Pandas Task 30 July.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "de64146e",
   "metadata": {},
   "source": [
    "1.Find out there avarage rating on weekly basis keep this in a mind that they take two days of leave\n",
    "\n",
    "2.Total working days for each agents \n",
    "\n",
    "3.Total query that you hvae taken \n",
    "\n",
    "4.total Feedback that you have received \n",
    "\n",
    "5.a agent name who have average rating between 3.5 to 4 \n",
    "\n",
    "6.Agent name who have rating lesss then 3.5 \n",
    "\n",
    "7.agent name who have rating more then 4.5 \n",
    "\n",
    "8.how many feedaback agents have received more then 4.5 average\n",
    "\n",
    "9.average weekly response time for each agent \n",
    "\n",
    "10.average weekely resolution time for each agents \n",
    "\n",
    "11.list of all agents name \n",
    "\n",
    "12.percentage of chat on which they have received a feedback \n",
    "\n",
    "13.Total contributation hour for each and every agents weekly basis \n",
    "\n",
    "14.total percentage of active hour for a month "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "84cee6b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5cb4d9eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('Agent Login Report.csv',skiprows=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7d167a3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SL No</th>\n",
       "      <th>Agent</th>\n",
       "      <th>Date</th>\n",
       "      <th>Login Time</th>\n",
       "      <th>Logout Time</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Shivananda Sonwane</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>03:35:29 PM</td>\n",
       "      <td>05:39:39 PM</td>\n",
       "      <td>02:04:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Khushboo Priya</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>03:06:59 PM</td>\n",
       "      <td>03:07:16 PM</td>\n",
       "      <td>00:00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Nandani Gupta</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>03:04:24 PM</td>\n",
       "      <td>05:31:07 PM</td>\n",
       "      <td>02:26:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Hrisikesh Neogi</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>02:34:29 PM</td>\n",
       "      <td>03:19:35 PM</td>\n",
       "      <td>00:45:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Mukesh</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>02:03:15 PM</td>\n",
       "      <td>03:11:52 PM</td>\n",
       "      <td>01:08:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>996</td>\n",
       "      <td>Manjunatha A</td>\n",
       "      <td>20-Jul-22</td>\n",
       "      <td>09:03:51 AM</td>\n",
       "      <td>03:02:28 PM</td>\n",
       "      <td>05:58:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>Bharath</td>\n",
       "      <td>20-Jul-22</td>\n",
       "      <td>09:00:49 AM</td>\n",
       "      <td>03:01:32 PM</td>\n",
       "      <td>06:00:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>Khushboo Priya</td>\n",
       "      <td>20-Jul-22</td>\n",
       "      <td>08:59:20 AM</td>\n",
       "      <td>02:26:55 PM</td>\n",
       "      <td>05:27:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>999</td>\n",
       "      <td>Nishtha Jain</td>\n",
       "      <td>20-Jul-22</td>\n",
       "      <td>08:43:55 AM</td>\n",
       "      <td>02:00:33 PM</td>\n",
       "      <td>05:16:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>Tarun</td>\n",
       "      <td>01-Jul-22</td>\n",
       "      <td>01:52:47 AM</td>\n",
       "      <td>12:01:07 PM</td>\n",
       "      <td>514:08:20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SL No               Agent       Date   Login Time  Logout Time   Duration\n",
       "0        1  Shivananda Sonwane  30-Jul-22  03:35:29 PM  05:39:39 PM   02:04:10\n",
       "1        2      Khushboo Priya  30-Jul-22  03:06:59 PM  03:07:16 PM   00:00:17\n",
       "2        3       Nandani Gupta  30-Jul-22  03:04:24 PM  05:31:07 PM   02:26:42\n",
       "3        4     Hrisikesh Neogi  30-Jul-22  02:34:29 PM  03:19:35 PM   00:45:06\n",
       "4        5             Mukesh   30-Jul-22  02:03:15 PM  03:11:52 PM   01:08:36\n",
       "..     ...                 ...        ...          ...          ...        ...\n",
       "995    996        Manjunatha A  20-Jul-22  09:03:51 AM  03:02:28 PM   05:58:37\n",
       "996    997            Bharath   20-Jul-22  09:00:49 AM  03:01:32 PM   06:00:43\n",
       "997    998      Khushboo Priya  20-Jul-22  08:59:20 AM  02:26:55 PM   05:27:34\n",
       "998    999        Nishtha Jain  20-Jul-22  08:43:55 AM  02:00:33 PM   05:16:38\n",
       "999   1000              Tarun   01-Jul-22  01:52:47 AM  12:01:07 PM  514:08:20\n",
       "\n",
       "[1000 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6ecc2350",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('Agent Performance Report.csv',skiprows = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "36fb65a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SL No</th>\n",
       "      <th>Date</th>\n",
       "      <th>Agent Name</th>\n",
       "      <th>Total Chats</th>\n",
       "      <th>Average Response Time</th>\n",
       "      <th>Average Resolution Time</th>\n",
       "      <th>Average Rating</th>\n",
       "      <th>Total Feedback</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>Prerna Singh</td>\n",
       "      <td>11</td>\n",
       "      <td>00:00:38</td>\n",
       "      <td>00:04:20</td>\n",
       "      <td>4.11</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>Nandani Gupta</td>\n",
       "      <td>11</td>\n",
       "      <td>00:01:15</td>\n",
       "      <td>00:28:25</td>\n",
       "      <td>3.14</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>Ameya Jain</td>\n",
       "      <td>14</td>\n",
       "      <td>00:00:30</td>\n",
       "      <td>00:11:36</td>\n",
       "      <td>4.55</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>Mahesh Sarade</td>\n",
       "      <td>14</td>\n",
       "      <td>00:01:04</td>\n",
       "      <td>00:15:46</td>\n",
       "      <td>4.71</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>Swati</td>\n",
       "      <td>14</td>\n",
       "      <td>00:01:11</td>\n",
       "      <td>00:16:33</td>\n",
       "      <td>3.67</td>\n",
       "      <td>6</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>2155</th>\n",
       "      <td>2156</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>Sowmiya Sivakumar</td>\n",
       "      <td>0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>2157</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>Nitin M</td>\n",
       "      <td>0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157</th>\n",
       "      <td>2158</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>Vivek</td>\n",
       "      <td>0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>2159</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>Ayushi Mishra</td>\n",
       "      <td>0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>2160</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>Chaitra K Hiremath</td>\n",
       "      <td>0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>2160 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SL No        Date          Agent Name  Total Chats  \\\n",
       "0         1  2022-07-30        Prerna Singh           11   \n",
       "1         2  2022-07-30       Nandani Gupta           11   \n",
       "2         3  2022-07-30          Ameya Jain           14   \n",
       "3         4  2022-07-30       Mahesh Sarade           14   \n",
       "4         5  2022-07-30              Swati            14   \n",
       "...     ...         ...                 ...          ...   \n",
       "2155   2156  2022-07-01   Sowmiya Sivakumar            0   \n",
       "2156   2157  2022-07-01             Nitin M            0   \n",
       "2157   2158  2022-07-01              Vivek             0   \n",
       "2158   2159  2022-07-01       Ayushi Mishra            0   \n",
       "2159   2160  2022-07-01  Chaitra K Hiremath            0   \n",
       "\n",
       "     Average Response Time Average Resolution Time  Average Rating  \\\n",
       "0                 00:00:38                00:04:20            4.11   \n",
       "1                 00:01:15                00:28:25            3.14   \n",
       "2                 00:00:30                00:11:36            4.55   \n",
       "3                 00:01:04                00:15:46            4.71   \n",
       "4                 00:01:11                00:16:33            3.67   \n",
       "...                    ...                     ...             ...   \n",
       "2155              00:00:00                00:00:00            0.00   \n",
       "2156              00:00:00                00:00:00            0.00   \n",
       "2157              00:00:00                00:00:00            0.00   \n",
       "2158              00:00:00                00:00:00            0.00   \n",
       "2159              00:00:00                00:00:00            0.00   \n",
       "\n",
       "      Total Feedback  \n",
       "0                  9  \n",
       "1                  7  \n",
       "2                 11  \n",
       "3                  7  \n",
       "4                  6  \n",
       "...              ...  \n",
       "2155               0  \n",
       "2156               0  \n",
       "2157               0  \n",
       "2158               0  \n",
       "2159               0  \n",
       "\n",
       "[2160 rows x 8 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "64ec4f22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SL No', 'Agent', 'Date', 'Login Time', 'Logout Time', 'Duration'], dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "03b8fe89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SL No           int64\n",
       "Agent          object\n",
       "Date           object\n",
       "Login Time     object\n",
       "Logout Time    object\n",
       "Duration       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c695a447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SL No', 'Date', 'Agent Name', 'Total Chats', 'Average Response Time',\n",
       "       'Average Resolution Time', 'Average Rating', 'Total Feedback'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "72baf31d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SL No                        int64\n",
       "Date                        object\n",
       "Agent Name                  object\n",
       "Total Chats                  int64\n",
       "Average Response Time       object\n",
       "Average Resolution Time     object\n",
       "Average Rating             float64\n",
       "Total Feedback               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bd71332",
   "metadata": {},
   "source": [
    "# 1.Find out there avarage rating on weekly basis keep this in a mind that they take two days of leave\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "208b2f5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SL No</th>\n",
       "      <th>Date</th>\n",
       "      <th>Agent Name</th>\n",
       "      <th>Total Chats</th>\n",
       "      <th>Average Response Time</th>\n",
       "      <th>Average Resolution Time</th>\n",
       "      <th>Average Rating</th>\n",
       "      <th>Total Feedback</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>Prerna Singh</td>\n",
       "      <td>11</td>\n",
       "      <td>00:00:38</td>\n",
       "      <td>00:04:20</td>\n",
       "      <td>4.11</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>Nandani Gupta</td>\n",
       "      <td>11</td>\n",
       "      <td>00:01:15</td>\n",
       "      <td>00:28:25</td>\n",
       "      <td>3.14</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>Ameya Jain</td>\n",
       "      <td>14</td>\n",
       "      <td>00:00:30</td>\n",
       "      <td>00:11:36</td>\n",
       "      <td>4.55</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>Mahesh Sarade</td>\n",
       "      <td>14</td>\n",
       "      <td>00:01:04</td>\n",
       "      <td>00:15:46</td>\n",
       "      <td>4.71</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>Swati</td>\n",
       "      <td>14</td>\n",
       "      <td>00:01:11</td>\n",
       "      <td>00:16:33</td>\n",
       "      <td>3.67</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2155</th>\n",
       "      <td>2156</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>Sowmiya Sivakumar</td>\n",
       "      <td>0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>2157</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>Nitin M</td>\n",
       "      <td>0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157</th>\n",
       "      <td>2158</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>Vivek</td>\n",
       "      <td>0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>2159</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>Ayushi Mishra</td>\n",
       "      <td>0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>2160</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>Chaitra K Hiremath</td>\n",
       "      <td>0</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2160 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SL No        Date          Agent Name  Total Chats  \\\n",
       "0         1  2022-07-30        Prerna Singh           11   \n",
       "1         2  2022-07-30       Nandani Gupta           11   \n",
       "2         3  2022-07-30          Ameya Jain           14   \n",
       "3         4  2022-07-30       Mahesh Sarade           14   \n",
       "4         5  2022-07-30              Swati            14   \n",
       "...     ...         ...                 ...          ...   \n",
       "2155   2156  2022-07-01   Sowmiya Sivakumar            0   \n",
       "2156   2157  2022-07-01             Nitin M            0   \n",
       "2157   2158  2022-07-01              Vivek             0   \n",
       "2158   2159  2022-07-01       Ayushi Mishra            0   \n",
       "2159   2160  2022-07-01  Chaitra K Hiremath            0   \n",
       "\n",
       "     Average Response Time Average Resolution Time  Average Rating  \\\n",
       "0                 00:00:38                00:04:20            4.11   \n",
       "1                 00:01:15                00:28:25            3.14   \n",
       "2                 00:00:30                00:11:36            4.55   \n",
       "3                 00:01:04                00:15:46            4.71   \n",
       "4                 00:01:11                00:16:33            3.67   \n",
       "...                    ...                     ...             ...   \n",
       "2155              00:00:00                00:00:00            0.00   \n",
       "2156              00:00:00                00:00:00            0.00   \n",
       "2157              00:00:00                00:00:00            0.00   \n",
       "2158              00:00:00                00:00:00            0.00   \n",
       "2159              00:00:00                00:00:00            0.00   \n",
       "\n",
       "      Total Feedback  \n",
       "0                  9  \n",
       "1                  7  \n",
       "2                 11  \n",
       "3                  7  \n",
       "4                  6  \n",
       "...              ...  \n",
       "2155               0  \n",
       "2156               0  \n",
       "2157               0  \n",
       "2158               0  \n",
       "2159               0  \n",
       "\n",
       "[2160 rows x 8 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b997c586",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['date']=pd.to_datetime(df2['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2c01b989",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['week']=df2['date'].dt.isocalendar().week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bf51eeaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['days']=df2['date'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "472830e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_mean=df2.groupby(['Agent Name','week'])['Average Rating'].mean().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a0e14a25",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>Average Rating</th>\n",
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       "    <tr>\n",
       "      <th>Agent Name</th>\n",
       "      <th>week</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Abhishek</th>\n",
       "      <th>26</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Zeeshan</th>\n",
       "      <th>26</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.307143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.948571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2.760000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Average Rating\n",
       "Agent Name week                \n",
       "Abhishek   26          0.000000\n",
       "           27          0.000000\n",
       "           28          0.000000\n",
       "           29          0.000000\n",
       "           30          0.000000\n",
       "...                         ...\n",
       "Zeeshan    26          0.000000\n",
       "           27          1.307143\n",
       "           28          2.948571\n",
       "           29          3.180000\n",
       "           30          2.760000\n",
       "\n",
       "[350 rows x 1 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5518c0bd",
   "metadata": {},
   "source": [
    "# 2.Total working days for each agents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0d012e1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agent Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abhishek</th>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>Aditya</th>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>Aditya Shinde</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aditya_iot</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amersh</th>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>Uday Mishra</th>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>Vasanth P</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vivek</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wasim</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zeeshan</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               days\n",
       "Agent Name         \n",
       "Abhishek         30\n",
       "Aditya           30\n",
       "Aditya Shinde    30\n",
       "Aditya_iot       30\n",
       "Amersh           30\n",
       "...             ...\n",
       "Uday Mishra      30\n",
       "Vasanth P        30\n",
       "Vivek            30\n",
       "Wasim            30\n",
       "Zeeshan          30\n",
       "\n",
       "[70 rows x 1 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('Agent Name')['days'].count().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeb41a75",
   "metadata": {},
   "source": [
    "# 3.Total query that you hvae taken "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "605e3aab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SL No', 'Date', 'Agent Name', 'Total Chats', 'Average Response Time',\n",
       "       'Average Resolution Time', 'Average Rating', 'Total Feedback', 'date',\n",
       "       'week', 'days'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c3828dbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Chats</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agent Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abhishek</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Aditya</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aditya Shinde</th>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aditya_iot</th>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amersh</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uday Mishra</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vasanth P</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vivek</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wasim</th>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zeeshan</th>\n",
       "      <td>542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Total Chats\n",
       "Agent Name                \n",
       "Abhishek                 0\n",
       "Aditya                   0\n",
       "Aditya Shinde          277\n",
       "Aditya_iot             231\n",
       "Amersh                   0\n",
       "...                    ...\n",
       "Uday Mishra              0\n",
       "Vasanth P                0\n",
       "Vivek                   44\n",
       "Wasim                  433\n",
       "Zeeshan                542\n",
       "\n",
       "[70 rows x 1 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('Agent Name')['Total Chats'].sum().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8135ff33",
   "metadata": {},
   "source": [
    "# 4.total Feedback that you have received"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c3b9aa92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agent Name</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abhishek</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Aditya</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Aditya Shinde</th>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aditya_iot</th>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amersh</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uday Mishra</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vasanth P</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vivek</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wasim</th>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zeeshan</th>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Total Feedback\n",
       "Agent Name                   \n",
       "Abhishek                    0\n",
       "Aditya                      0\n",
       "Aditya Shinde             153\n",
       "Aditya_iot                131\n",
       "Amersh                      0\n",
       "...                       ...\n",
       "Uday Mishra                 0\n",
       "Vasanth P                   0\n",
       "Vivek                      20\n",
       "Wasim                     284\n",
       "Zeeshan                   335\n",
       "\n",
       "[70 rows x 1 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('Agent Name')['Total Feedback'].sum().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a93ec52",
   "metadata": {},
   "source": [
    "# 5.a agent name who have average rating between 3.5 to 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "95099bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SL No', 'Date', 'Agent Name', 'Total Chats', 'Average Response Time',\n",
       "       'Average Resolution Time', 'Average Rating', 'Total Feedback', 'date',\n",
       "       'week', 'days'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "de1f7eb8",
   "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>Agent Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Swati</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Manjunatha A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Boktiar Ahmed Bappy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prateek _iot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Nandani Gupta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>Jawala Prakash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>Sanjeev Kumar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>Aditya Shinde</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>Deepranjan Gupta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2097</th>\n",
       "      <td>Sanjeev Kumar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Agent Name\n",
       "4                  Swati \n",
       "10           Manjunatha A\n",
       "73    Boktiar Ahmed Bappy\n",
       "76          Prateek _iot \n",
       "78          Nandani Gupta\n",
       "...                   ...\n",
       "1941       Jawala Prakash\n",
       "2018        Sanjeev Kumar\n",
       "2021        Aditya Shinde\n",
       "2023     Deepranjan Gupta\n",
       "2097        Sanjeev Kumar\n",
       "\n",
       "[114 rows x 1 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Agent Name'][(df2['Average Rating']>=3.5) & (df2['Average Rating']<=4)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b2c18b",
   "metadata": {},
   "source": [
    "# 6 . Agent name who have rating lesss then 3.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cf4ff10a",
   "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>Agent Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nandani Gupta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Hitesh Choudhary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Sanjeevan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Anirudh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Shiva Srivastava</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2155</th>\n",
       "      <td>Sowmiya Sivakumar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>Nitin M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157</th>\n",
       "      <td>Vivek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>Ayushi Mishra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>Chaitra K Hiremath</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1474 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Agent Name\n",
       "1          Nandani Gupta\n",
       "19      Hitesh Choudhary\n",
       "20            Sanjeevan \n",
       "21              Anirudh \n",
       "22      Shiva Srivastava\n",
       "...                  ...\n",
       "2155   Sowmiya Sivakumar\n",
       "2156             Nitin M\n",
       "2157              Vivek \n",
       "2158       Ayushi Mishra\n",
       "2159  Chaitra K Hiremath\n",
       "\n",
       "[1474 rows x 1 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Agent Name'][(df2['Average Rating'] < 3.5)].to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f212940b",
   "metadata": {},
   "source": [
    "# 8 . How many feedaback agents have received more then 4.5 average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "53a72fb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df2.groupby('Agent Name')['Total Feedback'].mean()>4.5).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffe2132f",
   "metadata": {},
   "source": [
    "# 9 . Average weekly response time for each agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fa420ee0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SL No', 'Date', 'Agent Name', 'Total Chats', 'Average Response Time',\n",
       "       'Average Resolution Time', 'Average Rating', 'Total Feedback', 'date',\n",
       "       'week', 'days'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3cc1dd0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Average_response_time'] = pd.to_datetime(df2['Average Response Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2ed3adb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['hrs']=df2['Average_response_time'].dt.hour\n",
    "df2['mins']=df2['Average_response_time'].dt.minute\n",
    "df2['sec']=df2['Average_response_time'].dt.second\n",
    "df2['mins']=df2['mins']*60\n",
    "df2['time in sec']=df2['mins']+df2['sec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5b67f34d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>SL No</th>\n",
       "      <th>Date</th>\n",
       "      <th>Agent Name</th>\n",
       "      <th>Total Chats</th>\n",
       "      <th>Average Response Time</th>\n",
       "      <th>Average Resolution Time</th>\n",
       "      <th>Average Rating</th>\n",
       "      <th>Total Feedback</th>\n",
       "      <th>date</th>\n",
       "      <th>week</th>\n",
       "      <th>days</th>\n",
       "      <th>Average_weekly_response</th>\n",
       "      <th>hrs</th>\n",
       "      <th>mins</th>\n",
       "      <th>sec</th>\n",
       "      <th>time in sec</th>\n",
       "      <th>Average_response_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>Prerna Singh</td>\n",
       "      <td>11</td>\n",
       "      <td>00:00:38</td>\n",
       "      <td>00:04:20</td>\n",
       "      <td>4.11</td>\n",
       "      <td>9</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>2022-07-31 00:00:38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>2022-07-31 00:00:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>Nandani Gupta</td>\n",
       "      <td>11</td>\n",
       "      <td>00:01:15</td>\n",
       "      <td>00:28:25</td>\n",
       "      <td>3.14</td>\n",
       "      <td>7</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>2022-07-31 00:01:15</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>15</td>\n",
       "      <td>75</td>\n",
       "      <td>2022-07-31 00:01:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>Ameya Jain</td>\n",
       "      <td>14</td>\n",
       "      <td>00:00:30</td>\n",
       "      <td>00:11:36</td>\n",
       "      <td>4.55</td>\n",
       "      <td>11</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>2022-07-31 00:00:30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>2022-07-31 00:00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>Mahesh Sarade</td>\n",
       "      <td>14</td>\n",
       "      <td>00:01:04</td>\n",
       "      <td>00:15:46</td>\n",
       "      <td>4.71</td>\n",
       "      <td>7</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>2022-07-31 00:01:04</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>4</td>\n",
       "      <td>64</td>\n",
       "      <td>2022-07-31 00:01:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>Swati</td>\n",
       "      <td>14</td>\n",
       "      <td>00:01:11</td>\n",
       "      <td>00:16:33</td>\n",
       "      <td>3.67</td>\n",
       "      <td>6</td>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>2022-07-31 00:01:11</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>71</td>\n",
       "      <td>2022-07-31 00:01:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SL No        Date     Agent Name  Total Chats Average Response Time  \\\n",
       "0      1  2022-07-30   Prerna Singh           11              00:00:38   \n",
       "1      2  2022-07-30  Nandani Gupta           11              00:01:15   \n",
       "2      3  2022-07-30     Ameya Jain           14              00:00:30   \n",
       "3      4  2022-07-30  Mahesh Sarade           14              00:01:04   \n",
       "4      5  2022-07-30         Swati            14              00:01:11   \n",
       "\n",
       "  Average Resolution Time  Average Rating  Total Feedback       date  week  \\\n",
       "0                00:04:20            4.11               9 2022-07-30    30   \n",
       "1                00:28:25            3.14               7 2022-07-30    30   \n",
       "2                00:11:36            4.55              11 2022-07-30    30   \n",
       "3                00:15:46            4.71               7 2022-07-30    30   \n",
       "4                00:16:33            3.67               6 2022-07-30    30   \n",
       "\n",
       "   days Average_weekly_response  hrs  mins  sec  time in sec  \\\n",
       "0    30     2022-07-31 00:00:38    0     0   38           38   \n",
       "1    30     2022-07-31 00:01:15    0    60   15           75   \n",
       "2    30     2022-07-31 00:00:30    0     0   30           30   \n",
       "3    30     2022-07-31 00:01:04    0    60    4           64   \n",
       "4    30     2022-07-31 00:01:11    0    60   11           71   \n",
       "\n",
       "  Average_response_time  \n",
       "0   2022-07-31 00:00:38  \n",
       "1   2022-07-31 00:01:15  \n",
       "2   2022-07-31 00:00:30  \n",
       "3   2022-07-31 00:01:04  \n",
       "4   2022-07-31 00:01:11  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ef900b21",
   "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></th>\n",
       "      <th>time in sec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agent Name</th>\n",
       "      <th>week</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Abhishek</th>\n",
       "      <th>26</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Zeeshan</th>\n",
       "      <th>26</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>23.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>123.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>62.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>63.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 time in sec\n",
       "Agent Name week             \n",
       "Abhishek   26       0.000000\n",
       "           27       0.000000\n",
       "           28       0.000000\n",
       "           29       0.000000\n",
       "           30       0.000000\n",
       "...                      ...\n",
       "Zeeshan    26       0.000000\n",
       "           27      23.571429\n",
       "           28     123.428571\n",
       "           29      62.857143\n",
       "           30      63.833333\n",
       "\n",
       "[350 rows x 1 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby(['Agent Name','week'])['time in sec'].mean().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c2c1ee1",
   "metadata": {},
   "source": [
    "# 10 . Average weekely resolution time for each agents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d78e2004",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Average_Resolution_Time'] = pd.to_datetime(df2['Average Resolution Time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "af41b78e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['hrs_art']=df2['Average_Resolution_Time'].dt.hour\n",
    "df2['mins_art']=df2['Average_Resolution_Time'].dt.minute\n",
    "df2['sec_art']=df2['Average_Resolution_Time'].dt.second\n",
    "df2['hrs_art']=df2['hrs_art']*3600\n",
    "df2['mins_art']=df2['mins_art']*60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0b0e7734",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['art_time_in_sec']=df2['hrs_art']+df2['mins_art']+df2['sec_art']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d87e34db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>art_time_in_sec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agent Name</th>\n",
       "      <th>week</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Abhishek</th>\n",
       "      <th>26</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Zeeshan</th>\n",
       "      <th>26</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>323.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>984.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>794.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>773.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 art_time_in_sec\n",
       "Agent Name week                 \n",
       "Abhishek   26           0.000000\n",
       "           27           0.000000\n",
       "           28           0.000000\n",
       "           29           0.000000\n",
       "           30           0.000000\n",
       "...                          ...\n",
       "Zeeshan    26           0.000000\n",
       "           27         323.000000\n",
       "           28         984.285714\n",
       "           29         794.285714\n",
       "           30         773.500000\n",
       "\n",
       "[350 rows x 1 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby(['Agent Name','week'])['art_time_in_sec'].mean().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea8672bc",
   "metadata": {},
   "source": [
    "# 11.List of all agents name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "2c0f3432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Prerna Singh', 'Nandani Gupta', 'Ameya Jain', 'Mahesh Sarade',\n",
       "       'Swati ', 'Mukesh ', 'Saikumarreddy N', 'Sanjeev Kumar',\n",
       "       'Shubham Sharma', 'Nishtha Jain', 'Manjunatha A', 'Khushboo Priya',\n",
       "       'Suraj S Bilgi', 'Harikrishnan Shaji', 'Hrisikesh Neogi',\n",
       "       'Shivan K', 'Sowmiya Sivakumar', 'Madhulika G', 'Mithun S',\n",
       "       'Hitesh Choudhary', 'Sanjeevan ', 'Anirudh ', 'Shiva Srivastava',\n",
       "       'Dibyanshu ', 'Ashish ', 'Uday Mishra', 'Aditya Shinde',\n",
       "       'Jayant Kumar', 'Aditya_iot ', 'Prabir Kumar Satapathy',\n",
       "       'Saurabh Shukla', 'Wasim ', 'Samprit ', 'Maitry ', 'Abhishek ',\n",
       "       'Rishav Dash', 'Aravind ', 'Tarun ', 'Muskan Garg', 'Ankit Sharma',\n",
       "       'Bharath ', 'Ashad Nasim', 'Vasanth P', 'Aditya ', 'Amersh ',\n",
       "       'Sandipan Saha', 'Ineuron Intelligence ', 'Mahak ', 'Mukesh Rao ',\n",
       "       'Zeeshan ', 'Anurag Tiwari', 'Ankitjha ', 'Saif Khan',\n",
       "       'Jaydeep Dixit', 'Sudhanshu Kumar', 'Maneesh ', 'Hyder Abbas',\n",
       "       'Rohan ', 'Vivek ', 'Nitin M', 'Shivan_S ', 'Spuri ',\n",
       "       'Ayushi Mishra', 'Chaitra K Hiremath', 'Prateek _iot ',\n",
       "       'Boktiar Ahmed Bappy', 'Shivananda Sonwane', 'Ishawant Kumar',\n",
       "       'Deepranjan Gupta', 'Jawala Prakash'], dtype=object)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Agent Name'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "505d797b",
   "metadata": {},
   "source": [
    "# 12 . percentage of chat on which they have received a feedback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c1a6cf1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Agent Name\n",
       "Abhishek               NaN\n",
       "Aditya                 NaN\n",
       "Aditya Shinde    55.234657\n",
       "Aditya_iot       56.709957\n",
       "Amersh                 NaN\n",
       "                   ...    \n",
       "Uday Mishra            NaN\n",
       "Vasanth P              NaN\n",
       "Vivek            45.454545\n",
       "Wasim            65.588915\n",
       "Zeeshan          61.808118\n",
       "Length: 70, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((df2.groupby('Agent Name')['Total Feedback'].sum())/(df2.groupby('Agent Name')['Total Chats'].sum()))*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05902324",
   "metadata": {},
   "source": [
    "# 13 . Total contributation hour for each and every agents weekly basis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "f1370505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>SL No</th>\n",
       "      <th>Agent</th>\n",
       "      <th>Date</th>\n",
       "      <th>Login Time</th>\n",
       "      <th>Logout Time</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Shivananda Sonwane</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>03:35:29 PM</td>\n",
       "      <td>05:39:39 PM</td>\n",
       "      <td>02:04:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Khushboo Priya</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>03:06:59 PM</td>\n",
       "      <td>03:07:16 PM</td>\n",
       "      <td>00:00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Nandani Gupta</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>03:04:24 PM</td>\n",
       "      <td>05:31:07 PM</td>\n",
       "      <td>02:26:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Hrisikesh Neogi</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>02:34:29 PM</td>\n",
       "      <td>03:19:35 PM</td>\n",
       "      <td>00:45:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Mukesh</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>02:03:15 PM</td>\n",
       "      <td>03:11:52 PM</td>\n",
       "      <td>01:08:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>996</td>\n",
       "      <td>Manjunatha A</td>\n",
       "      <td>20-Jul-22</td>\n",
       "      <td>09:03:51 AM</td>\n",
       "      <td>03:02:28 PM</td>\n",
       "      <td>05:58:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>Bharath</td>\n",
       "      <td>20-Jul-22</td>\n",
       "      <td>09:00:49 AM</td>\n",
       "      <td>03:01:32 PM</td>\n",
       "      <td>06:00:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>Khushboo Priya</td>\n",
       "      <td>20-Jul-22</td>\n",
       "      <td>08:59:20 AM</td>\n",
       "      <td>02:26:55 PM</td>\n",
       "      <td>05:27:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>999</td>\n",
       "      <td>Nishtha Jain</td>\n",
       "      <td>20-Jul-22</td>\n",
       "      <td>08:43:55 AM</td>\n",
       "      <td>02:00:33 PM</td>\n",
       "      <td>05:16:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>Tarun</td>\n",
       "      <td>01-Jul-22</td>\n",
       "      <td>01:52:47 AM</td>\n",
       "      <td>12:01:07 PM</td>\n",
       "      <td>514:08:20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SL No               Agent       Date   Login Time  Logout Time   Duration\n",
       "0        1  Shivananda Sonwane  30-Jul-22  03:35:29 PM  05:39:39 PM   02:04:10\n",
       "1        2      Khushboo Priya  30-Jul-22  03:06:59 PM  03:07:16 PM   00:00:17\n",
       "2        3       Nandani Gupta  30-Jul-22  03:04:24 PM  05:31:07 PM   02:26:42\n",
       "3        4     Hrisikesh Neogi  30-Jul-22  02:34:29 PM  03:19:35 PM   00:45:06\n",
       "4        5             Mukesh   30-Jul-22  02:03:15 PM  03:11:52 PM   01:08:36\n",
       "..     ...                 ...        ...          ...          ...        ...\n",
       "995    996        Manjunatha A  20-Jul-22  09:03:51 AM  03:02:28 PM   05:58:37\n",
       "996    997            Bharath   20-Jul-22  09:00:49 AM  03:01:32 PM   06:00:43\n",
       "997    998      Khushboo Priya  20-Jul-22  08:59:20 AM  02:26:55 PM   05:27:34\n",
       "998    999        Nishtha Jain  20-Jul-22  08:43:55 AM  02:00:33 PM   05:16:38\n",
       "999   1000              Tarun   01-Jul-22  01:52:47 AM  12:01:07 PM  514:08:20\n",
       "\n",
       "[1000 rows x 6 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "a24d4af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['updated_duration'] = (pd.to_datetime(df1.Date) + pd.to_timedelta(df1.Duration))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "d9611b8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['hrs_contribution']=df1['updated_duration'].dt.hour\n",
    "df1['min_contribution']=df1['updated_duration'].dt.minute\n",
    "df1['sec_contribution']=df1['updated_duration'].dt.second\n",
    "df1['hrs_contribution']=df1['hrs_contribution']*3600\n",
    "df1['min_contribution']=df1['min_contribution']*60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "914e94a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['total_contribution_in_sec']=df1['hrs_contribution']+df1['min_contribution']+df1['sec_contribution']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "eed952d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['total_contribution_in_hrs']=df1['total_contribution_in_sec']/3600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "fe0f7423",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_16720\\2284199305.py:1: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated. Please use Series.dt.isocalendar().week instead.\n",
      "  df1['week']=df1['updated_duration'].dt.week\n"
     ]
    }
   ],
   "source": [
    "df1['week']=df1['updated_duration'].dt.week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "3be2a83f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SL No</th>\n",
       "      <th>Agent</th>\n",
       "      <th>Date</th>\n",
       "      <th>Login Time</th>\n",
       "      <th>Logout Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>updated_duration</th>\n",
       "      <th>hrs_contribution</th>\n",
       "      <th>min_contribution</th>\n",
       "      <th>sec_contribution</th>\n",
       "      <th>total_contribution_in_sec</th>\n",
       "      <th>week</th>\n",
       "      <th>total_contribution_in_hrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Shivananda Sonwane</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>03:35:29 PM</td>\n",
       "      <td>05:39:39 PM</td>\n",
       "      <td>02:04:10</td>\n",
       "      <td>2022-07-30 02:04:10</td>\n",
       "      <td>7200</td>\n",
       "      <td>240</td>\n",
       "      <td>10</td>\n",
       "      <td>2.069444</td>\n",
       "      <td>30</td>\n",
       "      <td>0.000575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Khushboo Priya</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>03:06:59 PM</td>\n",
       "      <td>03:07:16 PM</td>\n",
       "      <td>00:00:17</td>\n",
       "      <td>2022-07-30 00:00:17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.004722</td>\n",
       "      <td>30</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Nandani Gupta</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>03:04:24 PM</td>\n",
       "      <td>05:31:07 PM</td>\n",
       "      <td>02:26:42</td>\n",
       "      <td>2022-07-30 02:26:42</td>\n",
       "      <td>7200</td>\n",
       "      <td>1560</td>\n",
       "      <td>42</td>\n",
       "      <td>2.445000</td>\n",
       "      <td>30</td>\n",
       "      <td>0.000679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Hrisikesh Neogi</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>02:34:29 PM</td>\n",
       "      <td>03:19:35 PM</td>\n",
       "      <td>00:45:06</td>\n",
       "      <td>2022-07-30 00:45:06</td>\n",
       "      <td>0</td>\n",
       "      <td>2700</td>\n",
       "      <td>6</td>\n",
       "      <td>0.751667</td>\n",
       "      <td>30</td>\n",
       "      <td>0.000209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Mukesh</td>\n",
       "      <td>30-Jul-22</td>\n",
       "      <td>02:03:15 PM</td>\n",
       "      <td>03:11:52 PM</td>\n",
       "      <td>01:08:36</td>\n",
       "      <td>2022-07-30 01:08:36</td>\n",
       "      <td>3600</td>\n",
       "      <td>480</td>\n",
       "      <td>36</td>\n",
       "      <td>1.143333</td>\n",
       "      <td>30</td>\n",
       "      <td>0.000318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>996</td>\n",
       "      <td>Manjunatha A</td>\n",
       "      <td>20-Jul-22</td>\n",
       "      <td>09:03:51 AM</td>\n",
       "      <td>03:02:28 PM</td>\n",
       "      <td>05:58:37</td>\n",
       "      <td>2022-07-20 05:58:37</td>\n",
       "      <td>18000</td>\n",
       "      <td>3480</td>\n",
       "      <td>37</td>\n",
       "      <td>5.976944</td>\n",
       "      <td>29</td>\n",
       "      <td>0.001660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>Bharath</td>\n",
       "      <td>20-Jul-22</td>\n",
       "      <td>09:00:49 AM</td>\n",
       "      <td>03:01:32 PM</td>\n",
       "      <td>06:00:43</td>\n",
       "      <td>2022-07-20 06:00:43</td>\n",
       "      <td>21600</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>6.011944</td>\n",
       "      <td>29</td>\n",
       "      <td>0.001670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>Khushboo Priya</td>\n",
       "      <td>20-Jul-22</td>\n",
       "      <td>08:59:20 AM</td>\n",
       "      <td>02:26:55 PM</td>\n",
       "      <td>05:27:34</td>\n",
       "      <td>2022-07-20 05:27:34</td>\n",
       "      <td>18000</td>\n",
       "      <td>1620</td>\n",
       "      <td>34</td>\n",
       "      <td>5.459444</td>\n",
       "      <td>29</td>\n",
       "      <td>0.001517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>999</td>\n",
       "      <td>Nishtha Jain</td>\n",
       "      <td>20-Jul-22</td>\n",
       "      <td>08:43:55 AM</td>\n",
       "      <td>02:00:33 PM</td>\n",
       "      <td>05:16:38</td>\n",
       "      <td>2022-07-20 05:16:38</td>\n",
       "      <td>18000</td>\n",
       "      <td>960</td>\n",
       "      <td>38</td>\n",
       "      <td>5.277222</td>\n",
       "      <td>29</td>\n",
       "      <td>0.001466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>Tarun</td>\n",
       "      <td>01-Jul-22</td>\n",
       "      <td>01:52:47 AM</td>\n",
       "      <td>12:01:07 PM</td>\n",
       "      <td>514:08:20</td>\n",
       "      <td>2022-07-22 10:08:20</td>\n",
       "      <td>36000</td>\n",
       "      <td>480</td>\n",
       "      <td>20</td>\n",
       "      <td>10.138889</td>\n",
       "      <td>29</td>\n",
       "      <td>0.002816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SL No               Agent       Date   Login Time  Logout Time  \\\n",
       "0        1  Shivananda Sonwane  30-Jul-22  03:35:29 PM  05:39:39 PM   \n",
       "1        2      Khushboo Priya  30-Jul-22  03:06:59 PM  03:07:16 PM   \n",
       "2        3       Nandani Gupta  30-Jul-22  03:04:24 PM  05:31:07 PM   \n",
       "3        4     Hrisikesh Neogi  30-Jul-22  02:34:29 PM  03:19:35 PM   \n",
       "4        5             Mukesh   30-Jul-22  02:03:15 PM  03:11:52 PM   \n",
       "..     ...                 ...        ...          ...          ...   \n",
       "995    996        Manjunatha A  20-Jul-22  09:03:51 AM  03:02:28 PM   \n",
       "996    997            Bharath   20-Jul-22  09:00:49 AM  03:01:32 PM   \n",
       "997    998      Khushboo Priya  20-Jul-22  08:59:20 AM  02:26:55 PM   \n",
       "998    999        Nishtha Jain  20-Jul-22  08:43:55 AM  02:00:33 PM   \n",
       "999   1000              Tarun   01-Jul-22  01:52:47 AM  12:01:07 PM   \n",
       "\n",
       "      Duration    updated_duration  hrs_contribution  min_contribution  \\\n",
       "0     02:04:10 2022-07-30 02:04:10              7200               240   \n",
       "1     00:00:17 2022-07-30 00:00:17                 0                 0   \n",
       "2     02:26:42 2022-07-30 02:26:42              7200              1560   \n",
       "3     00:45:06 2022-07-30 00:45:06                 0              2700   \n",
       "4     01:08:36 2022-07-30 01:08:36              3600               480   \n",
       "..         ...                 ...               ...               ...   \n",
       "995   05:58:37 2022-07-20 05:58:37             18000              3480   \n",
       "996   06:00:43 2022-07-20 06:00:43             21600                 0   \n",
       "997   05:27:34 2022-07-20 05:27:34             18000              1620   \n",
       "998   05:16:38 2022-07-20 05:16:38             18000               960   \n",
       "999  514:08:20 2022-07-22 10:08:20             36000               480   \n",
       "\n",
       "     sec_contribution  total_contribution_in_sec  week  \\\n",
       "0                  10                   2.069444    30   \n",
       "1                  17                   0.004722    30   \n",
       "2                  42                   2.445000    30   \n",
       "3                   6                   0.751667    30   \n",
       "4                  36                   1.143333    30   \n",
       "..                ...                        ...   ...   \n",
       "995                37                   5.976944    29   \n",
       "996                43                   6.011944    29   \n",
       "997                34                   5.459444    29   \n",
       "998                38                   5.277222    29   \n",
       "999                20                  10.138889    29   \n",
       "\n",
       "     total_contribution_in_hrs  \n",
       "0                     0.000575  \n",
       "1                     0.000001  \n",
       "2                     0.000679  \n",
       "3                     0.000209  \n",
       "4                     0.000318  \n",
       "..                         ...  \n",
       "995                   0.001660  \n",
       "996                   0.001670  \n",
       "997                   0.001517  \n",
       "998                   0.001466  \n",
       "999                   0.002816  \n",
       "\n",
       "[1000 rows x 13 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "5f90da2c",
   "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></th>\n",
       "      <th>total_contribution_in_hrs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agent</th>\n",
       "      <th>week</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aditya Shinde</th>\n",
       "      <th>30</th>\n",
       "      <td>0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Aditya_iot</th>\n",
       "      <th>29</th>\n",
       "      <td>0.001693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.002677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amersh</th>\n",
       "      <th>30</th>\n",
       "      <td>0.000851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ameya Jain</th>\n",
       "      <th>29</th>\n",
       "      <td>0.006690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tarun</th>\n",
       "      <th>29</th>\n",
       "      <td>0.002816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Wasim</th>\n",
       "      <th>29</th>\n",
       "      <td>0.005452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.007932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Zeeshan</th>\n",
       "      <th>29</th>\n",
       "      <td>0.006785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.006850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    total_contribution_in_hrs\n",
       "Agent         week                           \n",
       "Aditya Shinde 30                     0.000010\n",
       "Aditya_iot    29                     0.001693\n",
       "              30                     0.002677\n",
       "Amersh        30                     0.000851\n",
       "Ameya Jain    29                     0.006690\n",
       "...                                       ...\n",
       "Tarun         29                     0.002816\n",
       "Wasim         29                     0.005452\n",
       "              30                     0.007932\n",
       "Zeeshan       29                     0.006785\n",
       "              30                     0.006850\n",
       "\n",
       "[89 rows x 1 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby(['Agent','week'])['total_contribution_in_hrs'].sum().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29c144d6",
   "metadata": {},
   "source": [
    "# 14. total percentage of active hour for a month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "40184cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Agent\n",
       "Aditya Shinde             0.000002\n",
       "Aditya_iot                0.000828\n",
       "Amersh                    0.000161\n",
       "Ameya Jain                0.002214\n",
       "Ankitjha                  0.000119\n",
       "Anurag Tiwari             0.000146\n",
       "Aravind                   0.001278\n",
       "Ayushi Mishra             0.002006\n",
       "Bharath                   0.002529\n",
       "Boktiar Ahmed Bappy       0.002119\n",
       "Chaitra K Hiremath        0.001806\n",
       "Deepranjan Gupta          0.005591\n",
       "Dibyanshu                 0.002767\n",
       "Harikrishnan Shaji        0.002826\n",
       "Hrisikesh Neogi           0.003029\n",
       "Hyder Abbas               0.000020\n",
       "Ineuron Intelligence      0.000076\n",
       "Ishawant Kumar            0.002724\n",
       "Jawala Prakash            0.002441\n",
       "Jaydeep Dixit             0.001886\n",
       "Khushboo Priya            0.002292\n",
       "Madhulika G               0.002423\n",
       "Mahesh Sarade             0.002258\n",
       "Maitry                    0.001628\n",
       "Manjunatha A              0.002171\n",
       "Mithun S                  0.002377\n",
       "Mukesh                    0.000468\n",
       "Muskan Garg               0.000912\n",
       "Nandani Gupta             0.002113\n",
       "Nishtha Jain              0.002307\n",
       "Nitin M                   0.000042\n",
       "Prabir Kumar Satapathy    0.001756\n",
       "Prateek _iot              0.000969\n",
       "Prerna Singh              0.002405\n",
       "Rishav Dash               0.002198\n",
       "Saikumarreddy N           0.002269\n",
       "Sanjeev Kumar             0.002351\n",
       "Saurabh Shukla            0.000877\n",
       "Shiva Srivastava          0.000789\n",
       "Shivan K                  0.001899\n",
       "Shivananda Sonwane        0.002593\n",
       "Shubham Sharma            0.002830\n",
       "Sowmiya Sivakumar         0.002354\n",
       "Sudhanshu Kumar           0.002432\n",
       "Suraj S Bilgi             0.000661\n",
       "Swati                     0.001315\n",
       "Tarun                     0.000533\n",
       "Wasim                     0.002535\n",
       "Zeeshan                   0.002583\n",
       "Name: total_contribution_in_hrs, dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((df1.groupby(['Agent'])['total_contribution_in_hrs'].sum())/528)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c1d0d90",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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