Gmail Extract and Convert to Excel.ipynb

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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![gmaillogonew.png](attachment:gmaillogonew.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div style=\"text-align: center\">Extract Emails from Gmail and convert to Excel</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code -\n",
    "1. Extracts emails from Gmail from 'Inbox' and 'Spam' folders in .txt format.<br> \n",
    "2. Then converts each folder to Excel files.<br>\n",
    "3. Adds 'spam' and 'non-spam' Labels and 1/0 Label_Numbers.<br>\n",
    "4. Merges both Excel files into one and shuffles them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Important Note : enable 'Less Secure App' access for the code to work**<br>\n",
    "https://myaccount.google.com/lesssecureapps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import Libraries**<br>\n",
    "(install the required packages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, re\n",
    "import pandas as pd\n",
    "import codecs\n",
    "from getpass import getpass\n",
    "import imaplib, email\n",
    "from bs4 import BeautifulSoup\n",
    "from nltk import word_tokenize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Class emails to download emails**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class emails:\n",
    "   \n",
    "\n",
    "    def __init__(self,username,password):\n",
    "    \n",
    "        self.mail = imaplib.IMAP4_SSL('imap.gmail.com')\n",
    "        self.mail.login(username, password)\n",
    "        self.select_folder('Inbox',True)\n",
    "\n",
    "    def select_folder(self,folder, read_only):\n",
    "        self.mail.select(folder,readonly=read_only)\n",
    "\n",
    "    def get_all_emails(self):\n",
    "        result,data=self.mail.uid('search',None,'All')\n",
    "        return result,data[0].decode('utf-8').split()\n",
    "\n",
    "    def raw_email(self,uid):\n",
    "        result, data = self.mail.uid('fetch', uid, '(RFC822)')\n",
    "        return result,data[0][1]\n",
    "\n",
    "\n",
    "    def __clean__(self,text):\n",
    "        text = text.replace('  ', ' ')\n",
    "        text = text.replace('</n', '<')\n",
    "        text = text.replace('{*}', '')\n",
    "        #replacing patterns\n",
    "        text = re.sub(r'<[^>]+>', '', text)\n",
    "        text = re.sub(r'\\n+', '\\n', text)\n",
    "        output=''\n",
    "        words=[word for word in word_tokenize(text) if len(word)<=15 and re.match('[A-Za-z0-9,._]+',word)]\n",
    "        for word in words:\n",
    "            if output=='':\n",
    "                output=word\n",
    "            else:\n",
    "                output = output + ' ' + word\n",
    "        return output\n",
    "\n",
    "    def parse_email(self,uid):\n",
    "        dict={}\n",
    "        result, raw_email = self.raw_email(uid)\n",
    "        raw_email = raw_email.decode('utf-8','ignore')\n",
    "        parsed = email.message_from_string(raw_email)\n",
    "        dict['To']=email.utils.parseaddr(parsed['To'])[-1]\n",
    "        dict['From']=email.utils.parseaddr(parsed['From'])[-1]\n",
    "        dict['Subject']=parsed['Subject']\n",
    "\n",
    "        body=''\n",
    "        for part in parsed.walk():\n",
    "            if part.get_content_type()=='text/html':\n",
    "                html=str(part.get_payload())\n",
    "                soup = BeautifulSoup(html,'html5lib')\n",
    "                try:\n",
    "                    text=soup.find('body').text.strip()\n",
    "                except Exception:\n",
    "                    continue\n",
    "\n",
    "                text=self.__clean__(text)\n",
    "                body=body + text\n",
    "\n",
    "        body=self.__clean__(body)\n",
    "        dict['Body']=body\n",
    "        return dict\n",
    "    \n",
    "    #Directory\n",
    "    def directory(label):\n",
    "        cdir =os.getcwd()\n",
    "        #Setting target folder\n",
    "        if label.find('/')>0:\n",
    "            folder=label[label.find('/')+1:].lower()\n",
    "        else:\n",
    "            folder=label.lower()\n",
    "        \n",
    "        #Create folder, if it does not exist\n",
    "        if not os.path.exists(cdir + '\\Downloads\\\\' + folder):\n",
    "            os.mkdir(cdir + '\\Downloads\\\\' + folder)\n",
    "        return folder\n",
    "    \n",
    "    def download_emails(label):\n",
    "        #Getting all the emails\n",
    "        mail.select_folder(label,True)\n",
    "        result, data = mail.get_all_emails()\n",
    "    \n",
    "        #Exit, if no emails returned\n",
    "        if not result=='OK':\n",
    "            exit(1)\n",
    "\n",
    "        #Saving the Data\n",
    "        cdir =os.getcwd()\n",
    "        print('Downloading ' + emails.directory(label) + ' emails...')\n",
    "        for uid in data:\n",
    "            dict = mail.parse_email(uid)\n",
    "            subject = dict['Subject']\n",
    "            subject=re.sub('[^a-zA-Z ]','',subject)\n",
    "            if len(subject) > 50:\n",
    "                subject = subject[:50]\n",
    "            file = cdir + '\\Downloads\\\\' + emails.directory(label) + '\\\\' + subject + '.txt'\n",
    "            with open(file, 'w', encoding='utf-8') as f:\n",
    "                body = subject + '\\n' + str(dict['Body'])\n",
    "                f.write(body)\n",
    "    \n",
    "        #Status Display\n",
    "        print('Downloaded ' + emails.directory(label) + ' emails at '+ cdir + '\\Downloads\\\\' + emails.directory(label) + '.')\n",
    "        print('------------------------------')\n",
    "\n",
    "        \n",
    "    #Creates List of Emails\n",
    "    def create_email_list(folder_path):\n",
    "        email_list = []\n",
    "        folder = os.listdir(folder_path)\n",
    "        for txt in folder:\n",
    "            file_name = fr'{folder_path}/{txt}'\n",
    "        \n",
    "            with codecs.open(file_name, 'r', encoding='utf-8',errors='ignore') as f:\n",
    "                email = f.read()\n",
    "                email_list.append(email)\n",
    "        return email_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Input Email ID and Password**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter Gmail ID :········\n",
      "Enter Password :········\n"
     ]
    }
   ],
   "source": [
    "#main\n",
    "if __name__=='__main__':\n",
    "    email_id = getpass('Enter Gmail ID :')\n",
    "    email_password = getpass('Enter Password :')\n",
    "    mail=emails(email_id,email_password)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Download Spam and Inbox mails from Gmail**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading spam emails...\n",
      "Downloaded spam emails at C:\\Users\\mgman\\Downloads\\spam.\n",
      "------------------------------\n",
      "Downloading inbox emails...\n",
      "Downloaded inbox emails at C:\\Users\\mgman\\Downloads\\inbox.\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "#This might take a while\n",
    "emails.download_emails('[Gmail]/Spam')\n",
    "emails.download_emails('INBOX')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Convert spam emails folder to Excel file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converted spam emails to Excel file.\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "cdir = os.getcwd()\n",
    "spam_list = emails.create_email_list(cdir + '\\Downloads\\\\' + emails.directory('[Gmail]/Spam'))\n",
    "spam_df = pd.DataFrame(spam_list)\n",
    "spam_df.to_excel(cdir + '\\Downloads\\spam.xlsx')\n",
    "\n",
    "print('Converted spam emails to Excel file.')\n",
    "print('------------------------------')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Convert non spam emails folder to Excel file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converted inbox emails to Excel file.\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "non_spam_list = emails.create_email_list(cdir + '\\Downloads\\\\' + emails.directory('INBOX'))\n",
    "non_spam_df = pd.DataFrame(non_spam_list)\n",
    "non_spam_df.to_excel(cdir + '\\Downloads\\inbox.xlsx')\n",
    "    \n",
    "print('Converted inbox emails to Excel file.')\n",
    "print('------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read both files\n",
    "df1 = pd.read_excel(cdir + '\\Downloads\\inbox.xlsx')\n",
    "df2 = pd.read_excel(cdir + '\\Downloads\\spam.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 0], dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 0], dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Add 'non_spam' Label and 0 Label_Number to non spam file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>ID</th>\n",
       "      <th>Label</th>\n",
       "      <th>Text</th>\n",
       "      <th>Label_Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>non_spam</td>\n",
       "      <td>BHK Apartments in North Goa  Rs  Lakh Onwar...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>non_spam</td>\n",
       "      <td>BHK Apt starts From  Lakh With Free Registr...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>non_spam</td>\n",
       "      <td>BHK Ready to Move in Apartments in Raj Naga...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>non_spam</td>\n",
       "      <td>BHK Apt Starts From Rs  Lakhs With Free Regi...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>non_spam</td>\n",
       "      <td>BHK Luxury Ready to Move Apartments Rs  Lakh ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID     Label                                               Text  \\\n",
       "0   0  non_spam     BHK Apartments in North Goa  Rs  Lakh Onwar...   \n",
       "1   1  non_spam     BHK Apt starts From  Lakh With Free Registr...   \n",
       "2   2  non_spam     BHK Ready to Move in Apartments in Raj Naga...   \n",
       "3   3  non_spam    BHK Apt Starts From Rs  Lakhs With Free Regi...   \n",
       "4   4  non_spam   BHK Luxury Ready to Move Apartments Rs  Lakh ...   \n",
       "\n",
       "   Label_Number  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.insert(1,\"Label\", \"non_spam\")\n",
    "df1.insert(3,\"Label_Number\", 0)\n",
    "df1.rename(columns = {'Unnamed: 0': 'ID',0: 'Text'}, inplace = True)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Add 'spam' Label and 1 Label_Number to spam file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>ID</th>\n",
       "      <th>Label</th>\n",
       "      <th>Text</th>\n",
       "      <th>Label_Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>spam</td>\n",
       "      <td>Activity Sheet_x000D_\\nDear Rupal , Hello , I ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>spam</td>\n",
       "      <td>Advertise your property till it gets rented ou...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>spam</td>\n",
       "      <td>Application Fee Waiver  Apply to St Lawrence C...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>spam</td>\n",
       "      <td>Apply for Nipissing University Canada For Sep ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>spam</td>\n",
       "      <td>Apply for Northern College at Pures  Toronto f...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID Label                                               Text  Label_Number\n",
       "0   0  spam  Activity Sheet_x000D_\\nDear Rupal , Hello , I ...             1\n",
       "1   1  spam  Advertise your property till it gets rented ou...             1\n",
       "2   2  spam  Application Fee Waiver  Apply to St Lawrence C...             1\n",
       "3   3  spam  Apply for Nipissing University Canada For Sep ...             1\n",
       "4   4  spam  Apply for Northern College at Pures  Toronto f...             1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.insert(1,\"Label\", \"spam\")\n",
    "df2.insert(3,\"Label_Number\", 1)\n",
    "df2.rename(columns = {'Unnamed: 0': 'ID',0: 'Text'}, inplace = True)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Merge spam and non spam Excel files in one and shuffle**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>Label</th>\n",
       "      <th>Text</th>\n",
       "      <th>Label_Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>spam</td>\n",
       "      <td>Why United Kingdom is best study destination_x...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>non_spam</td>\n",
       "      <td>Homeowners are looking for a tenant like you z...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>non_spam</td>\n",
       "      <td>Shop Assigned  Mi Home VM JanakpuriHigh Street...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>non_spam</td>\n",
       "      <td>Profile picture pending approval_x000D_\\nHi Ru...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>non_spam</td>\n",
       "      <td>Mahimagoyal JEE Main New Exam Dates Out_x000D_\\n</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>non_spam</td>\n",
       "      <td>UTFBKSoKSPIOCkuOCkpuCkuOCljeCkryE_x000D_\\nmahi...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>non_spam</td>\n",
       "      <td>UTFBUnMgMTUwICgKSKSwKWNKSKWHIOCkruClhCkgiDgpKg...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>non_spam</td>\n",
       "      <td>Mahima Your request is now registered with us_...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>non_spam</td>\n",
       "      <td>UTFBTWFoaWhZYWwsIEhpZhlcQgUGFjaFnZSAIElOUiAxNS...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>non_spam</td>\n",
       "      <td>utfBKSwKWBKSaKSKSVKSIOCkpCgKSuKWHKSCIOCkhuCkqg...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>980 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Label                                               Text  Label_Number\n",
       "0        spam  Why United Kingdom is best study destination_x...             1\n",
       "1    non_spam  Homeowners are looking for a tenant like you z...             0\n",
       "2    non_spam  Shop Assigned  Mi Home VM JanakpuriHigh Street...             0\n",
       "3    non_spam  Profile picture pending approval_x000D_\\nHi Ru...             0\n",
       "4    non_spam   Mahimagoyal JEE Main New Exam Dates Out_x000D_\\n             0\n",
       "..        ...                                                ...           ...\n",
       "975  non_spam  UTFBKSoKSPIOCkuOCkpuCkuOCljeCkryE_x000D_\\nmahi...             0\n",
       "976  non_spam  UTFBUnMgMTUwICgKSKSwKWNKSKWHIOCkruClhCkgiDgpKg...             0\n",
       "977  non_spam  Mahima Your request is now registered with us_...             0\n",
       "978  non_spam  UTFBTWFoaWhZYWwsIEhpZhlcQgUGFjaFnZSAIElOUiAxNS...             0\n",
       "979  non_spam  utfBKSwKWBKSaKSKSVKSIOCkpCgKSuKWHKSCIOCkhuCkqg...             0\n",
       "\n",
       "[980 rows x 3 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = pd.concat([df1, df2])\n",
    "df_all.reset_index(inplace=True, drop=True) \n",
    "df_all.pop('ID')\n",
    "df_final = df_all.sample(frac = 1)\n",
    "df_final.reset_index(inplace=True, drop=True) \n",
    "df_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Enter the name of Excel file and Download final dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter name of Excel File: All_Emails\n",
      "Downloaded all emails dataset in C:\\Users\\mgman\\Downloads\\All_Emails.xlsx\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "excel_file_name = input(\"Enter name of Excel File: \")\n",
    "df_final.to_excel(cdir + '\\Downloads\\\\'+excel_file_name + '.xlsx')\n",
    "print('Downloaded all emails dataset in '+ cdir +  '\\Downloads\\\\'+excel_file_name + '.xlsx')\n",
    "print('------------------------------')"
   ]
  }
 ],
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