karthik']\n",
    "required_repeated_words_df = pd.DataFrame({})\n",
    "for words in words_to_search:\n",
    "    required_repeated_words_df = required_repeated_words_df.append(msgs_word_count_df[msgs_word_count_df['words']==words])\n",
    "required_repeated_words_df.sort_values(by=['count'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9b02168c",
   "metadata": {},
   "outputs": [],
   "source": [
    "words_x = required_repeated_words_df['words']\n",
    "words_y = required_repeated_words_df['count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8c5dc7e7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram for count of desired words\n",
    "plt.figure(figsize=(30,30))\n",
    "plt.bar(words_x,words_y,align='center') # A bar chart\n",
    "plt.title('count of desired words',fontsize=80)\n",
    "plt.xlabel('Words',fontsize=50)\n",
    "plt.ylabel('Count',fontsize=50)\n",
    "plt.rcParams[\"font.size\"] = \"25\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0957a8a2",
   "metadata": {},
   "source": [
    "## Top 10 most used emoji"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "269d32e1",
   "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>words</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23922</th>\n",
       "      <td>:)</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>😂</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>to</td>\n",
       "      <td>808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>I</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>is</td>\n",
       "      <td>767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>the</td>\n",
       "      <td>731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>in</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>and</td>\n",
       "      <td>503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>of</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>😂😂</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      words  count\n",
       "23922    :)   1989\n",
       "90        😂    840\n",
       "45       to    808\n",
       "279       I    800\n",
       "25       is    767\n",
       "16      the    731\n",
       "22       in    535\n",
       "102     and    503\n",
       "88       of    475\n",
       "161      😂😂    390"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msgs_word_count_df.sort_values(by=['count'],ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3e196339",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_emojis(s):\n",
    "    return ''.join(c for c in s if c in emoji.UNICODE_EMOJI['en'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "415d89d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "emoji_string = extract_emojis(msgs)\n",
    "emoji_list = []\n",
    "for emoji in emoji_string:\n",
    "    emoji_list.append(emoji)\n",
    "#     print(emoji)\n",
    "# emoji_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8418c03f",
   "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>emoji</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>🔥</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>🙏</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>🏻</td>\n",
       "      <td>528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>😂</td>\n",
       "      <td>5011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>💯</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>🎵</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>💀</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>💉</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>💸</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>😫</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>277 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    emoji  count\n",
       "0       🔥    276\n",
       "1       🙏    146\n",
       "2       🏻    528\n",
       "3       😂   5011\n",
       "4       💯     78\n",
       "..    ...    ...\n",
       "272     🎵      1\n",
       "273     💀      1\n",
       "274     💉      1\n",
       "275     💸      1\n",
       "276     😫      1\n",
       "\n",
       "[277 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emoji_occurence_dict = {}\n",
    "\n",
    "for item in emoji_list:\n",
    "    if item not in emoji_occurence_dict:\n",
    "        emoji_occurence_dict[item] = 1\n",
    "    else:\n",
    "        emoji_occurence_dict[item] +=1\n",
    "emoji_occurence_df = pd.DataFrame.from_dict(emoji_occurence_dict.items())\n",
    "# print(emoji_occurence_dict)\n",
    "emoji_occurence_df = emoji_occurence_df.rename(columns={0: \"emoji\", 1: \"count\"})\n",
    "emoji_occurence_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8b58572f",
   "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>emoji</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>😂</td>\n",
       "      <td>5011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>🦛</td>\n",
       "      <td>4306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>😀</td>\n",
       "      <td>2870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>🥳</td>\n",
       "      <td>1597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>🙂</td>\n",
       "      <td>1195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>🤣</td>\n",
       "      <td>1113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>🏻</td>\n",
       "      <td>528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>♂</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>🔥</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>🤩</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emoji  count\n",
       "3      😂   5011\n",
       "10     🦛   4306\n",
       "65     😀   2870\n",
       "24     🥳   1597\n",
       "68     🙂   1195\n",
       "13     🤣   1113\n",
       "2      🏻    528\n",
       "21     ♂    289\n",
       "0      🔥    276\n",
       "9      🤩    262"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#most used emojis\n",
    "top_used_emojis_df = emoji_occurence_df.sort_values(by=['count'],ascending=False).head(10)\n",
    "top_used_emojis_df"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}