0_ALL1-6STEPS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oceanographic Analysis - CalCOFI Capstone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"900\"\n",
       "            height=\"569\"\n",
       "            src=\"https://docs.google.com/presentation/d/e/2PACX-1vTFUKkshFjS3SpFm392ru6L5CVemXbfU2Op1NaaEFiW16x4Je70wKbiR0u_TcR0UyqOiINeGCNVUquK/embed??frameborder=0&align=center&allowfullscreen=true&mozallowfullscreen=true&webkitallowfullscreen=true\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fad7d001350>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "embed = \"https://docs.google.com/presentation/d/e/2PACX-1vTFUKkshFjS3SpFm392ru6L5CVemXbfU2Op1NaaEFiW16x4Je70wKbiR0u_TcR0UyqOiINeGCNVUquK/embed?\"\n",
    "IFrame(embed,frameborder=\"0\", width=\"900\", align=\"center\", height=\"569\", allowfullscreen=\"true\", mozallowfullscreen=\"true\", webkitallowfullscreen=\"true\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Problem Identification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  <font color='blue'>Problem Statement</font>\n",
    "<strong>Help policy makers understand oceanographic trends to make intelligent choices that will save the marine ecosystem.</strong>\n",
    "\n",
    "### Summary\n",
    "The California Cooperative Oceanic Fisheries Investigations (CalCOFI) are a unique partnership of the California Department of Fish & Wildlife, NOAA Fisheries Service and Scripps Institution of Oceanography. The organization was formed in 1949 to study the ecological aspects of the sardine population collapse off California. Today their focus has shifted to the study of the marine environment off the coast of California, the management of its living resources, and monitoring the indicators of El Nino and climate change. CalCOFI conducts quarterly cruises off southern & central California, collecting a suite of hydrographic and biological data on station and underway.  Data collected at depths down to 500 meters include: temperature, salinity, oxygen, phosphate, silicate, nitrate and nitrite, chlorophyll, transmissometer, PAR, C14 primary productivity, phytoplankton biodiversity, zooplankton biomass, and zooplankton biodiversity.\n",
    "\n",
    "### Overview\n",
    "<p>Oceonography, the study of the biological features of the ocean, is important to determine the factors threatening the ocean and its marine life. Studying the ocean is also importance since it covers more than 70 percent of the surface of our planet.\n",
    "According to NASA, big shifts in salinity could be a warning that more severe droughts and floods are on their way, or even that global warming is speeding up.\n",
    "</p>\n",
    "\n",
    "### Context\n",
    "Since climate changes today has been altering the ocean’s chemistry, it is important to understand the trends that are threatening the ocean and its marine life. The changes in temperature effects the melting of ice, and changes in sea levels and ocean currents.  \n",
    "The migration pattern of marine species disrupts, and some marine species are on the verge of extinction.\n",
    "\n",
    "### Criteria for Success\n",
    "Build a model that can accurately obtain and predict the factors affecting the ocean.\n",
    "\n",
    "### Scope of Solution Space\n",
    "Determine the top 5 important features that are useful to predict the factors threatening the ocean.\n",
    "Build additional features out of existing data (Feature Engineering) and perform Exploratory Data Analysis.\n",
    "\n",
    "### Constraints within Solution Space\n",
    "There are 61 features, but most of them have missing data.\n",
    "\n",
    "### Data Acquisition and Key Data Sources\n",
    "The data is provided from CalCOFI \n",
    "\n",
    "https://calcofi.org/ccdata/database.html\n",
    "\n",
    "The following table includes the most important features of the dataset and their description.\n",
    "\n",
    "| | <strong>Features</strong> | <strong>Description</strong> |\n",
    "|------|------:|------|\n",
    "| 1 | Depthm | Depth of ocean in meters|\n",
    "| 2 | TempDegF | Temperature of Water in Fahrenheit|\n",
    "| 3 | Salinity | Salinity of water|\n",
    "| 4 | STheta | Potential Density of Water|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data Wrangling\n",
    "- Collect, organize, define, and clean relevant datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "# Analysing datetime\n",
    "from datetime import datetime as dt\n",
    "from datetime import timedelta\n",
    "\n",
    "# File system manangement\n",
    "import os,sys\n",
    "\n",
    "# Suppress warnings \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "#Interactive Shell\n",
    "from IPython.core.interactiveshell import InteractiveShell  \n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "#Pandas profiling\n",
    "from pandas_profiling import ProfileReport\n",
    "\n",
    "import missingno as msno\n",
    "import re \n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/asaadkhattab/Documents/DSProjects/OceanographicAnalysisCalCOFI\n"
     ]
    }
   ],
   "source": [
    "cwd = os.getcwd()\n",
    "print(cwd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.DS_Store',\n",
       " 'LICENSE',\n",
       " '3_ExploratoryDataAnalysis.ipynb',\n",
       " '0_ALL1-6STEPS.ipynb',\n",
       " '2_DataWrangling.ipynb',\n",
       " 'models',\n",
       " 'README.md',\n",
       " '1_ProblemIdentification.ipynb',\n",
       " '.gitignore',\n",
       " 'figures',\n",
       " '6_Documentation.ipynb',\n",
       " '.ipynb_checkpoints',\n",
       " '.git',\n",
       " '4_Preprocessing_Modelling.ipynb',\n",
       " 'data']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(cwd)\n",
    "#os.listdir( os.getcwd() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Data from CSV File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#KAGGLE.com\n",
    "#path = '/kaggle/input/calcofi/bottle.csv'\n",
    "#bottle = pd.read_csv(path) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import CSV file and read the dataset\n",
    "path = '../data/calcofi/bottle.csv'\n",
    "bottle = pd.read_csv(path, encoding='latin-1') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show all columns\n",
    "pd.set_option('max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First 5 rows "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</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",
       "      <td>NaN</td>\n",
       "      <td>25.643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cst_Cnt  Btl_Cnt       Sta_ID                                Depth_ID  \\\n",
       "0        1        1  054.0 056.0  19-4903CR-HY-060-0930-05400560-0000A-3   \n",
       "1        1        2  054.0 056.0  19-4903CR-HY-060-0930-05400560-0008A-3   \n",
       "2        1        3  054.0 056.0  19-4903CR-HY-060-0930-05400560-0010A-7   \n",
       "3        1        4  054.0 056.0  19-4903CR-HY-060-0930-05400560-0019A-3   \n",
       "4        1        5  054.0 056.0  19-4903CR-HY-060-0930-05400560-0020A-7   \n",
       "\n",
       "   Depthm  T_degC  Salnty  O2ml_L  STheta  O2Sat  Oxy_µmol/Kg  BtlNum  RecInd  \\\n",
       "0       0   10.50  33.440     NaN  25.649    NaN          NaN     NaN       3   \n",
       "1       8   10.46  33.440     NaN  25.656    NaN          NaN     NaN       3   \n",
       "2      10   10.46  33.437     NaN  25.654    NaN          NaN     NaN       7   \n",
       "3      19   10.45  33.420     NaN  25.643    NaN          NaN     NaN       3   \n",
       "4      20   10.45  33.421     NaN  25.643    NaN          NaN     NaN       7   \n",
       "\n",
       "   T_prec  T_qual  S_prec  S_qual  P_qual  O_qual  SThtaq  O2Satq  ChlorA  \\\n",
       "0     1.0     NaN     2.0     NaN     9.0     9.0     NaN     9.0     NaN   \n",
       "1     2.0     NaN     2.0     NaN     9.0     9.0     NaN     9.0     NaN   \n",
       "2     2.0     NaN     3.0     NaN     9.0     9.0     NaN     9.0     NaN   \n",
       "3     2.0     NaN     2.0     NaN     9.0     9.0     NaN     9.0     NaN   \n",
       "4     2.0     NaN     3.0     NaN     9.0     9.0     NaN     9.0     NaN   \n",
       "\n",
       "   Chlqua  Phaeop  Phaqua  PO4uM  PO4q  SiO3uM  SiO3qu  NO2uM  NO2q  NO3uM  \\\n",
       "0     9.0     NaN     9.0    NaN   9.0     NaN     9.0    NaN   9.0    NaN   \n",
       "1     9.0     NaN     9.0    NaN   9.0     NaN     9.0    NaN   9.0    NaN   \n",
       "2     9.0     NaN     9.0    NaN   9.0     NaN     9.0    NaN   9.0    NaN   \n",
       "3     9.0     NaN     9.0    NaN   9.0     NaN     9.0    NaN   9.0    NaN   \n",
       "4     9.0     NaN     9.0    NaN   9.0     NaN     9.0    NaN   9.0    NaN   \n",
       "\n",
       "   NO3q  NH3uM  NH3q  C14As1  C14A1p  C14A1q  C14As2  C14A2p  C14A2q  DarkAs  \\\n",
       "0   9.0    NaN   9.0     NaN     NaN     9.0     NaN     NaN     9.0     NaN   \n",
       "1   9.0    NaN   9.0     NaN     NaN     9.0     NaN     NaN     9.0     NaN   \n",
       "2   9.0    NaN   9.0     NaN     NaN     9.0     NaN     NaN     9.0     NaN   \n",
       "3   9.0    NaN   9.0     NaN     NaN     9.0     NaN     NaN     9.0     NaN   \n",
       "4   9.0    NaN   9.0     NaN     NaN     9.0     NaN     NaN     9.0     NaN   \n",
       "\n",
       "   DarkAp  DarkAq  MeanAs  MeanAp  MeanAq IncTim  LightP  R_Depth  R_TEMP  \\\n",
       "0     NaN     9.0     NaN     NaN     9.0    NaN     NaN      0.0   10.50   \n",
       "1     NaN     9.0     NaN     NaN     9.0    NaN     NaN      8.0   10.46   \n",
       "2     NaN     9.0     NaN     NaN     9.0    NaN     NaN     10.0   10.46   \n",
       "3     NaN     9.0     NaN     NaN     9.0    NaN     NaN     19.0   10.45   \n",
       "4     NaN     9.0     NaN     NaN     9.0    NaN     NaN     20.0   10.45   \n",
       "\n",
       "   R_Sal  R_DYNHT  R_Nuts  R_Oxy_µmol/Kg  DIC1  DIC2  TA1  TA2  pH1  pH2  \\\n",
       "0  233.0     0.00     NaN            NaN   NaN   NaN  NaN  NaN  NaN  NaN   \n",
       "1  232.5     0.01     NaN            NaN   NaN   NaN  NaN  NaN  NaN  NaN   \n",
       "2  232.8     0.02     NaN            NaN   NaN   NaN  NaN  NaN  NaN  NaN   \n",
       "3  234.1     0.04     NaN            NaN   NaN   NaN  NaN  NaN  NaN  NaN   \n",
       "4  234.0     0.04     NaN            NaN   NaN   NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "  DIC Quality Comment  \n",
       "0                 NaN  \n",
       "1                 NaN  \n",
       "2                 NaN  \n",
       "3                 NaN  \n",
       "4                 NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottle.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Last 3 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cst_Cnt</th>\n",
       "      <th>Btl_Cnt</th>\n",
       "      <th>Sta_ID</th>\n",
       "      <th>Depth_ID</th>\n",
       "      <th>Depthm</th>\n",
       "      <th>T_degC</th>\n",
       "      <th>Salnty</th>\n",
       "      <th>O2ml_L</th>\n",
       "      <th>STheta</th>\n",
       "      <th>O2Sat</th>\n",
       "      <th>Oxy_µmol/Kg</th>\n",
       "      <th>BtlNum</th>\n",
       "      <th>RecInd</th>\n",
       "      <th>T_prec</th>\n",
       "      <th>T_qual</th>\n",
       "      <th>S_prec</th>\n",
       "      <th>S_qual</th>\n",
       "      <th>P_qual</th>\n",
       "      <th>O_qual</th>\n",
       "      <th>SThtaq</th>\n",
       "      <th>O2Satq</th>\n",
       "      <th>ChlorA</th>\n",
       "      <th>Chlqua</th>\n",
       "      <th>Phaeop</th>\n",
       "      <th>Phaqua</th>\n",
       "      <th>PO4uM</th>\n",
       "      <th>PO4q</th>\n",
       "      <th>SiO3uM</th>\n",
       "      <th>SiO3qu</th>\n",
       "      <th>NO2uM</th>\n",
       "      <th>NO2q</th>\n",
       "      <th>NO3uM</th>\n",
       "      <th>NO3q</th>\n",
       "      <th>NH3uM</th>\n",
       "      <th>NH3q</th>\n",
       "      <th>C14As1</th>\n",
       "      <th>C14A1p</th>\n",
       "      <th>C14A1q</th>\n",
       "      <th>C14As2</th>\n",
       "      <th>C14A2p</th>\n",
       "      <th>C14A2q</th>\n",
       "      <th>DarkAs</th>\n",
       "      <th>DarkAp</th>\n",
       "      <th>DarkAq</th>\n",
       "      <th>MeanAs</th>\n",
       "      <th>MeanAp</th>\n",
       "      <th>MeanAq</th>\n",
       "      <th>IncTim</th>\n",
       "      <th>LightP</th>\n",
       "      <th>R_Depth</th>\n",
       "      <th>R_TEMP</th>\n",
       "      <th>R_Sal</th>\n",
       "      <th>R_DYNHT</th>\n",
       "      <th>R_Nuts</th>\n",
       "      <th>R_Oxy_µmol/Kg</th>\n",
       "      <th>DIC1</th>\n",
       "      <th>DIC2</th>\n",
       "      <th>TA1</th>\n",
       "      <th>TA2</th>\n",
       "      <th>pH1</th>\n",
       "      <th>pH2</th>\n",
       "      <th>DIC Quality Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>887015</th>\n",
       "      <td>35272</td>\n",
       "      <td>887016</td>\n",
       "      <td>093.4 026.4</td>\n",
       "      <td>20-1911OC-MX-308-2233-09340264-0006A-3</td>\n",
       "      <td>6</td>\n",
       "      <td>17.278</td>\n",
       "      <td>33.6271</td>\n",
       "      <td>6.058</td>\n",
       "      <td>24.39638</td>\n",
       "      <td>110.47</td>\n",
       "      <td>264.11</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.587</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.28</td>\n",
       "      <td>352.5</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.24</td>\n",
       "      <td>264.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887016</th>\n",
       "      <td>35272</td>\n",
       "      <td>887017</td>\n",
       "      <td>093.4 026.4</td>\n",
       "      <td>20-1911OC-MX-308-2233-09340264-0010A-7</td>\n",
       "      <td>10</td>\n",
       "      <td>17.177</td>\n",
       "      <td>33.6199</td>\n",
       "      <td>5.985</td>\n",
       "      <td>24.41515</td>\n",
       "      <td>108.91</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.178</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.61</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.18</td>\n",
       "      <td>350.8</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.61</td>\n",
       "      <td>260.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887017</th>\n",
       "      <td>35272</td>\n",
       "      <td>887018</td>\n",
       "      <td>093.4 026.4</td>\n",
       "      <td>20-1911OC-MX-308-2233-09340264-0011A-3</td>\n",
       "      <td>11</td>\n",
       "      <td>17.145</td>\n",
       "      <td>33.6187</td>\n",
       "      <td>5.921</td>\n",
       "      <td>24.42188</td>\n",
       "      <td>107.69</td>\n",
       "      <td>258.13</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.70</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.14</td>\n",
       "      <td>350.2</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.70</td>\n",
       "      <td>258.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cst_Cnt  Btl_Cnt       Sta_ID                                Depth_ID  \\\n",
       "887015    35272   887016  093.4 026.4  20-1911OC-MX-308-2233-09340264-0006A-3   \n",
       "887016    35272   887017  093.4 026.4  20-1911OC-MX-308-2233-09340264-0010A-7   \n",
       "887017    35272   887018  093.4 026.4  20-1911OC-MX-308-2233-09340264-0011A-3   \n",
       "\n",
       "        Depthm  T_degC   Salnty  O2ml_L    STheta   O2Sat  Oxy_µmol/Kg  \\\n",
       "887015       6  17.278  33.6271   6.058  24.39638  110.47       264.11   \n",
       "887016      10  17.177  33.6199   5.985  24.41515  108.91       260.92   \n",
       "887017      11  17.145  33.6187   5.921  24.42188  107.69       258.13   \n",
       "\n",
       "        BtlNum  RecInd  T_prec  T_qual  S_prec  S_qual  P_qual  O_qual  \\\n",
       "887015     2.0       3     2.0     NaN     3.0     NaN     NaN     NaN   \n",
       "887016     NaN       7     2.0     NaN     3.0     6.0     NaN     6.0   \n",
       "887017     1.0       3     2.0     NaN     3.0     NaN     NaN     NaN   \n",
       "\n",
       "        SThtaq  O2Satq  ChlorA  Chlqua  Phaeop  Phaqua  PO4uM  PO4q  SiO3uM  \\\n",
       "887015     NaN     NaN   2.664     NaN   0.587     NaN   0.21   NaN    2.40   \n",
       "887016     6.0     6.0   3.178     NaN   0.708     NaN   0.25   NaN    2.70   \n",
       "887017     NaN     NaN   3.306     NaN   0.738     NaN   0.26   NaN    2.77   \n",
       "\n",
       "        SiO3qu  NO2uM  NO2q  NO3uM  NO3q  NH3uM  NH3q  C14As1  C14A1p  C14A1q  \\\n",
       "887015     NaN  0.000   8.0    0.0   8.0   0.24   NaN     NaN     NaN     9.0   \n",
       "887016     NaN    NaN   9.0    0.0   8.0   0.61   NaN     NaN     NaN     9.0   \n",
       "887017     NaN  0.028   NaN    0.0   8.0   0.70   NaN     NaN     NaN     9.0   \n",
       "\n",
       "        C14As2  C14A2p  C14A2q  DarkAs  DarkAp  DarkAq  MeanAs  MeanAp  \\\n",
       "887015     NaN     NaN     9.0     NaN     NaN     9.0     NaN     NaN   \n",
       "887016     NaN     NaN     9.0     NaN     NaN     9.0     NaN     NaN   \n",
       "887017     NaN     NaN     9.0     NaN     NaN     9.0     NaN     NaN   \n",
       "\n",
       "        MeanAq IncTim  LightP  R_Depth  R_TEMP  R_Sal  R_DYNHT  R_Nuts  \\\n",
       "887015     9.0    NaN     NaN      6.0   17.28  352.5    0.022    0.24   \n",
       "887016     9.0    NaN     NaN     10.0   17.18  350.8    0.032    0.61   \n",
       "887017     9.0    NaN     NaN     11.0   17.14  350.2    0.039    0.70   \n",
       "\n",
       "        R_Oxy_µmol/Kg  DIC1  DIC2  TA1  TA2  pH1  pH2 DIC Quality Comment  \n",
       "887015         264.11   NaN   NaN  NaN  NaN  NaN  NaN                 NaN  \n",
       "887016         260.92   NaN   NaN  NaN  NaN  NaN  NaN                 NaN  \n",
       "887017         258.13   NaN   NaN  NaN  NaN  NaN  NaN                 NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottle.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Celcius to Fahr\n",
    "def cel_to_fahr(x):\n",
    "    x = x * 1.8 + 32\n",
    "    return float(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 887018 Rows(Observations).\n",
      "There are 62 Columns(Features).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(887018, 62)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dimensions of the dataset. #(samples,features)\n",
    "print(\"There are\", bottle.shape[0], \"Rows(Observations).\")\n",
    "print(\"There are\", bottle.shape[1], \"Columns(Features).\")\n",
    "bottle.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Organization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create SubFolders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creation of the directory ../OceanographicAnalysisCalCOFI/data failed\n"
     ]
    }
   ],
   "source": [
    "newfolder = \"../OceanographicAnalysisCalCOFI/data\"\n",
    "\n",
    "try:\n",
    "    os.mkdir(newfolder)\n",
    "except OSError:\n",
    "    print (\"Creation of the directory %s failed\" % newfolder)\n",
    "else:\n",
    "    print (\"Successfully created the directory %s \" % newfolder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creation of the directory ../OceanographicAnalysisCalCOFI/figures failed\n"
     ]
    }
   ],
   "source": [
    "newfolder = \"../OceanographicAnalysisCalCOFI/figures\"\n",
    "\n",
    "try:\n",
    "    os.mkdir(newfolder)\n",
    "except OSError:\n",
    "    print (\"Creation of the directory %s failed\" % newfolder)\n",
    "else:\n",
    "    print (\"Successfully created the directory %s \" % newfolder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creation of the directory ../OceanographicAnalysisCalCOFI/models failed\n"
     ]
    }
   ],
   "source": [
    "newfolder = \"../OceanographicAnalysisCalCOFI/models\"\n",
    "\n",
    "try:\n",
    "    os.mkdir(newfolder)\n",
    "except OSError:\n",
    "    print (\"Creation of the directory %s failed\" % newfolder)\n",
    "else:\n",
    "    print (\"Successfully created the directory %s \" % newfolder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 887018 entries, 0 to 887017\n",
      "Data columns (total 62 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   Cst_Cnt              887018 non-null  int64  \n",
      " 1   Btl_Cnt              887018 non-null  int64  \n",
      " 2   Sta_ID               887018 non-null  object \n",
      " 3   Depth_ID             887018 non-null  object \n",
      " 4   Depthm               887018 non-null  int64  \n",
      " 5   T_degC               876053 non-null  float64\n",
      " 6   Salnty               839663 non-null  float64\n",
      " 7   O2ml_L               717589 non-null  float64\n",
      " 8   STheta               834327 non-null  float64\n",
      " 9   O2Sat                682668 non-null  float64\n",
      " 10  Oxy_µmol/Kg          682657 non-null  float64\n",
      " 11  BtlNum               133892 non-null  float64\n",
      " 12  RecInd               887018 non-null  int64  \n",
      " 13  T_prec               876055 non-null  float64\n",
      " 14  T_qual               23129 non-null   float64\n",
      " 15  S_prec               839663 non-null  float64\n",
      " 16  S_qual               81802 non-null   float64\n",
      " 17  P_qual               673756 non-null  float64\n",
      " 18  O_qual               195017 non-null  float64\n",
      " 19  SThtaq               72726 non-null   float64\n",
      " 20  O2Satq               228133 non-null  float64\n",
      " 21  ChlorA               240027 non-null  float64\n",
      " 22  Chlqua               646628 non-null  float64\n",
      " 23  Phaeop               240026 non-null  float64\n",
      " 24  Phaqua               646632 non-null  float64\n",
      " 25  PO4uM                433429 non-null  float64\n",
      " 26  PO4q                 453832 non-null  float64\n",
      " 27  SiO3uM               374205 non-null  float64\n",
      " 28  SiO3qu               512983 non-null  float64\n",
      " 29  NO2uM                357429 non-null  float64\n",
      " 30  NO2q                 543565 non-null  float64\n",
      " 31  NO3uM                357518 non-null  float64\n",
      " 32  NO3q                 535783 non-null  float64\n",
      " 33  NH3uM                84329 non-null   float64\n",
      " 34  NH3q                 825936 non-null  float64\n",
      " 35  C14As1               15440 non-null   float64\n",
      " 36  C14A1p               12760 non-null   float64\n",
      " 37  C14A1q               869746 non-null  float64\n",
      " 38  C14As2               15422 non-null   float64\n",
      " 39  C14A2p               12742 non-null   float64\n",
      " 40  C14A2q               869764 non-null  float64\n",
      " 41  DarkAs               23657 non-null   float64\n",
      " 42  DarkAp               20457 non-null   float64\n",
      " 43  DarkAq               861581 non-null  float64\n",
      " 44  MeanAs               23658 non-null   float64\n",
      " 45  MeanAp               20457 non-null   float64\n",
      " 46  MeanAq               861580 non-null  float64\n",
      " 47  IncTim               15445 non-null   object \n",
      " 48  LightP               19662 non-null   float64\n",
      " 49  R_Depth              887017 non-null  float64\n",
      " 50  R_TEMP               840969 non-null  float64\n",
      " 51  R_Sal                834240 non-null  float64\n",
      " 52  R_DYNHT              840354 non-null  float64\n",
      " 53  R_Nuts               84349 non-null   float64\n",
      " 54  R_Oxy_µmol/Kg        682657 non-null  float64\n",
      " 55  DIC1                 1999 non-null    float64\n",
      " 56  DIC2                 224 non-null     float64\n",
      " 57  TA1                  2084 non-null    float64\n",
      " 58  TA2                  234 non-null     float64\n",
      " 59  pH1                  84 non-null      float64\n",
      " 60  pH2                  10 non-null      float64\n",
      " 61  DIC Quality Comment  55 non-null      object \n",
      "dtypes: float64(54), int64(4), object(4)\n",
      "memory usage: 419.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# Get DataFrame Information\n",
    "bottle.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values. AppointmentDay and ScheduledDay should be converted to datetime. There are 3 objects (Gender, Neighborhood, and No-show)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variable Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    54\n",
       "object      4\n",
       "int64       4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottle.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Cst_Cnt', 'Btl_Cnt', 'Sta_ID', 'Depth_ID', 'Depthm', 'T_degC',\n",
      "       'Salnty', 'O2ml_L', 'STheta', 'O2Sat', 'Oxy_µmol/Kg', 'BtlNum',\n",
      "       'RecInd', 'T_prec', 'T_qual', 'S_prec', 'S_qual', 'P_qual', 'O_qual',\n",
      "       'SThtaq', 'O2Satq', 'ChlorA', 'Chlqua', 'Phaeop', 'Phaqua', 'PO4uM',\n",
      "       'PO4q', 'SiO3uM', 'SiO3qu', 'NO2uM', 'NO2q', 'NO3uM', 'NO3q', 'NH3uM',\n",
      "       'NH3q', 'C14As1', 'C14A1p', 'C14A1q', 'C14As2', 'C14A2p', 'C14A2q',\n",
      "       'DarkAs', 'DarkAp', 'DarkAq', 'MeanAs', 'MeanAp', 'MeanAq', 'IncTim',\n",
      "       'LightP', 'R_Depth', 'R_TEMP', 'R_Sal', 'R_DYNHT', 'R_Nuts',\n",
      "       'R_Oxy_µmol/Kg', 'DIC1', 'DIC2', 'TA1', 'TA2', 'pH1', 'pH2',\n",
      "       'DIC Quality Comment'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(bottle.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>index</th>\n",
       "      <th>NullCount</th>\n",
       "      <th>NullPercent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pH2</td>\n",
       "      <td>887008</td>\n",
       "      <td>99.998873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DIC Quality Comment</td>\n",
       "      <td>886963</td>\n",
       "      <td>99.993799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pH1</td>\n",
       "      <td>886934</td>\n",
       "      <td>99.990530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DIC2</td>\n",
       "      <td>886794</td>\n",
       "      <td>99.974747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TA2</td>\n",
       "      <td>886784</td>\n",
       "      <td>99.973619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DIC1</td>\n",
       "      <td>885019</td>\n",
       "      <td>99.774638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TA1</td>\n",
       "      <td>884934</td>\n",
       "      <td>99.765056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C14A2p</td>\n",
       "      <td>874276</td>\n",
       "      <td>98.563502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C14A1p</td>\n",
       "      <td>874258</td>\n",
       "      <td>98.561472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C14As2</td>\n",
       "      <td>871596</td>\n",
       "      <td>98.261366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C14As1</td>\n",
       "      <td>871578</td>\n",
       "      <td>98.259336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>IncTim</td>\n",
       "      <td>871573</td>\n",
       "      <td>98.258773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LightP</td>\n",
       "      <td>867356</td>\n",
       "      <td>97.783360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DarkAp</td>\n",
       "      <td>866561</td>\n",
       "      <td>97.693733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MeanAp</td>\n",
       "      <td>866561</td>\n",
       "      <td>97.693733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>T_qual</td>\n",
       "      <td>863889</td>\n",
       "      <td>97.392499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DarkAs</td>\n",
       "      <td>863361</td>\n",
       "      <td>97.332974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MeanAs</td>\n",
       "      <td>863360</td>\n",
       "      <td>97.332861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>SThtaq</td>\n",
       "      <td>814292</td>\n",
       "      <td>91.801068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>S_qual</td>\n",
       "      <td>805216</td>\n",
       "      <td>90.777865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NH3uM</td>\n",
       "      <td>802689</td>\n",
       "      <td>90.492978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>R_Nuts</td>\n",
       "      <td>802669</td>\n",
       "      <td>90.490723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>BtlNum</td>\n",
       "      <td>753126</td>\n",
       "      <td>84.905380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>O_qual</td>\n",
       "      <td>692001</td>\n",
       "      <td>78.014313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>O2Satq</td>\n",
       "      <td>658885</td>\n",
       "      <td>74.280905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Phaeop</td>\n",
       "      <td>646992</td>\n",
       "      <td>72.940121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ChlorA</td>\n",
       "      <td>646991</td>\n",
       "      <td>72.940008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NO2uM</td>\n",
       "      <td>529589</td>\n",
       "      <td>59.704425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NO3uM</td>\n",
       "      <td>529500</td>\n",
       "      <td>59.694392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SiO3uM</td>\n",
       "      <td>512813</td>\n",
       "      <td>57.813145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PO4uM</td>\n",
       "      <td>453589</td>\n",
       "      <td>51.136392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>PO4q</td>\n",
       "      <td>433186</td>\n",
       "      <td>48.836213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>SiO3qu</td>\n",
       "      <td>374035</td>\n",
       "      <td>42.167690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NO3q</td>\n",
       "      <td>351235</td>\n",
       "      <td>39.597280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NO2q</td>\n",
       "      <td>343453</td>\n",
       "      <td>38.719958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Chlqua</td>\n",
       "      <td>240390</td>\n",
       "      <td>27.100916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Phaqua</td>\n",
       "      <td>240386</td>\n",
       "      <td>27.100465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>P_qual</td>\n",
       "      <td>213262</td>\n",
       "      <td>24.042579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Oxy_µmol/Kg</td>\n",
       "      <td>204361</td>\n",
       "      <td>23.039104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>R_Oxy_µmol/Kg</td>\n",
       "      <td>204361</td>\n",
       "      <td>23.039104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>O2Sat</td>\n",
       "      <td>204350</td>\n",
       "      <td>23.037864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>O2ml_L</td>\n",
       "      <td>169429</td>\n",
       "      <td>19.100965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>NH3q</td>\n",
       "      <td>61082</td>\n",
       "      <td>6.886219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>R_Sal</td>\n",
       "      <td>52778</td>\n",
       "      <td>5.950048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>STheta</td>\n",
       "      <td>52691</td>\n",
       "      <td>5.940240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>S_prec</td>\n",
       "      <td>47355</td>\n",
       "      <td>5.338674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Salnty</td>\n",
       "      <td>47355</td>\n",
       "      <td>5.338674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>R_DYNHT</td>\n",
       "      <td>46664</td>\n",
       "      <td>5.260773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>R_TEMP</td>\n",
       "      <td>46049</td>\n",
       "      <td>5.191439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>MeanAq</td>\n",
       "      <td>25438</td>\n",
       "      <td>2.867811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>DarkAq</td>\n",
       "      <td>25437</td>\n",
       "      <td>2.867698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>C14A1q</td>\n",
       "      <td>17272</td>\n",
       "      <td>1.947198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>C14A2q</td>\n",
       "      <td>17254</td>\n",
       "      <td>1.945169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>T_degC</td>\n",
       "      <td>10965</td>\n",
       "      <td>1.236164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>T_prec</td>\n",
       "      <td>10963</td>\n",
       "      <td>1.235939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>R_Depth</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  index  NullCount  NullPercent\n",
       "0                   pH2     887008    99.998873\n",
       "1   DIC Quality Comment     886963    99.993799\n",
       "2                   pH1     886934    99.990530\n",
       "3                  DIC2     886794    99.974747\n",
       "4                   TA2     886784    99.973619\n",
       "5                  DIC1     885019    99.774638\n",
       "6                   TA1     884934    99.765056\n",
       "7                C14A2p     874276    98.563502\n",
       "8                C14A1p     874258    98.561472\n",
       "9                C14As2     871596    98.261366\n",
       "10               C14As1     871578    98.259336\n",
       "11               IncTim     871573    98.258773\n",
       "12               LightP     867356    97.783360\n",
       "13               DarkAp     866561    97.693733\n",
       "14               MeanAp     866561    97.693733\n",
       "15               T_qual     863889    97.392499\n",
       "16               DarkAs     863361    97.332974\n",
       "17               MeanAs     863360    97.332861\n",
       "18               SThtaq     814292    91.801068\n",
       "19               S_qual     805216    90.777865\n",
       "20                NH3uM     802689    90.492978\n",
       "21               R_Nuts     802669    90.490723\n",
       "22               BtlNum     753126    84.905380\n",
       "23               O_qual     692001    78.014313\n",
       "24               O2Satq     658885    74.280905\n",
       "25               Phaeop     646992    72.940121\n",
       "26               ChlorA     646991    72.940008\n",
       "27                NO2uM     529589    59.704425\n",
       "28                NO3uM     529500    59.694392\n",
       "29               SiO3uM     512813    57.813145\n",
       "30                PO4uM     453589    51.136392\n",
       "31                 PO4q     433186    48.836213\n",
       "32               SiO3qu     374035    42.167690\n",
       "33                 NO3q     351235    39.597280\n",
       "34                 NO2q     343453    38.719958\n",
       "35               Chlqua     240390    27.100916\n",
       "36               Phaqua     240386    27.100465\n",
       "37               P_qual     213262    24.042579\n",
       "38          Oxy_µmol/Kg     204361    23.039104\n",
       "39        R_Oxy_µmol/Kg     204361    23.039104\n",
       "40                O2Sat     204350    23.037864\n",
       "41               O2ml_L     169429    19.100965\n",
       "42                 NH3q      61082     6.886219\n",
       "43                R_Sal      52778     5.950048\n",
       "44               STheta      52691     5.940240\n",
       "45               S_prec      47355     5.338674\n",
       "46               Salnty      47355     5.338674\n",
       "47              R_DYNHT      46664     5.260773\n",
       "48               R_TEMP      46049     5.191439\n",
       "49               MeanAq      25438     2.867811\n",
       "50               DarkAq      25437     2.867698\n",
       "51               C14A1q      17272     1.947198\n",
       "52               C14A2q      17254     1.945169\n",
       "53               T_degC      10965     1.236164\n",
       "54               T_prec      10963     1.235939\n",
       "55              R_Depth          1     0.000113"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Counts and percentage of null values \n",
    "dictionary = {\n",
    "    \"NullCount\":bottle.isnull().sum().sort_values(ascending=False),\n",
    "    \"NullPercent\":bottle.isnull().sum().sort_values(ascending=False)/len(bottle)*100\n",
    "}\n",
    "\n",
    "na_df = pd.DataFrame(dictionary)\n",
    "na_df.columns = ['NullCount','NullPercent']\n",
    "na_df[(na_df['NullCount'] > 0)].reset_index()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On line 40 in the previous DataFrame we see that O2ml_L is missing 19.10% of data.  So we will delete all rows that has more than 19% of missing data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pct_null = bottle.isnull().sum() / len(bottle)\n",
    "missing_features = pct_null[pct_null > 0.19].index\n",
    "bottle.drop(missing_features, axis=1, inplace=True)\n",
    "df = bottle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize Missingness\n",
    "msno.matrix(df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cst_Cnt       3.976469\n",
      "Btl_Cnt     100.000000\n",
      "Sta_ID        0.296950\n",
      "Depth_ID     99.997520\n",
      "Depthm        0.362901\n",
      "T_degC        1.718680\n",
      "Salnty        1.766255\n",
      "STheta        6.213741\n",
      "RecInd        0.000564\n",
      "T_prec        0.000451\n",
      "S_prec        0.000225\n",
      "NH3q          0.000225\n",
      "C14A1q        0.000225\n",
      "C14A2q        0.000225\n",
      "DarkAq        0.000225\n",
      "MeanAq        0.000225\n",
      "R_Depth       0.375190\n",
      "R_TEMP        0.322090\n",
      "R_Sal         0.592547\n",
      "R_DYNHT       0.208451\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print ( df.nunique() / df.shape[0] * 100 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Cst_Cnt</th>\n",
       "      <th>Btl_Cnt</th>\n",
       "      <th>Sta_ID</th>\n",
       "      <th>Depth_ID</th>\n",
       "      <th>Depthm</th>\n",
       "      <th>T_degC</th>\n",
       "      <th>Salnty</th>\n",
       "      <th>STheta</th>\n",
       "      <th>RecInd</th>\n",
       "      <th>T_prec</th>\n",
       "      <th>S_prec</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>054.0 056.0</td>\n",
       "      <td>19-4903CR-HY-060-0930-05400560-0000A-3</td>\n",
       "      <td>0</td>\n",
       "      <td>10.50</td>\n",
       "      <td>33.440</td>\n",
       "      <td>25.649</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>10.50</td>\n",
       "      <td>233.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>054.0 056.0</td>\n",
       "      <td>19-4903CR-HY-060-0930-05400560-0008A-3</td>\n",
       "      <td>8</td>\n",
       "      <td>10.46</td>\n",
       "      <td>33.440</td>\n",
       "      <td>25.656</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>8.0</td>\n",
       "      <td>10.46</td>\n",
       "      <td>232.5</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>054.0 056.0</td>\n",
       "      <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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</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",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cst_Cnt  Btl_Cnt       Sta_ID                                Depth_ID  \\\n",
       "0        1        1  054.0 056.0  19-4903CR-HY-060-0930-05400560-0000A-3   \n",
       "1        1        2  054.0 056.0  19-4903CR-HY-060-0930-05400560-0008A-3   \n",
       "2        1        3  054.0 056.0  19-4903CR-HY-060-0930-05400560-0010A-7   \n",
       "3        1        4  054.0 056.0  19-4903CR-HY-060-0930-05400560-0019A-3   \n",
       "4        1        5  054.0 056.0  19-4903CR-HY-060-0930-05400560-0020A-7   \n",
       "\n",
       "   Depthm  T_degC  Salnty  STheta  RecInd  T_prec  S_prec  NH3q  C14A1q  \\\n",
       "0       0   10.50  33.440  25.649       3     1.0     2.0   9.0     9.0   \n",
       "1       8   10.46  33.440  25.656       3     2.0     2.0   9.0     9.0   \n",
       "2      10   10.46  33.437  25.654       7     2.0     3.0   9.0     9.0   \n",
       "3      19   10.45  33.420  25.643       3     2.0     2.0   9.0     9.0   \n",
       "4      20   10.45  33.421  25.643       7     2.0     3.0   9.0     9.0   \n",
       "\n",
       "   C14A2q  DarkAq  MeanAq  R_Depth  R_TEMP  R_Sal  R_DYNHT  \n",
       "0     9.0     9.0     9.0      0.0   10.50  233.0     0.00  \n",
       "1     9.0     9.0     9.0      8.0   10.46  232.5     0.01  \n",
       "2     9.0     9.0     9.0     10.0   10.46  232.8     0.02  \n",
       "3     9.0     9.0     9.0     19.0   10.45  234.1     0.04  \n",
       "4     9.0     9.0     9.0     20.0   10.45  234.0     0.04  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rename Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Cst_Cnt', 'Btl_Cnt', 'Sta_ID', 'Depth_ID', 'Depthm', 'T_degC',\n",
       "       'Salnty', 'STheta', 'RecInd', 'T_prec', 'S_prec', 'NH3q', 'C14A1q',\n",
       "       'C14A2q', 'DarkAq', 'MeanAq', 'R_Depth', 'R_TEMP', 'R_Sal', 'R_DYNHT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns = { \n",
    "    \"Cst_Cnt\": \"CastCount\",\n",
    "    \"Btl_Cnt\": \"BottleCount\",\n",
    "    \"Depthm\": \"DepthMeters\",\n",
    "    \"T_degC\": \"TempDegC\",\n",
    "    \"Salnty\": \"Salinity\",    \n",
    "    \"STheta\": \"PDensity\"\n",
    "    \n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set BottleCount to be Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index('BottleCount')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract Year and Month from Depth_ID "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract Year\n",
    "search = []    \n",
    "\n",
    "for values in df['Depth_ID']:\n",
    "    search.append(re.search(r'\\d{2}-\\d{2}', values).group())\n",
    "    \n",
    "df['Year'] = search\n",
    "df['Year'] = df['Year'].replace(to_replace='-',value='', regex = True) \n",
    "\n",
    "df['Year'] = pd.to_datetime(df['Year']).values.astype('datetime64[Y]')\n",
    "df['Year'] =  pd.DatetimeIndex(df['Year']).year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract Month \n",
    "search = []    \n",
    "\n",
    "for values in df['Depth_ID']:\n",
    "    search.append(re.search(r'-\\d+', values).group())\n",
    "    \n",
    "df['Month'] = search\n",
    "df['Month'] = df['Month'].str[-2:]\n",
    "\n",
    "df['Month'] = df['Month'].astype('int64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert from Celsius to Fahrenheit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TempDegF'] = df['TempDegC'].apply(cel_to_fahr)\n",
    "df = df.drop(\"TempDegC\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Salinity: [33.44   33.437  33.42   ... 34.0373 33.8616 33.7855]\n",
      "TempDegF: [50.9    50.828  50.81   ... 68.4482 68.4536 63.1004]\n"
     ]
    }
   ],
   "source": [
    "print('Salinity:', df.Salinity.unique() ) \n",
    "print('TempDegF:', df.TempDegF.unique() ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CastCount</th>\n",
       "      <td>887018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17581</td>\n",
       "      <td>10483</td>\n",
       "      <td>1</td>\n",
       "      <td>8462</td>\n",
       "      <td>17463</td>\n",
       "      <td>27190</td>\n",
       "      <td>35272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sta_ID</th>\n",
       "      <td>887018</td>\n",
       "      <td>2634</td>\n",
       "      <td>090.0 070.0</td>\n",
       "      <td>10417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depth_ID</th>\n",
       "      <td>887018</td>\n",
       "      <td>886996</td>\n",
       "      <td>20-1911OC-PR-319-1837-08000600-0013B-6</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepthMeters</th>\n",
       "      <td>887018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>225.038</td>\n",
       "      <td>313.2</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>125</td>\n",
       "      <td>300</td>\n",
       "      <td>5351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salinity</th>\n",
       "      <td>839663</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.8378</td>\n",
       "      <td>0.459417</td>\n",
       "      <td>28.3652</td>\n",
       "      <td>33.487</td>\n",
       "      <td>33.86</td>\n",
       "      <td>34.1912</td>\n",
       "      <td>37.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PDensity</th>\n",
       "      <td>834327</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.8153</td>\n",
       "      <td>1.16246</td>\n",
       "      <td>20.934</td>\n",
       "      <td>24.961</td>\n",
       "      <td>25.99</td>\n",
       "      <td>26.643</td>\n",
       "      <td>250.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RecInd</th>\n",
       "      <td>887018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.6921</td>\n",
       "      <td>1.87779</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_prec</th>\n",
       "      <td>876055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.01673</td>\n",
       "      <td>0.147012</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S_prec</th>\n",
       "      <td>839663</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.72426</td>\n",
       "      <td>0.446887</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH3q</th>\n",
       "      <td>825936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.85888</td>\n",
       "      <td>0.828058</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C14A1q</th>\n",
       "      <td>869746</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.99999</td>\n",
       "      <td>0.00283695</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C14A2q</th>\n",
       "      <td>869764</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.99999</td>\n",
       "      <td>0.00283692</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DarkAq</th>\n",
       "      <td>861581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.99999</td>\n",
       "      <td>0.00285036</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MeanAq</th>\n",
       "      <td>861580</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.99999</td>\n",
       "      <td>0.00285036</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R_Depth</th>\n",
       "      <td>887017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>226.594</td>\n",
       "      <td>316.569</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>126</td>\n",
       "      <td>302</td>\n",
       "      <td>5458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R_TEMP</th>\n",
       "      <td>840969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.8562</td>\n",
       "      <td>4.22591</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75</td>\n",
       "      <td>10.11</td>\n",
       "      <td>13.94</td>\n",
       "      <td>31.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R_Sal</th>\n",
       "      <td>834240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>221.307</td>\n",
       "      <td>93.3373</td>\n",
       "      <td>0.4</td>\n",
       "      <td>144</td>\n",
       "      <td>203.7</td>\n",
       "      <td>300.2</td>\n",
       "      <td>683.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R_DYNHT</th>\n",
       "      <td>840354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.429753</td>\n",
       "      <td>0.380306</td>\n",
       "      <td>0</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.63</td>\n",
       "      <td>3.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>887018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1975.22</td>\n",
       "      <td>21.2032</td>\n",
       "      <td>1949</td>\n",
       "      <td>1956</td>\n",
       "      <td>1972</td>\n",
       "      <td>1992</td>\n",
       "      <td>2093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <td>887018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.52558</td>\n",
       "      <td>5.98896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TempDegF</th>\n",
       "      <td>876053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.4662</td>\n",
       "      <td>7.62027</td>\n",
       "      <td>34.592</td>\n",
       "      <td>45.86</td>\n",
       "      <td>50.126</td>\n",
       "      <td>57.02</td>\n",
       "      <td>88.052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count  unique                                     top   freq  \\\n",
       "CastCount    887018     NaN                                     NaN    NaN   \n",
       "Sta_ID       887018    2634                             090.0 070.0  10417   \n",
       "Depth_ID     887018  886996  20-1911OC-PR-319-1837-08000600-0013B-6      2   \n",
       "DepthMeters  887018     NaN                                     NaN    NaN   \n",
       "Salinity     839663     NaN                                     NaN    NaN   \n",
       "PDensity     834327     NaN                                     NaN    NaN   \n",
       "RecInd       887018     NaN                                     NaN    NaN   \n",
       "T_prec       876055     NaN                                     NaN    NaN   \n",
       "S_prec       839663     NaN                                     NaN    NaN   \n",
       "NH3q         825936     NaN                                     NaN    NaN   \n",
       "C14A1q       869746     NaN                                     NaN    NaN   \n",
       "C14A2q       869764     NaN                                     NaN    NaN   \n",
       "DarkAq       861581     NaN                                     NaN    NaN   \n",
       "MeanAq       861580     NaN                                     NaN    NaN   \n",
       "R_Depth      887017     NaN                                     NaN    NaN   \n",
       "R_TEMP       840969     NaN                                     NaN    NaN   \n",
       "R_Sal        834240     NaN                                     NaN    NaN   \n",
       "R_DYNHT      840354     NaN                                     NaN    NaN   \n",
       "Year         887018     NaN                                     NaN    NaN   \n",
       "Month        887018     NaN                                     NaN    NaN   \n",
       "TempDegF     876053     NaN                                     NaN    NaN   \n",
       "\n",
       "                 mean         std      min     25%     50%      75%      max  \n",
       "CastCount       17581       10483        1    8462   17463    27190    35272  \n",
       "Sta_ID            NaN         NaN      NaN     NaN     NaN      NaN      NaN  \n",
       "Depth_ID          NaN         NaN      NaN     NaN     NaN      NaN      NaN  \n",
       "DepthMeters   225.038       313.2        0      45     125      300     5351  \n",
       "Salinity      33.8378    0.459417  28.3652  33.487   33.86  34.1912   37.034  \n",
       "PDensity      25.8153     1.16246   20.934  24.961   25.99   26.643  250.784  \n",
       "RecInd         4.6921     1.87779        3       3       3        7        7  \n",
       "T_prec        2.01673    0.147012        1       2       2        2        9  \n",
       "S_prec        2.72426    0.446887        2       2       3        3        3  \n",
       "NH3q          8.85888    0.828058        4       9       9        9        9  \n",
       "C14A1q        8.99999  0.00283695        8       9       9        9        9  \n",
       "C14A2q        8.99999  0.00283692        8       9       9        9        9  \n",
       "DarkAq        8.99999  0.00285036        8       9       9        9        9  \n",
       "MeanAq        8.99999  0.00285036        8       9       9        9        9  \n",
       "R_Depth       226.594     316.569        0      45     126      302     5458  \n",
       "R_TEMP        10.8562     4.22591        0    7.75   10.11    13.94    31.14  \n",
       "R_Sal         221.307     93.3373      0.4     144   203.7    300.2    683.4  \n",
       "R_DYNHT      0.429753    0.380306        0    0.13    0.34     0.63     3.88  \n",
       "Year          1975.22     21.2032     1949    1956    1972     1992     2093  \n",
       "Month         6.52558     5.98896        1       3       6        9       95  \n",
       "TempDegF      51.4662     7.62027   34.592   45.86  50.126    57.02   88.052  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"all\").T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detect Anomalies & Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Range of values per column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     CastCount       Sta_ID                                Depth_ID  \\\n",
      "min          1  001.0 168.0  19-4903CR-HY-060-0930-05400560-0000A-3   \n",
      "max      35272  176.7 030.0  20-93.3OC-MX-311-1010-09331100-0515A-3   \n",
      "\n",
      "     DepthMeters  Salinity  PDensity  RecInd  T_prec  S_prec  NH3q  C14A1q  \\\n",
      "min            0   28.3652    20.934       3     1.0     2.0   4.0     8.0   \n",
      "max         5351   37.0340   250.784       7     9.0     3.0   9.0     9.0   \n",
      "\n",
      "     C14A2q  DarkAq  MeanAq  R_Depth  R_TEMP  R_Sal  R_DYNHT  Year  Month  \\\n",
      "min     8.0     8.0     8.0      0.0    0.00    0.4     0.00  1949      1   \n",
      "max     9.0     9.0     9.0   5458.0   31.14  683.4     3.88  2093     95   \n",
      "\n",
      "     TempDegF  \n",
      "min    34.592  \n",
      "max    88.052  \n"
     ]
    }
   ],
   "source": [
    "range = df.aggregate([min, max])\n",
    "print(range)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Year - Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Year',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently, we are in 2020. We will remove all the years after that including 2093"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Year'] = df['Year'].drop(df[df['Year']>2020].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has months of more than 12, so we will drop them too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Month'] = df['Month'].drop(df[df['Month']>12].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    839663.000000\n",
       "mean         33.837784\n",
       "std           0.459417\n",
       "min          28.365200\n",
       "25%          33.487000\n",
       "50%          33.860000\n",
       "75%          34.191200\n",
       "max          37.034000\n",
       "Name: Salinity, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Salinity\"].describe(include=\"all\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    876053.000000\n",
       "mean         51.466178\n",
       "std           7.620268\n",
       "min          34.592000\n",
       "25%          45.860000\n",
       "50%          50.126000\n",
       "75%          57.020000\n",
       "max          88.052000\n",
       "Name: TempDegF, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"TempDegF\"].describe(include=\"all\").T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check for Duplicated Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>CastCount</th>\n",
       "      <th>Sta_ID</th>\n",
       "      <th>Depth_ID</th>\n",
       "      <th>DepthMeters</th>\n",
       "      <th>Salinity</th>\n",
       "      <th>PDensity</th>\n",
       "      <th>RecInd</th>\n",
       "      <th>T_prec</th>\n",
       "      <th>S_prec</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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>TempDegF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BottleCount</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>858746</th>\n",
       "      <td>34170</td>\n",
       "      <td>063.3 090.0</td>\n",
       "      <td>20-16045S-MX-110-2018-06330900-0002A-3</td>\n",
       "      <td>2</td>\n",
       "      <td>32.9637</td>\n",
       "      <td>24.19060</td>\n",
       "      <td>3</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>2.0</td>\n",
       "      <td>15.96</td>\n",
       "      <td>371.9</td>\n",
       "      <td>0.007</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.7370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858747</th>\n",
       "      <td>34170</td>\n",
       "      <td>063.3 090.0</td>\n",
       "      <td>20-16045S-MX-110-2018-06330900-0010A-3</td>\n",
       "      <td>10</td>\n",
       "      <td>32.9630</td>\n",
       "      <td>24.24324</td>\n",
       "      <td>3</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>15.73</td>\n",
       "      <td>367.2</td>\n",
       "      <td>0.034</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.3140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858748</th>\n",
       "      <td>34170</td>\n",
       "      <td>063.3 090.0</td>\n",
       "      <td>20-16045S-MX-110-2018-06330900-0025A-3</td>\n",
       "      <td>25</td>\n",
       "      <td>32.9851</td>\n",
       "      <td>24.39601</td>\n",
       "      <td>3</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>25.0</td>\n",
       "      <td>15.11</td>\n",
       "      <td>353.1</td>\n",
       "      <td>0.088</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.2106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858749</th>\n",
       "      <td>34170</td>\n",
       "      <td>063.3 090.0</td>\n",
       "      <td>20-16045S-MX-110-2018-06330900-0063A-3</td>\n",
       "      <td>63</td>\n",
       "      <td>32.9274</td>\n",
       "      <td>24.50110</td>\n",
       "      <td>3</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>64.0</td>\n",
       "      <td>14.42</td>\n",
       "      <td>344.3</td>\n",
       "      <td>0.222</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.9704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858750</th>\n",
       "      <td>34170</td>\n",
       "      <td>063.3 090.0</td>\n",
       "      <td>20-16045S-MX-110-2018-06330900-0088A-3</td>\n",
       "      <td>88</td>\n",
       "      <td>33.0018</td>\n",
       "      <td>24.69834</td>\n",
       "      <td>3</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>89.0</td>\n",
       "      <td>13.75</td>\n",
       "      <td>326.2</td>\n",
       "      <td>0.307</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.7680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858880</th>\n",
       "      <td>34176</td>\n",
       "      <td>066.7 090.0</td>\n",
       "      <td>20-16045S-MX-110-1421-06670900-0003A-3</td>\n",
       "      <td>3</td>\n",
       "      <td>32.9452</td>\n",
       "      <td>24.29040</td>\n",
       "      <td>3</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>3.0</td>\n",
       "      <td>15.45</td>\n",
       "      <td>362.5</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.8154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858881</th>\n",
       "      <td>34176</td>\n",
       "      <td>066.7 090.0</td>\n",
       "      <td>20-16045S-MX-110-1421-06670900-0011A-3</td>\n",
       "      <td>11</td>\n",
       "      <td>32.9441</td>\n",
       "      <td>24.35396</td>\n",
       "      <td>3</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>11.0</td>\n",
       "      <td>15.16</td>\n",
       "      <td>356.7</td>\n",
       "      <td>0.035</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.2898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858882</th>\n",
       "      <td>34176</td>\n",
       "      <td>066.7 090.0</td>\n",
       "      <td>20-16045S-MX-110-1421-06670900-0026A-3</td>\n",
       "      <td>26</td>\n",
       "      <td>32.9534</td>\n",
       "      <td>24.49039</td>\n",
       "      <td>3</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>26.0</td>\n",
       "      <td>14.56</td>\n",
       "      <td>344.1</td>\n",
       "      <td>0.087</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.2134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858883</th>\n",
       "      <td>34176</td>\n",
       "      <td>066.7 090.0</td>\n",
       "      <td>20-16045S-MX-110-1421-06670900-0061A-3</td>\n",
       "      <td>61</td>\n",
       "      <td>32.9589</td>\n",
       "      <td>24.63745</td>\n",
       "      <td>3</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>62.0</td>\n",
       "      <td>13.88</td>\n",
       "      <td>331.2</td>\n",
       "      <td>0.207</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.9984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858884</th>\n",
       "      <td>34176</td>\n",
       "      <td>066.7 090.0</td>\n",
       "      <td>20-16045S-MX-110-1421-06670900-0088A-3</td>\n",
       "      <td>88</td>\n",
       "      <td>33.2387</td>\n",
       "      <td>25.26541</td>\n",
       "      <td>3</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>89.0</td>\n",
       "      <td>11.78</td>\n",
       "      <td>271.9</td>\n",
       "      <td>0.293</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.2202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886343</th>\n",
       "      <td>35248</td>\n",
       "      <td>090.0 053.0</td>\n",
       "      <td>20-1911OC-MX-313-1236-09000530-0002A-3</td>\n",
       "      <td>2</td>\n",
       "      <td>33.6809</td>\n",
       "      <td>24.26980</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.97</td>\n",
       "      <td>364.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>64.3514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886344</th>\n",
       "      <td>35248</td>\n",
       "      <td>090.0 053.0</td>\n",
       "      <td>20-1911OC-MX-313-1236-09000530-0010B-6</td>\n",
       "      <td>9</td>\n",
       "      <td>33.6761</td>\n",
       "      <td>24.26750</td>\n",
       "      <td>6</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>17.97</td>\n",
       "      <td>364.9</td>\n",
       "      <td>0.036</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>64.3460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886345</th>\n",
       "      <td>35248</td>\n",
       "      <td>090.0 053.0</td>\n",
       "      <td>20-1911OC-MX-313-1236-09000530-0010A-3</td>\n",
       "      <td>10</td>\n",
       "      <td>33.6810</td>\n",
       "      <td>24.27130</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</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>17.97</td>\n",
       "      <td>364.6</td>\n",
       "      <td>0.037</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>64.3460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886346</th>\n",
       "      <td>35248</td>\n",
       "      <td>090.0 053.0</td>\n",
       "      <td>20-1911OC-MX-313-1236-09000530-0020A-3</td>\n",
       "      <td>20</td>\n",
       "      <td>33.6721</td>\n",
       "      <td>24.26770</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</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>17.96</td>\n",
       "      <td>365.3</td>\n",
       "      <td>0.073</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>64.3280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886347</th>\n",
       "      <td>35248</td>\n",
       "      <td>090.0 053.0</td>\n",
       "      <td>20-1911OC-MX-313-1236-09000530-0030B-6</td>\n",
       "      <td>30</td>\n",
       "      <td>33.5654</td>\n",
       "      <td>24.75280</td>\n",
       "      <td>6</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>30.0</td>\n",
       "      <td>15.52</td>\n",
       "      <td>319.4</td>\n",
       "      <td>0.108</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>59.9522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CastCount       Sta_ID                                Depth_ID  \\\n",
       "BottleCount                                                                   \n",
       "858746           34170  063.3 090.0  20-16045S-MX-110-2018-06330900-0002A-3   \n",
       "858747           34170  063.3 090.0  20-16045S-MX-110-2018-06330900-0010A-3   \n",
       "858748           34170  063.3 090.0  20-16045S-MX-110-2018-06330900-0025A-3   \n",
       "858749           34170  063.3 090.0  20-16045S-MX-110-2018-06330900-0063A-3   \n",
       "858750           34170  063.3 090.0  20-16045S-MX-110-2018-06330900-0088A-3   \n",
       "858880           34176  066.7 090.0  20-16045S-MX-110-1421-06670900-0003A-3   \n",
       "858881           34176  066.7 090.0  20-16045S-MX-110-1421-06670900-0011A-3   \n",
       "858882           34176  066.7 090.0  20-16045S-MX-110-1421-06670900-0026A-3   \n",
       "858883           34176  066.7 090.0  20-16045S-MX-110-1421-06670900-0061A-3   \n",
       "858884           34176  066.7 090.0  20-16045S-MX-110-1421-06670900-0088A-3   \n",
       "886343           35248  090.0 053.0  20-1911OC-MX-313-1236-09000530-0002A-3   \n",
       "886344           35248  090.0 053.0  20-1911OC-MX-313-1236-09000530-0010B-6   \n",
       "886345           35248  090.0 053.0  20-1911OC-MX-313-1236-09000530-0010A-3   \n",
       "886346           35248  090.0 053.0  20-1911OC-MX-313-1236-09000530-0020A-3   \n",
       "886347           35248  090.0 053.0  20-1911OC-MX-313-1236-09000530-0030B-6   \n",
       "\n",
       "             DepthMeters  Salinity  PDensity  RecInd  T_prec  S_prec  NH3q  \\\n",
       "BottleCount                                                                  \n",
       "858746                 2   32.9637  24.19060       3     2.0     3.0   9.0   \n",
       "858747                10   32.9630  24.24324       3     2.0     3.0   9.0   \n",
       "858748                25   32.9851  24.39601       3     2.0     3.0   9.0   \n",
       "858749                63   32.9274  24.50110       3     2.0     3.0   9.0   \n",
       "858750                88   33.0018  24.69834       3     2.0     3.0   9.0   \n",
       "858880                 3   32.9452  24.29040       3     2.0     3.0   9.0   \n",
       "858881                11   32.9441  24.35396       3     2.0     3.0   9.0   \n",
       "858882                26   32.9534  24.49039       3     2.0     3.0   9.0   \n",
       "858883                61   32.9589  24.63745       3     2.0     3.0   9.0   \n",
       "858884                88   33.2387  25.26541       3     2.0     3.0   9.0   \n",
       "886343                 2   33.6809  24.26980       3     2.0     3.0   NaN   \n",
       "886344                 9   33.6761  24.26750       6     2.0     3.0   9.0   \n",
       "886345                10   33.6810  24.27130       3     2.0     3.0   4.0   \n",
       "886346                20   33.6721  24.26770       3     2.0     3.0   4.0   \n",
       "886347                30   33.5654  24.75280       6     2.0     3.0   9.0   \n",
       "\n",
       "             C14A1q  C14A2q  DarkAq  MeanAq  R_Depth  R_TEMP  R_Sal  R_DYNHT  \\\n",
       "BottleCount                                                                    \n",
       "858746          9.0     9.0     9.0     9.0      2.0   15.96  371.9    0.007   \n",
       "858747          9.0     9.0     9.0     9.0     10.0   15.73  367.2    0.034   \n",
       "858748          9.0     9.0     9.0     9.0     25.0   15.11  353.1    0.088   \n",
       "858749          9.0     9.0     9.0     9.0     64.0   14.42  344.3    0.222   \n",
       "858750          9.0     9.0     9.0     9.0     89.0   13.75  326.2    0.307   \n",
       "858880          9.0     9.0     9.0     9.0      3.0   15.45  362.5    0.005   \n",
       "858881          9.0     9.0     9.0     9.0     11.0   15.16  356.7    0.035   \n",
       "858882          9.0     9.0     9.0     9.0     26.0   14.56  344.1    0.087   \n",
       "858883          9.0     9.0     9.0     9.0     62.0   13.88  331.2    0.207   \n",
       "858884          9.0     9.0     9.0     9.0     89.0   11.78  271.9    0.293   \n",
       "886343          9.0     9.0     9.0     9.0      2.0   17.97  364.4    0.007   \n",
       "886344          9.0     9.0     9.0     9.0     10.0   17.97  364.9    0.036   \n",
       "886345          9.0     9.0     9.0     9.0     10.0   17.97  364.6    0.037   \n",
       "886346          9.0     9.0     9.0     9.0     20.0   17.96  365.3    0.073   \n",
       "886347          9.0     9.0     9.0     9.0     30.0   15.52  319.4    0.108   \n",
       "\n",
       "               Year  Month  TempDegF  \n",
       "BottleCount                           \n",
       "858746       2016.0    NaN   60.7370  \n",
       "858747       2016.0    NaN   60.3140  \n",
       "858748       2016.0    NaN   59.2106  \n",
       "858749       2016.0    NaN   57.9704  \n",
       "858750       2016.0    NaN   56.7680  \n",
       "858880       2016.0    NaN   59.8154  \n",
       "858881       2016.0    NaN   59.2898  \n",
       "858882       2016.0    NaN   58.2134  \n",
       "858883       2016.0    NaN   56.9984  \n",
       "858884       2016.0    NaN   53.2202  \n",
       "886343       2019.0   11.0   64.3514  \n",
       "886344       2019.0   11.0   64.3460  \n",
       "886345       2019.0   11.0   64.3460  \n",
       "886346       2019.0   11.0   64.3280  \n",
       "886347       2019.0   11.0   59.9522  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicateRowsDF = df.duplicated() \n",
    "df[duplicateRowsDF]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(887003, 21)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  3. EXPLORATORY DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Categorical columns and their associated levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(887003, 2)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>VarName</th>\n",
       "      <th>LevelsCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Depth_ID</td>\n",
       "      <td>886996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sta_ID</td>\n",
       "      <td>2634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    VarName  LevelsCount\n",
       "1  Depth_ID       886996\n",
       "0    Sta_ID         2634"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfo = df.select_dtypes(include=['object'], exclude=['datetime'])\n",
    "dfo.shape\n",
    "#get levels for all variables\n",
    "vn = pd.DataFrame(dfo.nunique()).reset_index()\n",
    "vn.columns = ['VarName', 'LevelsCount']\n",
    "vn.sort_values(by='LevelsCount', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Depth_ID','Sta_ID'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.heatmap(corr,\n",
    "            linecolor='blue',linewidths=.1, \n",
    "            cmap=\"YlGnBu\", annot=True)\n",
    "plt.yticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C14A1q, C14A2q, DarkAq, and MeanAq all have a high correlation so we will keep MeanAq. The Reported Depth, Salinity, temp, and R_DYNHT have a positive correlation with the actual findings so we will only keep the Reported Dynamic Height.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['CastCount','R_Depth','R_TEMP', 'R_Sal', 'C14A1q', 'C14A2q', 'DarkAq' ], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. PreProcessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('num',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['DepthMeters', 'Salinity', 'PDensity', 'RecInd', 'T_prec', 'S_prec',\n",
       "       'NH3q', 'MeanAq', 'R_DYNHT', 'Year', 'Month', 'TempDegF'],\n",
       "      dtype='object'))]))])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_transformer = Pipeline(\n",
    "    steps=[\n",
    "        ('imputer', SimpleImputer(strategy='mean')),\n",
    "        ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "numeric_features = df.select_dtypes(include=['int64', 'float64']).columns\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    remainder='passthrough',\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "    ]\n",
    ")\n",
    "\n",
    "steps = [('preprocessor', preprocessor)]\n",
    "\n",
    "pipeline = Pipeline(steps)\n",
    "\n",
    "pipeline.fit(df[:])\n",
    "df_pipe = pipeline.transform(df[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.718520</td>\n",
       "      <td>-0.889962</td>\n",
       "      <td>-0.147495</td>\n",
       "      <td>-0.901126</td>\n",
       "      <td>-6.959051</td>\n",
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       "      <td>-0.864111</td>\n",
       "      <td>-0.074743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.692977</td>\n",
       "      <td>-0.889962</td>\n",
       "      <td>-0.141286</td>\n",
       "      <td>-0.901126</td>\n",
       "      <td>-0.114516</td>\n",
       "      <td>-1.665733</td>\n",
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       "      <td>-1.133970</td>\n",
       "      <td>-1.236764</td>\n",
       "      <td>-0.864111</td>\n",
       "      <td>-0.084251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.686591</td>\n",
       "      <td>-0.896674</td>\n",
       "      <td>-0.143060</td>\n",
       "      <td>1.229039</td>\n",
       "      <td>-0.114516</td>\n",
       "      <td>0.634194</td>\n",
       "      <td>0.176604</td>\n",
       "      <td>0.002892</td>\n",
       "      <td>-1.106955</td>\n",
       "      <td>-1.236764</td>\n",
       "      <td>-0.864111</td>\n",
       "      <td>-0.084251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.657856</td>\n",
       "      <td>-0.934707</td>\n",
       "      <td>-0.152816</td>\n",
       "      <td>-0.901126</td>\n",
       "      <td>-0.114516</td>\n",
       "      <td>-1.665733</td>\n",
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       "      <td>0.002892</td>\n",
       "      <td>-1.052925</td>\n",
       "      <td>-1.236764</td>\n",
       "      <td>-0.864111</td>\n",
       "      <td>-0.086628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.654663</td>\n",
       "      <td>-0.932470</td>\n",
       "      <td>-0.152816</td>\n",
       "      <td>1.229039</td>\n",
       "      <td>-0.114516</td>\n",
       "      <td>0.634194</td>\n",
       "      <td>0.176604</td>\n",
       "      <td>0.002892</td>\n",
       "      <td>-1.052925</td>\n",
       "      <td>-1.236764</td>\n",
       "      <td>-0.864111</td>\n",
       "      <td>-0.086628</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886998</th>\n",
       "      <td>-0.718520</td>\n",
       "      <td>-0.466677</td>\n",
       "      <td>-1.366607</td>\n",
       "      <td>1.229039</td>\n",
       "      <td>-0.114516</td>\n",
       "      <td>0.634194</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002892</td>\n",
       "      <td>-1.160984</td>\n",
       "      <td>2.065920</td>\n",
       "      <td>1.551910</td>\n",
       "      <td>1.657994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886999</th>\n",
       "      <td>-0.712134</td>\n",
       "      <td>-0.466677</td>\n",
       "      <td>-1.366465</td>\n",
       "      <td>-0.901126</td>\n",
       "      <td>-0.114516</td>\n",
       "      <td>0.634194</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002892</td>\n",
       "      <td>-1.142074</td>\n",
       "      <td>2.065920</td>\n",
       "      <td>1.551910</td>\n",
       "      <td>1.657994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887000</th>\n",
       "      <td>-0.699363</td>\n",
       "      <td>-0.471375</td>\n",
       "      <td>-1.258562</td>\n",
       "      <td>-0.901126</td>\n",
       "      <td>-0.114516</td>\n",
       "      <td>0.634194</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002892</td>\n",
       "      <td>-1.101552</td>\n",
       "      <td>2.065920</td>\n",
       "      <td>1.551910</td>\n",
       "      <td>1.536298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887001</th>\n",
       "      <td>-0.686591</td>\n",
       "      <td>-0.487483</td>\n",
       "      <td>-1.241913</td>\n",
       "      <td>1.229039</td>\n",
       "      <td>-0.114516</td>\n",
       "      <td>0.634194</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002892</td>\n",
       "      <td>-1.074537</td>\n",
       "      <td>2.065920</td>\n",
       "      <td>1.551910</td>\n",
       "      <td>1.512292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887002</th>\n",
       "      <td>-0.683398</td>\n",
       "      <td>-0.490168</td>\n",
       "      <td>-1.235944</td>\n",
       "      <td>-0.901126</td>\n",
       "      <td>-0.114516</td>\n",
       "      <td>0.634194</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002892</td>\n",
       "      <td>-1.055627</td>\n",
       "      <td>2.065920</td>\n",
       "      <td>1.551910</td>\n",
       "      <td>1.504686</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>887003 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0         1         2         3         4         5         6  \\\n",
       "0      -0.718520 -0.889962 -0.147495 -0.901126 -6.959051 -1.665733  0.176604   \n",
       "1      -0.692977 -0.889962 -0.141286 -0.901126 -0.114516 -1.665733  0.176604   \n",
       "2      -0.686591 -0.896674 -0.143060  1.229039 -0.114516  0.634194  0.176604   \n",
       "3      -0.657856 -0.934707 -0.152816 -0.901126 -0.114516 -1.665733  0.176604   \n",
       "4      -0.654663 -0.932470 -0.152816  1.229039 -0.114516  0.634194  0.176604   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "886998 -0.718520 -0.466677 -1.366607  1.229039 -0.114516  0.634194  0.000000   \n",
       "886999 -0.712134 -0.466677 -1.366465 -0.901126 -0.114516  0.634194  0.000000   \n",
       "887000 -0.699363 -0.471375 -1.258562 -0.901126 -0.114516  0.634194  0.000000   \n",
       "887001 -0.686591 -0.487483 -1.241913  1.229039 -0.114516  0.634194  0.000000   \n",
       "887002 -0.683398 -0.490168 -1.235944 -0.901126 -0.114516  0.634194  0.000000   \n",
       "\n",
       "               7         8         9        10        11  \n",
       "0       0.002892 -1.160984 -1.236764 -0.864111 -0.074743  \n",
       "1       0.002892 -1.133970 -1.236764 -0.864111 -0.084251  \n",
       "2       0.002892 -1.106955 -1.236764 -0.864111 -0.084251  \n",
       "3       0.002892 -1.052925 -1.236764 -0.864111 -0.086628  \n",
       "4       0.002892 -1.052925 -1.236764 -0.864111 -0.086628  \n",
       "...          ...       ...       ...       ...       ...  \n",
       "886998  0.002892 -1.160984  2.065920  1.551910  1.657994  \n",
       "886999  0.002892 -1.142074  2.065920  1.551910  1.657994  \n",
       "887000  0.002892 -1.101552  2.065920  1.551910  1.536298  \n",
       "887001  0.002892 -1.074537  2.065920  1.551910  1.512292  \n",
       "887002  0.002892 -1.055627  2.065920  1.551910  1.504686  \n",
       "\n",
       "[887003 rows x 12 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pipe = pd.DataFrame(df_pipe)\n",
    "df_pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>DepthMeters</th>\n",
       "      <th>Salinity</th>\n",
       "      <th>PDensity</th>\n",
       "      <th>RecInd</th>\n",
       "      <th>T_prec</th>\n",
       "      <th>S_prec</th>\n",
       "      <th>NH3q</th>\n",
       "      <th>MeanAq</th>\n",
       "      <th>R_DYNHT</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>TempDegF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>401701</th>\n",
       "      <td>0.14993</td>\n",
       "      <td>0.743222</td>\n",
       "      <td>0.671202</td>\n",
       "      <td>-0.901126</td>\n",
       "      <td>-0.114516</td>\n",
       "      <td>0.634194</td>\n",
       "      <td>0.176604</td>\n",
       "      <td>0.002892</td>\n",
       "      <td>0.297815</td>\n",
       "      <td>-0.434684</td>\n",
       "      <td>1.853912</td>\n",
       "      <td>-0.583393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DepthMeters  Salinity  PDensity    RecInd    T_prec    S_prec  \\\n",
       "401701      0.14993  0.743222  0.671202 -0.901126 -0.114516  0.634194   \n",
       "\n",
       "            NH3q    MeanAq   R_DYNHT      Year     Month  TempDegF  \n",
       "401701  0.176604  0.002892  0.297815 -0.434684  1.853912 -0.583393  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Change the name of columns back to original names.\n",
    "df_pipe.columns = df.columns\n",
    "\n",
    "df_pipe.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. MODELING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = [\"Salinity\",\"TempDegF\"]\n",
    "sal_temp = df.reindex(columns=column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "sal_temp['Salinity'].fillna((sal_temp['Salinity'].mean()), inplace=True)\n",
    "sal_temp['TempDegF'].fillna((sal_temp['TempDegF'].mean()), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Linear Regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Salinity</th>\n",
       "      <th>TempDegF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BottleCount</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.440</td>\n",
       "      <td>50.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.440</td>\n",
       "      <td>50.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.437</td>\n",
       "      <td>50.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.420</td>\n",
       "      <td>50.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33.421</td>\n",
       "      <td>50.810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Salinity  TempDegF\n",
       "BottleCount                    \n",
       "1              33.440    50.900\n",
       "2              33.440    50.828\n",
       "3              33.437    50.828\n",
       "4              33.420    50.810\n",
       "5              33.421    50.810"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of y before reshaping: (887003,)\n",
      "Dimensions of X before reshaping: (887003,)\n",
      "Dimensions of y after reshaping: (887003, 1)\n",
      "Dimensions of X after reshaping: (887003, 1)\n"
     ]
    }
   ],
   "source": [
    "X = sal_temp.Salinity.values\n",
    "y = sal_temp.TempDegF.values\n",
    "\n",
    "# Print the dimensions of X and y before reshaping\n",
    "print(\"Dimensions of y before reshaping: {}\".format(y.shape))\n",
    "print(\"Dimensions of X before reshaping: {}\".format(X.shape))\n",
    "\n",
    "# Reshape X and y\n",
    "y = y.reshape(-1,1)\n",
    "X = X.reshape(-1,1)\n",
    "\n",
    "# Print the dimensions of X and y after reshaping\n",
    "print(\"Dimensions of y after reshaping: {}\".format(y.shape))\n",
    "print(\"Dimensions of X after reshaping: {}\".format(X.shape))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "#Predict the Test set results\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize the Training set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_= plt.scatter(X_train, y_train, color = 'red')\n",
    "_= plt.plot(X_train, model.predict(X_train), color = 'blue')\n",
    "_= plt.title('Temperature vs Salinity (Training set)')\n",
    "_= plt.xlabel('Salinity')\n",
    "_= plt.ylabel('Temperature')\n",
    "_= plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize the Test set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_= plt.scatter(X_test, y_test, color = 'red')\n",
    "_= plt.plot(X_train, model.predict(X_train), color = 'blue')\n",
    "_= plt.title('Temperature vs Salinity (Training set)')\n",
    "_= plt.xlabel('Salinity')\n",
    "_= plt.ylabel('Temperature')\n",
    "_= plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split DataSet to Training set and Test set - For entire Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(620902, 11)\n",
      "(620902,)\n",
      "(266101, 11)\n",
      "(266101,)\n"
     ]
    }
   ],
   "source": [
    "X = df_pipe.drop(['TempDegF'],axis=1).values\n",
    "y = df_pipe.TempDegF.values\n",
    "\n",
    "SEED = 42\n",
    "TS = 0.30\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",
    "\n",
    "print (X_test.shape)\n",
    "print (y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Linear Regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.8128243925355431"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Training Score: \n",
      " 73.33\n",
      "Linear Regression Test Score: \n",
      " 81.28\n",
      "Root Mean Squared Error: 0.43315127212770627\n"
     ]
    }
   ],
   "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)\n",
    "\n",
    "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": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average 5-Fold CV Score: 0.2548713529936538\n",
      "Average 15-Fold CV Score: 0.2903922119919096\n",
      "Average 25-Fold CV Score: 0.6092251340150706\n"
     ]
    }
   ],
   "source": [
    "# 5-fold cross-validation:\n",
    "cv_scores_5 = cross_val_score(linreg, X, y, cv=5)\n",
    "print(\"Average 5-Fold CV Score: {}\".format(np.mean(cv_scores_5)))\n",
    "\n",
    "\n",
    "# 15-fold cross-validation:\n",
    "cv_scores_15 = cross_val_score(linreg, X, y, cv=15)\n",
    "print(\"Average 15-Fold CV Score: {}\".format(np.mean(cv_scores_15)))\n",
    "\n",
    "# 25-fold cross-validation:\n",
    "cv_scores_25 = cross_val_score(linreg, X, y, cv=25)\n",
    "print(\"Average 25-Fold CV Score: {}\".format(np.mean(cv_scores_25)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DECISION TREE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Training Score: \n",
      " 99.7\n",
      "Decision Test Score: \n",
      " 99.36\n"
     ]
    }
   ],
   "source": [
    "dtr = DecisionTreeRegressor(random_state=42)\n",
    "model = dtr.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "\n",
    "dtr_training_score = round(model.score(X_train, y_train) * 100, 2)\n",
    "dtr_test_score = round(model.score(X_test, y_test) * 100, 2)\n",
    "\n",
    "print('Decision Tree Training Score: \\n', dtr_training_score)\n",
    "print('Decision Test Score: \\n', dtr_test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RANDOM FOREST:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Training Score: \n",
      " 99.68\n",
      "Random Forest Test Score: \n",
      " 99.48\n"
     ]
    }
   ],
   "source": [
    "rfr = RandomForestRegressor(random_state=0, n_jobs=-1)\n",
    "model = rfr.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "rfr_training_score = round(model.score(X_train, y_train) * 100, 2)\n",
    "rfr_test_score = round(model.score(X_test, y_test) * 100, 2)\n",
    "\n",
    "print('Random Forest Training Score: \\n', rfr_training_score)\n",
    "print('Random Forest Test Score: \\n', rfr_test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Model</th>\n",
       "      <th>Training Score</th>\n",
       "      <th>Test Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>99.68</td>\n",
       "      <td>99.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>99.70</td>\n",
       "      <td>99.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>73.33</td>\n",
       "      <td>81.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model  Training Score  Test Score\n",
       "2      Random Forest           99.68       99.48\n",
       "1      Decision Tree           99.70       99.36\n",
       "0  Linear Regression           73.33       81.28"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    \n",
    "    'Model': [ \n",
    "        'Linear Regression',\n",
    "        'Decision Tree',\n",
    "        'Random Forest',   \n",
    "    ],\n",
    "             \n",
    "    \n",
    "    'Training Score': [ \n",
    "        linreg_training_score,\n",
    "        dtr_training_score, \n",
    "        rfr_training_score,\n",
    "    ],\n",
    "    \n",
    "    'Test Score': [ \n",
    "        linreg_test_score,\n",
    "        dtr_test_score,\n",
    "        rfr_test_score,\n",
    "    ]})\n",
    "\n",
    "\n",
    "models.sort_values(by='Test Score', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. DOCUMENTATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "This is Supervised Regression project to analyze Oceanographic trends.\n",
    "\n",
    "Temperature (TempDegF) is the target variable. Celsius was converted to Fahrenheit. \n",
    "\n",
    "### Model Performance:\n",
    "Accuracy Score: R^2: was used to determine the best model.\n",
    "\n",
    "### Model Findings:\n",
    "The most important features where: \n",
    "- 1. Salinity()\n",
    "- 2. Temperature()\n",
    "\n",
    "The reported Dynamic Height improved the model was removed then added to model because it improved the R^2 score significantly. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXPORT DATA..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to_csv('../data/calcofi/wrangle_csv.csv', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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