Temp_Scrub.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Green Project:  Scrub Global Temperature File\n",
    "### Note:  Temperatures are in Celsius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies and Setup\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "# File to Load\n",
    "temp_data_to_load = \"data/GlobalTemperatures.csv\"\n",
    "\n",
    "# Read Temperature Data\n",
    "temp_data = pd.read_csv(temp_data_to_load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>LandAverageTemperature</th>\n",
       "      <th>LandAverageTemperatureUncertainty</th>\n",
       "      <th>LandMaxTemperature</th>\n",
       "      <th>LandMaxTemperatureUncertainty</th>\n",
       "      <th>LandMinTemperature</th>\n",
       "      <th>LandMinTemperatureUncertainty</th>\n",
       "      <th>LandAndOceanAverageTemperature</th>\n",
       "      <th>LandAndOceanAverageTemperatureUncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1750-01-01</td>\n",
       "      <td>3.034</td>\n",
       "      <td>3.574</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1750-02-01</td>\n",
       "      <td>3.083</td>\n",
       "      <td>3.702</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1750-03-01</td>\n",
       "      <td>5.626</td>\n",
       "      <td>3.076</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",
       "      <td>3</td>\n",
       "      <td>1750-04-01</td>\n",
       "      <td>8.490</td>\n",
       "      <td>2.451</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",
       "      <td>4</td>\n",
       "      <td>1750-05-01</td>\n",
       "      <td>11.573</td>\n",
       "      <td>2.072</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": [
       "           dt  LandAverageTemperature  LandAverageTemperatureUncertainty  LandMaxTemperature  LandMaxTemperatureUncertainty  LandMinTemperature  LandMinTemperatureUncertainty  LandAndOceanAverageTemperature  LandAndOceanAverageTemperatureUncertainty\n",
       "0  1750-01-01                   3.034                              3.574                 NaN                            NaN                 NaN                            NaN                             NaN                                        NaN\n",
       "1  1750-02-01                   3.083                              3.702                 NaN                            NaN                 NaN                            NaN                             NaN                                        NaN\n",
       "2  1750-03-01                   5.626                              3.076                 NaN                            NaN                 NaN                            NaN                             NaN                                        NaN\n",
       "3  1750-04-01                   8.490                              2.451                 NaN                            NaN                 NaN                            NaN                             NaN                                        NaN\n",
       "4  1750-05-01                  11.573                              2.072                 NaN                            NaN                 NaN                            NaN                             NaN                                        NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3187</td>\n",
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       "      <td>3188</td>\n",
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       "      <td>17.049</td>\n",
       "      <td>0.058</td>\n",
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       "    <tr>\n",
       "      <td>3189</td>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>16.290</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3190</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>15.252</td>\n",
       "      <td>0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3191</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>14.774</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3192 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "              dt  LandAndOceanAverageTemperature  LandAndOceanAverageTemperatureUncertainty\n",
       "0     1750-01-01                             NaN                                        NaN\n",
       "1     1750-02-01                             NaN                                        NaN\n",
       "2     1750-03-01                             NaN                                        NaN\n",
       "3     1750-04-01                             NaN                                        NaN\n",
       "4     1750-05-01                             NaN                                        NaN\n",
       "...          ...                             ...                                        ...\n",
       "3187  2015-08-01                          17.589                                      0.057\n",
       "3188  2015-09-01                          17.049                                      0.058\n",
       "3189  2015-10-01                          16.290                                      0.062\n",
       "3190  2015-11-01                          15.252                                      0.063\n",
       "3191  2015-12-01                          14.774                                      0.062\n",
       "\n",
       "[3192 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new dataset with only dt, LandAndOceanAverageTemperature, and LandAndOceanAverageTemperatureUncertainty\n",
    "temp_data_1 = temp_data[['dt', 'LandAndOceanAverageTemperature', 'LandAndOceanAverageTemperatureUncertainty']]\n",
    "temp_data_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3190</td>\n",
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       "<p>1992 rows × 3 columns</p>\n",
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      "text/plain": [
       "              dt  LandAndOceanAverageTemperature  LandAndOceanAverageTemperatureUncertainty\n",
       "1200  1850-01-01                          12.833                                      0.367\n",
       "1201  1850-02-01                          13.588                                      0.414\n",
       "1202  1850-03-01                          14.043                                      0.341\n",
       "1203  1850-04-01                          14.667                                      0.267\n",
       "1204  1850-05-01                          15.507                                      0.249\n",
       "...          ...                             ...                                        ...\n",
       "3187  2015-08-01                          17.589                                      0.057\n",
       "3188  2015-09-01                          17.049                                      0.058\n",
       "3189  2015-10-01                          16.290                                      0.062\n",
       "3190  2015-11-01                          15.252                                      0.063\n",
       "3191  2015-12-01                          14.774                                      0.062\n",
       "\n",
       "[1992 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop rows with NaN\n",
    "temp_data_2 = temp_data_1.dropna()\n",
    "temp_data_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <td>2015</td>\n",
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       "      <td>3190</td>\n",
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       "      <td>0.063</td>\n",
       "      <td>2015</td>\n",
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       "      <td>0.062</td>\n",
       "      <td>2015</td>\n",
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       "<p>1992 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "              dt  LandAndOceanAverageTemperature  LandAndOceanAverageTemperatureUncertainty  Year\n",
       "1200  1850-01-01                          12.833                                      0.367  1850\n",
       "1201  1850-02-01                          13.588                                      0.414  1850\n",
       "1202  1850-03-01                          14.043                                      0.341  1850\n",
       "1203  1850-04-01                          14.667                                      0.267  1850\n",
       "1204  1850-05-01                          15.507                                      0.249  1850\n",
       "...          ...                             ...                                        ...   ...\n",
       "3187  2015-08-01                          17.589                                      0.057  2015\n",
       "3188  2015-09-01                          17.049                                      0.058  2015\n",
       "3189  2015-10-01                          16.290                                      0.062  2015\n",
       "3190  2015-11-01                          15.252                                      0.063  2015\n",
       "3191  2015-12-01                          14.774                                      0.062  2015\n",
       "\n",
       "[1992 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Year Column\n",
    "# Disable the SettingWithCopyWarning warning\n",
    "pd.options.mode.chained_assignment = None\n",
    "temp_data_2['Year'] = temp_data_2.dt.str[:4]\n",
    "temp_data_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>1853</td>\n",
       "      <td>14.955167</td>\n",
       "      <td>0.283833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1854</td>\n",
       "      <td>14.991000</td>\n",
       "      <td>0.276417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1855</td>\n",
       "      <td>15.021083</td>\n",
       "      <td>0.291167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1856</td>\n",
       "      <td>14.879333</td>\n",
       "      <td>0.260167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1857</td>\n",
       "      <td>14.758250</td>\n",
       "      <td>0.259750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1858</td>\n",
       "      <td>14.881500</td>\n",
       "      <td>0.251833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1859</td>\n",
       "      <td>14.929917</td>\n",
       "      <td>0.258250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1860</td>\n",
       "      <td>14.882917</td>\n",
       "      <td>0.252167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1861</td>\n",
       "      <td>14.800667</td>\n",
       "      <td>0.278500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1862</td>\n",
       "      <td>14.740083</td>\n",
       "      <td>0.287667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1863</td>\n",
       "      <td>14.935750</td>\n",
       "      <td>0.316333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1864</td>\n",
       "      <td>14.896500</td>\n",
       "      <td>0.287833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1865</td>\n",
       "      <td>14.996667</td>\n",
       "      <td>0.304250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1866</td>\n",
       "      <td>15.057000</td>\n",
       "      <td>0.286083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1867</td>\n",
       "      <td>15.058667</td>\n",
       "      <td>0.285750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1868</td>\n",
       "      <td>15.096917</td>\n",
       "      <td>0.249333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1869</td>\n",
       "      <td>15.065500</td>\n",
       "      <td>0.243583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1870</td>\n",
       "      <td>15.004333</td>\n",
       "      <td>0.224833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1871</td>\n",
       "      <td>15.005917</td>\n",
       "      <td>0.232000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1872</td>\n",
       "      <td>15.019333</td>\n",
       "      <td>0.230500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1873</td>\n",
       "      <td>15.049250</td>\n",
       "      <td>0.222083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1874</td>\n",
       "      <td>14.957000</td>\n",
       "      <td>0.213333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1875</td>\n",
       "      <td>14.921917</td>\n",
       "      <td>0.197083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1876</td>\n",
       "      <td>14.909417</td>\n",
       "      <td>0.193333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1877</td>\n",
       "      <td>15.282667</td>\n",
       "      <td>0.196750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1878</td>\n",
       "      <td>15.357417</td>\n",
       "      <td>0.186250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1879</td>\n",
       "      <td>15.064417</td>\n",
       "      <td>0.183417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1880</td>\n",
       "      <td>15.008667</td>\n",
       "      <td>0.172917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1881</td>\n",
       "      <td>15.087167</td>\n",
       "      <td>0.170083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1882</td>\n",
       "      <td>15.056583</td>\n",
       "      <td>0.160083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1883</td>\n",
       "      <td>14.963250</td>\n",
       "      <td>0.151167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1884</td>\n",
       "      <td>14.833250</td>\n",
       "      <td>0.150667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1885</td>\n",
       "      <td>14.854417</td>\n",
       "      <td>0.154917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1886</td>\n",
       "      <td>14.835250</td>\n",
       "      <td>0.145333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1887</td>\n",
       "      <td>14.816000</td>\n",
       "      <td>0.150167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1888</td>\n",
       "      <td>14.994083</td>\n",
       "      <td>0.149250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1889</td>\n",
       "      <td>15.113417</td>\n",
       "      <td>0.152417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1890</td>\n",
       "      <td>14.840750</td>\n",
       "      <td>0.149667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1891</td>\n",
       "      <td>14.943833</td>\n",
       "      <td>0.153583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1892</td>\n",
       "      <td>14.882500</td>\n",
       "      <td>0.156000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1893</td>\n",
       "      <td>14.912250</td>\n",
       "      <td>0.154250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1894</td>\n",
       "      <td>14.914750</td>\n",
       "      <td>0.150333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1895</td>\n",
       "      <td>14.969000</td>\n",
       "      <td>0.146833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1896</td>\n",
       "      <td>15.090583</td>\n",
       "      <td>0.148000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1897</td>\n",
       "      <td>15.075333</td>\n",
       "      <td>0.149333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1898</td>\n",
       "      <td>14.886917</td>\n",
       "      <td>0.148500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1899</td>\n",
       "      <td>15.048333</td>\n",
       "      <td>0.147500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1900</td>\n",
       "      <td>15.143917</td>\n",
       "      <td>0.150250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1901</td>\n",
       "      <td>15.073333</td>\n",
       "      <td>0.145750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1902</td>\n",
       "      <td>14.958333</td>\n",
       "      <td>0.135500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1903</td>\n",
       "      <td>14.836583</td>\n",
       "      <td>0.134833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1904</td>\n",
       "      <td>14.810417</td>\n",
       "      <td>0.133083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1905</td>\n",
       "      <td>14.954667</td>\n",
       "      <td>0.129000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>1906</td>\n",
       "      <td>15.031917</td>\n",
       "      <td>0.128583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>1907</td>\n",
       "      <td>14.874833</td>\n",
       "      <td>0.125833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1908</td>\n",
       "      <td>14.838167</td>\n",
       "      <td>0.125083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>1909</td>\n",
       "      <td>14.790333</td>\n",
       "      <td>0.119500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>1910</td>\n",
       "      <td>14.819083</td>\n",
       "      <td>0.117583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>1911</td>\n",
       "      <td>14.776000</td>\n",
       "      <td>0.115167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>1912</td>\n",
       "      <td>14.877167</td>\n",
       "      <td>0.116917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>1913</td>\n",
       "      <td>14.908750</td>\n",
       "      <td>0.117083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>1914</td>\n",
       "      <td>15.076250</td>\n",
       "      <td>0.125250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>1915</td>\n",
       "      <td>15.143750</td>\n",
       "      <td>0.134250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>1916</td>\n",
       "      <td>14.901333</td>\n",
       "      <td>0.134833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>1917</td>\n",
       "      <td>14.809750</td>\n",
       "      <td>0.140500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>1918</td>\n",
       "      <td>14.959833</td>\n",
       "      <td>0.148167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>1919</td>\n",
       "      <td>15.006750</td>\n",
       "      <td>0.142833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>1920</td>\n",
       "      <td>15.026250</td>\n",
       "      <td>0.135583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>1921</td>\n",
       "      <td>15.102083</td>\n",
       "      <td>0.129167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>1922</td>\n",
       "      <td>15.012333</td>\n",
       "      <td>0.124083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>1923</td>\n",
       "      <td>15.027833</td>\n",
       "      <td>0.122667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>1924</td>\n",
       "      <td>15.039333</td>\n",
       "      <td>0.124500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>1925</td>\n",
       "      <td>15.090333</td>\n",
       "      <td>0.123917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>1926</td>\n",
       "      <td>15.241083</td>\n",
       "      <td>0.122417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>1927</td>\n",
       "      <td>15.122250</td>\n",
       "      <td>0.121667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>1928</td>\n",
       "      <td>15.132250</td>\n",
       "      <td>0.123333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>1929</td>\n",
       "      <td>14.967417</td>\n",
       "      <td>0.124667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>1930</td>\n",
       "      <td>15.172500</td>\n",
       "      <td>0.128917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>1931</td>\n",
       "      <td>15.228250</td>\n",
       "      <td>0.129083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>1932</td>\n",
       "      <td>15.188000</td>\n",
       "      <td>0.129417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>1933</td>\n",
       "      <td>15.012917</td>\n",
       "      <td>0.127083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>1934</td>\n",
       "      <td>15.144500</td>\n",
       "      <td>0.124417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>1935</td>\n",
       "      <td>15.118833</td>\n",
       "      <td>0.122333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>1936</td>\n",
       "      <td>15.166167</td>\n",
       "      <td>0.120333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>1937</td>\n",
       "      <td>15.300500</td>\n",
       "      <td>0.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>1938</td>\n",
       "      <td>15.293083</td>\n",
       "      <td>0.118583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>1939</td>\n",
       "      <td>15.302333</td>\n",
       "      <td>0.120583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>1940</td>\n",
       "      <td>15.372083</td>\n",
       "      <td>0.122833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>1941</td>\n",
       "      <td>15.384667</td>\n",
       "      <td>0.115917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>1942</td>\n",
       "      <td>15.325333</td>\n",
       "      <td>0.133083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>1943</td>\n",
       "      <td>15.345000</td>\n",
       "      <td>0.133667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>1944</td>\n",
       "      <td>15.449083</td>\n",
       "      <td>0.127750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>1945</td>\n",
       "      <td>15.330750</td>\n",
       "      <td>0.130333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>1946</td>\n",
       "      <td>15.262750</td>\n",
       "      <td>0.132417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>1947</td>\n",
       "      <td>15.310167</td>\n",
       "      <td>0.128750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>1948</td>\n",
       "      <td>15.261167</td>\n",
       "      <td>0.128500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>1949</td>\n",
       "      <td>15.222750</td>\n",
       "      <td>0.107417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>1950</td>\n",
       "      <td>15.139500</td>\n",
       "      <td>0.102333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>1951</td>\n",
       "      <td>15.311167</td>\n",
       "      <td>0.098917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>1952</td>\n",
       "      <td>15.363083</td>\n",
       "      <td>0.101667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>1953</td>\n",
       "      <td>15.421083</td>\n",
       "      <td>0.102250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>1954</td>\n",
       "      <td>15.230583</td>\n",
       "      <td>0.095333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>1955</td>\n",
       "      <td>15.174333</td>\n",
       "      <td>0.088083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>1956</td>\n",
       "      <td>15.106000</td>\n",
       "      <td>0.079667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>1957</td>\n",
       "      <td>15.374083</td>\n",
       "      <td>0.084417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>1958</td>\n",
       "      <td>15.382083</td>\n",
       "      <td>0.074500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>1959</td>\n",
       "      <td>15.340500</td>\n",
       "      <td>0.073917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>1960</td>\n",
       "      <td>15.291917</td>\n",
       "      <td>0.069333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>1961</td>\n",
       "      <td>15.379917</td>\n",
       "      <td>0.067167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>1962</td>\n",
       "      <td>15.325583</td>\n",
       "      <td>0.072500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>1963</td>\n",
       "      <td>15.376667</td>\n",
       "      <td>0.068333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>1964</td>\n",
       "      <td>15.117083</td>\n",
       "      <td>0.065167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>1965</td>\n",
       "      <td>15.195750</td>\n",
       "      <td>0.067250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>1966</td>\n",
       "      <td>15.264667</td>\n",
       "      <td>0.063667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>1967</td>\n",
       "      <td>15.294167</td>\n",
       "      <td>0.059250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>1968</td>\n",
       "      <td>15.263917</td>\n",
       "      <td>0.060167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>1969</td>\n",
       "      <td>15.390500</td>\n",
       "      <td>0.055167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>1970</td>\n",
       "      <td>15.332667</td>\n",
       "      <td>0.053250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>1971</td>\n",
       "      <td>15.200000</td>\n",
       "      <td>0.056167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>1972</td>\n",
       "      <td>15.292917</td>\n",
       "      <td>0.055833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>1973</td>\n",
       "      <td>15.404750</td>\n",
       "      <td>0.054917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>1974</td>\n",
       "      <td>15.168083</td>\n",
       "      <td>0.056250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>1975</td>\n",
       "      <td>15.238667</td>\n",
       "      <td>0.055333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>1976</td>\n",
       "      <td>15.131083</td>\n",
       "      <td>0.057417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>1977</td>\n",
       "      <td>15.408250</td>\n",
       "      <td>0.053000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>1978</td>\n",
       "      <td>15.300917</td>\n",
       "      <td>0.055500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>1979</td>\n",
       "      <td>15.414167</td>\n",
       "      <td>0.047500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>1980</td>\n",
       "      <td>15.491833</td>\n",
       "      <td>0.053667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>1981</td>\n",
       "      <td>15.516167</td>\n",
       "      <td>0.052333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>1982</td>\n",
       "      <td>15.341917</td>\n",
       "      <td>0.054750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>1983</td>\n",
       "      <td>15.520250</td>\n",
       "      <td>0.055833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>1984</td>\n",
       "      <td>15.344167</td>\n",
       "      <td>0.057167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>1985</td>\n",
       "      <td>15.340667</td>\n",
       "      <td>0.053917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>1986</td>\n",
       "      <td>15.384000</td>\n",
       "      <td>0.053250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>1987</td>\n",
       "      <td>15.524500</td>\n",
       "      <td>0.053333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>1988</td>\n",
       "      <td>15.555750</td>\n",
       "      <td>0.053250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>1989</td>\n",
       "      <td>15.441583</td>\n",
       "      <td>0.055917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>1990</td>\n",
       "      <td>15.629333</td>\n",
       "      <td>0.057250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>1991</td>\n",
       "      <td>15.598000</td>\n",
       "      <td>0.055083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>1992</td>\n",
       "      <td>15.453000</td>\n",
       "      <td>0.058167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>1993</td>\n",
       "      <td>15.466417</td>\n",
       "      <td>0.059167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>1994</td>\n",
       "      <td>15.535000</td>\n",
       "      <td>0.058583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>1995</td>\n",
       "      <td>15.637833</td>\n",
       "      <td>0.060417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>1996</td>\n",
       "      <td>15.524667</td>\n",
       "      <td>0.059500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>1997</td>\n",
       "      <td>15.713833</td>\n",
       "      <td>0.059167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>1998</td>\n",
       "      <td>15.826000</td>\n",
       "      <td>0.063000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>1999</td>\n",
       "      <td>15.600333</td>\n",
       "      <td>0.063333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>2000</td>\n",
       "      <td>15.610667</td>\n",
       "      <td>0.063500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>2001</td>\n",
       "      <td>15.767500</td>\n",
       "      <td>0.064583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>152</td>\n",
       "      <td>2002</td>\n",
       "      <td>15.829167</td>\n",
       "      <td>0.062917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>153</td>\n",
       "      <td>2003</td>\n",
       "      <td>15.826583</td>\n",
       "      <td>0.064333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>2004</td>\n",
       "      <td>15.757250</td>\n",
       "      <td>0.061250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155</td>\n",
       "      <td>2005</td>\n",
       "      <td>15.879250</td>\n",
       "      <td>0.060833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>2006</td>\n",
       "      <td>15.813500</td>\n",
       "      <td>0.061000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>157</td>\n",
       "      <td>2007</td>\n",
       "      <td>15.827333</td>\n",
       "      <td>0.059083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>2008</td>\n",
       "      <td>15.721250</td>\n",
       "      <td>0.057250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>2009</td>\n",
       "      <td>15.827167</td>\n",
       "      <td>0.058917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>2010</td>\n",
       "      <td>15.895500</td>\n",
       "      <td>0.058583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>2011</td>\n",
       "      <td>15.769500</td>\n",
       "      <td>0.059000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>2012</td>\n",
       "      <td>15.802333</td>\n",
       "      <td>0.061500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>2013</td>\n",
       "      <td>15.854417</td>\n",
       "      <td>0.064667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>2014</td>\n",
       "      <td>15.913000</td>\n",
       "      <td>0.063167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>2015</td>\n",
       "      <td>16.058583</td>\n",
       "      <td>0.060833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  LandAndOceanAverageTemperature  LandAndOceanAverageTemperatureUncertainty\n",
       "0    1850                       14.867167                                   0.308167\n",
       "1    1851                       14.991833                                   0.312083\n",
       "2    1852                       15.006500                                   0.316417\n",
       "3    1853                       14.955167                                   0.283833\n",
       "4    1854                       14.991000                                   0.276417\n",
       "5    1855                       15.021083                                   0.291167\n",
       "6    1856                       14.879333                                   0.260167\n",
       "7    1857                       14.758250                                   0.259750\n",
       "8    1858                       14.881500                                   0.251833\n",
       "9    1859                       14.929917                                   0.258250\n",
       "10   1860                       14.882917                                   0.252167\n",
       "11   1861                       14.800667                                   0.278500\n",
       "12   1862                       14.740083                                   0.287667\n",
       "13   1863                       14.935750                                   0.316333\n",
       "14   1864                       14.896500                                   0.287833\n",
       "15   1865                       14.996667                                   0.304250\n",
       "16   1866                       15.057000                                   0.286083\n",
       "17   1867                       15.058667                                   0.285750\n",
       "18   1868                       15.096917                                   0.249333\n",
       "19   1869                       15.065500                                   0.243583\n",
       "20   1870                       15.004333                                   0.224833\n",
       "21   1871                       15.005917                                   0.232000\n",
       "22   1872                       15.019333                                   0.230500\n",
       "23   1873                       15.049250                                   0.222083\n",
       "24   1874                       14.957000                                   0.213333\n",
       "25   1875                       14.921917                                   0.197083\n",
       "26   1876                       14.909417                                   0.193333\n",
       "27   1877                       15.282667                                   0.196750\n",
       "28   1878                       15.357417                                   0.186250\n",
       "29   1879                       15.064417                                   0.183417\n",
       "30   1880                       15.008667                                   0.172917\n",
       "31   1881                       15.087167                                   0.170083\n",
       "32   1882                       15.056583                                   0.160083\n",
       "33   1883                       14.963250                                   0.151167\n",
       "34   1884                       14.833250                                   0.150667\n",
       "35   1885                       14.854417                                   0.154917\n",
       "36   1886                       14.835250                                   0.145333\n",
       "37   1887                       14.816000                                   0.150167\n",
       "38   1888                       14.994083                                   0.149250\n",
       "39   1889                       15.113417                                   0.152417\n",
       "40   1890                       14.840750                                   0.149667\n",
       "41   1891                       14.943833                                   0.153583\n",
       "42   1892                       14.882500                                   0.156000\n",
       "43   1893                       14.912250                                   0.154250\n",
       "44   1894                       14.914750                                   0.150333\n",
       "45   1895                       14.969000                                   0.146833\n",
       "46   1896                       15.090583                                   0.148000\n",
       "47   1897                       15.075333                                   0.149333\n",
       "48   1898                       14.886917                                   0.148500\n",
       "49   1899                       15.048333                                   0.147500\n",
       "50   1900                       15.143917                                   0.150250\n",
       "51   1901                       15.073333                                   0.145750\n",
       "52   1902                       14.958333                                   0.135500\n",
       "53   1903                       14.836583                                   0.134833\n",
       "54   1904                       14.810417                                   0.133083\n",
       "55   1905                       14.954667                                   0.129000\n",
       "56   1906                       15.031917                                   0.128583\n",
       "57   1907                       14.874833                                   0.125833\n",
       "58   1908                       14.838167                                   0.125083\n",
       "59   1909                       14.790333                                   0.119500\n",
       "60   1910                       14.819083                                   0.117583\n",
       "61   1911                       14.776000                                   0.115167\n",
       "62   1912                       14.877167                                   0.116917\n",
       "63   1913                       14.908750                                   0.117083\n",
       "64   1914                       15.076250                                   0.125250\n",
       "65   1915                       15.143750                                   0.134250\n",
       "66   1916                       14.901333                                   0.134833\n",
       "67   1917                       14.809750                                   0.140500\n",
       "68   1918                       14.959833                                   0.148167\n",
       "69   1919                       15.006750                                   0.142833\n",
       "70   1920                       15.026250                                   0.135583\n",
       "71   1921                       15.102083                                   0.129167\n",
       "72   1922                       15.012333                                   0.124083\n",
       "73   1923                       15.027833                                   0.122667\n",
       "74   1924                       15.039333                                   0.124500\n",
       "75   1925                       15.090333                                   0.123917\n",
       "76   1926                       15.241083                                   0.122417\n",
       "77   1927                       15.122250                                   0.121667\n",
       "78   1928                       15.132250                                   0.123333\n",
       "79   1929                       14.967417                                   0.124667\n",
       "80   1930                       15.172500                                   0.128917\n",
       "81   1931                       15.228250                                   0.129083\n",
       "82   1932                       15.188000                                   0.129417\n",
       "83   1933                       15.012917                                   0.127083\n",
       "84   1934                       15.144500                                   0.124417\n",
       "85   1935                       15.118833                                   0.122333\n",
       "86   1936                       15.166167                                   0.120333\n",
       "87   1937                       15.300500                                   0.120000\n",
       "88   1938                       15.293083                                   0.118583\n",
       "89   1939                       15.302333                                   0.120583\n",
       "90   1940                       15.372083                                   0.122833\n",
       "91   1941                       15.384667                                   0.115917\n",
       "92   1942                       15.325333                                   0.133083\n",
       "93   1943                       15.345000                                   0.133667\n",
       "94   1944                       15.449083                                   0.127750\n",
       "95   1945                       15.330750                                   0.130333\n",
       "96   1946                       15.262750                                   0.132417\n",
       "97   1947                       15.310167                                   0.128750\n",
       "98   1948                       15.261167                                   0.128500\n",
       "99   1949                       15.222750                                   0.107417\n",
       "100  1950                       15.139500                                   0.102333\n",
       "101  1951                       15.311167                                   0.098917\n",
       "102  1952                       15.363083                                   0.101667\n",
       "103  1953                       15.421083                                   0.102250\n",
       "104  1954                       15.230583                                   0.095333\n",
       "105  1955                       15.174333                                   0.088083\n",
       "106  1956                       15.106000                                   0.079667\n",
       "107  1957                       15.374083                                   0.084417\n",
       "108  1958                       15.382083                                   0.074500\n",
       "109  1959                       15.340500                                   0.073917\n",
       "110  1960                       15.291917                                   0.069333\n",
       "111  1961                       15.379917                                   0.067167\n",
       "112  1962                       15.325583                                   0.072500\n",
       "113  1963                       15.376667                                   0.068333\n",
       "114  1964                       15.117083                                   0.065167\n",
       "115  1965                       15.195750                                   0.067250\n",
       "116  1966                       15.264667                                   0.063667\n",
       "117  1967                       15.294167                                   0.059250\n",
       "118  1968                       15.263917                                   0.060167\n",
       "119  1969                       15.390500                                   0.055167\n",
       "120  1970                       15.332667                                   0.053250\n",
       "121  1971                       15.200000                                   0.056167\n",
       "122  1972                       15.292917                                   0.055833\n",
       "123  1973                       15.404750                                   0.054917\n",
       "124  1974                       15.168083                                   0.056250\n",
       "125  1975                       15.238667                                   0.055333\n",
       "126  1976                       15.131083                                   0.057417\n",
       "127  1977                       15.408250                                   0.053000\n",
       "128  1978                       15.300917                                   0.055500\n",
       "129  1979                       15.414167                                   0.047500\n",
       "130  1980                       15.491833                                   0.053667\n",
       "131  1981                       15.516167                                   0.052333\n",
       "132  1982                       15.341917                                   0.054750\n",
       "133  1983                       15.520250                                   0.055833\n",
       "134  1984                       15.344167                                   0.057167\n",
       "135  1985                       15.340667                                   0.053917\n",
       "136  1986                       15.384000                                   0.053250\n",
       "137  1987                       15.524500                                   0.053333\n",
       "138  1988                       15.555750                                   0.053250\n",
       "139  1989                       15.441583                                   0.055917\n",
       "140  1990                       15.629333                                   0.057250\n",
       "141  1991                       15.598000                                   0.055083\n",
       "142  1992                       15.453000                                   0.058167\n",
       "143  1993                       15.466417                                   0.059167\n",
       "144  1994                       15.535000                                   0.058583\n",
       "145  1995                       15.637833                                   0.060417\n",
       "146  1996                       15.524667                                   0.059500\n",
       "147  1997                       15.713833                                   0.059167\n",
       "148  1998                       15.826000                                   0.063000\n",
       "149  1999                       15.600333                                   0.063333\n",
       "150  2000                       15.610667                                   0.063500\n",
       "151  2001                       15.767500                                   0.064583\n",
       "152  2002                       15.829167                                   0.062917\n",
       "153  2003                       15.826583                                   0.064333\n",
       "154  2004                       15.757250                                   0.061250\n",
       "155  2005                       15.879250                                   0.060833\n",
       "156  2006                       15.813500                                   0.061000\n",
       "157  2007                       15.827333                                   0.059083\n",
       "158  2008                       15.721250                                   0.057250\n",
       "159  2009                       15.827167                                   0.058917\n",
       "160  2010                       15.895500                                   0.058583\n",
       "161  2011                       15.769500                                   0.059000\n",
       "162  2012                       15.802333                                   0.061500\n",
       "163  2013                       15.854417                                   0.064667\n",
       "164  2014                       15.913000                                   0.063167\n",
       "165  2015                       16.058583                                   0.060833"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by Year and get average of both numerical columns\n",
    "temp_data_3 = temp_data_2.groupby('Year').mean().reset_index()\n",
    "temp_data_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "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>Year</th>\n",
       "      <th>LandAndOceanAverageTemperature</th>\n",
       "      <th>LandAndOceanAverageTemperatureUncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>1993</td>\n",
       "      <td>15.466417</td>\n",
       "      <td>0.059167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>1994</td>\n",
       "      <td>15.535000</td>\n",
       "      <td>0.058583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>1995</td>\n",
       "      <td>15.637833</td>\n",
       "      <td>0.060417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>1996</td>\n",
       "      <td>15.524667</td>\n",
       "      <td>0.059500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>1997</td>\n",
       "      <td>15.713833</td>\n",
       "      <td>0.059167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>1998</td>\n",
       "      <td>15.826000</td>\n",
       "      <td>0.063000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>1999</td>\n",
       "      <td>15.600333</td>\n",
       "      <td>0.063333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>2000</td>\n",
       "      <td>15.610667</td>\n",
       "      <td>0.063500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>2001</td>\n",
       "      <td>15.767500</td>\n",
       "      <td>0.064583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>152</td>\n",
       "      <td>2002</td>\n",
       "      <td>15.829167</td>\n",
       "      <td>0.062917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>153</td>\n",
       "      <td>2003</td>\n",
       "      <td>15.826583</td>\n",
       "      <td>0.064333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>2004</td>\n",
       "      <td>15.757250</td>\n",
       "      <td>0.061250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155</td>\n",
       "      <td>2005</td>\n",
       "      <td>15.879250</td>\n",
       "      <td>0.060833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>2006</td>\n",
       "      <td>15.813500</td>\n",
       "      <td>0.061000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>157</td>\n",
       "      <td>2007</td>\n",
       "      <td>15.827333</td>\n",
       "      <td>0.059083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>2008</td>\n",
       "      <td>15.721250</td>\n",
       "      <td>0.057250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>2009</td>\n",
       "      <td>15.827167</td>\n",
       "      <td>0.058917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>2010</td>\n",
       "      <td>15.895500</td>\n",
       "      <td>0.058583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>2011</td>\n",
       "      <td>15.769500</td>\n",
       "      <td>0.059000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>2012</td>\n",
       "      <td>15.802333</td>\n",
       "      <td>0.061500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>2013</td>\n",
       "      <td>15.854417</td>\n",
       "      <td>0.064667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>2014</td>\n",
       "      <td>15.913000</td>\n",
       "      <td>0.063167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>2015</td>\n",
       "      <td>16.058583</td>\n",
       "      <td>0.060833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  LandAndOceanAverageTemperature  LandAndOceanAverageTemperatureUncertainty\n",
       "143  1993                       15.466417                                   0.059167\n",
       "144  1994                       15.535000                                   0.058583\n",
       "145  1995                       15.637833                                   0.060417\n",
       "146  1996                       15.524667                                   0.059500\n",
       "147  1997                       15.713833                                   0.059167\n",
       "148  1998                       15.826000                                   0.063000\n",
       "149  1999                       15.600333                                   0.063333\n",
       "150  2000                       15.610667                                   0.063500\n",
       "151  2001                       15.767500                                   0.064583\n",
       "152  2002                       15.829167                                   0.062917\n",
       "153  2003                       15.826583                                   0.064333\n",
       "154  2004                       15.757250                                   0.061250\n",
       "155  2005                       15.879250                                   0.060833\n",
       "156  2006                       15.813500                                   0.061000\n",
       "157  2007                       15.827333                                   0.059083\n",
       "158  2008                       15.721250                                   0.057250\n",
       "159  2009                       15.827167                                   0.058917\n",
       "160  2010                       15.895500                                   0.058583\n",
       "161  2011                       15.769500                                   0.059000\n",
       "162  2012                       15.802333                                   0.061500\n",
       "163  2013                       15.854417                                   0.064667\n",
       "164  2014                       15.913000                                   0.063167\n",
       "165  2015                       16.058583                                   0.060833"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert Year column to int, then filter for Years 1990 on\n",
    "temp_data_3['Year'] = temp_data_3['Year'].astype(str).astype(int)\n",
    "target_years = temp_data_3['Year'] >= 1993\n",
    "temp_data_3 = temp_data_3[target_years]\n",
    "temp_data_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save Data to .csv\n",
    "# Save scrubbed file to .csv\n",
    "temp_data_3.to_csv(r'Data/temp_data.csv', index = False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt  # To visualize\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#Data\n",
    "data = temp_data_3  # load data set\n",
    "X = data.iloc[:, 0].values.reshape(-1, 1)  # values converts it into a numpy array\n",
    "Y = data.iloc[:, 1].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column\n",
    "linear_regressor = LinearRegression()  # create object for the class\n",
    "linear_regressor.fit(X, Y)  # perform linear regression\n",
    "Y_pred = linear_regressor.predict(X)  # make predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot \n",
    "plt.scatter(X, Y)\n",
    "plt.plot(X, Y_pred, color='red')\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Global Average Temperature (C)')\n",
    "plt.title('Global Temperature vs.Time')\n",
    "plt.show()\n"
   ]
  },
  {
   "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": 2
}