2_DataWrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oceanographic Analysis - CalCOFI Capstone"
   ]
  },
  {
   "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": 1,
   "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": 2,
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.DS_Store',\n",
       " 'LICENSE',\n",
       " '2_DataWrangling.ipynb',\n",
       " 'models',\n",
       " 'README.md',\n",
       " '1_ProblemIdentification.ipynb',\n",
       " '.gitignore',\n",
       " 'figures',\n",
       " '.ipynb_checkpoints',\n",
       " '.git',\n",
       " '4_Preprocessing_Modelling.ipynb',\n",
       " 'data']"
      ]
     },
     "execution_count": 3,
     "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": 4,
   "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') #parse_dates=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    {
     "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>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>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>NaN</td>\n",
       "      <td>25.649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</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>0.0</td>\n",
       "      <td>10.50</td>\n",
       "      <td>233.0</td>\n",
       "      <td>0.00</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",
       "    <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>NaN</td>\n",
       "      <td>25.656</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</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>8.0</td>\n",
       "      <td>10.46</td>\n",
       "      <td>232.5</td>\n",
       "      <td>0.01</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",
       "    <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>NaN</td>\n",
       "      <td>25.654</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>10.0</td>\n",
       "      <td>10.46</td>\n",
       "      <td>232.8</td>\n",
       "      <td>0.02</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",
       "    <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>NaN</td>\n",
       "      <td>25.643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</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>19.0</td>\n",
       "      <td>10.45</td>\n",
       "      <td>234.1</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",
       "    <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": [
       "<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>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",
       "      <td>260.92</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>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",
       "      <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>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",
       "      <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>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",
       "      <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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</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": {
      "image/png": "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\n",
      "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": 27,
   "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": 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>CastCount</th>\n",
       "      <th>Sta_ID</th>\n",
       "      <th>Depth_ID</th>\n",
       "      <th>DepthMeters</th>\n",
       "      <th>TempDegC</th>\n",
       "      <th>Salinity</th>\n",
       "      <th>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",
       "    </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",
       "      <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>1949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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",
       "      <td>1949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</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",
       "      <td>1949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>054.0 056.0</td>\n",
       "      <td>19-4903CR-HY-060-0930-05400560-0019A-3</td>\n",
       "      <td>19</td>\n",
       "      <td>10.45</td>\n",
       "      <td>33.420</td>\n",
       "      <td>25.643</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.45</td>\n",
       "      <td>234.1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>054.0 056.0</td>\n",
       "      <td>19-4903CR-HY-060-0930-05400560-0020A-7</td>\n",
       "      <td>20</td>\n",
       "      <td>10.45</td>\n",
       "      <td>33.421</td>\n",
       "      <td>25.643</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.45</td>\n",
       "      <td>234.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CastCount       Sta_ID                                Depth_ID  \\\n",
       "BottleCount                                                                   \n",
       "1                    1  054.0 056.0  19-4903CR-HY-060-0930-05400560-0000A-3   \n",
       "2                    1  054.0 056.0  19-4903CR-HY-060-0930-05400560-0008A-3   \n",
       "3                    1  054.0 056.0  19-4903CR-HY-060-0930-05400560-0010A-7   \n",
       "4                    1  054.0 056.0  19-4903CR-HY-060-0930-05400560-0019A-3   \n",
       "5                    1  054.0 056.0  19-4903CR-HY-060-0930-05400560-0020A-7   \n",
       "\n",
       "             DepthMeters  TempDegC  Salinity  PDensity  RecInd  T_prec  \\\n",
       "BottleCount                                                              \n",
       "1                      0     10.50    33.440    25.649       3     1.0   \n",
       "2                      8     10.46    33.440    25.656       3     2.0   \n",
       "3                     10     10.46    33.437    25.654       7     2.0   \n",
       "4                     19     10.45    33.420    25.643       3     2.0   \n",
       "5                     20     10.45    33.421    25.643       7     2.0   \n",
       "\n",
       "             S_prec  NH3q  C14A1q  C14A2q  DarkAq  MeanAq  R_Depth  R_TEMP  \\\n",
       "BottleCount                                                                  \n",
       "1               2.0   9.0     9.0     9.0     9.0     9.0      0.0   10.50   \n",
       "2               2.0   9.0     9.0     9.0     9.0     9.0      8.0   10.46   \n",
       "3               3.0   9.0     9.0     9.0     9.0     9.0     10.0   10.46   \n",
       "4               2.0   9.0     9.0     9.0     9.0     9.0     19.0   10.45   \n",
       "5               3.0   9.0     9.0     9.0     9.0     9.0     20.0   10.45   \n",
       "\n",
       "             R_Sal  R_DYNHT  Year  \n",
       "BottleCount                        \n",
       "1            233.0     0.00  1949  \n",
       "2            232.5     0.01  1949  \n",
       "3            232.8     0.02  1949  \n",
       "4            234.1     0.04  1949  \n",
       "5            234.0     0.04  1949  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TempDegF'] = df['TempDegC'].apply(cel_to_fahr)\n",
    "df = df.drop(\"TempDegC\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = [\"Salinity\",\"TempDegF\"]\n",
    "\n",
    "saltemp = df.reindex(columns=column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saltemp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 38,
   "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-MX-313-1236-09000530-0030A-3</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-MX-313-1236-09000530-0030A-3      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": 38,
     "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": 39,
   "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": "code",
   "execution_count": 40,
   "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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Salinity\"].describe(include=\"all\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"TempDegF\"].describe(include=\"all\").T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA CLEANING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check for Duplicated Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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",
       "      <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <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</td>\n",
       "      <td>45</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</td>\n",
       "      <td>45</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</td>\n",
       "      <td>45</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</td>\n",
       "      <td>45</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</td>\n",
       "      <td>45</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</td>\n",
       "      <td>45</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</td>\n",
       "      <td>45</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</td>\n",
       "      <td>45</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</td>\n",
       "      <td>45</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</td>\n",
       "      <td>45</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</td>\n",
       "      <td>11</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</td>\n",
       "      <td>11</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</td>\n",
       "      <td>11</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</td>\n",
       "      <td>11</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</td>\n",
       "      <td>11</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     45   60.7370  \n",
       "858747       2016     45   60.3140  \n",
       "858748       2016     45   59.2106  \n",
       "858749       2016     45   57.9704  \n",
       "858750       2016     45   56.7680  \n",
       "858880       2016     45   59.8154  \n",
       "858881       2016     45   59.2898  \n",
       "858882       2016     45   58.2134  \n",
       "858883       2016     45   56.9984  \n",
       "858884       2016     45   53.2202  \n",
       "886343       2019     11   64.3514  \n",
       "886344       2019     11   64.3460  \n",
       "886345       2019     11   64.3460  \n",
       "886346       2019     11   64.3280  \n",
       "886347       2019     11   59.9522  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#column_names = [\"\"] \n",
    "\n",
    "duplicateRowsDF = df.duplicated() \n",
    "df[duplicateRowsDF]\n",
    "\n",
    "#df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Note:</b> .\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical columns and their associated levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(887018, 2)"
      ]
     },
     "execution_count": 43,
     "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": 43,
     "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": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Depth_ID','Sta_ID'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 887018 entries, 1 to 887018\n",
      "Data columns (total 19 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   CastCount    887018 non-null  int64  \n",
      " 1   DepthMeters  887018 non-null  int64  \n",
      " 2   Salinity     839663 non-null  float64\n",
      " 3   PDensity     834327 non-null  float64\n",
      " 4   RecInd       887018 non-null  int64  \n",
      " 5   T_prec       876055 non-null  float64\n",
      " 6   S_prec       839663 non-null  float64\n",
      " 7   NH3q         825936 non-null  float64\n",
      " 8   C14A1q       869746 non-null  float64\n",
      " 9   C14A2q       869764 non-null  float64\n",
      " 10  DarkAq       861581 non-null  float64\n",
      " 11  MeanAq       861580 non-null  float64\n",
      " 12  R_Depth      887017 non-null  float64\n",
      " 13  R_TEMP       840969 non-null  float64\n",
      " 14  R_Sal        834240 non-null  float64\n",
      " 15  R_DYNHT      840354 non-null  float64\n",
      " 16  Year         887018 non-null  int64  \n",
      " 17  Month        887018 non-null  int64  \n",
      " 18  TempDegF     876053 non-null  float64\n",
      "dtypes: float64(14), int64(5)\n",
      "memory usage: 135.3 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>C14A1q</th>\n",
       "      <th>C14A2q</th>\n",
       "      <th>CastCount</th>\n",
       "      <th>DarkAq</th>\n",
       "      <th>DepthMeters</th>\n",
       "      <th>MeanAq</th>\n",
       "      <th>Month</th>\n",
       "      <th>NH3q</th>\n",
       "      <th>PDensity</th>\n",
       "      <th>R_DYNHT</th>\n",
       "      <th>R_Depth</th>\n",
       "      <th>R_Sal</th>\n",
       "      <th>R_TEMP</th>\n",
       "      <th>RecInd</th>\n",
       "      <th>S_prec</th>\n",
       "      <th>Salinity</th>\n",
       "      <th>T_prec</th>\n",
       "      <th>TempDegF</th>\n",
       "      <th>Year</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>313320</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11924</td>\n",
       "      <td>9.0</td>\n",
       "      <td>92</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.571</td>\n",
       "      <td>0.280</td>\n",
       "      <td>92.0</td>\n",
       "      <td>242.8</td>\n",
       "      <td>12.01</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>33.690</td>\n",
       "      <td>2.0</td>\n",
       "      <td>53.636</td>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224278</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8561</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.753</td>\n",
       "      <td>2.400</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>47.1</td>\n",
       "      <td>1.62</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.691</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35.330</td>\n",
       "      <td>1956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816842</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>32548</td>\n",
       "      <td>9.0</td>\n",
       "      <td>150</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.371</td>\n",
       "      <td>0.345</td>\n",
       "      <td>151.0</td>\n",
       "      <td>167.5</td>\n",
       "      <td>9.01</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.048</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48.254</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148387</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5604</td>\n",
       "      <td>9.0</td>\n",
       "      <td>115</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.953</td>\n",
       "      <td>0.350</td>\n",
       "      <td>115.0</td>\n",
       "      <td>302.9</td>\n",
       "      <td>15.60</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>33.850</td>\n",
       "      <td>2.0</td>\n",
       "      <td>60.116</td>\n",
       "      <td>1953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73541</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2460</td>\n",
       "      <td>9.0</td>\n",
       "      <td>800</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.245</td>\n",
       "      <td>1.080</td>\n",
       "      <td>806.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>4.83</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.433</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.820</td>\n",
       "      <td>1951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             C14A1q  C14A2q  CastCount  DarkAq  DepthMeters  MeanAq  Month  \\\n",
       "BottleCount                                                                  \n",
       "313320          9.0     9.0      11924     9.0           92     9.0      4   \n",
       "224278          9.0     9.0       8561     9.0         3000     9.0     12   \n",
       "816842          9.0     9.0      32548     9.0          150     9.0     11   \n",
       "148387          9.0     9.0       5604     9.0          115     9.0      4   \n",
       "73541           9.0     9.0       2460     9.0          800     9.0      3   \n",
       "\n",
       "             NH3q  PDensity  R_DYNHT  R_Depth  R_Sal  R_TEMP  RecInd  S_prec  \\\n",
       "BottleCount                                                                    \n",
       "313320        9.0    25.571    0.280     92.0  242.8   12.01       3     2.0   \n",
       "224278        9.0    27.753    2.400   3038.0   47.1    1.62       7     3.0   \n",
       "816842        NaN    26.371    0.345    151.0  167.5    9.01       7     3.0   \n",
       "148387        9.0    24.953    0.350    115.0  302.9   15.60       3     2.0   \n",
       "73541         9.0    27.245    1.080    806.0   91.0    4.83       7     3.0   \n",
       "\n",
       "             Salinity  T_prec  TempDegF  Year  \n",
       "BottleCount                                    \n",
       "313320         33.690     2.0    53.636  1960  \n",
       "224278         34.691     2.0    35.330  1956  \n",
       "816842         34.048     2.0    48.254  2010  \n",
       "148387         33.850     2.0    60.116  1953  \n",
       "73541          34.433     2.0    40.820  1951  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1, ascending=True).sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXPORT DATA..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To CSV\n",
    "df.to_csv('../data/calcofi/wrangle_csv.csv', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To Excel\n",
    "df.to_excel('data/wrangle_excel.xlsx', sheet_name='NewSheet')\n",
    "\n",
    "#To ClipBoard\n",
    "df.to_clipboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To HTML\n",
    "df.to_html('data/wrangle_html.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To SQL\n",
    "from sqlalchemy import create_engine\n",
    "engine = create_engine('sqlite:///:memory:')\n",
    "df.to_sql('sql_table',engine, index=False)\n",
    "\n",
    "sqldf = pd.read_sql('sql_table',con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pandas Profiling\n",
    "profile = ProfileReport(df, title='Medical Appointment NoShows Profiling Report', minimal=True)\n",
    "profile.to_file(\"data/wrangle_profiling.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}