view_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View/Extract Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import Dependencies\n",
    "import os\n",
    "import pandas as pd\n",
    "import csv\n",
    "import json\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Get data csv paths\n",
    "air_pollution_death_rates_path = os.path.join(os.getcwd(), 'Resources', 'death-rates-from-air-pollution.csv')\n",
    "temp_change_path = os.path.join(os.getcwd(), 'Resources', 'Environment_Temperature_change_E_All_Data_NOFLAG.csv')\n",
    "air_pollution_path = os.path.join(os.getcwd(), 'Resources', 'PM2.5 Global Air Pollution 2010-2017.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## load data into dataframes\n",
    "air_pollution_death_rate_df = pd.read_csv(air_pollution_death_rates_path)\n",
    "temp_change_df = pd.read_csv(temp_change_path, encoding='latin-1')\n",
    "air_pollution_df = pd.read_csv(air_pollution_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Air pollution (total) (deaths per 100,000)</th>\n",
       "      <th>Indoor air pollution (deaths per 100,000)</th>\n",
       "      <th>Outdoor particulate matter (deaths per 100,000)</th>\n",
       "      <th>Outdoor ozone pollution (deaths per 100,000)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1990</td>\n",
       "      <td>299.477309</td>\n",
       "      <td>250.362910</td>\n",
       "      <td>46.446589</td>\n",
       "      <td>5.616442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1991</td>\n",
       "      <td>291.277967</td>\n",
       "      <td>242.575125</td>\n",
       "      <td>46.033841</td>\n",
       "      <td>5.603960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1992</td>\n",
       "      <td>278.963056</td>\n",
       "      <td>232.043878</td>\n",
       "      <td>44.243766</td>\n",
       "      <td>5.611822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1993</td>\n",
       "      <td>278.790815</td>\n",
       "      <td>231.648134</td>\n",
       "      <td>44.440148</td>\n",
       "      <td>5.655266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1994</td>\n",
       "      <td>287.162923</td>\n",
       "      <td>238.837177</td>\n",
       "      <td>45.594328</td>\n",
       "      <td>5.718922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Entity Code  Year  Air pollution (total) (deaths per 100,000)  \\\n",
       "0  Afghanistan  AFG  1990                                  299.477309   \n",
       "1  Afghanistan  AFG  1991                                  291.277967   \n",
       "2  Afghanistan  AFG  1992                                  278.963056   \n",
       "3  Afghanistan  AFG  1993                                  278.790815   \n",
       "4  Afghanistan  AFG  1994                                  287.162923   \n",
       "\n",
       "   Indoor air pollution (deaths per 100,000)  \\\n",
       "0                                 250.362910   \n",
       "1                                 242.575125   \n",
       "2                                 232.043878   \n",
       "3                                 231.648134   \n",
       "4                                 238.837177   \n",
       "\n",
       "   Outdoor particulate matter (deaths per 100,000)  \\\n",
       "0                                        46.446589   \n",
       "1                                        46.033841   \n",
       "2                                        44.243766   \n",
       "3                                        44.440148   \n",
       "4                                        45.594328   \n",
       "\n",
       "   Outdoor ozone pollution (deaths per 100,000)  \n",
       "0                                      5.616442  \n",
       "1                                      5.603960  \n",
       "2                                      5.611822  \n",
       "3                                      5.655266  \n",
       "4                                      5.718922  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Take a peek at each df and clean if necessary\n",
    "air_pollution_death_rate_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Area</th>\n",
       "      <th>Months Code</th>\n",
       "      <th>Months</th>\n",
       "      <th>Element Code</th>\n",
       "      <th>Element</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Y1961</th>\n",
       "      <th>Y1962</th>\n",
       "      <th>Y1963</th>\n",
       "      <th>...</th>\n",
       "      <th>Y2010</th>\n",
       "      <th>Y2011</th>\n",
       "      <th>Y2012</th>\n",
       "      <th>Y2013</th>\n",
       "      <th>Y2014</th>\n",
       "      <th>Y2015</th>\n",
       "      <th>Y2016</th>\n",
       "      <th>Y2017</th>\n",
       "      <th>Y2018</th>\n",
       "      <th>Y2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>7001</td>\n",
       "      <td>January</td>\n",
       "      <td>7271</td>\n",
       "      <td>Temperature change</td>\n",
       "      <td>°C</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.062</td>\n",
       "      <td>2.744</td>\n",
       "      <td>...</td>\n",
       "      <td>3.601</td>\n",
       "      <td>1.179</td>\n",
       "      <td>-0.583</td>\n",
       "      <td>1.233</td>\n",
       "      <td>1.755</td>\n",
       "      <td>1.943</td>\n",
       "      <td>3.416</td>\n",
       "      <td>1.201</td>\n",
       "      <td>1.996</td>\n",
       "      <td>2.951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>7001</td>\n",
       "      <td>January</td>\n",
       "      <td>6078</td>\n",
       "      <td>Standard Deviation</td>\n",
       "      <td>°C</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>...</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>7002</td>\n",
       "      <td>February</td>\n",
       "      <td>7271</td>\n",
       "      <td>Temperature change</td>\n",
       "      <td>°C</td>\n",
       "      <td>-1.743</td>\n",
       "      <td>2.465</td>\n",
       "      <td>3.919</td>\n",
       "      <td>...</td>\n",
       "      <td>1.212</td>\n",
       "      <td>0.321</td>\n",
       "      <td>-3.201</td>\n",
       "      <td>1.494</td>\n",
       "      <td>-3.187</td>\n",
       "      <td>2.699</td>\n",
       "      <td>2.251</td>\n",
       "      <td>-0.323</td>\n",
       "      <td>2.705</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>7002</td>\n",
       "      <td>February</td>\n",
       "      <td>6078</td>\n",
       "      <td>Standard Deviation</td>\n",
       "      <td>°C</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>...</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>7003</td>\n",
       "      <td>March</td>\n",
       "      <td>7271</td>\n",
       "      <td>Temperature change</td>\n",
       "      <td>°C</td>\n",
       "      <td>0.516</td>\n",
       "      <td>1.336</td>\n",
       "      <td>0.403</td>\n",
       "      <td>...</td>\n",
       "      <td>3.390</td>\n",
       "      <td>0.748</td>\n",
       "      <td>-0.527</td>\n",
       "      <td>2.246</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>-0.497</td>\n",
       "      <td>2.296</td>\n",
       "      <td>0.834</td>\n",
       "      <td>4.418</td>\n",
       "      <td>0.234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area Code         Area  Months Code    Months  Element Code  \\\n",
       "0          2  Afghanistan         7001   January          7271   \n",
       "1          2  Afghanistan         7001   January          6078   \n",
       "2          2  Afghanistan         7002  February          7271   \n",
       "3          2  Afghanistan         7002  February          6078   \n",
       "4          2  Afghanistan         7003     March          7271   \n",
       "\n",
       "              Element Unit  Y1961  Y1962  Y1963  ...  Y2010  Y2011  Y2012  \\\n",
       "0  Temperature change   °C  0.777  0.062  2.744  ...  3.601  1.179 -0.583   \n",
       "1  Standard Deviation   °C  1.950  1.950  1.950  ...  1.950  1.950  1.950   \n",
       "2  Temperature change   °C -1.743  2.465  3.919  ...  1.212  0.321 -3.201   \n",
       "3  Standard Deviation   °C  2.597  2.597  2.597  ...  2.597  2.597  2.597   \n",
       "4  Temperature change   °C  0.516  1.336  0.403  ...  3.390  0.748 -0.527   \n",
       "\n",
       "   Y2013  Y2014  Y2015  Y2016  Y2017  Y2018  Y2019  \n",
       "0  1.233  1.755  1.943  3.416  1.201  1.996  2.951  \n",
       "1  1.950  1.950  1.950  1.950  1.950  1.950  1.950  \n",
       "2  1.494 -3.187  2.699  2.251 -0.323  2.705  0.086  \n",
       "3  2.597  2.597  2.597  2.597  2.597  2.597  2.597  \n",
       "4  2.246 -0.076 -0.497  2.296  0.834  4.418  0.234  \n",
       "\n",
       "[5 rows x 66 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_change_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>65.245592</td>\n",
       "      <td>66.835727</td>\n",
       "      <td>66.023174</td>\n",
       "      <td>61.366745</td>\n",
       "      <td>59.010330</td>\n",
       "      <td>61.252656</td>\n",
       "      <td>56.287047</td>\n",
       "      <td>56.910808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>33.787247</td>\n",
       "      <td>33.104195</td>\n",
       "      <td>33.415495</td>\n",
       "      <td>34.663923</td>\n",
       "      <td>32.974025</td>\n",
       "      <td>32.729873</td>\n",
       "      <td>31.785389</td>\n",
       "      <td>32.388505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>21.277828</td>\n",
       "      <td>22.772537</td>\n",
       "      <td>20.578259</td>\n",
       "      <td>19.938517</td>\n",
       "      <td>18.883955</td>\n",
       "      <td>19.512540</td>\n",
       "      <td>18.189934</td>\n",
       "      <td>18.200603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>AND</td>\n",
       "      <td>12.807197</td>\n",
       "      <td>13.273506</td>\n",
       "      <td>12.407053</td>\n",
       "      <td>11.813673</td>\n",
       "      <td>10.830418</td>\n",
       "      <td>11.462178</td>\n",
       "      <td>10.255834</td>\n",
       "      <td>10.307621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>53.787001</td>\n",
       "      <td>52.652279</td>\n",
       "      <td>53.297270</td>\n",
       "      <td>54.053822</td>\n",
       "      <td>52.583603</td>\n",
       "      <td>60.406813</td>\n",
       "      <td>58.764905</td>\n",
       "      <td>58.689259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country Name Country Code       2010       2011       2012       2013  \\\n",
       "0  Afghanistan          AFG  65.245592  66.835727  66.023174  61.366745   \n",
       "1       Angola          AGO  33.787247  33.104195  33.415495  34.663923   \n",
       "2      Albania          ALB  21.277828  22.772537  20.578259  19.938517   \n",
       "3      Andorra          AND  12.807197  13.273506  12.407053  11.813673   \n",
       "4   Arab World          ARB  53.787001  52.652279  53.297270  54.053822   \n",
       "\n",
       "        2014       2015       2016       2017  \n",
       "0  59.010330  61.252656  56.287047  56.910808  \n",
       "1  32.974025  32.729873  31.785389  32.388505  \n",
       "2  18.883955  19.512540  18.189934  18.200603  \n",
       "3  10.830418  11.462178  10.255834  10.307621  \n",
       "4  52.583603  60.406813  58.764905  58.689259  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_pollution_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Area</th>\n",
       "      <th>Months Code</th>\n",
       "      <th>Months</th>\n",
       "      <th>Element Code</th>\n",
       "      <th>Element</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Y1961</th>\n",
       "      <th>Y1962</th>\n",
       "      <th>Y1963</th>\n",
       "      <th>...</th>\n",
       "      <th>Y2010</th>\n",
       "      <th>Y2011</th>\n",
       "      <th>Y2012</th>\n",
       "      <th>Y2013</th>\n",
       "      <th>Y2014</th>\n",
       "      <th>Y2015</th>\n",
       "      <th>Y2016</th>\n",
       "      <th>Y2017</th>\n",
       "      <th>Y2018</th>\n",
       "      <th>Y2019</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>7001</td>\n",
       "      <td>January</td>\n",
       "      <td>7271</td>\n",
       "      <td>Temperature change</td>\n",
       "      <td>°C</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.062</td>\n",
       "      <td>2.744</td>\n",
       "      <td>...</td>\n",
       "      <td>3.601</td>\n",
       "      <td>1.179</td>\n",
       "      <td>-0.583</td>\n",
       "      <td>1.233</td>\n",
       "      <td>1.755</td>\n",
       "      <td>1.943</td>\n",
       "      <td>3.416</td>\n",
       "      <td>1.201</td>\n",
       "      <td>1.996</td>\n",
       "      <td>2.951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>7001</td>\n",
       "      <td>January</td>\n",
       "      <td>6078</td>\n",
       "      <td>Standard Deviation</td>\n",
       "      <td>°C</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>...</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "      <td>1.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>7002</td>\n",
       "      <td>February</td>\n",
       "      <td>7271</td>\n",
       "      <td>Temperature change</td>\n",
       "      <td>°C</td>\n",
       "      <td>-1.743</td>\n",
       "      <td>2.465</td>\n",
       "      <td>3.919</td>\n",
       "      <td>...</td>\n",
       "      <td>1.212</td>\n",
       "      <td>0.321</td>\n",
       "      <td>-3.201</td>\n",
       "      <td>1.494</td>\n",
       "      <td>-3.187</td>\n",
       "      <td>2.699</td>\n",
       "      <td>2.251</td>\n",
       "      <td>-0.323</td>\n",
       "      <td>2.705</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>7002</td>\n",
       "      <td>February</td>\n",
       "      <td>6078</td>\n",
       "      <td>Standard Deviation</td>\n",
       "      <td>°C</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>...</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "      <td>2.597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>7003</td>\n",
       "      <td>March</td>\n",
       "      <td>7271</td>\n",
       "      <td>Temperature change</td>\n",
       "      <td>°C</td>\n",
       "      <td>0.516</td>\n",
       "      <td>1.336</td>\n",
       "      <td>0.403</td>\n",
       "      <td>...</td>\n",
       "      <td>3.390</td>\n",
       "      <td>0.748</td>\n",
       "      <td>-0.527</td>\n",
       "      <td>2.246</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>-0.497</td>\n",
       "      <td>2.296</td>\n",
       "      <td>0.834</td>\n",
       "      <td>4.418</td>\n",
       "      <td>0.234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Area Code         Area  Months Code    Months  Element Code  \\\n",
       "0          2  Afghanistan         7001   January          7271   \n",
       "1          2  Afghanistan         7001   January          6078   \n",
       "2          2  Afghanistan         7002  February          7271   \n",
       "3          2  Afghanistan         7002  February          6078   \n",
       "4          2  Afghanistan         7003     March          7271   \n",
       "\n",
       "              Element Unit  Y1961  Y1962  Y1963  ...  Y2010  Y2011  Y2012  \\\n",
       "0  Temperature change   °C  0.777  0.062  2.744  ...  3.601  1.179 -0.583   \n",
       "1  Standard Deviation   °C  1.950  1.950  1.950  ...  1.950  1.950  1.950   \n",
       "2  Temperature change   °C -1.743  2.465  3.919  ...  1.212  0.321 -3.201   \n",
       "3  Standard Deviation   °C  2.597  2.597  2.597  ...  2.597  2.597  2.597   \n",
       "4  Temperature change   °C  0.516  1.336  0.403  ...  3.390  0.748 -0.527   \n",
       "\n",
       "   Y2013  Y2014  Y2015  Y2016  Y2017  Y2018  Y2019  \n",
       "0  1.233  1.755  1.943  3.416  1.201  1.996  2.951  \n",
       "1  1.950  1.950  1.950  1.950  1.950  1.950  1.950  \n",
       "2  1.494 -3.187  2.699  2.251 -0.323  2.705  0.086  \n",
       "3  2.597  2.597  2.597  2.597  2.597  2.597  2.597  \n",
       "4  2.246 -0.076 -0.497  2.296  0.834  4.418  0.234  \n",
       "\n",
       "[5 rows x 66 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_change_cleaned_path = os.path.join(os.getcwd(), 'Resources', 'temp_change_cleaned.csv')\n",
    "temp_change_cleaned_df = pd.read_csv(temp_change_path, encoding='latin-1')\n",
    "temp_change_cleaned_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_mean_df = temp_change_cleaned_df.groupby('Area').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Y1961</th>\n",
       "      <th>Y1962</th>\n",
       "      <th>Y1963</th>\n",
       "      <th>Y1964</th>\n",
       "      <th>Y1965</th>\n",
       "      <th>Y1966</th>\n",
       "      <th>Y1967</th>\n",
       "      <th>Y1968</th>\n",
       "      <th>Y1969</th>\n",
       "      <th>Y1970</th>\n",
       "      <th>...</th>\n",
       "      <th>Y2010</th>\n",
       "      <th>Y2011</th>\n",
       "      <th>Y2012</th>\n",
       "      <th>Y2013</th>\n",
       "      <th>Y2014</th>\n",
       "      <th>Y2015</th>\n",
       "      <th>Y2016</th>\n",
       "      <th>Y2017</th>\n",
       "      <th>Y2018</th>\n",
       "      <th>Y2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.791235</td>\n",
       "      <td>0.556382</td>\n",
       "      <td>0.816735</td>\n",
       "      <td>0.499088</td>\n",
       "      <td>0.646029</td>\n",
       "      <td>0.599029</td>\n",
       "      <td>0.738412</td>\n",
       "      <td>0.602382</td>\n",
       "      <td>0.588676</td>\n",
       "      <td>0.643824</td>\n",
       "      <td>...</td>\n",
       "      <td>1.087059</td>\n",
       "      <td>1.050235</td>\n",
       "      <td>0.630059</td>\n",
       "      <td>1.534941</td>\n",
       "      <td>0.932235</td>\n",
       "      <td>0.618412</td>\n",
       "      <td>0.850118</td>\n",
       "      <td>1.030441</td>\n",
       "      <td>1.484765</td>\n",
       "      <td>0.962912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.785529</td>\n",
       "      <td>0.509353</td>\n",
       "      <td>0.428853</td>\n",
       "      <td>0.167853</td>\n",
       "      <td>0.219647</td>\n",
       "      <td>0.718941</td>\n",
       "      <td>0.477735</td>\n",
       "      <td>0.401971</td>\n",
       "      <td>-0.097000</td>\n",
       "      <td>0.483941</td>\n",
       "      <td>...</td>\n",
       "      <td>0.808059</td>\n",
       "      <td>1.288559</td>\n",
       "      <td>1.163147</td>\n",
       "      <td>1.115118</td>\n",
       "      <td>1.242265</td>\n",
       "      <td>1.493706</td>\n",
       "      <td>1.446382</td>\n",
       "      <td>1.445000</td>\n",
       "      <td>1.336206</td>\n",
       "      <td>1.511735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.349529</td>\n",
       "      <td>0.324765</td>\n",
       "      <td>0.223265</td>\n",
       "      <td>0.220735</td>\n",
       "      <td>0.348441</td>\n",
       "      <td>0.123441</td>\n",
       "      <td>0.234353</td>\n",
       "      <td>0.168353</td>\n",
       "      <td>0.332206</td>\n",
       "      <td>0.297324</td>\n",
       "      <td>...</td>\n",
       "      <td>0.569912</td>\n",
       "      <td>0.380324</td>\n",
       "      <td>0.453941</td>\n",
       "      <td>0.958765</td>\n",
       "      <td>0.818147</td>\n",
       "      <td>0.775029</td>\n",
       "      <td>0.847647</td>\n",
       "      <td>0.839118</td>\n",
       "      <td>0.839176</td>\n",
       "      <td>0.985500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.223529</td>\n",
       "      <td>0.218588</td>\n",
       "      <td>0.323471</td>\n",
       "      <td>0.099235</td>\n",
       "      <td>0.194912</td>\n",
       "      <td>0.356676</td>\n",
       "      <td>0.030559</td>\n",
       "      <td>0.100647</td>\n",
       "      <td>0.170412</td>\n",
       "      <td>0.226647</td>\n",
       "      <td>...</td>\n",
       "      <td>0.831294</td>\n",
       "      <td>0.522059</td>\n",
       "      <td>0.542765</td>\n",
       "      <td>0.761794</td>\n",
       "      <td>0.655265</td>\n",
       "      <td>0.873441</td>\n",
       "      <td>0.941029</td>\n",
       "      <td>0.914853</td>\n",
       "      <td>0.856500</td>\n",
       "      <td>0.908000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.202029</td>\n",
       "      <td>0.196029</td>\n",
       "      <td>0.311206</td>\n",
       "      <td>0.050412</td>\n",
       "      <td>0.101118</td>\n",
       "      <td>0.140882</td>\n",
       "      <td>0.169500</td>\n",
       "      <td>0.173971</td>\n",
       "      <td>0.319382</td>\n",
       "      <td>0.224735</td>\n",
       "      <td>...</td>\n",
       "      <td>0.883118</td>\n",
       "      <td>0.657000</td>\n",
       "      <td>0.864529</td>\n",
       "      <td>0.598882</td>\n",
       "      <td>0.626824</td>\n",
       "      <td>0.903971</td>\n",
       "      <td>1.037353</td>\n",
       "      <td>0.895647</td>\n",
       "      <td>0.688765</td>\n",
       "      <td>0.828735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.124235</td>\n",
       "      <td>0.165559</td>\n",
       "      <td>0.207471</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.071088</td>\n",
       "      <td>0.242088</td>\n",
       "      <td>0.057441</td>\n",
       "      <td>0.077912</td>\n",
       "      <td>0.343882</td>\n",
       "      <td>0.260735</td>\n",
       "      <td>...</td>\n",
       "      <td>0.927765</td>\n",
       "      <td>0.615206</td>\n",
       "      <td>0.576206</td>\n",
       "      <td>0.669912</td>\n",
       "      <td>0.693324</td>\n",
       "      <td>0.772706</td>\n",
       "      <td>0.910706</td>\n",
       "      <td>0.763059</td>\n",
       "      <td>0.786765</td>\n",
       "      <td>0.882735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Y1961     Y1962     Y1963     Y1964     Y1965     Y1966     Y1967  \\\n",
       "0  0.791235  0.556382  0.816735  0.499088  0.646029  0.599029  0.738412   \n",
       "1  0.785529  0.509353  0.428853  0.167853  0.219647  0.718941  0.477735   \n",
       "2  0.349529  0.324765  0.223265  0.220735  0.348441  0.123441  0.234353   \n",
       "3  0.223529  0.218588  0.323471  0.099235  0.194912  0.356676  0.030559   \n",
       "4  0.202029  0.196029  0.311206  0.050412  0.101118  0.140882  0.169500   \n",
       "5  0.124235  0.165559  0.207471  0.074000  0.071088  0.242088  0.057441   \n",
       "\n",
       "      Y1968     Y1969     Y1970  ...     Y2010     Y2011     Y2012     Y2013  \\\n",
       "0  0.602382  0.588676  0.643824  ...  1.087059  1.050235  0.630059  1.534941   \n",
       "1  0.401971 -0.097000  0.483941  ...  0.808059  1.288559  1.163147  1.115118   \n",
       "2  0.168353  0.332206  0.297324  ...  0.569912  0.380324  0.453941  0.958765   \n",
       "3  0.100647  0.170412  0.226647  ...  0.831294  0.522059  0.542765  0.761794   \n",
       "4  0.173971  0.319382  0.224735  ...  0.883118  0.657000  0.864529  0.598882   \n",
       "5  0.077912  0.343882  0.260735  ...  0.927765  0.615206  0.576206  0.669912   \n",
       "\n",
       "      Y2014     Y2015     Y2016     Y2017     Y2018     Y2019  \n",
       "0  0.932235  0.618412  0.850118  1.030441  1.484765  0.962912  \n",
       "1  1.242265  1.493706  1.446382  1.445000  1.336206  1.511735  \n",
       "2  0.818147  0.775029  0.847647  0.839118  0.839176  0.985500  \n",
       "3  0.655265  0.873441  0.941029  0.914853  0.856500  0.908000  \n",
       "4  0.626824  0.903971  1.037353  0.895647  0.688765  0.828735  \n",
       "5  0.693324  0.772706  0.910706  0.763059  0.786765  0.882735  \n",
       "\n",
       "[6 rows x 59 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Create table for bar chart\n",
    "temp_mean_region_df = temp_mean_df.loc[[\"Antarctica\", \"Europe\", \"Oceania\", \"Asia\", \"Americas\", \"Africa\"]].drop(columns = [\"Area Code\", \"Months Code\", \"Element Code\"])\n",
    "temp_mean_region_df = temp_mean_region_df.set_index('Y1961')\n",
    "# temp_mean_region_df.to_csv(\"Resources/temp_region_mean.csv\")\n",
    "# Show the dataframe\n",
    "temp_mean_region_df= temp_mean_region_df.reset_index()\n",
    "temp_mean_region_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y1961    0.124235\n",
       "Y1962    0.165559\n",
       "Y1963    0.207471\n",
       "Y1964    0.050412\n",
       "Y1965    0.071088\n",
       "Y1966    0.123441\n",
       "Y1967    0.030559\n",
       "Y1968    0.077912\n",
       "Y1969   -0.097000\n",
       "Y1970    0.224735\n",
       "Y1971    0.060500\n",
       "Y1972   -0.030412\n",
       "Y1973    0.325353\n",
       "Y1974    0.038941\n",
       "Y1975    0.072382\n",
       "Y1976    0.056441\n",
       "Y1977    0.269794\n",
       "Y1978    0.184941\n",
       "Y1979    0.198029\n",
       "Y1980    0.332676\n",
       "Y1981    0.252647\n",
       "Y1982    0.153176\n",
       "Y1983    0.213794\n",
       "Y1984    0.112265\n",
       "Y1985    0.180235\n",
       "Y1986    0.237118\n",
       "Y1987    0.158206\n",
       "Y1988    0.422529\n",
       "Y1989    0.193647\n",
       "Y1990    0.390794\n",
       "Y1991    0.315441\n",
       "Y1992    0.182029\n",
       "Y1993    0.227265\n",
       "Y1994    0.354676\n",
       "Y1995    0.422382\n",
       "Y1996    0.307647\n",
       "Y1997    0.396000\n",
       "Y1998    0.425088\n",
       "Y1999    0.327912\n",
       "Y2000    0.368559\n",
       "Y2001    0.429588\n",
       "Y2002    0.612176\n",
       "Y2003    0.561529\n",
       "Y2004    0.522735\n",
       "Y2005    0.627794\n",
       "Y2006    0.542235\n",
       "Y2007    0.589088\n",
       "Y2008    0.483853\n",
       "Y2009    0.584176\n",
       "Y2010    0.569912\n",
       "Y2011    0.380324\n",
       "Y2012    0.453941\n",
       "Y2013    0.598882\n",
       "Y2014    0.626824\n",
       "Y2015    0.618412\n",
       "Y2016    0.847647\n",
       "Y2017    0.763059\n",
       "Y2018    0.688765\n",
       "Y2019    0.828735\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_mean_region_df.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## How to create year array for trace on bar graph\n",
    "temp_var = 1961\n",
    "year_array = []\n",
    "while temp_var <= 2019:\n",
    "    year_array.append(temp_var)\n",
    "    temp_var = temp_var + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "### How to create trace arrays for bar graph features\n",
    "antartica_array = []\n",
    "europe_array = []\n",
    "oceania_array = []\n",
    "asia_array = []\n",
    "americas_array = []\n",
    "\n",
    "for i, row in temp_mean_region_df.iteritems():\n",
    "    antartica_array.append(row[0])\n",
    "    europe_array.append(row[1])\n",
    "    oceania_array.append(row[2])\n",
    "    asia_array.append(row[3])\n",
    "    americas_array.append(row[4])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(antartica_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Angola', 'Albania', 'Andorra', 'Arab World',\n",
       "       'United Arab Emirates', 'Argentina', 'Armenia', 'American Samoa',\n",
       "       'Antigua and Barbuda', 'Australia', 'Austria', 'Azerbaijan',\n",
       "       'Burundi', 'Belgium', 'Benin', 'Burkina Faso', 'Bangladesh',\n",
       "       'Bulgaria', 'Bahrain', 'Bahamas, The', 'Bosnia and Herzegovina',\n",
       "       'Belarus', 'Belize', 'Bermuda', 'Bolivia', 'Brazil', 'Barbados',\n",
       "       'Brunei Darussalam', 'Bhutan', 'Botswana',\n",
       "       'Central African Republic', 'Canada',\n",
       "       'Central Europe and the Baltics', 'Switzerland', 'Chile', 'China',\n",
       "       \"Cote d'Ivoire\", 'Cameroon', 'Congo, Dem. Rep.', 'Congo, Rep.',\n",
       "       'Colombia', 'Comoros', 'Cabo Verde', 'Costa Rica',\n",
       "       'Caribbean small states', 'Cuba', 'Cyprus', 'Czech Republic',\n",
       "       'Germany', 'Djibouti', 'Dominica', 'Denmark', 'Dominican Republic',\n",
       "       'Algeria', 'East Asia & Pacific (excluding high income)',\n",
       "       'Early-demographic dividend', 'East Asia & Pacific',\n",
       "       'Europe & Central Asia (excluding high income)',\n",
       "       'Europe & Central Asia', 'Ecuador', 'Egypt, Arab Rep.',\n",
       "       'Euro area', 'Eritrea', 'Spain', 'Estonia', 'Ethiopia',\n",
       "       'European Union', 'Fragile and conflict affected situations',\n",
       "       'Finland', 'Fiji', 'France', 'Micronesia, Fed. Sts.', 'Gabon',\n",
       "       'United Kingdom', 'Georgia', 'Ghana', 'Guinea', 'Gambia, The',\n",
       "       'Guinea-Bissau', 'Equatorial Guinea', 'Greece', 'Grenada',\n",
       "       'Greenland', 'Guatemala', 'Guam', 'Guyana', 'High income',\n",
       "       'Honduras', 'Heavily indebted poor countries (HIPC)', 'Croatia',\n",
       "       'Haiti', 'Hungary', 'IBRD only', 'IDA & IBRD total', 'IDA total',\n",
       "       'IDA blend', 'Indonesia', 'IDA only', 'India', 'Ireland',\n",
       "       'Iran, Islamic Rep.', 'Iraq', 'Iceland', 'Israel', 'Italy',\n",
       "       'Jamaica', 'Jordan', 'Japan', 'Kazakhstan', 'Kenya',\n",
       "       'Kyrgyz Republic', 'Cambodia', 'Kiribati', 'Korea, Rep.', 'Kuwait',\n",
       "       'Latin America & Caribbean (excluding high income)', 'Lao PDR',\n",
       "       'Lebanon', 'Liberia', 'Libya', 'St. Lucia',\n",
       "       'Latin America & Caribbean',\n",
       "       'Least developed countries: UN classification', 'Low income',\n",
       "       'Sri Lanka', 'Lower middle income', 'Low & middle income',\n",
       "       'Lesotho', 'Late-demographic dividend', 'Lithuania', 'Luxembourg',\n",
       "       'Latvia', 'Morocco', 'Moldova', 'Madagascar', 'Maldives',\n",
       "       'Middle East & North Africa', 'Mexico', 'Marshall Islands',\n",
       "       'Middle income', 'North Macedonia', 'Mali', 'Malta', 'Myanmar',\n",
       "       'Middle East & North Africa (excluding high income)', 'Montenegro',\n",
       "       'Mongolia', 'Northern Mariana Islands', 'Mozambique', 'Mauritania',\n",
       "       'Mauritius', 'Malawi', 'Malaysia', 'North America', 'Namibia',\n",
       "       'Niger', 'Nigeria', 'Nicaragua', 'Netherlands', 'Norway', 'Nepal',\n",
       "       'New Zealand', 'OECD members', 'Oman', 'Other small states',\n",
       "       'Pakistan', 'Panama', 'Peru', 'Philippines', 'Papua New Guinea',\n",
       "       'Poland', 'Pre-demographic dividend', 'Puerto Rico',\n",
       "       'Korea, Dem. People’s Rep.', 'Portugal', 'Paraguay',\n",
       "       'West Bank and Gaza', 'Pacific island small states',\n",
       "       'Post-demographic dividend', 'Qatar', 'Romania',\n",
       "       'Russian Federation', 'Rwanda', 'South Asia', 'Saudi Arabia',\n",
       "       'Sudan', 'Senegal', 'Singapore', 'Solomon Islands', 'Sierra Leone',\n",
       "       'El Salvador', 'Somalia', 'Serbia',\n",
       "       'Sub-Saharan Africa (excluding high income)', 'South Sudan',\n",
       "       'Sub-Saharan Africa', 'Small states', 'Sao Tome and Principe',\n",
       "       'Suriname', 'Slovak Republic', 'Slovenia', 'Sweden', 'Eswatini',\n",
       "       'Seychelles', 'Syrian Arab Republic', 'Chad',\n",
       "       'East Asia & Pacific (IDA & IBRD countries)',\n",
       "       'Europe & Central Asia (IDA & IBRD countries)', 'Togo', 'Thailand',\n",
       "       'Tajikistan', 'Turkmenistan',\n",
       "       'Latin America & the Caribbean (IDA & IBRD countries)',\n",
       "       'Timor-Leste', 'Middle East & North Africa (IDA & IBRD countries)',\n",
       "       'Tonga', 'South Asia (IDA & IBRD)',\n",
       "       'Sub-Saharan Africa (IDA & IBRD countries)', 'Trinidad and Tobago',\n",
       "       'Tunisia', 'Turkey', 'Tanzania', 'Uganda', 'Ukraine',\n",
       "       'Upper middle income', 'Uruguay', 'United States', 'Uzbekistan',\n",
       "       'St. Vincent and the Grenadines', 'Venezuela, RB',\n",
       "       'Virgin Islands (U.S.)', 'Vietnam', 'Vanuatu', 'World', 'Samoa',\n",
       "       'Yemen, Rep.', 'South Africa', 'Zambia', 'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_pollution_df['Country Name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "world_df = air_pollution_df[air_pollution_df['Country Name'] == 'World'].reset_index().drop(columns=['Country Name', 'Country Code', 'index'])\n",
    "euro_df = air_pollution_df[air_pollution_df['Country Name'] == 'Europe & Central Asia'].reset_index().drop(columns=['Country Name', 'Country Code', 'index'])\n",
    "us_df = air_pollution_df[air_pollution_df['Country Name'] == 'United States'].reset_index().drop(columns=['Country Name', 'Country Code', 'index'])\n",
    "china_df = air_pollution_df[air_pollution_df['Country Name'] == 'China'].reset_index().drop(columns=['Country Name', 'Country Code', 'index'])\n",
    "india_df = air_pollution_df[air_pollution_df['Country Name'] == 'India'].reset_index().drop(columns=['Country Name', 'Country Code', 'index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## establish array for line graph\n",
    "world_array = []\n",
    "euro_array = []\n",
    "us_array = []\n",
    "china_array = []\n",
    "india_array = []\n",
    "for i, row in world_df.iteritems():\n",
    "    world_array.append(row[0])\n",
    "for i, row in euro_df.iteritems():\n",
    "    euro_array.append(row[0])\n",
    "for i, row in us_df.iteritems():\n",
    "    us_array.append(row[0])\n",
    "for i, row in china_df.iteritems():\n",
    "    china_array.append(row[0])\n",
    "for i, row in india_df.iteritems():\n",
    "    india_array.append(row[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[50.24771686,\n",
       " 50.76662896,\n",
       " 47.529404,\n",
       " 47.94324524,\n",
       " 45.93996796,\n",
       " 47.22442898,\n",
       " 45.14917075,\n",
       " 45.52185907]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[20.58259725,\n",
       " 20.5751036,\n",
       " 19.50011895,\n",
       " 19.07915982,\n",
       " 18.39049124,\n",
       " 18.99890934,\n",
       " 18.00777789,\n",
       " 18.08195597]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euro_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9.35863641,\n",
       " 9.740640607,\n",
       " 9.00316337,\n",
       " 8.734777211,\n",
       " 8.220720319,\n",
       " 8.063920222,\n",
       " 7.406464089,\n",
       " 7.409441662]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[69.47957041,\n",
       " 70.54202883,\n",
       " 63.82701684,\n",
       " 65.51455022,\n",
       " 59.76736751,\n",
       " 59.06342246,\n",
       " 52.21132204,\n",
       " 52.66459583]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[95.75817945,\n",
       " 97.59937863,\n",
       " 88.1694406,\n",
       " 91.80479161,\n",
       " 89.62238269,\n",
       " 89.3033343,\n",
       " 89.67217693,\n",
       " 90.87321047]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "india_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Annual CO₂ emissions (tonnes )</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1949</td>\n",
       "      <td>14656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1950</td>\n",
       "      <td>84272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1951</td>\n",
       "      <td>91600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1952</td>\n",
       "      <td>91600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1953</td>\n",
       "      <td>106256.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Entity Code  Year  Annual CO₂ emissions (tonnes )\n",
       "0  Afghanistan  AFG  1949                         14656.0\n",
       "1  Afghanistan  AFG  1950                         84272.0\n",
       "2  Afghanistan  AFG  1951                         91600.0\n",
       "3  Afghanistan  AFG  1952                         91600.0\n",
       "4  Afghanistan  AFG  1953                        106256.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_path = os.path.join(os.getcwd(), 'Resources', 'co2_emission.csv')\n",
    "co2_df1 = pd.read_csv(co2_path)\n",
    "co2_df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "328484894.5042253\n",
      "213\n"
     ]
    }
   ],
   "source": [
    "### Take out entities withour country codes (just want countries)\n",
    "co2_df = co2_df1[co2_df1['Code'].notna()].drop(columns = {'Code'})\n",
    "co2_df = co2_df[co2_df['Year'] == 2016]\n",
    "co2_mean = co2_df['Annual CO₂ emissions (tonnes )'].mean()\n",
    "print(co2_mean)\n",
    "print(co2_df['Entity'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "      <th>Year</th>\n",
       "      <th>Annual CO₂ emissions (tonnes )</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.133699e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3095</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.737414e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3861</th>\n",
       "      <td>Canada</td>\n",
       "      <td>2016</td>\n",
       "      <td>5.581890e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4483</th>\n",
       "      <td>China</td>\n",
       "      <td>2016</td>\n",
       "      <td>9.704479e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7441</th>\n",
       "      <td>France</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.501047e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Entity  Year  Annual CO₂ emissions (tonnes )\n",
       "1674  Australia  2016                    4.133699e+08\n",
       "3095     Brazil  2016                    4.737414e+08\n",
       "3861     Canada  2016                    5.581890e+08\n",
       "4483      China  2016                    9.704479e+09\n",
       "7441     France  2016                    3.501047e+08"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_df = co2_df[co2_df['Annual CO₂ emissions (tonnes )'] > co2_mean]\n",
    "# co2_df.to_csv(\"Resources/co2_cleaned.csv\")\n",
    "co2_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_df['Entity'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_df['Entity'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3149559829.005"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_df['Annual CO₂ emissions (tonnes )'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "### How to get trace arrays for bar\n",
    "country_array = []\n",
    "co2_vals_array = []\n",
    "for index, row in co2_df.iterrows():\n",
    "    country_array.append(row[0])\n",
    "    co2_vals_array.append(row[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[413369923.0,\n",
       " 473741376.0,\n",
       " 558188999.7,\n",
       " 9704479432.0,\n",
       " 350104687.1,\n",
       " 801753007.7,\n",
       " 2377447863.0,\n",
       " 464856818.4,\n",
       " 637562152.6,\n",
       " 350323014.9,\n",
       " 1204318623.0,\n",
       " 504979947.8,\n",
       " 1668069933.0,\n",
       " 631549206.5,\n",
       " 468045403.7,\n",
       " 595073678.8,\n",
       " 402820783.8,\n",
       " 398550884.1,\n",
       " 5310861406.0,\n",
       " 35675099439.0]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_vals_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sub-sector</th>\n",
       "      <th>GlobalShare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Road</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aviation</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rail</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pipeline</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ship</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sub-sector  GlobalShare\n",
       "0       Road         11.9\n",
       "1   Aviation          1.9\n",
       "2       Rail          0.4\n",
       "3   Pipeline          0.3\n",
       "4       Ship          1.7"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sectorco2_path = os.path.join(os.getcwd(), 'Resources', 'GHG-Emissions-by-sector.csv')\n",
    "sectorco2_df = pd.read_csv(sectorco2_path)\n",
    "sectorco2_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sectorco2_df['GlobalShare'].sum()\n",
    "sectorco2_df['GlobalShare'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "count = 0\n",
    "energy = 0\n",
    "industry = 0\n",
    "land = 0 \n",
    "waste = 0 \n",
    "for index, row in sectorco2_df.iterrows():\n",
    "    if count <= 18: \n",
    "        energy = energy + row[\"GlobalShare\"]\n",
    "        count = count + 1\n",
    "    else:\n",
    "        if count <= 20:\n",
    "            industry = industry + row[\"GlobalShare\"]\n",
    "            count = count + 1\n",
    "        else:\n",
    "            if count <= 27:\n",
    "                land = land + row[\"GlobalShare\"]\n",
    "                count = count + 1\n",
    "            else:\n",
    "                waste = waste + row[\"GlobalShare\"]\n",
    "                count = count + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.20000000000003"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.499999999999998"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "land"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.00000000000003"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy + industry + land + waste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_pollution_death_rate_df[\"Year\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Albania', 'Algeria', 'American Samoa',\n",
       "       'Andean Latin America', 'Andorra', 'Angola', 'Antigua and Barbuda',\n",
       "       'Argentina', 'Armenia', 'Australasia', 'Australia', 'Austria',\n",
       "       'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados',\n",
       "       'Belarus', 'Belgium', 'Belize', 'Benin', 'Bermuda', 'Bhutan',\n",
       "       'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n",
       "       'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia',\n",
       "       'Cameroon', 'Canada', 'Cape Verde', 'Caribbean',\n",
       "       'Central African Republic', 'Central Asia', 'Central Europe',\n",
       "       'Central Europe, Eastern Europe, and Central Asia',\n",
       "       'Central Latin America', 'Central Sub-Saharan Africa', 'Chad',\n",
       "       'Chile', 'China', 'Colombia', 'Comoros', 'Congo', 'Costa Rica',\n",
       "       \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Cyprus', 'Czech Republic',\n",
       "       'Democratic Republic of Congo', 'Denmark', 'Djibouti', 'Dominica',\n",
       "       'Dominican Republic', 'East Asia', 'Eastern Europe',\n",
       "       'Eastern Sub-Saharan Africa', 'Ecuador', 'Egypt', 'El Salvador',\n",
       "       'England', 'Equatorial Guinea', 'Eritrea', 'Estonia', 'Ethiopia',\n",
       "       'Fiji', 'Finland', 'France', 'Gabon', 'Gambia', 'Georgia',\n",
       "       'Germany', 'Ghana', 'Greece', 'Greenland', 'Grenada', 'Guam',\n",
       "       'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti',\n",
       "       'High SDI', 'High-income', 'High-income Asia Pacific',\n",
       "       'High-middle SDI', 'Honduras', 'Hungary', 'Iceland', 'India',\n",
       "       'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',\n",
       "       'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati',\n",
       "       'Kuwait', 'Kyrgyzstan', 'Laos', 'Latin America and Caribbean',\n",
       "       'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Lithuania',\n",
       "       'Low SDI', 'Low-middle SDI', 'Luxembourg', 'Macedonia',\n",
       "       'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta',\n",
       "       'Marshall Islands', 'Mauritania', 'Mauritius', 'Mexico',\n",
       "       'Micronesia (country)', 'Middle SDI', 'Moldova', 'Mongolia',\n",
       "       'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia',\n",
       "       'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua', 'Niger',\n",
       "       'Nigeria', 'North Africa and Middle East', 'North America',\n",
       "       'North Korea', 'Northern Ireland', 'Northern Mariana Islands',\n",
       "       'Norway', 'Oceania', 'Oman', 'Pakistan', 'Palestine', 'Panama',\n",
       "       'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland',\n",
       "       'Portugal', 'Puerto Rico', 'Qatar', 'Romania', 'Russia', 'Rwanda',\n",
       "       'Saint Lucia', 'Saint Vincent and the Grenadines', 'Samoa',\n",
       "       'Sao Tome and Principe', 'Saudi Arabia', 'Scotland', 'Senegal',\n",
       "       'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia',\n",
       "       'Slovenia', 'Solomon Islands', 'Somalia', 'South Africa',\n",
       "       'South Asia', 'South Korea', 'South Sudan', 'Southeast Asia',\n",
       "       'Southeast Asia, East Asia, and Oceania', 'Southern Latin America',\n",
       "       'Southern Sub-Saharan Africa', 'Spain', 'Sri Lanka',\n",
       "       'Sub-Saharan Africa', 'Sudan', 'Suriname', 'Swaziland', 'Sweden',\n",
       "       'Switzerland', 'Syria', 'Taiwan', 'Tajikistan', 'Tanzania',\n",
       "       'Thailand', 'Timor', 'Togo', 'Tonga', 'Trinidad and Tobago',\n",
       "       'Tropical Latin America', 'Tunisia', 'Turkey', 'Turkmenistan',\n",
       "       'Uganda', 'Ukraine', 'United Arab Emirates', 'United Kingdom',\n",
       "       'United States', 'United States Virgin Islands', 'Uruguay',\n",
       "       'Uzbekistan', 'Vanuatu', 'Venezuela', 'Vietnam', 'Wales',\n",
       "       'Western Europe', 'Western Sub-Saharan Africa', 'World', 'Yemen',\n",
       "       'Zambia', 'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_pollution_death_rate_df['Entity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Afghanistan', 'AFG', 1990, ..., 250.36290974237468,\n",
       "        46.44658943828465, 5.616442030749176],\n",
       "       ['Afghanistan', 'AFG', 1991, ..., 242.575124973334,\n",
       "        46.033840567028406, 5.6039601160366725],\n",
       "       ['Afghanistan', 'AFG', 1992, ..., 232.04387789481063,\n",
       "        44.24376603219239, 5.611822064825636],\n",
       "       ...,\n",
       "       ['Zimbabwe', 'ZWE', 2015, ..., 104.34050566619176,\n",
       "        25.71541516331892, 4.200906679464229],\n",
       "       ['Zimbabwe', 'ZWE', 2016, ..., 100.39228701198344,\n",
       "        25.64356952811576, 4.117173407020747],\n",
       "       ['Zimbabwe', 'ZWE', 2017, ..., 96.23599601926124,\n",
       "        26.166182363641425, 4.052494813246566]], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_pollution_death_rate_df.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = []\n",
    "for index, row in air_pollution_death_rate_df.iterrows():\n",
    "    temp_array = [row[0], row[2], row[3], row[4], row[5], row[6]]\n",
    "    all_data.append(temp_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Entity</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Annual CO₂ emissions (tonnes )</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1949</td>\n",
       "      <td>14656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1950</td>\n",
       "      <td>84272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1951</td>\n",
       "      <td>91600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1952</td>\n",
       "      <td>91600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1953</td>\n",
       "      <td>106256.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Entity Code  Year  Annual CO₂ emissions (tonnes )\n",
       "0  Afghanistan  AFG  1949                         14656.0\n",
       "1  Afghanistan  AFG  1950                         84272.0\n",
       "2  Afghanistan  AFG  1951                         91600.0\n",
       "3  Afghanistan  AFG  1952                         91600.0\n",
       "4  Afghanistan  AFG  1953                        106256.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "co2_years = co2_df1.groupby(['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "co2_years = co2_years.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "co2_years = co2_years.reset_index().rename(columns = {'Annual CO₂ emissions (tonnes )' : 'Annual Emissions'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# co2_years.to_csv(\"Resources/co2_by_year.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'flask'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_24236/597694825.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mflask\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mFlask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjsonify\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrender_template\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'flask'"
     ]
    }
   ],
   "source": [
    "from flask import Flask, jsonify, render_template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}