Assignment 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aiming to uncover the factors that  lead to employee attrition and answer the following questions:\n",
    "\n",
    "\n",
    "          1. Show a breakdown of distance from home by job role and attrition.\n",
    "          2. Compare average monthly income by education and attrition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
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       "      <th>EmployeeNumber</th>\n",
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       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
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       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
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       "      <td>No</td>\n",
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       "      <td>6</td>\n",
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       "      <td>2062</td>\n",
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       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>155</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
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       "      <td>2064</td>\n",
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       "      <td>49</td>\n",
       "      <td>No</td>\n",
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       "      <td>1023</td>\n",
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       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
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       "      <th>1469</th>\n",
       "      <td>34</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>628</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
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       "      <td>2</td>\n",
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       "<p>1470 rows × 35 columns</p>\n",
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      ],
      "text/plain": [
       "      Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0      41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1      49        No  Travel_Frequently        279  Research & Development   \n",
       "2      37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3      33        No  Travel_Frequently       1392  Research & Development   \n",
       "4      27        No      Travel_Rarely        591  Research & Development   \n",
       "...   ...       ...                ...        ...                     ...   \n",
       "1465   36        No  Travel_Frequently        884  Research & Development   \n",
       "1466   39        No      Travel_Rarely        613  Research & Development   \n",
       "1467   27        No      Travel_Rarely        155  Research & Development   \n",
       "1468   49        No  Travel_Frequently       1023                   Sales   \n",
       "1469   34        No      Travel_Rarely        628  Research & Development   \n",
       "\n",
       "      DistanceFromHome  Education EducationField  EmployeeCount  \\\n",
       "0                    1          2  Life Sciences              1   \n",
       "1                    8          1  Life Sciences              1   \n",
       "2                    2          2          Other              1   \n",
       "3                    3          4  Life Sciences              1   \n",
       "4                    2          1        Medical              1   \n",
       "...                ...        ...            ...            ...   \n",
       "1465                23          2        Medical              1   \n",
       "1466                 6          1        Medical              1   \n",
       "1467                 4          3  Life Sciences              1   \n",
       "1468                 2          3        Medical              1   \n",
       "1469                 8          3        Medical              1   \n",
       "\n",
       "      EmployeeNumber  ...  RelationshipSatisfaction StandardHours  \\\n",
       "0                  1  ...                         1            80   \n",
       "1                  2  ...                         4            80   \n",
       "2                  4  ...                         2            80   \n",
       "3                  5  ...                         3            80   \n",
       "4                  7  ...                         4            80   \n",
       "...              ...  ...                       ...           ...   \n",
       "1465            2061  ...                         3            80   \n",
       "1466            2062  ...                         1            80   \n",
       "1467            2064  ...                         2            80   \n",
       "1468            2065  ...                         4            80   \n",
       "1469            2068  ...                         1            80   \n",
       "\n",
       "      StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0                    0                  8                      0   \n",
       "1                    1                 10                      3   \n",
       "2                    0                  7                      3   \n",
       "3                    0                  8                      3   \n",
       "4                    1                  6                      3   \n",
       "...                ...                ...                    ...   \n",
       "1465                 1                 17                      3   \n",
       "1466                 1                  9                      5   \n",
       "1467                 1                  6                      0   \n",
       "1468                 0                 17                      3   \n",
       "1469                 0                  6                      3   \n",
       "\n",
       "     WorkLifeBalance  YearsAtCompany YearsInCurrentRole  \\\n",
       "0                  1               6                  4   \n",
       "1                  3              10                  7   \n",
       "2                  3               0                  0   \n",
       "3                  3               8                  7   \n",
       "4                  3               2                  2   \n",
       "...              ...             ...                ...   \n",
       "1465               3               5                  2   \n",
       "1466               3               7                  7   \n",
       "1467               3               6                  2   \n",
       "1468               2               9                  6   \n",
       "1469               4               4                  3   \n",
       "\n",
       "      YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                           0                     5  \n",
       "1                           1                     7  \n",
       "2                           0                     0  \n",
       "3                           3                     0  \n",
       "4                           2                     2  \n",
       "...                       ...                   ...  \n",
       "1465                        0                     3  \n",
       "1466                        1                     7  \n",
       "1467                        0                     3  \n",
       "1468                        0                     8  \n",
       "1469                        1                     2  \n",
       "\n",
       "[1470 rows x 35 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading in the CSV file\n",
    "df = pd.read_csv(\"WA_Fn-UseC_-HR-Employee-Attrition.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1470 entries, 0 to 1469\n",
      "Data columns (total 35 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   Age                       1470 non-null   int64 \n",
      " 1   Attrition                 1470 non-null   object\n",
      " 2   BusinessTravel            1470 non-null   object\n",
      " 3   DailyRate                 1470 non-null   int64 \n",
      " 4   Department                1470 non-null   object\n",
      " 5   DistanceFromHome          1470 non-null   int64 \n",
      " 6   Education                 1470 non-null   int64 \n",
      " 7   EducationField            1470 non-null   object\n",
      " 8   EmployeeCount             1470 non-null   int64 \n",
      " 9   EmployeeNumber            1470 non-null   int64 \n",
      " 10  EnvironmentSatisfaction   1470 non-null   int64 \n",
      " 11  Gender                    1470 non-null   object\n",
      " 12  HourlyRate                1470 non-null   int64 \n",
      " 13  JobInvolvement            1470 non-null   int64 \n",
      " 14  JobLevel                  1470 non-null   int64 \n",
      " 15  JobRole                   1470 non-null   object\n",
      " 16  JobSatisfaction           1470 non-null   int64 \n",
      " 17  MaritalStatus             1470 non-null   object\n",
      " 18  MonthlyIncome             1470 non-null   int64 \n",
      " 19  MonthlyRate               1470 non-null   int64 \n",
      " 20  NumCompaniesWorked        1470 non-null   int64 \n",
      " 21  Over18                    1470 non-null   object\n",
      " 22  OverTime                  1470 non-null   object\n",
      " 23  PercentSalaryHike         1470 non-null   int64 \n",
      " 24  PerformanceRating         1470 non-null   int64 \n",
      " 25  RelationshipSatisfaction  1470 non-null   int64 \n",
      " 26  StandardHours             1470 non-null   int64 \n",
      " 27  StockOptionLevel          1470 non-null   int64 \n",
      " 28  TotalWorkingYears         1470 non-null   int64 \n",
      " 29  TrainingTimesLastYear     1470 non-null   int64 \n",
      " 30  WorkLifeBalance           1470 non-null   int64 \n",
      " 31  YearsAtCompany            1470 non-null   int64 \n",
      " 32  YearsInCurrentRole        1470 non-null   int64 \n",
      " 33  YearsSinceLastPromotion   1470 non-null   int64 \n",
      " 34  YearsWithCurrManager      1470 non-null   int64 \n",
      "dtypes: int64(26), object(9)\n",
      "memory usage: 350.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n",
       "       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',\n",
       "       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',\n",
       "       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
       "       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n",
       "       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n",
       "       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n",
       "       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
       "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
       "       'YearsWithCurrManager'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show a breakdown of distance from home by job role and attrition\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>Education</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>1</td>\n",
       "      <td>5993</td>\n",
       "      <td>2</td>\n",
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       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>8</td>\n",
       "      <td>5130</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>2</td>\n",
       "      <td>2090</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>3</td>\n",
       "      <td>2909</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>2</td>\n",
       "      <td>3468</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>No</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>23</td>\n",
       "      <td>2571</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>No</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>6</td>\n",
       "      <td>9991</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>No</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>4</td>\n",
       "      <td>6142</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>No</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>2</td>\n",
       "      <td>5390</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>No</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>8</td>\n",
       "      <td>4404</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1470 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Attrition                    JobRole  DistanceFromHome  MonthlyIncome  \\\n",
       "0          Yes            Sales Executive                 1           5993   \n",
       "1           No         Research Scientist                 8           5130   \n",
       "2          Yes      Laboratory Technician                 2           2090   \n",
       "3           No         Research Scientist                 3           2909   \n",
       "4           No      Laboratory Technician                 2           3468   \n",
       "...        ...                        ...               ...            ...   \n",
       "1465        No      Laboratory Technician                23           2571   \n",
       "1466        No  Healthcare Representative                 6           9991   \n",
       "1467        No     Manufacturing Director                 4           6142   \n",
       "1468        No            Sales Executive                 2           5390   \n",
       "1469        No      Laboratory Technician                 8           4404   \n",
       "\n",
       "      Education  \n",
       "0             2  \n",
       "1             1  \n",
       "2             2  \n",
       "3             4  \n",
       "4             1  \n",
       "...         ...  \n",
       "1465          2  \n",
       "1466          1  \n",
       "1467          3  \n",
       "1468          3  \n",
       "1469          3  \n",
       "\n",
       "[1470 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df[['Attrition', 'JobRole', 'DistanceFromHome', 'MonthlyIncome', 'Education' ]]\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>Education</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>Healthcare Representative</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
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       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>29</td>\n",
       "      <td>Manager</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>29</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>29</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>29</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>29</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>224 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     DistanceFromHome                    JobRole  Attrition  MonthlyIncome  \\\n",
       "0                   1  Healthcare Representative         23             23   \n",
       "1                   1            Human Resources          9              9   \n",
       "2                   1      Laboratory Technician         31             31   \n",
       "3                   1                    Manager         13             13   \n",
       "4                   1     Manufacturing Director         24             24   \n",
       "..                ...                        ...        ...            ...   \n",
       "219                29                    Manager          4              4   \n",
       "220                29     Manufacturing Director          4              4   \n",
       "221                29         Research Scientist          4              4   \n",
       "222                29            Sales Executive          5              5   \n",
       "223                29       Sales Representative          1              1   \n",
       "\n",
       "     Education  \n",
       "0           23  \n",
       "1            9  \n",
       "2           31  \n",
       "3           13  \n",
       "4           24  \n",
       "..         ...  \n",
       "219          4  \n",
       "220          4  \n",
       "221          4  \n",
       "222          5  \n",
       "223          1  \n",
       "\n",
       "[224 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.groupby(['DistanceFromHome', 'JobRole']).count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Attrition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>JobRole</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">1</th>\n",
       "      <th>Human Resources</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>Laboratory Technician</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manufacturing Director</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>Research Scientist</th>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>Sales Executive</th>\n",
       "      <td>6</td>\n",
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       "      <td>7</td>\n",
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       "      <th rowspan=\"4\" valign=\"top\">2</th>\n",
       "      <th>Healthcare Representative</th>\n",
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       "      <td>1</td>\n",
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       "      <td>11</td>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Attrition\n",
       "DistanceFromHome JobRole                             \n",
       "1                Human Resources                    1\n",
       "                 Laboratory Technician              4\n",
       "                 Manufacturing Director             1\n",
       "                 Research Scientist                 7\n",
       "                 Sales Executive                    6\n",
       "                 Sales Representative               7\n",
       "2                Healthcare Representative          1\n",
       "                 Human Resources                    1\n",
       "                 Laboratory Technician             11\n",
       "                 Manager                            3"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Attrited_yes = df1[['DistanceFromHome', 'JobRole','Attrition' ]][df['Attrition'] == 'Yes']\n",
    "Attrited_yes.groupby(['DistanceFromHome','JobRole']).count().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Attrition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>JobRole</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">1</th>\n",
       "      <th>Healthcare Representative</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laboratory Technician</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manager</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manufacturing Director</th>\n",
       "      <td>23</td>\n",
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       "      <th>Research Director</th>\n",
       "      <td>13</td>\n",
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       "      <th>Research Scientist</th>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>Sales Executive</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales Representative</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>Healthcare Representative</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Attrition\n",
       "DistanceFromHome JobRole                             \n",
       "1                Healthcare Representative         23\n",
       "                 Human Resources                    8\n",
       "                 Laboratory Technician             27\n",
       "                 Manager                           13\n",
       "                 Manufacturing Director            23\n",
       "                 Research Director                 13\n",
       "                 Research Scientist                40\n",
       "                 Sales Executive                   33\n",
       "                 Sales Representative               2\n",
       "2                Healthcare Representative         14"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Attrited_no = df1[['DistanceFromHome', 'JobRole','Attrition' ]][df['Attrition'] == 'No']\n",
    "Attrited_no.groupby(['DistanceFromHome', 'JobRole',]).count().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Attrition', 'JobRole', 'DistanceFromHome', 'MonthlyIncome',\n",
       "       'Education'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sales Executive              326\n",
       "Research Scientist           292\n",
       "Laboratory Technician        259\n",
       "Manufacturing Director       145\n",
       "Healthcare Representative    131\n",
       "Manager                      102\n",
       "Sales Representative          83\n",
       "Research Director             80\n",
       "Human Resources               52\n",
       "Name: JobRole, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['JobRole'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Distance From Home')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,5))\n",
    "sns.barplot(x=\"JobRole\", y=\"DistanceFromHome\" ,hue=\"Attrition\", data=df1,)\n",
    "plt.xlabel(\"Job Role\")\n",
    "plt.ylabel(\"Distance From Home\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare average monthly income by education and attrition.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    572\n",
       "4    398\n",
       "2    282\n",
       "1    170\n",
       "5     48\n",
       "Name: Education, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Attrition</th>\n",
       "      <th>Education</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>5926.129496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>6586.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>6882.919662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>7087.814706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>8559.906977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>4360.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>4282.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>4770.242424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>5335.155172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>5850.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Attrition  Education  MonthlyIncome\n",
       "0        No          1    5926.129496\n",
       "1        No          2    6586.058824\n",
       "2        No          3    6882.919662\n",
       "3        No          4    7087.814706\n",
       "4        No          5    8559.906977\n",
       "5       Yes          1    4360.161290\n",
       "6       Yes          2    4282.545455\n",
       "7       Yes          3    4770.242424\n",
       "8       Yes          4    5335.155172\n",
       "9       Yes          5    5850.200000"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_monthlyincome = df1[['Attrition', 'MonthlyIncome', 'Education']]\n",
    "df2 = df_monthlyincome.groupby(['Attrition','Education']).mean().reset_index()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average MonthlyIncome')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3Vf0gySdoujnXIYPEaoj4C4NVFwAXAMyaNct32CRJ0qg2bNdnVX0buAvYqtnubGLDGQAGquoHzfGXaBVuDzfdmTSfj7Rdv2Nb+6nAg0186iBxSZKkMa2TlQneCpwNXE/r6dZ5Sd5fVV8aql1V/SzJA0l2qaq7gYOAO5ptHnBW8/nlpsnVwBeSfAz4A1qDBm6qqtVJVibZD/gBMBc4b/1/6rrt8/6LN+TtRsTNZ8/tdQqSJKnLOun6/CDwx1X1CECSycB1tJ6QDefdwKVJNgXuAd5J6yneoiTHASuAIwGq6vYki2gVcquAE6tqdXOfE/jd9BzXNJskSdKY1kmhttGaIq3xOJ2NFqWqlgKzBjl10DquXwAsGCS+hNZcbJIkaRxaceaevU5hve304WW/9z06KdS+nuTfgYXN8VH4REuSJKnrhi3Uqur9Sd4MHEDrHbULquqqrmcmSZI0znUymGA68LWqurI53jzJtKq6r9vJSZIkjWedvGv2RX63xie01uH8YnfSkSRJ0hqdFGobV9Uzaw6a/U27l5IkSZKgs0Lt0STPLReV5HDgse6lJEmSJOhs1OfxtOZC+0dagwkeoDXprCRJkrqok1GfPwX2S7IlkKpa2f20JEmS1Mmoz82AtwDTgI2T1hrpVXVmVzOTJEka5zrp+vwy8EvgZuDp7qYjSZKkNTop1KZW1eyuZyJJkqTn6WTU5/eSjL4FtiRJkka5Tp6oHQC8I8m9tLo+A1RV7dXVzCRJksa5Tgq1v+h6FpIkSXqBdRZqSSY1u07HIUka9VacOfre4tnpw8t6nYJ6bKgnajcDRaurc20FvKIrGUmSJAkYolCrqukjmYgkSZKeb9hRn0m+lOQvk3QyQlSSJEkbSCfF16eBY4CfJDkryau6nJMkSZLooFCrquuq6hhgb+A+4Nok30vyziSbdDtBSZKk8aqj7swk2wLvAP4ncAvwCVqF27Vdy0ySJGmc6+QdtSuB7wIvAQ6tqsOq6vKqejew5TBt70uyLMnSJEua2KQk1yb5SfO5Tdv1pyVZnuTuJAe3xfdp7rM8yblZszK8JEnSGNbJE7V/rKrdqur/raqH2k9U1awO2r+hqma2XXsqsLiqZgCLm2OS7AbMAXYHZgOfSjKhaXM+MB+Y0WyuPSpJksa8oSa8ffNg+2tU1ZUv8jsPB17f7F8EXA98oIlfVlVPA/cmWQ7sm+Q+YOuqurHJ5WLgCOCaF/n9kiRJo8JQE94eOsS5Ajop1Ar4RpIC/qmqLgC2X/NkrqoeSvLy5topwPfb2g40sWeb/bXjkiRJY9pQE96+cwPc/7VV9WBTjF2b5K4hrl3XCgjrir/wBsl8Wl2k7LTTTuubqyRJUl8ZdlH2JJsBbwGmtV9fVWcO17aqHmw+H0lyFbAv8HCSHZqnaTsAjzSXDwA7tjWfCjzYxKcOEh/s+y4ALgCYNWvWoMWcJEnSaNHJYIIv03p/bBXwZNs2pCRbJNlqzT7w58BtwNXAvOayec39aeJzkmyWZDqtQQM3Nd2kK5Ps14z2nNvWRpIkacwa9okaMLWqXswoy+2Bq5qZNDYGvlBVX0/yH8CiJMcBK4AjAarq9iSLgDtoFYUnVtXq5l4nAJ8HNqc1iMCBBJIkaczrpFD7XpI9q2rZ+ty4qu4BXj1I/HHgoHW0WQAsGCS+BNhjfb5fkiRptBtqeo5ltF7a3xh4Z5J7gKdpvdxfVbXXyKQoSZI0Pg31RO2QEctCkiRJLzDU9Bz3AyS5pKqObT+X5BLg2EEbStIYtuLMPXudwnrZ6cPr9daKpD7TyajP3dsPmmWd9ulOOpIkSVpjnYVas0D6SmCvJL9qtpW05j1zegxJkqQuW2eh1izCvhVwdlVt3WxbVdW2VXXaCOYoSZI0Lg07PUdVnZZkCvCHPH9lgu90MzFJkqTxrpMlpM4C5tCaiHbNBLQFWKhJkiR1UScT3r4J2KWqnu52MpIkSfqdTkZ93gNs0u1EJEmS9HydPFH7NbA0yWJaKxMAUFUndS0rSZIkdVSoXd1skiRJGkGdjPq8KMmmwM5N6O6qera7aUmSJKmTUZ+vBy4C7qO1IPuOSeY5PYckSVJ3ddL1+VHgz6vqboAkOwMLcRmpnnK9QUmSxr5ORn1usqZIA6iqH+MoUEmSpK7r5InakiQXApc0x8cAN3cvJWlsG21PQ8EnopLUK50UaicAJwIn0XpH7TvAp7qZlCRJkjob9fk08LFmkyRJ0ghZZ6GW5NahGlbVXhs+HUmSJK0x1BO139JafP0LwFeA34xIRpIkSQKGGPVZVTOBo4EtaRVrC4Ddgf+sqvs7/YIkE5LckuTfmuNJSa5N8pPmc5u2a09LsjzJ3UkObovvk2RZc+7cJFnvXypJkjTKDDk9R1XdVVWnV9XetJ6qXQy8Zz2/42TgzrbjU4HFVTUDWNwck2Q3YA6tYnA28KkkE5o25wPzgRnNNns9c5AkSRp1hizUkkxJ8t4kNwBvp1Wknd/pzZNMBf4K+Exb+HBaKx3QfB7RFr+sqp6uqnuB5cC+SXYAtq6qG6uqaBWLRyBJkjTGDTWY4NvAVsAi4B3Az5tTmyaZVFU/X1fbNv8H+LvmPmtsX1UPAVTVQ0le3sSnAN9vu26giT3b7K8dlyRJGtOGGkzwh7QGE/wtrW7HNdLEXzHUjZMcAjxSVTc364UOZ7D3zmqI+GDfOX9NrjvttFMHXylJktS/1lmoVdW03/PerwUOS/KXwERg6yT/AjycZIfmadoOwCPN9QPAjm3tpwIPNvGpg8QHy/kC4AKAWbNmDVrMSZIkjRadrPX5olTVaVU1tSn45gDfrKq3A1cD85rL5gFfbvavBuYk2SzJdFqDBm5quklXJtmvGe05t62NJEnSmNXJElIb2lnAoiTHASuAIwGq6vYki4A7gFXAiVW1umlzAvB5YHPgmmaTJEka00akUKuq64Hrm/3HgYPWcd0CWvO1rR1fAuzRvQwlSZL6T0ddn0kOSPLOZn9y0zUpSZKkLhq2UEtyOvAB4LQmtAnwL91MSpIkSZ09UXsTcBjwJEBVPcjz50WTJElSF3RSqD3TrAhQAEm26G5KkiRJgs4KtUVJ/gl4WZL/BVwH/HN305IkSdKwoz6r6pwk/wP4FbAL8OGqurbrmUmSJI1zHU3P0RRmFmeSJEkjaNhCLclKXri25i+BJcB7q+qebiQmSZI03nXyRO1jtNbW/AKtBdLnAP8NuBv4LPD6biUnSZI0nnUymGB2Vf1TVa2sql81C5//ZVVdDmzT5fwkSZLGrU4Ktd8meWuSjZrtrW3n1u4SlSRJ0gbSSaF2DHAs8AjwcLP/9iSbA+/qYm6SJEnjWifTc9wDHLqO0zds2HQkSZK0RiejPicCxwG7AxPXxKvqb7qYlyRJ0rjXSdfnJbRGeR4MfBuYCqzsZlKSJEnqrFD7o6r6EPBkVV0E/BWwZ3fTkiRJUieF2rPN5y+S7AG8FJjWtYwkSZIEdDbh7QVJtgH+N3A1sCXwoa5mJUmSpKELtSQbAb+qqv8CvgO8YkSykiRJ0tBdn1X1W5wrTZIkqSc6eUft2iTvS7Jjkklrtq5nJkmSNM51Uqj9DXAira7Pm5ttyXCNkkxMclOSHyW5PclHmvikJNcm+UnzuU1bm9OSLE9yd5KD2+L7JFnWnDs3Sdb3h0qSJI02wxZqVTV9kK2Td9WeBg6sqlcDM4HZSfYDTgUWV9UMYHFzTJLdgDm0JtadDXwqyYTmXucD84EZzTZ7fX6kJEnSaDRsoZbkJUn+d5ILmuMZSQ4Zrl21PNEcbtJsBRwOXNTELwKOaPYPBy6rqqer6l5gObBvkh2Aravqxqoq4OK2NpIkSWNWJ12fnwOeAfZvjgeA/6eTmyeZkGQprQXdr62qHwDbV9VDAM3ny5vLpwAPtDUfaGJTmv2145IkSWNaJ4XaK6vq/6OZ+LaqfgN09I5YVa2uqpm0lp3at5kwd10Gu2cNEX/hDZL5SZYkWfLoo492kqIkSVLf6qRQeybJ5jTFUZJX0nr/rGNV9Qvgelrvlj3cdGfSfD7SXDYA7NjWbCrwYBOfOkh8sO+5oKpmVdWsyZMnr0+KkiRJfaeTQu0M4OvAjkkupTUA4O+Ga5RkcpKXNfubA28E7qK1usG85rJ5wJeb/auBOUk2SzKd1qCBm5ru0ZVJ9mtGe85tayNJkjRmDbuEVFV9I8nNwH60uiFPrqrHOrj3DsBFzcjNjYBFVfVvSW4EFiU5DlgBHNl8z+1JFgF3AKuAE6tqdXOvE4DPA5sD1zSbJEnSmDZsoZbkamAhcHVVPdnpjavqVuA1g8QfBw5aR5sFwIJB4kuAod5vkyRJGnM66fr8KPA64I4kX0zy10kmdjkvSZKkca+Trs9vA99uujAPBP4X8Flg6y7nJkmSNK4NW6jBc4MBDgWOAvbmdxPWSpIkqUs6eUftcuBPaI38/CRwfVX9ttuJSZIkjXedPFH7HPC2NSMwk7w2yduq6sTupiZJkjS+dfKO2teTzExyNK2uz3uBK7uemSRJ0ji3zkItyc7AHOBo4HHgciBV9YYRyk2SJGlcG+qJ2l3Ad4FDq2o5QJL3jEhWkiRJGnIetbcAPwO+leSfkxxEh4uxS5Ik6fe3zkKtqq6qqqOAV9FaUP09wPZJzk/y5yOUnyRJ0rg17MoEVfVkVV1aVYcAU4GlwKndTkySJGm862QJqedU1c+r6p+q6sBuJSRJkqSW9SrUJEmSNHIs1CRJkvqUhZokSVKfslCTJEnqUxZqkiRJfcpCTZIkqU9ZqEmSJPUpCzVJkqQ+ZaEmSZLUpyzUJEmS+lTXCrUkOyb5VpI7k9ye5OQmPinJtUl+0nxu09bmtCTLk9yd5OC2+D5JljXnzk2SbuUtSZLUL7r5RG0V8N6q2hXYDzgxyW60FnRfXFUzgMXNMc25OcDuwGzgU0kmNPc6H5gPzGi22V3MW5IkqS90rVCrqoeq6ofN/krgTmAKcDhwUXPZRcARzf7hwGVV9XRV3QssB/ZNsgOwdVXdWFUFXNzWRpIkacwakXfUkkwDXgP8ANi+qh6CVjEHvLy5bArwQFuzgSY2pdlfOz7Y98xPsiTJkkcffXSD/gZJkqSR1vVCLcmWwBXAKVX1q6EuHSRWQ8RfGKy6oKpmVdWsyZMnr3+ykiRJfaSrhVqSTWgVaZdW1ZVN+OGmO5Pm85EmPgDs2NZ8KvBgE586SFySJGlM6+aozwAXAndW1cfaTl0NzGv25wFfbovPSbJZkum0Bg3c1HSPrkyyX3PPuW1tJEmSxqyNu3jv1wLHAsuSLG1i/zdwFrAoyXHACuBIgKq6Pcki4A5aI0ZPrKrVTbsTgM8DmwPXNJskSdKY1rVCrapuYPD3ywAOWkebBcCCQeJLgD02XHaSJEn9z5UJJEmS+pSFmiRJUp+yUJMkSepTFmqSJEl9ykJNkiSpT1moSZIk9SkLNUmSpD5loSZJktSnLNQkSZL6lIWaJElSn7JQkyRJ6lMWapIkSX3KQk2SJKlPWahJkiT1KQs1SZKkPmWhJkmS1Kcs1CRJkvqUhZokSVKfslCTJEnqUxZqkiRJfcpCTZIkqU91rVBL8tkkjyS5rS02Kcm1SX7SfG7Tdu60JMuT3J3k4Lb4PkmWNefOTZJu5SxJktRPuvlE7fPA7LVipwKLq2oGsLg5JsluwBxg96bNp5JMaNqcD8wHZjTb2veUJEkak7pWqFXVd4CfrxU+HLio2b8IOKItfllVPV1V9wLLgX2T7ABsXVU3VlUBF7e1kSRJGtNG+h217avqIYDm8+VNfArwQNt1A01sSrO/dlySJGnM65fBBIO9d1ZDxAe/STI/yZIkSx599NENlpwkSVIvjHSh9nDTnUnz+UgTHwB2bLtuKvBgE586SHxQVXVBVc2qqlmTJ0/eoIlLkiSNtJEu1K4G5jX784Avt8XnJNksyXRagwZuarpHVybZrxntObetjSRJ0pi2cbdunGQh8HpguyQDwOnAWcCiJMcBK4AjAarq9iSLgDuAVcCJVbW6udUJtEaQbg5c02ySJEljXtcKtao6eh2nDlrH9QuABYPElwB7bMDUJEmSRoV+GUwgSZKktVioSZIk9SkLNUmSpD5loSZJktSnLNQkSZL6lIWaJElSn7JQkyRJ6lMWapIkSX3KQk2SJKlPWahJkiT1KQs1SZKkPmWhJkmS1Kcs1CRJkvqUhZokSVKfslCTJEnqUxZqkiRJfcpCTZIkqU9ZqEmSJPUpCzVJkqQ+ZaEmSZLUpyzUJEmS+tSoKdSSzE5yd5LlSU7tdT6SJEndNioKtSQTgE8CfwHsBhydZLfeZiVJktRdo6JQA/YFllfVPVX1DHAZcHiPc5IkSeqq0VKoTQEeaDseaGKSJEljVqqq1zkMK8mRwMFV9T+b42OBfavq3WtdNx+Y3xzuAtw9oomOrO2Ax3qdhF4U/3ajm3+/0cu/3eg21v9+f1hVk9cObtyLTF6EAWDHtuOpwINrX1RVFwAXjFRSvZRkSVXN6nUeWn/+7UY3/36jl3+70W28/v1GS9fnfwAzkkxPsikwB7i6xzlJkiR11ah4olZVq5K8C/h3YALw2aq6vcdpSZIkddWoKNQAquprwNd6nUcfGRddvGOUf7vRzb/f6OXfbnQbl3+/UTGYQJIkaTwaLe+oSZIkjTsWaqNIks8meSTJbb3OResvyY5JvpXkziS3Jzm51zmpM0kmJrkpyY+av91Hep2T1l+SCUluSfJvvc5F6yfJfUmWJVmaZEmv8xlJdn2OIkn+O/AEcHFV7dHrfLR+kuwA7FBVP0yyFXAzcERV3dHj1DSMJAG2qKonkmwC3ACcXFXf73FqWg9J/i9gFrB1VR3S63zUuST3AbOqaizPozYon6iNIlX1HeDnvc5DL05VPVRVP2z2VwJ34gobo0K1PNEcbtJs/it3FEkyFfgr4DO9zkVaHxZqUg8kmQa8BvhBj1NRh5pus6XAI8C1VeXfbnT5P8DfAb/tcR56cQr4RpKbm1WIxg0LNWmEJdkSuAI4pap+1et81JmqWl1VM2mtjLJvEl8/GCWSHAI8UlU39zoXvWivraq9gb8ATmxeBRoXLNSkEdS833QFcGlVXdnrfLT+quoXwPXA7N5movXwWuCw5j2ny4ADk/xLb1PS+qiqB5vPR4CrgH17m9HIsVCTRkjzQvqFwJ1V9bFe56POJZmc5GXN/ubAG4G7epqUOlZVp1XV1KqaRmsJwm9W1dt7nJY6lGSLZgAWSbYA/hwYN7MfWKiNIkkWAjcCuyQZSHJcr3PSenktcCytf80vbba/7HVS6sgOwLeS3Epr7eFrq8opHqSRsT1wQ5IfATcBX62qr/c4pxHj9BySJEl9yidqkiRJfcpCTZIkqU9ZqEmSJPUpCzVJkqQ+ZaEmSZLUpyzUJI0pSVa3TX+yNMmpg1zz+iQbdHqN5p77tx0fn2TuhvwOSePPxr1OQJI2sN80Sz2NtNcDTwDfA6iqT/cgB0ljjE/UJI0LSWYnuSvJDcCb2+JnJHlf2/FtSaY1+3OT3JrkR0kuaWKHJvlBkluSXJdk++b644H3NE/xXtd+3yQzk3y/uddVSbZp4tcn+YckNyX5cZLXjdh/EEmjgoWapLFm87W6Po9KMhH4Z+BQ4HXAfxvuJkl2Bz4IHFhVrwZObk7dAOxXVa+htW7k31XVfcCngY9X1cyq+u5at7sY+EBV7QUsA05vO7dxVe0LnLJWXJLs+pQ05ryg6zPJTODeqvpJc/wvwPxh7nMg8KWqegygqn7exKcClyfZAdgUuHeomyR5KfCyqvp2E7oI+GLbJVc2nzcD04bJSdI44xM1SePFutbLW8Xz/184sfnMOtqcB/xjVe0J/G3b9S/W083navzHs6S1WKhJGg/uAqYneWVzfHTbufuAvQGS7A1Mb+KLgbcm2bY5N6mJvxT4z2Z/Xtt9VgJbrf3FVfVL4L/a3j87Fvj22tdJ0mAs1CSNNWu/o3ZWVT1Fq6vzq81ggvvbrr8CmJRkKXAC8GOAqrodWAB8O8mPgI81158BfDHJd4HH2u7zFeBNawYTrJXTPODsJLcCM4EzN9zPlTSWpWpdvQGSJEnqJZ+oSZIk9SkLNUmSpD5loSZJktSnLNQkSZL6lIWaJElSn7JQkyRJ6lMWapIkSX3KQk2SJKlP/f/8mAs/JsuLcAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.barplot(x=\"Education\", y=\"MonthlyIncome\" ,hue=\"Attrition\", data=df2)\n",
    "plt.xlabel(\"Education\")\n",
    "plt.ylabel(\"Average MonthlyIncome\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}