El Nino.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "el = pd.read_csv('elnino.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "el.columns = [col.strip() for col in el.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Observation</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Date</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Zonal Winds</th>\n",
       "      <th>Meridional Winds</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Air Temp</th>\n",
       "      <th>Sea Surface Temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>800307</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>.</td>\n",
       "      <td>26.14</td>\n",
       "      <td>26.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>800308</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>.</td>\n",
       "      <td>25.66</td>\n",
       "      <td>25.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>800309</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>.</td>\n",
       "      <td>25.69</td>\n",
       "      <td>25.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>800310</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>.</td>\n",
       "      <td>25.57</td>\n",
       "      <td>24.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>800311</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-109.46</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>.</td>\n",
       "      <td>25.30</td>\n",
       "      <td>23.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Observation  Year  Month  Day    Date  Latitude  Longitude Zonal Winds  \\\n",
       "0            1    80      3    7  800307     -0.02    -109.46        -6.8   \n",
       "1            2    80      3    8  800308     -0.02    -109.46        -4.9   \n",
       "2            3    80      3    9  800309     -0.02    -109.46        -4.5   \n",
       "3            4    80      3   10  800310     -0.02    -109.46        -3.8   \n",
       "4            5    80      3   11  800311     -0.02    -109.46        -4.2   \n",
       "\n",
       "  Meridional Winds Humidity  Air Temp  Sea Surface Temp  \n",
       "0              0.7        .     26.14             26.24  \n",
       "1              1.1        .     25.66             25.97  \n",
       "2              2.2        .     25.69             25.28  \n",
       "3              1.9        .     25.57             24.31  \n",
       "4              1.5        .     25.30             23.19  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "el.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 178080 entries, 0 to 178079\n",
      "Data columns (total 12 columns):\n",
      "Observation         178080 non-null int64\n",
      "Year                178080 non-null int64\n",
      "Month               178080 non-null int64\n",
      "Day                 178080 non-null int64\n",
      "Date                178080 non-null int64\n",
      "Latitude            178080 non-null float64\n",
      "Longitude           178080 non-null float64\n",
      "Zonal Winds         178080 non-null object\n",
      "Meridional Winds    178080 non-null object\n",
      "Humidity            178080 non-null object\n",
      "Air Temp            159843 non-null float64\n",
      "Sea Surface Temp    161073 non-null float64\n",
      "dtypes: float64(4), int64(5), object(3)\n",
      "memory usage: 16.3+ MB\n"
     ]
    }
   ],
   "source": [
    "el.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Observation', 'Year', 'Month', 'Day', 'Date', 'Latitude', 'Longitude',\n",
       "       'Zonal Winds', 'Meridional Winds', 'Humidity', 'Air Temp',\n",
       "       'Sea Surface Temp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "el.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Observation</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Date</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Zonal Winds</th>\n",
       "      <th>Meridional Winds</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Air Temp</th>\n",
       "      <th>Sea Surface Temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178050</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178051</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178052</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178053</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178054</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178055</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178056</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178057</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178058</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178059</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178060</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178061</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178062</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178063</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178064</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178065</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178066</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178067</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178068</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178069</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178070</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178071</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178072</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178073</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178074</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178075</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178076</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178077</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178078</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178079</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178080 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Observation   Year  Month    Day   Date Latitude Longitude Zonal Winds  \\\n",
       "0            False  False  False  False  False    False     False       False   \n",
       "1            False  False  False  False  False    False     False       False   \n",
       "2            False  False  False  False  False    False     False       False   \n",
       "3            False  False  False  False  False    False     False       False   \n",
       "4            False  False  False  False  False    False     False       False   \n",
       "5            False  False  False  False  False    False     False       False   \n",
       "6            False  False  False  False  False    False     False       False   \n",
       "7            False  False  False  False  False    False     False       False   \n",
       "8            False  False  False  False  False    False     False       False   \n",
       "9            False  False  False  False  False    False     False       False   \n",
       "10           False  False  False  False  False    False     False       False   \n",
       "11           False  False  False  False  False    False     False       False   \n",
       "12           False  False  False  False  False    False     False       False   \n",
       "13           False  False  False  False  False    False     False       False   \n",
       "14           False  False  False  False  False    False     False       False   \n",
       "15           False  False  False  False  False    False     False       False   \n",
       "16           False  False  False  False  False    False     False       False   \n",
       "17           False  False  False  False  False    False     False       False   \n",
       "18           False  False  False  False  False    False     False       False   \n",
       "19           False  False  False  False  False    False     False       False   \n",
       "20           False  False  False  False  False    False     False       False   \n",
       "21           False  False  False  False  False    False     False       False   \n",
       "22           False  False  False  False  False    False     False       False   \n",
       "23           False  False  False  False  False    False     False       False   \n",
       "24           False  False  False  False  False    False     False       False   \n",
       "25           False  False  False  False  False    False     False       False   \n",
       "26           False  False  False  False  False    False     False       False   \n",
       "27           False  False  False  False  False    False     False       False   \n",
       "28           False  False  False  False  False    False     False       False   \n",
       "29           False  False  False  False  False    False     False       False   \n",
       "...            ...    ...    ...    ...    ...      ...       ...         ...   \n",
       "178050       False  False  False  False  False    False     False       False   \n",
       "178051       False  False  False  False  False    False     False       False   \n",
       "178052       False  False  False  False  False    False     False       False   \n",
       "178053       False  False  False  False  False    False     False       False   \n",
       "178054       False  False  False  False  False    False     False       False   \n",
       "178055       False  False  False  False  False    False     False       False   \n",
       "178056       False  False  False  False  False    False     False       False   \n",
       "178057       False  False  False  False  False    False     False       False   \n",
       "178058       False  False  False  False  False    False     False       False   \n",
       "178059       False  False  False  False  False    False     False       False   \n",
       "178060       False  False  False  False  False    False     False       False   \n",
       "178061       False  False  False  False  False    False     False       False   \n",
       "178062       False  False  False  False  False    False     False       False   \n",
       "178063       False  False  False  False  False    False     False       False   \n",
       "178064       False  False  False  False  False    False     False       False   \n",
       "178065       False  False  False  False  False    False     False       False   \n",
       "178066       False  False  False  False  False    False     False       False   \n",
       "178067       False  False  False  False  False    False     False       False   \n",
       "178068       False  False  False  False  False    False     False       False   \n",
       "178069       False  False  False  False  False    False     False       False   \n",
       "178070       False  False  False  False  False    False     False       False   \n",
       "178071       False  False  False  False  False    False     False       False   \n",
       "178072       False  False  False  False  False    False     False       False   \n",
       "178073       False  False  False  False  False    False     False       False   \n",
       "178074       False  False  False  False  False    False     False       False   \n",
       "178075       False  False  False  False  False    False     False       False   \n",
       "178076       False  False  False  False  False    False     False       False   \n",
       "178077       False  False  False  False  False    False     False       False   \n",
       "178078       False  False  False  False  False    False     False       False   \n",
       "178079       False  False  False  False  False    False     False       False   \n",
       "\n",
       "       Meridional Winds Humidity Air Temp Sea Surface Temp  \n",
       "0                 False    False    False            False  \n",
       "1                 False    False    False            False  \n",
       "2                 False    False    False            False  \n",
       "3                 False    False    False            False  \n",
       "4                 False    False    False            False  \n",
       "5                 False    False    False            False  \n",
       "6                 False    False    False            False  \n",
       "7                 False    False    False            False  \n",
       "8                 False    False    False            False  \n",
       "9                 False    False    False            False  \n",
       "10                False    False    False            False  \n",
       "11                False    False    False            False  \n",
       "12                False    False    False            False  \n",
       "13                False    False    False            False  \n",
       "14                False    False    False            False  \n",
       "15                False    False    False             True  \n",
       "16                False    False    False             True  \n",
       "17                False    False    False             True  \n",
       "18                False    False    False             True  \n",
       "19                False    False    False             True  \n",
       "20                False    False    False             True  \n",
       "21                False    False    False             True  \n",
       "22                False    False    False             True  \n",
       "23                False    False    False            False  \n",
       "24                False    False    False            False  \n",
       "25                False    False    False            False  \n",
       "26                False    False    False            False  \n",
       "27                False    False    False            False  \n",
       "28                False    False    False            False  \n",
       "29                False    False    False            False  \n",
       "...                 ...      ...      ...              ...  \n",
       "178050            False    False    False            False  \n",
       "178051            False    False    False            False  \n",
       "178052            False    False    False            False  \n",
       "178053            False    False    False            False  \n",
       "178054            False    False    False            False  \n",
       "178055            False    False    False            False  \n",
       "178056            False    False    False            False  \n",
       "178057            False    False    False            False  \n",
       "178058            False    False    False            False  \n",
       "178059            False    False    False            False  \n",
       "178060            False    False    False            False  \n",
       "178061            False    False    False            False  \n",
       "178062            False    False    False            False  \n",
       "178063            False    False    False            False  \n",
       "178064            False    False    False            False  \n",
       "178065            False    False    False            False  \n",
       "178066            False    False    False            False  \n",
       "178067            False    False    False            False  \n",
       "178068            False    False    False            False  \n",
       "178069            False    False    False            False  \n",
       "178070            False    False    False            False  \n",
       "178071            False    False    False            False  \n",
       "178072            False    False    False            False  \n",
       "178073            False    False    False            False  \n",
       "178074            False    False    False            False  \n",
       "178075            False    False    False            False  \n",
       "178076            False    False    False            False  \n",
       "178077            False    False    False            False  \n",
       "178078            False    False    False            False  \n",
       "178079            False    False    False            False  \n",
       "\n",
       "[178080 rows x 12 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "el.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13d42fa0fd0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+fFiZmZlFXktKSoq2bdumrVu3av/+/apfv7569uypmTNnFnktXq+99pq2bt0q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\nYIDABADAAIEJAICB/wdNVw8iLh7E1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13d42ffeb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(el.isnull(),yticklabels=False,cbar=False,cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Not much null values that will be needed to be taken care of."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the Statistics - Air Temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\atse\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    159843.000000\n",
       "mean         26.887567\n",
       "std           1.816429\n",
       "min          17.050000\n",
       "25%                NaN\n",
       "50%                NaN\n",
       "75%                NaN\n",
       "max          31.660000\n",
       "Name: Air Temp, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "el['Air Temp'] = pd.to_numeric(el['Air Temp'], errors='coerce')\n",
    "el['Air Temp'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the Statistics - Sea Temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\atse\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    161073.000000\n",
       "mean         27.715309\n",
       "std           2.057428\n",
       "min          17.350000\n",
       "25%                NaN\n",
       "50%                NaN\n",
       "75%                NaN\n",
       "max          31.260000\n",
       "Name: Sea Surface Temp, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "el['Sea Surface Temp'] = pd.to_numeric(el['Sea Surface Temp'], errors='coerce')\n",
    "el['Sea Surface Temp'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performing the Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x13d418d85f8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I4aLWi3nHZe+sl5ioW4PI8qEgLCKywp3atituxxmtjlByi6QiLfzUmp+e8nXH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\nPvaILgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13d418d85c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x = 'Air Temp', y = 'Sea Surface Temp', data = el, color='green', size = 10, alpha = 0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}