FASE-1-2-EDA-Transformacion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "#pd.set_option('display.max_rows', None) \n",
    "pd.set_option('display.max_columns', None) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fase 1: Análisis Exploratorio de Datos(EDA)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comenzamos echando un vistazo a los datos que nos ha dado la empresa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>employeecount</th>\n",
       "      <th>employeenumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NUMCOMPANIESWORKED</th>\n",
       "      <th>Over18</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <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",
       "      <th>SameAsMonthlyIncome</th>\n",
       "      <th>DateBirth</th>\n",
       "      <th>Salary</th>\n",
       "      <th>RoleDepartament</th>\n",
       "      <th>NUMBERCHILDREN</th>\n",
       "      <th>RemoteWork</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>684,0$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>162,0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>resEArch DIREcToR</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19537,0</td>\n",
       "      <td>6462</td>\n",
       "      <td>7</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>13</td>\n",
       "      <td>3,0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3,0</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>19537,0</td>\n",
       "      <td>1972</td>\n",
       "      <td>1000000000$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>699,0$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>259,0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>ManAGeR</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19999,0</td>\n",
       "      <td>5678</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>3,0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>34,0</td>\n",
       "      <td>5</td>\n",
       "      <td>3,0</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>19999,0</td>\n",
       "      <td>1971</td>\n",
       "      <td>1000000000$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>532,0$</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Technical Degree</td>\n",
       "      <td>1</td>\n",
       "      <td>319,0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>ManaGER</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>19232,0</td>\n",
       "      <td>4933</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>11</td>\n",
       "      <td>3,0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>22,0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>19232,0</td>\n",
       "      <td>1981</td>\n",
       "      <td>1000000000$</td>\n",
       "      <td>ManaGER  -  Research &amp; Development</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>359,0$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>ReseArCH DIrECtOr</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>17169,0</td>\n",
       "      <td>26703</td>\n",
       "      <td>3</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>3,0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>17169,0</td>\n",
       "      <td>1976</td>\n",
       "      <td>1000000000$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1319,0$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Technical Degree</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>sAleS EXECUtIve</td>\n",
       "      <td>1</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7739</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3,0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3,0</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1977</td>\n",
       "      <td>1000000000$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Attrition BusinessTravel DailyRate                Department  \\\n",
       "0  51        No            NaN    684,0$                       NaN   \n",
       "1  52        No            NaN    699,0$                       NaN   \n",
       "2  42        No  travel_rarely    532,0$   Research & Development    \n",
       "3  47        No  travel_rarely    359,0$                       NaN   \n",
       "4  46        No            NaN   1319,0$                       NaN   \n",
       "\n",
       "   DistanceFromHome  Education    EducationField  employeecount  \\\n",
       "0                 6          3               NaN              1   \n",
       "1                 1          4     Life Sciences              1   \n",
       "2                 4          2  Technical Degree              1   \n",
       "3                 2          4           Medical              1   \n",
       "4                 3          3  Technical Degree              1   \n",
       "\n",
       "  employeenumber  EnvironmentSatisfaction  Gender HourlyRate  JobInvolvement  \\\n",
       "0          162,0                        1       0         51               3   \n",
       "1          259,0                        3       0         65               2   \n",
       "2          319,0                        3       0         58               3   \n",
       "3            NaN                        1       1         82               3   \n",
       "4            NaN                        1       1         45               4   \n",
       "\n",
       "   JobLevel              JobRole  JobSatisfaction MaritalStatus MonthlyIncome  \\\n",
       "0         5   resEArch DIREcToR                 3           NaN       19537,0   \n",
       "1         5             ManAGeR                 3           NaN       19999,0   \n",
       "2         5             ManaGER                 4       Married       19232,0   \n",
       "3         4   ReseArCH DIrECtOr                 3       Married       17169,0   \n",
       "4         4     sAleS EXECUtIve                 1      Divorced           NaN   \n",
       "\n",
       "   MonthlyRate  NUMCOMPANIESWORKED Over18 OverTime  PercentSalaryHike  \\\n",
       "0         6462                   7      Y       No                 13   \n",
       "1         5678                   0    NaN      NaN                 14   \n",
       "2         4933                   1    NaN       No                 11   \n",
       "3        26703                   3      Y      NaN                 19   \n",
       "4         7739                   2      Y       No                 12   \n",
       "\n",
       "  PerformanceRating  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0               3,0                         3           NaN                 0   \n",
       "1               3,0                         1           NaN                 1   \n",
       "2               3,0                         4           NaN                 0   \n",
       "3               3,0                         2           NaN                 2   \n",
       "4               3,0                         4           NaN                 1   \n",
       "\n",
       "  TOTALWORKINGYEARS  TrainingTimesLastYear WORKLIFEBALANCE  YearsAtCompany  \\\n",
       "0               NaN                      5             3,0              20   \n",
       "1              34,0                      5             3,0              33   \n",
       "2              22,0                      3             NaN              22   \n",
       "3               NaN                      2             NaN              20   \n",
       "4               NaN                      5             3,0              19   \n",
       "\n",
       "  YearsInCurrentRole  YearsSinceLastPromotion  YEARSWITHCURRMANAGER  \\\n",
       "0                NaN                       15                    15   \n",
       "1                NaN                       11                     9   \n",
       "2                NaN                       11                    15   \n",
       "3                NaN                        5                     6   \n",
       "4                NaN                        2                     8   \n",
       "\n",
       "  SameAsMonthlyIncome  DateBirth       Salary  \\\n",
       "0             19537,0       1972  1000000000$   \n",
       "1             19999,0       1971  1000000000$   \n",
       "2             19232,0       1981  1000000000$   \n",
       "3             17169,0       1976  1000000000$   \n",
       "4                 NaN       1977  1000000000$   \n",
       "\n",
       "                        RoleDepartament  NUMBERCHILDREN RemoteWork  \n",
       "0                                   NaN             NaN        Yes  \n",
       "1                                   NaN             NaN          1  \n",
       "2   ManaGER  -  Research & Development              NaN          1  \n",
       "3                                   NaN             NaN      False  \n",
       "4                                   NaN             NaN          0  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Files/HR RAW DATA.csv\", index_col = 0)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tenemos 1614 filas y 41 columnas\n"
     ]
    }
   ],
   "source": [
    "print(f'Tenemos {df.shape[0]} filas y {df.shape[1]} columnas')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al ver que hay algunos valores escritos de manera extraña, procedemos a ver cada valor de cada columna y la cantidad de los mismos y tener una vision más general"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores únicos en la columna 'Age':\n",
      "Age\n",
      "35              84\n",
      "34              83\n",
      "31              82\n",
      "29              78\n",
      "36              74\n",
      "32              64\n",
      "38              64\n",
      "30              63\n",
      "33              61\n",
      "40              60\n",
      "27              54\n",
      "37              53\n",
      "28              53\n",
      "42              50\n",
      "45              48\n",
      "41              46\n",
      "26              45\n",
      "39              45\n",
      "43              40\n",
      "46              38\n",
      "44              34\n",
      "50              31\n",
      "25              27\n",
      "24              26\n",
      "47              26\n",
      "49              25\n",
      "55              24\n",
      "54              20\n",
      "51              20\n",
      "53              20\n",
      "48              20\n",
      "52              18\n",
      "56              17\n",
      "21              16\n",
      "22              16\n",
      "23              15\n",
      "58              14\n",
      "20              11\n",
      "59              10\n",
      "19               9\n",
      "18               8\n",
      "60               5\n",
      "57               5\n",
      "thirty-two       2\n",
      "fifty-eight      1\n",
      "twenty-six       1\n",
      "thirty-seven     1\n",
      "thirty-one       1\n",
      "thirty           1\n",
      "fifty-two        1\n",
      "fifty-five       1\n",
      "thirty-six       1\n",
      "forty-seven      1\n",
      "twenty-four      1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'Attrition':\n",
      "Attrition\n",
      "No     1355\n",
      "Yes     259\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'BusinessTravel':\n",
      "BusinessTravel\n",
      "travel_rarely        586\n",
      "travel_frequently    165\n",
      "non-travel            91\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'DailyRate':\n",
      "DailyRate\n",
      "nan$      124\n",
      "691,0$      7\n",
      "329,0$      7\n",
      "147,0$      6\n",
      "530,0$      6\n",
      "         ... \n",
      "317,0$      1\n",
      "891,0$      1\n",
      "759,0$      1\n",
      "483,0$      1\n",
      "105,0$      1\n",
      "Name: count, Length: 849, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'Department':\n",
      "Department\n",
      "Research & Development     196\n",
      "Sales                       91\n",
      "Human Resources             15\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'DistanceFromHome':\n",
      "DistanceFromHome\n",
      " 2     217\n",
      " 1     203\n",
      " 10     86\n",
      " 9      85\n",
      " 8      81\n",
      "      ... \n",
      "-28      2\n",
      "-34      2\n",
      "-43      2\n",
      "-39      1\n",
      "-40      1\n",
      "Name: count, Length: 69, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'Education':\n",
      "Education\n",
      "3    621\n",
      "4    445\n",
      "2    314\n",
      "1    180\n",
      "5     54\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'EducationField':\n",
      "EducationField\n",
      "Life Sciences       349\n",
      "Medical             276\n",
      "Marketing           104\n",
      "Technical Degree     69\n",
      "Other                59\n",
      "Human Resources      12\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'employeecount':\n",
      "employeecount\n",
      "1    1614\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'employeenumber':\n",
      "employeenumber\n",
      "482,0     2\n",
      "530,0     2\n",
      "507,0     2\n",
      "517,0     2\n",
      "522,0     2\n",
      "         ..\n",
      "161,0     1\n",
      "164,0     1\n",
      "190,0     1\n",
      "194,0     1\n",
      "2040,0    1\n",
      "Name: count, Length: 1079, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'EnvironmentSatisfaction':\n",
      "EnvironmentSatisfaction\n",
      "4     460\n",
      "3     459\n",
      "1     298\n",
      "2     297\n",
      "12      7\n",
      "35      6\n",
      "13      6\n",
      "14      5\n",
      "24      5\n",
      "47      5\n",
      "36      4\n",
      "48      4\n",
      "41      4\n",
      "46      4\n",
      "42      4\n",
      "45      3\n",
      "11      3\n",
      "22      3\n",
      "17      3\n",
      "18      3\n",
      "20      3\n",
      "25      3\n",
      "27      3\n",
      "37      2\n",
      "29      2\n",
      "19      2\n",
      "38      2\n",
      "31      2\n",
      "15      2\n",
      "16      2\n",
      "26      1\n",
      "39      1\n",
      "10      1\n",
      "49      1\n",
      "21      1\n",
      "28      1\n",
      "33      1\n",
      "43      1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'Gender':\n",
      "Gender\n",
      "0    971\n",
      "1    643\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'HourlyRate':\n",
      "HourlyRate\n",
      "Not Available    84\n",
      "42               33\n",
      "66               32\n",
      "48               30\n",
      "57               29\n",
      "                 ..\n",
      "47               15\n",
      "53               13\n",
      "68               13\n",
      "38               12\n",
      "34               11\n",
      "Name: count, Length: 72, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'JobInvolvement':\n",
      "JobInvolvement\n",
      "3    955\n",
      "2    406\n",
      "4    164\n",
      "1     89\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'JobLevel':\n",
      "JobLevel\n",
      "2    597\n",
      "1    586\n",
      "3    242\n",
      "4    113\n",
      "5     76\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'JobRole':\n",
      "JobRole\n",
      "mANager                       4\n",
      "ManageR                       3\n",
      "ManagEr                       3\n",
      "mAnaGeR                       3\n",
      "MANAgER                       3\n",
      "                             ..\n",
      "ResEArch ScieNTiST            1\n",
      "HealthcARE RePreSENtAtiVe     1\n",
      "ReSearcH scIEntist            1\n",
      "LAbOrATOry techNicIan         1\n",
      "mAnUfactURInG DiRECTOr        1\n",
      "Name: count, Length: 1579, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'JobSatisfaction':\n",
      "JobSatisfaction\n",
      "4    514\n",
      "3    481\n",
      "1    317\n",
      "2    302\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'MaritalStatus':\n",
      "MaritalStatus\n",
      "Married     404\n",
      "Single      325\n",
      "Divorced    188\n",
      "Marreid      35\n",
      "divorced     11\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'MonthlyIncome':\n",
      "MonthlyIncome\n",
      "6347,0     4\n",
      "5304,0     4\n",
      "2657,0     3\n",
      "2258,0     3\n",
      "5405,0     2\n",
      "          ..\n",
      "3102,0     1\n",
      "4556,0     1\n",
      "4230,0     1\n",
      "4859,0     1\n",
      "19431,0    1\n",
      "Name: count, Length: 668, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'MonthlyRate':\n",
      "MonthlyRate\n",
      "9150     4\n",
      "11737    3\n",
      "17001    3\n",
      "6069     3\n",
      "21981    3\n",
      "        ..\n",
      "25470    1\n",
      "10205    1\n",
      "9973     1\n",
      "17089    1\n",
      "16642    1\n",
      "Name: count, Length: 1427, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'NUMCOMPANIESWORKED':\n",
      "NUMCOMPANIESWORKED\n",
      "1    573\n",
      "0    226\n",
      "3    169\n",
      "4    157\n",
      "2    156\n",
      "7     84\n",
      "6     73\n",
      "5     66\n",
      "9     59\n",
      "8     51\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'Over18':\n",
      "Over18\n",
      "Y    713\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'OverTime':\n",
      "OverTime\n",
      "No     682\n",
      "Yes    256\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'PercentSalaryHike':\n",
      "PercentSalaryHike\n",
      "11    232\n",
      "13    230\n",
      "12    225\n",
      "14    220\n",
      "15    110\n",
      "18     98\n",
      "17     88\n",
      "16     86\n",
      "19     82\n",
      "20     60\n",
      "22     59\n",
      "21     51\n",
      "23     29\n",
      "24     25\n",
      "25     19\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'PerformanceRating':\n",
      "PerformanceRating\n",
      "3,0    1205\n",
      "4,0     214\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'RelationshipSatisfaction':\n",
      "RelationshipSatisfaction\n",
      "3    504\n",
      "4    468\n",
      "2    339\n",
      "1    303\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'StandardHours':\n",
      "StandardHours\n",
      "80,0    419\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'StockOptionLevel':\n",
      "StockOptionLevel\n",
      "0    687\n",
      "1    666\n",
      "2    172\n",
      "3     89\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'TOTALWORKINGYEARS':\n",
      "TOTALWORKINGYEARS\n",
      "10,0    144\n",
      "8,0      86\n",
      "6,0      84\n",
      "9,0      69\n",
      "5,0      66\n",
      "7,0      56\n",
      "4,0      54\n",
      "1,0      53\n",
      "12,0     34\n",
      "3,0      32\n",
      "14,0     30\n",
      "13,0     30\n",
      "11,0     29\n",
      "15,0     28\n",
      "16,0     28\n",
      "20,0     28\n",
      "18,0     27\n",
      "21,0     23\n",
      "17,0     22\n",
      "2,0      21\n",
      "22,0     18\n",
      "19,0     17\n",
      "24,0     14\n",
      "23,0     13\n",
      "28,0     13\n",
      "26,0      8\n",
      "0,0       8\n",
      "29,0      6\n",
      "36,0      6\n",
      "25,0      6\n",
      "33,0      6\n",
      "37,0      5\n",
      "27,0      5\n",
      "31,0      4\n",
      "30,0      3\n",
      "32,0      3\n",
      "35,0      3\n",
      "40,0      3\n",
      "34,0      2\n",
      "38,0      1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'TrainingTimesLastYear':\n",
      "TrainingTimesLastYear\n",
      "2    598\n",
      "3    534\n",
      "4    137\n",
      "5    136\n",
      "1     77\n",
      "6     72\n",
      "0     60\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'WORKLIFEBALANCE':\n",
      "WORKLIFEBALANCE\n",
      "3,0    913\n",
      "2,0    359\n",
      "4,0    155\n",
      "1,0     79\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'YearsAtCompany':\n",
      "YearsAtCompany\n",
      "5     208\n",
      "1     171\n",
      "3     141\n",
      "2     141\n",
      "10    133\n",
      "7     115\n",
      "4     114\n",
      "8     106\n",
      "9      94\n",
      "6      78\n",
      "0      44\n",
      "11     36\n",
      "20     29\n",
      "13     26\n",
      "15     21\n",
      "14     19\n",
      "22     17\n",
      "12     15\n",
      "18     15\n",
      "16     14\n",
      "21     14\n",
      "19     12\n",
      "17      9\n",
      "24      7\n",
      "25      5\n",
      "33      5\n",
      "26      4\n",
      "32      3\n",
      "27      3\n",
      "31      3\n",
      "36      3\n",
      "29      2\n",
      "23      2\n",
      "40      2\n",
      "34      1\n",
      "37      1\n",
      "30      1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'YearsInCurrentRole':\n",
      "YearsInCurrentRole\n",
      "2,0     11\n",
      "7,0      5\n",
      "0,0      4\n",
      "4,0      3\n",
      "1,0      3\n",
      "11,0     2\n",
      "6,0      2\n",
      "3,0      2\n",
      "13,0     1\n",
      "12,0     1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'YearsSinceLastPromotion':\n",
      "YearsSinceLastPromotion\n",
      "0     625\n",
      "1     384\n",
      "2     177\n",
      "7      93\n",
      "4      67\n",
      "3      62\n",
      "5      53\n",
      "6      37\n",
      "11     26\n",
      "8      20\n",
      "9      18\n",
      "15     15\n",
      "12     11\n",
      "13     10\n",
      "14     10\n",
      "10      6\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'YEARSWITHCURRMANAGER':\n",
      "YEARSWITHCURRMANAGER\n",
      "2     380\n",
      "0     270\n",
      "7     267\n",
      "3     148\n",
      "8     115\n",
      "4     104\n",
      "1      84\n",
      "9      70\n",
      "5      36\n",
      "10     31\n",
      "6      30\n",
      "11     22\n",
      "12     20\n",
      "13     16\n",
      "17      8\n",
      "15      5\n",
      "14      5\n",
      "16      3\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'SameAsMonthlyIncome':\n",
      "SameAsMonthlyIncome\n",
      "6347,0     4\n",
      "5304,0     4\n",
      "2657,0     3\n",
      "2258,0     3\n",
      "5405,0     2\n",
      "          ..\n",
      "3102,0     1\n",
      "4556,0     1\n",
      "4230,0     1\n",
      "4859,0     1\n",
      "19431,0    1\n",
      "Name: count, Length: 668, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'DateBirth':\n",
      "DateBirth\n",
      "1988    84\n",
      "1992    83\n",
      "1989    83\n",
      "1994    78\n",
      "1987    75\n",
      "1991    65\n",
      "1985    64\n",
      "1993    64\n",
      "1990    61\n",
      "1983    60\n",
      "1986    55\n",
      "1996    54\n",
      "1995    53\n",
      "1981    50\n",
      "1978    48\n",
      "1982    46\n",
      "1997    46\n",
      "1984    45\n",
      "1980    40\n",
      "1977    38\n",
      "1979    34\n",
      "1973    31\n",
      "1999    27\n",
      "1976    27\n",
      "1998    27\n",
      "1968    25\n",
      "1974    25\n",
      "1969    20\n",
      "1972    20\n",
      "1970    20\n",
      "1975    20\n",
      "1971    19\n",
      "1967    17\n",
      "2001    16\n",
      "2002    16\n",
      "1965    15\n",
      "2000    15\n",
      "2003    11\n",
      "1964    10\n",
      "2004     9\n",
      "2005     8\n",
      "1963     5\n",
      "1966     5\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'Salary':\n",
      "Salary\n",
      "1000000000$    1614\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'RoleDepartament':\n",
      "RoleDepartament\n",
      "MaNAgeR  -  Sales                                        2\n",
      "ManaGER  -  Research & Development                       1\n",
      "ReseaRch scIENTisT  -  Research & Development            1\n",
      "ManufacTURInG DIRECtOR  -  Research & Development        1\n",
      "hEalthCaRe reprEseNTaTiVe  -  Research & Development     1\n",
      "                                                        ..\n",
      "saLES eXEcUTiVE  -  Sales                                1\n",
      "mANUfacTURiNG dIRectOR  -  Research & Development        1\n",
      "huMAn ResOurces  -  Human Resources                      1\n",
      "HUMAN ResoURCeS  -  Human Resources                      1\n",
      "sAleS EXECUtIvE  -  Sales                                1\n",
      "Name: count, Length: 301, dtype: int64\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'NUMBERCHILDREN':\n",
      "Series([], Name: count, dtype: int64)\n",
      "\n",
      "\n",
      "Valores únicos en la columna 'RemoteWork':\n",
      "RemoteWork\n",
      "1        360\n",
      "True     345\n",
      "0        309\n",
      "False    305\n",
      "Yes      295\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for columna in df.columns:\n",
    "    print(f\"Valores únicos en la columna '{columna}':\")\n",
    "    print(df[columna].value_counts())\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sabiendo ya los valores queremos ver los tipos que tiene para ver cómo trabajar y posteriormente cambiarlos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1614 entries, 0 to 1613\n",
      "Data columns (total 41 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Age                       1614 non-null   object \n",
      " 1   Attrition                 1614 non-null   object \n",
      " 2   BusinessTravel            842 non-null    object \n",
      " 3   DailyRate                 1614 non-null   object \n",
      " 4   Department                302 non-null    object \n",
      " 5   DistanceFromHome          1614 non-null   int64  \n",
      " 6   Education                 1614 non-null   int64  \n",
      " 7   EducationField            869 non-null    object \n",
      " 8   employeecount             1614 non-null   int64  \n",
      " 9   employeenumber            1183 non-null   object \n",
      " 10  EnvironmentSatisfaction   1614 non-null   int64  \n",
      " 11  Gender                    1614 non-null   int64  \n",
      " 12  HourlyRate                1614 non-null   object \n",
      " 13  JobInvolvement            1614 non-null   int64  \n",
      " 14  JobLevel                  1614 non-null   int64  \n",
      " 15  JobRole                   1614 non-null   object \n",
      " 16  JobSatisfaction           1614 non-null   int64  \n",
      " 17  MaritalStatus             963 non-null    object \n",
      " 18  MonthlyIncome             771 non-null    object \n",
      " 19  MonthlyRate               1614 non-null   int64  \n",
      " 20  NUMCOMPANIESWORKED        1614 non-null   int64  \n",
      " 21  Over18                    713 non-null    object \n",
      " 22  OverTime                  938 non-null    object \n",
      " 23  PercentSalaryHike         1614 non-null   int64  \n",
      " 24  PerformanceRating         1419 non-null   object \n",
      " 25  RelationshipSatisfaction  1614 non-null   int64  \n",
      " 26  StandardHours             419 non-null    object \n",
      " 27  StockOptionLevel          1614 non-null   int64  \n",
      " 28  TOTALWORKINGYEARS         1088 non-null   object \n",
      " 29  TrainingTimesLastYear     1614 non-null   int64  \n",
      " 30  WORKLIFEBALANCE           1506 non-null   object \n",
      " 31  YearsAtCompany            1614 non-null   int64  \n",
      " 32  YearsInCurrentRole        34 non-null     object \n",
      " 33  YearsSinceLastPromotion   1614 non-null   int64  \n",
      " 34  YEARSWITHCURRMANAGER      1614 non-null   int64  \n",
      " 35  SameAsMonthlyIncome       771 non-null    object \n",
      " 36  DateBirth                 1614 non-null   int64  \n",
      " 37  Salary                    1614 non-null   object \n",
      " 38  RoleDepartament           302 non-null    object \n",
      " 39  NUMBERCHILDREN            0 non-null      float64\n",
      " 40  RemoteWork                1614 non-null   object \n",
      "dtypes: float64(1), int64(18), object(22)\n",
      "memory usage: 529.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a mirar qué porcentaje hay de nulos en cada columna y lo guardamos en una variable ya que vamos a utilizar la formula para ir comprobando en la limpieza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BusinessTravel          47.83\n",
      "Department              81.29\n",
      "EducationField          46.16\n",
      "employeenumber          26.70\n",
      "MaritalStatus           40.33\n",
      "MonthlyIncome           52.23\n",
      "Over18                  55.82\n",
      "OverTime                41.88\n",
      "PerformanceRating       12.08\n",
      "StandardHours           74.04\n",
      "TOTALWORKINGYEARS       32.59\n",
      "WORKLIFEBALANCE          6.69\n",
      "YearsInCurrentRole      97.89\n",
      "SameAsMonthlyIncome     52.23\n",
      "RoleDepartament         81.29\n",
      "NUMBERCHILDREN         100.00\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "porcentaje_nulos = (df.isnull().sum()[df.isnull().sum() > 0]/df.shape[0] * 100).round(2)\n",
    "print(porcentaje_nulos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A priori parece que no hay duplicados pero esto se peude deber a la necesidad de homogeneizar el formato de los valores de las columnas por lo que después o durante la limpieza se volverá a contemplar los duplicados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fase 2: Transformación de los datos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las columnas estan ordenadas por orden alfabético, sin embargo debería ir en la primera columna lo que creemos que corresponde a su ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_to_move = df['employeenumber'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns='employeenumber', inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.insert(0, \"employeenumber\", column_to_move ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\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>employeenumber</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>employeecount</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NUMCOMPANIESWORKED</th>\n",
       "      <th>Over18</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <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",
       "      <th>SameAsMonthlyIncome</th>\n",
       "      <th>DateBirth</th>\n",
       "      <th>Salary</th>\n",
       "      <th>RoleDepartament</th>\n",
       "      <th>NUMBERCHILDREN</th>\n",
       "      <th>RemoteWork</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162,0</td>\n",
       "      <td>51</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>684,0$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>resEArch DIREcToR</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19537,0</td>\n",
       "      <td>6462</td>\n",
       "      <td>7</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>13</td>\n",
       "      <td>3,0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3,0</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>19537,0</td>\n",
       "      <td>1972</td>\n",
       "      <td>1000000000$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>259,0</td>\n",
       "      <td>52</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>699,0$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>ManAGeR</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19999,0</td>\n",
       "      <td>5678</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>3,0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>34,0</td>\n",
       "      <td>5</td>\n",
       "      <td>3,0</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>19999,0</td>\n",
       "      <td>1971</td>\n",
       "      <td>1000000000$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>319,0</td>\n",
       "      <td>42</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>532,0$</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Technical Degree</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>ManaGER</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>19232,0</td>\n",
       "      <td>4933</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>11</td>\n",
       "      <td>3,0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>22,0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>19232,0</td>\n",
       "      <td>1981</td>\n",
       "      <td>1000000000$</td>\n",
       "      <td>ManaGER  -  Research &amp; Development</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>359,0$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>ReseArCH DIrECtOr</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>17169,0</td>\n",
       "      <td>26703</td>\n",
       "      <td>3</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>3,0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>17169,0</td>\n",
       "      <td>1976</td>\n",
       "      <td>1000000000$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1319,0$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Technical Degree</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>sAleS EXECUtIve</td>\n",
       "      <td>1</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7739</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3,0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3,0</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1977</td>\n",
       "      <td>1000000000$</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employeenumber Age Attrition BusinessTravel DailyRate  \\\n",
       "0          162,0  51        No            NaN    684,0$   \n",
       "1          259,0  52        No            NaN    699,0$   \n",
       "2          319,0  42        No  travel_rarely    532,0$   \n",
       "3            NaN  47        No  travel_rarely    359,0$   \n",
       "4            NaN  46        No            NaN   1319,0$   \n",
       "\n",
       "                 Department  DistanceFromHome  Education    EducationField  \\\n",
       "0                       NaN                 6          3               NaN   \n",
       "1                       NaN                 1          4     Life Sciences   \n",
       "2   Research & Development                  4          2  Technical Degree   \n",
       "3                       NaN                 2          4           Medical   \n",
       "4                       NaN                 3          3  Technical Degree   \n",
       "\n",
       "   employeecount  EnvironmentSatisfaction  Gender HourlyRate  JobInvolvement  \\\n",
       "0              1                        1       0         51               3   \n",
       "1              1                        3       0         65               2   \n",
       "2              1                        3       0         58               3   \n",
       "3              1                        1       1         82               3   \n",
       "4              1                        1       1         45               4   \n",
       "\n",
       "   JobLevel              JobRole  JobSatisfaction MaritalStatus MonthlyIncome  \\\n",
       "0         5   resEArch DIREcToR                 3           NaN       19537,0   \n",
       "1         5             ManAGeR                 3           NaN       19999,0   \n",
       "2         5             ManaGER                 4       Married       19232,0   \n",
       "3         4   ReseArCH DIrECtOr                 3       Married       17169,0   \n",
       "4         4     sAleS EXECUtIve                 1      Divorced           NaN   \n",
       "\n",
       "   MonthlyRate  NUMCOMPANIESWORKED Over18 OverTime  PercentSalaryHike  \\\n",
       "0         6462                   7      Y       No                 13   \n",
       "1         5678                   0    NaN      NaN                 14   \n",
       "2         4933                   1    NaN       No                 11   \n",
       "3        26703                   3      Y      NaN                 19   \n",
       "4         7739                   2      Y       No                 12   \n",
       "\n",
       "  PerformanceRating  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0               3,0                         3           NaN                 0   \n",
       "1               3,0                         1           NaN                 1   \n",
       "2               3,0                         4           NaN                 0   \n",
       "3               3,0                         2           NaN                 2   \n",
       "4               3,0                         4           NaN                 1   \n",
       "\n",
       "  TOTALWORKINGYEARS  TrainingTimesLastYear WORKLIFEBALANCE  YearsAtCompany  \\\n",
       "0               NaN                      5             3,0              20   \n",
       "1              34,0                      5             3,0              33   \n",
       "2              22,0                      3             NaN              22   \n",
       "3               NaN                      2             NaN              20   \n",
       "4               NaN                      5             3,0              19   \n",
       "\n",
       "  YearsInCurrentRole  YearsSinceLastPromotion  YEARSWITHCURRMANAGER  \\\n",
       "0                NaN                       15                    15   \n",
       "1                NaN                       11                     9   \n",
       "2                NaN                       11                    15   \n",
       "3                NaN                        5                     6   \n",
       "4                NaN                        2                     8   \n",
       "\n",
       "  SameAsMonthlyIncome  DateBirth       Salary  \\\n",
       "0             19537,0       1972  1000000000$   \n",
       "1             19999,0       1971  1000000000$   \n",
       "2             19232,0       1981  1000000000$   \n",
       "3             17169,0       1976  1000000000$   \n",
       "4                 NaN       1977  1000000000$   \n",
       "\n",
       "                        RoleDepartament  NUMBERCHILDREN RemoteWork  \n",
       "0                                   NaN             NaN        Yes  \n",
       "1                                   NaN             NaN          1  \n",
       "2   ManaGER  -  Research & Development              NaN          1  \n",
       "3                                   NaN             NaN      False  \n",
       "4                                   NaN             NaN          0  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a renombras el nombre de las columnas EmployeeNumber, EmployeeCount, YearsWithCurrManager,YearsWithCurrManager, WorkLifeBalance y TotalWorkingYears para que esten como en el documento original que nos ha dado el cliente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={\"employeenumber\" : \"EmployeeNumber\", \n",
    "                   \"employeecount\" : \"EmployeeCount\", \n",
    "                   \"YEARSWITHCURRMANAGER\" : \"YearsWithCurrManager\",\n",
    "                   \"NUMCOMPANIESWORKED\" : \"NumCompaniesWorked\",\n",
    "                   \"WORKLIFEBALANCE\" : \"WorkLifeBalance\", \n",
    "                   \"TOTALWORKINGYEARS\" : \"TotalWorkingYears\" },inplace = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora procedemos a homogeneizar los nombres de la columna JobRole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"JobRole\"] = df[\"JobRole\"].str.title() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             Research Director \n",
       "1                       Manager \n",
       "2                       Manager \n",
       "3             Research Director \n",
       "4               Sales Executive \n",
       "                  ...           \n",
       "1609            Sales Executive \n",
       "1610      Laboratory Technician \n",
       "1611            Sales Executive \n",
       "1612            Sales Executive \n",
       "1613     Manufacturing Director \n",
       "Name: JobRole, Length: 1614, dtype: object"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"JobRole\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JobRole\n",
       "Sales Executive               369\n",
       "Research Scientist            314\n",
       "Laboratory Technician         278\n",
       "Manufacturing Director        158\n",
       "Healthcare Representative     149\n",
       "Manager                       111\n",
       "Sales Representative           90\n",
       "Research Director              88\n",
       "Human Resources                57\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"JobRole\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo mismo hacemos con la columna \"MaritualStatus\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"MaritalStatus\"] = df[\"MaritalStatus\"].str.title() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MaritalStatus\n",
       "Married     404\n",
       "Single      325\n",
       "Divorced    199\n",
       "Marreid      35\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"MaritalStatus\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "for item in df['MaritalStatus']:\n",
    "        df['MaritalStatus'].replace('Marreid', 'Married', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MaritalStatus\n",
       "Married     439\n",
       "Single      325\n",
       "Divorced    199\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"MaritalStatus\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se van a cambiar los tipos de ciertas columnas para poder operar de manera correcta con los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#En el caso de DailyRate no sólo hay que eliminar el simbolo del dolar y cambiar la coma por un punto\n",
    "df[\"DailyRate\"] = df[\"DailyRate\"].str.replace('$', '', regex=False)\n",
    "df[\"DailyRate\"] = df[\"DailyRate\"].str.replace(',', '.', regex=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"DailyRate\"] = df[\"DailyRate\"].astype(float) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Aqui todos los valores son número menos \"Not Available\". Entenemodes que es un nulo \n",
    "df[\"HourlyRate\"] = df[\"HourlyRate\"].replace('Not Available', np.nan, regex=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"HourlyRate\"] = df[\"HourlyRate\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"MonthlyIncome\"] = df[\"MonthlyIncome\"].str.replace(',', '.', regex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"MonthlyIncome\"] = df[\"MonthlyIncome\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"EnvironmentSatisfaction\"] = df[\"EnvironmentSatisfaction\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"MonthlyRate\"] = df[\"MonthlyRate\"].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ponemos la columna 'Gender' con valores más intuitivos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Gender\"].head()\n",
    "for item in df['Gender']:\n",
    "        df['Gender'].replace(0, 'Male', inplace=True)\n",
    "        df['Gender'].replace(1, 'Female', inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se ha decidido borrar varias columnas devido a diversas razones. A continuación se ha enumeran las columnas que se van a borrar y las razones del borrado:\n",
    "- 'Age' : Esta información también la podemos obtener en la columna que esta el año de nacimiento. Entre ambas opciones se ha decidido borrar esta ya que el año de nacimiento nos puede proporcionar con e paso del tiempo la edad real de la personas y la columna edad se quedaría obsoleta al pasar el tiempo. De hecho esta misma base de datos es de 2023 y ya estarían desactualizadas a fecha actual.\n",
    "- 'Department' : Con la gran cantidad de nulos que contiene esta columna (81,3% de los datos) nos es imposible trabajar con dicha columna.\n",
    "- 'RoleDepartament' por lo mismo que la anterior : Con la gran cantidad de nulos que contiene esta columna (81,3% de los datos) nos es imposible trabajar con dicha columna.\n",
    "- 'EmployeeCount' : Esta columna nos ha parecido innecesaria debido a que lo unico que hace es contar uno por uno los trabajadores, cosas que podemos hacer sin necesidad de ella.\n",
    "- 'Over18' : Al igual que la columna 'Age' esta información la podemos optener de manera más actualizada con la columna de año de nacimiento.\n",
    "- 'StandardHours' : Se ha decidido borrar devido a que estan mal los datos que aprecen, ya que es imposible que todo el mundo trabaje 80 horas semanales, doblando la jornada laboral usual.\n",
    "- 'YearsInCurrentRole' : Esta columna tiene demasiados nulos (97,9%) por lo que se hace imposible trabajar con ella y, además, podemos obtener informacion similar  aporatada por la columna 'YearsInCurrentRole'.\n",
    "- 'SameAsMonthlyIncome' : Esta columna esta repetida con 'MonthlyIncome'\n",
    "- 'Salary' : A pesar de que esta columna a priori es de las más importantes, la información de la misma esta mal debido a que aparece que todo el mundo cobra 1000000000$. Esto además de irreal, se puede ver en otras columna que nos pueden hacer de indicadores salariales que la gente cobra diferente y las utilizaremos para ver el salario \n",
    "- \"NumberChildren\": Esta columna tiene un 100% de nulos por lo que no podemos trabajar con ella "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Age','Department','RoleDepartament','EmployeeCount','Over18','StandardHours','YearsInCurrentRole','SameAsMonthlyIncome','Salary','NUMBERCHILDREN'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['EmployeeNumber', 'Attrition', 'BusinessTravel', 'DailyRate',\n",
       "       'DistanceFromHome', 'Education', 'EducationField',\n",
       "       'EnvironmentSatisfaction', 'Gender', 'HourlyRate', 'JobInvolvement',\n",
       "       'JobLevel', 'JobRole', 'JobSatisfaction', 'MaritalStatus',\n",
       "       'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'OverTime',\n",
       "       'PercentSalaryHike', 'PerformanceRating', 'RelationshipSatisfaction',\n",
       "       'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear',\n",
       "       'WorkLifeBalance', 'YearsAtCompany', 'YearsSinceLastPromotion',\n",
       "       'YearsWithCurrManager', 'DateBirth', 'RemoteWork'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sabemos que hay un EmployeeNumber pero tiene cierta cantidad de nulos. Como necesitamos tener una ID de los/as trabajadores/as se le va a asignar una nueva creando una columna que pueda completar la de los nulos con una ID nueva a todo el mundo. Además, dentro de los nulos puede haber filas duplicadas por lo que también se eliminan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Primero filtramos y almacenamos en un dataframe\n",
    "df_nulos = df[df['EmployeeNumber'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nulos.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Esto lo que hace es almacenar en un DF las filas que no contienen un nulo en la columna EmployeeNumber:\n",
    "df_no_nulos = df[df['EmployeeNumber'].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Juntamos los DF, por una parte el filtrado con los duplicados de nulos eliminados y por otro lado, \n",
    "# el DF que contiene todos los que no tiene nulo en su EmployeeNumber:\n",
    "df = pd.concat([df_no_nulos, df_nulos], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora tenemos un Df con 1.605 filas donde nuestros nulos en EmployeeNumber no están duplicados. El siguiente paso, es asignar a estos nulos de EmployeeNumber su correspondiente identificación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>DateBirth</th>\n",
       "      <th>RemoteWork</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>162,0</td>\n",
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       "      <td>6</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>Research Director</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19537.0</td>\n",
       "      <td>6462.0</td>\n",
       "      <td>7</td>\n",
       "      <td>No</td>\n",
       "      <td>13</td>\n",
       "      <td>3,0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3,0</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1972</td>\n",
       "      <td>Yes</td>\n",
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       "      <th>1</th>\n",
       "      <td>259,0</td>\n",
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       "      <td>699.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Manager</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19999.0</td>\n",
       "      <td>5678.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>3,0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>34,0</td>\n",
       "      <td>5</td>\n",
       "      <td>3,0</td>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>1971</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  EmployeeNumber Attrition BusinessTravel  DailyRate  DistanceFromHome  \\\n",
       "0          162,0        No            NaN      684.0                 6   \n",
       "1          259,0        No            NaN      699.0                 1   \n",
       "\n",
       "   Education EducationField  EnvironmentSatisfaction Gender  HourlyRate  \\\n",
       "0          3            NaN                        1   Male        51.0   \n",
       "1          4  Life Sciences                        3   Male        65.0   \n",
       "\n",
       "   JobInvolvement  JobLevel              JobRole  JobSatisfaction  \\\n",
       "0               3         5   Research Director                 3   \n",
       "1               2         5             Manager                 3   \n",
       "\n",
       "  MaritalStatus  MonthlyIncome  MonthlyRate  NumCompaniesWorked OverTime  \\\n",
       "0           NaN        19537.0       6462.0                   7       No   \n",
       "1           NaN        19999.0       5678.0                   0      NaN   \n",
       "\n",
       "   PercentSalaryHike PerformanceRating  RelationshipSatisfaction  \\\n",
       "0                 13               3,0                         3   \n",
       "1                 14               3,0                         1   \n",
       "\n",
       "   StockOptionLevel TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  \\\n",
       "0                 0               NaN                      5             3,0   \n",
       "1                 1              34,0                      5             3,0   \n",
       "\n",
       "   YearsAtCompany  YearsSinceLastPromotion  YearsWithCurrManager  DateBirth  \\\n",
       "0              20                       15                    15       1972   \n",
       "1              33                       11                     9       1971   \n",
       "\n",
       "  RemoteWork  \n",
       "0        Yes  \n",
       "1          1  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación vamos a eliminar los que son totalmente duplicados del DF, antes de pasar a valorar los duplicados del ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df.duplicated()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Manager</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>1435.0</td>\n",
       "      <td>25</td>\n",
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       "      <td>2</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3698.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>4,0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8,0</td>\n",
       "      <td>2</td>\n",
       "      <td>3,0</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1601</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>2366.0</td>\n",
       "      <td>20898.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>3,0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8,0</td>\n",
       "      <td>2</td>\n",
       "      <td>3,0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1997</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1602</th>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>616.0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>41.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17369.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>3,0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10,0</td>\n",
       "      <td>2</td>\n",
       "      <td>1,0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1603</th>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>903.0</td>\n",
       "      <td>-13</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>Male</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2560.0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>18</td>\n",
       "      <td>3,0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9,0</td>\n",
       "      <td>3</td>\n",
       "      <td>3,0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1984</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1604</th>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>566.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>75.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10845.0</td>\n",
       "      <td>24208.0</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>3,0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3,0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1977</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1593 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     EmployeeNumber Attrition BusinessTravel  DailyRate  DistanceFromHome  \\\n",
       "0             162,0        No            NaN      684.0                 6   \n",
       "1             259,0        No            NaN      699.0                 1   \n",
       "2             319,0        No  travel_rarely      532.0                 4   \n",
       "3            1900,0        No            NaN      117.0                22   \n",
       "4              81,0        No            NaN     1435.0                25   \n",
       "...             ...       ...            ...        ...               ...   \n",
       "1600            NaN        No            NaN      760.0               -43   \n",
       "1601            NaN       Yes            NaN        NaN                 5   \n",
       "1602            NaN        No            NaN      616.0                12   \n",
       "1603            NaN        No  travel_rarely      903.0               -13   \n",
       "1604            NaN        No            NaN      566.0                 7   \n",
       "\n",
       "      Education    EducationField  EnvironmentSatisfaction  Gender  \\\n",
       "0             3               NaN                        1    Male   \n",
       "1             4     Life Sciences                        3    Male   \n",
       "2             2  Technical Degree                        3    Male   \n",
       "3             3           Medical                        4  Female   \n",
       "4             3     Life Sciences                        1  Female   \n",
       "...         ...               ...                      ...     ...   \n",
       "1600          4               NaN                       43  Female   \n",
       "1601          2           Medical                        3  Female   \n",
       "1602          3           Medical                        4  Female   \n",
       "1603          5               NaN                       13    Male   \n",
       "1604          2           Medical                        4    Male   \n",
       "\n",
       "      HourlyRate  JobInvolvement  JobLevel                      JobRole  \\\n",
       "0           51.0               3         5           Research Director    \n",
       "1           65.0               2         5                     Manager    \n",
       "2           58.0               3         5                     Manager    \n",
       "3           58.0               3         4                     Manager    \n",
       "4           99.0               3         3             Sales Executive    \n",
       "...          ...             ...       ...                          ...   \n",
       "1600        81.0               3         2             Sales Executive    \n",
       "1601        88.0               2         1          Research Scientist    \n",
       "1602        41.0               3         2   Healthcare Representative    \n",
       "1603        41.0               4         3             Sales Executive    \n",
       "1604        75.0               3         3      Manufacturing Director    \n",
       "\n",
       "      JobSatisfaction MaritalStatus  MonthlyIncome  MonthlyRate  \\\n",
       "0                   3           NaN        19537.0       6462.0   \n",
       "1                   3           NaN        19999.0       5678.0   \n",
       "2                   4       Married        19232.0       4933.0   \n",
       "3                   4           NaN        17174.0       2437.0   \n",
       "4                   1           NaN            NaN       2354.0   \n",
       "...               ...           ...            ...          ...   \n",
       "1600                2           NaN            NaN       3698.0   \n",
       "1601                3       Married         2366.0      20898.0   \n",
       "1602                4           NaN            NaN      17369.0   \n",
       "1603                3        Single            NaN       2560.0   \n",
       "1604                3           NaN        10845.0      24208.0   \n",
       "\n",
       "      NumCompaniesWorked OverTime  PercentSalaryHike PerformanceRating  \\\n",
       "0                      7       No                 13               3,0   \n",
       "1                      0      NaN                 14               3,0   \n",
       "2                      1       No                 11               3,0   \n",
       "3                      3       No                 11               3,0   \n",
       "4                      7      NaN                 11               3,0   \n",
       "...                  ...      ...                ...               ...   \n",
       "1600                   1      NaN                 20               4,0   \n",
       "1601                   1      NaN                 14               3,0   \n",
       "1602                   0      NaN                 11               3,0   \n",
       "1603                   0       No                 18               3,0   \n",
       "1604                   6      NaN                 13               3,0   \n",
       "\n",
       "      RelationshipSatisfaction  StockOptionLevel TotalWorkingYears  \\\n",
       "0                            3                 0               NaN   \n",
       "1                            1                 1              34,0   \n",
       "2                            4                 0              22,0   \n",
       "3                            2                 1               NaN   \n",
       "4                            4                 0              28,0   \n",
       "...                        ...               ...               ...   \n",
       "1600                         1                 0               8,0   \n",
       "1601                         1                 1               8,0   \n",
       "1602                         3                 2              10,0   \n",
       "1603                         4                 0               9,0   \n",
       "1604                         2                 1               NaN   \n",
       "\n",
       "      TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                         5             3,0              20   \n",
       "1                         5             3,0              33   \n",
       "2                         3             NaN              22   \n",
       "3                         3             3,0              22   \n",
       "4                         3             2,0              21   \n",
       "...                     ...             ...             ...   \n",
       "1600                      2             3,0               8   \n",
       "1601                      2             3,0               8   \n",
       "1602                      2             1,0               9   \n",
       "1603                      3             3,0               8   \n",
       "1604                      3             3,0               8   \n",
       "\n",
       "      YearsSinceLastPromotion  YearsWithCurrManager  DateBirth RemoteWork  \n",
       "0                          15                    15       1972        Yes  \n",
       "1                          11                     9       1971          1  \n",
       "2                          11                    15       1981          1  \n",
       "3                           4                     7       1975        Yes  \n",
       "4                           7                     9       1964       True  \n",
       "...                       ...                   ...        ...        ...  \n",
       "1600                        7                     5       1995          0  \n",
       "1601                        1                     7       1997       True  \n",
       "1602                        8                     5       1992       True  \n",
       "1603                        0                     7       1984        Yes  \n",
       "1604                        0                     7       1977          0  \n",
       "\n",
       "[1593 rows x 31 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Teníamos 1.605 filas, y pasamos a 1.593 filas sin datos totalmente duplicados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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",
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       "        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>EmployeeNumber</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>DateBirth</th>\n",
       "      <th>RemoteWork</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>1541,0</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Technical Degree</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2308.0</td>\n",
       "      <td>4944.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>25</td>\n",
       "      <td>4,0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12,0</td>\n",
       "      <td>4</td>\n",
       "      <td>3,0</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>1947,0</td>\n",
       "      <td>No</td>\n",
       "      <td>non-travel</td>\n",
       "      <td>1103.0</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>2144.0</td>\n",
       "      <td>2122.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>14</td>\n",
       "      <td>3,0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5,0</td>\n",
       "      <td>3</td>\n",
       "      <td>2,0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1995</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>1954,0</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>136.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>89.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>1</td>\n",
       "      <td>Married</td>\n",
       "      <td>5373.0</td>\n",
       "      <td>6225.0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3,0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6,0</td>\n",
       "      <td>5</td>\n",
       "      <td>2,0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>307,0</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>3</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9647.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>3,0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>2,0</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>1985</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>374,0</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>452.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>Manager</td>\n",
       "      <td>1</td>\n",
       "      <td>Single</td>\n",
       "      <td>19045.0</td>\n",
       "      <td>18938.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>3,0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>37,0</td>\n",
       "      <td>2</td>\n",
       "      <td>3,0</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>1968</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1600</th>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>760.0</td>\n",
       "      <td>-43</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43</td>\n",
       "      <td>Female</td>\n",
       "      <td>81.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3698.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>4,0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8,0</td>\n",
       "      <td>2</td>\n",
       "      <td>3,0</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1601</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>2366.0</td>\n",
       "      <td>20898.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>3,0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8,0</td>\n",
       "      <td>2</td>\n",
       "      <td>3,0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1997</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1602</th>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>616.0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>41.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17369.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>3,0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10,0</td>\n",
       "      <td>2</td>\n",
       "      <td>1,0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1603</th>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>903.0</td>\n",
       "      <td>-13</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>Male</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2560.0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>18</td>\n",
       "      <td>3,0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9,0</td>\n",
       "      <td>3</td>\n",
       "      <td>3,0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1984</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1604</th>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>566.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>75.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10845.0</td>\n",
       "      <td>24208.0</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>3,0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3,0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1977</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>525 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     EmployeeNumber Attrition BusinessTravel  DailyRate  DistanceFromHome  \\\n",
       "1079         1541,0        No  travel_rarely     1440.0                 7   \n",
       "1080         1947,0        No     non-travel     1103.0                16   \n",
       "1081         1954,0        No            NaN      136.0                 1   \n",
       "1082          307,0        No            NaN      119.0                 3   \n",
       "1083          374,0        No  travel_rarely      452.0                 1   \n",
       "...             ...       ...            ...        ...               ...   \n",
       "1600            NaN        No            NaN      760.0               -43   \n",
       "1601            NaN       Yes            NaN        NaN                 5   \n",
       "1602            NaN        No            NaN      616.0                12   \n",
       "1603            NaN        No  travel_rarely      903.0               -13   \n",
       "1604            NaN        No            NaN      566.0                 7   \n",
       "\n",
       "      Education    EducationField  EnvironmentSatisfaction  Gender  \\\n",
       "1079          2  Technical Degree                        2    Male   \n",
       "1080          3           Medical                        3    Male   \n",
       "1081          3     Life Sciences                        1    Male   \n",
       "1082          3     Life Sciences                        1    Male   \n",
       "1083          3           Medical                        4    Male   \n",
       "...         ...               ...                      ...     ...   \n",
       "1600          4               NaN                       43  Female   \n",
       "1601          2           Medical                        3  Female   \n",
       "1602          3           Medical                        4  Female   \n",
       "1603          5               NaN                       13    Male   \n",
       "1604          2           Medical                        4    Male   \n",
       "\n",
       "      HourlyRate  JobInvolvement  JobLevel                      JobRole  \\\n",
       "1079        55.0               3         1        Sales Representative    \n",
       "1080        49.0               3         1          Research Scientist    \n",
       "1081        89.0               3         2   Healthcare Representative    \n",
       "1082        76.0               3         3             Sales Executive    \n",
       "1083        81.0               3         5                     Manager    \n",
       "...          ...             ...       ...                          ...   \n",
       "1600        81.0               3         2             Sales Executive    \n",
       "1601        88.0               2         1          Research Scientist    \n",
       "1602        41.0               3         2   Healthcare Representative    \n",
       "1603        41.0               4         3             Sales Executive    \n",
       "1604        75.0               3         3      Manufacturing Director    \n",
       "\n",
       "      JobSatisfaction MaritalStatus  MonthlyIncome  MonthlyRate  \\\n",
       "1079                3           NaN         2308.0       4944.0   \n",
       "1080                3        Single         2144.0       2122.0   \n",
       "1081                1       Married         5373.0       6225.0   \n",
       "1082                3      Divorced            NaN       9647.0   \n",
       "1083                1        Single        19045.0      18938.0   \n",
       "...               ...           ...            ...          ...   \n",
       "1600                2           NaN            NaN       3698.0   \n",
       "1601                3       Married         2366.0      20898.0   \n",
       "1602                4           NaN            NaN      17369.0   \n",
       "1603                3        Single            NaN       2560.0   \n",
       "1604                3           NaN        10845.0      24208.0   \n",
       "\n",
       "      NumCompaniesWorked OverTime  PercentSalaryHike PerformanceRating  \\\n",
       "1079                   0      Yes                 25               4,0   \n",
       "1080                   1       No                 14               3,0   \n",
       "1081                   0       No                 12               3,0   \n",
       "1082                   0      NaN                 12               3,0   \n",
       "1083                   0      NaN                 14               3,0   \n",
       "...                  ...      ...                ...               ...   \n",
       "1600                   1      NaN                 20               4,0   \n",
       "1601                   1      NaN                 14               3,0   \n",
       "1602                   0      NaN                 11               3,0   \n",
       "1603                   0       No                 18               3,0   \n",
       "1604                   6      NaN                 13               3,0   \n",
       "\n",
       "      RelationshipSatisfaction  StockOptionLevel TotalWorkingYears  \\\n",
       "1079                         2                 1              12,0   \n",
       "1080                         3                 0               5,0   \n",
       "1081                         1                 1               6,0   \n",
       "1082                         3                 2               NaN   \n",
       "1083                         3                 0              37,0   \n",
       "...                        ...               ...               ...   \n",
       "1600                         1                 0               8,0   \n",
       "1601                         1                 1               8,0   \n",
       "1602                         3                 2              10,0   \n",
       "1603                         4                 0               9,0   \n",
       "1604                         2                 1               NaN   \n",
       "\n",
       "      TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "1079                      4             3,0              11   \n",
       "1080                      3             2,0               5   \n",
       "1081                      5             2,0               5   \n",
       "1082                      6             2,0              16   \n",
       "1083                      2             3,0              36   \n",
       "...                     ...             ...             ...   \n",
       "1600                      2             3,0               8   \n",
       "1601                      2             3,0               8   \n",
       "1602                      2             1,0               9   \n",
       "1603                      3             3,0               8   \n",
       "1604                      3             3,0               8   \n",
       "\n",
       "      YearsSinceLastPromotion  YearsWithCurrManager  DateBirth RemoteWork  \n",
       "1079                        5                     7       1989          1  \n",
       "1080                        1                     4       1995      False  \n",
       "1081                        0                     2       1994          0  \n",
       "1082                        5                    13       1985        Yes  \n",
       "1083                        4                    13       1968        Yes  \n",
       "...                       ...                   ...        ...        ...  \n",
       "1600                        7                     5       1995          0  \n",
       "1601                        1                     7       1997       True  \n",
       "1602                        8                     5       1992       True  \n",
       "1603                        0                     7       1984        Yes  \n",
       "1604                        0                     7       1977          0  \n",
       "\n",
       "[525 rows x 31 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['EmployeeNumber'].duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     EmployeeNumber Attrition     BusinessTravel  DailyRate  DistanceFromHome  \\\n",
      "1079         1541,0        No      travel_rarely     1440.0                 7   \n",
      "1080         1947,0        No         non-travel     1103.0                16   \n",
      "1081         1954,0        No                NaN      136.0                 1   \n",
      "1082          307,0        No                NaN      119.0                 3   \n",
      "1083          374,0        No      travel_rarely      452.0                 1   \n",
      "...             ...       ...                ...        ...               ...   \n",
      "1178          956,0        No  travel_frequently     1283.0                 1   \n",
      "1179          966,0        No                NaN      498.0                 3   \n",
      "1180          967,0       Yes      travel_rarely      530.0                 3   \n",
      "1181          972,0        No         non-travel      805.0                 4   \n",
      "1182          990,0        No         non-travel     1229.0                 8   \n",
      "\n",
      "      Education    EducationField  EnvironmentSatisfaction  Gender  \\\n",
      "1079          2  Technical Degree                        2    Male   \n",
      "1080          3           Medical                        3    Male   \n",
      "1081          3     Life Sciences                        1    Male   \n",
      "1082          3     Life Sciences                        1    Male   \n",
      "1083          3           Medical                        4    Male   \n",
      "...         ...               ...                      ...     ...   \n",
      "1178          3           Medical                        3    Male   \n",
      "1179          4           Medical                        3  Female   \n",
      "1180          1     Life Sciences                        3    Male   \n",
      "1181          2               NaN                        3    Male   \n",
      "1182          4  Technical Degree                        1    Male   \n",
      "\n",
      "      HourlyRate  JobInvolvement  JobLevel                      JobRole  \\\n",
      "1079        55.0               3         1        Sales Representative    \n",
      "1080        49.0               3         1          Research Scientist    \n",
      "1081        89.0               3         2   Healthcare Representative    \n",
      "1082        76.0               3         3             Sales Executive    \n",
      "1083        81.0               3         5                     Manager    \n",
      "...          ...             ...       ...                          ...   \n",
      "1178        52.0               2         2             Sales Executive    \n",
      "1179        93.0               3         2      Manufacturing Director    \n",
      "1180        51.0               2         3             Sales Executive    \n",
      "1181        57.0               3         2       Laboratory Technician    \n",
      "1182        84.0               3         2             Sales Executive    \n",
      "\n",
      "      JobSatisfaction MaritalStatus  MonthlyIncome  MonthlyRate  \\\n",
      "1079                3           NaN         2308.0       4944.0   \n",
      "1080                3        Single         2144.0       2122.0   \n",
      "1081                1       Married         5373.0       6225.0   \n",
      "1082                3      Divorced            NaN       9647.0   \n",
      "1083                1        Single        19045.0      18938.0   \n",
      "...               ...           ...            ...          ...   \n",
      "1178                1           NaN         4294.0      11148.0   \n",
      "1179                1       Married         6725.0      13554.0   \n",
      "1180                4       Married        10325.0       5518.0   \n",
      "1181                2           NaN         4447.0      23163.0   \n",
      "1182                4      Divorced            NaN      25952.0   \n",
      "\n",
      "      NumCompaniesWorked OverTime  PercentSalaryHike PerformanceRating  \\\n",
      "1079                   0      Yes                 25               4,0   \n",
      "1080                   1       No                 14               3,0   \n",
      "1081                   0       No                 12               3,0   \n",
      "1082                   0      NaN                 12               3,0   \n",
      "1083                   0      NaN                 14               3,0   \n",
      "...                  ...      ...                ...               ...   \n",
      "1178                   1      NaN                 12               3,0   \n",
      "1179                   1      NaN                 12               3,0   \n",
      "1180                   1      NaN                 11               NaN   \n",
      "1181                   1      NaN                 12               3,0   \n",
      "1182                   4       No                 13               NaN   \n",
      "\n",
      "      RelationshipSatisfaction  StockOptionLevel TotalWorkingYears  \\\n",
      "1079                         2                 1              12,0   \n",
      "1080                         3                 0               5,0   \n",
      "1081                         1                 1               6,0   \n",
      "1082                         3                 2               NaN   \n",
      "1083                         3                 0              37,0   \n",
      "...                        ...               ...               ...   \n",
      "1178                         2                 0               NaN   \n",
      "1179                         3                 1               8,0   \n",
      "1180                         1                 1               NaN   \n",
      "1181                         2                 0               NaN   \n",
      "1182                         4                 2              12,0   \n",
      "\n",
      "      TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
      "1079                      4             3,0              11   \n",
      "1080                      3             2,0               5   \n",
      "1081                      5             2,0               5   \n",
      "1082                      6             2,0              16   \n",
      "1083                      2             3,0              36   \n",
      "...                     ...             ...             ...   \n",
      "1178                      2             3,0               7   \n",
      "1179                      2             4,0               8   \n",
      "1180                      6             3,0              16   \n",
      "1181                      5             2,0               9   \n",
      "1182                      3             3,0               7   \n",
      "\n",
      "      YearsSinceLastPromotion  YearsWithCurrManager  DateBirth RemoteWork  \n",
      "1079                        5                     7       1989          1  \n",
      "1080                        1                     4       1995      False  \n",
      "1081                        0                     2       1994          0  \n",
      "1082                        5                    13       1985        Yes  \n",
      "1083                        4                    13       1968        Yes  \n",
      "...                       ...                   ...        ...        ...  \n",
      "1178                        0                     7       1997          1  \n",
      "1179                        6                     3       1991          1  \n",
      "1180                        3                     7       1987          0  \n",
      "1181                        0                     8       1978          1  \n",
      "1182                        0                     7       1987       True  \n",
      "\n",
      "[104 rows x 31 columns]\n"
     ]
    }
   ],
   "source": [
    "# Filtrar los duplicados en 'EmployeeNumber', excluyendo los nulos\n",
    "duplicados_sin_nulos = df[df['EmployeeNumber'].duplicated() & df['EmployeeNumber'].notna()]\n",
    "\n",
    "# Mostrar los duplicados sin nulos\n",
    "print(duplicados_sin_nulos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tras hacer un filtrado de los duplicados según EmployeeNumber + EmployeeNumber not null (recordamos que si hay nulo en E.N. nos lo detecta como duplicado de otro nulo), eliminamos los duplicados del dataframe donde no hay nulos. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hemos eliminado en total 104 filas que se repetían y tenían duplicada toda la información, incluyendo el EN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     EmployeeNumber Attrition BusinessTravel  DailyRate  DistanceFromHome  \\\n",
      "0             162,0        No            NaN      684.0                 6   \n",
      "1             259,0        No            NaN      699.0                 1   \n",
      "2             319,0        No  travel_rarely      532.0                 4   \n",
      "3            1900,0        No            NaN      117.0                22   \n",
      "4              81,0        No            NaN     1435.0                25   \n",
      "...             ...       ...            ...        ...               ...   \n",
      "1600            NaN        No            NaN      760.0               -43   \n",
      "1601            NaN       Yes            NaN        NaN                 5   \n",
      "1602            NaN        No            NaN      616.0                12   \n",
      "1603            NaN        No  travel_rarely      903.0               -13   \n",
      "1604            NaN        No            NaN      566.0                 7   \n",
      "\n",
      "      Education    EducationField  EnvironmentSatisfaction  Gender  \\\n",
      "0             3               NaN                        1    Male   \n",
      "1             4     Life Sciences                        3    Male   \n",
      "2             2  Technical Degree                        3    Male   \n",
      "3             3           Medical                        4  Female   \n",
      "4             3     Life Sciences                        1  Female   \n",
      "...         ...               ...                      ...     ...   \n",
      "1600          4               NaN                       43  Female   \n",
      "1601          2           Medical                        3  Female   \n",
      "1602          3           Medical                        4  Female   \n",
      "1603          5               NaN                       13    Male   \n",
      "1604          2           Medical                        4    Male   \n",
      "\n",
      "      HourlyRate  JobInvolvement  JobLevel                      JobRole  \\\n",
      "0           51.0               3         5           Research Director    \n",
      "1           65.0               2         5                     Manager    \n",
      "2           58.0               3         5                     Manager    \n",
      "3           58.0               3         4                     Manager    \n",
      "4           99.0               3         3             Sales Executive    \n",
      "...          ...             ...       ...                          ...   \n",
      "1600        81.0               3         2             Sales Executive    \n",
      "1601        88.0               2         1          Research Scientist    \n",
      "1602        41.0               3         2   Healthcare Representative    \n",
      "1603        41.0               4         3             Sales Executive    \n",
      "1604        75.0               3         3      Manufacturing Director    \n",
      "\n",
      "      JobSatisfaction MaritalStatus  MonthlyIncome  MonthlyRate  \\\n",
      "0                   3           NaN        19537.0       6462.0   \n",
      "1                   3           NaN        19999.0       5678.0   \n",
      "2                   4       Married        19232.0       4933.0   \n",
      "3                   4           NaN        17174.0       2437.0   \n",
      "4                   1           NaN            NaN       2354.0   \n",
      "...               ...           ...            ...          ...   \n",
      "1600                2           NaN            NaN       3698.0   \n",
      "1601                3       Married         2366.0      20898.0   \n",
      "1602                4           NaN            NaN      17369.0   \n",
      "1603                3        Single            NaN       2560.0   \n",
      "1604                3           NaN        10845.0      24208.0   \n",
      "\n",
      "      NumCompaniesWorked OverTime  PercentSalaryHike PerformanceRating  \\\n",
      "0                      7       No                 13               3,0   \n",
      "1                      0      NaN                 14               3,0   \n",
      "2                      1       No                 11               3,0   \n",
      "3                      3       No                 11               3,0   \n",
      "4                      7      NaN                 11               3,0   \n",
      "...                  ...      ...                ...               ...   \n",
      "1600                   1      NaN                 20               4,0   \n",
      "1601                   1      NaN                 14               3,0   \n",
      "1602                   0      NaN                 11               3,0   \n",
      "1603                   0       No                 18               3,0   \n",
      "1604                   6      NaN                 13               3,0   \n",
      "\n",
      "      RelationshipSatisfaction  StockOptionLevel TotalWorkingYears  \\\n",
      "0                            3                 0               NaN   \n",
      "1                            1                 1              34,0   \n",
      "2                            4                 0              22,0   \n",
      "3                            2                 1               NaN   \n",
      "4                            4                 0              28,0   \n",
      "...                        ...               ...               ...   \n",
      "1600                         1                 0               8,0   \n",
      "1601                         1                 1               8,0   \n",
      "1602                         3                 2              10,0   \n",
      "1603                         4                 0               9,0   \n",
      "1604                         2                 1               NaN   \n",
      "\n",
      "      TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
      "0                         5             3,0              20   \n",
      "1                         5             3,0              33   \n",
      "2                         3             NaN              22   \n",
      "3                         3             3,0              22   \n",
      "4                         3             2,0              21   \n",
      "...                     ...             ...             ...   \n",
      "1600                      2             3,0               8   \n",
      "1601                      2             3,0               8   \n",
      "1602                      2             1,0               9   \n",
      "1603                      3             3,0               8   \n",
      "1604                      3             3,0               8   \n",
      "\n",
      "      YearsSinceLastPromotion  YearsWithCurrManager  DateBirth RemoteWork  \n",
      "0                          15                    15       1972        Yes  \n",
      "1                          11                     9       1971          1  \n",
      "2                          11                    15       1981          1  \n",
      "3                           4                     7       1975        Yes  \n",
      "4                           7                     9       1964       True  \n",
      "...                       ...                   ...        ...        ...  \n",
      "1600                        7                     5       1995          0  \n",
      "1601                        1                     7       1997       True  \n",
      "1602                        8                     5       1992       True  \n",
      "1603                        0                     7       1984        Yes  \n",
      "1604                        0                     7       1977          0  \n",
      "\n",
      "[1501 rows x 31 columns]\n"
     ]
    }
   ],
   "source": [
    "# Si quieres eliminar esos duplicados del dataframe original\n",
    "df_sin_duplicados = df.drop(duplicados_sin_nulos.index)\n",
    "\n",
    "# Mostrar el dataframe sin los duplicados\n",
    "print(df_sin_duplicados) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_sin_duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como es de tipo objeto y al hacer la conversión a tipo float nos saca todo como nulo, hemos decidido consensuadamente construir una nueva columna e imputar un identificador único a cada fila (a cada empleado)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['IdEmployee'] = range(0, len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['EmployeeNumber', 'Attrition', 'BusinessTravel', 'DailyRate',\n",
       "       'DistanceFromHome', 'Education', 'EducationField',\n",
       "       'EnvironmentSatisfaction', 'Gender', 'HourlyRate', 'JobInvolvement',\n",
       "       'JobLevel', 'JobRole', 'JobSatisfaction', 'MaritalStatus',\n",
       "       'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'OverTime',\n",
       "       'PercentSalaryHike', 'PerformanceRating', 'RelationshipSatisfaction',\n",
       "       'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear',\n",
       "       'WorkLifeBalance', 'YearsAtCompany', 'YearsSinceLastPromotion',\n",
       "       'YearsWithCurrManager', 'DateBirth', 'RemoteWork', 'IdEmployee'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['IdEmployee','EmployeeNumber', 'Attrition', 'BusinessTravel', 'DailyRate',\n",
    "       'DistanceFromHome', 'Education', 'EducationField',\n",
    "       'EnvironmentSatisfaction', 'Gender', 'HourlyRate', 'JobInvolvement',\n",
    "       'JobLevel', 'JobRole', 'JobSatisfaction', 'MaritalStatus',\n",
    "       'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'OverTime',\n",
    "       'PercentSalaryHike', 'PerformanceRating', 'RelationshipSatisfaction',\n",
    "       'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear',\n",
    "       'WorkLifeBalance', 'YearsAtCompany', 'YearsSinceLastPromotion',\n",
    "       'YearsWithCurrManager', 'DateBirth', 'RemoteWork']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pasamos a valores absolutos la columna DistanceFromHome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>IdEmployee</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>DateBirth</th>\n",
       "      <th>RemoteWork</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>1815,0</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-37</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37</td>\n",
       "      <td>Female</td>\n",
       "      <td>81.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>3</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>10447.0</td>\n",
       "      <td>26458.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>3,0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4,0</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1982</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>170,0</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-26</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>96.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5549.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>14</td>\n",
       "      <td>3,0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>16,0</td>\n",
       "      <td>2</td>\n",
       "      <td>2,0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>1974</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>1375,0</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>-25</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "      <td>89.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13888.0</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>20</td>\n",
       "      <td>4,0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>15,0</td>\n",
       "      <td>0</td>\n",
       "      <td>3,0</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>1983</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>54</td>\n",
       "      <td>131,0</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>682.0</td>\n",
       "      <td>-18</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>37.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>13872.0</td>\n",
       "      <td>24409.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>3,0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>38,0</td>\n",
       "      <td>1</td>\n",
       "      <td>2,0</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1965</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>58</td>\n",
       "      <td>524,0</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>480.0</td>\n",
       "      <td>-30</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>1</td>\n",
       "      <td>Married</td>\n",
       "      <td>4306.0</td>\n",
       "      <td>4156.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3,0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13,0</td>\n",
       "      <td>5</td>\n",
       "      <td>1,0</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>1992</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1581</th>\n",
       "      <td>1477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>non-travel</td>\n",
       "      <td>589.0</td>\n",
       "      <td>-48</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "      <td>Female</td>\n",
       "      <td>67.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>5154.0</td>\n",
       "      <td>19665.0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>4,0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10,0</td>\n",
       "      <td>3</td>\n",
       "      <td>4,0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1978</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1588</th>\n",
       "      <td>1484</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>1385.0</td>\n",
       "      <td>-33</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>79.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7501.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14</td>\n",
       "      <td>3,0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>21,0</td>\n",
       "      <td>2</td>\n",
       "      <td>3,0</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1978</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>1491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>397.0</td>\n",
       "      <td>-24</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>88.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Human Resources</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6729.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16,0</td>\n",
       "      <td>1</td>\n",
       "      <td>4,0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1969</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1600</th>\n",
       "      <td>1496</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>760.0</td>\n",
       "      <td>-43</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43</td>\n",
       "      <td>Female</td>\n",
       "      <td>81.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3698.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>4,0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8,0</td>\n",
       "      <td>2</td>\n",
       "      <td>3,0</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1603</th>\n",
       "      <td>1499</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>travel_rarely</td>\n",
       "      <td>903.0</td>\n",
       "      <td>-13</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>Male</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2560.0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>18</td>\n",
       "      <td>3,0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9,0</td>\n",
       "      <td>3</td>\n",
       "      <td>3,0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1984</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      IdEmployee EmployeeNumber Attrition BusinessTravel  DailyRate  \\\n",
       "16            16         1815,0        No            NaN        NaN   \n",
       "37            37          170,0        No            NaN        NaN   \n",
       "47            47         1375,0        No            NaN     1202.0   \n",
       "54            54          131,0        No  travel_rarely      682.0   \n",
       "58            58          524,0        No            NaN      480.0   \n",
       "...          ...            ...       ...            ...        ...   \n",
       "1581        1477            NaN        No     non-travel      589.0   \n",
       "1588        1484            NaN        No  travel_rarely     1385.0   \n",
       "1595        1491            NaN        No            NaN      397.0   \n",
       "1600        1496            NaN        No            NaN      760.0   \n",
       "1603        1499            NaN        No  travel_rarely      903.0   \n",
       "\n",
       "      DistanceFromHome  Education EducationField  EnvironmentSatisfaction  \\\n",
       "16                 -37          1            NaN                       37   \n",
       "37                 -26          4        Medical                        3   \n",
       "47                 -25          1            NaN                       25   \n",
       "54                 -18          4            NaN                        4   \n",
       "58                 -30          2            NaN                        2   \n",
       "...                ...        ...            ...                      ...   \n",
       "1581               -48          4            NaN                       48   \n",
       "1588               -33          2        Medical                        3   \n",
       "1595               -24          4            NaN                        3   \n",
       "1600               -43          4            NaN                       43   \n",
       "1603               -13          5            NaN                       13   \n",
       "\n",
       "      Gender  HourlyRate  JobInvolvement  JobLevel  \\\n",
       "16    Female        81.0               3         3   \n",
       "37    Female        96.0               3         2   \n",
       "47    Female        89.0               4         2   \n",
       "54      Male        37.0               3         4   \n",
       "58    Female        31.0               3         2   \n",
       "...      ...         ...             ...       ...   \n",
       "1581  Female        67.0               3         2   \n",
       "1588    Male        79.0               3         4   \n",
       "1595    Male        88.0               3         3   \n",
       "1600  Female        81.0               3         2   \n",
       "1603    Male        41.0               4         3   \n",
       "\n",
       "                          JobRole  JobSatisfaction MaritalStatus  \\\n",
       "16               Sales Executive                 3      Divorced   \n",
       "37        Manufacturing Director                 1           NaN   \n",
       "47     Healthcare Representative                 3           NaN   \n",
       "54               Sales Executive                 3        Single   \n",
       "58        Manufacturing Director                 1       Married   \n",
       "...                           ...              ...           ...   \n",
       "1581             Sales Executive                 3       Married   \n",
       "1588   Healthcare Representative                 4       Married   \n",
       "1595             Human Resources                 2           NaN   \n",
       "1600             Sales Executive                 2           NaN   \n",
       "1603             Sales Executive                 3        Single   \n",
       "\n",
       "      MonthlyIncome  MonthlyRate  NumCompaniesWorked OverTime  \\\n",
       "16          10447.0      26458.0                   0      NaN   \n",
       "37              NaN       5549.0                   1       No   \n",
       "47              NaN      13888.0                   5       No   \n",
       "54          13872.0      24409.0                   0      NaN   \n",
       "58           4306.0       4156.0                   1       No   \n",
       "...             ...          ...                 ...      ...   \n",
       "1581         5154.0      19665.0                   4      NaN   \n",
       "1588            NaN       7501.0                   0      Yes   \n",
       "1595            NaN       6729.0                   2      NaN   \n",
       "1600            NaN       3698.0                   1      NaN   \n",
       "1603            NaN       2560.0                   0       No   \n",
       "\n",
       "      PercentSalaryHike PerformanceRating  RelationshipSatisfaction  \\\n",
       "16                   13               3,0                         4   \n",
       "37                   14               3,0                         3   \n",
       "47                   20               4,0                         2   \n",
       "54                   13               3,0                         3   \n",
       "58                   12               3,0                         2   \n",
       "...                 ...               ...                       ...   \n",
       "1581                 22               4,0                         2   \n",
       "1588                 14               3,0                         2   \n",
       "1595                 15               NaN                         3   \n",
       "1600                 20               4,0                         1   \n",
       "1603                 18               3,0                         4   \n",
       "\n",
       "      StockOptionLevel TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "16                   1               NaN                      3   \n",
       "37                   0              16,0                      2   \n",
       "47                   3              15,0                      0   \n",
       "54                   0              38,0                      1   \n",
       "58                   1              13,0                      5   \n",
       "...                ...               ...                    ...   \n",
       "1581                 2              10,0                      3   \n",
       "1588                 0              21,0                      2   \n",
       "1595                 1              16,0                      1   \n",
       "1600                 0               8,0                      2   \n",
       "1603                 0               9,0                      3   \n",
       "\n",
       "     WorkLifeBalance  YearsAtCompany  YearsSinceLastPromotion  \\\n",
       "16               4,0              22                       13   \n",
       "37               2,0              15                        5   \n",
       "47               3,0              12                       11   \n",
       "54               2,0              37                        1   \n",
       "58               1,0              13                        3   \n",
       "...              ...             ...                      ...   \n",
       "1581             4,0               8                        5   \n",
       "1588             3,0              20                        4   \n",
       "1595             4,0               9                        7   \n",
       "1600             3,0               8                        7   \n",
       "1603             3,0               8                        0   \n",
       "\n",
       "      YearsWithCurrManager  DateBirth RemoteWork  \n",
       "16                       5       1982        Yes  \n",
       "37                      11       1974          0  \n",
       "47                       8       1983       True  \n",
       "54                       8       1965          1  \n",
       "58                      12       1992        Yes  \n",
       "...                    ...        ...        ...  \n",
       "1581                     7       1978       True  \n",
       "1588                    10       1978        Yes  \n",
       "1595                     1       1969      False  \n",
       "1600                     5       1995          0  \n",
       "1603                     7       1984        Yes  \n",
       "\n",
       "[179 rows x 32 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['DistanceFromHome'] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DistanceFromHome'] = abs(df['DistanceFromHome'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hemos eliminado la columna 'Age' por poder estar desactualizada. Sin embargo, como posiblemente querremos ver la edad de los trabajadores en nuestro análisis, ya que es más cómodo a la hora de presentar los datos que si ponemos el año de nacimiento, la calculamos y creamos una columna de edad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'] = (2024 - df['DateBirth'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['IdEmployee', 'EmployeeNumber','Age', 'DateBirth','Attrition', 'BusinessTravel',\n",
    "       'DailyRate', 'DistanceFromHome', 'Education', 'EducationField',\n",
    "       'EnvironmentSatisfaction', 'Gender', 'HourlyRate', 'JobInvolvement',\n",
    "       'JobLevel', 'JobRole', 'JobSatisfaction', 'MaritalStatus',\n",
    "       'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'OverTime',\n",
    "       'PercentSalaryHike', 'PerformanceRating', 'RelationshipSatisfaction',\n",
    "       'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear',\n",
    "       'WorkLifeBalance', 'YearsAtCompany', 'YearsSinceLastPromotion',\n",
    "       'YearsWithCurrManager', 'RemoteWork' ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1501.000000\n",
       "mean       37.940040\n",
       "std         9.153965\n",
       "min        19.000000\n",
       "25%        31.000000\n",
       "50%        37.000000\n",
       "75%        44.000000\n",
       "max        61.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hemos visto que en 'EnviromenteSatisfaction' hay números del 1 al 49, sin embargo el cliente nos dice que esta columna corresponde a una escala del 1 al 4. ´Se podría pensar que son números de una unidad con un decimal, sin embargo eso correspondería a una escala del 1 al 5 y no del 1 al 4. Finalmente sedecide transformar los datos de la siguiente manera:\n",
    "- 1 al 19: 1\n",
    "- 20 al 29: 2\n",
    "- 30 al 39: 3\n",
    "- 40 al 49: 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['EnvironmentSatisfaction'] = np.where(\n",
    "    df['EnvironmentSatisfaction'] <= 19.99,\n",
    "    1.0,\n",
    "    np.where(\n",
    "        df['EnvironmentSatisfaction'] <= 29.99,\n",
    "        2.0,\n",
    "        np.where(\n",
    "            df['EnvironmentSatisfaction'] <= 39.99,\n",
    "            3.0,\n",
    "            4.0\n",
    "        )\n",
    "    )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1501.000000\n",
       "mean        1.085943\n",
       "std         0.447149\n",
       "min         1.000000\n",
       "25%         1.000000\n",
       "50%         1.000000\n",
       "75%         1.000000\n",
       "max         4.000000\n",
       "Name: EnvironmentSatisfaction, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EnvironmentSatisfaction'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ya con los datos homogeneizados, se quiere explotar un poco qué significa la escala del 1 al 5 que hay en JobLevel,es decir si, por ejemplo, el 5 corresponden a altos directivos o a personas baja en la escala empresarial "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JobLevel  JobRole                   \n",
       "1         Research Scientist            238\n",
       "          Laboratory Technician         203\n",
       "          Sales Representative           77\n",
       "          Human Resources                33\n",
       "2         Sales Executive               237\n",
       "          Manufacturing Director         90\n",
       "          Healthcare Representative      80\n",
       "          Research Scientist             59\n",
       "          Laboratory Technician          56\n",
       "          Human Resources                13\n",
       "          Sales Representative            7\n",
       "3         Sales Executive                81\n",
       "          Healthcare Representative      47\n",
       "          Manufacturing Director         46\n",
       "          Research Director              30\n",
       "          Manager                        12\n",
       "          Human Resources                 7\n",
       "          Laboratory Technician           3\n",
       "          Research Scientist              1\n",
       "4         Manager                        49\n",
       "          Research Director              27\n",
       "          Sales Executive                15\n",
       "          Healthcare Representative      10\n",
       "          Manufacturing Director         10\n",
       "5         Manager                        44\n",
       "          Research Director              26\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agrupando el nivel y el rol podremos ver qué roles hay en cada nivel\n",
    "df.groupby('JobLevel')['JobRole'].value_counts() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se ve que en el valor 5 hay menos grente y sólo hay  'Manager' y 'Research Director', por lo que entendemos que corresponde a los niveles más altos de la empresa, mientras que en el valor 1 no hay ningún puesto de mando  (ningún tipo de director ni de manager) y es el lugar donde más empleados hay. En el nivel 2 tampoco hay manager aunque empieza a haber puestos de directores y ya aparece en el valor 3 y 4 Manager. Esto nos hace entender que los niveles corresponden en una escala del 1 al 5 donde 1 es el nivel más bajo y 5 el más alto "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La columna de RemoteWork nos ha informado el Proyect Owner que el cliente quiere tratar los True como 1 y 0 como False asi que procedemos a limpiarla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['RemoteWork'] = df['RemoteWork'].replace('Yes',True)\n",
    "df['RemoteWork'] = df['RemoteWork'].replace('True',True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([True, '1', '0', 'False'], dtype=object)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['RemoteWork'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>RemoteWork</th>\n",
       "      <th>IdEmployee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  RemoteWork  IdEmployee\n",
       "0       True         598\n",
       "1          0         287\n",
       "2          1         329\n",
       "3      False         287"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ver_remotes = df.groupby('RemoteWork')['IdEmployee'].count()\n",
    "ver_remotes.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['RemoteWork'] = df['RemoteWork'].replace('1',True)\n",
    "df['RemoteWork'] = df['RemoteWork'].replace('0',False)\n",
    "df['RemoteWork'] = df['RemoteWork'].replace('False',False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>RemoteWork</th>\n",
       "      <th>IdEmployee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RemoteWork  IdEmployee\n",
       "0       False         574\n",
       "1        True         927"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ver_remotes = df.groupby('RemoteWork')['IdEmployee'].count()\n",
    "ver_remotes.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gestión de nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmployeeNumber       28.11\n",
       "BusinessTravel       47.90\n",
       "DailyRate             7.66\n",
       "EducationField       46.57\n",
       "HourlyRate            5.33\n",
       "MaritalStatus        40.11\n",
       "MonthlyIncome        52.90\n",
       "OverTime             41.77\n",
       "PerformanceRating    11.99\n",
       "TotalWorkingYears    32.64\n",
       "WorkLifeBalance       6.66\n",
       "dtype: float64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.isnull().sum()[df.isnull().sum() > 0]/df.shape[0] * 100).round(2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se podrian imputar por su bajo porcentaje de nulos:\n",
    "- DailyRate\n",
    "- HourlyRate\n",
    "- WorkLifeBalance  \n",
    "\n",
    "MonthlyIncome: al faltarnos información sobre qué es esa columna, tener tantos nulos que tampoco podemos trabajar con ella y teniendo otros indicadores economicos que si podemos usar , se decide borrar esta fila.  \n",
    "Mientras, los nulos que faltan al ser un porcentaje tan alto se decide cambiar a \"no-data\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns= 'MonthlyIncome',inplace= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DailyRate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1386.000000\n",
       "mean      806.674603\n",
       "std       401.462128\n",
       "min       103.000000\n",
       "25%       472.500000\n",
       "50%       805.000000\n",
       "75%      1157.000000\n",
       "max      1499.000000\n",
       "Name: DailyRate, dtype: float64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DailyRate'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decidimos hacer una imputación en función del JobRole y JobLevel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JobRole                     JobLevel\n",
       "Healthcare Representative   2            848.368421\n",
       "                            3            913.590909\n",
       "                            4            753.666667\n",
       "Human Resources             1            763.093750\n",
       "                            2            829.454545\n",
       "                            3            687.428571\n",
       "Laboratory Technician       1            846.155080\n",
       "                            2            663.780000\n",
       "                            3            800.666667\n",
       "Manager                     3            869.916667\n",
       "                            4            803.918367\n",
       "                            5            715.581395\n",
       "Manufacturing Director      2            786.012346\n",
       "                            3            849.166667\n",
       "                            4            694.000000\n",
       "Research Director           3            719.807692\n",
       "                            4            763.545455\n",
       "                            5            946.000000\n",
       "Research Scientist          1            789.368182\n",
       "                            2            862.036364\n",
       "                            3           1339.000000\n",
       "Sales Executive             2            794.023364\n",
       "                            3            832.896104\n",
       "                            4            783.500000\n",
       "Sales Representative        1            786.642857\n",
       "                            2            939.285714\n",
       "Name: DailyRate, dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"JobRole\", \"JobLevel\"])[\"DailyRate\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "moda_dailyrate = round(df.groupby([\"JobRole\",\"JobLevel\"])[\"DailyRate\"].apply(lambda x: x.mode()[0]), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crear una función para reemplazar los valores nulos con la moda correspondiente\n",
    "def reemplazar_moda(row):\n",
    "    if pd.isnull(row['DailyRate']):\n",
    "        return moda_dailyrate.loc[row['JobRole'], row['JobLevel']]\n",
    "    else:\n",
    "        return row['DailyRate']\n",
    "\n",
    "# Aplicar la función fila por fila\n",
    "df['DailyRate'] = df.apply(reemplazar_moda, axis=1)\n",
    "\n",
    "df[\"DailyRate\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HourlyRate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "moda_hourly = round(df.groupby([\"JobRole\",\"JobLevel\"])[\"HourlyRate\"].apply(lambda x: x.mode()[0]), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crear una función para reemplazar los valores nulos con la moda correspondiente\n",
    "def reemplazar_moda(row):\n",
    "    if pd.isnull(row['HourlyRate']):\n",
    "        return moda_hourly.loc[row['JobRole'], row['JobLevel']]\n",
    "    else:\n",
    "        return row['HourlyRate']\n",
    "\n",
    "# Aplicar la función fila por fila\n",
    "df['HourlyRate'] = df.apply(reemplazar_moda, axis=1)\n",
    "\n",
    "df[\"HourlyRate\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PerformanceRating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "moda_performance = round(df.groupby([\"JobRole\",\"JobLevel\"])[\"PerformanceRating\"].apply(lambda x: x.mode()[0]), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crear una función para reemplazar los valores nulos con la moda correspondiente\n",
    "def reemplazar_moda(row):\n",
    "    if pd.isnull(row['PerformanceRating']):\n",
    "        return moda_performance.loc[row['JobRole'], row['JobLevel']]\n",
    "    else:\n",
    "        return row['PerformanceRating']\n",
    "\n",
    "# Aplicar la función fila por fila\n",
    "df['PerformanceRating'] = df.apply(reemplazar_moda, axis=1)\n",
    "\n",
    "df[\"PerformanceRating\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WorkLifeBalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "moda_worklife = round(df.groupby([\"JobRole\",\"JobLevel\"])[\"WorkLifeBalance\"].apply(lambda x: x.mode()[0]), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crear una función para reemplazar los valores nulos con la moda correspondiente\n",
    "def reemplazar_moda(row):\n",
    "    if pd.isnull(row['WorkLifeBalance']):\n",
    "        return moda_worklife.loc[row['JobRole'], row['JobLevel']]\n",
    "    else:\n",
    "        return row['WorkLifeBalance']\n",
    "\n",
    "# Aplicar la función fila por fila\n",
    "df['WorkLifeBalance'] = df.apply(reemplazar_moda, axis=1)\n",
    "\n",
    "df[\"WorkLifeBalance\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pasamos los nulos  a \"no-data\" de las columnas 'BusinessTravel','EducationField','EmployeeNumber','MaritalStatus','TotalWorkingYears' y 'OverTime'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_nodata = ['BusinessTravel','EducationField','EmployeeNumber','MaritalStatus','TotalWorkingYears','OverTime']\n",
    "\n",
    "df[lista_nodata] = df[lista_nodata].fillna(\"no-data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cambio de valores a int para la Fase3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pasar a int la columna de Environment Satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"EnvironmentSatisfaction\"] = df[\"EnvironmentSatisfaction\"].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pasar aint la columna Performance Rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"PerformanceRating\"] = df[\"PerformanceRating\"].replace(\",\", \".\", regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PerformanceRating\n",
       "3.0    1295\n",
       "4.0     206\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"PerformanceRating\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"PerformanceRating\"] = df[\"PerformanceRating\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"PerformanceRating\"] = df[\"PerformanceRating\"].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pasar a int la columna de WorkLifeBalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"WorkLifeBalance\"] = df[\"WorkLifeBalance\"].replace(\",\", \".\", regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"WorkLifeBalance\"] = df[\"WorkLifeBalance\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"WorkLifeBalance\"] = df[\"WorkLifeBalance\"].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como vamos a hacer la FASE 3 en otro jupiter para el esquema pasamos el df a un nuevo csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"files/df_fase1.csv\")"
   ]
  }
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