notebooks/dataprocessing/module_tests/encoding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "875d7df8",
   "metadata": {},
   "source": [
    "# Encoding Examples\n",
    "\n",
    "In this notebook, we will demonstrate the different ways the Encoder classes can be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "193967f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../../../notebooks')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from raimitigations.dataprocessing import EncoderOHE, EncoderOrdinal\n",
    "\n",
    "from download import download_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b30182ce",
   "metadata": {},
   "source": [
    "## Ordinal Encoding\n",
    "\n",
    "In Ordinal Encoding, an integer is given to each value of the categorical variable. This is typically used when the categorical variable is ordered (e.g. education, where High School < Bachelors < Masters). In this example, we will see the different ways `EncoderOrdinal` can be used.\n",
    "\n",
    "### Using a dataset with headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "514b2d08",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>department</th>\n",
       "      <th>region</th>\n",
       "      <th>education</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
       "      <th>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65438</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_7</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>f</td>\n",
       "      <td>sourcing</td>\n",
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       "      <td>Bachelor's</td>\n",
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       "      <td>3.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>region_23</td>\n",
       "      <td>Bachelor's</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48945</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_26</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>region_14</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
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       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>54804</th>\n",
       "      <td>74592</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_27</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>54806</th>\n",
       "      <td>13614</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>51526</td>\n",
       "      <td>HR</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       employee_id         department     region         education gender  \\\n",
       "0            65438  Sales & Marketing   region_7  Master's & above      f   \n",
       "1            65141         Operations  region_22        Bachelor's      m   \n",
       "2             7513  Sales & Marketing  region_19        Bachelor's      m   \n",
       "3             2542  Sales & Marketing  region_23        Bachelor's      m   \n",
       "4            48945         Technology  region_26        Bachelor's      m   \n",
       "...            ...                ...        ...               ...    ...   \n",
       "54803         3030         Technology  region_14        Bachelor's      m   \n",
       "54804        74592         Operations  region_27  Master's & above      f   \n",
       "54805        13918          Analytics   region_1        Bachelor's      m   \n",
       "54806        13614  Sales & Marketing   region_9               NaN      m   \n",
       "54807        51526                 HR  region_22        Bachelor's      m   \n",
       "\n",
       "      recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "0                sourcing                1   35                   5.0   \n",
       "1                   other                1   30                   5.0   \n",
       "2                sourcing                1   34                   3.0   \n",
       "3                   other                2   39                   1.0   \n",
       "4                   other                1   45                   3.0   \n",
       "...                   ...              ...  ...                   ...   \n",
       "54803            sourcing                1   48                   3.0   \n",
       "54804               other                1   37                   2.0   \n",
       "54805               other                1   27                   5.0   \n",
       "54806            sourcing                1   29                   1.0   \n",
       "54807               other                1   27                   1.0   \n",
       "\n",
       "       length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
       "0                      8              1            0                  49   \n",
       "1                      4              0            0                  60   \n",
       "2                      7              0            0                  50   \n",
       "3                     10              0            0                  50   \n",
       "4                      2              0            0                  73   \n",
       "...                  ...            ...          ...                 ...   \n",
       "54803                 17              0            0                  78   \n",
       "54804                  6              0            0                  56   \n",
       "54805                  3              1            0                  79   \n",
       "54806                  2              0            0                  45   \n",
       "54807                  5              0            0                  49   \n",
       "\n",
       "       is_promoted  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "...            ...  \n",
       "54803            0  \n",
       "54804            0  \n",
       "54805            0  \n",
       "54806            0  \n",
       "54807            0  \n",
       "\n",
       "[54808 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = '../../../datasets/'\n",
    "download_datasets(data_dir)\n",
    "dataset =  pd.read_csv(data_dir + 'hr_promotion/train.csv')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25c92b63",
   "metadata": {},
   "source": [
    "Here we don't specify any columns that should be encoded. We let the class automatically identify which columns should be encoded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "93e0f69f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No columns specified for encoding. These columns have been automatically identfied as the following:\n",
      "['department', 'region', 'education', 'gender', 'recruitment_channel']\n"
     ]
    },
    {
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       "      <td>0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "       employee_id  department  region  education  gender  \\\n",
       "0            65438           7      31          2       0   \n",
       "1            65141           4      14          0       1   \n",
       "2             7513           7      10          0       1   \n",
       "3             2542           7      15          0       1   \n",
       "4            48945           8      18          0       1   \n",
       "...            ...         ...     ...        ...     ...   \n",
       "54803         3030           8       5          0       1   \n",
       "54804        74592           4      19          2       0   \n",
       "54805        13918           0       0          0       1   \n",
       "54806        13614           7      33         -1       1   \n",
       "54807        51526           2      14          0       1   \n",
       "\n",
       "       recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "0                        2                1   35                   5.0   \n",
       "1                        0                1   30                   5.0   \n",
       "2                        2                1   34                   3.0   \n",
       "3                        0                2   39                   1.0   \n",
       "4                        0                1   45                   3.0   \n",
       "...                    ...              ...  ...                   ...   \n",
       "54803                    2                1   48                   3.0   \n",
       "54804                    0                1   37                   2.0   \n",
       "54805                    0                1   27                   5.0   \n",
       "54806                    2                1   29                   1.0   \n",
       "54807                    0                1   27                   1.0   \n",
       "\n",
       "       length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
       "0                      8              1            0                  49   \n",
       "1                      4              0            0                  60   \n",
       "2                      7              0            0                  50   \n",
       "3                     10              0            0                  50   \n",
       "4                      2              0            0                  73   \n",
       "...                  ...            ...          ...                 ...   \n",
       "54803                 17              0            0                  78   \n",
       "54804                  6              0            0                  56   \n",
       "54805                  3              1            0                  79   \n",
       "54806                  2              0            0                  45   \n",
       "54807                  5              0            0                  49   \n",
       "\n",
       "       is_promoted  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "...            ...  \n",
       "54803            0  \n",
       "54804            0  \n",
       "54805            0  \n",
       "54806            0  \n",
       "54807            0  \n",
       "\n",
       "[54808 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode = EncoderOrdinal(\n",
    "\t\t\t\tdf=dataset,\n",
    "\t\t\t\tcol_encode=None\n",
    "\t\t\t)\n",
    "encode.fit()\n",
    "new_df = encode.transform(dataset)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9894ff34",
   "metadata": {},
   "source": [
    "We can then get the mapping dictionary used by the encoder by calling the **get_mapping()** method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a95c6e8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'department': {'values': ['Analytics',\n",
       "   'Finance',\n",
       "   'HR',\n",
       "   'Legal',\n",
       "   'Operations',\n",
       "   'Procurement',\n",
       "   'R&D',\n",
       "   'Sales & Marketing',\n",
       "   'Technology',\n",
       "   'UNKNOWN'],\n",
       "  'labels': [0, 1, 2, 3, 4, 5, 6, 7, 8, -1],\n",
       "  'n_labels': 10},\n",
       " 'region': {'values': ['region_1',\n",
       "   'region_10',\n",
       "   'region_11',\n",
       "   'region_12',\n",
       "   'region_13',\n",
       "   'region_14',\n",
       "   'region_15',\n",
       "   'region_16',\n",
       "   'region_17',\n",
       "   'region_18',\n",
       "   'region_19',\n",
       "   'region_2',\n",
       "   'region_20',\n",
       "   'region_21',\n",
       "   'region_22',\n",
       "   'region_23',\n",
       "   'region_24',\n",
       "   'region_25',\n",
       "   'region_26',\n",
       "   'region_27',\n",
       "   'region_28',\n",
       "   'region_29',\n",
       "   'region_3',\n",
       "   'region_30',\n",
       "   'region_31',\n",
       "   'region_32',\n",
       "   'region_33',\n",
       "   'region_34',\n",
       "   'region_4',\n",
       "   'region_5',\n",
       "   'region_6',\n",
       "   'region_7',\n",
       "   'region_8',\n",
       "   'region_9',\n",
       "   'UNKNOWN'],\n",
       "  'labels': [0,\n",
       "   1,\n",
       "   2,\n",
       "   3,\n",
       "   4,\n",
       "   5,\n",
       "   6,\n",
       "   7,\n",
       "   8,\n",
       "   9,\n",
       "   10,\n",
       "   11,\n",
       "   12,\n",
       "   13,\n",
       "   14,\n",
       "   15,\n",
       "   16,\n",
       "   17,\n",
       "   18,\n",
       "   19,\n",
       "   20,\n",
       "   21,\n",
       "   22,\n",
       "   23,\n",
       "   24,\n",
       "   25,\n",
       "   26,\n",
       "   27,\n",
       "   28,\n",
       "   29,\n",
       "   30,\n",
       "   31,\n",
       "   32,\n",
       "   33,\n",
       "   -1],\n",
       "  'n_labels': 35},\n",
       " 'education': {'values': [\"Bachelor's\",\n",
       "   'Below Secondary',\n",
       "   \"Master's & above\",\n",
       "   'UNKNOWN'],\n",
       "  'labels': [0, 1, 2, -1],\n",
       "  'n_labels': 4},\n",
       " 'gender': {'values': ['f', 'm', 'UNKNOWN'],\n",
       "  'labels': [0, 1, -1],\n",
       "  'n_labels': 3},\n",
       " 'recruitment_channel': {'values': ['other',\n",
       "   'referred',\n",
       "   'sourcing',\n",
       "   'UNKNOWN'],\n",
       "  'labels': [0, 1, 2, -1],\n",
       "  'n_labels': 4}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode.get_mapping()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a01e9929",
   "metadata": {},
   "source": [
    "We can also provide the dataset only when calling the fit method, instead of providing it when instantiating the object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a64baed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No columns specified for encoding. These columns have been automatically identfied as the following:\n",
      "['department', 'region', 'education', 'gender', 'recruitment_channel']\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "       employee_id  department  region  education  gender  \\\n",
       "0            65438           7      31          2       0   \n",
       "1            65141           4      14          0       1   \n",
       "2             7513           7      10          0       1   \n",
       "3             2542           7      15          0       1   \n",
       "4            48945           8      18          0       1   \n",
       "...            ...         ...     ...        ...     ...   \n",
       "54803         3030           8       5          0       1   \n",
       "54804        74592           4      19          2       0   \n",
       "54805        13918           0       0          0       1   \n",
       "54806        13614           7      33         -1       1   \n",
       "54807        51526           2      14          0       1   \n",
       "\n",
       "       recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "0                        2                1   35                   5.0   \n",
       "1                        0                1   30                   5.0   \n",
       "2                        2                1   34                   3.0   \n",
       "3                        0                2   39                   1.0   \n",
       "4                        0                1   45                   3.0   \n",
       "...                    ...              ...  ...                   ...   \n",
       "54803                    2                1   48                   3.0   \n",
       "54804                    0                1   37                   2.0   \n",
       "54805                    0                1   27                   5.0   \n",
       "54806                    2                1   29                   1.0   \n",
       "54807                    0                1   27                   1.0   \n",
       "\n",
       "       length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
       "0                      8              1            0                  49   \n",
       "1                      4              0            0                  60   \n",
       "2                      7              0            0                  50   \n",
       "3                     10              0            0                  50   \n",
       "4                      2              0            0                  73   \n",
       "...                  ...            ...          ...                 ...   \n",
       "54803                 17              0            0                  78   \n",
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       "54805                  3              1            0                  79   \n",
       "54806                  2              0            0                  45   \n",
       "54807                  5              0            0                  49   \n",
       "\n",
       "       is_promoted  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "...            ...  \n",
       "54803            0  \n",
       "54804            0  \n",
       "54805            0  \n",
       "54806            0  \n",
       "54807            0  \n",
       "\n",
       "[54808 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode = EncoderOrdinal(col_encode=None)\n",
    "encode.fit(df=dataset)\n",
    "new_df = encode.transform(dataset)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbd3fa08",
   "metadata": {},
   "source": [
    "We can also specify which columns to encode using the column names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "efd64a31",
   "metadata": {},
   "outputs": [
    {
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       "      <td>13614</td>\n",
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       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       employee_id  department  region  education gender recruitment_channel  \\\n",
       "0            65438           7      31          2      f            sourcing   \n",
       "1            65141           4      14          0      m               other   \n",
       "2             7513           7      10          0      m            sourcing   \n",
       "3             2542           7      15          0      m               other   \n",
       "4            48945           8      18          0      m               other   \n",
       "...            ...         ...     ...        ...    ...                 ...   \n",
       "54803         3030           8       5          0      m            sourcing   \n",
       "54804        74592           4      19          2      f               other   \n",
       "54805        13918           0       0          0      m               other   \n",
       "54806        13614           7      33         -1      m            sourcing   \n",
       "54807        51526           2      14          0      m               other   \n",
       "\n",
       "       no_of_trainings  age  previous_year_rating  length_of_service  \\\n",
       "0                    1   35                   5.0                  8   \n",
       "1                    1   30                   5.0                  4   \n",
       "2                    1   34                   3.0                  7   \n",
       "3                    2   39                   1.0                 10   \n",
       "4                    1   45                   3.0                  2   \n",
       "...                ...  ...                   ...                ...   \n",
       "54803                1   48                   3.0                 17   \n",
       "54804                1   37                   2.0                  6   \n",
       "54805                1   27                   5.0                  3   \n",
       "54806                1   29                   1.0                  2   \n",
       "54807                1   27                   1.0                  5   \n",
       "\n",
       "       KPIs_met >80%  awards_won?  avg_training_score  is_promoted  \n",
       "0                  1            0                  49            0  \n",
       "1                  0            0                  60            0  \n",
       "2                  0            0                  50            0  \n",
       "3                  0            0                  50            0  \n",
       "4                  0            0                  73            0  \n",
       "...              ...          ...                 ...          ...  \n",
       "54803              0            0                  78            0  \n",
       "54804              0            0                  56            0  \n",
       "54805              1            0                  79            0  \n",
       "54806              0            0                  45            0  \n",
       "54807              0            0                  49            0  \n",
       "\n",
       "[54808 rows x 14 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode = EncoderOrdinal(\n",
    "\t\t\t\tdf=dataset,\n",
    "\t\t\t\tcol_encode=[\"department\", \"region\", \"education\"]\n",
    "\t\t\t)\n",
    "encode.fit()\n",
    "new_df = encode.transform(dataset)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c3e967d",
   "metadata": {},
   "source": [
    "or the column indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "948f63c4",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "       employee_id  department  region  education gender recruitment_channel  \\\n",
       "0            65438           7      31          2      f            sourcing   \n",
       "1            65141           4      14          0      m               other   \n",
       "2             7513           7      10          0      m            sourcing   \n",
       "3             2542           7      15          0      m               other   \n",
       "4            48945           8      18          0      m               other   \n",
       "...            ...         ...     ...        ...    ...                 ...   \n",
       "54803         3030           8       5          0      m            sourcing   \n",
       "54804        74592           4      19          2      f               other   \n",
       "54805        13918           0       0          0      m               other   \n",
       "54806        13614           7      33         -1      m            sourcing   \n",
       "54807        51526           2      14          0      m               other   \n",
       "\n",
       "       no_of_trainings  age  previous_year_rating  length_of_service  \\\n",
       "0                    1   35                   5.0                  8   \n",
       "1                    1   30                   5.0                  4   \n",
       "2                    1   34                   3.0                  7   \n",
       "3                    2   39                   1.0                 10   \n",
       "4                    1   45                   3.0                  2   \n",
       "...                ...  ...                   ...                ...   \n",
       "54803                1   48                   3.0                 17   \n",
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       "54805                1   27                   5.0                  3   \n",
       "54806                1   29                   1.0                  2   \n",
       "54807                1   27                   1.0                  5   \n",
       "\n",
       "       KPIs_met >80%  awards_won?  avg_training_score  is_promoted  \n",
       "0                  1            0                  49            0  \n",
       "1                  0            0                  60            0  \n",
       "2                  0            0                  50            0  \n",
       "3                  0            0                  50            0  \n",
       "4                  0            0                  73            0  \n",
       "...              ...          ...                 ...          ...  \n",
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       "54804              0            0                  56            0  \n",
       "54805              1            0                  79            0  \n",
       "54806              0            0                  45            0  \n",
       "54807              0            0                  49            0  \n",
       "\n",
       "[54808 rows x 14 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode = EncoderOrdinal(\n",
    "\t\t\t\tdf=dataset,\n",
    "\t\t\t\tcol_encode=[1,2,3]\n",
    "\t\t\t)\n",
    "encode.fit()\n",
    "new_df = encode.transform(dataset)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c85276f",
   "metadata": {},
   "source": [
    "We can also specify the order of the encodings for the ordinal encoder. For example, let's specify the order of the labels for the 'education' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a108cad1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"Master's & above\", \"Bachelor's\", nan, 'Below Secondary'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "42cecced",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7513</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2542</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48945</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54803</th>\n",
       "      <td>3030</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54804</th>\n",
       "      <td>74592</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_27</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54805</th>\n",
       "      <td>13918</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>region_1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54806</th>\n",
       "      <td>13614</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_9</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>51526</td>\n",
       "      <td>HR</td>\n",
       "      <td>region_22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       employee_id         department     region  education  gender  \\\n",
       "0            65438  Sales & Marketing   region_7          2       0   \n",
       "1            65141         Operations  region_22          1       1   \n",
       "2             7513  Sales & Marketing  region_19          1       1   \n",
       "3             2542  Sales & Marketing  region_23          1       1   \n",
       "4            48945         Technology  region_26          1       1   \n",
       "...            ...                ...        ...        ...     ...   \n",
       "54803         3030         Technology  region_14          1       1   \n",
       "54804        74592         Operations  region_27          2       0   \n",
       "54805        13918          Analytics   region_1          1       1   \n",
       "54806        13614  Sales & Marketing   region_9         -1       1   \n",
       "54807        51526                 HR  region_22          1       1   \n",
       "\n",
       "      recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "0                sourcing                1   35                   5.0   \n",
       "1                   other                1   30                   5.0   \n",
       "2                sourcing                1   34                   3.0   \n",
       "3                   other                2   39                   1.0   \n",
       "4                   other                1   45                   3.0   \n",
       "...                   ...              ...  ...                   ...   \n",
       "54803            sourcing                1   48                   3.0   \n",
       "54804               other                1   37                   2.0   \n",
       "54805               other                1   27                   5.0   \n",
       "54806            sourcing                1   29                   1.0   \n",
       "54807               other                1   27                   1.0   \n",
       "\n",
       "       length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
       "0                      8              1            0                  49   \n",
       "1                      4              0            0                  60   \n",
       "2                      7              0            0                  50   \n",
       "3                     10              0            0                  50   \n",
       "4                      2              0            0                  73   \n",
       "...                  ...            ...          ...                 ...   \n",
       "54803                 17              0            0                  78   \n",
       "54804                  6              0            0                  56   \n",
       "54805                  3              1            0                  79   \n",
       "54806                  2              0            0                  45   \n",
       "54807                  5              0            0                  49   \n",
       "\n",
       "       is_promoted  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "...            ...  \n",
       "54803            0  \n",
       "54804            0  \n",
       "54805            0  \n",
       "54806            0  \n",
       "54807            0  \n",
       "\n",
       "[54808 rows x 14 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode = EncoderOrdinal(\n",
    "\t\t\t\tdf=dataset,\n",
    "\t\t\t\tcol_encode=[\"education\", \"gender\"],\n",
    "\t\t\t\tcategories={'education': [\"Below Secondary\", \"Bachelor's\", \"Master's & above\"]}\n",
    "\t\t\t)\n",
    "encode.fit()\n",
    "new_df = encode.transform(dataset)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2d8febc",
   "metadata": {},
   "source": [
    "The **get_encoded_columns()** method returns the list of columns encoded. For the ordinal encoding, this is the same list passed to the **col_encode** parameter if it's provided or the list of categorical features if the latter parameter is not provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "402bc5c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['education', 'gender']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode.get_encoded_columns()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7b12ef6",
   "metadata": {},
   "source": [
    "Finally, we can recover the original values by calling the inverse_transform() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e1935eab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead 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>employee_id</th>\n",
       "      <th>department</th>\n",
       "      <th>region</th>\n",
       "      <th>education</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
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       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65438</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_7</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>f</td>\n",
       "      <td>sourcing</td>\n",
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       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
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       "      <td>other</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7513</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_19</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2542</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_23</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
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       "      <th>4</th>\n",
       "      <td>48945</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_26</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>54803</th>\n",
       "      <td>3030</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_14</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54804</th>\n",
       "      <td>74592</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_27</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54805</th>\n",
       "      <td>13918</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>region_1</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54806</th>\n",
       "      <td>13614</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_9</td>\n",
       "      <td>None</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>51526</td>\n",
       "      <td>HR</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       employee_id         department     region         education gender  \\\n",
       "0            65438  Sales & Marketing   region_7  Master's & above      f   \n",
       "1            65141         Operations  region_22        Bachelor's      m   \n",
       "2             7513  Sales & Marketing  region_19        Bachelor's      m   \n",
       "3             2542  Sales & Marketing  region_23        Bachelor's      m   \n",
       "4            48945         Technology  region_26        Bachelor's      m   \n",
       "...            ...                ...        ...               ...    ...   \n",
       "54803         3030         Technology  region_14        Bachelor's      m   \n",
       "54804        74592         Operations  region_27  Master's & above      f   \n",
       "54805        13918          Analytics   region_1        Bachelor's      m   \n",
       "54806        13614  Sales & Marketing   region_9              None      m   \n",
       "54807        51526                 HR  region_22        Bachelor's      m   \n",
       "\n",
       "      recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "0                sourcing                1   35                   5.0   \n",
       "1                   other                1   30                   5.0   \n",
       "2                sourcing                1   34                   3.0   \n",
       "3                   other                2   39                   1.0   \n",
       "4                   other                1   45                   3.0   \n",
       "...                   ...              ...  ...                   ...   \n",
       "54803            sourcing                1   48                   3.0   \n",
       "54804               other                1   37                   2.0   \n",
       "54805               other                1   27                   5.0   \n",
       "54806            sourcing                1   29                   1.0   \n",
       "54807               other                1   27                   1.0   \n",
       "\n",
       "       length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
       "0                      8              1            0                  49   \n",
       "1                      4              0            0                  60   \n",
       "2                      7              0            0                  50   \n",
       "3                     10              0            0                  50   \n",
       "4                      2              0            0                  73   \n",
       "...                  ...            ...          ...                 ...   \n",
       "54803                 17              0            0                  78   \n",
       "54804                  6              0            0                  56   \n",
       "54805                  3              1            0                  79   \n",
       "54806                  2              0            0                  45   \n",
       "54807                  5              0            0                  49   \n",
       "\n",
       "       is_promoted  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "...            ...  \n",
       "54803            0  \n",
       "54804            0  \n",
       "54805            0  \n",
       "54806            0  \n",
       "54807            0  \n",
       "\n",
       "[54808 rows x 14 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_df = encode.inverse_transform(new_df)\n",
    "org_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c60394",
   "metadata": {},
   "source": [
    "### Using a dataset without headers\n",
    "\n",
    "The same proceedure can be performed with a dataset without headers. Here, column indices are used instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a473b067",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>54808 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      1          2                 3  4         5   6   7   \\\n",
       "0      Sales & Marketing   region_7  Master's & above  f  sourcing   1  35   \n",
       "1             Operations  region_22        Bachelor's  m     other   1  30   \n",
       "2      Sales & Marketing  region_19        Bachelor's  m  sourcing   1  34   \n",
       "3      Sales & Marketing  region_23        Bachelor's  m     other   2  39   \n",
       "4             Technology  region_26        Bachelor's  m     other   1  45   \n",
       "...                  ...        ...               ... ..       ...  ..  ..   \n",
       "54803         Technology  region_14        Bachelor's  m  sourcing   1  48   \n",
       "54804         Operations  region_27  Master's & above  f     other   1  37   \n",
       "54805          Analytics   region_1        Bachelor's  m     other   1  27   \n",
       "54806  Sales & Marketing   region_9               NaN  m  sourcing   1  29   \n",
       "54807                 HR  region_22        Bachelor's  m     other   1  27   \n",
       "\n",
       "        8   9   10  11  12  13  \n",
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       "3      1.0  10   0   0  50   0  \n",
       "4      3.0   2   0   0  73   0  \n",
       "...    ...  ..  ..  ..  ..  ..  \n",
       "54803  3.0  17   0   0  78   0  \n",
       "54804  2.0   6   0   0  56   0  \n",
       "54805  5.0   3   1   0  79   0  \n",
       "54806  1.0   2   0   0  45   0  \n",
       "54807  1.0   5   0   0  49   0  \n",
       "\n",
       "[54808 rows x 13 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset =  pd.read_csv(data_dir + 'hr_promotion/train.csv', header=None, skiprows=1)\n",
    "dataset.drop(columns=[0], inplace=True)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "785c9f26",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "                       0          1  2  3         4  5   6    7   8  9  10  \\\n",
       "0      Sales & Marketing   region_7  2  0  sourcing  1  35  5.0   8  1   0   \n",
       "1             Operations  region_22  0  1     other  1  30  5.0   4  0   0   \n",
       "2      Sales & Marketing  region_19  0  1  sourcing  1  34  3.0   7  0   0   \n",
       "3      Sales & Marketing  region_23  0  1     other  2  39  1.0  10  0   0   \n",
       "4             Technology  region_26  0  1     other  1  45  3.0   2  0   0   \n",
       "...                  ...        ... .. ..       ... ..  ..  ...  .. ..  ..   \n",
       "54803         Technology  region_14  0  1  sourcing  1  48  3.0  17  0   0   \n",
       "54804         Operations  region_27  2  0     other  1  37  2.0   6  0   0   \n",
       "54805          Analytics   region_1  0  1     other  1  27  5.0   3  1   0   \n",
       "54806  Sales & Marketing   region_9 -1  1  sourcing  1  29  1.0   2  0   0   \n",
       "54807                 HR  region_22  0  1     other  1  27  1.0   5  0   0   \n",
       "\n",
       "       11  12  \n",
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       "2      50   0  \n",
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       "4      73   0  \n",
       "...    ..  ..  \n",
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       "54804  56   0  \n",
       "54805  79   0  \n",
       "54806  45   0  \n",
       "54807  49   0  \n",
       "\n",
       "[54808 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode = EncoderOrdinal(\n",
    "\t\t\t\tdf=dataset,\n",
    "\t\t\t\tcol_encode=[3, 2],\n",
    "\t\t\t\tcategories={2: [\"Below Secondary\", \"Bachelor's\", \"Master's & above\"]}\n",
    "\t\t\t)\n",
    "encode.fit()\n",
    "new_df = encode.transform(dataset)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ae606d80",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>Operations</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_19</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_23</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Technology</td>\n",
       "      <td>region_26</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54803</th>\n",
       "      <td>Technology</td>\n",
       "      <td>region_14</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54804</th>\n",
       "      <td>Operations</td>\n",
       "      <td>region_27</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54805</th>\n",
       "      <td>Analytics</td>\n",
       "      <td>region_1</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54806</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_9</td>\n",
       "      <td>None</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>HR</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0          1                 2  3         4  5   6  \\\n",
       "0      Sales & Marketing   region_7  Master's & above  f  sourcing  1  35   \n",
       "1             Operations  region_22        Bachelor's  m     other  1  30   \n",
       "2      Sales & Marketing  region_19        Bachelor's  m  sourcing  1  34   \n",
       "3      Sales & Marketing  region_23        Bachelor's  m     other  2  39   \n",
       "4             Technology  region_26        Bachelor's  m     other  1  45   \n",
       "...                  ...        ...               ... ..       ... ..  ..   \n",
       "54803         Technology  region_14        Bachelor's  m  sourcing  1  48   \n",
       "54804         Operations  region_27  Master's & above  f     other  1  37   \n",
       "54805          Analytics   region_1        Bachelor's  m     other  1  27   \n",
       "54806  Sales & Marketing   region_9              None  m  sourcing  1  29   \n",
       "54807                 HR  region_22        Bachelor's  m     other  1  27   \n",
       "\n",
       "         7   8  9  10  11  12  \n",
       "0      5.0   8  1   0  49   0  \n",
       "1      5.0   4  0   0  60   0  \n",
       "2      3.0   7  0   0  50   0  \n",
       "3      1.0  10  0   0  50   0  \n",
       "4      3.0   2  0   0  73   0  \n",
       "...    ...  .. ..  ..  ..  ..  \n",
       "54803  3.0  17  0   0  78   0  \n",
       "54804  2.0   6  0   0  56   0  \n",
       "54805  5.0   3  1   0  79   0  \n",
       "54806  1.0   2  0   0  45   0  \n",
       "54807  1.0   5  0   0  49   0  \n",
       "\n",
       "[54808 rows x 13 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_df = encode.inverse_transform(new_df)\n",
    "org_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2252a5dd",
   "metadata": {},
   "source": [
    "## One-Hot Encoding\n",
    "\n",
    "The following cell shows an example using the one-hot encoding class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "447ec273",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No columns specified for encoding. These columns have been automatically identfied as the following:\n",
      "['department', 'region', 'education', 'gender', 'recruitment_channel']\n"
     ]
    },
    {
     "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>employee_id</th>\n",
       "      <th>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "      <th>department_Finance</th>\n",
       "      <th>...</th>\n",
       "      <th>region_region_6</th>\n",
       "      <th>region_region_7</th>\n",
       "      <th>region_region_8</th>\n",
       "      <th>region_region_9</th>\n",
       "      <th>education_Below Secondary</th>\n",
       "      <th>education_Master's &amp; above</th>\n",
       "      <th>education_nan</th>\n",
       "      <th>gender_m</th>\n",
       "      <th>recruitment_channel_referred</th>\n",
       "      <th>recruitment_channel_sourcing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>1</th>\n",
       "      <td>65141</td>\n",
       "      <td>1</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>7513</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2542</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48945</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>54803</th>\n",
       "      <td>3030</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>54804</th>\n",
       "      <td>74592</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54805</th>\n",
       "      <td>13918</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>54806</th>\n",
       "      <td>13614</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>51526</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       employee_id  no_of_trainings  age  previous_year_rating  \\\n",
       "0            65438                1   35                   5.0   \n",
       "1            65141                1   30                   5.0   \n",
       "2             7513                1   34                   3.0   \n",
       "3             2542                2   39                   1.0   \n",
       "4            48945                1   45                   3.0   \n",
       "...            ...              ...  ...                   ...   \n",
       "54803         3030                1   48                   3.0   \n",
       "54804        74592                1   37                   2.0   \n",
       "54805        13918                1   27                   5.0   \n",
       "54806        13614                1   29                   1.0   \n",
       "54807        51526                1   27                   1.0   \n",
       "\n",
       "       length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
       "0                      8              1            0                  49   \n",
       "1                      4              0            0                  60   \n",
       "2                      7              0            0                  50   \n",
       "3                     10              0            0                  50   \n",
       "4                      2              0            0                  73   \n",
       "...                  ...            ...          ...                 ...   \n",
       "54803                 17              0            0                  78   \n",
       "54804                  6              0            0                  56   \n",
       "54805                  3              1            0                  79   \n",
       "54806                  2              0            0                  45   \n",
       "54807                  5              0            0                  49   \n",
       "\n",
       "       is_promoted  department_Finance  ...  region_region_6  region_region_7  \\\n",
       "0                0                   0  ...                0                1   \n",
       "1                0                   0  ...                0                0   \n",
       "2                0                   0  ...                0                0   \n",
       "3                0                   0  ...                0                0   \n",
       "4                0                   0  ...                0                0   \n",
       "...            ...                 ...  ...              ...              ...   \n",
       "54803            0                   0  ...                0                0   \n",
       "54804            0                   0  ...                0                0   \n",
       "54805            0                   0  ...                0                0   \n",
       "54806            0                   0  ...                0                0   \n",
       "54807            0                   0  ...                0                0   \n",
       "\n",
       "       region_region_8  region_region_9  education_Below Secondary  \\\n",
       "0                    0                0                          0   \n",
       "1                    0                0                          0   \n",
       "2                    0                0                          0   \n",
       "3                    0                0                          0   \n",
       "4                    0                0                          0   \n",
       "...                ...              ...                        ...   \n",
       "54803                0                0                          0   \n",
       "54804                0                0                          0   \n",
       "54805                0                0                          0   \n",
       "54806                0                1                          0   \n",
       "54807                0                0                          0   \n",
       "\n",
       "       education_Master's & above  education_nan  gender_m  \\\n",
       "0                               1              0         0   \n",
       "1                               0              0         1   \n",
       "2                               0              0         1   \n",
       "3                               0              0         1   \n",
       "4                               0              0         1   \n",
       "...                           ...            ...       ...   \n",
       "54803                           0              0         1   \n",
       "54804                           1              0         0   \n",
       "54805                           0              0         1   \n",
       "54806                           0              1         1   \n",
       "54807                           0              0         1   \n",
       "\n",
       "       recruitment_channel_referred  recruitment_channel_sourcing  \n",
       "0                                 0                             1  \n",
       "1                                 0                             0  \n",
       "2                                 0                             1  \n",
       "3                                 0                             0  \n",
       "4                                 0                             0  \n",
       "...                             ...                           ...  \n",
       "54803                             0                             1  \n",
       "54804                             0                             0  \n",
       "54805                             0                             0  \n",
       "54806                             0                             1  \n",
       "54807                             0                             0  \n",
       "\n",
       "[54808 rows x 56 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset =  pd.read_csv(data_dir + 'hr_promotion/train.csv')\n",
    "dataset.head()\n",
    "\n",
    "encode = EncoderOHE(col_encode=None)\n",
    "encode.fit(df=dataset)\n",
    "new_df = encode.transform(dataset)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4975c416",
   "metadata": {},
   "source": [
    "When using the EncoderOHE, the **get_encoded_columns()** method will return the list of the original columns that were encoded, but that are not present in the transformed dataset anymore, because these were replaced by one or more one-hot encoded columns (one for each value in the column)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "611f2f00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['department', 'region', 'education', 'gender', 'recruitment_channel']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode.get_encoded_columns()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b1cb8c2",
   "metadata": {},
   "source": [
    "To get the list of all new columns created with the one-hot encodings, use the **get_one_hot_columns()** method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1c362303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['department_Finance',\n",
       " 'department_HR',\n",
       " 'department_Legal',\n",
       " 'department_Operations',\n",
       " 'department_Procurement',\n",
       " 'department_R&D',\n",
       " 'department_Sales & Marketing',\n",
       " 'department_Technology',\n",
       " 'region_region_10',\n",
       " 'region_region_11',\n",
       " 'region_region_12',\n",
       " 'region_region_13',\n",
       " 'region_region_14',\n",
       " 'region_region_15',\n",
       " 'region_region_16',\n",
       " 'region_region_17',\n",
       " 'region_region_18',\n",
       " 'region_region_19',\n",
       " 'region_region_2',\n",
       " 'region_region_20',\n",
       " 'region_region_21',\n",
       " 'region_region_22',\n",
       " 'region_region_23',\n",
       " 'region_region_24',\n",
       " 'region_region_25',\n",
       " 'region_region_26',\n",
       " 'region_region_27',\n",
       " 'region_region_28',\n",
       " 'region_region_29',\n",
       " 'region_region_3',\n",
       " 'region_region_30',\n",
       " 'region_region_31',\n",
       " 'region_region_32',\n",
       " 'region_region_33',\n",
       " 'region_region_34',\n",
       " 'region_region_4',\n",
       " 'region_region_5',\n",
       " 'region_region_6',\n",
       " 'region_region_7',\n",
       " 'region_region_8',\n",
       " 'region_region_9',\n",
       " 'education_Below Secondary',\n",
       " \"education_Master's & above\",\n",
       " 'education_nan',\n",
       " 'gender_m',\n",
       " 'recruitment_channel_referred',\n",
       " 'recruitment_channel_sourcing']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode.get_one_hot_columns()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b67f46f",
   "metadata": {},
   "source": [
    "We can also specify which columns to encode using the column names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e2b5893d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>department</th>\n",
       "      <th>region</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
       "      <th>no_of_trainings</th>\n",
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       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
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       "      <th>4</th>\n",
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       "      <td>sourcing</td>\n",
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       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>51526</td>\n",
       "      <td>HR</td>\n",
       "      <td>region_22</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       employee_id         department     region gender recruitment_channel  \\\n",
       "0            65438  Sales & Marketing   region_7      f            sourcing   \n",
       "1            65141         Operations  region_22      m               other   \n",
       "2             7513  Sales & Marketing  region_19      m            sourcing   \n",
       "3             2542  Sales & Marketing  region_23      m               other   \n",
       "4            48945         Technology  region_26      m               other   \n",
       "...            ...                ...        ...    ...                 ...   \n",
       "54803         3030         Technology  region_14      m            sourcing   \n",
       "54804        74592         Operations  region_27      f               other   \n",
       "54805        13918          Analytics   region_1      m               other   \n",
       "54806        13614  Sales & Marketing   region_9      m            sourcing   \n",
       "54807        51526                 HR  region_22      m               other   \n",
       "\n",
       "       no_of_trainings  age  previous_year_rating  length_of_service  \\\n",
       "0                    1   35                   5.0                  8   \n",
       "1                    1   30                   5.0                  4   \n",
       "2                    1   34                   3.0                  7   \n",
       "3                    2   39                   1.0                 10   \n",
       "4                    1   45                   3.0                  2   \n",
       "...                ...  ...                   ...                ...   \n",
       "54803                1   48                   3.0                 17   \n",
       "54804                1   37                   2.0                  6   \n",
       "54805                1   27                   5.0                  3   \n",
       "54806                1   29                   1.0                  2   \n",
       "54807                1   27                   1.0                  5   \n",
       "\n",
       "       KPIs_met >80%  awards_won?  avg_training_score  is_promoted  \\\n",
       "0                  1            0                  49            0   \n",
       "1                  0            0                  60            0   \n",
       "2                  0            0                  50            0   \n",
       "3                  0            0                  50            0   \n",
       "4                  0            0                  73            0   \n",
       "...              ...          ...                 ...          ...   \n",
       "54803              0            0                  78            0   \n",
       "54804              0            0                  56            0   \n",
       "54805              1            0                  79            0   \n",
       "54806              0            0                  45            0   \n",
       "54807              0            0                  49            0   \n",
       "\n",
       "       education_Below Secondary  education_Master's & above  education_nan  \n",
       "0                              0                           1              0  \n",
       "1                              0                           0              0  \n",
       "2                              0                           0              0  \n",
       "3                              0                           0              0  \n",
       "4                              0                           0              0  \n",
       "...                          ...                         ...            ...  \n",
       "54803                          0                           0              0  \n",
       "54804                          0                           1              0  \n",
       "54805                          0                           0              0  \n",
       "54806                          0                           0              1  \n",
       "54807                          0                           0              0  \n",
       "\n",
       "[54808 rows x 16 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode = EncoderOHE(col_encode=['education'])\n",
    "encode.fit(df=dataset)\n",
    "new_df = encode.transform(dataset)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b059b57",
   "metadata": {},
   "source": [
    "or the column indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "44d38682",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2542</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_23</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48945</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_26</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54803</th>\n",
       "      <td>3030</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_14</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54804</th>\n",
       "      <td>74592</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_27</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54805</th>\n",
       "      <td>13918</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>region_1</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54806</th>\n",
       "      <td>13614</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_9</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>51526</td>\n",
       "      <td>HR</td>\n",
       "      <td>region_22</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       employee_id         department     region gender recruitment_channel  \\\n",
       "0            65438  Sales & Marketing   region_7      f            sourcing   \n",
       "1            65141         Operations  region_22      m               other   \n",
       "2             7513  Sales & Marketing  region_19      m            sourcing   \n",
       "3             2542  Sales & Marketing  region_23      m               other   \n",
       "4            48945         Technology  region_26      m               other   \n",
       "...            ...                ...        ...    ...                 ...   \n",
       "54803         3030         Technology  region_14      m            sourcing   \n",
       "54804        74592         Operations  region_27      f               other   \n",
       "54805        13918          Analytics   region_1      m               other   \n",
       "54806        13614  Sales & Marketing   region_9      m            sourcing   \n",
       "54807        51526                 HR  region_22      m               other   \n",
       "\n",
       "       no_of_trainings  age  previous_year_rating  length_of_service  \\\n",
       "0                    1   35                   5.0                  8   \n",
       "1                    1   30                   5.0                  4   \n",
       "2                    1   34                   3.0                  7   \n",
       "3                    2   39                   1.0                 10   \n",
       "4                    1   45                   3.0                  2   \n",
       "...                ...  ...                   ...                ...   \n",
       "54803                1   48                   3.0                 17   \n",
       "54804                1   37                   2.0                  6   \n",
       "54805                1   27                   5.0                  3   \n",
       "54806                1   29                   1.0                  2   \n",
       "54807                1   27                   1.0                  5   \n",
       "\n",
       "       KPIs_met >80%  awards_won?  avg_training_score  is_promoted  \\\n",
       "0                  1            0                  49            0   \n",
       "1                  0            0                  60            0   \n",
       "2                  0            0                  50            0   \n",
       "3                  0            0                  50            0   \n",
       "4                  0            0                  73            0   \n",
       "...              ...          ...                 ...          ...   \n",
       "54803              0            0                  78            0   \n",
       "54804              0            0                  56            0   \n",
       "54805              1            0                  79            0   \n",
       "54806              0            0                  45            0   \n",
       "54807              0            0                  49            0   \n",
       "\n",
       "       education_Below Secondary  education_Master's & above  education_nan  \n",
       "0                              0                           1              0  \n",
       "1                              0                           0              0  \n",
       "2                              0                           0              0  \n",
       "3                              0                           0              0  \n",
       "4                              0                           0              0  \n",
       "...                          ...                         ...            ...  \n",
       "54803                          0                           0              0  \n",
       "54804                          0                           1              0  \n",
       "54805                          0                           0              0  \n",
       "54806                          0                           0              1  \n",
       "54807                          0                           0              0  \n",
       "\n",
       "[54808 rows x 16 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode = EncoderOHE(col_encode=[3])\n",
    "encode.fit(df=dataset)\n",
    "new_df = encode.transform(dataset)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a984778",
   "metadata": {},
   "source": [
    "We can also revert the encoded column to their original state by using the inverse_transform() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "13ef1b7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education</th>\n",
       "      <th>employee_id</th>\n",
       "      <th>department</th>\n",
       "      <th>region</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
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       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>65438</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_7</td>\n",
       "      <td>f</td>\n",
       "      <td>sourcing</td>\n",
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       "      <td>Operations</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>7513</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_19</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>2542</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_23</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>48945</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_26</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>54803</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>3030</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_14</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54804</th>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>74592</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_27</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54805</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>13918</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>region_1</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54806</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13614</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_9</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>51526</td>\n",
       "      <td>HR</td>\n",
       "      <td>region_22</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              education  employee_id         department     region gender  \\\n",
       "0      Master's & above        65438  Sales & Marketing   region_7      f   \n",
       "1            Bachelor's        65141         Operations  region_22      m   \n",
       "2            Bachelor's         7513  Sales & Marketing  region_19      m   \n",
       "3            Bachelor's         2542  Sales & Marketing  region_23      m   \n",
       "4            Bachelor's        48945         Technology  region_26      m   \n",
       "...                 ...          ...                ...        ...    ...   \n",
       "54803        Bachelor's         3030         Technology  region_14      m   \n",
       "54804  Master's & above        74592         Operations  region_27      f   \n",
       "54805        Bachelor's        13918          Analytics   region_1      m   \n",
       "54806               NaN        13614  Sales & Marketing   region_9      m   \n",
       "54807        Bachelor's        51526                 HR  region_22      m   \n",
       "\n",
       "      recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "0                sourcing                1   35                   5.0   \n",
       "1                   other                1   30                   5.0   \n",
       "2                sourcing                1   34                   3.0   \n",
       "3                   other                2   39                   1.0   \n",
       "4                   other                1   45                   3.0   \n",
       "...                   ...              ...  ...                   ...   \n",
       "54803            sourcing                1   48                   3.0   \n",
       "54804               other                1   37                   2.0   \n",
       "54805               other                1   27                   5.0   \n",
       "54806            sourcing                1   29                   1.0   \n",
       "54807               other                1   27                   1.0   \n",
       "\n",
       "       length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
       "0                      8              1            0                  49   \n",
       "1                      4              0            0                  60   \n",
       "2                      7              0            0                  50   \n",
       "3                     10              0            0                  50   \n",
       "4                      2              0            0                  73   \n",
       "...                  ...            ...          ...                 ...   \n",
       "54803                 17              0            0                  78   \n",
       "54804                  6              0            0                  56   \n",
       "54805                  3              1            0                  79   \n",
       "54806                  2              0            0                  45   \n",
       "54807                  5              0            0                  49   \n",
       "\n",
       "       is_promoted  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "...            ...  \n",
       "54803            0  \n",
       "54804            0  \n",
       "54805            0  \n",
       "54806            0  \n",
       "54807            0  \n",
       "\n",
       "[54808 rows x 14 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_df = encode.inverse_transform(new_df)\n",
    "org_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d07931fc",
   "metadata": {},
   "source": [
    "Let us now test the 'drop' parameter. For that, we will repeat the same procedure as the previous cell, but this time use drop = False, since drop = True is the default behavior. We can see that the cell that uses drop = False (below) has one extra column: education_Bachelor's, while the cell that uses drop = True (above) doesn't have this column, since it was droped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "88015bc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>department</th>\n",
       "      <th>region</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
       "      <th>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "      <th>education_Bachelor's</th>\n",
       "      <th>education_Below Secondary</th>\n",
       "      <th>education_Master's &amp; above</th>\n",
       "      <th>education_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65438</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_7</td>\n",
       "      <td>f</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65141</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_22</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7513</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_19</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2542</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_23</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48945</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_26</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54803</th>\n",
       "      <td>3030</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_14</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54804</th>\n",
       "      <td>74592</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_27</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54805</th>\n",
       "      <td>13918</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>region_1</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54806</th>\n",
       "      <td>13614</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_9</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>51526</td>\n",
       "      <td>HR</td>\n",
       "      <td>region_22</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       employee_id         department     region gender recruitment_channel  \\\n",
       "0            65438  Sales & Marketing   region_7      f            sourcing   \n",
       "1            65141         Operations  region_22      m               other   \n",
       "2             7513  Sales & Marketing  region_19      m            sourcing   \n",
       "3             2542  Sales & Marketing  region_23      m               other   \n",
       "4            48945         Technology  region_26      m               other   \n",
       "...            ...                ...        ...    ...                 ...   \n",
       "54803         3030         Technology  region_14      m            sourcing   \n",
       "54804        74592         Operations  region_27      f               other   \n",
       "54805        13918          Analytics   region_1      m               other   \n",
       "54806        13614  Sales & Marketing   region_9      m            sourcing   \n",
       "54807        51526                 HR  region_22      m               other   \n",
       "\n",
       "       no_of_trainings  age  previous_year_rating  length_of_service  \\\n",
       "0                    1   35                   5.0                  8   \n",
       "1                    1   30                   5.0                  4   \n",
       "2                    1   34                   3.0                  7   \n",
       "3                    2   39                   1.0                 10   \n",
       "4                    1   45                   3.0                  2   \n",
       "...                ...  ...                   ...                ...   \n",
       "54803                1   48                   3.0                 17   \n",
       "54804                1   37                   2.0                  6   \n",
       "54805                1   27                   5.0                  3   \n",
       "54806                1   29                   1.0                  2   \n",
       "54807                1   27                   1.0                  5   \n",
       "\n",
       "       KPIs_met >80%  awards_won?  avg_training_score  is_promoted  \\\n",
       "0                  1            0                  49            0   \n",
       "1                  0            0                  60            0   \n",
       "2                  0            0                  50            0   \n",
       "3                  0            0                  50            0   \n",
       "4                  0            0                  73            0   \n",
       "...              ...          ...                 ...          ...   \n",
       "54803              0            0                  78            0   \n",
       "54804              0            0                  56            0   \n",
       "54805              1            0                  79            0   \n",
       "54806              0            0                  45            0   \n",
       "54807              0            0                  49            0   \n",
       "\n",
       "       education_Bachelor's  education_Below Secondary  \\\n",
       "0                         0                          0   \n",
       "1                         1                          0   \n",
       "2                         1                          0   \n",
       "3                         1                          0   \n",
       "4                         1                          0   \n",
       "...                     ...                        ...   \n",
       "54803                     1                          0   \n",
       "54804                     0                          0   \n",
       "54805                     1                          0   \n",
       "54806                     0                          0   \n",
       "54807                     1                          0   \n",
       "\n",
       "       education_Master's & above  education_nan  \n",
       "0                               1              0  \n",
       "1                               0              0  \n",
       "2                               0              0  \n",
       "3                               0              0  \n",
       "4                               0              0  \n",
       "...                           ...            ...  \n",
       "54803                           0              0  \n",
       "54804                           1              0  \n",
       "54805                           0              0  \n",
       "54806                           0              1  \n",
       "54807                           0              0  \n",
       "\n",
       "[54808 rows x 17 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode = EncoderOHE(col_encode=['education'], drop=False)\n",
    "encode.fit(df=dataset)\n",
    "new_df = encode.transform(dataset)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9b95c6f0",
   "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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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education</th>\n",
       "      <th>employee_id</th>\n",
       "      <th>department</th>\n",
       "      <th>region</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
       "      <th>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>65438</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_7</td>\n",
       "      <td>f</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>65141</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_22</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>7513</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_19</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>2542</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_23</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>48945</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_26</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54803</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>3030</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_14</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54804</th>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>74592</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_27</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54805</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>13918</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>region_1</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54806</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13614</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_9</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>51526</td>\n",
       "      <td>HR</td>\n",
       "      <td>region_22</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              education  employee_id         department     region gender  \\\n",
       "0      Master's & above        65438  Sales & Marketing   region_7      f   \n",
       "1            Bachelor's        65141         Operations  region_22      m   \n",
       "2            Bachelor's         7513  Sales & Marketing  region_19      m   \n",
       "3            Bachelor's         2542  Sales & Marketing  region_23      m   \n",
       "4            Bachelor's        48945         Technology  region_26      m   \n",
       "...                 ...          ...                ...        ...    ...   \n",
       "54803        Bachelor's         3030         Technology  region_14      m   \n",
       "54804  Master's & above        74592         Operations  region_27      f   \n",
       "54805        Bachelor's        13918          Analytics   region_1      m   \n",
       "54806               NaN        13614  Sales & Marketing   region_9      m   \n",
       "54807        Bachelor's        51526                 HR  region_22      m   \n",
       "\n",
       "      recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "0                sourcing                1   35                   5.0   \n",
       "1                   other                1   30                   5.0   \n",
       "2                sourcing                1   34                   3.0   \n",
       "3                   other                2   39                   1.0   \n",
       "4                   other                1   45                   3.0   \n",
       "...                   ...              ...  ...                   ...   \n",
       "54803            sourcing                1   48                   3.0   \n",
       "54804               other                1   37                   2.0   \n",
       "54805               other                1   27                   5.0   \n",
       "54806            sourcing                1   29                   1.0   \n",
       "54807               other                1   27                   1.0   \n",
       "\n",
       "       length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
       "0                      8              1            0                  49   \n",
       "1                      4              0            0                  60   \n",
       "2                      7              0            0                  50   \n",
       "3                     10              0            0                  50   \n",
       "4                      2              0            0                  73   \n",
       "...                  ...            ...          ...                 ...   \n",
       "54803                 17              0            0                  78   \n",
       "54804                  6              0            0                  56   \n",
       "54805                  3              1            0                  79   \n",
       "54806                  2              0            0                  45   \n",
       "54807                  5              0            0                  49   \n",
       "\n",
       "       is_promoted  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "...            ...  \n",
       "54803            0  \n",
       "54804            0  \n",
       "54805            0  \n",
       "54806            0  \n",
       "54807            0  \n",
       "\n",
       "[54808 rows x 14 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_df = encode.inverse_transform(new_df)\n",
    "org_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79aeb17",
   "metadata": {},
   "source": [
    "### Using a dataset without headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "869e6c44",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>Operations</td>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "<p>54808 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      1          2                 3  4         5   6   7   \\\n",
       "0      Sales & Marketing   region_7  Master's & above  f  sourcing   1  35   \n",
       "1             Operations  region_22        Bachelor's  m     other   1  30   \n",
       "2      Sales & Marketing  region_19        Bachelor's  m  sourcing   1  34   \n",
       "3      Sales & Marketing  region_23        Bachelor's  m     other   2  39   \n",
       "4             Technology  region_26        Bachelor's  m     other   1  45   \n",
       "...                  ...        ...               ... ..       ...  ..  ..   \n",
       "54803         Technology  region_14        Bachelor's  m  sourcing   1  48   \n",
       "54804         Operations  region_27  Master's & above  f     other   1  37   \n",
       "54805          Analytics   region_1        Bachelor's  m     other   1  27   \n",
       "54806  Sales & Marketing   region_9               NaN  m  sourcing   1  29   \n",
       "54807                 HR  region_22        Bachelor's  m     other   1  27   \n",
       "\n",
       "        8   9   10  11  12  13  \n",
       "0      5.0   8   1   0  49   0  \n",
       "1      5.0   4   0   0  60   0  \n",
       "2      3.0   7   0   0  50   0  \n",
       "3      1.0  10   0   0  50   0  \n",
       "4      3.0   2   0   0  73   0  \n",
       "...    ...  ..  ..  ..  ..  ..  \n",
       "54803  3.0  17   0   0  78   0  \n",
       "54804  2.0   6   0   0  56   0  \n",
       "54805  5.0   3   1   0  79   0  \n",
       "54806  1.0   2   0   0  45   0  \n",
       "54807  1.0   5   0   0  49   0  \n",
       "\n",
       "[54808 rows x 13 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset =  pd.read_csv(data_dir + 'hr_promotion/train.csv', header=None, skiprows=1)\n",
    "dataset.drop(columns=[0], inplace=True)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bbc69242",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54806</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>HR</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0  3         4  5   6    7   8  9  10  11  ...  \\\n",
       "0      Sales & Marketing  f  sourcing  1  35  5.0   8  1   0  49  ...   \n",
       "1             Operations  m     other  1  30  5.0   4  0   0  60  ...   \n",
       "2      Sales & Marketing  m  sourcing  1  34  3.0   7  0   0  50  ...   \n",
       "3      Sales & Marketing  m     other  2  39  1.0  10  0   0  50  ...   \n",
       "4             Technology  m     other  1  45  3.0   2  0   0  73  ...   \n",
       "...                  ... ..       ... ..  ..  ...  .. ..  ..  ..  ...   \n",
       "54803         Technology  m  sourcing  1  48  3.0  17  0   0  78  ...   \n",
       "54804         Operations  f     other  1  37  2.0   6  0   0  56  ...   \n",
       "54805          Analytics  m     other  1  27  5.0   3  1   0  79  ...   \n",
       "54806  Sales & Marketing  m  sourcing  1  29  1.0   2  0   0  45  ...   \n",
       "54807                 HR  m     other  1  27  1.0   5  0   0  49  ...   \n",
       "\n",
       "       1_region_31  1_region_32  1_region_33  1_region_34  1_region_4  \\\n",
       "0                0            0            0            0           0   \n",
       "1                0            0            0            0           0   \n",
       "2                0            0            0            0           0   \n",
       "3                0            0            0            0           0   \n",
       "4                0            0            0            0           0   \n",
       "...            ...          ...          ...          ...         ...   \n",
       "54803            0            0            0            0           0   \n",
       "54804            0            0            0            0           0   \n",
       "54805            0            0            0            0           0   \n",
       "54806            0            0            0            0           0   \n",
       "54807            0            0            0            0           0   \n",
       "\n",
       "       1_region_5  1_region_6  1_region_7  1_region_8  1_region_9  \n",
       "0               0           0           1           0           0  \n",
       "1               0           0           0           0           0  \n",
       "2               0           0           0           0           0  \n",
       "3               0           0           0           0           0  \n",
       "4               0           0           0           0           0  \n",
       "...           ...         ...         ...         ...         ...  \n",
       "54803           0           0           0           0           0  \n",
       "54804           0           0           0           0           0  \n",
       "54805           0           0           0           0           0  \n",
       "54806           0           0           0           0           1  \n",
       "54807           0           0           0           0           0  \n",
       "\n",
       "[54808 rows x 47 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode = EncoderOHE(col_encode=[2, 1])\n",
    "encode.fit(df=dataset)\n",
    "new_df = encode.transform(dataset)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ff2cc979",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2</th>\n",
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       "      <td>Master's &amp; above</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bachelor's</td>\n",
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       "      <td>Sales &amp; Marketing</td>\n",
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       "      <td>50</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>region_26</td>\n",
       "      <td>Technology</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54803</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>region_14</td>\n",
       "      <td>Technology</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54804</th>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>region_27</td>\n",
       "      <td>Operations</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54805</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>region_1</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54806</th>\n",
       "      <td>NaN</td>\n",
       "      <td>region_9</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>region_22</td>\n",
       "      <td>HR</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      2          1                  0  3         4  5   6  \\\n",
       "0      Master's & above   region_7  Sales & Marketing  f  sourcing  1  35   \n",
       "1            Bachelor's  region_22         Operations  m     other  1  30   \n",
       "2            Bachelor's  region_19  Sales & Marketing  m  sourcing  1  34   \n",
       "3            Bachelor's  region_23  Sales & Marketing  m     other  2  39   \n",
       "4            Bachelor's  region_26         Technology  m     other  1  45   \n",
       "...                 ...        ...                ... ..       ... ..  ..   \n",
       "54803        Bachelor's  region_14         Technology  m  sourcing  1  48   \n",
       "54804  Master's & above  region_27         Operations  f     other  1  37   \n",
       "54805        Bachelor's   region_1          Analytics  m     other  1  27   \n",
       "54806               NaN   region_9  Sales & Marketing  m  sourcing  1  29   \n",
       "54807        Bachelor's  region_22                 HR  m     other  1  27   \n",
       "\n",
       "         7   8  9  10  11  12  \n",
       "0      5.0   8  1   0  49   0  \n",
       "1      5.0   4  0   0  60   0  \n",
       "2      3.0   7  0   0  50   0  \n",
       "3      1.0  10  0   0  50   0  \n",
       "4      3.0   2  0   0  73   0  \n",
       "...    ...  .. ..  ..  ..  ..  \n",
       "54803  3.0  17  0   0  78   0  \n",
       "54804  2.0   6  0   0  56   0  \n",
       "54805  5.0   3  1   0  79   0  \n",
       "54806  1.0   2  0   0  45   0  \n",
       "54807  1.0   5  0   0  49   0  \n",
       "\n",
       "[54808 rows x 13 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_df = encode.inverse_transform(new_df)\n",
    "org_df"
   ]
  }
 ],
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