4_Preprocessing_Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oceanographic Analysis - CalCOFI - CAPSTONE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Feature Engineering - PreProcessing - Training Data Development"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Plotting modules\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# File system manangement\n",
    "import os,sys\n",
    "\n",
    "# Suppress warnings \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell  \n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "%matplotlib inline\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SKLEARN\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, PowerTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "from sklearn.metrics import mean_squared_error,accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BottleCount</th>\n",
       "      <th>CastCount</th>\n",
       "      <th>Sta_ID</th>\n",
       "      <th>Depth_ID</th>\n",
       "      <th>DepthMeters</th>\n",
       "      <th>TempDegC</th>\n",
       "      <th>Salinity</th>\n",
       "      <th>STheta</th>\n",
       "      <th>RecInd</th>\n",
       "      <th>T_prec</th>\n",
       "      <th>...</th>\n",
       "      <th>NH3q</th>\n",
       "      <th>C14A1q</th>\n",
       "      <th>C14A2q</th>\n",
       "      <th>DarkAq</th>\n",
       "      <th>MeanAq</th>\n",
       "      <th>R_Depth</th>\n",
       "      <th>R_TEMP</th>\n",
       "      <th>R_Sal</th>\n",
       "      <th>R_DYNHT</th>\n",
       "      <th>TempDegF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>10.46</td>\n",
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       "      <td>3</td>\n",
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       "      <td>19-4903CR-HY-060-0930-05400560-0010A-7</td>\n",
       "      <td>10</td>\n",
       "      <td>10.46</td>\n",
       "      <td>33.437</td>\n",
       "      <td>25.654</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.46</td>\n",
       "      <td>232.8</td>\n",
       "      <td>0.02</td>\n",
       "      <td>50.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>054.0 056.0</td>\n",
       "      <td>19-4903CR-HY-060-0930-05400560-0019A-3</td>\n",
       "      <td>19</td>\n",
       "      <td>10.45</td>\n",
       "      <td>33.420</td>\n",
       "      <td>25.643</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>19.0</td>\n",
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       "      <td>234.1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>50.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>054.0 056.0</td>\n",
       "      <td>19-4903CR-HY-060-0930-05400560-0020A-7</td>\n",
       "      <td>20</td>\n",
       "      <td>10.45</td>\n",
       "      <td>33.421</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
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       "      <td>234.0</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 21 columns</p>\n",
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      ],
      "text/plain": [
       "   BottleCount  CastCount       Sta_ID  \\\n",
       "0            1          1  054.0 056.0   \n",
       "1            2          1  054.0 056.0   \n",
       "2            3          1  054.0 056.0   \n",
       "3            4          1  054.0 056.0   \n",
       "4            5          1  054.0 056.0   \n",
       "\n",
       "                                 Depth_ID  DepthMeters  TempDegC  Salinity  \\\n",
       "0  19-4903CR-HY-060-0930-05400560-0000A-3            0     10.50    33.440   \n",
       "1  19-4903CR-HY-060-0930-05400560-0008A-3            8     10.46    33.440   \n",
       "2  19-4903CR-HY-060-0930-05400560-0010A-7           10     10.46    33.437   \n",
       "3  19-4903CR-HY-060-0930-05400560-0019A-3           19     10.45    33.420   \n",
       "4  19-4903CR-HY-060-0930-05400560-0020A-7           20     10.45    33.421   \n",
       "\n",
       "   STheta  RecInd  T_prec  ...  NH3q  C14A1q  C14A2q  DarkAq  MeanAq  R_Depth  \\\n",
       "0  25.649       3     1.0  ...   9.0     9.0     9.0     9.0     9.0      0.0   \n",
       "1  25.656       3     2.0  ...   9.0     9.0     9.0     9.0     9.0      8.0   \n",
       "2  25.654       7     2.0  ...   9.0     9.0     9.0     9.0     9.0     10.0   \n",
       "3  25.643       3     2.0  ...   9.0     9.0     9.0     9.0     9.0     19.0   \n",
       "4  25.643       7     2.0  ...   9.0     9.0     9.0     9.0     9.0     20.0   \n",
       "\n",
       "   R_TEMP  R_Sal  R_DYNHT  TempDegF  \n",
       "0   10.50  233.0     0.00    50.900  \n",
       "1   10.46  232.5     0.01    50.828  \n",
       "2   10.46  232.8     0.02    50.828  \n",
       "3   10.45  234.1     0.04    50.810  \n",
       "4   10.45  234.0     0.04    50.810  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = '../data/calcofi/wrangle_csv.csv'\n",
    "df = pd.read_csv(path, ) #index_col=\"BottleCount\"\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 887018 entries, 0 to 887017\n",
      "Data columns (total 21 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   BottleCount  887018 non-null  int64  \n",
      " 1   CastCount    887018 non-null  int64  \n",
      " 2   Sta_ID       887018 non-null  object \n",
      " 3   Depth_ID     887018 non-null  object \n",
      " 4   DepthMeters  887018 non-null  int64  \n",
      " 5   TempDegC     876053 non-null  float64\n",
      " 6   Salinity     839663 non-null  float64\n",
      " 7   STheta       834327 non-null  float64\n",
      " 8   RecInd       887018 non-null  int64  \n",
      " 9   T_prec       876055 non-null  float64\n",
      " 10  S_prec       839663 non-null  float64\n",
      " 11  NH3q         825936 non-null  float64\n",
      " 12  C14A1q       869746 non-null  float64\n",
      " 13  C14A2q       869764 non-null  float64\n",
      " 14  DarkAq       861581 non-null  float64\n",
      " 15  MeanAq       861580 non-null  float64\n",
      " 16  R_Depth      887017 non-null  float64\n",
      " 17  R_TEMP       840969 non-null  float64\n",
      " 18  R_Sal        834240 non-null  float64\n",
      " 19  R_DYNHT      840354 non-null  float64\n",
      " 20  TempDegF     876053 non-null  float64\n",
      "dtypes: float64(15), int64(4), object(2)\n",
      "memory usage: 142.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>887016</th>\n",
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       "      <td>20-1911OC-MX-308-2233-09340264-0010A-7</td>\n",
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       "      <th>887017</th>\n",
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       "      <td>20-1911OC-MX-308-2233-09340264-0011A-3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>887018 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "             Sta_ID                                Depth_ID\n",
       "0       054.0 056.0  19-4903CR-HY-060-0930-05400560-0000A-3\n",
       "1       054.0 056.0  19-4903CR-HY-060-0930-05400560-0008A-3\n",
       "2       054.0 056.0  19-4903CR-HY-060-0930-05400560-0010A-7\n",
       "3       054.0 056.0  19-4903CR-HY-060-0930-05400560-0019A-3\n",
       "4       054.0 056.0  19-4903CR-HY-060-0930-05400560-0020A-7\n",
       "...             ...                                     ...\n",
       "887013  093.4 026.4  20-1911OC-MX-308-2233-09340264-0000A-7\n",
       "887014  093.4 026.4  20-1911OC-MX-308-2233-09340264-0002A-3\n",
       "887015  093.4 026.4  20-1911OC-MX-308-2233-09340264-0006A-3\n",
       "887016  093.4 026.4  20-1911OC-MX-308-2233-09340264-0010A-7\n",
       "887017  093.4 026.4  20-1911OC-MX-308-2233-09340264-0011A-3\n",
       "\n",
       "[887018 rows x 2 columns]"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include=[object])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"Sta_ID\",\"Depth_ID\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
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       "      <th>DepthMeters</th>\n",
       "      <th>TempDegC</th>\n",
       "      <th>Salinity</th>\n",
       "      <th>STheta</th>\n",
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       "      <th>T_prec</th>\n",
       "      <th>S_prec</th>\n",
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       "      <th>C14A1q</th>\n",
       "      <th>C14A2q</th>\n",
       "      <th>DarkAq</th>\n",
       "      <th>MeanAq</th>\n",
       "      <th>R_Depth</th>\n",
       "      <th>R_TEMP</th>\n",
       "      <th>R_Sal</th>\n",
       "      <th>R_DYNHT</th>\n",
       "      <th>TempDegF</th>\n",
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       "      <td>10.46</td>\n",
       "      <td>232.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>50.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10.46</td>\n",
       "      <td>33.437</td>\n",
       "      <td>25.654</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.46</td>\n",
       "      <td>232.8</td>\n",
       "      <td>0.02</td>\n",
       "      <td>50.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>10.45</td>\n",
       "      <td>33.420</td>\n",
       "      <td>25.643</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.45</td>\n",
       "      <td>234.1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>50.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>10.45</td>\n",
       "      <td>33.421</td>\n",
       "      <td>25.643</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.45</td>\n",
       "      <td>234.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>50.810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BottleCount  CastCount  DepthMeters  TempDegC  Salinity  STheta  RecInd  \\\n",
       "0            1          1            0     10.50    33.440  25.649       3   \n",
       "1            2          1            8     10.46    33.440  25.656       3   \n",
       "2            3          1           10     10.46    33.437  25.654       7   \n",
       "3            4          1           19     10.45    33.420  25.643       3   \n",
       "4            5          1           20     10.45    33.421  25.643       7   \n",
       "\n",
       "   T_prec  S_prec  NH3q  C14A1q  C14A2q  DarkAq  MeanAq  R_Depth  R_TEMP  \\\n",
       "0     1.0     2.0   9.0     9.0     9.0     9.0     9.0      0.0   10.50   \n",
       "1     2.0     2.0   9.0     9.0     9.0     9.0     9.0      8.0   10.46   \n",
       "2     2.0     3.0   9.0     9.0     9.0     9.0     9.0     10.0   10.46   \n",
       "3     2.0     2.0   9.0     9.0     9.0     9.0     9.0     19.0   10.45   \n",
       "4     2.0     3.0   9.0     9.0     9.0     9.0     9.0     20.0   10.45   \n",
       "\n",
       "   R_Sal  R_DYNHT  TempDegF  \n",
       "0  233.0     0.00    50.900  \n",
       "1  232.5     0.01    50.828  \n",
       "2  232.8     0.02    50.828  \n",
       "3  234.1     0.04    50.810  \n",
       "4  234.0     0.04    50.810  "
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TempDegF'].fillna((df['TempDegF'].mean()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create arrays for features and target variable\n",
    "X = df.iloc[:, :-1].values\n",
    "y = df.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00000e+00, 1.00000e+00, 0.00000e+00, ..., 1.05000e+01,\n",
       "        2.33000e+02, 0.00000e+00],\n",
       "       [2.00000e+00, 1.00000e+00, 8.00000e+00, ..., 1.04600e+01,\n",
       "        2.32500e+02, 1.00000e-02],\n",
       "       [3.00000e+00, 1.00000e+00, 1.00000e+01, ..., 1.04600e+01,\n",
       "        2.32800e+02, 2.00000e-02],\n",
       "       ...,\n",
       "       [8.87016e+05, 3.52720e+04, 6.00000e+00, ..., 1.72800e+01,\n",
       "        3.52500e+02, 2.20000e-02],\n",
       "       [8.87017e+05, 3.52720e+04, 1.00000e+01, ..., 1.71800e+01,\n",
       "        3.50800e+02, 3.20000e-02],\n",
       "       [8.87018e+05, 3.52720e+04, 1.10000e+01, ..., 1.71400e+01,\n",
       "        3.50200e+02, 3.90000e-02]])"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50.9   , 50.828 , 50.828 , ..., 63.1004, 62.9186, 62.861 ])"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Taking Care of Missing Data\n",
    "### Simple Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer()"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "#object\n",
    "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "#methods\n",
    "imputer.fit(X[:,:])\n",
    "X[:,:] = imputer.transform(X[:,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split DataSet to Training set and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(665263, 18)\n",
      "(665263,)\n",
      "(221755, 18)\n",
      "(221755,)\n"
     ]
    }
   ],
   "source": [
    "SEED = 42\n",
    "TS = 0.25\n",
    "\n",
    "# Create training and test sets\n",
    "X_train, X_test, y_train, y_test = \\\n",
    "    train_test_split(X, y, test_size = TS, random_state=SEED)\n",
    "\n",
    "#Feature Scaling to prevent information leakage\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "\n",
    "print (X_train.shape)\n",
    "print (y_train.shape)\n",
    "print (X_test.shape)\n",
    "print (y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create logistic regression model\n",
    "linreg = LinearRegression()\n",
    "\n",
    "# Train the model using the training sets\n",
    "linreg.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions using the testing set\n",
    "y_pred = linreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Training Score: \n",
      " 100.0\n",
      "Linear Regression Test Score: \n",
      " 100.0\n",
      "Root Mean Squared Error: 1.8365382577761532e-13\n"
     ]
    }
   ],
   "source": [
    "linreg.score(X_test,y_test)\n",
    "\n",
    "linreg_training_score = round(linreg.score(X_train, y_train) * 100, 2)\n",
    "linreg_test_score = round(linreg.score(X_test, y_test) * 100, 2)\n",
    "\n",
    "print('Linear Regression Training Score: \\n', linreg_training_score)\n",
    "print('Linear Regression Test Score: \\n', linreg_test_score)\n",
    "\n",
    "# Compute and print R^2 and RMSE\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(\"Root Mean Squared Error: {}\".format(rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BottleCount</th>\n",
       "      <th>CastCount</th>\n",
       "      <th>DepthMeters</th>\n",
       "      <th>TempDegC</th>\n",
       "      <th>Salinity</th>\n",
       "      <th>O2ml_L</th>\n",
       "      <th>STheta</th>\n",
       "      <th>O2Sat</th>\n",
       "      <th>Oxy_µmol/Kg</th>\n",
       "      <th>RecInd</th>\n",
       "      <th>...</th>\n",
       "      <th>C14A1q</th>\n",
       "      <th>C14A2q</th>\n",
       "      <th>DarkAq</th>\n",
       "      <th>MeanAq</th>\n",
       "      <th>R_Depth</th>\n",
       "      <th>R_TEMP</th>\n",
       "      <th>R_Sal</th>\n",
       "      <th>R_DYNHT</th>\n",
       "      <th>R_Oxy_µmol/Kg</th>\n",
       "      <th>TempDegF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.50</td>\n",
       "      <td>33.440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.50</td>\n",
       "      <td>233.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>10.46</td>\n",
       "      <td>33.440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.656</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.46</td>\n",
       "      <td>232.5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10.46</td>\n",
       "      <td>33.437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.46</td>\n",
       "      <td>232.8</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>10.45</td>\n",
       "      <td>33.420</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.45</td>\n",
       "      <td>234.1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>10.45</td>\n",
       "      <td>33.421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.45</td>\n",
       "      <td>234.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   BottleCount  CastCount  DepthMeters  TempDegC  Salinity  O2ml_L  STheta  \\\n",
       "0            1          1            0     10.50    33.440     NaN  25.649   \n",
       "1            2          1            8     10.46    33.440     NaN  25.656   \n",
       "2            3          1           10     10.46    33.437     NaN  25.654   \n",
       "3            4          1           19     10.45    33.420     NaN  25.643   \n",
       "4            5          1           20     10.45    33.421     NaN  25.643   \n",
       "\n",
       "   O2Sat  Oxy_µmol/Kg  RecInd  ...  C14A1q  C14A2q  DarkAq  MeanAq  R_Depth  \\\n",
       "0    NaN          NaN       3  ...     9.0     9.0     9.0     9.0      0.0   \n",
       "1    NaN          NaN       3  ...     9.0     9.0     9.0     9.0      8.0   \n",
       "2    NaN          NaN       7  ...     9.0     9.0     9.0     9.0     10.0   \n",
       "3    NaN          NaN       3  ...     9.0     9.0     9.0     9.0     19.0   \n",
       "4    NaN          NaN       7  ...     9.0     9.0     9.0     9.0     20.0   \n",
       "\n",
       "   R_TEMP  R_Sal  R_DYNHT  R_Oxy_µmol/Kg  TempDegF  \n",
       "0   10.50  233.0     0.00            NaN    50.900  \n",
       "1   10.46  232.5     0.01            NaN    50.828  \n",
       "2   10.46  232.8     0.02            NaN    50.828  \n",
       "3   10.45  234.1     0.04            NaN    50.810  \n",
       "4   10.45  234.0     0.04            NaN    50.810  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LabelEncoder 1 \n",
    "df_le1.to_csv('data/df_le1.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LabelEncoder 2\n",
    "df_le2.to_csv('data/df_le2.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get_dummies data\n",
    "df_gd.to_csv('data/df_gd.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#OneHotEncoder data\n",
    "df_ohe.to_csv('data/df_ohe.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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