Cityla.ipynb

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   "cell_type": "markdown",
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    " <div style=\"text-align: center; background-color: #D4AF37; font-family: 'Trebuchet MS', Arial, sans-serif; color:white; padding: 20px; font-size: 40px; font-weight: bold; border-radius: 10px 10px 0 0; box-shadow: 0px 6px 8px rgba(0, 0, 0, 0.2);\">Compensation Project\n",
    "</div>\n",
    "<p style=\"text-align: center;\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f72f3775",
   "metadata": {},
   "source": [
    "<div style=\"border-radius: 10px; border: #b09507 solid; padding: 10px; background-color:white; font-size: 120%; text-align: left;\">\n",
    "\n",
    " <b><span style='color:#fa762f'> </span><span style='color:#00B0F0'> About the Project</span></b>\n",
    "\n",
    "\n",
    "\n",
    "Managing compensation is a complex endeavor that covers multiple facets including salaries, wages, bonuses, benefits, and additional incentives given to employees for their work. It involves navigating a delicate balance between budget constraints and providing fair rewards to employees.\n",
    "In the practical scenario of our dummy data project, we address similar elements. This entails a fictional organization handling compensation management across various outlined factors. I've utilized Pandas to analyse data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b1e8ab",
   "metadata": {},
   "source": [
    "<div style=\"border-radius: 10px; border: #b09507 solid; padding: 10px; background-color:white; font-size: 120%; text-align: left;\">\n",
    "\n",
    " <b><span style='color:#fa762f'> </span><span style='color:#00B0F0'> About the Dataset</span></b>\n",
    "\n",
    "The dataset comprises fabricated data pertaining to a company named Cityla. Among the three projects undertaken by the company, the Venice project surpassed expectations, the Tokyo project met expectations, and the Gemcom project yielded results below expectations.  \n",
    "    \n",
    "Based on the outcomes, the organization developed monetary rewards, such as regular bonuses, additional bonuses, and revised base pay.\n",
    "    \n",
    "Project Venice (Exceeded Expectations)\n",
    "    \n",
    "•\tEmployees (3 OR ABOVE years in the company):\n",
    "    \n",
    "Bonus: Regular bonus (10% for Grade A/B, 15% for C/D) + 50k additional bonus (paid in full).\n",
    "    \n",
    "o\tHike:\n",
    "    \n",
    "\tBelow 50th percentile: 18% hike.\n",
    "\tAbove 50th percentile: 12% hike.\n",
    "    \n",
    "•\tEmployees (2-3 years in the company):\n",
    "    \n",
    "o\tBonus: Regular bonus (prorated for those joined after April 1, 2023)+ 25K\n",
    "    \n",
    "•\tEmployees (Below Rating 7):\n",
    "    \n",
    "o\tRegular Bonus: 50% bonus (paid in full).\n",
    "o\tHike: 5%\n",
    "    \n",
    "Project Tokyo (Met Expectations)\n",
    "    \n",
    "•\tEmployees (3 OR ABOVE years in the company):\n",
    "    \n",
    "o\tBonus: Regular bonus (10% for Grade A/B, 15% for C/D) + 20k additional bonus (paid in full).\n",
    "    \n",
    "o\tHike:\n",
    "    \n",
    "\tBelow 50th percentile: 12% hike.\n",
    "\tAbove 50th percentile: 10% hike.\n",
    "    \n",
    "•\tEmployees (2-3 years in the company):\n",
    "    \n",
    "Bonus: Regular bonus (prorated for those joined after April 1, 2023)+10K\n",
    "    \n",
    "•\tEmployees (Below Rating 7):\n",
    "    \n",
    "Regular Bonus: 50% bonus (paid in full).\n",
    "Hike: 5%\n",
    " \n",
    "(if joined after April 1, 2023): Prorated hike and bonus.\n",
    "    \n",
    "    \n",
    "Project Gemcom (Below Expectations)- 5% Hike. 50% Bonus for all. no additional bonus.\n",
    "    \n",
    "\n",
    "    \n",
    "Note:\n",
    "    \n",
    "•\tRegular bonus percentages are based on employee grade.\n",
    "    \n",
    "•\tJoining after April 1, 2023 impacts bonus and hike calculations (prorated).\n",
    "\n",
    "\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba04fec0",
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   "outputs": [],
   "source": [
    "#Import the Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92a9457a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Cityla.csv', parse_dates=['Start_Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cda5581c",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Start_Year</th>\n",
       "      <th>Start_Date</th>\n",
       "      <th>Employee_Name</th>\n",
       "      <th>unique_ID</th>\n",
       "      <th>Level_ID</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Project</th>\n",
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       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>S.Kumar</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Gemcon</td>\n",
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       "      <td>A. Swati</td>\n",
       "      <td>2</td>\n",
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       "      <td>Senior Software Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2530100</td>\n",
       "      <td>0.15</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
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       "      <td>C. Poornima</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>D. Manish</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2767233</td>\n",
       "      <td>0.10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>R. Manoj</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2023602</td>\n",
       "      <td>0.10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Start_Year Start_Date Employee_Name  unique_ID  Level_ID  \\\n",
       "0        2019 2019-01-07       S.Kumar          1         4   \n",
       "1        2019 2019-01-14      A. Swati          2         3   \n",
       "2        2019 2019-01-14   C. Poornima          3         4   \n",
       "3        2019 2019-01-21     D. Manish          4         2   \n",
       "4        2019 2019-01-21      R. Manoj          5         2   \n",
       "\n",
       "                      Grade Project  Current_Base_Pay  Bonus_entitlement  \\\n",
       "0             Lead Engineer  Gemcon           3219629               0.15   \n",
       "1  Senior Software Engineer   Tokyo           2530100               0.15   \n",
       "2             Lead Engineer  Gemcon           3330113               0.15   \n",
       "3         Software Engineer  Gemcon           2767233               0.10   \n",
       "4         Software Engineer  Gemcon           2023602               0.10   \n",
       "\n",
       "   Rating  \n",
       "0      10  \n",
       "1       7  \n",
       "2      10  \n",
       "3      10  \n",
       "4       8  "
      ]
     },
     "execution_count": 3,
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   ],
   "source": [
    "df.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "4f862f31",
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   "outputs": [
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       "      <th>Start_Year</th>\n",
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       "    <tr>\n",
       "      <th>Employee_Name</th>\n",
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       "      <td>object</td>\n",
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       "    <tr>\n",
       "      <th>Level_ID</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grade</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Project</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Current_Base_Pay</th>\n",
       "      <td>535</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bonus_entitlement</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>float64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rating</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                   unique  Null  Nullpercent            type  duplicated\n",
       "Start_Year              5     0          0.0           int64           0\n",
       "Start_Date            148     0          0.0  datetime64[ns]           0\n",
       "Employee_Name         556     0          0.0          object           0\n",
       "unique_ID             561     0          0.0           int64           0\n",
       "Level_ID                4     0          0.0           int64           0\n",
       "Grade                   4     0          0.0          object           0\n",
       "Project                 3     0          0.0          object           0\n",
       "Current_Base_Pay      535     0          0.0           int64           0\n",
       "Bonus_entitlement       2     0          0.0         float64           0\n",
       "Rating                  5     0          0.0           int64           0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "table= pd.DataFrame({'unique':df.nunique(),\n",
    "                   'Null':df.isnull().sum(),\n",
    "                   'Nullpercent':df.isnull().sum()/len(df)*100,\n",
    "                   'type':df.dtypes, \"duplicated\":df.duplicated().sum()})\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60aacb87",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['unique_ID']=df['unique_ID'].astype(object)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a59bcb9",
   "metadata": {},
   "source": [
    "How many employees are there in the records?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9584aa0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(561, 10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "901bc999",
   "metadata": {},
   "source": [
    "561 records include"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0080668e",
   "metadata": {},
   "source": [
    "Employees by Grade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9c78d166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Software Engineer           190\n",
       "Senior Software Engineer    164\n",
       "Associate                   122\n",
       "Lead Engineer                85\n",
       "Name: Grade, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Grade'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e086cbf",
   "metadata": {},
   "source": [
    "Employees joined by Year and still with the company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f8d617fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020    163\n",
       "2023    132\n",
       "2021    106\n",
       "2022    104\n",
       "2019     56\n",
       "Name: Start_Year, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Start_Year'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e8991af",
   "metadata": {},
   "source": [
    "employee project allocation & grade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b68cdd6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_project_grade = df.pivot_table(index='Grade', columns='Project', values='unique_ID',aggfunc='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b1b53aaa",
   "metadata": {},
   "outputs": [
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       "      <td>43</td>\n",
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       "      <th>Lead Engineer</th>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>Senior Software Engineer</th>\n",
       "      <td>43</td>\n",
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      "text/plain": [
       "Project                   Gemcon  Tokyo  Venice\n",
       "Grade                                          \n",
       "Associate                     49     30      43\n",
       "Lead Engineer                 33     25      27\n",
       "Senior Software Engineer      43     61      60\n",
       "Software Engineer             71     56      63"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_project_grade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "687609a1",
   "metadata": {},
   "outputs": [
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       "      <td>27</td>\n",
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       "    <tr>\n",
       "      <th>Senior Software Engineer</th>\n",
       "      <td>43</td>\n",
       "      <td>61</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Software Engineer</th>\n",
       "      <td>71</td>\n",
       "      <td>56</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Project                   Gemcon  Tokyo  Venice\n",
       "Grade                                          \n",
       "Associate                     49     30      43\n",
       "Lead Engineer                 33     25      27\n",
       "Senior Software Engineer      43     61      60\n",
       "Software Engineer             71     56      63"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_project_grade"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5e9d1e7",
   "metadata": {},
   "source": [
    "Avg Ratings across project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0760665c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_project_rating = df.pivot_table(index ='Grade', columns ='Project',values = 'Rating',aggfunc='mean')\n",
    "pivot_project_rating = pivot_project_rating.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "603f74e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Project</th>\n",
       "      <th>Gemcon</th>\n",
       "      <th>Tokyo</th>\n",
       "      <th>Venice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Associate</th>\n",
       "      <td>7.96</td>\n",
       "      <td>8.57</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lead Engineer</th>\n",
       "      <td>8.27</td>\n",
       "      <td>8.00</td>\n",
       "      <td>7.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior Software Engineer</th>\n",
       "      <td>7.74</td>\n",
       "      <td>8.03</td>\n",
       "      <td>7.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Software Engineer</th>\n",
       "      <td>7.96</td>\n",
       "      <td>7.77</td>\n",
       "      <td>7.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Project                   Gemcon  Tokyo  Venice\n",
       "Grade                                          \n",
       "Associate                   7.96   8.57    7.88\n",
       "Lead Engineer               8.27   8.00    7.89\n",
       "Senior Software Engineer    7.74   8.03    7.93\n",
       "Software Engineer           7.96   7.77    7.98"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_project_rating"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9f9ef32",
   "metadata": {},
   "source": [
    "Average Base Pay Across Projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9ddd45aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Project</th>\n",
       "      <th>Gemcon</th>\n",
       "      <th>Tokyo</th>\n",
       "      <th>Venice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Associate</th>\n",
       "      <td>1488618.0</td>\n",
       "      <td>1489989.0</td>\n",
       "      <td>1523179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lead Engineer</th>\n",
       "      <td>2766288.0</td>\n",
       "      <td>2535402.0</td>\n",
       "      <td>2858086.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior Software Engineer</th>\n",
       "      <td>2378832.0</td>\n",
       "      <td>2421193.0</td>\n",
       "      <td>2417559.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Software Engineer</th>\n",
       "      <td>2127771.0</td>\n",
       "      <td>2052550.0</td>\n",
       "      <td>2076845.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Project                      Gemcon      Tokyo     Venice\n",
       "Grade                                                    \n",
       "Associate                 1488618.0  1489989.0  1523179.0\n",
       "Lead Engineer             2766288.0  2535402.0  2858086.0\n",
       "Senior Software Engineer  2378832.0  2421193.0  2417559.0\n",
       "Software Engineer         2127771.0  2052550.0  2076845.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_project_Base = df.pivot_table(index ='Grade', columns ='Project',values = 'Current_Base_Pay',aggfunc='mean')\n",
    "pivot_project_Base = pivot_project_Base.round(0)\n",
    "pivot_project_Base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "91787fa3",
   "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>Start_Year</th>\n",
       "      <th>Start_Date</th>\n",
       "      <th>Employee_Name</th>\n",
       "      <th>unique_ID</th>\n",
       "      <th>Level_ID</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Project</th>\n",
       "      <th>Current_Base_Pay</th>\n",
       "      <th>Bonus_entitlement</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>S.Kumar</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>3219629</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>A. Swati</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2530100</td>\n",
       "      <td>0.15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Start_Year Start_Date Employee_Name unique_ID  Level_ID  \\\n",
       "0        2019 2019-01-07       S.Kumar         1         4   \n",
       "1        2019 2019-01-14      A. Swati         2         3   \n",
       "\n",
       "                      Grade Project  Current_Base_Pay  Bonus_entitlement  \\\n",
       "0             Lead Engineer  Gemcon           3219629               0.15   \n",
       "1  Senior Software Engineer   Tokyo           2530100               0.15   \n",
       "\n",
       "   Rating  \n",
       "0      10  \n",
       "1       7  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bb7610da",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Current_Base_Pay</th>\n",
       "      <th>Bonus_entitlement</th>\n",
       "      <th>Rating</th>\n",
       "      <th>percentile_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>S.Kumar</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>A. Swati</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>C. Poornima</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>3330113</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>D. Manish</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2767233</td>\n",
       "      <td>0.10</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>R. Manoj</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2023602</td>\n",
       "      <td>0.10</td>\n",
       "      <td>8</td>\n",
       "      <td>Between 25th to 50th</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>K. Shreya</td>\n",
       "      <td>557</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2697410</td>\n",
       "      <td>0.15</td>\n",
       "      <td>7</td>\n",
       "      <td>Above 75th</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>S. Mani</td>\n",
       "      <td>558</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2467364</td>\n",
       "      <td>0.15</td>\n",
       "      <td>8</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>M. Bhavana</td>\n",
       "      <td>559</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2397438</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>N. Naveen</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>Associate</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>9</td>\n",
       "      <td>Below 25th</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>R. Renuka</td>\n",
       "      <td>561</td>\n",
       "      <td>1</td>\n",
       "      <td>Associate</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>9</td>\n",
       "      <td>Below 25th</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Start_Year Start_Date Employee_Name unique_ID  Level_ID  \\\n",
       "0          2019 2019-01-07       S.Kumar         1         4   \n",
       "1          2019 2019-01-14      A. Swati         2         3   \n",
       "2          2019 2019-01-14   C. Poornima         3         4   \n",
       "3          2019 2019-01-21     D. Manish         4         2   \n",
       "4          2019 2019-01-21      R. Manoj         5         2   \n",
       "..          ...        ...           ...       ...       ...   \n",
       "556        2023 2023-08-21     K. Shreya       557         3   \n",
       "557        2023 2023-08-21       S. Mani       558         4   \n",
       "558        2023 2023-08-21    M. Bhavana       559         3   \n",
       "559        2023 2023-08-21     N. Naveen       560         1   \n",
       "560        2023 2023-08-21     R. Renuka       561         1   \n",
       "\n",
       "                        Grade Project  Current_Base_Pay  Bonus_entitlement  \\\n",
       "0               Lead Engineer  Gemcon           3219629               0.15   \n",
       "1    Senior Software Engineer   Tokyo           2530100               0.15   \n",
       "2               Lead Engineer  Gemcon           3330113               0.15   \n",
       "3           Software Engineer  Gemcon           2767233               0.10   \n",
       "4           Software Engineer  Gemcon           2023602               0.10   \n",
       "..                        ...     ...               ...                ...   \n",
       "556  Senior Software Engineer  Gemcon           2697410               0.15   \n",
       "557             Lead Engineer   Tokyo           2467364               0.15   \n",
       "558  Senior Software Engineer   Tokyo           2397438               0.15   \n",
       "559                 Associate   Tokyo           1000000               0.10   \n",
       "560                 Associate   Tokyo           1000000               0.10   \n",
       "\n",
       "     Rating   percentile_category  \n",
       "0        10            Above 75th  \n",
       "1         7  Between 50th to 75th  \n",
       "2        10            Above 75th  \n",
       "3        10            Above 75th  \n",
       "4         8  Between 25th to 50th  \n",
       "..      ...                   ...  \n",
       "556       7            Above 75th  \n",
       "557       8  Between 25th to 50th  \n",
       "558      10  Between 25th to 50th  \n",
       "559       9            Below 25th  \n",
       "560       9            Below 25th  \n",
       "\n",
       "[561 rows x 11 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Calculate quartiles\n",
    "quartiles = df.groupby('Grade')['Current_Base_Pay'].transform(lambda x: pd.qcut(x, q=[0, 0.25, 0.5, 0.75, 1], labels=False))\n",
    "\n",
    "# Assign percentile categories based on quartiles\n",
    "percentile_category = quartiles.apply(lambda x: {\n",
    "    0: 'Below 25th',\n",
    "    1: 'Between 25th to 50th',\n",
    "    2: 'Between 50th to 75th',\n",
    "    3: 'Above 75th'\n",
    "}[x])\n",
    "\n",
    "# Add new column for percentile category\n",
    "df['percentile_category'] = percentile_category\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d8f27a00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1765745.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Current_Base_Pay'].quantile(0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "99af737a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Project</th>\n",
       "      <th>Gemcon</th>\n",
       "      <th>Tokyo</th>\n",
       "      <th>Venice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percentile_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Above 75th</th>\n",
       "      <td>46</td>\n",
       "      <td>36</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Below 25th</th>\n",
       "      <td>47</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Between 25th to 50th</th>\n",
       "      <td>54</td>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Between 50th to 75th</th>\n",
       "      <td>49</td>\n",
       "      <td>48</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Project               Gemcon  Tokyo  Venice\n",
       "percentile_category                        \n",
       "Above 75th                46     36      59\n",
       "Below 25th                47     48      47\n",
       "Between 25th to 50th      54     40      45\n",
       "Between 50th to 75th      49     48      42"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_project_percentile = df.pivot_table(index ='percentile_category', columns ='Project',values = 'unique_ID',aggfunc='count')\n",
    "pivot_project_percentile = pivot_project_percentile.round(0)\n",
    "pivot_project_percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a761d634",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start_Year</th>\n",
       "      <th>Start_Date</th>\n",
       "      <th>Employee_Name</th>\n",
       "      <th>unique_ID</th>\n",
       "      <th>Level_ID</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Project</th>\n",
       "      <th>Current_Base_Pay</th>\n",
       "      <th>Bonus_entitlement</th>\n",
       "      <th>Rating</th>\n",
       "      <th>percentile_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>S.Kumar</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>3219629</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Start_Year Start_Date Employee_Name unique_ID  Level_ID          Grade  \\\n",
       "0        2019 2019-01-07       S.Kumar         1         4  Lead Engineer   \n",
       "\n",
       "  Project  Current_Base_Pay  Bonus_entitlement  Rating percentile_category  \n",
       "0  Gemcon           3219629               0.15      10          Above 75th  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['unique_ID']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "edb7f736",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the reference date\n",
    "reference_date = pd.to_datetime('2024-04-01')\n",
    "\n",
    "# Calculate time delta in years\n",
    "time_delta = (reference_date - df['Start_Date']).dt.days\n",
    "years = time_delta // 365  # Calculate years\n",
    "remaining_days = time_delta % 365  # Calculate remaining days\n",
    "time_in_years = years + remaining_days / 365  # Combine years and remaining days as decimal\n",
    "\n",
    "# Add the time delta in years to the DataFrame\n",
    "df['Time_In_Company'] = time_in_years.round(2)\n",
    "df['Time_In_Company'] = np.floor(time_in_years * 100) / 100  # Round to 2 decimal places\n",
    "df\n",
    "\n",
    "df['Prorated'] = df['Time_In_Company'].apply(lambda x: 1 if x > 1 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5f4cddaa",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>S.Kumar</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>3219629</td>\n",
       "      <td>0.15</td>\n",
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       "      <td>5.23</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>A. Swati</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2530100</td>\n",
       "      <td>0.15</td>\n",
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       "      <td>5.21</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>C. Poornima</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>3330113</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.21</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>D. Manish</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2767233</td>\n",
       "      <td>0.10</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.19</td>\n",
       "      <td>1.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>R. Manoj</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2023602</td>\n",
       "      <td>0.10</td>\n",
       "      <td>8</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>5.19</td>\n",
       "      <td>1.00</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>K. Shreya</td>\n",
       "      <td>557</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2697410</td>\n",
       "      <td>0.15</td>\n",
       "      <td>7</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>S. Mani</td>\n",
       "      <td>558</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2467364</td>\n",
       "      <td>0.15</td>\n",
       "      <td>8</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>M. Bhavana</td>\n",
       "      <td>559</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2397438</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>N. Naveen</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>Associate</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>9</td>\n",
       "      <td>Below 25th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
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       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>R. Renuka</td>\n",
       "      <td>561</td>\n",
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       "      <td>Associate</td>\n",
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       "      <td>9</td>\n",
       "      <td>Below 25th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Start_Year Start_Date Employee_Name unique_ID  Level_ID  \\\n",
       "0          2019 2019-01-07       S.Kumar         1         4   \n",
       "1          2019 2019-01-14      A. Swati         2         3   \n",
       "2          2019 2019-01-14   C. Poornima         3         4   \n",
       "3          2019 2019-01-21     D. Manish         4         2   \n",
       "4          2019 2019-01-21      R. Manoj         5         2   \n",
       "..          ...        ...           ...       ...       ...   \n",
       "556        2023 2023-08-21     K. Shreya       557         3   \n",
       "557        2023 2023-08-21       S. Mani       558         4   \n",
       "558        2023 2023-08-21    M. Bhavana       559         3   \n",
       "559        2023 2023-08-21     N. Naveen       560         1   \n",
       "560        2023 2023-08-21     R. Renuka       561         1   \n",
       "\n",
       "                        Grade Project  Current_Base_Pay  Bonus_entitlement  \\\n",
       "0               Lead Engineer  Gemcon           3219629               0.15   \n",
       "1    Senior Software Engineer   Tokyo           2530100               0.15   \n",
       "2               Lead Engineer  Gemcon           3330113               0.15   \n",
       "3           Software Engineer  Gemcon           2767233               0.10   \n",
       "4           Software Engineer  Gemcon           2023602               0.10   \n",
       "..                        ...     ...               ...                ...   \n",
       "556  Senior Software Engineer  Gemcon           2697410               0.15   \n",
       "557             Lead Engineer   Tokyo           2467364               0.15   \n",
       "558  Senior Software Engineer   Tokyo           2397438               0.15   \n",
       "559                 Associate   Tokyo           1000000               0.10   \n",
       "560                 Associate   Tokyo           1000000               0.10   \n",
       "\n",
       "     Rating   percentile_category  Time_In_Company  Prorated  \n",
       "0        10            Above 75th             5.23      1.00  \n",
       "1         7  Between 50th to 75th             5.21      1.00  \n",
       "2        10            Above 75th             5.21      1.00  \n",
       "3        10            Above 75th             5.19      1.00  \n",
       "4         8  Between 25th to 50th             5.19      1.00  \n",
       "..      ...                   ...              ...       ...  \n",
       "556       7            Above 75th             0.61      0.61  \n",
       "557       8  Between 25th to 50th             0.61      0.61  \n",
       "558      10  Between 25th to 50th             0.61      0.61  \n",
       "559       9            Below 25th             0.61      0.61  \n",
       "560       9            Below 25th             0.61      0.61  \n",
       "\n",
       "[561 rows x 13 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a0aa0d6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>2019</td>\n",
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       "      <td>C. Poornima</td>\n",
       "      <td>3</td>\n",
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       "      <th>3</th>\n",
       "      <td>2019</td>\n",
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       "      <td>4</td>\n",
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       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.19</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
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       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2023602</td>\n",
       "      <td>0.10</td>\n",
       "      <td>8</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>5.19</td>\n",
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       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>K. Shreya</td>\n",
       "      <td>557</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2697410</td>\n",
       "      <td>0.15</td>\n",
       "      <td>7</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>S. Mani</td>\n",
       "      <td>558</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2467364</td>\n",
       "      <td>0.15</td>\n",
       "      <td>8</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>M. Bhavana</td>\n",
       "      <td>559</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2397438</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>N. Naveen</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>Associate</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>9</td>\n",
       "      <td>Below 25th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>R. Renuka</td>\n",
       "      <td>561</td>\n",
       "      <td>1</td>\n",
       "      <td>Associate</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>9</td>\n",
       "      <td>Below 25th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Start_Year Start_Date Employee_Name unique_ID  Level_ID  \\\n",
       "0          2019 2019-01-07       S.Kumar         1         4   \n",
       "1          2019 2019-01-14      A. Swati         2         3   \n",
       "2          2019 2019-01-14   C. Poornima         3         4   \n",
       "3          2019 2019-01-21     D. Manish         4         2   \n",
       "4          2019 2019-01-21      R. Manoj         5         2   \n",
       "..          ...        ...           ...       ...       ...   \n",
       "556        2023 2023-08-21     K. Shreya       557         3   \n",
       "557        2023 2023-08-21       S. Mani       558         4   \n",
       "558        2023 2023-08-21    M. Bhavana       559         3   \n",
       "559        2023 2023-08-21     N. Naveen       560         1   \n",
       "560        2023 2023-08-21     R. Renuka       561         1   \n",
       "\n",
       "                        Grade Project  Current_Base_Pay  Bonus_entitlement  \\\n",
       "0               Lead Engineer  Gemcon           3219629               0.15   \n",
       "1    Senior Software Engineer   Tokyo           2530100               0.15   \n",
       "2               Lead Engineer  Gemcon           3330113               0.15   \n",
       "3           Software Engineer  Gemcon           2767233               0.10   \n",
       "4           Software Engineer  Gemcon           2023602               0.10   \n",
       "..                        ...     ...               ...                ...   \n",
       "556  Senior Software Engineer  Gemcon           2697410               0.15   \n",
       "557             Lead Engineer   Tokyo           2467364               0.15   \n",
       "558  Senior Software Engineer   Tokyo           2397438               0.15   \n",
       "559                 Associate   Tokyo           1000000               0.10   \n",
       "560                 Associate   Tokyo           1000000               0.10   \n",
       "\n",
       "     Rating   percentile_category  Time_In_Company  Prorated  \n",
       "0        10            Above 75th             5.23      1.00  \n",
       "1         7  Between 50th to 75th             5.21      1.00  \n",
       "2        10            Above 75th             5.21      1.00  \n",
       "3        10            Above 75th             5.19      1.00  \n",
       "4         8  Between 25th to 50th             5.19      1.00  \n",
       "..      ...                   ...              ...       ...  \n",
       "556       7            Above 75th             0.61      0.61  \n",
       "557       8  Between 25th to 50th             0.61      0.61  \n",
       "558      10  Between 25th to 50th             0.61      0.61  \n",
       "559       9            Below 25th             0.61      0.61  \n",
       "560       9            Below 25th             0.61      0.61  \n",
       "\n",
       "[561 rows x 13 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c34b2b3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Start_Year', 'Start_Date', 'Employee_Name', 'unique_ID', 'Level_ID',\n",
       "       'Grade', 'Project', 'Current_Base_Pay', 'Bonus_entitlement', 'Rating',\n",
       "       'percentile_category', 'Time_In_Company', 'Prorated'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3ad09b51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply condition to calculate Regular_Bonus\n",
    "df['Regular_Bonus'] = df.apply(lambda row: row['Current_Base_Pay'] * row['Bonus_entitlement'] * row['Prorated'] \n",
    "                                if row['Project'] in ['Venice', 'Tokyo'] and row['Rating'] >= 7 \n",
    "                                else row['Current_Base_Pay'] * row['Bonus_entitlement'] * row['Prorated'] * 0.5, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8358fe6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Regular_Bonus']=df['Regular_Bonus'].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c8537bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start_Year</th>\n",
       "      <th>Start_Date</th>\n",
       "      <th>Employee_Name</th>\n",
       "      <th>unique_ID</th>\n",
       "      <th>Level_ID</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Project</th>\n",
       "      <th>Current_Base_Pay</th>\n",
       "      <th>Bonus_entitlement</th>\n",
       "      <th>Rating</th>\n",
       "      <th>percentile_category</th>\n",
       "      <th>Time_In_Company</th>\n",
       "      <th>Prorated</th>\n",
       "      <th>Regular_Bonus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>S.Kumar</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>3219629</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.00</td>\n",
       "      <td>241472.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>A. Swati</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2530100</td>\n",
       "      <td>0.15</td>\n",
       "      <td>7</td>\n",
       "      <td>Between 50th to 75th</td>\n",
       "      <td>5.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>379515.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>C. Poornima</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>3330113</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>249758.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>D. Manish</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2767233</td>\n",
       "      <td>0.10</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.19</td>\n",
       "      <td>1.00</td>\n",
       "      <td>138361.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>R. Manoj</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2023602</td>\n",
       "      <td>0.10</td>\n",
       "      <td>8</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>5.19</td>\n",
       "      <td>1.00</td>\n",
       "      <td>101180.10</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>K. Shreya</td>\n",
       "      <td>557</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2697410</td>\n",
       "      <td>0.15</td>\n",
       "      <td>7</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>123406.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>S. Mani</td>\n",
       "      <td>558</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2467364</td>\n",
       "      <td>0.15</td>\n",
       "      <td>8</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>225763.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>M. Bhavana</td>\n",
       "      <td>559</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2397438</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>219365.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>N. Naveen</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>Associate</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>9</td>\n",
       "      <td>Below 25th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>61000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>R. Renuka</td>\n",
       "      <td>561</td>\n",
       "      <td>1</td>\n",
       "      <td>Associate</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>9</td>\n",
       "      <td>Below 25th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>61000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Start_Year Start_Date Employee_Name unique_ID  Level_ID  \\\n",
       "0          2019 2019-01-07       S.Kumar         1         4   \n",
       "1          2019 2019-01-14      A. Swati         2         3   \n",
       "2          2019 2019-01-14   C. Poornima         3         4   \n",
       "3          2019 2019-01-21     D. Manish         4         2   \n",
       "4          2019 2019-01-21      R. Manoj         5         2   \n",
       "..          ...        ...           ...       ...       ...   \n",
       "556        2023 2023-08-21     K. Shreya       557         3   \n",
       "557        2023 2023-08-21       S. Mani       558         4   \n",
       "558        2023 2023-08-21    M. Bhavana       559         3   \n",
       "559        2023 2023-08-21     N. Naveen       560         1   \n",
       "560        2023 2023-08-21     R. Renuka       561         1   \n",
       "\n",
       "                        Grade Project  Current_Base_Pay  Bonus_entitlement  \\\n",
       "0               Lead Engineer  Gemcon           3219629               0.15   \n",
       "1    Senior Software Engineer   Tokyo           2530100               0.15   \n",
       "2               Lead Engineer  Gemcon           3330113               0.15   \n",
       "3           Software Engineer  Gemcon           2767233               0.10   \n",
       "4           Software Engineer  Gemcon           2023602               0.10   \n",
       "..                        ...     ...               ...                ...   \n",
       "556  Senior Software Engineer  Gemcon           2697410               0.15   \n",
       "557             Lead Engineer   Tokyo           2467364               0.15   \n",
       "558  Senior Software Engineer   Tokyo           2397438               0.15   \n",
       "559                 Associate   Tokyo           1000000               0.10   \n",
       "560                 Associate   Tokyo           1000000               0.10   \n",
       "\n",
       "     Rating   percentile_category  Time_In_Company  Prorated  Regular_Bonus  \n",
       "0        10            Above 75th             5.23      1.00      241472.18  \n",
       "1         7  Between 50th to 75th             5.21      1.00      379515.00  \n",
       "2        10            Above 75th             5.21      1.00      249758.47  \n",
       "3        10            Above 75th             5.19      1.00      138361.65  \n",
       "4         8  Between 25th to 50th             5.19      1.00      101180.10  \n",
       "..      ...                   ...              ...       ...            ...  \n",
       "556       7            Above 75th             0.61      0.61      123406.51  \n",
       "557       8  Between 25th to 50th             0.61      0.61      225763.81  \n",
       "558      10  Between 25th to 50th             0.61      0.61      219365.58  \n",
       "559       9            Below 25th             0.61      0.61       61000.00  \n",
       "560       9            Below 25th             0.61      0.61       61000.00  \n",
       "\n",
       "[561 rows x 14 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d177db27",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start_Year</th>\n",
       "      <th>Start_Date</th>\n",
       "      <th>Employee_Name</th>\n",
       "      <th>unique_ID</th>\n",
       "      <th>Level_ID</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Project</th>\n",
       "      <th>Current_Base_Pay</th>\n",
       "      <th>Bonus_entitlement</th>\n",
       "      <th>Rating</th>\n",
       "      <th>percentile_category</th>\n",
       "      <th>Time_In_Company</th>\n",
       "      <th>Prorated</th>\n",
       "      <th>Regular_Bonus</th>\n",
       "      <th>Additional_Bonus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>S.Kumar</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>3219629</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.00</td>\n",
       "      <td>241472.18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>A. Swati</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2530100</td>\n",
       "      <td>0.15</td>\n",
       "      <td>7</td>\n",
       "      <td>Between 50th to 75th</td>\n",
       "      <td>5.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>379515.00</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>C. Poornima</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>3330113</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>249758.47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>D. Manish</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2767233</td>\n",
       "      <td>0.10</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.19</td>\n",
       "      <td>1.00</td>\n",
       "      <td>138361.65</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>R. Manoj</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2023602</td>\n",
       "      <td>0.10</td>\n",
       "      <td>8</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>5.19</td>\n",
       "      <td>1.00</td>\n",
       "      <td>101180.10</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>K. Shreya</td>\n",
       "      <td>557</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2697410</td>\n",
       "      <td>0.15</td>\n",
       "      <td>7</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>123406.51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>S. Mani</td>\n",
       "      <td>558</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2467364</td>\n",
       "      <td>0.15</td>\n",
       "      <td>8</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>225763.81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>M. Bhavana</td>\n",
       "      <td>559</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2397438</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>219365.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>N. Naveen</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>Associate</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>9</td>\n",
       "      <td>Below 25th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>61000.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>R. Renuka</td>\n",
       "      <td>561</td>\n",
       "      <td>1</td>\n",
       "      <td>Associate</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>9</td>\n",
       "      <td>Below 25th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>61000.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Start_Year Start_Date Employee_Name unique_ID  Level_ID  \\\n",
       "0          2019 2019-01-07       S.Kumar         1         4   \n",
       "1          2019 2019-01-14      A. Swati         2         3   \n",
       "2          2019 2019-01-14   C. Poornima         3         4   \n",
       "3          2019 2019-01-21     D. Manish         4         2   \n",
       "4          2019 2019-01-21      R. Manoj         5         2   \n",
       "..          ...        ...           ...       ...       ...   \n",
       "556        2023 2023-08-21     K. Shreya       557         3   \n",
       "557        2023 2023-08-21       S. Mani       558         4   \n",
       "558        2023 2023-08-21    M. Bhavana       559         3   \n",
       "559        2023 2023-08-21     N. Naveen       560         1   \n",
       "560        2023 2023-08-21     R. Renuka       561         1   \n",
       "\n",
       "                        Grade Project  Current_Base_Pay  Bonus_entitlement  \\\n",
       "0               Lead Engineer  Gemcon           3219629               0.15   \n",
       "1    Senior Software Engineer   Tokyo           2530100               0.15   \n",
       "2               Lead Engineer  Gemcon           3330113               0.15   \n",
       "3           Software Engineer  Gemcon           2767233               0.10   \n",
       "4           Software Engineer  Gemcon           2023602               0.10   \n",
       "..                        ...     ...               ...                ...   \n",
       "556  Senior Software Engineer  Gemcon           2697410               0.15   \n",
       "557             Lead Engineer   Tokyo           2467364               0.15   \n",
       "558  Senior Software Engineer   Tokyo           2397438               0.15   \n",
       "559                 Associate   Tokyo           1000000               0.10   \n",
       "560                 Associate   Tokyo           1000000               0.10   \n",
       "\n",
       "     Rating   percentile_category  Time_In_Company  Prorated  Regular_Bonus  \\\n",
       "0        10            Above 75th             5.23      1.00      241472.18   \n",
       "1         7  Between 50th to 75th             5.21      1.00      379515.00   \n",
       "2        10            Above 75th             5.21      1.00      249758.47   \n",
       "3        10            Above 75th             5.19      1.00      138361.65   \n",
       "4         8  Between 25th to 50th             5.19      1.00      101180.10   \n",
       "..      ...                   ...              ...       ...            ...   \n",
       "556       7            Above 75th             0.61      0.61      123406.51   \n",
       "557       8  Between 25th to 50th             0.61      0.61      225763.81   \n",
       "558      10  Between 25th to 50th             0.61      0.61      219365.58   \n",
       "559       9            Below 25th             0.61      0.61       61000.00   \n",
       "560       9            Below 25th             0.61      0.61       61000.00   \n",
       "\n",
       "     Additional_Bonus  \n",
       "0                   0  \n",
       "1               20000  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  \n",
       "..                ...  \n",
       "556                 0  \n",
       "557                 0  \n",
       "558                 0  \n",
       "559                 0  \n",
       "560                 0  \n",
       "\n",
       "[561 rows x 15 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Function to calculate Additional_Bonus\n",
    "def calculate_additional_bonus(row):\n",
    "    if row['Project'] == 'Venice':\n",
    "        if row['Rating'] >= 7:\n",
    "            if row['Time_In_Company'] >= 3:\n",
    "                return 50000\n",
    "            elif row['Time_In_Company'] >= 2:\n",
    "                return 25000\n",
    "    elif row['Project'] == 'Tokyo':\n",
    "        if row['Rating'] >= 7:\n",
    "            if row['Time_In_Company'] >= 3:\n",
    "                return 20000\n",
    "            elif row['Time_In_Company'] >= 2:\n",
    "                return 10000\n",
    "    return 0\n",
    "\n",
    "# Apply function to calculate Additional_Bonus\n",
    "df['Additional_Bonus'] = df.apply(calculate_additional_bonus, axis=1)\n",
    "\n",
    "df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f523d0b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Total_Bonus'] = df['Regular_Bonus'] + df['Additional_Bonus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "328c839a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start_Year</th>\n",
       "      <th>Start_Date</th>\n",
       "      <th>Employee_Name</th>\n",
       "      <th>unique_ID</th>\n",
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       "      <th>percentile_category</th>\n",
       "      <th>Time_In_Company</th>\n",
       "      <th>Prorated</th>\n",
       "      <th>Regular_Bonus</th>\n",
       "      <th>Additional_Bonus</th>\n",
       "      <th>Total_Bonus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>S.Kumar</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Gemcon</td>\n",
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       "      <td>0.15</td>\n",
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       "      <td>0</td>\n",
       "      <td>241472.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>A. Swati</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2530100</td>\n",
       "      <td>0.15</td>\n",
       "      <td>7</td>\n",
       "      <td>Between 50th to 75th</td>\n",
       "      <td>5.21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>379515.00</td>\n",
       "      <td>20000</td>\n",
       "      <td>399515.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>C. Poornima</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>3330113</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>249758.47</td>\n",
       "      <td>0</td>\n",
       "      <td>249758.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>D. Manish</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2767233</td>\n",
       "      <td>0.10</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>138361.65</td>\n",
       "      <td>0</td>\n",
       "      <td>138361.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>R. Manoj</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2023602</td>\n",
       "      <td>0.10</td>\n",
       "      <td>8</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>5.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101180.10</td>\n",
       "      <td>0</td>\n",
       "      <td>101180.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Start_Year Start_Date Employee_Name unique_ID  Level_ID  \\\n",
       "0        2019 2019-01-07       S.Kumar         1         4   \n",
       "1        2019 2019-01-14      A. Swati         2         3   \n",
       "2        2019 2019-01-14   C. Poornima         3         4   \n",
       "3        2019 2019-01-21     D. Manish         4         2   \n",
       "4        2019 2019-01-21      R. Manoj         5         2   \n",
       "\n",
       "                      Grade Project  Current_Base_Pay  Bonus_entitlement  \\\n",
       "0             Lead Engineer  Gemcon           3219629               0.15   \n",
       "1  Senior Software Engineer   Tokyo           2530100               0.15   \n",
       "2             Lead Engineer  Gemcon           3330113               0.15   \n",
       "3         Software Engineer  Gemcon           2767233               0.10   \n",
       "4         Software Engineer  Gemcon           2023602               0.10   \n",
       "\n",
       "   Rating   percentile_category  Time_In_Company  Prorated  Regular_Bonus  \\\n",
       "0      10            Above 75th             5.23       1.0      241472.18   \n",
       "1       7  Between 50th to 75th             5.21       1.0      379515.00   \n",
       "2      10            Above 75th             5.21       1.0      249758.47   \n",
       "3      10            Above 75th             5.19       1.0      138361.65   \n",
       "4       8  Between 25th to 50th             5.19       1.0      101180.10   \n",
       "\n",
       "   Additional_Bonus  Total_Bonus  \n",
       "0                 0    241472.18  \n",
       "1             20000    399515.00  \n",
       "2                 0    249758.47  \n",
       "3                 0    138361.65  \n",
       "4                 0    101180.10  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cf74df8",
   "metadata": {},
   "source": [
    "Percent Hike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7bdf4364",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Regular_Bonus</th>\n",
       "      <th>Additional_Bonus</th>\n",
       "      <th>Total_Bonus</th>\n",
       "      <th>Hike Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>S.Kumar</td>\n",
       "      <td>1</td>\n",
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       "      <td>241472.18</td>\n",
       "      <td>0</td>\n",
       "      <td>241472.18</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>A. Swati</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2530100</td>\n",
       "      <td>0.15</td>\n",
       "      <td>7</td>\n",
       "      <td>Between 50th to 75th</td>\n",
       "      <td>5.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>379515.00</td>\n",
       "      <td>20000</td>\n",
       "      <td>399515.00</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>C. Poornima</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>3330113</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>249758.47</td>\n",
       "      <td>0</td>\n",
       "      <td>249758.47</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>D. Manish</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2767233</td>\n",
       "      <td>0.10</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.19</td>\n",
       "      <td>1.00</td>\n",
       "      <td>138361.65</td>\n",
       "      <td>0</td>\n",
       "      <td>138361.65</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>R. Manoj</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2023602</td>\n",
       "      <td>0.10</td>\n",
       "      <td>8</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>5.19</td>\n",
       "      <td>1.00</td>\n",
       "      <td>101180.10</td>\n",
       "      <td>0</td>\n",
       "      <td>101180.10</td>\n",
       "      <td>0.05</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>K. Shreya</td>\n",
       "      <td>557</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2697410</td>\n",
       "      <td>0.15</td>\n",
       "      <td>7</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>123406.51</td>\n",
       "      <td>0</td>\n",
       "      <td>123406.51</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>S. Mani</td>\n",
       "      <td>558</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2467364</td>\n",
       "      <td>0.15</td>\n",
       "      <td>8</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>225763.81</td>\n",
       "      <td>0</td>\n",
       "      <td>225763.81</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>M. Bhavana</td>\n",
       "      <td>559</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2397438</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>219365.58</td>\n",
       "      <td>0</td>\n",
       "      <td>219365.58</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>N. Naveen</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>Associate</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>9</td>\n",
       "      <td>Below 25th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>61000.00</td>\n",
       "      <td>0</td>\n",
       "      <td>61000.00</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>R. Renuka</td>\n",
       "      <td>561</td>\n",
       "      <td>1</td>\n",
       "      <td>Associate</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>9</td>\n",
       "      <td>Below 25th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>61000.00</td>\n",
       "      <td>0</td>\n",
       "      <td>61000.00</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Start_Year Start_Date Employee_Name unique_ID  Level_ID  \\\n",
       "0          2019 2019-01-07       S.Kumar         1         4   \n",
       "1          2019 2019-01-14      A. Swati         2         3   \n",
       "2          2019 2019-01-14   C. Poornima         3         4   \n",
       "3          2019 2019-01-21     D. Manish         4         2   \n",
       "4          2019 2019-01-21      R. Manoj         5         2   \n",
       "..          ...        ...           ...       ...       ...   \n",
       "556        2023 2023-08-21     K. Shreya       557         3   \n",
       "557        2023 2023-08-21       S. Mani       558         4   \n",
       "558        2023 2023-08-21    M. Bhavana       559         3   \n",
       "559        2023 2023-08-21     N. Naveen       560         1   \n",
       "560        2023 2023-08-21     R. Renuka       561         1   \n",
       "\n",
       "                        Grade Project  Current_Base_Pay  Bonus_entitlement  \\\n",
       "0               Lead Engineer  Gemcon           3219629               0.15   \n",
       "1    Senior Software Engineer   Tokyo           2530100               0.15   \n",
       "2               Lead Engineer  Gemcon           3330113               0.15   \n",
       "3           Software Engineer  Gemcon           2767233               0.10   \n",
       "4           Software Engineer  Gemcon           2023602               0.10   \n",
       "..                        ...     ...               ...                ...   \n",
       "556  Senior Software Engineer  Gemcon           2697410               0.15   \n",
       "557             Lead Engineer   Tokyo           2467364               0.15   \n",
       "558  Senior Software Engineer   Tokyo           2397438               0.15   \n",
       "559                 Associate   Tokyo           1000000               0.10   \n",
       "560                 Associate   Tokyo           1000000               0.10   \n",
       "\n",
       "     Rating   percentile_category  Time_In_Company  Prorated  Regular_Bonus  \\\n",
       "0        10            Above 75th             5.23      1.00      241472.18   \n",
       "1         7  Between 50th to 75th             5.21      1.00      379515.00   \n",
       "2        10            Above 75th             5.21      1.00      249758.47   \n",
       "3        10            Above 75th             5.19      1.00      138361.65   \n",
       "4         8  Between 25th to 50th             5.19      1.00      101180.10   \n",
       "..      ...                   ...              ...       ...            ...   \n",
       "556       7            Above 75th             0.61      0.61      123406.51   \n",
       "557       8  Between 25th to 50th             0.61      0.61      225763.81   \n",
       "558      10  Between 25th to 50th             0.61      0.61      219365.58   \n",
       "559       9            Below 25th             0.61      0.61       61000.00   \n",
       "560       9            Below 25th             0.61      0.61       61000.00   \n",
       "\n",
       "     Additional_Bonus  Total_Bonus  Hike Percent  \n",
       "0                   0    241472.18          0.05  \n",
       "1               20000    399515.00          0.10  \n",
       "2                   0    249758.47          0.05  \n",
       "3                   0    138361.65          0.05  \n",
       "4                   0    101180.10          0.05  \n",
       "..                ...          ...           ...  \n",
       "556                 0    123406.51          0.05  \n",
       "557                 0    225763.81          0.12  \n",
       "558                 0    219365.58          0.12  \n",
       "559                 0     61000.00          0.12  \n",
       "560                 0     61000.00          0.12  \n",
       "\n",
       "[561 rows x 17 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Function to calculate Hike Percent\n",
    "def calculate_hike_percent(row):\n",
    "    if row['Project'] == 'Venice':\n",
    "        if row['Rating'] >= 7:\n",
    "            if row['percentile_category'] == 'Below 25th' or row['percentile_category'] == 'Between 25th to 50th':\n",
    "                return 0.18\n",
    "            elif row['percentile_category'] == 'Between 50th to 75th' or row['percentile_category'] == 'Above 75th':\n",
    "                return 0.12\n",
    "    elif row['Project'] == 'Tokyo':\n",
    "        if row['Rating'] >= 7:\n",
    "            if row['percentile_category'] == 'Below 25th' or row['percentile_category'] == 'Between 25th to 50th':\n",
    "                return 0.12\n",
    "            elif row['percentile_category'] == 'Between 50th to 75th' or row['percentile_category'] == 'Above 75th':\n",
    "                return 0.10\n",
    "    return 0.05\n",
    "\n",
    "# Apply function to calculate Hike Percent\n",
    "df['Hike Percent'] = df.apply(calculate_hike_percent, axis=1)\n",
    "\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "920175d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['New_Base_Pay'] =  df['Current_Base_Pay'] + (df['Current_Base_Pay']*df['Prorated']* df['Hike Percent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0c4c7e6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['New_Base_Pay']=df['New_Base_Pay'].round(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "87d7212c",
   "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>Start_Year</th>\n",
       "      <th>Start_Date</th>\n",
       "      <th>Employee_Name</th>\n",
       "      <th>unique_ID</th>\n",
       "      <th>Level_ID</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Project</th>\n",
       "      <th>Current_Base_Pay</th>\n",
       "      <th>Bonus_entitlement</th>\n",
       "      <th>Rating</th>\n",
       "      <th>percentile_category</th>\n",
       "      <th>Time_In_Company</th>\n",
       "      <th>Prorated</th>\n",
       "      <th>Regular_Bonus</th>\n",
       "      <th>Additional_Bonus</th>\n",
       "      <th>Total_Bonus</th>\n",
       "      <th>Hike Percent</th>\n",
       "      <th>New_Base_Pay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>S.Kumar</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>3219629</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.00</td>\n",
       "      <td>241472.18</td>\n",
       "      <td>0</td>\n",
       "      <td>241472.18</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3380610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>A. Swati</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2530100</td>\n",
       "      <td>0.15</td>\n",
       "      <td>7</td>\n",
       "      <td>Between 50th to 75th</td>\n",
       "      <td>5.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>379515.00</td>\n",
       "      <td>20000</td>\n",
       "      <td>399515.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2783110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>C. Poornima</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>3330113</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.21</td>\n",
       "      <td>1.00</td>\n",
       "      <td>249758.47</td>\n",
       "      <td>0</td>\n",
       "      <td>249758.47</td>\n",
       "      <td>0.05</td>\n",
       "      <td>3496619.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>D. Manish</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2767233</td>\n",
       "      <td>0.10</td>\n",
       "      <td>10</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>5.19</td>\n",
       "      <td>1.00</td>\n",
       "      <td>138361.65</td>\n",
       "      <td>0</td>\n",
       "      <td>138361.65</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2905595.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>R. Manoj</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2023602</td>\n",
       "      <td>0.10</td>\n",
       "      <td>8</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>5.19</td>\n",
       "      <td>1.00</td>\n",
       "      <td>101180.10</td>\n",
       "      <td>0</td>\n",
       "      <td>101180.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2124782.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>K. Shreya</td>\n",
       "      <td>557</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Gemcon</td>\n",
       "      <td>2697410</td>\n",
       "      <td>0.15</td>\n",
       "      <td>7</td>\n",
       "      <td>Above 75th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>123406.51</td>\n",
       "      <td>0</td>\n",
       "      <td>123406.51</td>\n",
       "      <td>0.05</td>\n",
       "      <td>2779681.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>S. Mani</td>\n",
       "      <td>558</td>\n",
       "      <td>4</td>\n",
       "      <td>Lead Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2467364</td>\n",
       "      <td>0.15</td>\n",
       "      <td>8</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>225763.81</td>\n",
       "      <td>0</td>\n",
       "      <td>225763.81</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2647975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>M. Bhavana</td>\n",
       "      <td>559</td>\n",
       "      <td>3</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2397438</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10</td>\n",
       "      <td>Between 25th to 50th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>219365.58</td>\n",
       "      <td>0</td>\n",
       "      <td>219365.58</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2572930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>N. Naveen</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>Associate</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>9</td>\n",
       "      <td>Below 25th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>61000.00</td>\n",
       "      <td>0</td>\n",
       "      <td>61000.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1073200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>2023</td>\n",
       "      <td>2023-08-21</td>\n",
       "      <td>R. Renuka</td>\n",
       "      <td>561</td>\n",
       "      <td>1</td>\n",
       "      <td>Associate</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>9</td>\n",
       "      <td>Below 25th</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>61000.00</td>\n",
       "      <td>0</td>\n",
       "      <td>61000.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1073200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 18 columns</p>\n",
       "</div>"
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       "     Start_Year Start_Date Employee_Name unique_ID  Level_ID  \\\n",
       "0          2019 2019-01-07       S.Kumar         1         4   \n",
       "1          2019 2019-01-14      A. Swati         2         3   \n",
       "2          2019 2019-01-14   C. Poornima         3         4   \n",
       "3          2019 2019-01-21     D. Manish         4         2   \n",
       "4          2019 2019-01-21      R. Manoj         5         2   \n",
       "..          ...        ...           ...       ...       ...   \n",
       "556        2023 2023-08-21     K. Shreya       557         3   \n",
       "557        2023 2023-08-21       S. Mani       558         4   \n",
       "558        2023 2023-08-21    M. Bhavana       559         3   \n",
       "559        2023 2023-08-21     N. Naveen       560         1   \n",
       "560        2023 2023-08-21     R. Renuka       561         1   \n",
       "\n",
       "                        Grade Project  Current_Base_Pay  Bonus_entitlement  \\\n",
       "0               Lead Engineer  Gemcon           3219629               0.15   \n",
       "1    Senior Software Engineer   Tokyo           2530100               0.15   \n",
       "2               Lead Engineer  Gemcon           3330113               0.15   \n",
       "3           Software Engineer  Gemcon           2767233               0.10   \n",
       "4           Software Engineer  Gemcon           2023602               0.10   \n",
       "..                        ...     ...               ...                ...   \n",
       "556  Senior Software Engineer  Gemcon           2697410               0.15   \n",
       "557             Lead Engineer   Tokyo           2467364               0.15   \n",
       "558  Senior Software Engineer   Tokyo           2397438               0.15   \n",
       "559                 Associate   Tokyo           1000000               0.10   \n",
       "560                 Associate   Tokyo           1000000               0.10   \n",
       "\n",
       "     Rating   percentile_category  Time_In_Company  Prorated  Regular_Bonus  \\\n",
       "0        10            Above 75th             5.23      1.00      241472.18   \n",
       "1         7  Between 50th to 75th             5.21      1.00      379515.00   \n",
       "2        10            Above 75th             5.21      1.00      249758.47   \n",
       "3        10            Above 75th             5.19      1.00      138361.65   \n",
       "4         8  Between 25th to 50th             5.19      1.00      101180.10   \n",
       "..      ...                   ...              ...       ...            ...   \n",
       "556       7            Above 75th             0.61      0.61      123406.51   \n",
       "557       8  Between 25th to 50th             0.61      0.61      225763.81   \n",
       "558      10  Between 25th to 50th             0.61      0.61      219365.58   \n",
       "559       9            Below 25th             0.61      0.61       61000.00   \n",
       "560       9            Below 25th             0.61      0.61       61000.00   \n",
       "\n",
       "     Additional_Bonus  Total_Bonus  Hike Percent  New_Base_Pay  \n",
       "0                   0    241472.18          0.05     3380610.0  \n",
       "1               20000    399515.00          0.10     2783110.0  \n",
       "2                   0    249758.47          0.05     3496619.0  \n",
       "3                   0    138361.65          0.05     2905595.0  \n",
       "4                   0    101180.10          0.05     2124782.0  \n",
       "..                ...          ...           ...           ...  \n",
       "556                 0    123406.51          0.05     2779681.0  \n",
       "557                 0    225763.81          0.12     2647975.0  \n",
       "558                 0    219365.58          0.12     2572930.0  \n",
       "559                 0     61000.00          0.12     1073200.0  \n",
       "560                 0     61000.00          0.12     1073200.0  \n",
       "\n",
       "[561 rows x 18 columns]"
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       "     Start_Year Start_Date Employee_Name unique_ID  Level_ID  \\\n",
       "556        2023 2023-08-21     K. Shreya       557         3   \n",
       "\n",
       "                        Grade Project  Current_Base_Pay  Bonus_entitlement  \\\n",
       "556  Senior Software Engineer  Gemcon           2697410               0.15   \n",
       "\n",
       "     Rating percentile_category  Time_In_Company  Prorated  Regular_Bonus  \\\n",
       "556       7          Above 75th             0.61      0.61      123406.51   \n",
       "\n",
       "     Additional_Bonus  Total_Bonus  Hike Percent  New_Base_Pay  \n",
       "556                 0    123406.51          0.05     2779681.0  "
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       "      <td>2019</td>\n",
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       "      <td>A. Swati</td>\n",
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       "   Start_Year Start_Date Employee_Name unique_ID  Level_ID  \\\n",
       "0        2019 2019-01-07       S.Kumar         1         4   \n",
       "1        2019 2019-01-14      A. Swati         2         3   \n",
       "2        2019 2019-01-14   C. Poornima         3         4   \n",
       "3        2019 2019-01-21     D. Manish         4         2   \n",
       "4        2019 2019-01-21      R. Manoj         5         2   \n",
       "\n",
       "                      Grade Project  Current_Base_Pay  Bonus_entitlement  \\\n",
       "0             Lead Engineer  Gemcon           3219629               0.15   \n",
       "1  Senior Software Engineer   Tokyo           2530100               0.15   \n",
       "2             Lead Engineer  Gemcon           3330113               0.15   \n",
       "3         Software Engineer  Gemcon           2767233               0.10   \n",
       "4         Software Engineer  Gemcon           2023602               0.10   \n",
       "\n",
       "   Rating   percentile_category  Time_In_Company  Prorated  Regular_Bonus  \\\n",
       "0      10            Above 75th             5.23       1.0      241472.18   \n",
       "1       7  Between 50th to 75th             5.21       1.0      379515.00   \n",
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       "4       8  Between 25th to 50th             5.19       1.0      101180.10   \n",
       "\n",
       "   Additional_Bonus  Total_Bonus  Hike Percent  New_Base_Pay  \n",
       "0                 0    241472.18          0.05     3380610.0  \n",
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   ],
   "source": [
    "fig = px.scatter(df, x='Grade', y='Current_Base_Pay', title='Scatter Plot of Salaries')\n",
    "fig.update_traces(textposition='top center') \n",
    "fig.update_layout(yaxis_title='Salary')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b1df8b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "group_1=df.groupby(by=['Additional_Bonus','Rating'])['unique_ID'].count().reset_index(name='Counts').sort_values(by='Counts', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9c3ed664",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Additional_Bonus</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Counts</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>112</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>69</td>\n",
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       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>65</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>61</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>57</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20000</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>50000</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>50000</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>50000</td>\n",
       "      <td>9</td>\n",
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       "      <th>11</th>\n",
       "      <td>20000</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
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       "      <th>17</th>\n",
       "      <td>50000</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20000</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
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       "      <th>13</th>\n",
       "      <td>25000</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>25000</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
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       "      <th>7</th>\n",
       "      <td>10000</td>\n",
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       "      <th>10</th>\n",
       "      <td>20000</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10000</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25000</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25000</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10000</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10000</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Additional_Bonus  Rating  Counts\n",
       "0                  0       6     112\n",
       "2                  0       8      69\n",
       "1                  0       7      65\n",
       "4                  0      10      61\n",
       "3                  0       9      57\n",
       "9              20000       7      20\n",
       "18             50000       8      20\n",
       "20             50000      10      20\n",
       "19             50000       9      19\n",
       "11             20000       9      17\n",
       "17             50000       7      17\n",
       "12             20000      10      16\n",
       "13             25000       7      12\n",
       "14             25000       8      10\n",
       "7              10000       9       9\n",
       "10             20000       8       9\n",
       "8              10000      10       7\n",
       "15             25000       9       6\n",
       "16             25000      10       6\n",
       "6              10000       8       5\n",
       "5              10000       7       4"
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     },
     "execution_count": 67,
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   "cell_type": "code",
   "execution_count": 86,
   "id": "92759922",
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   "outputs": [
    {
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      "text/plain": [
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\n",
      "text/plain": [
       "<Figure size 1242.5x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='Rating',y='Current_Base_Pay', col= 'Project',hue='Grade', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "394a8a51",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}