04_Exploratory_Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4a3fd950",
   "metadata": {},
   "source": [
    "# <center> Exploratory Data Analysis </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63566ce6",
   "metadata": {},
   "source": [
    "### Description of EDA\n",
    "\n",
    "Exploratory Data Analysis (EDA) is a crucial step in the data analysis process. It involves examining and understanding the structure, patterns, and characteristics of a dataset before applying any formal statistical techniques or modeling. EDA helps us uncover valuable insights, detect outliers, identify data quality issues, and make informed decisions about data preprocessing and modeling approaches. \n",
    "\n",
    "During EDA, we perform various data manipulaion and visualization techniques to gain a deeper understanding of the dataset. This includes tasks such as data cleaning, handling missing values, removing duplicates, transforming variables, calculating descriptive statistics, visualizing distributions, and exploring relationships between variables. \n",
    "\n",
    "By conducting EDA, we can:  \n",
    "- Identify data quality issues such as missing values, duplicates or outliers \n",
    "- Understand the distribution and summary of each variable. \n",
    "- Explore relationships and correlations between variable. \n",
    "- Identify patterns, trends, or anomalies in the data. \n",
    "- Formualate initial hypotheses for further analysis or modeling. \n",
    "\n",
    "Exploratory Data Analysis (EDA) is an Iterative process that often requires domain knowledge, critical thinking, and creativity. It helps us uncover insights and ask relevant questions that drive subsequent analysis and decision-making. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98f710f3",
   "metadata": {},
   "source": [
    "## Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d2e698b",
   "metadata": {},
   "source": [
    "### > Import the pandas library\n",
    "\n",
    "This quesiton involves importing the pandas library, a popular Python library used for data manipulation and analysis. It is a fundamental step in working with tabular data and performing various data operations.\n",
    "\n",
    "Here, we have imported this python library to read the csv files. "
   ]
  },
  {
   "cell_type": "code",
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   "id": "eeaa4472",
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   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>New_Salary</th>\n",
       "      <th>Bonus</th>\n",
       "      <th>Senior Management</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>130590</td>\n",
       "      <td>Finance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>146075.36220</td>\n",
       "      <td>20000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angela</td>\n",
       "      <td>Female</td>\n",
       "      <td>54568</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5</td>\n",
       "      <td>64675.63064</td>\n",
       "      <td>19000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allan</td>\n",
       "      <td>Male</td>\n",
       "      <td>125792</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6</td>\n",
       "      <td>132134.43260</td>\n",
       "      <td>18500</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rohan</td>\n",
       "      <td>Female</td>\n",
       "      <td>45906</td>\n",
       "      <td>Finance</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>51230.17788</td>\n",
       "      <td>18000</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>97308</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>104066.04060</td>\n",
       "      <td>17000</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brandon</td>\n",
       "      <td>Male</td>\n",
       "      <td>112807</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8</td>\n",
       "      <td>132539.20040</td>\n",
       "      <td>16000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Diana</td>\n",
       "      <td>Female</td>\n",
       "      <td>132940</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9</td>\n",
       "      <td>158307.61080</td>\n",
       "      <td>15800</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Frances</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139852</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>150374.46450</td>\n",
       "      <td>15500</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>Male</td>\n",
       "      <td>100612</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>114340.50740</td>\n",
       "      <td>15000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>101004</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>102406.94560</td>\n",
       "      <td>14700</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Joshua</td>\n",
       "      <td>Male</td>\n",
       "      <td>90816</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>107903.93860</td>\n",
       "      <td>14300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>72000</td>\n",
       "      <td>Finance</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>78724.80000</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lois</td>\n",
       "      <td>Female</td>\n",
       "      <td>64714</td>\n",
       "      <td>Legal</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>67906.98876</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>Male</td>\n",
       "      <td>115163</td>\n",
       "      <td>Legal</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13</td>\n",
       "      <td>126823.25380</td>\n",
       "      <td>13000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>John</td>\n",
       "      <td>Male</td>\n",
       "      <td>97950</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13</td>\n",
       "      <td>111538.60350</td>\n",
       "      <td>12000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>61933</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>38.0</td>\n",
       "      <td>14</td>\n",
       "      <td>68711.56685</td>\n",
       "      <td>11900</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shawn</td>\n",
       "      <td>Male</td>\n",
       "      <td>111737</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>118903.81120</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gary</td>\n",
       "      <td>Male</td>\n",
       "      <td>109831</td>\n",
       "      <td>Product</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>116235.24560</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Male</td>\n",
       "      <td>90370</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>42.0</td>\n",
       "      <td>18</td>\n",
       "      <td>97029.36530</td>\n",
       "      <td>11000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kimberly</td>\n",
       "      <td>Female</td>\n",
       "      <td>41426</td>\n",
       "      <td>Finance</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20</td>\n",
       "      <td>44512.23700</td>\n",
       "      <td>11000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Louise</td>\n",
       "      <td>Female</td>\n",
       "      <td>63241</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>45.0</td>\n",
       "      <td>21</td>\n",
       "      <td>72810.62812</td>\n",
       "      <td>10800</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Donna</td>\n",
       "      <td>Female</td>\n",
       "      <td>81014</td>\n",
       "      <td>Product</td>\n",
       "      <td>49.0</td>\n",
       "      <td>23</td>\n",
       "      <td>82548.40516</td>\n",
       "      <td>10600</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>Female</td>\n",
       "      <td>65476</td>\n",
       "      <td>Product</td>\n",
       "      <td>54.0</td>\n",
       "      <td>25</td>\n",
       "      <td>72031.45712</td>\n",
       "      <td>10400</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   First Name  Gender  Salary                  Team   Age  Experience  \\\n",
       "0       Maria  Female  130590               Finance   NaN           5   \n",
       "1      Angela  Female   54568  Business Development  27.0           5   \n",
       "2       Allan    Male  125792       Client Services  28.0           6   \n",
       "3       Rohan  Female   45906               Finance  28.0           7   \n",
       "4     Douglas    Male   97308             Marketing  28.0           7   \n",
       "5     Brandon    Male  112807       Human Resources  30.0           8   \n",
       "6       Diana  Female  132940       Client Services  31.0           9   \n",
       "7     Frances     NaN  139852  Business Development  34.0          10   \n",
       "8     Matthew    Male  100612             Marketing  34.0          10   \n",
       "9       Larry    Male  101004       Client Services  35.0          11   \n",
       "10     Joshua    Male   90816       Client Services  35.0          11   \n",
       "11      Jerry    Male   72000               Finance  35.0          12   \n",
       "12       Lois  Female   64714                 Legal  35.0          12   \n",
       "13     Dennis    Male  115163                 Legal  36.0          13   \n",
       "14       John    Male   97950       Client Services  37.0          13   \n",
       "15     Thomas    Male   61933             Marketing  38.0          14   \n",
       "16      Shawn    Male  111737       Human Resources  39.0          15   \n",
       "17       Gary    Male  109831               Product  39.0          15   \n",
       "18     Jeremy    Male   90370       Human Resources  42.0          18   \n",
       "19   Kimberly  Female   41426               Finance  44.0          20   \n",
       "20     Louise  Female   63241  Business Development  45.0          21   \n",
       "21      Donna  Female   81014               Product  49.0          23   \n",
       "22       Ruby  Female   65476               Product  54.0          25   \n",
       "23    Lillian  Female   59414               Product  55.0          26   \n",
       "24    Lillian  Female   59414               Product  55.0          26   \n",
       "\n",
       "      New_Salary  Bonus  Senior Management  \n",
       "0   146075.36220  20000              False  \n",
       "1    64675.63064  19000               True  \n",
       "2   132134.43260  18500              False  \n",
       "3    51230.17788  18000               True  \n",
       "4   104066.04060  17000               True  \n",
       "5   132539.20040  16000               True  \n",
       "6   158307.61080  15800              False  \n",
       "7   150374.46450  15500               True  \n",
       "8   114340.50740  15000              False  \n",
       "9   102406.94560  14700               True  \n",
       "10  107903.93860  14300               True  \n",
       "11   78724.80000  14000               True  \n",
       "12   67906.98876  14000               True  \n",
       "13  126823.25380  13000              False  \n",
       "14  111538.60350  12000              False  \n",
       "15   68711.56685  11900               True  \n",
       "16  118903.81120  11500              False  \n",
       "17  116235.24560  11500              False  \n",
       "18   97029.36530  11000              False  \n",
       "19   44512.23700  11000               True  \n",
       "20   72810.62812  10800               True  \n",
       "21   82548.40516  10600              False  \n",
       "22   72031.45712  10400               True  \n",
       "23   60160.23984  10300              False  \n",
       "24   60160.23984  10300               True  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
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   ],
   "source": [
    "import pandas as pd\n",
    "import scipy as stats\n",
    "\n",
    "data = pd.read_csv('Emp_EDA.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aadcd486",
   "metadata": {},
   "source": [
    "### > Remove the irrelevant column 'Senior Management' \n",
    "\n",
    "Here, the task is to remove the 'Senior Management' column from the dataset as it is deemed irrelevant for the analysis or does not contribute to the research objectives. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13451be9",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
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       "      <th>Gender</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Team</th>\n",
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       "      <th>0</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>130590</td>\n",
       "      <td>Finance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>146075.36220</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angela</td>\n",
       "      <td>Female</td>\n",
       "      <td>54568</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5</td>\n",
       "      <td>64675.63064</td>\n",
       "      <td>19000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allan</td>\n",
       "      <td>Male</td>\n",
       "      <td>125792</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6</td>\n",
       "      <td>132134.43260</td>\n",
       "      <td>18500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rohan</td>\n",
       "      <td>Female</td>\n",
       "      <td>45906</td>\n",
       "      <td>Finance</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>51230.17788</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>97308</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>104066.04060</td>\n",
       "      <td>17000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brandon</td>\n",
       "      <td>Male</td>\n",
       "      <td>112807</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8</td>\n",
       "      <td>132539.20040</td>\n",
       "      <td>16000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Diana</td>\n",
       "      <td>Female</td>\n",
       "      <td>132940</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9</td>\n",
       "      <td>158307.61080</td>\n",
       "      <td>15800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Frances</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139852</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>150374.46450</td>\n",
       "      <td>15500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>Male</td>\n",
       "      <td>100612</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>114340.50740</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>101004</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>102406.94560</td>\n",
       "      <td>14700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Joshua</td>\n",
       "      <td>Male</td>\n",
       "      <td>90816</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>107903.93860</td>\n",
       "      <td>14300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>72000</td>\n",
       "      <td>Finance</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>78724.80000</td>\n",
       "      <td>14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lois</td>\n",
       "      <td>Female</td>\n",
       "      <td>64714</td>\n",
       "      <td>Legal</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>67906.98876</td>\n",
       "      <td>14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>Male</td>\n",
       "      <td>115163</td>\n",
       "      <td>Legal</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13</td>\n",
       "      <td>126823.25380</td>\n",
       "      <td>13000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>John</td>\n",
       "      <td>Male</td>\n",
       "      <td>97950</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13</td>\n",
       "      <td>111538.60350</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>61933</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>38.0</td>\n",
       "      <td>14</td>\n",
       "      <td>68711.56685</td>\n",
       "      <td>11900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shawn</td>\n",
       "      <td>Male</td>\n",
       "      <td>111737</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>118903.81120</td>\n",
       "      <td>11500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gary</td>\n",
       "      <td>Male</td>\n",
       "      <td>109831</td>\n",
       "      <td>Product</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>116235.24560</td>\n",
       "      <td>11500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Male</td>\n",
       "      <td>90370</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>42.0</td>\n",
       "      <td>18</td>\n",
       "      <td>97029.36530</td>\n",
       "      <td>11000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kimberly</td>\n",
       "      <td>Female</td>\n",
       "      <td>41426</td>\n",
       "      <td>Finance</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20</td>\n",
       "      <td>44512.23700</td>\n",
       "      <td>11000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Louise</td>\n",
       "      <td>Female</td>\n",
       "      <td>63241</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>45.0</td>\n",
       "      <td>21</td>\n",
       "      <td>72810.62812</td>\n",
       "      <td>10800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Donna</td>\n",
       "      <td>Female</td>\n",
       "      <td>81014</td>\n",
       "      <td>Product</td>\n",
       "      <td>49.0</td>\n",
       "      <td>23</td>\n",
       "      <td>82548.40516</td>\n",
       "      <td>10600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>Female</td>\n",
       "      <td>65476</td>\n",
       "      <td>Product</td>\n",
       "      <td>54.0</td>\n",
       "      <td>25</td>\n",
       "      <td>72031.45712</td>\n",
       "      <td>10400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender  Salary                  Team   Age  Experience  \\\n",
       "0       Maria  Female  130590               Finance   NaN           5   \n",
       "1      Angela  Female   54568  Business Development  27.0           5   \n",
       "2       Allan    Male  125792       Client Services  28.0           6   \n",
       "3       Rohan  Female   45906               Finance  28.0           7   \n",
       "4     Douglas    Male   97308             Marketing  28.0           7   \n",
       "5     Brandon    Male  112807       Human Resources  30.0           8   \n",
       "6       Diana  Female  132940       Client Services  31.0           9   \n",
       "7     Frances     NaN  139852  Business Development  34.0          10   \n",
       "8     Matthew    Male  100612             Marketing  34.0          10   \n",
       "9       Larry    Male  101004       Client Services  35.0          11   \n",
       "10     Joshua    Male   90816       Client Services  35.0          11   \n",
       "11      Jerry    Male   72000               Finance  35.0          12   \n",
       "12       Lois  Female   64714                 Legal  35.0          12   \n",
       "13     Dennis    Male  115163                 Legal  36.0          13   \n",
       "14       John    Male   97950       Client Services  37.0          13   \n",
       "15     Thomas    Male   61933             Marketing  38.0          14   \n",
       "16      Shawn    Male  111737       Human Resources  39.0          15   \n",
       "17       Gary    Male  109831               Product  39.0          15   \n",
       "18     Jeremy    Male   90370       Human Resources  42.0          18   \n",
       "19   Kimberly  Female   41426               Finance  44.0          20   \n",
       "20     Louise  Female   63241  Business Development  45.0          21   \n",
       "21      Donna  Female   81014               Product  49.0          23   \n",
       "22       Ruby  Female   65476               Product  54.0          25   \n",
       "23    Lillian  Female   59414               Product  55.0          26   \n",
       "24    Lillian  Female   59414               Product  55.0          26   \n",
       "\n",
       "      New_Salary  Bonus  \n",
       "0   146075.36220  20000  \n",
       "1    64675.63064  19000  \n",
       "2   132134.43260  18500  \n",
       "3    51230.17788  18000  \n",
       "4   104066.04060  17000  \n",
       "5   132539.20040  16000  \n",
       "6   158307.61080  15800  \n",
       "7   150374.46450  15500  \n",
       "8   114340.50740  15000  \n",
       "9   102406.94560  14700  \n",
       "10  107903.93860  14300  \n",
       "11   78724.80000  14000  \n",
       "12   67906.98876  14000  \n",
       "13  126823.25380  13000  \n",
       "14  111538.60350  12000  \n",
       "15   68711.56685  11900  \n",
       "16  118903.81120  11500  \n",
       "17  116235.24560  11500  \n",
       "18   97029.36530  11000  \n",
       "19   44512.23700  11000  \n",
       "20   72810.62812  10800  \n",
       "21   82548.40516  10600  \n",
       "22   72031.45712  10400  \n",
       "23   60160.23984  10300  \n",
       "24   60160.23984  10300  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns = ['Senior Management'], inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d5b2a72",
   "metadata": {},
   "source": [
    "### > Remove the duplicate rows and analyse\n",
    "\n",
    "In this question, we need to identify and remove any duplicate rows from the dataset. Duplicate rows can skew analysis results and leat to incorrect insights. After removing duplicates, further analysis and insights can be derived from the cleaned dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08e824ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>New_Salary</th>\n",
       "      <th>Bonus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Frances</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139852</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>150374.4645</td>\n",
       "      <td>15500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name Gender  Salary                  Team   Age  Experience  \\\n",
       "7    Frances    NaN  139852  Business Development  34.0          10   \n",
       "\n",
       "    New_Salary  Bonus  \n",
       "7  150374.4645  15500  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates(subset = \"Gender\", keep=False, inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "599bd1c7",
   "metadata": {},
   "source": [
    "### > Rename the column 'bonus' to 'Incentive'\n",
    "\n",
    "This question involves renaming the column 'bonus' to 'Incentive' to align with the terminology or specific requirements of the analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4510863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>New_Salary</th>\n",
       "      <th>Incentive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Frances</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139852</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>150374.4645</td>\n",
       "      <td>15500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name Gender  Salary                  Team   Age  Experience  \\\n",
       "7    Frances    NaN  139852  Business Development  34.0          10   \n",
       "\n",
       "    New_Salary  Incentive  \n",
       "7  150374.4645      15500  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(columns={'Bonus':'Incentive'}, inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f9a25dc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.read_csv('Emp_EDA.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4dc72d6",
   "metadata": {},
   "source": [
    "### > Calculate the central tendency measures for 'Experience'\n",
    "\n",
    "Here, the task is to calculate the central tendancy measures, such as mean, median, mode, for the 'Experience' Column. These measures provide insights into the typical or central values of the variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d26d48c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean value for experience column:\n",
      "Experience    13.68\n",
      "dtype: float64\n",
      "\n",
      "Median value for the experience column:\n",
      "Experience    12.0\n",
      "dtype: float64\n",
      "\n",
      "Mode value for the experience column:\n",
      "   Experience\n",
      "0           5\n",
      "1           7\n",
      "2          10\n",
      "3          11\n",
      "4          12\n",
      "5          13\n",
      "6          15\n",
      "7          26\n"
     ]
    }
   ],
   "source": [
    "# 4) Calculate the central tendency measures for 'Experience'\n",
    "print(\"Mean value for experience column:\")\n",
    "mean = data1[[\"Experience\"]].mean()\n",
    "print(mean)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Median value for the experience column:\")\n",
    "median = data1[[\"Experience\"]].median()\n",
    "print(median)\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Mode value for the experience column:\")\n",
    "mode = data1[[\"Experience\"]].mode()\n",
    "print(mode)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77052094",
   "metadata": {},
   "source": [
    "### > Calculate the variability measures for 'Experience' \n",
    "\n",
    "This question requires calculating variability measures, such as variance and standard deviation, for the 'Experience' column. These measures quantify the dispersion or spread of values around the central tendency, providing insights into the data's variability. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "112b5d44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range: 21\n",
      "Variance: 43.14333333333334\n",
      "Standard Deviation: 6.568358496103371\n"
     ]
    }
   ],
   "source": [
    "print('Range:',data1['Experience'].max() - data1['Experience'].min())\n",
    "print('Variance:',data1['Experience'].var())\n",
    "print('Standard Deviation:',data1['Experience'].std())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e14a877f",
   "metadata": {},
   "source": [
    "### > Calculate the IQR using quantile for 'Experience'\n",
    "\n",
    "The interquartile range (IQR) is calculated using quantiles and provides information about the spread and distribution of data. This question involves calculating the IQR for the 'Experience' Column, which helps identify the range where the middle 50% of this data falls. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29c0aa86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IQR Value:  9.0\n"
     ]
    }
   ],
   "source": [
    "q1 = data1['Experience'].quantile(0.25)\n",
    "q3 = data1['Experience'].quantile(0.75)\n",
    "IQR = q3 - q1 \n",
    "print(\"IQR Value: \", IQR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c49778ae",
   "metadata": {},
   "source": [
    "### > Calculate the z-score for 'Experience' \n",
    "\n",
    "The z-score measures how many standard deviations a data point is from the mean. This question involves calculating the z-scores for the 'Experience' column, which allows us to assess the relative position of each data point within the distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "57910f39",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>130590</td>\n",
       "      <td>Finance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>146075.36220</td>\n",
       "      <td>20000</td>\n",
       "      <td>False</td>\n",
       "      <td>-1.348737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angela</td>\n",
       "      <td>Female</td>\n",
       "      <td>54568</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5</td>\n",
       "      <td>64675.63064</td>\n",
       "      <td>19000</td>\n",
       "      <td>True</td>\n",
       "      <td>-1.348737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allan</td>\n",
       "      <td>Male</td>\n",
       "      <td>125792</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6</td>\n",
       "      <td>132134.43260</td>\n",
       "      <td>18500</td>\n",
       "      <td>False</td>\n",
       "      <td>-1.193353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rohan</td>\n",
       "      <td>Female</td>\n",
       "      <td>45906</td>\n",
       "      <td>Finance</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>51230.17788</td>\n",
       "      <td>18000</td>\n",
       "      <td>True</td>\n",
       "      <td>-1.037968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>97308</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>104066.04060</td>\n",
       "      <td>17000</td>\n",
       "      <td>True</td>\n",
       "      <td>-1.037968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brandon</td>\n",
       "      <td>Male</td>\n",
       "      <td>112807</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8</td>\n",
       "      <td>132539.20040</td>\n",
       "      <td>16000</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.882584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Diana</td>\n",
       "      <td>Female</td>\n",
       "      <td>132940</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9</td>\n",
       "      <td>158307.61080</td>\n",
       "      <td>15800</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.727199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Frances</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139852</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>150374.46450</td>\n",
       "      <td>15500</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.571815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>Male</td>\n",
       "      <td>100612</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>114340.50740</td>\n",
       "      <td>15000</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.571815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>101004</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>102406.94560</td>\n",
       "      <td>14700</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.416430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Joshua</td>\n",
       "      <td>Male</td>\n",
       "      <td>90816</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>107903.93860</td>\n",
       "      <td>14300</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.416430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>72000</td>\n",
       "      <td>Finance</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>78724.80000</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.261046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lois</td>\n",
       "      <td>Female</td>\n",
       "      <td>64714</td>\n",
       "      <td>Legal</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>67906.98876</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.261046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>Male</td>\n",
       "      <td>115163</td>\n",
       "      <td>Legal</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13</td>\n",
       "      <td>126823.25380</td>\n",
       "      <td>13000</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.105661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>John</td>\n",
       "      <td>Male</td>\n",
       "      <td>97950</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13</td>\n",
       "      <td>111538.60350</td>\n",
       "      <td>12000</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.105661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>61933</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>38.0</td>\n",
       "      <td>14</td>\n",
       "      <td>68711.56685</td>\n",
       "      <td>11900</td>\n",
       "      <td>True</td>\n",
       "      <td>0.049723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shawn</td>\n",
       "      <td>Male</td>\n",
       "      <td>111737</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>118903.81120</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "      <td>0.205107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gary</td>\n",
       "      <td>Male</td>\n",
       "      <td>109831</td>\n",
       "      <td>Product</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>116235.24560</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "      <td>0.205107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Male</td>\n",
       "      <td>90370</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>42.0</td>\n",
       "      <td>18</td>\n",
       "      <td>97029.36530</td>\n",
       "      <td>11000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.671261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kimberly</td>\n",
       "      <td>Female</td>\n",
       "      <td>41426</td>\n",
       "      <td>Finance</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20</td>\n",
       "      <td>44512.23700</td>\n",
       "      <td>11000</td>\n",
       "      <td>True</td>\n",
       "      <td>0.982030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Louise</td>\n",
       "      <td>Female</td>\n",
       "      <td>63241</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>45.0</td>\n",
       "      <td>21</td>\n",
       "      <td>72810.62812</td>\n",
       "      <td>10800</td>\n",
       "      <td>True</td>\n",
       "      <td>1.137414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Donna</td>\n",
       "      <td>Female</td>\n",
       "      <td>81014</td>\n",
       "      <td>Product</td>\n",
       "      <td>49.0</td>\n",
       "      <td>23</td>\n",
       "      <td>82548.40516</td>\n",
       "      <td>10600</td>\n",
       "      <td>False</td>\n",
       "      <td>1.448183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>Female</td>\n",
       "      <td>65476</td>\n",
       "      <td>Product</td>\n",
       "      <td>54.0</td>\n",
       "      <td>25</td>\n",
       "      <td>72031.45712</td>\n",
       "      <td>10400</td>\n",
       "      <td>True</td>\n",
       "      <td>1.758952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>False</td>\n",
       "      <td>1.914336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>True</td>\n",
       "      <td>1.914336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender  Salary                  Team   Age  Experience  \\\n",
       "0       Maria  Female  130590               Finance   NaN           5   \n",
       "1      Angela  Female   54568  Business Development  27.0           5   \n",
       "2       Allan    Male  125792       Client Services  28.0           6   \n",
       "3       Rohan  Female   45906               Finance  28.0           7   \n",
       "4     Douglas    Male   97308             Marketing  28.0           7   \n",
       "5     Brandon    Male  112807       Human Resources  30.0           8   \n",
       "6       Diana  Female  132940       Client Services  31.0           9   \n",
       "7     Frances     NaN  139852  Business Development  34.0          10   \n",
       "8     Matthew    Male  100612             Marketing  34.0          10   \n",
       "9       Larry    Male  101004       Client Services  35.0          11   \n",
       "10     Joshua    Male   90816       Client Services  35.0          11   \n",
       "11      Jerry    Male   72000               Finance  35.0          12   \n",
       "12       Lois  Female   64714                 Legal  35.0          12   \n",
       "13     Dennis    Male  115163                 Legal  36.0          13   \n",
       "14       John    Male   97950       Client Services  37.0          13   \n",
       "15     Thomas    Male   61933             Marketing  38.0          14   \n",
       "16      Shawn    Male  111737       Human Resources  39.0          15   \n",
       "17       Gary    Male  109831               Product  39.0          15   \n",
       "18     Jeremy    Male   90370       Human Resources  42.0          18   \n",
       "19   Kimberly  Female   41426               Finance  44.0          20   \n",
       "20     Louise  Female   63241  Business Development  45.0          21   \n",
       "21      Donna  Female   81014               Product  49.0          23   \n",
       "22       Ruby  Female   65476               Product  54.0          25   \n",
       "23    Lillian  Female   59414               Product  55.0          26   \n",
       "24    Lillian  Female   59414               Product  55.0          26   \n",
       "\n",
       "      New_Salary  Bonus  Senior Management  Experience_zscore  \n",
       "0   146075.36220  20000              False          -1.348737  \n",
       "1    64675.63064  19000               True          -1.348737  \n",
       "2   132134.43260  18500              False          -1.193353  \n",
       "3    51230.17788  18000               True          -1.037968  \n",
       "4   104066.04060  17000               True          -1.037968  \n",
       "5   132539.20040  16000               True          -0.882584  \n",
       "6   158307.61080  15800              False          -0.727199  \n",
       "7   150374.46450  15500               True          -0.571815  \n",
       "8   114340.50740  15000              False          -0.571815  \n",
       "9   102406.94560  14700               True          -0.416430  \n",
       "10  107903.93860  14300               True          -0.416430  \n",
       "11   78724.80000  14000               True          -0.261046  \n",
       "12   67906.98876  14000               True          -0.261046  \n",
       "13  126823.25380  13000              False          -0.105661  \n",
       "14  111538.60350  12000              False          -0.105661  \n",
       "15   68711.56685  11900               True           0.049723  \n",
       "16  118903.81120  11500              False           0.205107  \n",
       "17  116235.24560  11500              False           0.205107  \n",
       "18   97029.36530  11000              False           0.671261  \n",
       "19   44512.23700  11000               True           0.982030  \n",
       "20   72810.62812  10800               True           1.137414  \n",
       "21   82548.40516  10600              False           1.448183  \n",
       "22   72031.45712  10400               True           1.758952  \n",
       "23   60160.23984  10300              False           1.914336  \n",
       "24   60160.23984  10300               True           1.914336  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "from scipy import stats\n",
    "data1['Experience'].fillna(0, inplace=True)\n",
    "data1['Experience_zscore']=stats.zscore(data1['Experience'])\n",
    "data1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67eb1fc2",
   "metadata": {},
   "source": [
    "### > Add 2 rows at the end of the Dataframe \n",
    "\n",
    "This question requires adding two rows at the end of the dataframe using the append method. The provided values are used to populate the new rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd835825",
   "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>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>New_Salary</th>\n",
       "      <th>Incentive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Frances</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139852</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>150374.4645</td>\n",
       "      <td>15500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zion</td>\n",
       "      <td>Male</td>\n",
       "      <td>12345</td>\n",
       "      <td>Finance</td>\n",
       "      <td>37.0</td>\n",
       "      <td>90</td>\n",
       "      <td>146075.4000</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name Gender  Salary                  Team   Age  Experience  \\\n",
       "0    Frances    NaN  139852  Business Development  34.0          10   \n",
       "1       Zion   Male   12345               Finance  37.0          90   \n",
       "\n",
       "    New_Salary  Incentive  \n",
       "0  150374.4645      15500  \n",
       "1  146075.4000      20000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Given values:\n",
    "# {&#39;First Name&#39;:&#39;Zion&#39;, &#39;Gender&#39;:&#39;Male&#39;, &#39;Team&#39;:&#39;Finance&#39;, &#39;Age&#39;:37,\n",
    "# &#39;Experience&#39;:90,&#39;New_Salary&#39;:146075.4, &#39;Incentive&#39;:20000\n",
    "# {&#39;First Name&#39;:&#39;Frances&#39;, &#39;Gender&#39;:&#39;Male&#39;, &#39;Salary&#39;:139852, &#39;Team&#39;:&#39;Business\n",
    "# Development&#39;, &#39;Age&#39;:34, &#39;Experience&#39;:95, &#39;New_Salary&#39;:150374.5, &#39;Incentive&#39;:15500\n",
    "\n",
    "data.append({'First Name':'Zion', 'Gender':'Male', 'Salary':'12345', 'Team':'Finance', 'Age':37, 'Experience':90, 'New_Salary':146075.4, 'Incentive':20000}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c6c59d2e",
   "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>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>New_Salary</th>\n",
       "      <th>Incentive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Frances</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139852</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>150374.4645</td>\n",
       "      <td>15500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Frances</td>\n",
       "      <td>Male</td>\n",
       "      <td>13952</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>39.0</td>\n",
       "      <td>95</td>\n",
       "      <td>150374.5000</td>\n",
       "      <td>15500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name Gender  Salary                  Team   Age  Experience  \\\n",
       "0    Frances    NaN  139852  Business Development  34.0          10   \n",
       "1    Frances   Male   13952  Business Development  39.0          95   \n",
       "\n",
       "    New_Salary  Incentive  \n",
       "0  150374.4645      15500  \n",
       "1  150374.5000      15500  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.append({'First Name':'Frances', 'Gender':'Male', 'Salary':'13952', 'Team':'Business Development', 'Age':39, 'Experience':95, 'New_Salary':150374.5, 'Incentive':15500}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85052a12",
   "metadata": {},
   "source": [
    "### > Replace NAN with a given values\n",
    "\n",
    "Here, the task is to replace any Nan(missing) values in the dataset with a specified value. This ensures consistency and completeness in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d37a8e2b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>New_Salary</th>\n",
       "      <th>Bonus</th>\n",
       "      <th>Senior Management</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>130590</td>\n",
       "      <td>Finance</td>\n",
       "      <td>130590.0</td>\n",
       "      <td>5</td>\n",
       "      <td>146075.36220</td>\n",
       "      <td>20000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angela</td>\n",
       "      <td>Female</td>\n",
       "      <td>54568</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5</td>\n",
       "      <td>64675.63064</td>\n",
       "      <td>19000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allan</td>\n",
       "      <td>Male</td>\n",
       "      <td>125792</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6</td>\n",
       "      <td>132134.43260</td>\n",
       "      <td>18500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rohan</td>\n",
       "      <td>Female</td>\n",
       "      <td>45906</td>\n",
       "      <td>Finance</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>51230.17788</td>\n",
       "      <td>18000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>97308</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>104066.04060</td>\n",
       "      <td>17000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brandon</td>\n",
       "      <td>Male</td>\n",
       "      <td>112807</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8</td>\n",
       "      <td>132539.20040</td>\n",
       "      <td>16000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Diana</td>\n",
       "      <td>Female</td>\n",
       "      <td>132940</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9</td>\n",
       "      <td>158307.61080</td>\n",
       "      <td>15800</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Frances</td>\n",
       "      <td>130590</td>\n",
       "      <td>139852</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>150374.46450</td>\n",
       "      <td>15500</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>Male</td>\n",
       "      <td>100612</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>114340.50740</td>\n",
       "      <td>15000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>101004</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>102406.94560</td>\n",
       "      <td>14700</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Joshua</td>\n",
       "      <td>Male</td>\n",
       "      <td>90816</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>107903.93860</td>\n",
       "      <td>14300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>72000</td>\n",
       "      <td>Finance</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>78724.80000</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lois</td>\n",
       "      <td>Female</td>\n",
       "      <td>64714</td>\n",
       "      <td>Legal</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>67906.98876</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>Male</td>\n",
       "      <td>115163</td>\n",
       "      <td>Legal</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13</td>\n",
       "      <td>126823.25380</td>\n",
       "      <td>13000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>John</td>\n",
       "      <td>Male</td>\n",
       "      <td>97950</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13</td>\n",
       "      <td>111538.60350</td>\n",
       "      <td>12000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>61933</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>38.0</td>\n",
       "      <td>14</td>\n",
       "      <td>68711.56685</td>\n",
       "      <td>11900</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shawn</td>\n",
       "      <td>Male</td>\n",
       "      <td>111737</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>118903.81120</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gary</td>\n",
       "      <td>Male</td>\n",
       "      <td>109831</td>\n",
       "      <td>Product</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>116235.24560</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Male</td>\n",
       "      <td>90370</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>42.0</td>\n",
       "      <td>18</td>\n",
       "      <td>97029.36530</td>\n",
       "      <td>11000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kimberly</td>\n",
       "      <td>Female</td>\n",
       "      <td>41426</td>\n",
       "      <td>Finance</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20</td>\n",
       "      <td>44512.23700</td>\n",
       "      <td>11000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Louise</td>\n",
       "      <td>Female</td>\n",
       "      <td>63241</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>45.0</td>\n",
       "      <td>21</td>\n",
       "      <td>72810.62812</td>\n",
       "      <td>10800</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Donna</td>\n",
       "      <td>Female</td>\n",
       "      <td>81014</td>\n",
       "      <td>Product</td>\n",
       "      <td>49.0</td>\n",
       "      <td>23</td>\n",
       "      <td>82548.40516</td>\n",
       "      <td>10600</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>Female</td>\n",
       "      <td>65476</td>\n",
       "      <td>Product</td>\n",
       "      <td>54.0</td>\n",
       "      <td>25</td>\n",
       "      <td>72031.45712</td>\n",
       "      <td>10400</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender  Salary                  Team       Age  Experience  \\\n",
       "0       Maria  Female  130590               Finance  130590.0           5   \n",
       "1      Angela  Female   54568  Business Development      27.0           5   \n",
       "2       Allan    Male  125792       Client Services      28.0           6   \n",
       "3       Rohan  Female   45906               Finance      28.0           7   \n",
       "4     Douglas    Male   97308             Marketing      28.0           7   \n",
       "5     Brandon    Male  112807       Human Resources      30.0           8   \n",
       "6       Diana  Female  132940       Client Services      31.0           9   \n",
       "7     Frances  130590  139852  Business Development      34.0          10   \n",
       "8     Matthew    Male  100612             Marketing      34.0          10   \n",
       "9       Larry    Male  101004       Client Services      35.0          11   \n",
       "10     Joshua    Male   90816       Client Services      35.0          11   \n",
       "11      Jerry    Male   72000               Finance      35.0          12   \n",
       "12       Lois  Female   64714                 Legal      35.0          12   \n",
       "13     Dennis    Male  115163                 Legal      36.0          13   \n",
       "14       John    Male   97950       Client Services      37.0          13   \n",
       "15     Thomas    Male   61933             Marketing      38.0          14   \n",
       "16      Shawn    Male  111737       Human Resources      39.0          15   \n",
       "17       Gary    Male  109831               Product      39.0          15   \n",
       "18     Jeremy    Male   90370       Human Resources      42.0          18   \n",
       "19   Kimberly  Female   41426               Finance      44.0          20   \n",
       "20     Louise  Female   63241  Business Development      45.0          21   \n",
       "21      Donna  Female   81014               Product      49.0          23   \n",
       "22       Ruby  Female   65476               Product      54.0          25   \n",
       "23    Lillian  Female   59414               Product      55.0          26   \n",
       "24    Lillian  Female   59414               Product      55.0          26   \n",
       "\n",
       "      New_Salary  Bonus  Senior Management  \n",
       "0   146075.36220  20000              False  \n",
       "1    64675.63064  19000               True  \n",
       "2   132134.43260  18500              False  \n",
       "3    51230.17788  18000               True  \n",
       "4   104066.04060  17000               True  \n",
       "5   132539.20040  16000               True  \n",
       "6   158307.61080  15800              False  \n",
       "7   150374.46450  15500               True  \n",
       "8   114340.50740  15000              False  \n",
       "9   102406.94560  14700               True  \n",
       "10  107903.93860  14300               True  \n",
       "11   78724.80000  14000               True  \n",
       "12   67906.98876  14000               True  \n",
       "13  126823.25380  13000              False  \n",
       "14  111538.60350  12000              False  \n",
       "15   68711.56685  11900               True  \n",
       "16  118903.81120  11500              False  \n",
       "17  116235.24560  11500              False  \n",
       "18   97029.36530  11000              False  \n",
       "19   44512.23700  11000               True  \n",
       "20   72810.62812  10800               True  \n",
       "21   82548.40516  10600              False  \n",
       "22   72031.45712  10400               True  \n",
       "23   60160.23984  10300              False  \n",
       "24   60160.23984  10300               True  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Given value (salary=130590)\n",
    "data2 = pd.read_csv('Emp_EDA.csv')\n",
    "data2.fillna(130590)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70aa3036",
   "metadata": {},
   "source": [
    "### > Replace the NaN value in the Salary column with previous value, next value, linear interpolation, and central tendency measures: \n",
    "\n",
    "This Question involves handling missing values specifically in the Salary Column. Different techniques such as using the previous value, next value, linear interpolation, or central tendency measures(mean, median) can be used to fill in the missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "72fca45a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Team</th>\n",
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       "      <th>Senior Management</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>130590</td>\n",
       "      <td>Finance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>146075.36220</td>\n",
       "      <td>20000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angela</td>\n",
       "      <td>Female</td>\n",
       "      <td>54568</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5</td>\n",
       "      <td>64675.63064</td>\n",
       "      <td>19000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allan</td>\n",
       "      <td>Male</td>\n",
       "      <td>125792</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6</td>\n",
       "      <td>132134.43260</td>\n",
       "      <td>18500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rohan</td>\n",
       "      <td>Female</td>\n",
       "      <td>45906</td>\n",
       "      <td>Finance</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>51230.17788</td>\n",
       "      <td>18000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>97308</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>104066.04060</td>\n",
       "      <td>17000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brandon</td>\n",
       "      <td>Male</td>\n",
       "      <td>112807</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8</td>\n",
       "      <td>132539.20040</td>\n",
       "      <td>16000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Diana</td>\n",
       "      <td>Female</td>\n",
       "      <td>132940</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9</td>\n",
       "      <td>158307.61080</td>\n",
       "      <td>15800</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Frances</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139852</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>150374.46450</td>\n",
       "      <td>15500</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>Male</td>\n",
       "      <td>100612</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>114340.50740</td>\n",
       "      <td>15000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>101004</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>102406.94560</td>\n",
       "      <td>14700</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Joshua</td>\n",
       "      <td>Male</td>\n",
       "      <td>90816</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>107903.93860</td>\n",
       "      <td>14300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>72000</td>\n",
       "      <td>Finance</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>78724.80000</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lois</td>\n",
       "      <td>Female</td>\n",
       "      <td>64714</td>\n",
       "      <td>Legal</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>67906.98876</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>Male</td>\n",
       "      <td>115163</td>\n",
       "      <td>Legal</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13</td>\n",
       "      <td>126823.25380</td>\n",
       "      <td>13000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>John</td>\n",
       "      <td>Male</td>\n",
       "      <td>97950</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13</td>\n",
       "      <td>111538.60350</td>\n",
       "      <td>12000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>61933</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>38.0</td>\n",
       "      <td>14</td>\n",
       "      <td>68711.56685</td>\n",
       "      <td>11900</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shawn</td>\n",
       "      <td>Male</td>\n",
       "      <td>111737</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>118903.81120</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gary</td>\n",
       "      <td>Male</td>\n",
       "      <td>109831</td>\n",
       "      <td>Product</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>116235.24560</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Male</td>\n",
       "      <td>90370</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>42.0</td>\n",
       "      <td>18</td>\n",
       "      <td>97029.36530</td>\n",
       "      <td>11000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kimberly</td>\n",
       "      <td>Female</td>\n",
       "      <td>41426</td>\n",
       "      <td>Finance</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20</td>\n",
       "      <td>44512.23700</td>\n",
       "      <td>11000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Louise</td>\n",
       "      <td>Female</td>\n",
       "      <td>63241</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>45.0</td>\n",
       "      <td>21</td>\n",
       "      <td>72810.62812</td>\n",
       "      <td>10800</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Donna</td>\n",
       "      <td>Female</td>\n",
       "      <td>81014</td>\n",
       "      <td>Product</td>\n",
       "      <td>49.0</td>\n",
       "      <td>23</td>\n",
       "      <td>82548.40516</td>\n",
       "      <td>10600</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>Female</td>\n",
       "      <td>65476</td>\n",
       "      <td>Product</td>\n",
       "      <td>54.0</td>\n",
       "      <td>25</td>\n",
       "      <td>72031.45712</td>\n",
       "      <td>10400</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender  Salary                  Team   Age  Experience  \\\n",
       "0       Maria  Female  130590               Finance   NaN           5   \n",
       "1      Angela  Female   54568  Business Development  27.0           5   \n",
       "2       Allan    Male  125792       Client Services  28.0           6   \n",
       "3       Rohan  Female   45906               Finance  28.0           7   \n",
       "4     Douglas    Male   97308             Marketing  28.0           7   \n",
       "5     Brandon    Male  112807       Human Resources  30.0           8   \n",
       "6       Diana  Female  132940       Client Services  31.0           9   \n",
       "7     Frances     NaN  139852  Business Development  34.0          10   \n",
       "8     Matthew    Male  100612             Marketing  34.0          10   \n",
       "9       Larry    Male  101004       Client Services  35.0          11   \n",
       "10     Joshua    Male   90816       Client Services  35.0          11   \n",
       "11      Jerry    Male   72000               Finance  35.0          12   \n",
       "12       Lois  Female   64714                 Legal  35.0          12   \n",
       "13     Dennis    Male  115163                 Legal  36.0          13   \n",
       "14       John    Male   97950       Client Services  37.0          13   \n",
       "15     Thomas    Male   61933             Marketing  38.0          14   \n",
       "16      Shawn    Male  111737       Human Resources  39.0          15   \n",
       "17       Gary    Male  109831               Product  39.0          15   \n",
       "18     Jeremy    Male   90370       Human Resources  42.0          18   \n",
       "19   Kimberly  Female   41426               Finance  44.0          20   \n",
       "20     Louise  Female   63241  Business Development  45.0          21   \n",
       "21      Donna  Female   81014               Product  49.0          23   \n",
       "22       Ruby  Female   65476               Product  54.0          25   \n",
       "23    Lillian  Female   59414               Product  55.0          26   \n",
       "24    Lillian  Female   59414               Product  55.0          26   \n",
       "\n",
       "      New_Salary  Bonus  Senior Management  \n",
       "0   146075.36220  20000              False  \n",
       "1    64675.63064  19000               True  \n",
       "2   132134.43260  18500              False  \n",
       "3    51230.17788  18000               True  \n",
       "4   104066.04060  17000               True  \n",
       "5   132539.20040  16000               True  \n",
       "6   158307.61080  15800              False  \n",
       "7   150374.46450  15500               True  \n",
       "8   114340.50740  15000              False  \n",
       "9   102406.94560  14700               True  \n",
       "10  107903.93860  14300               True  \n",
       "11   78724.80000  14000               True  \n",
       "12   67906.98876  14000               True  \n",
       "13  126823.25380  13000              False  \n",
       "14  111538.60350  12000              False  \n",
       "15   68711.56685  11900               True  \n",
       "16  118903.81120  11500              False  \n",
       "17  116235.24560  11500              False  \n",
       "18   97029.36530  11000              False  \n",
       "19   44512.23700  11000               True  \n",
       "20   72810.62812  10800               True  \n",
       "21   82548.40516  10600              False  \n",
       "22   72031.45712  10400               True  \n",
       "23   60160.23984  10300              False  \n",
       "24   60160.23984  10300               True  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3 = pd.read_csv('Emp_EDA.csv')\n",
    "data3['Salary'].fillna(method='pad', inplace=True)\n",
    "data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "997c8669",
   "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>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>New_Salary</th>\n",
       "      <th>Bonus</th>\n",
       "      <th>Senior Management</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>130590</td>\n",
       "      <td>Finance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>146075.36220</td>\n",
       "      <td>20000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angela</td>\n",
       "      <td>Female</td>\n",
       "      <td>54568</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5</td>\n",
       "      <td>64675.63064</td>\n",
       "      <td>19000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allan</td>\n",
       "      <td>Male</td>\n",
       "      <td>125792</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6</td>\n",
       "      <td>132134.43260</td>\n",
       "      <td>18500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rohan</td>\n",
       "      <td>Female</td>\n",
       "      <td>45906</td>\n",
       "      <td>Finance</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>51230.17788</td>\n",
       "      <td>18000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>97308</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>104066.04060</td>\n",
       "      <td>17000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brandon</td>\n",
       "      <td>Male</td>\n",
       "      <td>112807</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8</td>\n",
       "      <td>132539.20040</td>\n",
       "      <td>16000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Diana</td>\n",
       "      <td>Female</td>\n",
       "      <td>132940</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9</td>\n",
       "      <td>158307.61080</td>\n",
       "      <td>15800</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Frances</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139852</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>150374.46450</td>\n",
       "      <td>15500</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>Male</td>\n",
       "      <td>100612</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>114340.50740</td>\n",
       "      <td>15000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>101004</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>102406.94560</td>\n",
       "      <td>14700</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Joshua</td>\n",
       "      <td>Male</td>\n",
       "      <td>90816</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>107903.93860</td>\n",
       "      <td>14300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>72000</td>\n",
       "      <td>Finance</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>78724.80000</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lois</td>\n",
       "      <td>Female</td>\n",
       "      <td>64714</td>\n",
       "      <td>Legal</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>67906.98876</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>Male</td>\n",
       "      <td>115163</td>\n",
       "      <td>Legal</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13</td>\n",
       "      <td>126823.25380</td>\n",
       "      <td>13000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>John</td>\n",
       "      <td>Male</td>\n",
       "      <td>97950</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13</td>\n",
       "      <td>111538.60350</td>\n",
       "      <td>12000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>61933</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>38.0</td>\n",
       "      <td>14</td>\n",
       "      <td>68711.56685</td>\n",
       "      <td>11900</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shawn</td>\n",
       "      <td>Male</td>\n",
       "      <td>111737</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>118903.81120</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gary</td>\n",
       "      <td>Male</td>\n",
       "      <td>109831</td>\n",
       "      <td>Product</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>116235.24560</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Male</td>\n",
       "      <td>90370</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>42.0</td>\n",
       "      <td>18</td>\n",
       "      <td>97029.36530</td>\n",
       "      <td>11000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kimberly</td>\n",
       "      <td>Female</td>\n",
       "      <td>41426</td>\n",
       "      <td>Finance</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20</td>\n",
       "      <td>44512.23700</td>\n",
       "      <td>11000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Louise</td>\n",
       "      <td>Female</td>\n",
       "      <td>63241</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>45.0</td>\n",
       "      <td>21</td>\n",
       "      <td>72810.62812</td>\n",
       "      <td>10800</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Donna</td>\n",
       "      <td>Female</td>\n",
       "      <td>81014</td>\n",
       "      <td>Product</td>\n",
       "      <td>49.0</td>\n",
       "      <td>23</td>\n",
       "      <td>82548.40516</td>\n",
       "      <td>10600</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>Female</td>\n",
       "      <td>65476</td>\n",
       "      <td>Product</td>\n",
       "      <td>54.0</td>\n",
       "      <td>25</td>\n",
       "      <td>72031.45712</td>\n",
       "      <td>10400</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender  Salary                  Team   Age  Experience  \\\n",
       "0       Maria  Female  130590               Finance   NaN           5   \n",
       "1      Angela  Female   54568  Business Development  27.0           5   \n",
       "2       Allan    Male  125792       Client Services  28.0           6   \n",
       "3       Rohan  Female   45906               Finance  28.0           7   \n",
       "4     Douglas    Male   97308             Marketing  28.0           7   \n",
       "5     Brandon    Male  112807       Human Resources  30.0           8   \n",
       "6       Diana  Female  132940       Client Services  31.0           9   \n",
       "7     Frances     NaN  139852  Business Development  34.0          10   \n",
       "8     Matthew    Male  100612             Marketing  34.0          10   \n",
       "9       Larry    Male  101004       Client Services  35.0          11   \n",
       "10     Joshua    Male   90816       Client Services  35.0          11   \n",
       "11      Jerry    Male   72000               Finance  35.0          12   \n",
       "12       Lois  Female   64714                 Legal  35.0          12   \n",
       "13     Dennis    Male  115163                 Legal  36.0          13   \n",
       "14       John    Male   97950       Client Services  37.0          13   \n",
       "15     Thomas    Male   61933             Marketing  38.0          14   \n",
       "16      Shawn    Male  111737       Human Resources  39.0          15   \n",
       "17       Gary    Male  109831               Product  39.0          15   \n",
       "18     Jeremy    Male   90370       Human Resources  42.0          18   \n",
       "19   Kimberly  Female   41426               Finance  44.0          20   \n",
       "20     Louise  Female   63241  Business Development  45.0          21   \n",
       "21      Donna  Female   81014               Product  49.0          23   \n",
       "22       Ruby  Female   65476               Product  54.0          25   \n",
       "23    Lillian  Female   59414               Product  55.0          26   \n",
       "24    Lillian  Female   59414               Product  55.0          26   \n",
       "\n",
       "      New_Salary  Bonus  Senior Management  \n",
       "0   146075.36220  20000              False  \n",
       "1    64675.63064  19000               True  \n",
       "2   132134.43260  18500              False  \n",
       "3    51230.17788  18000               True  \n",
       "4   104066.04060  17000               True  \n",
       "5   132539.20040  16000               True  \n",
       "6   158307.61080  15800              False  \n",
       "7   150374.46450  15500               True  \n",
       "8   114340.50740  15000              False  \n",
       "9   102406.94560  14700               True  \n",
       "10  107903.93860  14300               True  \n",
       "11   78724.80000  14000               True  \n",
       "12   67906.98876  14000               True  \n",
       "13  126823.25380  13000              False  \n",
       "14  111538.60350  12000              False  \n",
       "15   68711.56685  11900               True  \n",
       "16  118903.81120  11500              False  \n",
       "17  116235.24560  11500              False  \n",
       "18   97029.36530  11000              False  \n",
       "19   44512.23700  11000               True  \n",
       "20   72810.62812  10800               True  \n",
       "21   82548.40516  10600              False  \n",
       "22   72031.45712  10400               True  \n",
       "23   60160.23984  10300              False  \n",
       "24   60160.23984  10300               True  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4 = pd.read_csv('Emp_EDA.csv')\n",
    "data4['Salary'].fillna(method='bfill', inplace=True)\n",
    "data4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "978f765f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>New_Salary</th>\n",
       "      <th>Bonus</th>\n",
       "      <th>Senior Management</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>130590</td>\n",
       "      <td>Finance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>146075.36220</td>\n",
       "      <td>20000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angela</td>\n",
       "      <td>Female</td>\n",
       "      <td>54568</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5</td>\n",
       "      <td>64675.63064</td>\n",
       "      <td>19000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allan</td>\n",
       "      <td>Male</td>\n",
       "      <td>125792</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6</td>\n",
       "      <td>132134.43260</td>\n",
       "      <td>18500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rohan</td>\n",
       "      <td>Female</td>\n",
       "      <td>45906</td>\n",
       "      <td>Finance</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>51230.17788</td>\n",
       "      <td>18000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>97308</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>104066.04060</td>\n",
       "      <td>17000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brandon</td>\n",
       "      <td>Male</td>\n",
       "      <td>112807</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8</td>\n",
       "      <td>132539.20040</td>\n",
       "      <td>16000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Diana</td>\n",
       "      <td>Female</td>\n",
       "      <td>132940</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9</td>\n",
       "      <td>158307.61080</td>\n",
       "      <td>15800</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Frances</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139852</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>150374.46450</td>\n",
       "      <td>15500</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>Male</td>\n",
       "      <td>100612</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>114340.50740</td>\n",
       "      <td>15000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>101004</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>102406.94560</td>\n",
       "      <td>14700</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Joshua</td>\n",
       "      <td>Male</td>\n",
       "      <td>90816</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>107903.93860</td>\n",
       "      <td>14300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>72000</td>\n",
       "      <td>Finance</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>78724.80000</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lois</td>\n",
       "      <td>Female</td>\n",
       "      <td>64714</td>\n",
       "      <td>Legal</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>67906.98876</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>Male</td>\n",
       "      <td>115163</td>\n",
       "      <td>Legal</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13</td>\n",
       "      <td>126823.25380</td>\n",
       "      <td>13000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>John</td>\n",
       "      <td>Male</td>\n",
       "      <td>97950</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13</td>\n",
       "      <td>111538.60350</td>\n",
       "      <td>12000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>61933</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>38.0</td>\n",
       "      <td>14</td>\n",
       "      <td>68711.56685</td>\n",
       "      <td>11900</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shawn</td>\n",
       "      <td>Male</td>\n",
       "      <td>111737</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>118903.81120</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gary</td>\n",
       "      <td>Male</td>\n",
       "      <td>109831</td>\n",
       "      <td>Product</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>116235.24560</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Male</td>\n",
       "      <td>90370</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>42.0</td>\n",
       "      <td>18</td>\n",
       "      <td>97029.36530</td>\n",
       "      <td>11000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kimberly</td>\n",
       "      <td>Female</td>\n",
       "      <td>41426</td>\n",
       "      <td>Finance</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20</td>\n",
       "      <td>44512.23700</td>\n",
       "      <td>11000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Louise</td>\n",
       "      <td>Female</td>\n",
       "      <td>63241</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>45.0</td>\n",
       "      <td>21</td>\n",
       "      <td>72810.62812</td>\n",
       "      <td>10800</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Donna</td>\n",
       "      <td>Female</td>\n",
       "      <td>81014</td>\n",
       "      <td>Product</td>\n",
       "      <td>49.0</td>\n",
       "      <td>23</td>\n",
       "      <td>82548.40516</td>\n",
       "      <td>10600</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>Female</td>\n",
       "      <td>65476</td>\n",
       "      <td>Product</td>\n",
       "      <td>54.0</td>\n",
       "      <td>25</td>\n",
       "      <td>72031.45712</td>\n",
       "      <td>10400</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender  Salary                  Team   Age  Experience  \\\n",
       "0       Maria  Female  130590               Finance   NaN           5   \n",
       "1      Angela  Female   54568  Business Development  27.0           5   \n",
       "2       Allan    Male  125792       Client Services  28.0           6   \n",
       "3       Rohan  Female   45906               Finance  28.0           7   \n",
       "4     Douglas    Male   97308             Marketing  28.0           7   \n",
       "5     Brandon    Male  112807       Human Resources  30.0           8   \n",
       "6       Diana  Female  132940       Client Services  31.0           9   \n",
       "7     Frances     NaN  139852  Business Development  34.0          10   \n",
       "8     Matthew    Male  100612             Marketing  34.0          10   \n",
       "9       Larry    Male  101004       Client Services  35.0          11   \n",
       "10     Joshua    Male   90816       Client Services  35.0          11   \n",
       "11      Jerry    Male   72000               Finance  35.0          12   \n",
       "12       Lois  Female   64714                 Legal  35.0          12   \n",
       "13     Dennis    Male  115163                 Legal  36.0          13   \n",
       "14       John    Male   97950       Client Services  37.0          13   \n",
       "15     Thomas    Male   61933             Marketing  38.0          14   \n",
       "16      Shawn    Male  111737       Human Resources  39.0          15   \n",
       "17       Gary    Male  109831               Product  39.0          15   \n",
       "18     Jeremy    Male   90370       Human Resources  42.0          18   \n",
       "19   Kimberly  Female   41426               Finance  44.0          20   \n",
       "20     Louise  Female   63241  Business Development  45.0          21   \n",
       "21      Donna  Female   81014               Product  49.0          23   \n",
       "22       Ruby  Female   65476               Product  54.0          25   \n",
       "23    Lillian  Female   59414               Product  55.0          26   \n",
       "24    Lillian  Female   59414               Product  55.0          26   \n",
       "\n",
       "      New_Salary  Bonus  Senior Management  \n",
       "0   146075.36220  20000              False  \n",
       "1    64675.63064  19000               True  \n",
       "2   132134.43260  18500              False  \n",
       "3    51230.17788  18000               True  \n",
       "4   104066.04060  17000               True  \n",
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       "19   44512.23700  11000               True  \n",
       "20   72810.62812  10800               True  \n",
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       "22   72031.45712  10400               True  \n",
       "23   60160.23984  10300              False  \n",
       "24   60160.23984  10300               True  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5 = pd.read_csv('Emp_EDA.csv')\n",
    "data5['Salary'].interpolate(method='linear', limit_direction='forward', inplace=True)\n",
    "data5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ffc42a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>130590</td>\n",
       "      <td>Finance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>146075.36220</td>\n",
       "      <td>20000</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angela</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allan</td>\n",
       "      <td>Male</td>\n",
       "      <td>125792</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6</td>\n",
       "      <td>132134.43260</td>\n",
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       "    <tr>\n",
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       "      <td>Finance</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>51230.17788</td>\n",
       "      <td>18000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>97308</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>28.0</td>\n",
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       "      <td>104066.04060</td>\n",
       "      <td>17000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brandon</td>\n",
       "      <td>Male</td>\n",
       "      <td>112807</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8</td>\n",
       "      <td>132539.20040</td>\n",
       "      <td>16000</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Diana</td>\n",
       "      <td>Female</td>\n",
       "      <td>132940</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9</td>\n",
       "      <td>158307.61080</td>\n",
       "      <td>15800</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Frances</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139852</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>150374.46450</td>\n",
       "      <td>15500</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>Male</td>\n",
       "      <td>100612</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>114340.50740</td>\n",
       "      <td>15000</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Larry</td>\n",
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       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>102406.94560</td>\n",
       "      <td>14700</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Joshua</td>\n",
       "      <td>Male</td>\n",
       "      <td>90816</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>107903.93860</td>\n",
       "      <td>14300</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>72000</td>\n",
       "      <td>Finance</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>78724.80000</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lois</td>\n",
       "      <td>Female</td>\n",
       "      <td>64714</td>\n",
       "      <td>Legal</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>67906.98876</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>Male</td>\n",
       "      <td>115163</td>\n",
       "      <td>Legal</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13</td>\n",
       "      <td>126823.25380</td>\n",
       "      <td>13000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>John</td>\n",
       "      <td>Male</td>\n",
       "      <td>97950</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13</td>\n",
       "      <td>111538.60350</td>\n",
       "      <td>12000</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>61933</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>38.0</td>\n",
       "      <td>14</td>\n",
       "      <td>68711.56685</td>\n",
       "      <td>11900</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shawn</td>\n",
       "      <td>Male</td>\n",
       "      <td>111737</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>118903.81120</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gary</td>\n",
       "      <td>Male</td>\n",
       "      <td>109831</td>\n",
       "      <td>Product</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>116235.24560</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Male</td>\n",
       "      <td>90370</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>42.0</td>\n",
       "      <td>18</td>\n",
       "      <td>97029.36530</td>\n",
       "      <td>11000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kimberly</td>\n",
       "      <td>Female</td>\n",
       "      <td>41426</td>\n",
       "      <td>Finance</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20</td>\n",
       "      <td>44512.23700</td>\n",
       "      <td>11000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Louise</td>\n",
       "      <td>Female</td>\n",
       "      <td>63241</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>45.0</td>\n",
       "      <td>21</td>\n",
       "      <td>72810.62812</td>\n",
       "      <td>10800</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Donna</td>\n",
       "      <td>Female</td>\n",
       "      <td>81014</td>\n",
       "      <td>Product</td>\n",
       "      <td>49.0</td>\n",
       "      <td>23</td>\n",
       "      <td>82548.40516</td>\n",
       "      <td>10600</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>Female</td>\n",
       "      <td>65476</td>\n",
       "      <td>Product</td>\n",
       "      <td>54.0</td>\n",
       "      <td>25</td>\n",
       "      <td>72031.45712</td>\n",
       "      <td>10400</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender  Salary                  Team   Age  Experience  \\\n",
       "0       Maria  Female  130590               Finance   NaN           5   \n",
       "1      Angela  Female   54568  Business Development  27.0           5   \n",
       "2       Allan    Male  125792       Client Services  28.0           6   \n",
       "3       Rohan  Female   45906               Finance  28.0           7   \n",
       "4     Douglas    Male   97308             Marketing  28.0           7   \n",
       "5     Brandon    Male  112807       Human Resources  30.0           8   \n",
       "6       Diana  Female  132940       Client Services  31.0           9   \n",
       "7     Frances     NaN  139852  Business Development  34.0          10   \n",
       "8     Matthew    Male  100612             Marketing  34.0          10   \n",
       "9       Larry    Male  101004       Client Services  35.0          11   \n",
       "10     Joshua    Male   90816       Client Services  35.0          11   \n",
       "11      Jerry    Male   72000               Finance  35.0          12   \n",
       "12       Lois  Female   64714                 Legal  35.0          12   \n",
       "13     Dennis    Male  115163                 Legal  36.0          13   \n",
       "14       John    Male   97950       Client Services  37.0          13   \n",
       "15     Thomas    Male   61933             Marketing  38.0          14   \n",
       "16      Shawn    Male  111737       Human Resources  39.0          15   \n",
       "17       Gary    Male  109831               Product  39.0          15   \n",
       "18     Jeremy    Male   90370       Human Resources  42.0          18   \n",
       "19   Kimberly  Female   41426               Finance  44.0          20   \n",
       "20     Louise  Female   63241  Business Development  45.0          21   \n",
       "21      Donna  Female   81014               Product  49.0          23   \n",
       "22       Ruby  Female   65476               Product  54.0          25   \n",
       "23    Lillian  Female   59414               Product  55.0          26   \n",
       "24    Lillian  Female   59414               Product  55.0          26   \n",
       "\n",
       "      New_Salary  Bonus  Senior Management  \n",
       "0   146075.36220  20000              False  \n",
       "1    64675.63064  19000               True  \n",
       "2   132134.43260  18500              False  \n",
       "3    51230.17788  18000               True  \n",
       "4   104066.04060  17000               True  \n",
       "5   132539.20040  16000               True  \n",
       "6   158307.61080  15800              False  \n",
       "7   150374.46450  15500               True  \n",
       "8   114340.50740  15000              False  \n",
       "9   102406.94560  14700               True  \n",
       "10  107903.93860  14300               True  \n",
       "11   78724.80000  14000               True  \n",
       "12   67906.98876  14000               True  \n",
       "13  126823.25380  13000              False  \n",
       "14  111538.60350  12000              False  \n",
       "15   68711.56685  11900               True  \n",
       "16  118903.81120  11500              False  \n",
       "17  116235.24560  11500              False  \n",
       "18   97029.36530  11000              False  \n",
       "19   44512.23700  11000               True  \n",
       "20   72810.62812  10800               True  \n",
       "21   82548.40516  10600              False  \n",
       "22   72031.45712  10400               True  \n",
       "23   60160.23984  10300              False  \n",
       "24   60160.23984  10300               True  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data6 = pd.read_csv('Emp_EDA.csv')\n",
    "data6['Salary'].fillna(data['Salary'].mean(), inplace=True)\n",
    "data6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7f788f47",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>Douglas</td>\n",
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       "      <td>97308</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>28.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brandon</td>\n",
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       "      <td>112807</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>30.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Diana</td>\n",
       "      <td>Female</td>\n",
       "      <td>132940</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9</td>\n",
       "      <td>158307.61080</td>\n",
       "      <td>15800</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Frances</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139852</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>150374.46450</td>\n",
       "      <td>15500</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>Male</td>\n",
       "      <td>100612</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>114340.50740</td>\n",
       "      <td>15000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>101004</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>102406.94560</td>\n",
       "      <td>14700</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Joshua</td>\n",
       "      <td>Male</td>\n",
       "      <td>90816</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>107903.93860</td>\n",
       "      <td>14300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>72000</td>\n",
       "      <td>Finance</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>78724.80000</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lois</td>\n",
       "      <td>Female</td>\n",
       "      <td>64714</td>\n",
       "      <td>Legal</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>67906.98876</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>Male</td>\n",
       "      <td>115163</td>\n",
       "      <td>Legal</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13</td>\n",
       "      <td>126823.25380</td>\n",
       "      <td>13000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>John</td>\n",
       "      <td>Male</td>\n",
       "      <td>97950</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13</td>\n",
       "      <td>111538.60350</td>\n",
       "      <td>12000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>61933</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>38.0</td>\n",
       "      <td>14</td>\n",
       "      <td>68711.56685</td>\n",
       "      <td>11900</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shawn</td>\n",
       "      <td>Male</td>\n",
       "      <td>111737</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>118903.81120</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gary</td>\n",
       "      <td>Male</td>\n",
       "      <td>109831</td>\n",
       "      <td>Product</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>116235.24560</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Male</td>\n",
       "      <td>90370</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>42.0</td>\n",
       "      <td>18</td>\n",
       "      <td>97029.36530</td>\n",
       "      <td>11000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kimberly</td>\n",
       "      <td>Female</td>\n",
       "      <td>41426</td>\n",
       "      <td>Finance</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20</td>\n",
       "      <td>44512.23700</td>\n",
       "      <td>11000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Louise</td>\n",
       "      <td>Female</td>\n",
       "      <td>63241</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>45.0</td>\n",
       "      <td>21</td>\n",
       "      <td>72810.62812</td>\n",
       "      <td>10800</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Donna</td>\n",
       "      <td>Female</td>\n",
       "      <td>81014</td>\n",
       "      <td>Product</td>\n",
       "      <td>49.0</td>\n",
       "      <td>23</td>\n",
       "      <td>82548.40516</td>\n",
       "      <td>10600</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>Female</td>\n",
       "      <td>65476</td>\n",
       "      <td>Product</td>\n",
       "      <td>54.0</td>\n",
       "      <td>25</td>\n",
       "      <td>72031.45712</td>\n",
       "      <td>10400</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender  Salary                  Team   Age  Experience  \\\n",
       "0       Maria  Female  130590               Finance   NaN           5   \n",
       "1      Angela  Female   54568  Business Development  27.0           5   \n",
       "2       Allan    Male  125792       Client Services  28.0           6   \n",
       "3       Rohan  Female   45906               Finance  28.0           7   \n",
       "4     Douglas    Male   97308             Marketing  28.0           7   \n",
       "5     Brandon    Male  112807       Human Resources  30.0           8   \n",
       "6       Diana  Female  132940       Client Services  31.0           9   \n",
       "7     Frances     NaN  139852  Business Development  34.0          10   \n",
       "8     Matthew    Male  100612             Marketing  34.0          10   \n",
       "9       Larry    Male  101004       Client Services  35.0          11   \n",
       "10     Joshua    Male   90816       Client Services  35.0          11   \n",
       "11      Jerry    Male   72000               Finance  35.0          12   \n",
       "12       Lois  Female   64714                 Legal  35.0          12   \n",
       "13     Dennis    Male  115163                 Legal  36.0          13   \n",
       "14       John    Male   97950       Client Services  37.0          13   \n",
       "15     Thomas    Male   61933             Marketing  38.0          14   \n",
       "16      Shawn    Male  111737       Human Resources  39.0          15   \n",
       "17       Gary    Male  109831               Product  39.0          15   \n",
       "18     Jeremy    Male   90370       Human Resources  42.0          18   \n",
       "19   Kimberly  Female   41426               Finance  44.0          20   \n",
       "20     Louise  Female   63241  Business Development  45.0          21   \n",
       "21      Donna  Female   81014               Product  49.0          23   \n",
       "22       Ruby  Female   65476               Product  54.0          25   \n",
       "23    Lillian  Female   59414               Product  55.0          26   \n",
       "24    Lillian  Female   59414               Product  55.0          26   \n",
       "\n",
       "      New_Salary  Bonus  Senior Management  \n",
       "0   146075.36220  20000              False  \n",
       "1    64675.63064  19000               True  \n",
       "2   132134.43260  18500              False  \n",
       "3    51230.17788  18000               True  \n",
       "4   104066.04060  17000               True  \n",
       "5   132539.20040  16000               True  \n",
       "6   158307.61080  15800              False  \n",
       "7   150374.46450  15500               True  \n",
       "8   114340.50740  15000              False  \n",
       "9   102406.94560  14700               True  \n",
       "10  107903.93860  14300               True  \n",
       "11   78724.80000  14000               True  \n",
       "12   67906.98876  14000               True  \n",
       "13  126823.25380  13000              False  \n",
       "14  111538.60350  12000              False  \n",
       "15   68711.56685  11900               True  \n",
       "16  118903.81120  11500              False  \n",
       "17  116235.24560  11500              False  \n",
       "18   97029.36530  11000              False  \n",
       "19   44512.23700  11000               True  \n",
       "20   72810.62812  10800               True  \n",
       "21   82548.40516  10600              False  \n",
       "22   72031.45712  10400               True  \n",
       "23   60160.23984  10300              False  \n",
       "24   60160.23984  10300               True  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data7 = pd.read_csv('Emp_EDA.csv')\n",
    "data7['Salary'].fillna(data['Salary'].median(), inplace=True)\n",
    "data7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e42e5d",
   "metadata": {},
   "source": [
    "### > Detect outliers in the updated 'Experience' column with boxplot, scatter plot, and histogram\n",
    "\n",
    "Outliers are extreme values that significantly differ from other data points. This question requires detecting outliers in the updated 'Experience' column using visual techniques such as boxplot, scatter plot, and histogram. These visualizations help identify values that fall outside the expected range. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cf1c1a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "data8 = pd.read_csv('Emp_EDA.csv')\n",
    "plt.boxplot(data8['Experience'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f55b72db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1e30a9d278>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data8['Experience'], linewidth=0, marker='o', color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9b8a91d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([5., 2., 4., 4., 3., 0., 1., 2., 1., 3.]),\n",
       " array([ 5. ,  7.1,  9.2, 11.3, 13.4, 15.5, 17.6, 19.7, 21.8, 23.9, 26. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data8['Experience'], edgecolor='black', color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed2982c3",
   "metadata": {},
   "source": [
    "### > Remove the outliers using IQR by recalculating IQR in the updated 'Experience' column and analyze with a box plot. \n",
    "\n",
    "This question involves removing outliers from the updated 'Experience' column using the interquartile range (IQR) method. By recalculating the IQR and removing values outside a certain range, outliers can be effectively eliminated. The analysis is then visualized using a box plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ef58f6e6",
   "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>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>New_Salary</th>\n",
       "      <th>Bonus</th>\n",
       "      <th>Senior Management</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>130590</td>\n",
       "      <td>Finance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Maria</td>\n",
       "      <td>146075.36220</td>\n",
       "      <td>20000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angela</td>\n",
       "      <td>Female</td>\n",
       "      <td>54568</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Angela</td>\n",
       "      <td>64675.63064</td>\n",
       "      <td>19000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allan</td>\n",
       "      <td>Male</td>\n",
       "      <td>125792</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Allan</td>\n",
       "      <td>132134.43260</td>\n",
       "      <td>18500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rohan</td>\n",
       "      <td>Female</td>\n",
       "      <td>45906</td>\n",
       "      <td>Finance</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Rohan</td>\n",
       "      <td>51230.17788</td>\n",
       "      <td>18000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>97308</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Douglas</td>\n",
       "      <td>104066.04060</td>\n",
       "      <td>17000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brandon</td>\n",
       "      <td>Male</td>\n",
       "      <td>112807</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Brandon</td>\n",
       "      <td>132539.20040</td>\n",
       "      <td>16000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Diana</td>\n",
       "      <td>Female</td>\n",
       "      <td>132940</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Diana</td>\n",
       "      <td>158307.61080</td>\n",
       "      <td>15800</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Frances</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139852</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Frances</td>\n",
       "      <td>150374.46450</td>\n",
       "      <td>15500</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>Male</td>\n",
       "      <td>100612</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Matthew</td>\n",
       "      <td>114340.50740</td>\n",
       "      <td>15000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>101004</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Larry</td>\n",
       "      <td>102406.94560</td>\n",
       "      <td>14700</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Joshua</td>\n",
       "      <td>Male</td>\n",
       "      <td>90816</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Joshua</td>\n",
       "      <td>107903.93860</td>\n",
       "      <td>14300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>72000</td>\n",
       "      <td>Finance</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Jerry</td>\n",
       "      <td>78724.80000</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lois</td>\n",
       "      <td>Female</td>\n",
       "      <td>64714</td>\n",
       "      <td>Legal</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Lois</td>\n",
       "      <td>67906.98876</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>Male</td>\n",
       "      <td>115163</td>\n",
       "      <td>Legal</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Dennis</td>\n",
       "      <td>126823.25380</td>\n",
       "      <td>13000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>John</td>\n",
       "      <td>Male</td>\n",
       "      <td>97950</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>37.0</td>\n",
       "      <td>John</td>\n",
       "      <td>111538.60350</td>\n",
       "      <td>12000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>61933</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>68711.56685</td>\n",
       "      <td>11900</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shawn</td>\n",
       "      <td>Male</td>\n",
       "      <td>111737</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Shawn</td>\n",
       "      <td>118903.81120</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gary</td>\n",
       "      <td>Male</td>\n",
       "      <td>109831</td>\n",
       "      <td>Product</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Gary</td>\n",
       "      <td>116235.24560</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Male</td>\n",
       "      <td>90370</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Jeremy</td>\n",
       "      <td>97029.36530</td>\n",
       "      <td>11000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kimberly</td>\n",
       "      <td>Female</td>\n",
       "      <td>41426</td>\n",
       "      <td>Finance</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Kimberly</td>\n",
       "      <td>44512.23700</td>\n",
       "      <td>11000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Louise</td>\n",
       "      <td>Female</td>\n",
       "      <td>63241</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Louise</td>\n",
       "      <td>72810.62812</td>\n",
       "      <td>10800</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Donna</td>\n",
       "      <td>Female</td>\n",
       "      <td>81014</td>\n",
       "      <td>Product</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Donna</td>\n",
       "      <td>82548.40516</td>\n",
       "      <td>10600</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>Female</td>\n",
       "      <td>65476</td>\n",
       "      <td>Product</td>\n",
       "      <td>54.0</td>\n",
       "      <td>Ruby</td>\n",
       "      <td>72031.45712</td>\n",
       "      <td>10400</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Lillian</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>59414</td>\n",
       "      <td>Product</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Lillian</td>\n",
       "      <td>60160.23984</td>\n",
       "      <td>10300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender  Salary                  Team   Age Experience  \\\n",
       "0       Maria  Female  130590               Finance   NaN      Maria   \n",
       "1      Angela  Female   54568  Business Development  27.0     Angela   \n",
       "2       Allan    Male  125792       Client Services  28.0      Allan   \n",
       "3       Rohan  Female   45906               Finance  28.0      Rohan   \n",
       "4     Douglas    Male   97308             Marketing  28.0    Douglas   \n",
       "5     Brandon    Male  112807       Human Resources  30.0    Brandon   \n",
       "6       Diana  Female  132940       Client Services  31.0      Diana   \n",
       "7     Frances     NaN  139852  Business Development  34.0    Frances   \n",
       "8     Matthew    Male  100612             Marketing  34.0    Matthew   \n",
       "9       Larry    Male  101004       Client Services  35.0      Larry   \n",
       "10     Joshua    Male   90816       Client Services  35.0     Joshua   \n",
       "11      Jerry    Male   72000               Finance  35.0      Jerry   \n",
       "12       Lois  Female   64714                 Legal  35.0       Lois   \n",
       "13     Dennis    Male  115163                 Legal  36.0     Dennis   \n",
       "14       John    Male   97950       Client Services  37.0       John   \n",
       "15     Thomas    Male   61933             Marketing  38.0     Thomas   \n",
       "16      Shawn    Male  111737       Human Resources  39.0      Shawn   \n",
       "17       Gary    Male  109831               Product  39.0       Gary   \n",
       "18     Jeremy    Male   90370       Human Resources  42.0     Jeremy   \n",
       "19   Kimberly  Female   41426               Finance  44.0   Kimberly   \n",
       "20     Louise  Female   63241  Business Development  45.0     Louise   \n",
       "21      Donna  Female   81014               Product  49.0      Donna   \n",
       "22       Ruby  Female   65476               Product  54.0       Ruby   \n",
       "23    Lillian  Female   59414               Product  55.0    Lillian   \n",
       "24    Lillian  Female   59414               Product  55.0    Lillian   \n",
       "\n",
       "      New_Salary  Bonus  Senior Management  \n",
       "0   146075.36220  20000              False  \n",
       "1    64675.63064  19000               True  \n",
       "2   132134.43260  18500              False  \n",
       "3    51230.17788  18000               True  \n",
       "4   104066.04060  17000               True  \n",
       "5   132539.20040  16000               True  \n",
       "6   158307.61080  15800              False  \n",
       "7   150374.46450  15500               True  \n",
       "8   114340.50740  15000              False  \n",
       "9   102406.94560  14700               True  \n",
       "10  107903.93860  14300               True  \n",
       "11   78724.80000  14000               True  \n",
       "12   67906.98876  14000               True  \n",
       "13  126823.25380  13000              False  \n",
       "14  111538.60350  12000              False  \n",
       "15   68711.56685  11900               True  \n",
       "16  118903.81120  11500              False  \n",
       "17  116235.24560  11500              False  \n",
       "18   97029.36530  11000              False  \n",
       "19   44512.23700  11000               True  \n",
       "20   72810.62812  10800               True  \n",
       "21   82548.40516  10600              False  \n",
       "22   72031.45712  10400               True  \n",
       "23   60160.23984  10300              False  \n",
       "24   60160.23984  10300               True  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1c=data7['Experience'].quantile(0.25)\n",
    "Q3c=data7['Experience'].quantile(0.75)\n",
    "IQRc = Q3c-Q1c\n",
    "l=Q1c-1.5*IQRc\n",
    "h=Q3c+1.5*IQRc\n",
    "data7['Experience']=data7[(data7['Experience']>l) | (data7['Experience']< h)]\n",
    "data7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "044abdd3",
   "metadata": {},
   "source": [
    "### > Remove the outliers using z-score by recalculating z-score in the updated 'Experience' column and analyze it with a box plot.\n",
    "\n",
    "This question requirres removing outliers from the updated 'Experience' column using the z-score method. By recalculating the z-scores and removing values exceed a certain threshold, outliers can be identified and removed. The analysis is visualized using a box plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "76d6390f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0          NaN\n",
      "1    -1.297767\n",
      "2    -1.180234\n",
      "3    -1.180234\n",
      "4    -1.180234\n",
      "5    -0.945166\n",
      "6    -0.827633\n",
      "7    -0.475032\n",
      "8    -0.475032\n",
      "9    -0.357498\n",
      "10   -0.357498\n",
      "11   -0.357498\n",
      "12   -0.357498\n",
      "13   -0.239965\n",
      "14   -0.122431\n",
      "15   -0.004897\n",
      "16    0.112636\n",
      "17    0.112636\n",
      "18    0.465237\n",
      "19    0.700305\n",
      "20    0.817838\n",
      "21    1.287973\n",
      "22    1.875641\n",
      "23    1.993175\n",
      "24    1.993175\n",
      "Name: Age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df_zscore = (data7['Age'] - data7['Age'].mean())/data7['Age'].std()\n",
    "print(df_zscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae08633c",
   "metadata": {},
   "source": [
    "### > Plot the Heatmap using correlation\n",
    "\n",
    "This question involves plotting a heatmap to visualize the correlation between different variables in the dataset. Heatmaps provide an effective way to identify and analyze relationships and dependencies between variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2a64b58f",
   "metadata": {},
   "outputs": [
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       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Salary</th>        <th class=\"col_heading level0 col1\" >Age</th>        <th class=\"col_heading level0 col2\" >New_Salary</th>        <th class=\"col_heading level0 col3\" >Bonus</th>        <th class=\"col_heading level0 col4\" >Senior Management</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56level0_row0\" class=\"row_heading level0 row0\" >Salary</th>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row0_col1\" class=\"data row0 col1\" >-0.407259</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row0_col2\" class=\"data row0 col2\" >0.98788</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row0_col3\" class=\"data row0 col3\" >0.368744</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row0_col4\" class=\"data row0 col4\" >-0.49921</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56level0_row1\" class=\"row_heading level0 row1\" >Age</th>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row1_col0\" class=\"data row1 col0\" >-0.407259</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row1_col1\" class=\"data row1 col1\" >1</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row1_col2\" class=\"data row1 col2\" >-0.453648</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row1_col3\" class=\"data row1 col3\" >-0.88841</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row1_col4\" class=\"data row1 col4\" >-0.0972426</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56level0_row2\" class=\"row_heading level0 row2\" >New_Salary</th>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row2_col0\" class=\"data row2 col0\" >0.98788</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row2_col1\" class=\"data row2 col1\" >-0.453648</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row2_col3\" class=\"data row2 col3\" >0.403568</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row2_col4\" class=\"data row2 col4\" >-0.474023</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56level0_row3\" class=\"row_heading level0 row3\" >Bonus</th>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row3_col0\" class=\"data row3 col0\" >0.368744</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row3_col1\" class=\"data row3 col1\" >-0.88841</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row3_col2\" class=\"data row3 col2\" >0.403568</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row3_col3\" class=\"data row3 col3\" >1</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row3_col4\" class=\"data row3 col4\" >0.0840114</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56level0_row4\" class=\"row_heading level0 row4\" >Senior Management</th>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row4_col0\" class=\"data row4 col0\" >-0.49921</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row4_col1\" class=\"data row4 col1\" >-0.0972426</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row4_col2\" class=\"data row4 col2\" >-0.474023</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row4_col3\" class=\"data row4 col3\" >0.0840114</td>\n",
       "                        <td id=\"T_c9c91f86_2e04_11ed_a0fe_00505693aa56row4_col4\" class=\"data row4 col4\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f1e30521ba8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = data7.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8211da7",
   "metadata": {},
   "source": [
    "### > Drop the last rows added in the dataframe\n",
    "\n",
    "Finally, this question requires dropping the last two rows that were previously added to the dataframe. This ensures that the dataframe is reverted to its original state or structure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6a2148a9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>New_Salary</th>\n",
       "      <th>Bonus</th>\n",
       "      <th>Senior Management</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>130590</td>\n",
       "      <td>Finance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>146075.36220</td>\n",
       "      <td>20000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angela</td>\n",
       "      <td>Female</td>\n",
       "      <td>54568</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5</td>\n",
       "      <td>64675.63064</td>\n",
       "      <td>19000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allan</td>\n",
       "      <td>Male</td>\n",
       "      <td>125792</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>28.0</td>\n",
       "      <td>6</td>\n",
       "      <td>132134.43260</td>\n",
       "      <td>18500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rohan</td>\n",
       "      <td>Female</td>\n",
       "      <td>45906</td>\n",
       "      <td>Finance</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>51230.17788</td>\n",
       "      <td>18000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>97308</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>104066.04060</td>\n",
       "      <td>17000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Brandon</td>\n",
       "      <td>Male</td>\n",
       "      <td>112807</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8</td>\n",
       "      <td>132539.20040</td>\n",
       "      <td>16000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Diana</td>\n",
       "      <td>Female</td>\n",
       "      <td>132940</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>31.0</td>\n",
       "      <td>9</td>\n",
       "      <td>158307.61080</td>\n",
       "      <td>15800</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Frances</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139852</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>150374.46450</td>\n",
       "      <td>15500</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>Male</td>\n",
       "      <td>100612</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>114340.50740</td>\n",
       "      <td>15000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>101004</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>102406.94560</td>\n",
       "      <td>14700</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Joshua</td>\n",
       "      <td>Male</td>\n",
       "      <td>90816</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11</td>\n",
       "      <td>107903.93860</td>\n",
       "      <td>14300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>72000</td>\n",
       "      <td>Finance</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>78724.80000</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lois</td>\n",
       "      <td>Female</td>\n",
       "      <td>64714</td>\n",
       "      <td>Legal</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12</td>\n",
       "      <td>67906.98876</td>\n",
       "      <td>14000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>Male</td>\n",
       "      <td>115163</td>\n",
       "      <td>Legal</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13</td>\n",
       "      <td>126823.25380</td>\n",
       "      <td>13000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>John</td>\n",
       "      <td>Male</td>\n",
       "      <td>97950</td>\n",
       "      <td>Client Services</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13</td>\n",
       "      <td>111538.60350</td>\n",
       "      <td>12000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>61933</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>38.0</td>\n",
       "      <td>14</td>\n",
       "      <td>68711.56685</td>\n",
       "      <td>11900</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Shawn</td>\n",
       "      <td>Male</td>\n",
       "      <td>111737</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>118903.81120</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gary</td>\n",
       "      <td>Male</td>\n",
       "      <td>109831</td>\n",
       "      <td>Product</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15</td>\n",
       "      <td>116235.24560</td>\n",
       "      <td>11500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Male</td>\n",
       "      <td>90370</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>42.0</td>\n",
       "      <td>18</td>\n",
       "      <td>97029.36530</td>\n",
       "      <td>11000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kimberly</td>\n",
       "      <td>Female</td>\n",
       "      <td>41426</td>\n",
       "      <td>Finance</td>\n",
       "      <td>44.0</td>\n",
       "      <td>20</td>\n",
       "      <td>44512.23700</td>\n",
       "      <td>11000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Louise</td>\n",
       "      <td>Female</td>\n",
       "      <td>63241</td>\n",
       "      <td>Business Development</td>\n",
       "      <td>45.0</td>\n",
       "      <td>21</td>\n",
       "      <td>72810.62812</td>\n",
       "      <td>10800</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Donna</td>\n",
       "      <td>Female</td>\n",
       "      <td>81014</td>\n",
       "      <td>Product</td>\n",
       "      <td>49.0</td>\n",
       "      <td>23</td>\n",
       "      <td>82548.40516</td>\n",
       "      <td>10600</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>Female</td>\n",
       "      <td>65476</td>\n",
       "      <td>Product</td>\n",
       "      <td>54.0</td>\n",
       "      <td>25</td>\n",
       "      <td>72031.45712</td>\n",
       "      <td>10400</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender  Salary                  Team   Age  Experience  \\\n",
       "0       Maria  Female  130590               Finance   NaN           5   \n",
       "1      Angela  Female   54568  Business Development  27.0           5   \n",
       "2       Allan    Male  125792       Client Services  28.0           6   \n",
       "3       Rohan  Female   45906               Finance  28.0           7   \n",
       "4     Douglas    Male   97308             Marketing  28.0           7   \n",
       "5     Brandon    Male  112807       Human Resources  30.0           8   \n",
       "6       Diana  Female  132940       Client Services  31.0           9   \n",
       "7     Frances     NaN  139852  Business Development  34.0          10   \n",
       "8     Matthew    Male  100612             Marketing  34.0          10   \n",
       "9       Larry    Male  101004       Client Services  35.0          11   \n",
       "10     Joshua    Male   90816       Client Services  35.0          11   \n",
       "11      Jerry    Male   72000               Finance  35.0          12   \n",
       "12       Lois  Female   64714                 Legal  35.0          12   \n",
       "13     Dennis    Male  115163                 Legal  36.0          13   \n",
       "14       John    Male   97950       Client Services  37.0          13   \n",
       "15     Thomas    Male   61933             Marketing  38.0          14   \n",
       "16      Shawn    Male  111737       Human Resources  39.0          15   \n",
       "17       Gary    Male  109831               Product  39.0          15   \n",
       "18     Jeremy    Male   90370       Human Resources  42.0          18   \n",
       "19   Kimberly  Female   41426               Finance  44.0          20   \n",
       "20     Louise  Female   63241  Business Development  45.0          21   \n",
       "21      Donna  Female   81014               Product  49.0          23   \n",
       "22       Ruby  Female   65476               Product  54.0          25   \n",
       "\n",
       "      New_Salary  Bonus  Senior Management  \n",
       "0   146075.36220  20000              False  \n",
       "1    64675.63064  19000               True  \n",
       "2   132134.43260  18500              False  \n",
       "3    51230.17788  18000               True  \n",
       "4   104066.04060  17000               True  \n",
       "5   132539.20040  16000               True  \n",
       "6   158307.61080  15800              False  \n",
       "7   150374.46450  15500               True  \n",
       "8   114340.50740  15000              False  \n",
       "9   102406.94560  14700               True  \n",
       "10  107903.93860  14300               True  \n",
       "11   78724.80000  14000               True  \n",
       "12   67906.98876  14000               True  \n",
       "13  126823.25380  13000              False  \n",
       "14  111538.60350  12000              False  \n",
       "15   68711.56685  11900               True  \n",
       "16  118903.81120  11500              False  \n",
       "17  116235.24560  11500              False  \n",
       "18   97029.36530  11000              False  \n",
       "19   44512.23700  11000               True  \n",
       "20   72810.62812  10800               True  \n",
       "21   82548.40516  10600              False  \n",
       "22   72031.45712  10400               True  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.drop([23, 24], inplace=True)\n",
    "data2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca292f89",
   "metadata": {},
   "source": [
    "**In Conclusion** Exploratory Data Analysis (EDA) Plays a crucial role in the data analysis process by providing valuable insights, identifying patterns, and uncovering relationships within the dataset. Through various techniques such as data cleaning, visualization, and statistical calculations EDA helps us understand the characteristics and structure of the data, detect outliers or missing values and formulate hypotheses for further analysis. \n",
    "\n",
    "By conducting EDA, we can make informed decisions about data preprocessing, variable selection, and modeling approaches. It allows us to gain a deeper understanding of the data, identify potential data quality issues, and derive meaningful insights. EDA Serves as a foundation for more advanced analysis techniques, such as predictive modeling, hypothesis testing, and machine learning. "
   ]
  },
  {
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    "**Contact Information:**\n",
    "\n",
    "For any inquiries or further discussions related to this exploratory data analysis notebook, please feel free to reach out to me. I welcome the opportunity to connect and engage in data-related conversations. \n",
    "\n",
    "👤 Email: [info@rubangino.in](https://info@rubangino.in/)\n",
    "\n",
    "🌐 LinkedIn: [https://www.linkedin.com/in/ruban-gino-singh/](https://www.linkedin.com/in/ruban-gino-singh/)\n",
    "\n",
    "📧 Twitter: [https://twitter.com/Rubangino](https://twitter.com/Rubangino)\n",
    "\n",
    "🌐 Github: [https://github.com/Ruban2205](https://github.com/Ruban2205)\n",
    "\n",
    "Let's connect and Explore the fascinating world of data analysis together!"
   ]
  }
 ],
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