Temp_Scrub-AllYears.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": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th>0</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>LandAndOceanAverageTemperature</th>\n",
       "      <th>LandAndOceanAverageTemperatureUncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1750-01-01</td>\n",
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       "      <th>1</th>\n",
       "      <td>1750-02-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1750-03-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1750-04-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1750-05-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "# Create a new dataset with only dt, LandAndOceanAverageTemperature, and LandAndOceanAverageTemperatureUncertainty\n",
    "temp_data_1 = temp_data[['dt', 'LandAndOceanAverageTemperature', 'LandAndOceanAverageTemperatureUncertainty']]\n",
    "temp_data_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>LandAndOceanAverageTemperature</th>\n",
       "      <th>LandAndOceanAverageTemperatureUncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>1850-01-01</td>\n",
       "      <td>12.833</td>\n",
       "      <td>0.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>1850-02-01</td>\n",
       "      <td>13.588</td>\n",
       "      <td>0.414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>1850-03-01</td>\n",
       "      <td>14.043</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>1850-04-01</td>\n",
       "      <td>14.667</td>\n",
       "      <td>0.267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>1850-05-01</td>\n",
       "      <td>15.507</td>\n",
       "      <td>0.249</td>\n",
       "    </tr>\n",
       "  </tbody>\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"
      ]
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     "execution_count": 4,
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   ],
   "source": [
    "# Drop rows with NaN\n",
    "temp_data_2 = temp_data_1.dropna()\n",
    "temp_data_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>LandAndOceanAverageTemperature</th>\n",
       "      <th>LandAndOceanAverageTemperatureUncertainty</th>\n",
       "      <th>Year</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>1850-01-01</td>\n",
       "      <td>12.833</td>\n",
       "      <td>0.367</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>1850-02-01</td>\n",
       "      <td>13.588</td>\n",
       "      <td>0.414</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>1850-03-01</td>\n",
       "      <td>14.043</td>\n",
       "      <td>0.341</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>1850-04-01</td>\n",
       "      <td>14.667</td>\n",
       "      <td>0.267</td>\n",
       "      <td>1850</td>\n",
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       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>1850-05-01</td>\n",
       "      <td>15.507</td>\n",
       "      <td>0.249</td>\n",
       "      <td>1850</td>\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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "# Disable the SettingWithCopyWarning warning\n",
    "pd.options.mode.chained_assignment = None\n",
    "# Create a Year Column\n",
    "temp_data_2['Year'] = temp_data_2.dt.str[:4]\n",
    "temp_data_2\n",
    "# Convert dt to datetime format\n",
    "temp_data_2['dt'] = pd.to_datetime(temp_data_2['dt'])\n",
    "temp_data_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
       "      <th>0</th>\n",
       "      <td>1850</td>\n",
       "      <td>14.867167</td>\n",
       "      <td>0.308167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1851</td>\n",
       "      <td>14.991833</td>\n",
       "      <td>0.312083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1852</td>\n",
       "      <td>15.006500</td>\n",
       "      <td>0.316417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1853</td>\n",
       "      <td>14.955167</td>\n",
       "      <td>0.283833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1854</td>\n",
       "      <td>14.991000</td>\n",
       "      <td>0.276417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1855</td>\n",
       "      <td>15.021083</td>\n",
       "      <td>0.291167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1856</td>\n",
       "      <td>14.879333</td>\n",
       "      <td>0.260167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1857</td>\n",
       "      <td>14.758250</td>\n",
       "      <td>0.259750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1858</td>\n",
       "      <td>14.881500</td>\n",
       "      <td>0.251833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1859</td>\n",
       "      <td>14.929917</td>\n",
       "      <td>0.258250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1860</td>\n",
       "      <td>14.882917</td>\n",
       "      <td>0.252167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1861</td>\n",
       "      <td>14.800667</td>\n",
       "      <td>0.278500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1862</td>\n",
       "      <td>14.740083</td>\n",
       "      <td>0.287667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1863</td>\n",
       "      <td>14.935750</td>\n",
       "      <td>0.316333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1864</td>\n",
       "      <td>14.896500</td>\n",
       "      <td>0.287833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1865</td>\n",
       "      <td>14.996667</td>\n",
       "      <td>0.304250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1866</td>\n",
       "      <td>15.057000</td>\n",
       "      <td>0.286083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1867</td>\n",
       "      <td>15.058667</td>\n",
       "      <td>0.285750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1868</td>\n",
       "      <td>15.096917</td>\n",
       "      <td>0.249333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1869</td>\n",
       "      <td>15.065500</td>\n",
       "      <td>0.243583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1870</td>\n",
       "      <td>15.004333</td>\n",
       "      <td>0.224833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1871</td>\n",
       "      <td>15.005917</td>\n",
       "      <td>0.232000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1872</td>\n",
       "      <td>15.019333</td>\n",
       "      <td>0.230500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1873</td>\n",
       "      <td>15.049250</td>\n",
       "      <td>0.222083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1874</td>\n",
       "      <td>14.957000</td>\n",
       "      <td>0.213333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1875</td>\n",
       "      <td>14.921917</td>\n",
       "      <td>0.197083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1876</td>\n",
       "      <td>14.909417</td>\n",
       "      <td>0.193333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1877</td>\n",
       "      <td>15.282667</td>\n",
       "      <td>0.196750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1878</td>\n",
       "      <td>15.357417</td>\n",
       "      <td>0.186250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1879</td>\n",
       "      <td>15.064417</td>\n",
       "      <td>0.183417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1880</td>\n",
       "      <td>15.008667</td>\n",
       "      <td>0.172917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1881</td>\n",
       "      <td>15.087167</td>\n",
       "      <td>0.170083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1882</td>\n",
       "      <td>15.056583</td>\n",
       "      <td>0.160083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1883</td>\n",
       "      <td>14.963250</td>\n",
       "      <td>0.151167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1884</td>\n",
       "      <td>14.833250</td>\n",
       "      <td>0.150667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1885</td>\n",
       "      <td>14.854417</td>\n",
       "      <td>0.154917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1886</td>\n",
       "      <td>14.835250</td>\n",
       "      <td>0.145333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1887</td>\n",
       "      <td>14.816000</td>\n",
       "      <td>0.150167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1888</td>\n",
       "      <td>14.994083</td>\n",
       "      <td>0.149250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1889</td>\n",
       "      <td>15.113417</td>\n",
       "      <td>0.152417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1890</td>\n",
       "      <td>14.840750</td>\n",
       "      <td>0.149667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1891</td>\n",
       "      <td>14.943833</td>\n",
       "      <td>0.153583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1892</td>\n",
       "      <td>14.882500</td>\n",
       "      <td>0.156000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1893</td>\n",
       "      <td>14.912250</td>\n",
       "      <td>0.154250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1894</td>\n",
       "      <td>14.914750</td>\n",
       "      <td>0.150333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1895</td>\n",
       "      <td>14.969000</td>\n",
       "      <td>0.146833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1896</td>\n",
       "      <td>15.090583</td>\n",
       "      <td>0.148000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1897</td>\n",
       "      <td>15.075333</td>\n",
       "      <td>0.149333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1898</td>\n",
       "      <td>14.886917</td>\n",
       "      <td>0.148500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1899</td>\n",
       "      <td>15.048333</td>\n",
       "      <td>0.147500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1900</td>\n",
       "      <td>15.143917</td>\n",
       "      <td>0.150250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1901</td>\n",
       "      <td>15.073333</td>\n",
       "      <td>0.145750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1902</td>\n",
       "      <td>14.958333</td>\n",
       "      <td>0.135500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1903</td>\n",
       "      <td>14.836583</td>\n",
       "      <td>0.134833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1904</td>\n",
       "      <td>14.810417</td>\n",
       "      <td>0.133083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1905</td>\n",
       "      <td>14.954667</td>\n",
       "      <td>0.129000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1906</td>\n",
       "      <td>15.031917</td>\n",
       "      <td>0.128583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1907</td>\n",
       "      <td>14.874833</td>\n",
       "      <td>0.125833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1908</td>\n",
       "      <td>14.838167</td>\n",
       "      <td>0.125083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1909</td>\n",
       "      <td>14.790333</td>\n",
       "      <td>0.119500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1910</td>\n",
       "      <td>14.819083</td>\n",
       "      <td>0.117583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1911</td>\n",
       "      <td>14.776000</td>\n",
       "      <td>0.115167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1912</td>\n",
       "      <td>14.877167</td>\n",
       "      <td>0.116917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1913</td>\n",
       "      <td>14.908750</td>\n",
       "      <td>0.117083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1914</td>\n",
       "      <td>15.076250</td>\n",
       "      <td>0.125250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1915</td>\n",
       "      <td>15.143750</td>\n",
       "      <td>0.134250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1916</td>\n",
       "      <td>14.901333</td>\n",
       "      <td>0.134833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1917</td>\n",
       "      <td>14.809750</td>\n",
       "      <td>0.140500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1918</td>\n",
       "      <td>14.959833</td>\n",
       "      <td>0.148167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1919</td>\n",
       "      <td>15.006750</td>\n",
       "      <td>0.142833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1920</td>\n",
       "      <td>15.026250</td>\n",
       "      <td>0.135583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1921</td>\n",
       "      <td>15.102083</td>\n",
       "      <td>0.129167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1922</td>\n",
       "      <td>15.012333</td>\n",
       "      <td>0.124083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1923</td>\n",
       "      <td>15.027833</td>\n",
       "      <td>0.122667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1924</td>\n",
       "      <td>15.039333</td>\n",
       "      <td>0.124500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1925</td>\n",
       "      <td>15.090333</td>\n",
       "      <td>0.123917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1926</td>\n",
       "      <td>15.241083</td>\n",
       "      <td>0.122417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>1927</td>\n",
       "      <td>15.122250</td>\n",
       "      <td>0.121667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1928</td>\n",
       "      <td>15.132250</td>\n",
       "      <td>0.123333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1929</td>\n",
       "      <td>14.967417</td>\n",
       "      <td>0.124667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1930</td>\n",
       "      <td>15.172500</td>\n",
       "      <td>0.128917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1931</td>\n",
       "      <td>15.228250</td>\n",
       "      <td>0.129083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1932</td>\n",
       "      <td>15.188000</td>\n",
       "      <td>0.129417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1933</td>\n",
       "      <td>15.012917</td>\n",
       "      <td>0.127083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1934</td>\n",
       "      <td>15.144500</td>\n",
       "      <td>0.124417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1935</td>\n",
       "      <td>15.118833</td>\n",
       "      <td>0.122333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1936</td>\n",
       "      <td>15.166167</td>\n",
       "      <td>0.120333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1937</td>\n",
       "      <td>15.300500</td>\n",
       "      <td>0.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1938</td>\n",
       "      <td>15.293083</td>\n",
       "      <td>0.118583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>1939</td>\n",
       "      <td>15.302333</td>\n",
       "      <td>0.120583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1940</td>\n",
       "      <td>15.372083</td>\n",
       "      <td>0.122833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>1941</td>\n",
       "      <td>15.384667</td>\n",
       "      <td>0.115917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1942</td>\n",
       "      <td>15.325333</td>\n",
       "      <td>0.133083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>1943</td>\n",
       "      <td>15.345000</td>\n",
       "      <td>0.133667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1944</td>\n",
       "      <td>15.449083</td>\n",
       "      <td>0.127750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1945</td>\n",
       "      <td>15.330750</td>\n",
       "      <td>0.130333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1946</td>\n",
       "      <td>15.262750</td>\n",
       "      <td>0.132417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1947</td>\n",
       "      <td>15.310167</td>\n",
       "      <td>0.128750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1948</td>\n",
       "      <td>15.261167</td>\n",
       "      <td>0.128500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1949</td>\n",
       "      <td>15.222750</td>\n",
       "      <td>0.107417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1950</td>\n",
       "      <td>15.139500</td>\n",
       "      <td>0.102333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1951</td>\n",
       "      <td>15.311167</td>\n",
       "      <td>0.098917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1952</td>\n",
       "      <td>15.363083</td>\n",
       "      <td>0.101667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1953</td>\n",
       "      <td>15.421083</td>\n",
       "      <td>0.102250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1954</td>\n",
       "      <td>15.230583</td>\n",
       "      <td>0.095333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1955</td>\n",
       "      <td>15.174333</td>\n",
       "      <td>0.088083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1956</td>\n",
       "      <td>15.106000</td>\n",
       "      <td>0.079667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>1957</td>\n",
       "      <td>15.374083</td>\n",
       "      <td>0.084417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>1958</td>\n",
       "      <td>15.382083</td>\n",
       "      <td>0.074500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>1959</td>\n",
       "      <td>15.340500</td>\n",
       "      <td>0.073917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1960</td>\n",
       "      <td>15.291917</td>\n",
       "      <td>0.069333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>1961</td>\n",
       "      <td>15.379917</td>\n",
       "      <td>0.067167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>1962</td>\n",
       "      <td>15.325583</td>\n",
       "      <td>0.072500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>1963</td>\n",
       "      <td>15.376667</td>\n",
       "      <td>0.068333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>1964</td>\n",
       "      <td>15.117083</td>\n",
       "      <td>0.065167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1965</td>\n",
       "      <td>15.195750</td>\n",
       "      <td>0.067250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1966</td>\n",
       "      <td>15.264667</td>\n",
       "      <td>0.063667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1967</td>\n",
       "      <td>15.294167</td>\n",
       "      <td>0.059250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>1968</td>\n",
       "      <td>15.263917</td>\n",
       "      <td>0.060167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1969</td>\n",
       "      <td>15.390500</td>\n",
       "      <td>0.055167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1970</td>\n",
       "      <td>15.332667</td>\n",
       "      <td>0.053250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>1971</td>\n",
       "      <td>15.200000</td>\n",
       "      <td>0.056167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1972</td>\n",
       "      <td>15.292917</td>\n",
       "      <td>0.055833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1973</td>\n",
       "      <td>15.404750</td>\n",
       "      <td>0.054917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1974</td>\n",
       "      <td>15.168083</td>\n",
       "      <td>0.056250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1975</td>\n",
       "      <td>15.238667</td>\n",
       "      <td>0.055333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1976</td>\n",
       "      <td>15.131083</td>\n",
       "      <td>0.057417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1977</td>\n",
       "      <td>15.408250</td>\n",
       "      <td>0.053000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1978</td>\n",
       "      <td>15.300917</td>\n",
       "      <td>0.055500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1979</td>\n",
       "      <td>15.414167</td>\n",
       "      <td>0.047500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1980</td>\n",
       "      <td>15.491833</td>\n",
       "      <td>0.053667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>1981</td>\n",
       "      <td>15.516167</td>\n",
       "      <td>0.052333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1982</td>\n",
       "      <td>15.341917</td>\n",
       "      <td>0.054750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>1983</td>\n",
       "      <td>15.520250</td>\n",
       "      <td>0.055833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>1984</td>\n",
       "      <td>15.344167</td>\n",
       "      <td>0.057167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>1985</td>\n",
       "      <td>15.340667</td>\n",
       "      <td>0.053917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>1986</td>\n",
       "      <td>15.384000</td>\n",
       "      <td>0.053250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1987</td>\n",
       "      <td>15.524500</td>\n",
       "      <td>0.053333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1988</td>\n",
       "      <td>15.555750</td>\n",
       "      <td>0.053250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1989</td>\n",
       "      <td>15.441583</td>\n",
       "      <td>0.055917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1990</td>\n",
       "      <td>15.629333</td>\n",
       "      <td>0.057250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1991</td>\n",
       "      <td>15.598000</td>\n",
       "      <td>0.055083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1992</td>\n",
       "      <td>15.453000</td>\n",
       "      <td>0.058167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1993</td>\n",
       "      <td>15.466417</td>\n",
       "      <td>0.059167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>1994</td>\n",
       "      <td>15.535000</td>\n",
       "      <td>0.058583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1995</td>\n",
       "      <td>15.637833</td>\n",
       "      <td>0.060417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>1996</td>\n",
       "      <td>15.524667</td>\n",
       "      <td>0.059500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1997</td>\n",
       "      <td>15.713833</td>\n",
       "      <td>0.059167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1998</td>\n",
       "      <td>15.826000</td>\n",
       "      <td>0.063000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>1999</td>\n",
       "      <td>15.600333</td>\n",
       "      <td>0.063333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>2000</td>\n",
       "      <td>15.610667</td>\n",
       "      <td>0.063500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>2001</td>\n",
       "      <td>15.767500</td>\n",
       "      <td>0.064583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>2002</td>\n",
       "      <td>15.829167</td>\n",
       "      <td>0.062917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>2003</td>\n",
       "      <td>15.826583</td>\n",
       "      <td>0.064333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>2004</td>\n",
       "      <td>15.757250</td>\n",
       "      <td>0.061250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>2005</td>\n",
       "      <td>15.879250</td>\n",
       "      <td>0.060833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>2006</td>\n",
       "      <td>15.813500</td>\n",
       "      <td>0.061000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>2007</td>\n",
       "      <td>15.827333</td>\n",
       "      <td>0.059083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>2008</td>\n",
       "      <td>15.721250</td>\n",
       "      <td>0.057250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>2009</td>\n",
       "      <td>15.827167</td>\n",
       "      <td>0.058917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>2010</td>\n",
       "      <td>15.895500</td>\n",
       "      <td>0.058583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>2011</td>\n",
       "      <td>15.769500</td>\n",
       "      <td>0.059000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2012</td>\n",
       "      <td>15.802333</td>\n",
       "      <td>0.061500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2013</td>\n",
       "      <td>15.854417</td>\n",
       "      <td>0.064667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2014</td>\n",
       "      <td>15.913000</td>\n",
       "      <td>0.063167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\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": 6,
     "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save Data to .csv\n",
    "# Save scrubbed file to .csv\n",
    "temp_data_3.to_csv(r'Data/temp_data-AllYears.csv', index = False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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[:, 1].values.reshape(-1, 1)  # values converts it into a numpy array\n",
    "Y = data.iloc[:, 2].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": 11,
   "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('Average Annual Temperature (C)')\n",
    "plt.ylabel('Temperature Uncertainty %')\n",
    "plt.title('Global Temperature vs Uncertainty')\n",
    "fig1 = plt.gcf()\n",
    "fig1.savefig('images/Temp_to_Uncertainty_scatter.png', bbox_inches='tight')"
   ]
  },
  {
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}