visualizations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read in data\n",
    "original = pd.read_csv('original_data_predictions.csv')\n",
    "new = pd.read_csv('climate_results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual_temp</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>predicted_temp</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.034</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.527702</td>\n",
       "      <td>1750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.083</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.972995</td>\n",
       "      <td>1750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.626</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.733904</td>\n",
       "      <td>1750.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   actual_temp  day  month  predicted_temp    year\n",
       "0        3.034  1.0    1.0        1.527702  1750.0\n",
       "1        3.083  1.0    2.0        1.972995  1750.0\n",
       "2        5.626  1.0    3.0        4.733904  1750.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view original data + predicted temperatures for those years based on the TPOT optimizied pipeline\n",
    "original.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>landavgtemp</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.514622</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.055345</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.168799</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   landavgtemp  month    year\n",
       "0     3.514622    1.0  2016.0\n",
       "1     4.055345    2.0  2016.0\n",
       "2     6.168799    3.0  2016.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view predictions for next 5 years\n",
    "new.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# remove day column from original dataframe\n",
    "original = original.drop('day',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# rename predicted temps in results dataframe\n",
    "new.rename(columns={'landavgtemp': 'predicted_temp'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# add actual_temp column, full of NA's\n",
    "new['actual_temp'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted_temp</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>actual_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.514622</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.055345</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.168799</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.463200</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.422633</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   predicted_temp  month    year  actual_temp\n",
       "0        3.514622    1.0  2016.0          NaN\n",
       "1        4.055345    2.0  2016.0          NaN\n",
       "2        6.168799    3.0  2016.0          NaN\n",
       "3        9.463200    4.0  2016.0          NaN\n",
       "4       12.422633    5.0  2016.0          NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view changes\n",
    "new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual_temp</th>\n",
       "      <th>month</th>\n",
       "      <th>predicted_temp</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.034</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.527702</td>\n",
       "      <td>1750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.083</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.972995</td>\n",
       "      <td>1750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.626</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.733904</td>\n",
       "      <td>1750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.490</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.560140</td>\n",
       "      <td>1750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.573</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.015585</td>\n",
       "      <td>1750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.937</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.209926</td>\n",
       "      <td>1750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.868</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.877536</td>\n",
       "      <td>1750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.750</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.323442</td>\n",
       "      <td>1750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.413</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.347094</td>\n",
       "      <td>1750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.367</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.427641</td>\n",
       "      <td>1750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.772</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.851475</td>\n",
       "      <td>1750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.495</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.495898</td>\n",
       "      <td>1751.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.963</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.941192</td>\n",
       "      <td>1751.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.800</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.773817</td>\n",
       "      <td>1751.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.670</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.631856</td>\n",
       "      <td>1751.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>13.827</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.309941</td>\n",
       "      <td>1751.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14.405</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.521994</td>\n",
       "      <td>1751.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10.673</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.596462</td>\n",
       "      <td>1751.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.348</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.463813</td>\n",
       "      <td>1752.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.806</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.741731</td>\n",
       "      <td>1752.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>8.265</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.599771</td>\n",
       "      <td>1752.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.839</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.147444</td>\n",
       "      <td>1752.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.335</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.309720</td>\n",
       "      <td>1752.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.086</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.005696</td>\n",
       "      <td>1752.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.039</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.463813</td>\n",
       "      <td>1753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.715</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.909107</td>\n",
       "      <td>1753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.424</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.741731</td>\n",
       "      <td>1753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8.272</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.599771</td>\n",
       "      <td>1753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>12.186</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.109692</td>\n",
       "      <td>1753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14.585</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.482331</td>\n",
       "      <td>1753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3210</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.414539</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3211</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.041952</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3212</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.173717</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3213</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.424560</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3214</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.270557</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3215</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.997195</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3216</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.514622</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3217</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.055345</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3218</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.168799</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3219</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.463200</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3220</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.422633</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3221</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.623092</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3222</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.414539</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3223</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.041952</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3224</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.173717</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3225</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.424560</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3226</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.270557</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3227</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.997195</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3228</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.514622</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3229</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.055345</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.168799</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3231</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.463200</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3232</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.422633</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3233</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.623092</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3234</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.414539</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3235</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.041952</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3236</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.173717</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3237</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.424560</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3238</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.270557</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3239</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.997195</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3240 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      actual_temp  month  predicted_temp    year\n",
       "0           3.034    1.0        1.527702  1750.0\n",
       "1           3.083    2.0        1.972995  1750.0\n",
       "2           5.626    3.0        4.733904  1750.0\n",
       "3           8.490    4.0        8.560140  1750.0\n",
       "4          11.573    5.0       12.015585  1750.0\n",
       "5          12.937    6.0       14.209926  1750.0\n",
       "6          15.868    7.0       14.877536  1750.0\n",
       "7          14.750    8.0       14.323442  1750.0\n",
       "8          11.413    9.0       11.347094  1750.0\n",
       "9           6.367   10.0        7.427641  1750.0\n",
       "10          2.772   12.0        2.851475  1750.0\n",
       "11          2.495    1.0        1.495898  1751.0\n",
       "12          0.963    2.0        1.941192  1751.0\n",
       "13          5.800    3.0        4.773817  1751.0\n",
       "14          7.670    4.0        8.631856  1751.0\n",
       "15         13.827    6.0       14.309941  1751.0\n",
       "16         14.405    8.0       14.521994  1751.0\n",
       "17         10.673    9.0       11.596462  1751.0\n",
       "18          0.348    1.0        1.463813  1752.0\n",
       "19          5.806    3.0        4.741731  1752.0\n",
       "20          8.265    4.0        8.599771  1752.0\n",
       "21          7.839   10.0        8.147444  1752.0\n",
       "22          7.335   11.0        5.309720  1752.0\n",
       "23          5.086   12.0        4.005696  1752.0\n",
       "24          2.039    1.0        1.463813  1753.0\n",
       "25          0.715    2.0        1.909107  1753.0\n",
       "26          5.424    3.0        4.741731  1753.0\n",
       "27          8.272    4.0        8.599771  1753.0\n",
       "28         12.186    5.0       12.109692  1753.0\n",
       "29         14.585    6.0       14.482331  1753.0\n",
       "...           ...    ...             ...     ...\n",
       "3210          NaN    7.0       15.414539  2018.0\n",
       "3211          NaN    8.0       15.041952  2018.0\n",
       "3212          NaN    9.0       13.173717  2018.0\n",
       "3213          NaN   10.0       10.424560  2018.0\n",
       "3214          NaN   11.0        7.270557  2018.0\n",
       "3215          NaN   12.0        4.997195  2018.0\n",
       "3216          NaN    1.0        3.514622  2019.0\n",
       "3217          NaN    2.0        4.055345  2019.0\n",
       "3218          NaN    3.0        6.168799  2019.0\n",
       "3219          NaN    4.0        9.463200  2019.0\n",
       "3220          NaN    5.0       12.422633  2019.0\n",
       "3221          NaN    6.0       14.623092  2019.0\n",
       "3222          NaN    7.0       15.414539  2019.0\n",
       "3223          NaN    8.0       15.041952  2019.0\n",
       "3224          NaN    9.0       13.173717  2019.0\n",
       "3225          NaN   10.0       10.424560  2019.0\n",
       "3226          NaN   11.0        7.270557  2019.0\n",
       "3227          NaN   12.0        4.997195  2019.0\n",
       "3228          NaN    1.0        3.514622  2020.0\n",
       "3229          NaN    2.0        4.055345  2020.0\n",
       "3230          NaN    3.0        6.168799  2020.0\n",
       "3231          NaN    4.0        9.463200  2020.0\n",
       "3232          NaN    5.0       12.422633  2020.0\n",
       "3233          NaN    6.0       14.623092  2020.0\n",
       "3234          NaN    7.0       15.414539  2020.0\n",
       "3235          NaN    8.0       15.041952  2020.0\n",
       "3236          NaN    9.0       13.173717  2020.0\n",
       "3237          NaN   10.0       10.424560  2020.0\n",
       "3238          NaN   11.0        7.270557  2020.0\n",
       "3239          NaN   12.0        4.997195  2020.0\n",
       "\n",
       "[3240 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine dataframes\n",
    "complete = original.append(new, ignore_index=True)\n",
    "\n",
    "# view\n",
    "complete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual_temp</th>\n",
       "      <th>month</th>\n",
       "      <th>predicted_temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1750.0</th>\n",
       "      <td>8.719364</td>\n",
       "      <td>6.090909</td>\n",
       "      <td>8.531585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1751.0</th>\n",
       "      <td>7.976143</td>\n",
       "      <td>4.714286</td>\n",
       "      <td>8.181594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752.0</th>\n",
       "      <td>5.779833</td>\n",
       "      <td>6.833333</td>\n",
       "      <td>5.378029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        actual_temp     month  predicted_temp\n",
       "year                                         \n",
       "1750.0     8.719364  6.090909        8.531585\n",
       "1751.0     7.976143  4.714286        8.181594\n",
       "1752.0     5.779833  6.833333        5.378029"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by year, and compute yearly mean temps \n",
    "grouped = complete.groupby('year').mean()\n",
    "\n",
    "# view dataframe\n",
    "grouped.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# remove averaged month column. its unnecessary\n",
    "grouped = grouped.drop('month',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# reset index, so year is now column\n",
    "grouped.reset_index(level=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>actual_temp</th>\n",
       "      <th>predicted_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1750.0</td>\n",
       "      <td>8.719364</td>\n",
       "      <td>8.531585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1751.0</td>\n",
       "      <td>7.976143</td>\n",
       "      <td>8.181594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1752.0</td>\n",
       "      <td>5.779833</td>\n",
       "      <td>5.378029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  actual_temp  predicted_temp\n",
       "0  1750.0     8.719364        8.531585\n",
       "1  1751.0     7.976143        8.181594\n",
       "2  1752.0     5.779833        5.378029"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view\n",
    "grouped.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# rename columns appropriately\n",
    "grouped.rename(columns={'actual_temp': 'Yearly Average Actual Temperature','predicted_temp':'Yearly Average Predicted Temperature'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>Yearly Average Actual Temperature</th>\n",
       "      <th>Yearly Average Predicted Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1750.0</td>\n",
       "      <td>8.719364</td>\n",
       "      <td>8.531585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1751.0</td>\n",
       "      <td>7.976143</td>\n",
       "      <td>8.181594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1752.0</td>\n",
       "      <td>5.779833</td>\n",
       "      <td>5.378029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1753.0</td>\n",
       "      <td>8.388083</td>\n",
       "      <td>8.397631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1754.0</td>\n",
       "      <td>8.469333</td>\n",
       "      <td>8.453283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1755.0</td>\n",
       "      <td>8.355583</td>\n",
       "      <td>8.402144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1756.0</td>\n",
       "      <td>8.849583</td>\n",
       "      <td>8.879887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1757.0</td>\n",
       "      <td>9.022000</td>\n",
       "      <td>8.615583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1758.0</td>\n",
       "      <td>6.743583</td>\n",
       "      <td>7.563559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1759.0</td>\n",
       "      <td>7.985083</td>\n",
       "      <td>7.662195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1760.0</td>\n",
       "      <td>7.185167</td>\n",
       "      <td>7.607092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1761.0</td>\n",
       "      <td>8.772500</td>\n",
       "      <td>8.606180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1762.0</td>\n",
       "      <td>8.606500</td>\n",
       "      <td>8.650137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1763.0</td>\n",
       "      <td>7.496750</td>\n",
       "      <td>8.136665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1764.0</td>\n",
       "      <td>8.400333</td>\n",
       "      <td>8.254162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1765.0</td>\n",
       "      <td>8.251917</td>\n",
       "      <td>8.238961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1766.0</td>\n",
       "      <td>8.405667</td>\n",
       "      <td>8.237836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1767.0</td>\n",
       "      <td>8.221500</td>\n",
       "      <td>8.230645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1768.0</td>\n",
       "      <td>6.781333</td>\n",
       "      <td>7.574910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1769.0</td>\n",
       "      <td>7.694583</td>\n",
       "      <td>7.655278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1770.0</td>\n",
       "      <td>7.691917</td>\n",
       "      <td>7.585166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1771.0</td>\n",
       "      <td>7.853167</td>\n",
       "      <td>7.566953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1772.0</td>\n",
       "      <td>8.193500</td>\n",
       "      <td>8.126636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1773.0</td>\n",
       "      <td>8.221500</td>\n",
       "      <td>8.147879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1774.0</td>\n",
       "      <td>8.772167</td>\n",
       "      <td>8.702846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1775.0</td>\n",
       "      <td>9.183083</td>\n",
       "      <td>8.526097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1776.0</td>\n",
       "      <td>8.304000</td>\n",
       "      <td>8.412500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1777.0</td>\n",
       "      <td>8.256250</td>\n",
       "      <td>8.412500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1778.0</td>\n",
       "      <td>8.542250</td>\n",
       "      <td>8.412500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1779.0</td>\n",
       "      <td>8.983250</td>\n",
       "      <td>8.563554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>1991.0</td>\n",
       "      <td>9.179417</td>\n",
       "      <td>9.056884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>1992.0</td>\n",
       "      <td>8.836583</td>\n",
       "      <td>9.024677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>1993.0</td>\n",
       "      <td>8.866583</td>\n",
       "      <td>9.024677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>1994.0</td>\n",
       "      <td>9.038750</td>\n",
       "      <td>9.030688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>1995.0</td>\n",
       "      <td>9.347083</td>\n",
       "      <td>9.040408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>1996.0</td>\n",
       "      <td>9.038917</td>\n",
       "      <td>9.040408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>1997.0</td>\n",
       "      <td>9.202583</td>\n",
       "      <td>9.075481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>1998.0</td>\n",
       "      <td>9.522667</td>\n",
       "      <td>9.342537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1999.0</td>\n",
       "      <td>9.285083</td>\n",
       "      <td>9.209282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>9.201167</td>\n",
       "      <td>9.209282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>2001.0</td>\n",
       "      <td>9.414583</td>\n",
       "      <td>9.226697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2002.0</td>\n",
       "      <td>9.570417</td>\n",
       "      <td>9.217747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>2003.0</td>\n",
       "      <td>9.525583</td>\n",
       "      <td>9.755476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>2004.0</td>\n",
       "      <td>9.324583</td>\n",
       "      <td>9.476650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>2005.0</td>\n",
       "      <td>9.700917</td>\n",
       "      <td>9.556233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>2006.0</td>\n",
       "      <td>9.532500</td>\n",
       "      <td>9.610321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>2007.0</td>\n",
       "      <td>9.732167</td>\n",
       "      <td>9.610321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>9.431750</td>\n",
       "      <td>9.606114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>9.505250</td>\n",
       "      <td>9.606114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>2010.0</td>\n",
       "      <td>9.703083</td>\n",
       "      <td>9.606114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>9.516000</td>\n",
       "      <td>9.606114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>9.507333</td>\n",
       "      <td>9.606114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>9.606500</td>\n",
       "      <td>9.606114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>9.570667</td>\n",
       "      <td>9.596290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>9.831000</td>\n",
       "      <td>9.625246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.714184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.714184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.714184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.714184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.714184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>271 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  Yearly Average Actual Temperature  \\\n",
       "0    1750.0                           8.719364   \n",
       "1    1751.0                           7.976143   \n",
       "2    1752.0                           5.779833   \n",
       "3    1753.0                           8.388083   \n",
       "4    1754.0                           8.469333   \n",
       "5    1755.0                           8.355583   \n",
       "6    1756.0                           8.849583   \n",
       "7    1757.0                           9.022000   \n",
       "8    1758.0                           6.743583   \n",
       "9    1759.0                           7.985083   \n",
       "10   1760.0                           7.185167   \n",
       "11   1761.0                           8.772500   \n",
       "12   1762.0                           8.606500   \n",
       "13   1763.0                           7.496750   \n",
       "14   1764.0                           8.400333   \n",
       "15   1765.0                           8.251917   \n",
       "16   1766.0                           8.405667   \n",
       "17   1767.0                           8.221500   \n",
       "18   1768.0                           6.781333   \n",
       "19   1769.0                           7.694583   \n",
       "20   1770.0                           7.691917   \n",
       "21   1771.0                           7.853167   \n",
       "22   1772.0                           8.193500   \n",
       "23   1773.0                           8.221500   \n",
       "24   1774.0                           8.772167   \n",
       "25   1775.0                           9.183083   \n",
       "26   1776.0                           8.304000   \n",
       "27   1777.0                           8.256250   \n",
       "28   1778.0                           8.542250   \n",
       "29   1779.0                           8.983250   \n",
       "..      ...                                ...   \n",
       "241  1991.0                           9.179417   \n",
       "242  1992.0                           8.836583   \n",
       "243  1993.0                           8.866583   \n",
       "244  1994.0                           9.038750   \n",
       "245  1995.0                           9.347083   \n",
       "246  1996.0                           9.038917   \n",
       "247  1997.0                           9.202583   \n",
       "248  1998.0                           9.522667   \n",
       "249  1999.0                           9.285083   \n",
       "250  2000.0                           9.201167   \n",
       "251  2001.0                           9.414583   \n",
       "252  2002.0                           9.570417   \n",
       "253  2003.0                           9.525583   \n",
       "254  2004.0                           9.324583   \n",
       "255  2005.0                           9.700917   \n",
       "256  2006.0                           9.532500   \n",
       "257  2007.0                           9.732167   \n",
       "258  2008.0                           9.431750   \n",
       "259  2009.0                           9.505250   \n",
       "260  2010.0                           9.703083   \n",
       "261  2011.0                           9.516000   \n",
       "262  2012.0                           9.507333   \n",
       "263  2013.0                           9.606500   \n",
       "264  2014.0                           9.570667   \n",
       "265  2015.0                           9.831000   \n",
       "266  2016.0                                NaN   \n",
       "267  2017.0                                NaN   \n",
       "268  2018.0                                NaN   \n",
       "269  2019.0                                NaN   \n",
       "270  2020.0                                NaN   \n",
       "\n",
       "     Yearly Average Predicted Temperature  \n",
       "0                                8.531585  \n",
       "1                                8.181594  \n",
       "2                                5.378029  \n",
       "3                                8.397631  \n",
       "4                                8.453283  \n",
       "5                                8.402144  \n",
       "6                                8.879887  \n",
       "7                                8.615583  \n",
       "8                                7.563559  \n",
       "9                                7.662195  \n",
       "10                               7.607092  \n",
       "11                               8.606180  \n",
       "12                               8.650137  \n",
       "13                               8.136665  \n",
       "14                               8.254162  \n",
       "15                               8.238961  \n",
       "16                               8.237836  \n",
       "17                               8.230645  \n",
       "18                               7.574910  \n",
       "19                               7.655278  \n",
       "20                               7.585166  \n",
       "21                               7.566953  \n",
       "22                               8.126636  \n",
       "23                               8.147879  \n",
       "24                               8.702846  \n",
       "25                               8.526097  \n",
       "26                               8.412500  \n",
       "27                               8.412500  \n",
       "28                               8.412500  \n",
       "29                               8.563554  \n",
       "..                                    ...  \n",
       "241                              9.056884  \n",
       "242                              9.024677  \n",
       "243                              9.024677  \n",
       "244                              9.030688  \n",
       "245                              9.040408  \n",
       "246                              9.040408  \n",
       "247                              9.075481  \n",
       "248                              9.342537  \n",
       "249                              9.209282  \n",
       "250                              9.209282  \n",
       "251                              9.226697  \n",
       "252                              9.217747  \n",
       "253                              9.755476  \n",
       "254                              9.476650  \n",
       "255                              9.556233  \n",
       "256                              9.610321  \n",
       "257                              9.610321  \n",
       "258                              9.606114  \n",
       "259                              9.606114  \n",
       "260                              9.606114  \n",
       "261                              9.606114  \n",
       "262                              9.606114  \n",
       "263                              9.606114  \n",
       "264                              9.596290  \n",
       "265                              9.625246  \n",
       "266                              9.714184  \n",
       "267                              9.714184  \n",
       "268                              9.714184  \n",
       "269                              9.714184  \n",
       "270                              9.714184  \n",
       "\n",
       "[271 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11304fda0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113010b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DSsyd6iCghBazbFllMpTq6tToko9kKAEAAABoz2bO9KutZelPlMX2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D9omlYlhPin2e987nOfQ9++fbFlC/Dss+UoLT3jv7lJqU5WVFSgoQH44AO6umz2uJfbLVrQ8rw8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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130109e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets see what the average yearly global land temperatures look like for the original data (actual temperatures)\n",
    "fig = plt.figure()\n",
    "ax = grouped.plot('year','Yearly Average Actual Temperature',figsize=(20,10),grid=True,ylim=(5,10),fontsize=14,legend=False)\n",
    "plt.suptitle('Average Yearly Global Land Temperatures', fontsize=20)\n",
    "plt.xlabel('Year', fontsize=18)\n",
    "plt.ylabel('Degrees Celcius', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11324eb38>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11309dd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AodD7F9RNPvB/Cfa9Ef9DS+zyuvgfUTYG5+Kn+IDSPvPbz3GG6qvQ/uKkPTo4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e1aYM8e4LrrXD+GOph7xQr5EPv0091//ilT5L3upk3uzVByJ1CqrGTFK/UcDJQcPV6bHmkJDgKl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jXgm8n+ntb6uBXUEvrBwxk38odyUCJS8tb7WsUFLLW3GqUKq9BQsgcaCL/jF/M5T6DvSxpK2X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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11323b780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets compare that to what our optimized TPOT pipeline predicts\n",
    "fig = plt.figure()\n",
    "ax = grouped.plot('year',['Yearly Average Actual Temperature','Yearly Average Predicted Temperature'],figsize=(20,10),grid=True,ylim=(5,10),fontsize=14,legend=True)\n",
    "plt.suptitle('Average Actual & Predicted Yearly Global Land Temperatures', fontsize=20)\n",
    "plt.xlabel('Year', fontsize=18)\n",
    "plt.ylabel('Degrees Celcius', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual_temp</th>\n",
       "      <th>predicted_temp</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.034</td>\n",
       "      <td>1.527702</td>\n",
       "      <td>1750-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.083</td>\n",
       "      <td>1.972995</td>\n",
       "      <td>1750-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.626</td>\n",
       "      <td>4.733904</td>\n",
       "      <td>1750-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.490</td>\n",
       "      <td>8.560140</td>\n",
       "      <td>1750-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.573</td>\n",
       "      <td>12.015585</td>\n",
       "      <td>1750-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.937</td>\n",
       "      <td>14.209926</td>\n",
       "      <td>1750-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.868</td>\n",
       "      <td>14.877536</td>\n",
       "      <td>1750-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.750</td>\n",
       "      <td>14.323442</td>\n",
       "      <td>1750-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.413</td>\n",
       "      <td>11.347094</td>\n",
       "      <td>1750-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.367</td>\n",
       "      <td>7.427641</td>\n",
       "      <td>1750-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.772</td>\n",
       "      <td>2.851475</td>\n",
       "      <td>1750-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.495</td>\n",
       "      <td>1.495898</td>\n",
       "      <td>1751-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.963</td>\n",
       "      <td>1.941192</td>\n",
       "      <td>1751-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.800</td>\n",
       "      <td>4.773817</td>\n",
       "      <td>1751-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.670</td>\n",
       "      <td>8.631856</td>\n",
       "      <td>1751-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>13.827</td>\n",
       "      <td>14.309941</td>\n",
       "      <td>1751-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14.405</td>\n",
       "      <td>14.521994</td>\n",
       "      <td>1751-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10.673</td>\n",
       "      <td>11.596462</td>\n",
       "      <td>1751-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.348</td>\n",
       "      <td>1.463813</td>\n",
       "      <td>1752-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.806</td>\n",
       "      <td>4.741731</td>\n",
       "      <td>1752-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>8.265</td>\n",
       "      <td>8.599771</td>\n",
       "      <td>1752-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.839</td>\n",
       "      <td>8.147444</td>\n",
       "      <td>1752-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.335</td>\n",
       "      <td>5.309720</td>\n",
       "      <td>1752-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.086</td>\n",
       "      <td>4.005696</td>\n",
       "      <td>1752-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.039</td>\n",
       "      <td>1.463813</td>\n",
       "      <td>1753-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.715</td>\n",
       "      <td>1.909107</td>\n",
       "      <td>1753-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.424</td>\n",
       "      <td>4.741731</td>\n",
       "      <td>1753-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8.272</td>\n",
       "      <td>8.599771</td>\n",
       "      <td>1753-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>12.186</td>\n",
       "      <td>12.109692</td>\n",
       "      <td>1753-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14.585</td>\n",
       "      <td>14.482331</td>\n",
       "      <td>1753-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3210</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.414539</td>\n",
       "      <td>2018-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3211</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.041952</td>\n",
       "      <td>2018-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3212</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13.173717</td>\n",
       "      <td>2018-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3213</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.424560</td>\n",
       "      <td>2018-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3214</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.270557</td>\n",
       "      <td>2018-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3215</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.997195</td>\n",
       "      <td>2018-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3216</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.514622</td>\n",
       "      <td>2019-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3217</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.055345</td>\n",
       "      <td>2019-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3218</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.168799</td>\n",
       "      <td>2019-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3219</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.463200</td>\n",
       "      <td>2019-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3220</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.422633</td>\n",
       "      <td>2019-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3221</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14.623092</td>\n",
       "      <td>2019-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3222</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.414539</td>\n",
       "      <td>2019-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3223</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.041952</td>\n",
       "      <td>2019-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3224</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13.173717</td>\n",
       "      <td>2019-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3225</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.424560</td>\n",
       "      <td>2019-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3226</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.270557</td>\n",
       "      <td>2019-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3227</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.997195</td>\n",
       "      <td>2019-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3228</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.514622</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3229</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.055345</td>\n",
       "      <td>2020-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3230</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.168799</td>\n",
       "      <td>2020-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3231</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.463200</td>\n",
       "      <td>2020-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3232</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.422633</td>\n",
       "      <td>2020-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3233</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14.623092</td>\n",
       "      <td>2020-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3234</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.414539</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3235</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.041952</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3236</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13.173717</td>\n",
       "      <td>2020-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3237</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.424560</td>\n",
       "      <td>2020-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3238</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.270557</td>\n",
       "      <td>2020-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3239</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.997195</td>\n",
       "      <td>2020-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3240 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      actual_temp  predicted_temp   datetime\n",
       "0           3.034        1.527702 1750-01-01\n",
       "1           3.083        1.972995 1750-02-01\n",
       "2           5.626        4.733904 1750-03-01\n",
       "3           8.490        8.560140 1750-04-01\n",
       "4          11.573       12.015585 1750-05-01\n",
       "5          12.937       14.209926 1750-06-01\n",
       "6          15.868       14.877536 1750-07-01\n",
       "7          14.750       14.323442 1750-08-01\n",
       "8          11.413       11.347094 1750-09-01\n",
       "9           6.367        7.427641 1750-10-01\n",
       "10          2.772        2.851475 1750-12-01\n",
       "11          2.495        1.495898 1751-01-01\n",
       "12          0.963        1.941192 1751-02-01\n",
       "13          5.800        4.773817 1751-03-01\n",
       "14          7.670        8.631856 1751-04-01\n",
       "15         13.827       14.309941 1751-06-01\n",
       "16         14.405       14.521994 1751-08-01\n",
       "17         10.673       11.596462 1751-09-01\n",
       "18          0.348        1.463813 1752-01-01\n",
       "19          5.806        4.741731 1752-03-01\n",
       "20          8.265        8.599771 1752-04-01\n",
       "21          7.839        8.147444 1752-10-01\n",
       "22          7.335        5.309720 1752-11-01\n",
       "23          5.086        4.005696 1752-12-01\n",
       "24          2.039        1.463813 1753-01-01\n",
       "25          0.715        1.909107 1753-02-01\n",
       "26          5.424        4.741731 1753-03-01\n",
       "27          8.272        8.599771 1753-04-01\n",
       "28         12.186       12.109692 1753-05-01\n",
       "29         14.585       14.482331 1753-06-01\n",
       "...           ...             ...        ...\n",
       "3210          NaN       15.414539 2018-07-01\n",
       "3211          NaN       15.041952 2018-08-01\n",
       "3212          NaN       13.173717 2018-09-01\n",
       "3213          NaN       10.424560 2018-10-01\n",
       "3214          NaN        7.270557 2018-11-01\n",
       "3215          NaN        4.997195 2018-12-01\n",
       "3216          NaN        3.514622 2019-01-01\n",
       "3217          NaN        4.055345 2019-02-01\n",
       "3218          NaN        6.168799 2019-03-01\n",
       "3219          NaN        9.463200 2019-04-01\n",
       "3220          NaN       12.422633 2019-05-01\n",
       "3221          NaN       14.623092 2019-06-01\n",
       "3222          NaN       15.414539 2019-07-01\n",
       "3223          NaN       15.041952 2019-08-01\n",
       "3224          NaN       13.173717 2019-09-01\n",
       "3225          NaN       10.424560 2019-10-01\n",
       "3226          NaN        7.270557 2019-11-01\n",
       "3227          NaN        4.997195 2019-12-01\n",
       "3228          NaN        3.514622 2020-01-01\n",
       "3229          NaN        4.055345 2020-02-01\n",
       "3230          NaN        6.168799 2020-03-01\n",
       "3231          NaN        9.463200 2020-04-01\n",
       "3232          NaN       12.422633 2020-05-01\n",
       "3233          NaN       14.623092 2020-06-01\n",
       "3234          NaN       15.414539 2020-07-01\n",
       "3235          NaN       15.041952 2020-08-01\n",
       "3236          NaN       13.173717 2020-09-01\n",
       "3237          NaN       10.424560 2020-10-01\n",
       "3238          NaN        7.270557 2020-11-01\n",
       "3239          NaN        4.997195 2020-12-01\n",
       "\n",
       "[3240 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finally, lets check out the answer to our question:\n",
    "# What will the monthly global average temperatures be for the next 5 years?\n",
    "\n",
    "# We can look at this in relation to the past several years (will make the plot more readable than if we included all points since 1750)\n",
    "\n",
    "# convert to date-time\n",
    "complete['datetime']=pd.to_datetime(complete.year*10000+complete.month*100+1,format='%Y%m%d')\n",
    "\n",
    "# drop month year columns\n",
    "complete = complete.drop('month',1)\n",
    "complete = complete.drop('year',1)\n",
    "\n",
    "# view\n",
    "complete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# rename columns appropriately\n",
    "complete.rename(columns={'actual_temp': 'Yearly Average Actual Temperature','predicted_temp':'Yearly Average Predicted Temperature','datetime':'Date'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1132e1240>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1132a7518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Nt9U5PvKw54MH8TmfYCfHLgc+ClLOLuxT/QK9thD7JETfZXc45V3EwaT0HmxS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RJtMzlzyiPPYz4soVkfYOy8TXq5huuqtkQ9o/GIeq/OyvyKalMuRcJKsDq/ajJ3n+C1NM2KMNkt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EGgMD4px8sNzy7G4VS7EBZtXk4xxu4nhwwADZQC1LqqBbNGntv3NyYt2vZYPbXHylpSMXoUR0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11325dcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = complete.iloc[3120:3239].plot('Date',['Yearly Average Actual Temperature','Yearly Average Predicted Temperature'],figsize=(20,10),grid=True,fontsize=14,legend=True)\n",
    "plt.suptitle('Average Actual & Predicted Monthly Global Land Temperatures', fontsize=20)\n",
    "plt.xlabel('Date', fontsize=18)\n",
    "plt.ylabel('Degrees Celcius', fontsize=16)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}