Absenteeism+Preprocessing+-+ChP+-+df_date_reason_mod.ipynb

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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Reason for Absence</th>\n",
       "      <th>Date</th>\n",
       "      <th>Transportation Expense</th>\n",
       "      <th>Distance to Work</th>\n",
       "      <th>Age</th>\n",
       "      <th>Daily Work Load Average</th>\n",
       "      <th>Body Mass Index</th>\n",
       "      <th>Education</th>\n",
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       "<p>700 rows × 12 columns</p>\n",
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      "text/plain": [
       "     ID  Reason for Absence        Date  Transportation Expense  \\\n",
       "0    11                  26  07/07/2015                     289   \n",
       "1    36                   0  14/07/2015                     118   \n",
       "2     3                  23  15/07/2015                     179   \n",
       "3     7                   7  16/07/2015                     279   \n",
       "4    11                  23  23/07/2015                     289   \n",
       "..   ..                 ...         ...                     ...   \n",
       "695  17                  10  23/05/2018                     179   \n",
       "696  28                   6  23/05/2018                     225   \n",
       "697  18                  10  24/05/2018                     330   \n",
       "698  25                  23  24/05/2018                     235   \n",
       "699  15                  28  31/05/2018                     291   \n",
       "\n",
       "     Distance to Work  Age  Daily Work Load Average  Body Mass Index  \\\n",
       "0                  36   33                  239.554               30   \n",
       "1                  13   50                  239.554               31   \n",
       "2                  51   38                  239.554               31   \n",
       "3                   5   39                  239.554               24   \n",
       "4                  36   33                  239.554               30   \n",
       "..                ...  ...                      ...              ...   \n",
       "695                22   40                  237.656               22   \n",
       "696                26   28                  237.656               24   \n",
       "697                16   28                  237.656               25   \n",
       "698                16   32                  237.656               25   \n",
       "699                31   40                  237.656               25   \n",
       "\n",
       "     Education  Children  Pets  Absenteeism Time in Hours  \n",
       "0            1         2     1                          4  \n",
       "1            1         1     0                          0  \n",
       "2            1         0     0                          2  \n",
       "3            1         2     0                          4  \n",
       "4            1         2     1                          2  \n",
       "..         ...       ...   ...                        ...  \n",
       "695          2         2     0                          8  \n",
       "696          1         1     2                          3  \n",
       "697          2         0     0                          8  \n",
       "698          3         0     0                          2  \n",
       "699          1         1     1                          2  \n",
       "\n",
       "[700 rows x 12 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "raw_csv_data = pd.read_csv('Absenteeism_data.csv')\n",
    "df = raw_csv_data.copy()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df=df.drop(['ID'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "     Reason_1  Reason_2  Reason_3  Reason_4  Month Value  Day of the Week  \\\n",
       "0           0         0         0         1            7                1   \n",
       "1           0         0         0         0            7                1   \n",
       "2           0         0         0         1            7                2   \n",
       "3           1         0         0         0            7                3   \n",
       "4           0         0         0         1            7                3   \n",
       "..        ...       ...       ...       ...          ...              ...   \n",
       "695         1         0         0         0            5                2   \n",
       "696         1         0         0         0            5                2   \n",
       "697         1         0         0         0            5                3   \n",
       "698         0         0         0         1            5                3   \n",
       "699         0         0         0         1            5                3   \n",
       "\n",
       "     Transportation Expense  Distance to Work  Age  Daily Work Load Average  \\\n",
       "0                       289                36   33                  239.554   \n",
       "1                       118                13   50                  239.554   \n",
       "2                       179                51   38                  239.554   \n",
       "3                       279                 5   39                  239.554   \n",
       "4                       289                36   33                  239.554   \n",
       "..                      ...               ...  ...                      ...   \n",
       "695                     179                22   40                  237.656   \n",
       "696                     225                26   28                  237.656   \n",
       "697                     330                16   28                  237.656   \n",
       "698                     235                16   32                  237.656   \n",
       "699                     291                31   40                  237.656   \n",
       "\n",
       "     Body Mass Index  Education  Children  Pets  Absenteeism Time in Hours  \n",
       "0                 30          1         2     1                          4  \n",
       "1                 31          1         1     0                          0  \n",
       "2                 31          1         0     0                          2  \n",
       "3                 24          1         2     0                          4  \n",
       "4                 30          1         2     1                          2  \n",
       "..               ...        ...       ...   ...                        ...  \n",
       "695               22          2         2     0                          8  \n",
       "696               24          1         1     2                          3  \n",
       "697               25          2         0     0                          8  \n",
       "698               25          3         0     0                          2  \n",
       "699               25          1         1     1                          2  \n",
       "\n",
       "[700 rows x 15 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reason_columns = pd.get_dummies(df['Reason for Absence'])\n",
    "\n",
    "\n",
    "reason_columns['check'] = reason_columns.sum(axis=1)\n",
    "\n",
    "\n",
    "reason_columns = reason_columns.drop(['check'], axis=1)\n",
    "\n",
    "\n",
    "reason_columns = pd.get_dummies(df['Reason for Absence'], drop_first = True)\n",
    "\n",
    "\n",
    "df = df.drop(['Reason for Absence'], axis=1)\n",
    "\n",
    "reason_type_1 = reason_columns.loc[:, 1:14].max(axis=1)\n",
    "reason_type_2 = reason_columns.loc[:, 15:17].max(axis=1)\n",
    "reason_type_3 = reason_columns.loc[:, 18:21].max(axis=1)\n",
    "reason_type_4 = reason_columns.loc[:, 22:].max(axis=1)\n",
    "\n",
    "\n",
    "df = pd.concat([df, reason_type_1, reason_type_2, reason_type_3, reason_type_4], axis = 1)\n",
    "\n",
    "column_names = ['Date', 'Transportation Expense', 'Distance to Work', 'Age',\n",
    "       'Daily Work Load Average', 'Body Mass Index', 'Education',\n",
    "       'Children', 'Pets', 'Absenteeism Time in Hours', 'Reason_1', 'Reason_2', 'Reason_3', 'Reason_4']\n",
    "\n",
    "df.columns = column_names\n",
    "df.head()\n",
    "\n",
    "\n",
    "\n",
    "column_names_reordered = ['Reason_1', 'Reason_2', 'Reason_3', 'Reason_4', \n",
    "                          'Date', 'Transportation Expense', 'Distance to Work', 'Age',\n",
    "       'Daily Work Load Average', 'Body Mass Index', 'Education',\n",
    "       'Children', 'Pets', 'Absenteeism Time in Hours']\n",
    "\n",
    "df = df[column_names_reordered]\n",
    "\n",
    "\n",
    "\n",
    "df_reason_mod = df.copy()\n",
    "\n",
    "\n",
    "df_reason_mod['Date'] = pd.to_datetime(df_reason_mod['Date'], format = '%d/%m/%Y')\n",
    "\n",
    "\n",
    "list_months = []\n",
    "\n",
    "\n",
    "for i in range(df_reason_mod.shape[0]):\n",
    "    list_months.append(df_reason_mod['Date'][i].month)\n",
    "    \n",
    "df_reason_mod['Month Value'] = list_months\n",
    "\n",
    "\n",
    "\n",
    "def date_to_weekday(date_value):\n",
    "    return date_value.weekday()\n",
    "\n",
    "df_reason_mod['Day of the Week'] = df_reason_mod['Date'].apply(date_to_weekday)\n",
    "\n",
    "\n",
    "\n",
    "df_reason_mod = df_reason_mod.drop(['Date'], axis = 1)\n",
    "\n",
    "column_names_upd = ['Reason_1', 'Reason_2', 'Reason_3', 'Reason_4', 'Month Value', 'Day of the Week',\n",
    "       'Transportation Expense', 'Distance to Work', 'Age',\n",
    "       'Daily Work Load Average', 'Body Mass Index', 'Education', 'Children',\n",
    "       'Pets', 'Absenteeism Time in Hours']\n",
    "df_reason_mod = df_reason_mod[column_names_upd]\n",
    "\n",
    "\n",
    "\n",
    "df_reason_date_mod = df_reason_mod.copy()\n",
    "df_reason_date_mod"
   ]
  }
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