global_CO2_emissions_prep.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as npy\n",
    "import pandas as pds\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from matplotlib.gridspec import GridSpec\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install --upgrade pip\n",
    "# %pip install openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_headers=['ObjectId', 'Country', 'ISO2', 'ISO3', 'Indicator', 'Unit', 'Source',\n",
    "'CTS_Code', 'CTS_Name', 'CTS_Full_Descriptor', 'Industry', 'Scale',\n",
    "'1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002',\n",
    "'2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n",
    "'2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']\n",
    "       \n",
    "df = pds.read_csv('../dataset/global_CO2_emissions/global_CO2_emissions.csv')\n",
    "# Global CO2\n",
    "df_co2 = pds.read_csv('../dataset/global_CO2_emissions/Global Fossil Carbon Dioxide emissions by Year-https___www.worldometers.info_co2-emissions_co2-emissions-by-year.csv', index_col='Year') #CO2 emission\n",
    "# World GDP\n",
    "df_gdp = pds.read_csv('../dataset/global_CO2_emissions/World GDP 1960-2023-https___www.macrotrends.net_countries_WLD_world_gdp-gross-domestic-product.csv', index_col='Year') #GDP\n",
    "# pds.options.display.max_columns = None\n",
    "# pds.options.display.max_rows = None\n",
    "df.columns = column_headers\n",
    "\n",
    "# df2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ObjectId</th>\n",
       "      <th>Country</th>\n",
       "      <th>ISO2</th>\n",
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       "      <th>CTS_Code</th>\n",
       "      <th>CTS_Name</th>\n",
       "      <th>CTS_Full_Descriptor</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</th>\n",
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       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
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       "      <th>2018</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>AR</td>\n",
       "      <td>ARG</td>\n",
       "      <td>CO2 emissions</td>\n",
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       "      <td>CO2 Emissions</td>\n",
       "      <td>Environment, Climate Change, Economic Activity...</td>\n",
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       "      <td>0.377000</td>\n",
       "      <td>0.378000</td>\n",
       "      <td>0.369000</td>\n",
       "      <td>0.371000</td>\n",
       "      <td>0.303000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>AR</td>\n",
       "      <td>ARG</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>OECD (2021), OECD Inter-Country Input-Output D...</td>\n",
       "      <td>ECNC</td>\n",
       "      <td>CO2 Emissions</td>\n",
       "      <td>Environment, Climate Change, Economic Activity...</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>AR</td>\n",
       "      <td>ARG</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>OECD (2021), OECD Inter-Country Input-Output D...</td>\n",
       "      <td>ECNC</td>\n",
       "      <td>CO2 Emissions</td>\n",
       "      <td>Environment, Climate Change, Economic Activity...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.738000</td>\n",
       "      <td>0.765000</td>\n",
       "      <td>0.777000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.794000</td>\n",
       "      <td>0.852000</td>\n",
       "      <td>0.789000</td>\n",
       "      <td>0.846000</td>\n",
       "      <td>0.857000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>AR</td>\n",
       "      <td>ARG</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>OECD (2021), OECD Inter-Country Input-Output D...</td>\n",
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       "      <td>CO2 Emissions</td>\n",
       "      <td>Environment, Climate Change, Economic Activity...</td>\n",
       "      <td>...</td>\n",
       "      <td>13.804000</td>\n",
       "      <td>14.110000</td>\n",
       "      <td>14.616000</td>\n",
       "      <td>14.120000</td>\n",
       "      <td>15.358000</td>\n",
       "      <td>13.728000</td>\n",
       "      <td>14.273000</td>\n",
       "      <td>13.838000</td>\n",
       "      <td>14.121000</td>\n",
       "      <td>13.067000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>AR</td>\n",
       "      <td>ARG</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>OECD (2021), OECD Inter-Country Input-Output D...</td>\n",
       "      <td>ECNC</td>\n",
       "      <td>CO2 Emissions</td>\n",
       "      <td>Environment, Climate Change, Economic Activity...</td>\n",
       "      <td>...</td>\n",
       "      <td>2.648000</td>\n",
       "      <td>2.989000</td>\n",
       "      <td>3.094000</td>\n",
       "      <td>3.129000</td>\n",
       "      <td>3.327000</td>\n",
       "      <td>3.323000</td>\n",
       "      <td>3.345000</td>\n",
       "      <td>3.435000</td>\n",
       "      <td>3.564000</td>\n",
       "      <td>3.584000</td>\n",
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       "      <td>Vietnam</td>\n",
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       "      ObjectId    Country ISO2 ISO3                  Indicator  \\\n",
       "0            1  Argentina   AR  ARG              CO2 emissions   \n",
       "1            2  Argentina   AR  ARG              CO2 emissions   \n",
       "2            3  Argentina   AR  ARG              CO2 emissions   \n",
       "3            4  Argentina   AR  ARG              CO2 emissions   \n",
       "4            5  Argentina   AR  ARG              CO2 emissions   \n",
       "...        ...        ...  ...  ...                        ...   \n",
       "8905      8906    Vietnam   VN  VNM  CO2 emissions multipliers   \n",
       "8906      8907    Vietnam   VN  VNM  CO2 emissions multipliers   \n",
       "8907      8908    Vietnam   VN  VNM  CO2 emissions multipliers   \n",
       "8908      8909    Vietnam   VN  VNM  CO2 emissions multipliers   \n",
       "8909      8910    Vietnam   VN  VNM  CO2 emissions multipliers   \n",
       "\n",
       "                                                   Unit  \\\n",
       "0                        Millions of Metric tons of CO2   \n",
       "1                        Millions of Metric tons of CO2   \n",
       "2                        Millions of Metric tons of CO2   \n",
       "3                        Millions of Metric tons of CO2   \n",
       "4                        Millions of Metric tons of CO2   \n",
       "...                                                 ...   \n",
       "8905  Metric Tons of CO2 Emissions per $1million USD...   \n",
       "8906  Metric Tons of CO2 Emissions per $1million USD...   \n",
       "8907  Metric Tons of CO2 Emissions per $1million USD...   \n",
       "8908  Metric Tons of CO2 Emissions per $1million USD...   \n",
       "8909  Metric Tons of CO2 Emissions per $1million USD...   \n",
       "\n",
       "                                                 Source CTS_Code  \\\n",
       "0     OECD (2021), OECD Inter-Country Input-Output D...     ECNC   \n",
       "1     OECD (2021), OECD Inter-Country Input-Output D...     ECNC   \n",
       "2     OECD (2021), OECD Inter-Country Input-Output D...     ECNC   \n",
       "3     OECD (2021), OECD Inter-Country Input-Output D...     ECNC   \n",
       "4     OECD (2021), OECD Inter-Country Input-Output D...     ECNC   \n",
       "...                                                 ...      ...   \n",
       "8905  OECD (2021), OECD Inter-Country Input-Output D...     ECNM   \n",
       "8906  OECD (2021), OECD Inter-Country Input-Output D...     ECNM   \n",
       "8907  OECD (2021), OECD Inter-Country Input-Output D...     ECNM   \n",
       "8908  OECD (2021), OECD Inter-Country Input-Output D...     ECNM   \n",
       "8909  OECD (2021), OECD Inter-Country Input-Output D...     ECNM   \n",
       "\n",
       "                       CTS_Name  \\\n",
       "0                 CO2 Emissions   \n",
       "1                 CO2 Emissions   \n",
       "2                 CO2 Emissions   \n",
       "3                 CO2 Emissions   \n",
       "4                 CO2 Emissions   \n",
       "...                         ...   \n",
       "8905  CO2 Emissions Multipliers   \n",
       "8906  CO2 Emissions Multipliers   \n",
       "8907  CO2 Emissions Multipliers   \n",
       "8908  CO2 Emissions Multipliers   \n",
       "8909  CO2 Emissions Multipliers   \n",
       "\n",
       "                                    CTS_Full_Descriptor  ...         2009  \\\n",
       "0     Environment, Climate Change, Economic Activity...  ...     0.284000   \n",
       "1     Environment, Climate Change, Economic Activity...  ...     0.000000   \n",
       "2     Environment, Climate Change, Economic Activity...  ...     0.725000   \n",
       "3     Environment, Climate Change, Economic Activity...  ...    13.804000   \n",
       "4     Environment, Climate Change, Economic Activity...  ...     2.648000   \n",
       "...                                                 ...  ...          ...   \n",
       "8905  Environment, Climate Change, Economic Activity...  ...   571.409871   \n",
       "8906  Environment, Climate Change, Economic Activity...  ...   906.449860   \n",
       "8907  Environment, Climate Change, Economic Activity...  ...  3807.684853   \n",
       "8908  Environment, Climate Change, Economic Activity...  ...   449.066931   \n",
       "8909  Environment, Climate Change, Economic Activity...  ...   564.308833   \n",
       "\n",
       "             2010         2011         2012         2013         2014  \\\n",
       "0        0.319000     0.338000     0.377000     0.378000     0.369000   \n",
       "1        0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "2        0.738000     0.765000     0.777000     0.840000     0.794000   \n",
       "3       14.110000    14.616000    14.120000    15.358000    13.728000   \n",
       "4        2.989000     3.094000     3.129000     3.327000     3.323000   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "8905   460.836156   347.350194   299.661077   282.529754   297.871935   \n",
       "8906   889.629032   725.459644   659.111915   573.819271   585.155874   \n",
       "8907  3020.134010  2253.246177  1917.872096  1908.984147  2093.765490   \n",
       "8908   440.446520   378.152049   357.297470   350.369327   377.126416   \n",
       "8909   558.348722   446.045111   389.419367   335.815256   340.664071   \n",
       "\n",
       "             2015         2016         2017         2018  \n",
       "0        0.371000     0.303000     0.358000     0.344000  \n",
       "1        0.000000     0.000000     0.000000     0.000000  \n",
       "2        0.852000     0.789000     0.846000     0.857000  \n",
       "3       14.273000    13.838000    14.121000    13.067000  \n",
       "4        3.345000     3.435000     3.564000     3.584000  \n",
       "...           ...          ...          ...          ...  \n",
       "8905   365.437022   333.755635   303.371701   326.056753  \n",
       "8906   693.572818   702.200536   613.915080   694.331871  \n",
       "8907  2982.160528  3974.738515  3664.875652  3295.556416  \n",
       "8908   479.038527   506.595843   451.921218   508.214053  \n",
       "8909   570.908659   460.042792   398.818037   441.426688  \n",
       "\n",
       "[8910 rows x 36 columns]"
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     },
     "execution_count": 4,
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       "        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>ObjectId</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</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",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "      <td>8910.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4455.500000</td>\n",
       "      <td>642.690767</td>\n",
       "      <td>626.247949</td>\n",
       "      <td>597.889064</td>\n",
       "      <td>634.523417</td>\n",
       "      <td>628.532695</td>\n",
       "      <td>584.815129</td>\n",
       "      <td>581.440616</td>\n",
       "      <td>545.903132</td>\n",
       "      <td>490.212486</td>\n",
       "      <td>...</td>\n",
       "      <td>277.552219</td>\n",
       "      <td>255.599049</td>\n",
       "      <td>228.419131</td>\n",
       "      <td>226.670976</td>\n",
       "      <td>219.907381</td>\n",
       "      <td>217.131214</td>\n",
       "      <td>254.947335</td>\n",
       "      <td>300.596451</td>\n",
       "      <td>256.180262</td>\n",
       "      <td>224.472003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2572.239783</td>\n",
       "      <td>4630.754009</td>\n",
       "      <td>4370.387146</td>\n",
       "      <td>3440.589143</td>\n",
       "      <td>3472.768617</td>\n",
       "      <td>3072.488297</td>\n",
       "      <td>2403.487481</td>\n",
       "      <td>2405.330851</td>\n",
       "      <td>2168.233614</td>\n",
       "      <td>2003.781611</td>\n",
       "      <td>...</td>\n",
       "      <td>1073.148968</td>\n",
       "      <td>967.957555</td>\n",
       "      <td>885.294193</td>\n",
       "      <td>913.127550</td>\n",
       "      <td>911.191397</td>\n",
       "      <td>800.634281</td>\n",
       "      <td>1154.516988</td>\n",
       "      <td>3413.770262</td>\n",
       "      <td>1516.822242</td>\n",
       "      <td>793.934788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2228.250000</td>\n",
       "      <td>0.551750</td>\n",
       "      <td>0.579000</td>\n",
       "      <td>0.574250</td>\n",
       "      <td>0.583250</td>\n",
       "      <td>0.606250</td>\n",
       "      <td>0.672000</td>\n",
       "      <td>0.677000</td>\n",
       "      <td>0.702750</td>\n",
       "      <td>0.747000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.831250</td>\n",
       "      <td>0.864750</td>\n",
       "      <td>0.870250</td>\n",
       "      <td>0.895000</td>\n",
       "      <td>0.906250</td>\n",
       "      <td>0.889250</td>\n",
       "      <td>0.905000</td>\n",
       "      <td>0.897750</td>\n",
       "      <td>0.962250</td>\n",
       "      <td>0.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4455.500000</td>\n",
       "      <td>51.483010</td>\n",
       "      <td>51.680268</td>\n",
       "      <td>52.319637</td>\n",
       "      <td>54.133453</td>\n",
       "      <td>54.645225</td>\n",
       "      <td>57.685882</td>\n",
       "      <td>59.304477</td>\n",
       "      <td>56.832295</td>\n",
       "      <td>50.775041</td>\n",
       "      <td>...</td>\n",
       "      <td>31.513547</td>\n",
       "      <td>29.964705</td>\n",
       "      <td>26.775893</td>\n",
       "      <td>27.436395</td>\n",
       "      <td>26.485056</td>\n",
       "      <td>25.452176</td>\n",
       "      <td>27.371744</td>\n",
       "      <td>27.459369</td>\n",
       "      <td>26.590652</td>\n",
       "      <td>25.698487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6682.750000</td>\n",
       "      <td>342.525963</td>\n",
       "      <td>339.773235</td>\n",
       "      <td>349.418155</td>\n",
       "      <td>358.978451</td>\n",
       "      <td>357.182242</td>\n",
       "      <td>362.076765</td>\n",
       "      <td>363.397858</td>\n",
       "      <td>340.890874</td>\n",
       "      <td>302.102272</td>\n",
       "      <td>...</td>\n",
       "      <td>181.621510</td>\n",
       "      <td>170.145047</td>\n",
       "      <td>149.792878</td>\n",
       "      <td>150.786022</td>\n",
       "      <td>146.083990</td>\n",
       "      <td>148.533203</td>\n",
       "      <td>167.667274</td>\n",
       "      <td>167.091640</td>\n",
       "      <td>158.101021</td>\n",
       "      <td>150.373212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8910.000000</td>\n",
       "      <td>268619.614800</td>\n",
       "      <td>244816.005900</td>\n",
       "      <td>180811.599800</td>\n",
       "      <td>172174.706800</td>\n",
       "      <td>131047.244200</td>\n",
       "      <td>82288.011300</td>\n",
       "      <td>84204.541120</td>\n",
       "      <td>70922.998900</td>\n",
       "      <td>63207.730870</td>\n",
       "      <td>...</td>\n",
       "      <td>29960.060010</td>\n",
       "      <td>28251.186870</td>\n",
       "      <td>29678.748310</td>\n",
       "      <td>29089.169930</td>\n",
       "      <td>32033.948990</td>\n",
       "      <td>17089.568230</td>\n",
       "      <td>53393.751770</td>\n",
       "      <td>220076.705300</td>\n",
       "      <td>85065.783690</td>\n",
       "      <td>20139.395610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ObjectId           1995           1996           1997  \\\n",
       "count  8910.000000    8910.000000    8910.000000    8910.000000   \n",
       "mean   4455.500000     642.690767     626.247949     597.889064   \n",
       "std    2572.239783    4630.754009    4370.387146    3440.589143   \n",
       "min       1.000000       0.000000       0.000000       0.000000   \n",
       "25%    2228.250000       0.551750       0.579000       0.574250   \n",
       "50%    4455.500000      51.483010      51.680268      52.319637   \n",
       "75%    6682.750000     342.525963     339.773235     349.418155   \n",
       "max    8910.000000  268619.614800  244816.005900  180811.599800   \n",
       "\n",
       "                1998           1999          2000          2001          2002  \\\n",
       "count    8910.000000    8910.000000   8910.000000   8910.000000   8910.000000   \n",
       "mean      634.523417     628.532695    584.815129    581.440616    545.903132   \n",
       "std      3472.768617    3072.488297   2403.487481   2405.330851   2168.233614   \n",
       "min         0.000000       0.000000      0.000000      0.000000      0.000000   \n",
       "25%         0.583250       0.606250      0.672000      0.677000      0.702750   \n",
       "50%        54.133453      54.645225     57.685882     59.304477     56.832295   \n",
       "75%       358.978451     357.182242    362.076765    363.397858    340.890874   \n",
       "max    172174.706800  131047.244200  82288.011300  84204.541120  70922.998900   \n",
       "\n",
       "               2003  ...          2009          2010          2011  \\\n",
       "count   8910.000000  ...   8910.000000   8910.000000   8910.000000   \n",
       "mean     490.212486  ...    277.552219    255.599049    228.419131   \n",
       "std     2003.781611  ...   1073.148968    967.957555    885.294193   \n",
       "min        0.000000  ...      0.000000      0.000000      0.000000   \n",
       "25%        0.747000  ...      0.831250      0.864750      0.870250   \n",
       "50%       50.775041  ...     31.513547     29.964705     26.775893   \n",
       "75%      302.102272  ...    181.621510    170.145047    149.792878   \n",
       "max    63207.730870  ...  29960.060010  28251.186870  29678.748310   \n",
       "\n",
       "               2012          2013          2014          2015           2016  \\\n",
       "count   8910.000000   8910.000000   8910.000000   8910.000000    8910.000000   \n",
       "mean     226.670976    219.907381    217.131214    254.947335     300.596451   \n",
       "std      913.127550    911.191397    800.634281   1154.516988    3413.770262   \n",
       "min        0.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "25%        0.895000      0.906250      0.889250      0.905000       0.897750   \n",
       "50%       27.436395     26.485056     25.452176     27.371744      27.459369   \n",
       "75%      150.786022    146.083990    148.533203    167.667274     167.091640   \n",
       "max    29089.169930  32033.948990  17089.568230  53393.751770  220076.705300   \n",
       "\n",
       "               2017          2018  \n",
       "count   8910.000000   8910.000000  \n",
       "mean     256.180262    224.472003  \n",
       "std     1516.822242    793.934788  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.962250      0.975000  \n",
       "50%       26.590652     25.698487  \n",
       "75%      158.101021    150.373212  \n",
       "max    85065.783690  20139.395610  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Fossil CO2 Emissions (tons)</th>\n",
       "      <th>CO2 emisions change</th>\n",
       "      <th>CO2 emissions per capita</th>\n",
       "      <th>Population</th>\n",
       "      <th>Pop. Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>15,679,743,000</td>\n",
       "      <td>-0.50%</td>\n",
       "      <td>4.15</td>\n",
       "      <td>3,775,759,617</td>\n",
       "      <td>2.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>16,464,900,000</td>\n",
       "      <td>5.01%</td>\n",
       "      <td>4.27</td>\n",
       "      <td>3,851,650,245</td>\n",
       "      <td>2.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>17,441,791,000</td>\n",
       "      <td>5.93%</td>\n",
       "      <td>4.44</td>\n",
       "      <td>3,927,780,238</td>\n",
       "      <td>1.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>17,396,363,000</td>\n",
       "      <td>-0.26%</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4,003,794,172</td>\n",
       "      <td>1.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>17,346,512,000</td>\n",
       "      <td>-0.29%</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4,079,480,606</td>\n",
       "      <td>1.89%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>18,329,016,000</td>\n",
       "      <td>5.66%</td>\n",
       "      <td>4.41</td>\n",
       "      <td>4,154,666,864</td>\n",
       "      <td>1.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>18,874,320,000</td>\n",
       "      <td>2.98%</td>\n",
       "      <td>4.46</td>\n",
       "      <td>4,229,506,060</td>\n",
       "      <td>1.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>19,477,371,000</td>\n",
       "      <td>3.20%</td>\n",
       "      <td>4.52</td>\n",
       "      <td>4,304,533,501</td>\n",
       "      <td>1.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>20,031,918,000</td>\n",
       "      <td>2.85%</td>\n",
       "      <td>4.57</td>\n",
       "      <td>4,380,506,100</td>\n",
       "      <td>1.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>19,803,842,000</td>\n",
       "      <td>-1.14%</td>\n",
       "      <td>4.44</td>\n",
       "      <td>4,458,003,514</td>\n",
       "      <td>1.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>19,439,731,000</td>\n",
       "      <td>-1.84%</td>\n",
       "      <td>4.28</td>\n",
       "      <td>4,536,996,762</td>\n",
       "      <td>1.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>19,184,207,000</td>\n",
       "      <td>-1.31%</td>\n",
       "      <td>4.15</td>\n",
       "      <td>4,617,386,542</td>\n",
       "      <td>1.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>19,301,703,000</td>\n",
       "      <td>0.61%</td>\n",
       "      <td>4.11</td>\n",
       "      <td>4,699,569,304</td>\n",
       "      <td>1.78%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>19,904,653,000</td>\n",
       "      <td>3.12%</td>\n",
       "      <td>4.16</td>\n",
       "      <td>4,784,011,621</td>\n",
       "      <td>1.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>20,176,693,000</td>\n",
       "      <td>1.37%</td>\n",
       "      <td>4.14</td>\n",
       "      <td>4,870,921,740</td>\n",
       "      <td>1.82%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>20,530,265,000</td>\n",
       "      <td>1.75%</td>\n",
       "      <td>4.14</td>\n",
       "      <td>4,960,567,912</td>\n",
       "      <td>1.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>21,195,418,000</td>\n",
       "      <td>3.24%</td>\n",
       "      <td>4.20</td>\n",
       "      <td>5,052,522,147</td>\n",
       "      <td>1.85%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>21,945,689,000</td>\n",
       "      <td>3.54%</td>\n",
       "      <td>4.27</td>\n",
       "      <td>5,145,426,008</td>\n",
       "      <td>1.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>22,341,747,000</td>\n",
       "      <td>1.80%</td>\n",
       "      <td>4.27</td>\n",
       "      <td>5,237,441,558</td>\n",
       "      <td>1.79%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>22,450,442,000</td>\n",
       "      <td>0.49%</td>\n",
       "      <td>4.21</td>\n",
       "      <td>5,327,231,061</td>\n",
       "      <td>1.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>22,591,041,000</td>\n",
       "      <td>0.63%</td>\n",
       "      <td>4.17</td>\n",
       "      <td>5,414,289,444</td>\n",
       "      <td>1.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>22,488,598,000</td>\n",
       "      <td>-0.45%</td>\n",
       "      <td>4.09</td>\n",
       "      <td>5,498,919,809</td>\n",
       "      <td>1.56%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>22,664,346,000</td>\n",
       "      <td>0.78%</td>\n",
       "      <td>4.06</td>\n",
       "      <td>5,581,597,546</td>\n",
       "      <td>1.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>22,898,963,000</td>\n",
       "      <td>1.04%</td>\n",
       "      <td>4.04</td>\n",
       "      <td>5,663,150,427</td>\n",
       "      <td>1.46%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>23,619,144,000</td>\n",
       "      <td>3.15%</td>\n",
       "      <td>4.11</td>\n",
       "      <td>5,744,212,979</td>\n",
       "      <td>1.43%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>24,075,938,000</td>\n",
       "      <td>1.93%</td>\n",
       "      <td>4.13</td>\n",
       "      <td>5,824,891,951</td>\n",
       "      <td>1.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>24,398,282,000</td>\n",
       "      <td>1.34%</td>\n",
       "      <td>4.13</td>\n",
       "      <td>5,905,045,788</td>\n",
       "      <td>1.38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>24,541,647,000</td>\n",
       "      <td>0.59%</td>\n",
       "      <td>4.10</td>\n",
       "      <td>5,984,793,942</td>\n",
       "      <td>1.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>24,733,708,000</td>\n",
       "      <td>0.78%</td>\n",
       "      <td>4.08</td>\n",
       "      <td>6,064,239,055</td>\n",
       "      <td>1.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>25,593,733,000</td>\n",
       "      <td>3.48%</td>\n",
       "      <td>4.17</td>\n",
       "      <td>6,143,493,823</td>\n",
       "      <td>1.31%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>25,877,903,000</td>\n",
       "      <td>1.11%</td>\n",
       "      <td>4.16</td>\n",
       "      <td>6,222,626,606</td>\n",
       "      <td>1.29%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>26,350,081,000</td>\n",
       "      <td>1.82%</td>\n",
       "      <td>4.18</td>\n",
       "      <td>6,301,773,188</td>\n",
       "      <td>1.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>27,513,926,000</td>\n",
       "      <td>4.42%</td>\n",
       "      <td>4.31</td>\n",
       "      <td>6,381,185,114</td>\n",
       "      <td>1.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>28,729,309,000</td>\n",
       "      <td>4.42%</td>\n",
       "      <td>4.45</td>\n",
       "      <td>6,461,159,389</td>\n",
       "      <td>1.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>29,769,008,000</td>\n",
       "      <td>3.62%</td>\n",
       "      <td>4.55</td>\n",
       "      <td>6,541,907,027</td>\n",
       "      <td>1.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>30,756,174,000</td>\n",
       "      <td>3.32%</td>\n",
       "      <td>4.64</td>\n",
       "      <td>6,623,517,833</td>\n",
       "      <td>1.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>31,916,504,000</td>\n",
       "      <td>3.77%</td>\n",
       "      <td>4.76</td>\n",
       "      <td>6,705,946,610</td>\n",
       "      <td>1.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>32,124,428,000</td>\n",
       "      <td>0.65%</td>\n",
       "      <td>4.73</td>\n",
       "      <td>6,789,088,686</td>\n",
       "      <td>1.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>31,770,516,000</td>\n",
       "      <td>-1.10%</td>\n",
       "      <td>4.62</td>\n",
       "      <td>6,872,767,093</td>\n",
       "      <td>1.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>33,587,787,000</td>\n",
       "      <td>5.72%</td>\n",
       "      <td>4.83</td>\n",
       "      <td>6,956,823,603</td>\n",
       "      <td>1.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>34,578,390,000</td>\n",
       "      <td>2.95%</td>\n",
       "      <td>4.91</td>\n",
       "      <td>7,041,194,301</td>\n",
       "      <td>1.21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>34,790,564,000</td>\n",
       "      <td>0.61%</td>\n",
       "      <td>4.88</td>\n",
       "      <td>7,125,828,059</td>\n",
       "      <td>1.20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>35,416,599,000</td>\n",
       "      <td>1.80%</td>\n",
       "      <td>4.91</td>\n",
       "      <td>7,210,581,976</td>\n",
       "      <td>1.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>35,686,780,000</td>\n",
       "      <td>0.76%</td>\n",
       "      <td>4.89</td>\n",
       "      <td>7,295,290,765</td>\n",
       "      <td>1.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>35,631,078,000</td>\n",
       "      <td>-0.16%</td>\n",
       "      <td>4.83</td>\n",
       "      <td>7,379,797,139</td>\n",
       "      <td>1.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>35,753,305,000</td>\n",
       "      <td>0.34%</td>\n",
       "      <td>4.79</td>\n",
       "      <td>7,464,022,049</td>\n",
       "      <td>1.14%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fossil CO2 Emissions (tons) CO2 emisions change  \\\n",
       "Year                                                   \n",
       "1971              15,679,743,000              -0.50%   \n",
       "1972              16,464,900,000               5.01%   \n",
       "1973              17,441,791,000               5.93%   \n",
       "1974              17,396,363,000              -0.26%   \n",
       "1975              17,346,512,000              -0.29%   \n",
       "1976              18,329,016,000               5.66%   \n",
       "1977              18,874,320,000               2.98%   \n",
       "1978              19,477,371,000               3.20%   \n",
       "1979              20,031,918,000               2.85%   \n",
       "1980              19,803,842,000              -1.14%   \n",
       "1981              19,439,731,000              -1.84%   \n",
       "1982              19,184,207,000              -1.31%   \n",
       "1983              19,301,703,000               0.61%   \n",
       "1984              19,904,653,000               3.12%   \n",
       "1985              20,176,693,000               1.37%   \n",
       "1986              20,530,265,000               1.75%   \n",
       "1987              21,195,418,000               3.24%   \n",
       "1988              21,945,689,000               3.54%   \n",
       "1989              22,341,747,000               1.80%   \n",
       "1990              22,450,442,000               0.49%   \n",
       "1991              22,591,041,000               0.63%   \n",
       "1992              22,488,598,000              -0.45%   \n",
       "1993              22,664,346,000               0.78%   \n",
       "1994              22,898,963,000               1.04%   \n",
       "1995              23,619,144,000               3.15%   \n",
       "1996              24,075,938,000               1.93%   \n",
       "1997              24,398,282,000               1.34%   \n",
       "1998              24,541,647,000               0.59%   \n",
       "1999              24,733,708,000               0.78%   \n",
       "2000              25,593,733,000               3.48%   \n",
       "2001              25,877,903,000               1.11%   \n",
       "2002              26,350,081,000               1.82%   \n",
       "2003              27,513,926,000               4.42%   \n",
       "2004              28,729,309,000               4.42%   \n",
       "2005              29,769,008,000               3.62%   \n",
       "2006              30,756,174,000               3.32%   \n",
       "2007              31,916,504,000               3.77%   \n",
       "2008              32,124,428,000               0.65%   \n",
       "2009              31,770,516,000              -1.10%   \n",
       "2010              33,587,787,000               5.72%   \n",
       "2011              34,578,390,000               2.95%   \n",
       "2012              34,790,564,000               0.61%   \n",
       "2013              35,416,599,000               1.80%   \n",
       "2014              35,686,780,000               0.76%   \n",
       "2015              35,631,078,000              -0.16%   \n",
       "2016              35,753,305,000               0.34%   \n",
       "\n",
       "      CO2 emissions per capita     Population Pop. Change  \n",
       "Year                                                       \n",
       "1971                      4.15  3,775,759,617       2.04%  \n",
       "1972                      4.27  3,851,650,245       2.01%  \n",
       "1973                      4.44  3,927,780,238       1.98%  \n",
       "1974                      4.34  4,003,794,172       1.94%  \n",
       "1975                      4.25  4,079,480,606       1.89%  \n",
       "1976                      4.41  4,154,666,864       1.84%  \n",
       "1977                      4.46  4,229,506,060       1.80%  \n",
       "1978                      4.52  4,304,533,501       1.77%  \n",
       "1979                      4.57  4,380,506,100       1.76%  \n",
       "1980                      4.44  4,458,003,514       1.77%  \n",
       "1981                      4.28  4,536,996,762       1.77%  \n",
       "1982                      4.15  4,617,386,542       1.77%  \n",
       "1983                      4.11  4,699,569,304       1.78%  \n",
       "1984                      4.16  4,784,011,621       1.80%  \n",
       "1985                      4.14  4,870,921,740       1.82%  \n",
       "1986                      4.14  4,960,567,912       1.84%  \n",
       "1987                      4.20  5,052,522,147       1.85%  \n",
       "1988                      4.27  5,145,426,008       1.84%  \n",
       "1989                      4.27  5,237,441,558       1.79%  \n",
       "1990                      4.21  5,327,231,061       1.71%  \n",
       "1991                      4.17  5,414,289,444       1.63%  \n",
       "1992                      4.09  5,498,919,809       1.56%  \n",
       "1993                      4.06  5,581,597,546       1.50%  \n",
       "1994                      4.04  5,663,150,427       1.46%  \n",
       "1995                      4.11  5,744,212,979       1.43%  \n",
       "1996                      4.13  5,824,891,951       1.40%  \n",
       "1997                      4.13  5,905,045,788       1.38%  \n",
       "1998                      4.10  5,984,793,942       1.35%  \n",
       "1999                      4.08  6,064,239,055       1.33%  \n",
       "2000                      4.17  6,143,493,823       1.31%  \n",
       "2001                      4.16  6,222,626,606       1.29%  \n",
       "2002                      4.18  6,301,773,188       1.27%  \n",
       "2003                      4.31  6,381,185,114       1.26%  \n",
       "2004                      4.45  6,461,159,389       1.25%  \n",
       "2005                      4.55  6,541,907,027       1.25%  \n",
       "2006                      4.64  6,623,517,833       1.25%  \n",
       "2007                      4.76  6,705,946,610       1.24%  \n",
       "2008                      4.73  6,789,088,686       1.24%  \n",
       "2009                      4.62  6,872,767,093       1.23%  \n",
       "2010                      4.83  6,956,823,603       1.22%  \n",
       "2011                      4.91  7,041,194,301       1.21%  \n",
       "2012                      4.88  7,125,828,059       1.20%  \n",
       "2013                      4.91  7,210,581,976       1.19%  \n",
       "2014                      4.89  7,295,290,765       1.17%  \n",
       "2015                      4.83  7,379,797,139       1.16%  \n",
       "2016                      4.79  7,464,022,049       1.14%  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_co2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>GDP (Billion US Dollars)</th>\n",
       "      <th>Per Capita (in US Dollars)</th>\n",
       "      <th>Percentage Growth</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>1960</th>\n",
       "      <td>1,392.28</td>\n",
       "      <td>459</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>1,448.63</td>\n",
       "      <td>471</td>\n",
       "      <td>0.0379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>1,550.55</td>\n",
       "      <td>496</td>\n",
       "      <td>0.0532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>1,671.61</td>\n",
       "      <td>523</td>\n",
       "      <td>0.0519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>1,830.29</td>\n",
       "      <td>561</td>\n",
       "      <td>0.0656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>81,400.67</td>\n",
       "      <td>10,741</td>\n",
       "      <td>0.0339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>86,456.89</td>\n",
       "      <td>11,284</td>\n",
       "      <td>0.0329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>87,645.26</td>\n",
       "      <td>11,320</td>\n",
       "      <td>0.0259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>85,105.60</td>\n",
       "      <td>10,882</td>\n",
       "      <td>-0.0312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>96,513.08</td>\n",
       "      <td>12,235</td>\n",
       "      <td>0.0587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GDP (Billion US Dollars) Per Capita (in US Dollars)  Percentage Growth \n",
       "Year                                                                        \n",
       "1960                 1,392.28                        459                 NaN\n",
       "1961                 1,448.63                        471              0.0379\n",
       "1962                 1,550.55                        496              0.0532\n",
       "1963                 1,671.61                        523              0.0519\n",
       "1964                 1,830.29                        561              0.0656\n",
       "...                       ...                        ...                 ...\n",
       "2017                81,400.67                     10,741              0.0339\n",
       "2018                86,456.89                     11,284              0.0329\n",
       "2019                87,645.26                     11,320              0.0259\n",
       "2020                85,105.60                     10,882             -0.0312\n",
       "2021                96,513.08                     12,235              0.0587\n",
       "\n",
       "[62 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gdp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Main dataset:  (8910, 36)\n",
      "Global CO2 dataset:  (46, 5)\n",
      "World GDP dataset:  (62, 3)\n"
     ]
    }
   ],
   "source": [
    "print('Main dataset: ',df.shape)\n",
    "print('Global CO2 dataset: ', df_co2.shape)\n",
    "print('World GDP dataset: ', df_gdp.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Argentina', 'Australia', 'Austria', 'Belgium', 'Brazil',\n",
       "       'Brunei Darussalam', 'Bulgaria', 'Cambodia', 'Canada', 'Chile',\n",
       "       'China, P.R.: Hong Kong', 'China, P.R.: Mainland', 'Colombia',\n",
       "       'Costa Rica', 'Croatia, Rep. of', 'Cyprus', 'Czech Rep.',\n",
       "       'Denmark', 'Estonia, Rep. of', 'Finland', 'France', 'Germany',\n",
       "       'Greece', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Ireland',\n",
       "       'Israel', 'Italy', 'Japan', 'Kazakhstan, Rep. of',\n",
       "       'Korea, Rep. of', \"Lao People's Dem. Rep.\", 'Latvia', 'Lithuania',\n",
       "       'Luxembourg', 'Malaysia', 'Malta', 'Mexico', 'Morocco', 'Myanmar',\n",
       "       'Netherlands, The', 'New Zealand', 'Norway', 'Peru', 'Philippines',\n",
       "       'Poland, Rep. of', 'Portugal', 'Romania', 'Russian Federation',\n",
       "       'Saudi Arabia', 'Singapore', 'Slovak Rep.', 'Slovenia, Rep. of',\n",
       "       'South Africa', 'Spain', 'Sweden', 'Switzerland',\n",
       "       'Taiwan Province of China', 'Thailand', 'Tunisia', 'Turkey',\n",
       "       'United Kingdom', 'United States', 'Vietnam'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Country.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The length of column Industry:  45\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Accomodation and food services',\n",
       "       'Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use',\n",
       "       'Administrative and support services',\n",
       "       'Agriculture, hunting, forestry', 'Air transport',\n",
       "       'Arts, entertainment and recreation', 'Basic metals',\n",
       "       'Chemicals and pharmaceutical products',\n",
       "       'Coke and refined petroleum products',\n",
       "       'Computer, electronic and optical products', 'Construction',\n",
       "       'Education', 'Electrical equipment',\n",
       "       'Electricity, gas, steam and air conditioning supply',\n",
       "       'Fabricated metal products', 'Financial and insurance activities',\n",
       "       'Fishing and aquaculture', 'Food products, beverages and tobacco',\n",
       "       'Human health and social work activities',\n",
       "       'IT and other information services',\n",
       "       'Land transport and transport via pipelines',\n",
       "       'Machinery and equipment n.e.c.',\n",
       "       'Manufacturing nec; repair and installation of machinery and equipment',\n",
       "       'Mining and quarrying, energy producing products',\n",
       "       'Mining and quarrying, non-energy producing products',\n",
       "       'Mining support service activities',\n",
       "       'Motor vehicles, trailers and semi-trailers',\n",
       "       'Other non-metallic mineral products', 'Other service activities',\n",
       "       'Other transport equipment', 'Paper products and printing',\n",
       "       'Pharmaceuticals, medicinal chemical and botanical products',\n",
       "       'Postal and courier activities',\n",
       "       'Professional, scientific and technical activities',\n",
       "       'Public administration and defence; compulsory social security',\n",
       "       'Publishing, audiovisual and broadcasting activities',\n",
       "       'Real estate activities', 'Rubber and plastics products',\n",
       "       'Telecommunications',\n",
       "       'Textiles, textile products, leather and footwear',\n",
       "       'Warehousing and support activities for transportation',\n",
       "       'Water supply; sewerage, waste management and remediation activities',\n",
       "       'Water transport',\n",
       "       'Wholesale and retail trade; repair of motor vehicles',\n",
       "       'Wood and products of wood and cork'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('The length of column Industry: ', len(df.Industry.unique()))\n",
    "df.Industry.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectId               0\n",
       "Country                0\n",
       "ISO2                   0\n",
       "ISO3                   0\n",
       "Indicator              0\n",
       "Unit                   0\n",
       "Source                 0\n",
       "CTS_Code               0\n",
       "CTS_Name               0\n",
       "CTS_Full_Descriptor    0\n",
       "Industry               0\n",
       "Scale                  0\n",
       "1995                   0\n",
       "1996                   0\n",
       "1997                   0\n",
       "1998                   0\n",
       "1999                   0\n",
       "2000                   0\n",
       "2001                   0\n",
       "2002                   0\n",
       "2003                   0\n",
       "2004                   0\n",
       "2005                   0\n",
       "2006                   0\n",
       "2007                   0\n",
       "2008                   0\n",
       "2009                   0\n",
       "2010                   0\n",
       "2011                   0\n",
       "2012                   0\n",
       "2013                   0\n",
       "2014                   0\n",
       "2015                   0\n",
       "2016                   0\n",
       "2017                   0\n",
       "2018                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8910 entries, 0 to 8909\n",
      "Data columns (total 36 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   ObjectId             8910 non-null   int64  \n",
      " 1   Country              8910 non-null   object \n",
      " 2   ISO2                 8910 non-null   object \n",
      " 3   ISO3                 8910 non-null   object \n",
      " 4   Indicator            8910 non-null   object \n",
      " 5   Unit                 8910 non-null   object \n",
      " 6   Source               8910 non-null   object \n",
      " 7   CTS_Code             8910 non-null   object \n",
      " 8   CTS_Name             8910 non-null   object \n",
      " 9   CTS_Full_Descriptor  8910 non-null   object \n",
      " 10  Industry             8910 non-null   object \n",
      " 11  Scale                8910 non-null   object \n",
      " 12  1995                 8910 non-null   float64\n",
      " 13  1996                 8910 non-null   float64\n",
      " 14  1997                 8910 non-null   float64\n",
      " 15  1998                 8910 non-null   float64\n",
      " 16  1999                 8910 non-null   float64\n",
      " 17  2000                 8910 non-null   float64\n",
      " 18  2001                 8910 non-null   float64\n",
      " 19  2002                 8910 non-null   float64\n",
      " 20  2003                 8910 non-null   float64\n",
      " 21  2004                 8910 non-null   float64\n",
      " 22  2005                 8910 non-null   float64\n",
      " 23  2006                 8910 non-null   float64\n",
      " 24  2007                 8910 non-null   float64\n",
      " 25  2008                 8910 non-null   float64\n",
      " 26  2009                 8910 non-null   float64\n",
      " 27  2010                 8910 non-null   float64\n",
      " 28  2011                 8910 non-null   float64\n",
      " 29  2012                 8910 non-null   float64\n",
      " 30  2013                 8910 non-null   float64\n",
      " 31  2014                 8910 non-null   float64\n",
      " 32  2015                 8910 non-null   float64\n",
      " 33  2016                 8910 non-null   float64\n",
      " 34  2017                 8910 non-null   float64\n",
      " 35  2018                 8910 non-null   float64\n",
      "dtypes: float64(24), int64(1), object(11)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values on df but on df_co2 and df_gdp\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>ObjectId</th>\n",
       "      <th>Country</th>\n",
       "      <th>ISO2</th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Source</th>\n",
       "      <th>CTS_Code</th>\n",
       "      <th>CTS_Name</th>\n",
       "      <th>CTS_Full_Descriptor</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</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",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>AR</td>\n",
       "      <td>ARG</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>OECD (2021), OECD Inter-Country Input-Output D...</td>\n",
       "      <td>ECNC</td>\n",
       "      <td>CO2 Emissions</td>\n",
       "      <td>Environment, Climate Change, Economic Activity...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.319</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.377</td>\n",
       "      <td>0.378</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>AR</td>\n",
       "      <td>ARG</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>OECD (2021), OECD Inter-Country Input-Output D...</td>\n",
       "      <td>ECNC</td>\n",
       "      <td>CO2 Emissions</td>\n",
       "      <td>Environment, Climate Change, Economic Activity...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>AR</td>\n",
       "      <td>ARG</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>OECD (2021), OECD Inter-Country Input-Output D...</td>\n",
       "      <td>ECNC</td>\n",
       "      <td>CO2 Emissions</td>\n",
       "      <td>Environment, Climate Change, Economic Activity...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.738</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.846</td>\n",
       "      <td>0.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>AR</td>\n",
       "      <td>ARG</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>OECD (2021), OECD Inter-Country Input-Output D...</td>\n",
       "      <td>ECNC</td>\n",
       "      <td>CO2 Emissions</td>\n",
       "      <td>Environment, Climate Change, Economic Activity...</td>\n",
       "      <td>...</td>\n",
       "      <td>13.804</td>\n",
       "      <td>14.110</td>\n",
       "      <td>14.616</td>\n",
       "      <td>14.120</td>\n",
       "      <td>15.358</td>\n",
       "      <td>13.728</td>\n",
       "      <td>14.273</td>\n",
       "      <td>13.838</td>\n",
       "      <td>14.121</td>\n",
       "      <td>13.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>AR</td>\n",
       "      <td>ARG</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>OECD (2021), OECD Inter-Country Input-Output D...</td>\n",
       "      <td>ECNC</td>\n",
       "      <td>CO2 Emissions</td>\n",
       "      <td>Environment, Climate Change, Economic Activity...</td>\n",
       "      <td>...</td>\n",
       "      <td>2.648</td>\n",
       "      <td>2.989</td>\n",
       "      <td>3.094</td>\n",
       "      <td>3.129</td>\n",
       "      <td>3.327</td>\n",
       "      <td>3.323</td>\n",
       "      <td>3.345</td>\n",
       "      <td>3.435</td>\n",
       "      <td>3.564</td>\n",
       "      <td>3.584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ObjectId    Country ISO2 ISO3      Indicator  \\\n",
       "0         1  Argentina   AR  ARG  CO2 emissions   \n",
       "1         2  Argentina   AR  ARG  CO2 emissions   \n",
       "2         3  Argentina   AR  ARG  CO2 emissions   \n",
       "3         4  Argentina   AR  ARG  CO2 emissions   \n",
       "4         5  Argentina   AR  ARG  CO2 emissions   \n",
       "\n",
       "                             Unit  \\\n",
       "0  Millions of Metric tons of CO2   \n",
       "1  Millions of Metric tons of CO2   \n",
       "2  Millions of Metric tons of CO2   \n",
       "3  Millions of Metric tons of CO2   \n",
       "4  Millions of Metric tons of CO2   \n",
       "\n",
       "                                              Source CTS_Code       CTS_Name  \\\n",
       "0  OECD (2021), OECD Inter-Country Input-Output D...     ECNC  CO2 Emissions   \n",
       "1  OECD (2021), OECD Inter-Country Input-Output D...     ECNC  CO2 Emissions   \n",
       "2  OECD (2021), OECD Inter-Country Input-Output D...     ECNC  CO2 Emissions   \n",
       "3  OECD (2021), OECD Inter-Country Input-Output D...     ECNC  CO2 Emissions   \n",
       "4  OECD (2021), OECD Inter-Country Input-Output D...     ECNC  CO2 Emissions   \n",
       "\n",
       "                                 CTS_Full_Descriptor  ...    2009    2010  \\\n",
       "0  Environment, Climate Change, Economic Activity...  ...   0.284   0.319   \n",
       "1  Environment, Climate Change, Economic Activity...  ...   0.000   0.000   \n",
       "2  Environment, Climate Change, Economic Activity...  ...   0.725   0.738   \n",
       "3  Environment, Climate Change, Economic Activity...  ...  13.804  14.110   \n",
       "4  Environment, Climate Change, Economic Activity...  ...   2.648   2.989   \n",
       "\n",
       "     2011    2012    2013    2014    2015    2016    2017    2018  \n",
       "0   0.338   0.377   0.378   0.369   0.371   0.303   0.358   0.344  \n",
       "1   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000  \n",
       "2   0.765   0.777   0.840   0.794   0.852   0.789   0.846   0.857  \n",
       "3  14.616  14.120  15.358  13.728  14.273  13.838  14.121  13.067  \n",
       "4   3.094   3.129   3.327   3.323   3.345   3.435   3.564   3.584  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a checkpoint - \n",
    "df_main = df.copy()\n",
    "df_main.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Argentina', 'Australia', 'Austria', 'Belgium', 'Brazil',\n",
       "       'Brunei Darussalam', 'Bulgaria', 'Cambodia', 'Canada', 'Chile',\n",
       "       'Hong Kong', 'China', 'Colombia', 'Costa Rica', 'Croatia',\n",
       "       'Cyprus', 'Czech', 'Denmark', 'Estonia', 'Finland', 'France',\n",
       "       'Germany', 'Greece', 'Hungary', 'Iceland', 'India', 'Indonesia',\n",
       "       'Ireland', 'Israel', 'Italy', 'Japan', 'Kazakhstan', 'Korea',\n",
       "       'Lao', 'Latvia', 'Lithuania', 'Luxembourg', 'Malaysia', 'Malta',\n",
       "       'Mexico', 'Morocco', 'Myanmar', 'Netherlands', 'New Zealand',\n",
       "       'Norway', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Romania',\n",
       "       'Russian', 'Saudi Arabia', 'Singapore', 'Slovak', 'Slovenia',\n",
       "       'South Africa', 'Spain', 'Sweden', 'Switzerland', 'Taiwan',\n",
       "       'Thailand', 'Tunisia', 'Turkey', 'United Kingdom', 'United States',\n",
       "       'Vietnam'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename the incorrectly named countries\n",
    "df_mod_country = df_main.replace({\n",
    "       'Country': {\n",
    "              'China, P.R.: Hong Kong' : 'Hong Kong',\n",
    "              'China, P.R.: Mainland' : 'China',\n",
    "              'Croatia, Rep. of' : 'Croatia',\n",
    "              'Czech Rep.' : 'Czech',\n",
    "              'Estonia, Rep. of' : 'Estonia',\n",
    "              'Kazakhstan, Rep. of' : 'Kazakhstan',\n",
    "              'Korea, Rep. of' : 'Korea',\n",
    "              \"Lao People's Dem. Rep.\" : 'Lao',\n",
    "              'Netherlands, The' : 'Netherlands',\n",
    "              'Poland, Rep. of' : 'Poland',\n",
    "              'Russian Federation' : 'Russian',\n",
    "              'Slovak Rep.' : 'Slovak',\n",
    "              'Slovenia, Rep. of' : 'Slovenia',\n",
    "              'Taiwan Province of China' : 'Taiwan'\n",
    "       }\n",
    "})\n",
    "\n",
    "df_mod_country.Country.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ObjectId', 'Country', 'ISO2', 'ISO3', 'Indicator', 'Unit', 'Source',\n",
       "       'CTS_Code', 'CTS_Name', 'CTS_Full_Descriptor', 'Industry', 'Scale',\n",
       "       '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003',\n",
       "       '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012',\n",
       "       '2013', '2014', '2015', '2016', '2017', '2018'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mod_country.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Accomodation and food services',\n",
       "       'Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use',\n",
       "       'Administrative and support services',\n",
       "       'Agriculture, hunting, forestry', 'Air transport',\n",
       "       'Arts, entertainment and recreation', 'Basic metals',\n",
       "       'Chemicals and pharmaceutical products',\n",
       "       'Coke and refined petroleum products',\n",
       "       'Computer, electronic and optical products', 'Construction',\n",
       "       'Education', 'Electrical equipment',\n",
       "       'Electricity, gas, steam and air conditioning supply',\n",
       "       'Fabricated metal products', 'Financial and insurance activities',\n",
       "       'Fishing and aquaculture', 'Food products, beverages and tobacco',\n",
       "       'Human health and social work activities',\n",
       "       'IT and other information services',\n",
       "       'Land transport and transport via pipelines',\n",
       "       'Machinery and equipment n.e.c.',\n",
       "       'Manufacturing nec; repair and installation of machinery and equipment',\n",
       "       'Mining and quarrying, energy producing products',\n",
       "       'Mining and quarrying, non-energy producing products',\n",
       "       'Mining support service activities',\n",
       "       'Motor vehicles, trailers and semi-trailers',\n",
       "       'Other non-metallic mineral products', 'Other service activities',\n",
       "       'Other transport equipment', 'Paper products and printing',\n",
       "       'Pharmaceuticals, medicinal chemical and botanical products',\n",
       "       'Postal and courier activities',\n",
       "       'Professional, scientific and technical activities',\n",
       "       'Public administration and defence; compulsory social security',\n",
       "       'Publishing, audiovisual and broadcasting activities',\n",
       "       'Real estate activities', 'Rubber and plastics products',\n",
       "       'Telecommunications',\n",
       "       'Textiles, textile products, leather and footwear',\n",
       "       'Warehousing and support activities for transportation',\n",
       "       'Water supply; sewerage, waste management and remediation activities',\n",
       "       'Water transport',\n",
       "       'Wholesale and retail trade; repair of motor vehicles',\n",
       "       'Wood and products of wood and cork'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mod_country.Industry.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Industry</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</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",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>Accomodation and food services</td>\n",
       "      <td>0.159000</td>\n",
       "      <td>0.158000</td>\n",
       "      <td>0.163000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.175000</td>\n",
       "      <td>0.183000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.284000</td>\n",
       "      <td>0.319000</td>\n",
       "      <td>0.338000</td>\n",
       "      <td>0.377000</td>\n",
       "      <td>0.378000</td>\n",
       "      <td>0.369000</td>\n",
       "      <td>0.371000</td>\n",
       "      <td>0.303000</td>\n",
       "      <td>0.358000</td>\n",
       "      <td>0.344000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>Activities of households as employers; undiffe...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>Administrative and support services</td>\n",
       "      <td>0.326000</td>\n",
       "      <td>0.351000</td>\n",
       "      <td>0.363000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.336000</td>\n",
       "      <td>0.338000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.738000</td>\n",
       "      <td>0.765000</td>\n",
       "      <td>0.777000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.794000</td>\n",
       "      <td>0.852000</td>\n",
       "      <td>0.789000</td>\n",
       "      <td>0.846000</td>\n",
       "      <td>0.857000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>Agriculture, hunting, forestry</td>\n",
       "      <td>8.914000</td>\n",
       "      <td>9.580000</td>\n",
       "      <td>9.625000</td>\n",
       "      <td>9.180000</td>\n",
       "      <td>9.124000</td>\n",
       "      <td>9.020000</td>\n",
       "      <td>...</td>\n",
       "      <td>13.804000</td>\n",
       "      <td>14.110000</td>\n",
       "      <td>14.616000</td>\n",
       "      <td>14.120000</td>\n",
       "      <td>15.358000</td>\n",
       "      <td>13.728000</td>\n",
       "      <td>14.273000</td>\n",
       "      <td>13.838000</td>\n",
       "      <td>14.121000</td>\n",
       "      <td>13.067000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>Air transport</td>\n",
       "      <td>1.957000</td>\n",
       "      <td>2.518000</td>\n",
       "      <td>2.523000</td>\n",
       "      <td>2.664000</td>\n",
       "      <td>2.602000</td>\n",
       "      <td>2.499000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.648000</td>\n",
       "      <td>2.989000</td>\n",
       "      <td>3.094000</td>\n",
       "      <td>3.129000</td>\n",
       "      <td>3.327000</td>\n",
       "      <td>3.323000</td>\n",
       "      <td>3.345000</td>\n",
       "      <td>3.435000</td>\n",
       "      <td>3.564000</td>\n",
       "      <td>3.584000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8905</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>CO2 emissions multipliers</td>\n",
       "      <td>Metric Tons of CO2 Emissions per $1million USD...</td>\n",
       "      <td>Warehousing and support activities for transpo...</td>\n",
       "      <td>519.764644</td>\n",
       "      <td>466.062272</td>\n",
       "      <td>382.618770</td>\n",
       "      <td>415.963400</td>\n",
       "      <td>414.567551</td>\n",
       "      <td>446.926284</td>\n",
       "      <td>...</td>\n",
       "      <td>571.409871</td>\n",
       "      <td>460.836156</td>\n",
       "      <td>347.350194</td>\n",
       "      <td>299.661077</td>\n",
       "      <td>282.529754</td>\n",
       "      <td>297.871935</td>\n",
       "      <td>365.437022</td>\n",
       "      <td>333.755635</td>\n",
       "      <td>303.371701</td>\n",
       "      <td>326.056753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8906</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>CO2 emissions multipliers</td>\n",
       "      <td>Metric Tons of CO2 Emissions per $1million USD...</td>\n",
       "      <td>Water supply; sewerage, waste management and r...</td>\n",
       "      <td>674.488008</td>\n",
       "      <td>646.824053</td>\n",
       "      <td>678.445578</td>\n",
       "      <td>752.219607</td>\n",
       "      <td>711.425741</td>\n",
       "      <td>708.719932</td>\n",
       "      <td>...</td>\n",
       "      <td>906.449860</td>\n",
       "      <td>889.629032</td>\n",
       "      <td>725.459644</td>\n",
       "      <td>659.111915</td>\n",
       "      <td>573.819271</td>\n",
       "      <td>585.155874</td>\n",
       "      <td>693.572818</td>\n",
       "      <td>702.200536</td>\n",
       "      <td>613.915080</td>\n",
       "      <td>694.331871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8907</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>CO2 emissions multipliers</td>\n",
       "      <td>Metric Tons of CO2 Emissions per $1million USD...</td>\n",
       "      <td>Water transport</td>\n",
       "      <td>4962.411721</td>\n",
       "      <td>4524.064409</td>\n",
       "      <td>4186.056061</td>\n",
       "      <td>5198.122248</td>\n",
       "      <td>5090.530714</td>\n",
       "      <td>4466.546521</td>\n",
       "      <td>...</td>\n",
       "      <td>3807.684853</td>\n",
       "      <td>3020.134010</td>\n",
       "      <td>2253.246177</td>\n",
       "      <td>1917.872096</td>\n",
       "      <td>1908.984147</td>\n",
       "      <td>2093.765490</td>\n",
       "      <td>2982.160528</td>\n",
       "      <td>3974.738515</td>\n",
       "      <td>3664.875652</td>\n",
       "      <td>3295.556416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8908</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>CO2 emissions multipliers</td>\n",
       "      <td>Metric Tons of CO2 Emissions per $1million USD...</td>\n",
       "      <td>Wholesale and retail trade; repair of motor ve...</td>\n",
       "      <td>640.400379</td>\n",
       "      <td>598.307544</td>\n",
       "      <td>582.122660</td>\n",
       "      <td>626.587673</td>\n",
       "      <td>600.809924</td>\n",
       "      <td>591.404834</td>\n",
       "      <td>...</td>\n",
       "      <td>449.066931</td>\n",
       "      <td>440.446520</td>\n",
       "      <td>378.152049</td>\n",
       "      <td>357.297470</td>\n",
       "      <td>350.369327</td>\n",
       "      <td>377.126416</td>\n",
       "      <td>479.038527</td>\n",
       "      <td>506.595843</td>\n",
       "      <td>451.921218</td>\n",
       "      <td>508.214053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8909</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>CO2 emissions multipliers</td>\n",
       "      <td>Metric Tons of CO2 Emissions per $1million USD...</td>\n",
       "      <td>Wood and products of wood and cork</td>\n",
       "      <td>947.581828</td>\n",
       "      <td>834.537034</td>\n",
       "      <td>903.049310</td>\n",
       "      <td>1007.909313</td>\n",
       "      <td>937.881972</td>\n",
       "      <td>895.990150</td>\n",
       "      <td>...</td>\n",
       "      <td>564.308833</td>\n",
       "      <td>558.348722</td>\n",
       "      <td>446.045111</td>\n",
       "      <td>389.419367</td>\n",
       "      <td>335.815256</td>\n",
       "      <td>340.664071</td>\n",
       "      <td>570.908659</td>\n",
       "      <td>460.042792</td>\n",
       "      <td>398.818037</td>\n",
       "      <td>441.426688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8910 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country                  Indicator  \\\n",
       "0     Argentina              CO2 emissions   \n",
       "1     Argentina              CO2 emissions   \n",
       "2     Argentina              CO2 emissions   \n",
       "3     Argentina              CO2 emissions   \n",
       "4     Argentina              CO2 emissions   \n",
       "...         ...                        ...   \n",
       "8905    Vietnam  CO2 emissions multipliers   \n",
       "8906    Vietnam  CO2 emissions multipliers   \n",
       "8907    Vietnam  CO2 emissions multipliers   \n",
       "8908    Vietnam  CO2 emissions multipliers   \n",
       "8909    Vietnam  CO2 emissions multipliers   \n",
       "\n",
       "                                                   Unit  \\\n",
       "0                        Millions of Metric tons of CO2   \n",
       "1                        Millions of Metric tons of CO2   \n",
       "2                        Millions of Metric tons of CO2   \n",
       "3                        Millions of Metric tons of CO2   \n",
       "4                        Millions of Metric tons of CO2   \n",
       "...                                                 ...   \n",
       "8905  Metric Tons of CO2 Emissions per $1million USD...   \n",
       "8906  Metric Tons of CO2 Emissions per $1million USD...   \n",
       "8907  Metric Tons of CO2 Emissions per $1million USD...   \n",
       "8908  Metric Tons of CO2 Emissions per $1million USD...   \n",
       "8909  Metric Tons of CO2 Emissions per $1million USD...   \n",
       "\n",
       "                                               Industry         1995  \\\n",
       "0                        Accomodation and food services     0.159000   \n",
       "1     Activities of households as employers; undiffe...     0.000000   \n",
       "2                   Administrative and support services     0.326000   \n",
       "3                        Agriculture, hunting, forestry     8.914000   \n",
       "4                                         Air transport     1.957000   \n",
       "...                                                 ...          ...   \n",
       "8905  Warehousing and support activities for transpo...   519.764644   \n",
       "8906  Water supply; sewerage, waste management and r...   674.488008   \n",
       "8907                                    Water transport  4962.411721   \n",
       "8908  Wholesale and retail trade; repair of motor ve...   640.400379   \n",
       "8909                 Wood and products of wood and cork   947.581828   \n",
       "\n",
       "             1996         1997         1998         1999         2000  ...  \\\n",
       "0        0.158000     0.163000     0.170000     0.175000     0.183000  ...   \n",
       "1        0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "2        0.351000     0.363000     0.360000     0.336000     0.338000  ...   \n",
       "3        9.580000     9.625000     9.180000     9.124000     9.020000  ...   \n",
       "4        2.518000     2.523000     2.664000     2.602000     2.499000  ...   \n",
       "...           ...          ...          ...          ...          ...  ...   \n",
       "8905   466.062272   382.618770   415.963400   414.567551   446.926284  ...   \n",
       "8906   646.824053   678.445578   752.219607   711.425741   708.719932  ...   \n",
       "8907  4524.064409  4186.056061  5198.122248  5090.530714  4466.546521  ...   \n",
       "8908   598.307544   582.122660   626.587673   600.809924   591.404834  ...   \n",
       "8909   834.537034   903.049310  1007.909313   937.881972   895.990150  ...   \n",
       "\n",
       "             2009         2010         2011         2012         2013  \\\n",
       "0        0.284000     0.319000     0.338000     0.377000     0.378000   \n",
       "1        0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "2        0.725000     0.738000     0.765000     0.777000     0.840000   \n",
       "3       13.804000    14.110000    14.616000    14.120000    15.358000   \n",
       "4        2.648000     2.989000     3.094000     3.129000     3.327000   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "8905   571.409871   460.836156   347.350194   299.661077   282.529754   \n",
       "8906   906.449860   889.629032   725.459644   659.111915   573.819271   \n",
       "8907  3807.684853  3020.134010  2253.246177  1917.872096  1908.984147   \n",
       "8908   449.066931   440.446520   378.152049   357.297470   350.369327   \n",
       "8909   564.308833   558.348722   446.045111   389.419367   335.815256   \n",
       "\n",
       "             2014         2015         2016         2017         2018  \n",
       "0        0.369000     0.371000     0.303000     0.358000     0.344000  \n",
       "1        0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "2        0.794000     0.852000     0.789000     0.846000     0.857000  \n",
       "3       13.728000    14.273000    13.838000    14.121000    13.067000  \n",
       "4        3.323000     3.345000     3.435000     3.564000     3.584000  \n",
       "...           ...          ...          ...          ...          ...  \n",
       "8905   297.871935   365.437022   333.755635   303.371701   326.056753  \n",
       "8906   585.155874   693.572818   702.200536   613.915080   694.331871  \n",
       "8907  2093.765490  2982.160528  3974.738515  3664.875652  3295.556416  \n",
       "8908   377.126416   479.038527   506.595843   451.921218   508.214053  \n",
       "8909   340.664071   570.908659   460.042792   398.818037   441.426688  \n",
       "\n",
       "[8910 rows x 28 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop 'ObjectId', 'ISO2', 'ISO3', 'Source', 'CTS_Code', 'CTS_Name', 'CTS_Full_Descriptor', 'Scale'\n",
    "df_drop_iso = df_mod_country.drop(['ObjectId', 'ISO2', 'ISO3', 'Source', 'CTS_Code', 'CTS_Name', 'CTS_Full_Descriptor', 'Scale'], axis=1)\n",
    "df_drop_iso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country Field -> \n",
      " Argentina     135\n",
      "Romania       135\n",
      "Lithuania     135\n",
      "Luxembourg    135\n",
      "Malaysia      135\n",
      "             ... \n",
      "Ireland       135\n",
      "Israel        135\n",
      "Italy         135\n",
      "Japan         135\n",
      "Vietnam       135\n",
      "Name: Country, Length: 66, dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#  Analyzing the Categorical values in the non-numerical value fields\n",
    "\n",
    "print(\"Country Field -> \\n\", df_drop_iso.Country.value_counts(), \"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indicator Field -> \n",
      " CO2 emissions                2970\n",
      "CO2 emissions intensities    2970\n",
      "CO2 emissions multipliers    2970\n",
      "Name: Indicator, dtype: int64 \n",
      "\n",
      "Unit Field -> \n",
      " Metric Tons of CO2 Emissions per $1million USD of output    5940\n",
      "Millions of Metric tons of CO2                              2970\n",
      "Name: Unit, dtype: int64 \n",
      "\n",
      "Industry Field -> \n",
      " Accomodation and food services                                                                                                198\n",
      "Mining and quarrying, energy producing products                                                                               198\n",
      "Mining support service activities                                                                                             198\n",
      "Motor vehicles, trailers and semi-trailers                                                                                    198\n",
      "Other non-metallic mineral products                                                                                           198\n",
      "Other service activities                                                                                                      198\n",
      "Other transport equipment                                                                                                     198\n",
      "Paper products and printing                                                                                                   198\n",
      "Pharmaceuticals, medicinal chemical and botanical products                                                                    198\n",
      "Postal and courier activities                                                                                                 198\n",
      "Professional, scientific and technical activities                                                                             198\n",
      "Public administration and defence; compulsory social security                                                                 198\n",
      "Publishing, audiovisual and broadcasting activities                                                                           198\n",
      "Real estate activities                                                                                                        198\n",
      "Rubber and plastics products                                                                                                  198\n",
      "Telecommunications                                                                                                            198\n",
      "Textiles, textile products, leather and footwear                                                                              198\n",
      "Warehousing and support activities for transportation                                                                         198\n",
      "Water supply; sewerage, waste management and remediation activities                                                           198\n",
      "Water transport                                                                                                               198\n",
      "Wholesale and retail trade; repair of motor vehicles                                                                          198\n",
      "Mining and quarrying, non-energy producing products                                                                           198\n",
      "Manufacturing nec; repair and installation of machinery and equipment                                                         198\n",
      "Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use    198\n",
      "Machinery and equipment n.e.c.                                                                                                198\n",
      "Administrative and support services                                                                                           198\n",
      "Agriculture, hunting, forestry                                                                                                198\n",
      "Air transport                                                                                                                 198\n",
      "Arts, entertainment and recreation                                                                                            198\n",
      "Basic metals                                                                                                                  198\n",
      "Chemicals and pharmaceutical products                                                                                         198\n",
      "Coke and refined petroleum products                                                                                           198\n",
      "Computer, electronic and optical products                                                                                     198\n",
      "Construction                                                                                                                  198\n",
      "Education                                                                                                                     198\n",
      "Electrical equipment                                                                                                          198\n",
      "Electricity, gas, steam and air conditioning supply                                                                           198\n",
      "Fabricated metal products                                                                                                     198\n",
      "Financial and insurance activities                                                                                            198\n",
      "Fishing and aquaculture                                                                                                       198\n",
      "Food products, beverages and tobacco                                                                                          198\n",
      "Human health and social work activities                                                                                       198\n",
      "IT and other information services                                                                                             198\n",
      "Land transport and transport via pipelines                                                                                    198\n",
      "Wood and products of wood and cork                                                                                            198\n",
      "Name: Industry, dtype: int64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Indicator Field -> \\n\", df_drop_iso.Indicator.value_counts(), \"\\n\")\n",
    "print(\"Unit Field -> \\n\", df_drop_iso.Unit.value_counts(), \"\\n\")\n",
    "print(\"Industry Field -> \\n\", df_drop_iso.Industry.value_counts(), \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "categories = {\n",
    "        'Agriculture, Fishing and Forestry':['Fishing', 'Agriculture', 'hunting', 'forestry'],\n",
    "        'Transport':['Air transport', 'Land transport', 'Water transport', 'vehicles', 'motor', 'road'],\n",
    "        'Health, Finance and Education':['Human health', 'Financial', 'Education'],\n",
    "        'Energy sources':['electricity', 'gas', 'steam'],\n",
    "        'Waste':['waste'],\n",
    "        'Defence and Security':['defence', 'security'],\n",
    "        'Consumable Product':['Food products', 'beverages', 'tobacco', 'medicinal chemical', 'botanical'],\n",
    "        'Direct Industrial product':['metal', 'Chemicals', 'Coke', 'mineral', 'Paper', 'Rubber', 'cements', 'petroleum', 'plastic'],\n",
    "        'Technology and Communication Activities':['computer', 'electronic', 'IT', 'courier', 'scientific', 'Publishing,', 'audiovisual and broadcasting', 'broadcasting', 'Telecommunications'],\n",
    "        'Other industry and services':['Construction', 'Other service', 'textiles', 'wood', 'footwear', 'leather'],\n",
    "        'Engineering, Machinery and Equipment':['machinery', 'equipment', 'warehouse'],\n",
    "        'Mining Product and Activities':['Mining', 'quarrying'],\n",
    "        'Housing and Entertainment Services':['Accommodation and food service', 'Administrative and support services', 'Real estate activities', 'Arts', 'entertainment', 'recreation', 'own use', 'Activities of households', 'entertainment']\n",
    "        }\n",
    "\n",
    "def count_categories(Industry):\n",
    "\n",
    "\n",
    "    counts = {category: 0 for category in categories}\n",
    "\n",
    "    for value in df.Industry:\n",
    "        for category, keywords in categories.items():\n",
    "            if any(keyword in str(value) for keyword in keywords):\n",
    "                counts[category] += 1\n",
    "                break\n",
    "\n",
    "    for category, count in counts.items():\n",
    "        # print(f'{category} is {count}')\n",
    "        return counts \n",
    "\n",
    "# counts = {category: 0 for category in categories}\n",
    "\n",
    "# for value in df_drop_iso.Industry:\n",
    "#     for category, keywords in categories.items():\n",
    "#         if any(keyword in str(value) for keyword in keywords):\n",
    "#             counts[category] += 1\n",
    "#             break\n",
    "\n",
    "# for category, count in counts.items():\n",
    "#     print(f'{category} is {count}')\n",
    "    \n",
    "# df_drop_iso['new_industry'] = df_drop_iso['Industry'].apply(count_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Industry</th>\n",
       "      <th>Country</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>Unit</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</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",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Housing and Entertainment Services</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>0.159000</td>\n",
       "      <td>0.158000</td>\n",
       "      <td>0.163000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.175000</td>\n",
       "      <td>0.183000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.284000</td>\n",
       "      <td>0.319000</td>\n",
       "      <td>0.338000</td>\n",
       "      <td>0.377000</td>\n",
       "      <td>0.378000</td>\n",
       "      <td>0.369000</td>\n",
       "      <td>0.371000</td>\n",
       "      <td>0.303000</td>\n",
       "      <td>0.358000</td>\n",
       "      <td>0.344000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Housing and Entertainment Services</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Housing and Entertainment Services</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>0.326000</td>\n",
       "      <td>0.351000</td>\n",
       "      <td>0.363000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.336000</td>\n",
       "      <td>0.338000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.738000</td>\n",
       "      <td>0.765000</td>\n",
       "      <td>0.777000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.794000</td>\n",
       "      <td>0.852000</td>\n",
       "      <td>0.789000</td>\n",
       "      <td>0.846000</td>\n",
       "      <td>0.857000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Agriculture, Fishing and Forestry</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>8.914000</td>\n",
       "      <td>9.580000</td>\n",
       "      <td>9.625000</td>\n",
       "      <td>9.180000</td>\n",
       "      <td>9.124000</td>\n",
       "      <td>9.020000</td>\n",
       "      <td>...</td>\n",
       "      <td>13.804000</td>\n",
       "      <td>14.110000</td>\n",
       "      <td>14.616000</td>\n",
       "      <td>14.120000</td>\n",
       "      <td>15.358000</td>\n",
       "      <td>13.728000</td>\n",
       "      <td>14.273000</td>\n",
       "      <td>13.838000</td>\n",
       "      <td>14.121000</td>\n",
       "      <td>13.067000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Transport</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>CO2 emissions</td>\n",
       "      <td>Millions of Metric tons of CO2</td>\n",
       "      <td>1.957000</td>\n",
       "      <td>2.518000</td>\n",
       "      <td>2.523000</td>\n",
       "      <td>2.664000</td>\n",
       "      <td>2.602000</td>\n",
       "      <td>2.499000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.648000</td>\n",
       "      <td>2.989000</td>\n",
       "      <td>3.094000</td>\n",
       "      <td>3.129000</td>\n",
       "      <td>3.327000</td>\n",
       "      <td>3.323000</td>\n",
       "      <td>3.345000</td>\n",
       "      <td>3.435000</td>\n",
       "      <td>3.564000</td>\n",
       "      <td>3.584000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8905</th>\n",
       "      <td>Engineering, Machinery and Equipment</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>CO2 emissions multipliers</td>\n",
       "      <td>Metric Tons of CO2 Emissions per $1million USD...</td>\n",
       "      <td>519.764644</td>\n",
       "      <td>466.062272</td>\n",
       "      <td>382.618770</td>\n",
       "      <td>415.963400</td>\n",
       "      <td>414.567551</td>\n",
       "      <td>446.926284</td>\n",
       "      <td>...</td>\n",
       "      <td>571.409871</td>\n",
       "      <td>460.836156</td>\n",
       "      <td>347.350194</td>\n",
       "      <td>299.661077</td>\n",
       "      <td>282.529754</td>\n",
       "      <td>297.871935</td>\n",
       "      <td>365.437022</td>\n",
       "      <td>333.755635</td>\n",
       "      <td>303.371701</td>\n",
       "      <td>326.056753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8906</th>\n",
       "      <td>Waste</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>CO2 emissions multipliers</td>\n",
       "      <td>Metric Tons of CO2 Emissions per $1million USD...</td>\n",
       "      <td>674.488008</td>\n",
       "      <td>646.824053</td>\n",
       "      <td>678.445578</td>\n",
       "      <td>752.219607</td>\n",
       "      <td>711.425741</td>\n",
       "      <td>708.719932</td>\n",
       "      <td>...</td>\n",
       "      <td>906.449860</td>\n",
       "      <td>889.629032</td>\n",
       "      <td>725.459644</td>\n",
       "      <td>659.111915</td>\n",
       "      <td>573.819271</td>\n",
       "      <td>585.155874</td>\n",
       "      <td>693.572818</td>\n",
       "      <td>702.200536</td>\n",
       "      <td>613.915080</td>\n",
       "      <td>694.331871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8907</th>\n",
       "      <td>Transport</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>CO2 emissions multipliers</td>\n",
       "      <td>Metric Tons of CO2 Emissions per $1million USD...</td>\n",
       "      <td>4962.411721</td>\n",
       "      <td>4524.064409</td>\n",
       "      <td>4186.056061</td>\n",
       "      <td>5198.122248</td>\n",
       "      <td>5090.530714</td>\n",
       "      <td>4466.546521</td>\n",
       "      <td>...</td>\n",
       "      <td>3807.684853</td>\n",
       "      <td>3020.134010</td>\n",
       "      <td>2253.246177</td>\n",
       "      <td>1917.872096</td>\n",
       "      <td>1908.984147</td>\n",
       "      <td>2093.765490</td>\n",
       "      <td>2982.160528</td>\n",
       "      <td>3974.738515</td>\n",
       "      <td>3664.875652</td>\n",
       "      <td>3295.556416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8908</th>\n",
       "      <td>Transport</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>CO2 emissions multipliers</td>\n",
       "      <td>Metric Tons of CO2 Emissions per $1million USD...</td>\n",
       "      <td>640.400379</td>\n",
       "      <td>598.307544</td>\n",
       "      <td>582.122660</td>\n",
       "      <td>626.587673</td>\n",
       "      <td>600.809924</td>\n",
       "      <td>591.404834</td>\n",
       "      <td>...</td>\n",
       "      <td>449.066931</td>\n",
       "      <td>440.446520</td>\n",
       "      <td>378.152049</td>\n",
       "      <td>357.297470</td>\n",
       "      <td>350.369327</td>\n",
       "      <td>377.126416</td>\n",
       "      <td>479.038527</td>\n",
       "      <td>506.595843</td>\n",
       "      <td>451.921218</td>\n",
       "      <td>508.214053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8909</th>\n",
       "      <td>Other industry and services</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>CO2 emissions multipliers</td>\n",
       "      <td>Metric Tons of CO2 Emissions per $1million USD...</td>\n",
       "      <td>947.581828</td>\n",
       "      <td>834.537034</td>\n",
       "      <td>903.049310</td>\n",
       "      <td>1007.909313</td>\n",
       "      <td>937.881972</td>\n",
       "      <td>895.990150</td>\n",
       "      <td>...</td>\n",
       "      <td>564.308833</td>\n",
       "      <td>558.348722</td>\n",
       "      <td>446.045111</td>\n",
       "      <td>389.419367</td>\n",
       "      <td>335.815256</td>\n",
       "      <td>340.664071</td>\n",
       "      <td>570.908659</td>\n",
       "      <td>460.042792</td>\n",
       "      <td>398.818037</td>\n",
       "      <td>441.426688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8910 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Industry    Country  \\\n",
       "0       Housing and Entertainment Services  Argentina   \n",
       "1       Housing and Entertainment Services  Argentina   \n",
       "2       Housing and Entertainment Services  Argentina   \n",
       "3        Agriculture, Fishing and Forestry  Argentina   \n",
       "4                                Transport  Argentina   \n",
       "...                                    ...        ...   \n",
       "8905  Engineering, Machinery and Equipment    Vietnam   \n",
       "8906                                 Waste    Vietnam   \n",
       "8907                             Transport    Vietnam   \n",
       "8908                             Transport    Vietnam   \n",
       "8909           Other industry and services    Vietnam   \n",
       "\n",
       "                      Indicator  \\\n",
       "0                 CO2 emissions   \n",
       "1                 CO2 emissions   \n",
       "2                 CO2 emissions   \n",
       "3                 CO2 emissions   \n",
       "4                 CO2 emissions   \n",
       "...                         ...   \n",
       "8905  CO2 emissions multipliers   \n",
       "8906  CO2 emissions multipliers   \n",
       "8907  CO2 emissions multipliers   \n",
       "8908  CO2 emissions multipliers   \n",
       "8909  CO2 emissions multipliers   \n",
       "\n",
       "                                                   Unit         1995  \\\n",
       "0                        Millions of Metric tons of CO2     0.159000   \n",
       "1                        Millions of Metric tons of CO2     0.000000   \n",
       "2                        Millions of Metric tons of CO2     0.326000   \n",
       "3                        Millions of Metric tons of CO2     8.914000   \n",
       "4                        Millions of Metric tons of CO2     1.957000   \n",
       "...                                                 ...          ...   \n",
       "8905  Metric Tons of CO2 Emissions per $1million USD...   519.764644   \n",
       "8906  Metric Tons of CO2 Emissions per $1million USD...   674.488008   \n",
       "8907  Metric Tons of CO2 Emissions per $1million USD...  4962.411721   \n",
       "8908  Metric Tons of CO2 Emissions per $1million USD...   640.400379   \n",
       "8909  Metric Tons of CO2 Emissions per $1million USD...   947.581828   \n",
       "\n",
       "             1996         1997         1998         1999         2000  ...  \\\n",
       "0        0.158000     0.163000     0.170000     0.175000     0.183000  ...   \n",
       "1        0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "2        0.351000     0.363000     0.360000     0.336000     0.338000  ...   \n",
       "3        9.580000     9.625000     9.180000     9.124000     9.020000  ...   \n",
       "4        2.518000     2.523000     2.664000     2.602000     2.499000  ...   \n",
       "...           ...          ...          ...          ...          ...  ...   \n",
       "8905   466.062272   382.618770   415.963400   414.567551   446.926284  ...   \n",
       "8906   646.824053   678.445578   752.219607   711.425741   708.719932  ...   \n",
       "8907  4524.064409  4186.056061  5198.122248  5090.530714  4466.546521  ...   \n",
       "8908   598.307544   582.122660   626.587673   600.809924   591.404834  ...   \n",
       "8909   834.537034   903.049310  1007.909313   937.881972   895.990150  ...   \n",
       "\n",
       "             2009         2010         2011         2012         2013  \\\n",
       "0        0.284000     0.319000     0.338000     0.377000     0.378000   \n",
       "1        0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "2        0.725000     0.738000     0.765000     0.777000     0.840000   \n",
       "3       13.804000    14.110000    14.616000    14.120000    15.358000   \n",
       "4        2.648000     2.989000     3.094000     3.129000     3.327000   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "8905   571.409871   460.836156   347.350194   299.661077   282.529754   \n",
       "8906   906.449860   889.629032   725.459644   659.111915   573.819271   \n",
       "8907  3807.684853  3020.134010  2253.246177  1917.872096  1908.984147   \n",
       "8908   449.066931   440.446520   378.152049   357.297470   350.369327   \n",
       "8909   564.308833   558.348722   446.045111   389.419367   335.815256   \n",
       "\n",
       "             2014         2015         2016         2017         2018  \n",
       "0        0.369000     0.371000     0.303000     0.358000     0.344000  \n",
       "1        0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "2        0.794000     0.852000     0.789000     0.846000     0.857000  \n",
       "3       13.728000    14.273000    13.838000    14.121000    13.067000  \n",
       "4        3.323000     3.345000     3.435000     3.564000     3.584000  \n",
       "...           ...          ...          ...          ...          ...  \n",
       "8905   297.871935   365.437022   333.755635   303.371701   326.056753  \n",
       "8906   585.155874   693.572818   702.200536   613.915080   694.331871  \n",
       "8907  2093.765490  2982.160528  3974.738515  3664.875652  3295.556416  \n",
       "8908   377.126416   479.038527   506.595843   451.921218   508.214053  \n",
       "8909   340.664071   570.908659   460.042792   398.818037   441.426688  \n",
       "\n",
       "[8910 rows x 28 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_category(industry):\n",
    "    if  ('Fishing' in str(industry)) or ('Agriculture' in str(industry)) or ('hunting' in str(industry)) or ('forestry' in str(industry)): \n",
    "        return 'Agriculture, Fishing and Forestry'\n",
    "    elif('Air transport' in str(industry)) or ('Land transport' in str(industry)) or ('Water transport' in str(industry)) or ('vehicles' in str(industry)) or ('motor' in str(industry)) or ('road' in str(industry)): \n",
    "        return 'Transport'\n",
    "    elif('Human health' in str(industry)) or ('Financial' in str(industry)) or ('Education' in str(industry)): \n",
    "        return 'Health, Finance and Education'\n",
    "    elif('electricity' in str(industry)) or ('gas' in str(industry)) or ('steam' in str(industry)): \n",
    "        return 'Energy sources'\n",
    "    elif('waste' in str(industry)): \n",
    "        return 'Waste'\n",
    "    elif('defence' in str(industry)) or ('security' in str(industry)): \n",
    "        return 'Defence and Security'\n",
    "    elif('Food products' in str(industry)) or ('beverages' in str(industry)) or ('tobacco' in str(industry)) or ('medicinal chemical' in str(industry)) or ('botanical' in str(industry)): \n",
    "        return 'Consumable Product'\n",
    "    elif('metal' in str(industry)) or ('Chemicals' in str(industry)) or ('Coke' in str(industry)) or ('mineral' in str(industry)) or ('Paper' in str(industry)) or ('Rubber' in str(industry)) or ('cements' in str(industry)) or ('petroleum' in str(industry)) or ('plastic' in str(industry)): \n",
    "        return 'Direct Industrial product'\n",
    "    elif('computer' in str(industry)) or ('electronic' in str(industry)) or ('IT' in str(industry)) or ('courier' in str(industry)) or ('scientific' in str(industry)) or ('Publishing,' in str(industry)) or ('audiovisual and broadcasting' in str(industry)) or ('broadcasting' in str(industry)) or ('Telecommunications' in str(industry)): \n",
    "        return 'Technology and Communication Activities'\n",
    "    elif('Construction' in str(industry)) or ('Other service' in str(industry)) or ('textiles' in str(industry)) or ('wood' in str(industry)) or ('footwear' in str(industry)) or ('leather' in str(industry)): \n",
    "        return 'Other industry and services'\n",
    "    elif('machinery' in str(industry)) or ('equipment' in str(industry)) or ('warehouse' in str(industry)) or ('Warehousing' in str(industry)): \n",
    "        return 'Engineering, Machinery and Equipment'\n",
    "    elif('Mining' in str(industry)) or ('quarrying' in str(industry)): \n",
    "        return 'Mining Product and Activities'\n",
    "    elif('Accomodation and food service' in str(industry)) or ('Administrative and support services' in str(industry)) or ('Real estate activities' in str(industry)) or ('Arts' in str(industry)) or ('entertainment' in str(industry)) or ('recreation' in str(industry)) or ('own use' in str(industry)) or ('Activities of households' in str(industry)) or ('entertainment' in str(industry)): \n",
    "        return 'Housing and Entertainment Services'\n",
    "    \n",
    "\n",
    "new_industry = df_drop_iso['Industry'].apply(get_category)\n",
    "df_drop_iso = pds.concat([new_industry, df_drop_iso.drop(['Industry'], axis=1)], axis=1)\n",
    "df_drop_iso"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Date processing for df_co2 and df_gdp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_co2_date = df_co2.copy()\n",
    "df_gdp_date = df_gdp.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968',\n",
       "       '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977',\n",
       "       '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',\n",
       "       '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',\n",
       "       '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004',\n",
       "       '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',\n",
       "       '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021'],\n",
       "      dtype='object', name='Year')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the index to string\n",
    "df_co2_date.index = df_co2_date.index.astype(str)\n",
    "df_gdp_date.index = df_gdp_date.index.astype(str)\n",
    "df_gdp_date.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 62 entries, 1960 to 2021\n",
      "Data columns (total 3 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   GDP (Billion US Dollars)    62 non-null     float64\n",
      " 1   Per Capita (in US Dollars)  62 non-null     object \n",
      " 2   Percentage Growth           61 non-null     float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 1.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Remove commas from the columns and convert the column GDP and Emission to float\n",
    "df_gdp_date['GDP (Billion US Dollars)'] = df_gdp_date['GDP (Billion US Dollars)'].str.replace(',', '').astype(float)\n",
    "df_co2_date['Fossil CO2 Emissions (tons)'] = df_co2_date['Fossil CO2 Emissions (tons)'].str.replace(',', '').astype(float)\n",
    "df_gdp_date.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GroupBy Country, and Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ctry_grouped = df_drop_iso.copy()\n",
    "df_itry_grouped = df_drop_iso.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5125/1560369489.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df_ctry_grouped = df_ctry_grouped.groupby('Country').mean() #.apply(list)\n"
     ]
    },
    {
     "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>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</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",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>199.113145</td>\n",
       "      <td>210.710816</td>\n",
       "      <td>208.617855</td>\n",
       "      <td>212.023909</td>\n",
       "      <td>220.759486</td>\n",
       "      <td>206.627666</td>\n",
       "      <td>211.711808</td>\n",
       "      <td>478.350317</td>\n",
       "      <td>419.929414</td>\n",
       "      <td>403.561228</td>\n",
       "      <td>...</td>\n",
       "      <td>251.040374</td>\n",
       "      <td>223.501481</td>\n",
       "      <td>201.515711</td>\n",
       "      <td>194.543280</td>\n",
       "      <td>192.453974</td>\n",
       "      <td>208.970789</td>\n",
       "      <td>198.419772</td>\n",
       "      <td>205.726688</td>\n",
       "      <td>170.645462</td>\n",
       "      <td>184.428879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>446.433185</td>\n",
       "      <td>427.876937</td>\n",
       "      <td>415.410754</td>\n",
       "      <td>475.533920</td>\n",
       "      <td>488.622471</td>\n",
       "      <td>484.281380</td>\n",
       "      <td>530.958697</td>\n",
       "      <td>528.861985</td>\n",
       "      <td>450.412324</td>\n",
       "      <td>372.893350</td>\n",
       "      <td>...</td>\n",
       "      <td>253.903552</td>\n",
       "      <td>221.025659</td>\n",
       "      <td>175.408749</td>\n",
       "      <td>160.231771</td>\n",
       "      <td>160.936181</td>\n",
       "      <td>168.817016</td>\n",
       "      <td>192.761417</td>\n",
       "      <td>208.782794</td>\n",
       "      <td>196.113395</td>\n",
       "      <td>190.578131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>180.212639</td>\n",
       "      <td>167.727262</td>\n",
       "      <td>188.146965</td>\n",
       "      <td>196.844343</td>\n",
       "      <td>188.694436</td>\n",
       "      <td>185.233738</td>\n",
       "      <td>192.258401</td>\n",
       "      <td>176.966039</td>\n",
       "      <td>160.306526</td>\n",
       "      <td>166.099776</td>\n",
       "      <td>...</td>\n",
       "      <td>80.943297</td>\n",
       "      <td>84.300003</td>\n",
       "      <td>74.822105</td>\n",
       "      <td>80.044182</td>\n",
       "      <td>81.195744</td>\n",
       "      <td>78.201356</td>\n",
       "      <td>92.761038</td>\n",
       "      <td>96.180581</td>\n",
       "      <td>96.250423</td>\n",
       "      <td>83.110403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>172.493900</td>\n",
       "      <td>178.241313</td>\n",
       "      <td>200.056064</td>\n",
       "      <td>209.195320</td>\n",
       "      <td>207.669814</td>\n",
       "      <td>227.181705</td>\n",
       "      <td>232.561387</td>\n",
       "      <td>227.173179</td>\n",
       "      <td>191.617581</td>\n",
       "      <td>155.949615</td>\n",
       "      <td>...</td>\n",
       "      <td>111.066166</td>\n",
       "      <td>113.327968</td>\n",
       "      <td>94.775016</td>\n",
       "      <td>98.726428</td>\n",
       "      <td>105.722343</td>\n",
       "      <td>101.667696</td>\n",
       "      <td>126.150311</td>\n",
       "      <td>127.497498</td>\n",
       "      <td>122.224762</td>\n",
       "      <td>113.448047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>245.805947</td>\n",
       "      <td>252.176539</td>\n",
       "      <td>247.418430</td>\n",
       "      <td>264.846003</td>\n",
       "      <td>378.061121</td>\n",
       "      <td>345.734200</td>\n",
       "      <td>397.416863</td>\n",
       "      <td>445.025405</td>\n",
       "      <td>380.881574</td>\n",
       "      <td>330.972477</td>\n",
       "      <td>...</td>\n",
       "      <td>158.749732</td>\n",
       "      <td>129.600835</td>\n",
       "      <td>110.912040</td>\n",
       "      <td>122.584603</td>\n",
       "      <td>131.427292</td>\n",
       "      <td>140.194820</td>\n",
       "      <td>180.244222</td>\n",
       "      <td>172.488602</td>\n",
       "      <td>153.381243</td>\n",
       "      <td>153.663402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>493.069907</td>\n",
       "      <td>484.994329</td>\n",
       "      <td>531.356527</td>\n",
       "      <td>537.104540</td>\n",
       "      <td>534.398261</td>\n",
       "      <td>591.771776</td>\n",
       "      <td>603.970463</td>\n",
       "      <td>558.580410</td>\n",
       "      <td>474.177835</td>\n",
       "      <td>435.871230</td>\n",
       "      <td>...</td>\n",
       "      <td>280.072713</td>\n",
       "      <td>299.589390</td>\n",
       "      <td>284.665878</td>\n",
       "      <td>305.848446</td>\n",
       "      <td>308.891294</td>\n",
       "      <td>326.626802</td>\n",
       "      <td>329.978075</td>\n",
       "      <td>340.995302</td>\n",
       "      <td>357.125736</td>\n",
       "      <td>345.513016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>359.423174</td>\n",
       "      <td>371.162717</td>\n",
       "      <td>376.125178</td>\n",
       "      <td>369.083815</td>\n",
       "      <td>391.154496</td>\n",
       "      <td>412.803585</td>\n",
       "      <td>498.561335</td>\n",
       "      <td>438.428763</td>\n",
       "      <td>359.671892</td>\n",
       "      <td>286.558099</td>\n",
       "      <td>...</td>\n",
       "      <td>232.975908</td>\n",
       "      <td>192.474197</td>\n",
       "      <td>181.792244</td>\n",
       "      <td>179.876633</td>\n",
       "      <td>168.246174</td>\n",
       "      <td>181.244245</td>\n",
       "      <td>228.525390</td>\n",
       "      <td>248.481321</td>\n",
       "      <td>254.145339</td>\n",
       "      <td>259.232245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>256.542513</td>\n",
       "      <td>240.872475</td>\n",
       "      <td>215.204153</td>\n",
       "      <td>218.430018</td>\n",
       "      <td>217.034848</td>\n",
       "      <td>221.004876</td>\n",
       "      <td>235.626650</td>\n",
       "      <td>226.453607</td>\n",
       "      <td>206.056279</td>\n",
       "      <td>175.504989</td>\n",
       "      <td>...</td>\n",
       "      <td>146.108957</td>\n",
       "      <td>139.669050</td>\n",
       "      <td>121.402202</td>\n",
       "      <td>121.298774</td>\n",
       "      <td>115.723751</td>\n",
       "      <td>105.667450</td>\n",
       "      <td>114.122354</td>\n",
       "      <td>116.029686</td>\n",
       "      <td>108.475918</td>\n",
       "      <td>97.646616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>483.567892</td>\n",
       "      <td>467.103502</td>\n",
       "      <td>472.643729</td>\n",
       "      <td>470.013765</td>\n",
       "      <td>439.219519</td>\n",
       "      <td>411.103664</td>\n",
       "      <td>383.511760</td>\n",
       "      <td>407.001833</td>\n",
       "      <td>379.590808</td>\n",
       "      <td>374.104286</td>\n",
       "      <td>...</td>\n",
       "      <td>277.990120</td>\n",
       "      <td>270.626170</td>\n",
       "      <td>262.424945</td>\n",
       "      <td>251.092844</td>\n",
       "      <td>245.943573</td>\n",
       "      <td>233.376149</td>\n",
       "      <td>231.169140</td>\n",
       "      <td>233.094826</td>\n",
       "      <td>218.877377</td>\n",
       "      <td>205.894227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>785.946890</td>\n",
       "      <td>709.348114</td>\n",
       "      <td>766.493743</td>\n",
       "      <td>837.999053</td>\n",
       "      <td>791.904043</td>\n",
       "      <td>776.314563</td>\n",
       "      <td>788.893024</td>\n",
       "      <td>829.830253</td>\n",
       "      <td>752.131536</td>\n",
       "      <td>719.400432</td>\n",
       "      <td>...</td>\n",
       "      <td>501.902895</td>\n",
       "      <td>489.110715</td>\n",
       "      <td>411.070200</td>\n",
       "      <td>361.872472</td>\n",
       "      <td>336.164594</td>\n",
       "      <td>339.882335</td>\n",
       "      <td>419.419856</td>\n",
       "      <td>430.759758</td>\n",
       "      <td>396.143133</td>\n",
       "      <td>446.830924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      1995        1996        1997        1998        1999  \\\n",
       "Country                                                                      \n",
       "Argentina       199.113145  210.710816  208.617855  212.023909  220.759486   \n",
       "Australia       446.433185  427.876937  415.410754  475.533920  488.622471   \n",
       "Austria         180.212639  167.727262  188.146965  196.844343  188.694436   \n",
       "Belgium         172.493900  178.241313  200.056064  209.195320  207.669814   \n",
       "Brazil          245.805947  252.176539  247.418430  264.846003  378.061121   \n",
       "...                    ...         ...         ...         ...         ...   \n",
       "Tunisia         493.069907  484.994329  531.356527  537.104540  534.398261   \n",
       "Turkey          359.423174  371.162717  376.125178  369.083815  391.154496   \n",
       "United Kingdom  256.542513  240.872475  215.204153  218.430018  217.034848   \n",
       "United States   483.567892  467.103502  472.643729  470.013765  439.219519   \n",
       "Vietnam         785.946890  709.348114  766.493743  837.999053  791.904043   \n",
       "\n",
       "                      2000        2001        2002        2003        2004  \\\n",
       "Country                                                                      \n",
       "Argentina       206.627666  211.711808  478.350317  419.929414  403.561228   \n",
       "Australia       484.281380  530.958697  528.861985  450.412324  372.893350   \n",
       "Austria         185.233738  192.258401  176.966039  160.306526  166.099776   \n",
       "Belgium         227.181705  232.561387  227.173179  191.617581  155.949615   \n",
       "Brazil          345.734200  397.416863  445.025405  380.881574  330.972477   \n",
       "...                    ...         ...         ...         ...         ...   \n",
       "Tunisia         591.771776  603.970463  558.580410  474.177835  435.871230   \n",
       "Turkey          412.803585  498.561335  438.428763  359.671892  286.558099   \n",
       "United Kingdom  221.004876  235.626650  226.453607  206.056279  175.504989   \n",
       "United States   411.103664  383.511760  407.001833  379.590808  374.104286   \n",
       "Vietnam         776.314563  788.893024  829.830253  752.131536  719.400432   \n",
       "\n",
       "                ...        2009        2010        2011        2012  \\\n",
       "Country         ...                                                   \n",
       "Argentina       ...  251.040374  223.501481  201.515711  194.543280   \n",
       "Australia       ...  253.903552  221.025659  175.408749  160.231771   \n",
       "Austria         ...   80.943297   84.300003   74.822105   80.044182   \n",
       "Belgium         ...  111.066166  113.327968   94.775016   98.726428   \n",
       "Brazil          ...  158.749732  129.600835  110.912040  122.584603   \n",
       "...             ...         ...         ...         ...         ...   \n",
       "Tunisia         ...  280.072713  299.589390  284.665878  305.848446   \n",
       "Turkey          ...  232.975908  192.474197  181.792244  179.876633   \n",
       "United Kingdom  ...  146.108957  139.669050  121.402202  121.298774   \n",
       "United States   ...  277.990120  270.626170  262.424945  251.092844   \n",
       "Vietnam         ...  501.902895  489.110715  411.070200  361.872472   \n",
       "\n",
       "                      2013        2014        2015        2016        2017  \\\n",
       "Country                                                                      \n",
       "Argentina       192.453974  208.970789  198.419772  205.726688  170.645462   \n",
       "Australia       160.936181  168.817016  192.761417  208.782794  196.113395   \n",
       "Austria          81.195744   78.201356   92.761038   96.180581   96.250423   \n",
       "Belgium         105.722343  101.667696  126.150311  127.497498  122.224762   \n",
       "Brazil          131.427292  140.194820  180.244222  172.488602  153.381243   \n",
       "...                    ...         ...         ...         ...         ...   \n",
       "Tunisia         308.891294  326.626802  329.978075  340.995302  357.125736   \n",
       "Turkey          168.246174  181.244245  228.525390  248.481321  254.145339   \n",
       "United Kingdom  115.723751  105.667450  114.122354  116.029686  108.475918   \n",
       "United States   245.943573  233.376149  231.169140  233.094826  218.877377   \n",
       "Vietnam         336.164594  339.882335  419.419856  430.759758  396.143133   \n",
       "\n",
       "                      2018  \n",
       "Country                     \n",
       "Argentina       184.428879  \n",
       "Australia       190.578131  \n",
       "Austria          83.110403  \n",
       "Belgium         113.448047  \n",
       "Brazil          153.663402  \n",
       "...                    ...  \n",
       "Tunisia         345.513016  \n",
       "Turkey          259.232245  \n",
       "United Kingdom   97.646616  \n",
       "United States   205.894227  \n",
       "Vietnam         446.830924  \n",
       "\n",
       "[66 rows x 24 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the dataframe by the 'Country' columns separately\n",
    "df_ctry_grouped = df_ctry_grouped.groupby('Country').mean() #.apply(list)\n",
    "\n",
    "df_ctry_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</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",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>642.690767</td>\n",
       "      <td>626.247949</td>\n",
       "      <td>597.889064</td>\n",
       "      <td>634.523417</td>\n",
       "      <td>628.532695</td>\n",
       "      <td>584.815129</td>\n",
       "      <td>581.440616</td>\n",
       "      <td>545.903132</td>\n",
       "      <td>490.212486</td>\n",
       "      <td>422.352039</td>\n",
       "      <td>...</td>\n",
       "      <td>277.552219</td>\n",
       "      <td>255.599049</td>\n",
       "      <td>228.419131</td>\n",
       "      <td>226.670976</td>\n",
       "      <td>219.907381</td>\n",
       "      <td>217.131214</td>\n",
       "      <td>254.947335</td>\n",
       "      <td>300.596451</td>\n",
       "      <td>256.180262</td>\n",
       "      <td>224.472003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>973.057991</td>\n",
       "      <td>881.337085</td>\n",
       "      <td>699.720830</td>\n",
       "      <td>719.694294</td>\n",
       "      <td>728.551356</td>\n",
       "      <td>571.852947</td>\n",
       "      <td>544.213433</td>\n",
       "      <td>485.870545</td>\n",
       "      <td>450.776272</td>\n",
       "      <td>372.466664</td>\n",
       "      <td>...</td>\n",
       "      <td>228.450499</td>\n",
       "      <td>191.355199</td>\n",
       "      <td>173.193286</td>\n",
       "      <td>162.106547</td>\n",
       "      <td>165.030653</td>\n",
       "      <td>152.731489</td>\n",
       "      <td>174.058353</td>\n",
       "      <td>419.777722</td>\n",
       "      <td>209.425483</td>\n",
       "      <td>146.889024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>74.095599</td>\n",
       "      <td>75.320227</td>\n",
       "      <td>86.937930</td>\n",
       "      <td>89.215162</td>\n",
       "      <td>91.662411</td>\n",
       "      <td>97.600868</td>\n",
       "      <td>98.771818</td>\n",
       "      <td>88.915034</td>\n",
       "      <td>75.728550</td>\n",
       "      <td>68.183751</td>\n",
       "      <td>...</td>\n",
       "      <td>48.556796</td>\n",
       "      <td>47.820463</td>\n",
       "      <td>41.322201</td>\n",
       "      <td>42.515230</td>\n",
       "      <td>41.037970</td>\n",
       "      <td>37.250773</td>\n",
       "      <td>40.836833</td>\n",
       "      <td>43.220565</td>\n",
       "      <td>42.427808</td>\n",
       "      <td>40.180103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>234.004370</td>\n",
       "      <td>245.499487</td>\n",
       "      <td>250.484834</td>\n",
       "      <td>248.910201</td>\n",
       "      <td>255.589026</td>\n",
       "      <td>288.746545</td>\n",
       "      <td>283.219615</td>\n",
       "      <td>278.974241</td>\n",
       "      <td>241.589093</td>\n",
       "      <td>215.446863</td>\n",
       "      <td>...</td>\n",
       "      <td>148.490639</td>\n",
       "      <td>140.798023</td>\n",
       "      <td>123.449250</td>\n",
       "      <td>122.449341</td>\n",
       "      <td>121.391947</td>\n",
       "      <td>118.612334</td>\n",
       "      <td>144.647989</td>\n",
       "      <td>141.974834</td>\n",
       "      <td>133.719366</td>\n",
       "      <td>122.512398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>397.010870</td>\n",
       "      <td>412.006230</td>\n",
       "      <td>423.267320</td>\n",
       "      <td>447.355617</td>\n",
       "      <td>418.738108</td>\n",
       "      <td>413.019903</td>\n",
       "      <td>446.695497</td>\n",
       "      <td>431.641274</td>\n",
       "      <td>371.032041</td>\n",
       "      <td>309.015258</td>\n",
       "      <td>...</td>\n",
       "      <td>207.370975</td>\n",
       "      <td>205.200685</td>\n",
       "      <td>186.047546</td>\n",
       "      <td>185.686099</td>\n",
       "      <td>170.417773</td>\n",
       "      <td>187.221100</td>\n",
       "      <td>213.617760</td>\n",
       "      <td>221.721498</td>\n",
       "      <td>200.817628</td>\n",
       "      <td>190.080741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>664.089813</td>\n",
       "      <td>651.001163</td>\n",
       "      <td>615.884420</td>\n",
       "      <td>671.165738</td>\n",
       "      <td>658.721083</td>\n",
       "      <td>611.848137</td>\n",
       "      <td>606.713058</td>\n",
       "      <td>576.933310</td>\n",
       "      <td>540.455482</td>\n",
       "      <td>480.822857</td>\n",
       "      <td>...</td>\n",
       "      <td>290.275763</td>\n",
       "      <td>285.577101</td>\n",
       "      <td>269.563390</td>\n",
       "      <td>265.898761</td>\n",
       "      <td>253.301794</td>\n",
       "      <td>256.157502</td>\n",
       "      <td>294.743134</td>\n",
       "      <td>319.573975</td>\n",
       "      <td>303.421281</td>\n",
       "      <td>257.540247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7574.086668</td>\n",
       "      <td>6785.168911</td>\n",
       "      <td>5159.563462</td>\n",
       "      <td>5176.366913</td>\n",
       "      <td>5059.375383</td>\n",
       "      <td>3714.387467</td>\n",
       "      <td>3557.662212</td>\n",
       "      <td>2998.479840</td>\n",
       "      <td>2966.254665</td>\n",
       "      <td>2491.447015</td>\n",
       "      <td>...</td>\n",
       "      <td>1173.723823</td>\n",
       "      <td>1056.512360</td>\n",
       "      <td>1000.206067</td>\n",
       "      <td>831.286161</td>\n",
       "      <td>973.594254</td>\n",
       "      <td>781.201023</td>\n",
       "      <td>853.501782</td>\n",
       "      <td>3374.591905</td>\n",
       "      <td>1376.142683</td>\n",
       "      <td>712.539664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              1995         1996         1997         1998         1999  \\\n",
       "count    66.000000    66.000000    66.000000    66.000000    66.000000   \n",
       "mean    642.690767   626.247949   597.889064   634.523417   628.532695   \n",
       "std     973.057991   881.337085   699.720830   719.694294   728.551356   \n",
       "min      74.095599    75.320227    86.937930    89.215162    91.662411   \n",
       "25%     234.004370   245.499487   250.484834   248.910201   255.589026   \n",
       "50%     397.010870   412.006230   423.267320   447.355617   418.738108   \n",
       "75%     664.089813   651.001163   615.884420   671.165738   658.721083   \n",
       "max    7574.086668  6785.168911  5159.563462  5176.366913  5059.375383   \n",
       "\n",
       "              2000         2001         2002         2003         2004  ...  \\\n",
       "count    66.000000    66.000000    66.000000    66.000000    66.000000  ...   \n",
       "mean    584.815129   581.440616   545.903132   490.212486   422.352039  ...   \n",
       "std     571.852947   544.213433   485.870545   450.776272   372.466664  ...   \n",
       "min      97.600868    98.771818    88.915034    75.728550    68.183751  ...   \n",
       "25%     288.746545   283.219615   278.974241   241.589093   215.446863  ...   \n",
       "50%     413.019903   446.695497   431.641274   371.032041   309.015258  ...   \n",
       "75%     611.848137   606.713058   576.933310   540.455482   480.822857  ...   \n",
       "max    3714.387467  3557.662212  2998.479840  2966.254665  2491.447015  ...   \n",
       "\n",
       "              2009         2010         2011        2012        2013  \\\n",
       "count    66.000000    66.000000    66.000000   66.000000   66.000000   \n",
       "mean    277.552219   255.599049   228.419131  226.670976  219.907381   \n",
       "std     228.450499   191.355199   173.193286  162.106547  165.030653   \n",
       "min      48.556796    47.820463    41.322201   42.515230   41.037970   \n",
       "25%     148.490639   140.798023   123.449250  122.449341  121.391947   \n",
       "50%     207.370975   205.200685   186.047546  185.686099  170.417773   \n",
       "75%     290.275763   285.577101   269.563390  265.898761  253.301794   \n",
       "max    1173.723823  1056.512360  1000.206067  831.286161  973.594254   \n",
       "\n",
       "             2014        2015         2016         2017        2018  \n",
       "count   66.000000   66.000000    66.000000    66.000000   66.000000  \n",
       "mean   217.131214  254.947335   300.596451   256.180262  224.472003  \n",
       "std    152.731489  174.058353   419.777722   209.425483  146.889024  \n",
       "min     37.250773   40.836833    43.220565    42.427808   40.180103  \n",
       "25%    118.612334  144.647989   141.974834   133.719366  122.512398  \n",
       "50%    187.221100  213.617760   221.721498   200.817628  190.080741  \n",
       "75%    256.157502  294.743134   319.573975   303.421281  257.540247  \n",
       "max    781.201023  853.501782  3374.591905  1376.142683  712.539664  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ctry_grouped.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5125/221424412.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df_itry_grouped = df_itry_grouped.groupby('Industry').mean() #.apply(list)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
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       "    <tr>\n",
       "      <th>Industry</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Agriculture, Fishing and Forestry</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>334.980277</td>\n",
       "      <td>328.091114</td>\n",
       "      <td>339.236941</td>\n",
       "      <td>366.202748</td>\n",
       "      <td>368.603839</td>\n",
       "      <td>358.760341</td>\n",
       "      <td>344.332024</td>\n",
       "      <td>314.954381</td>\n",
       "      <td>270.617646</td>\n",
       "      <td>...</td>\n",
       "      <td>182.829938</td>\n",
       "      <td>169.696655</td>\n",
       "      <td>145.198324</td>\n",
       "      <td>145.865801</td>\n",
       "      <td>139.929996</td>\n",
       "      <td>138.083049</td>\n",
       "      <td>149.337663</td>\n",
       "      <td>149.322367</td>\n",
       "      <td>142.961477</td>\n",
       "      <td>138.063747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consumable Product</th>\n",
       "      <td>376.594487</td>\n",
       "      <td>359.708621</td>\n",
       "      <td>362.764414</td>\n",
       "      <td>341.825512</td>\n",
       "      <td>363.612317</td>\n",
       "      <td>361.507763</td>\n",
       "      <td>346.782240</td>\n",
       "      <td>326.200426</td>\n",
       "      <td>289.425875</td>\n",
       "      <td>243.726550</td>\n",
       "      <td>...</td>\n",
       "      <td>147.515245</td>\n",
       "      <td>131.215768</td>\n",
       "      <td>116.619381</td>\n",
       "      <td>116.986821</td>\n",
       "      <td>111.616723</td>\n",
       "      <td>113.106319</td>\n",
       "      <td>129.663254</td>\n",
       "      <td>133.308797</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>119.594850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Defence and Security</th>\n",
       "      <td>153.550946</td>\n",
       "      <td>139.032819</td>\n",
       "      <td>133.063370</td>\n",
       "      <td>141.093432</td>\n",
       "      <td>141.314087</td>\n",
       "      <td>137.467924</td>\n",
       "      <td>138.174343</td>\n",
       "      <td>131.741926</td>\n",
       "      <td>117.192138</td>\n",
       "      <td>106.556505</td>\n",
       "      <td>...</td>\n",
       "      <td>65.830928</td>\n",
       "      <td>61.473579</td>\n",
       "      <td>55.241614</td>\n",
       "      <td>52.319747</td>\n",
       "      <td>51.513848</td>\n",
       "      <td>52.560685</td>\n",
       "      <td>57.512771</td>\n",
       "      <td>60.289399</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>55.259296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Direct Industrial product</th>\n",
       "      <td>1197.997726</td>\n",
       "      <td>1205.307972</td>\n",
       "      <td>1103.698933</td>\n",
       "      <td>1124.092125</td>\n",
       "      <td>1069.130972</td>\n",
       "      <td>879.425689</td>\n",
       "      <td>904.238729</td>\n",
       "      <td>797.278256</td>\n",
       "      <td>732.511219</td>\n",
       "      <td>642.185141</td>\n",
       "      <td>...</td>\n",
       "      <td>427.494768</td>\n",
       "      <td>407.437505</td>\n",
       "      <td>359.891472</td>\n",
       "      <td>370.122954</td>\n",
       "      <td>371.213096</td>\n",
       "      <td>342.656337</td>\n",
       "      <td>437.516546</td>\n",
       "      <td>665.604935</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>323.766872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy sources</th>\n",
       "      <td>6250.669630</td>\n",
       "      <td>6242.310383</td>\n",
       "      <td>6071.495339</td>\n",
       "      <td>6697.178310</td>\n",
       "      <td>6673.562862</td>\n",
       "      <td>6557.548472</td>\n",
       "      <td>6331.377252</td>\n",
       "      <td>6173.987481</td>\n",
       "      <td>5512.703014</td>\n",
       "      <td>4682.492903</td>\n",
       "      <td>...</td>\n",
       "      <td>2850.929763</td>\n",
       "      <td>2640.333719</td>\n",
       "      <td>2370.786162</td>\n",
       "      <td>2359.734416</td>\n",
       "      <td>2254.321774</td>\n",
       "      <td>2330.702165</td>\n",
       "      <td>2550.452487</td>\n",
       "      <td>2788.489017</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>2480.397014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering, Machinery and Equipment</th>\n",
       "      <td>464.305545</td>\n",
       "      <td>440.316131</td>\n",
       "      <td>426.238352</td>\n",
       "      <td>449.149721</td>\n",
       "      <td>435.552317</td>\n",
       "      <td>408.246487</td>\n",
       "      <td>405.047692</td>\n",
       "      <td>374.299824</td>\n",
       "      <td>347.071884</td>\n",
       "      <td>293.171566</td>\n",
       "      <td>...</td>\n",
       "      <td>212.692403</td>\n",
       "      <td>178.093206</td>\n",
       "      <td>156.453897</td>\n",
       "      <td>146.702062</td>\n",
       "      <td>141.471801</td>\n",
       "      <td>139.786990</td>\n",
       "      <td>153.166442</td>\n",
       "      <td>146.927808</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>126.871331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health, Finance and Education</th>\n",
       "      <td>146.846622</td>\n",
       "      <td>138.464796</td>\n",
       "      <td>130.259396</td>\n",
       "      <td>134.130024</td>\n",
       "      <td>135.006600</td>\n",
       "      <td>131.963029</td>\n",
       "      <td>128.789845</td>\n",
       "      <td>121.038531</td>\n",
       "      <td>108.883766</td>\n",
       "      <td>92.885297</td>\n",
       "      <td>...</td>\n",
       "      <td>54.824459</td>\n",
       "      <td>49.500304</td>\n",
       "      <td>43.488931</td>\n",
       "      <td>42.677069</td>\n",
       "      <td>41.422701</td>\n",
       "      <td>40.240192</td>\n",
       "      <td>44.743888</td>\n",
       "      <td>45.978601</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>39.600751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Housing and Entertainment Services</th>\n",
       "      <td>140.606581</td>\n",
       "      <td>133.392836</td>\n",
       "      <td>128.082494</td>\n",
       "      <td>130.540106</td>\n",
       "      <td>133.385996</td>\n",
       "      <td>129.267971</td>\n",
       "      <td>128.111615</td>\n",
       "      <td>121.157878</td>\n",
       "      <td>108.424134</td>\n",
       "      <td>92.938551</td>\n",
       "      <td>...</td>\n",
       "      <td>60.792025</td>\n",
       "      <td>55.192925</td>\n",
       "      <td>47.677940</td>\n",
       "      <td>45.598414</td>\n",
       "      <td>43.847905</td>\n",
       "      <td>42.280771</td>\n",
       "      <td>47.095921</td>\n",
       "      <td>48.103252</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>44.364078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mining Product and Activities</th>\n",
       "      <td>618.499748</td>\n",
       "      <td>570.048735</td>\n",
       "      <td>509.945366</td>\n",
       "      <td>568.962337</td>\n",
       "      <td>547.795917</td>\n",
       "      <td>532.889079</td>\n",
       "      <td>517.003381</td>\n",
       "      <td>481.048194</td>\n",
       "      <td>417.399571</td>\n",
       "      <td>355.675892</td>\n",
       "      <td>...</td>\n",
       "      <td>254.167017</td>\n",
       "      <td>233.729618</td>\n",
       "      <td>216.439464</td>\n",
       "      <td>233.085490</td>\n",
       "      <td>223.262295</td>\n",
       "      <td>233.047956</td>\n",
       "      <td>320.371506</td>\n",
       "      <td>358.125464</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>268.396975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other industry and services</th>\n",
       "      <td>246.796681</td>\n",
       "      <td>227.373793</td>\n",
       "      <td>222.012290</td>\n",
       "      <td>235.296420</td>\n",
       "      <td>242.767217</td>\n",
       "      <td>235.365827</td>\n",
       "      <td>228.590973</td>\n",
       "      <td>216.841700</td>\n",
       "      <td>190.880605</td>\n",
       "      <td>162.403600</td>\n",
       "      <td>...</td>\n",
       "      <td>103.185138</td>\n",
       "      <td>94.775075</td>\n",
       "      <td>83.250081</td>\n",
       "      <td>84.567723</td>\n",
       "      <td>81.801097</td>\n",
       "      <td>81.853067</td>\n",
       "      <td>91.792172</td>\n",
       "      <td>93.787896</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>87.014558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology and Communication Activities</th>\n",
       "      <td>182.340372</td>\n",
       "      <td>171.112309</td>\n",
       "      <td>162.990013</td>\n",
       "      <td>168.582750</td>\n",
       "      <td>171.891684</td>\n",
       "      <td>168.201025</td>\n",
       "      <td>163.360252</td>\n",
       "      <td>158.536545</td>\n",
       "      <td>140.280237</td>\n",
       "      <td>115.495576</td>\n",
       "      <td>...</td>\n",
       "      <td>76.569330</td>\n",
       "      <td>70.241203</td>\n",
       "      <td>63.265801</td>\n",
       "      <td>59.328301</td>\n",
       "      <td>56.327284</td>\n",
       "      <td>57.049262</td>\n",
       "      <td>64.934389</td>\n",
       "      <td>65.775258</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>59.250333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transport</th>\n",
       "      <td>852.606143</td>\n",
       "      <td>818.231750</td>\n",
       "      <td>816.327820</td>\n",
       "      <td>896.782990</td>\n",
       "      <td>911.028630</td>\n",
       "      <td>868.416759</td>\n",
       "      <td>886.384422</td>\n",
       "      <td>851.642525</td>\n",
       "      <td>753.055069</td>\n",
       "      <td>662.901437</td>\n",
       "      <td>...</td>\n",
       "      <td>449.653700</td>\n",
       "      <td>412.421395</td>\n",
       "      <td>374.980184</td>\n",
       "      <td>357.440346</td>\n",
       "      <td>342.299632</td>\n",
       "      <td>340.047115</td>\n",
       "      <td>390.582084</td>\n",
       "      <td>407.626522</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>375.469625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Waste</th>\n",
       "      <td>323.266921</td>\n",
       "      <td>304.748282</td>\n",
       "      <td>299.660921</td>\n",
       "      <td>311.988641</td>\n",
       "      <td>335.749216</td>\n",
       "      <td>330.363784</td>\n",
       "      <td>301.871972</td>\n",
       "      <td>284.445490</td>\n",
       "      <td>253.743564</td>\n",
       "      <td>212.072910</td>\n",
       "      <td>...</td>\n",
       "      <td>132.029432</td>\n",
       "      <td>125.307430</td>\n",
       "      <td>110.302630</td>\n",
       "      <td>103.229317</td>\n",
       "      <td>105.119309</td>\n",
       "      <td>103.524158</td>\n",
       "      <td>112.056034</td>\n",
       "      <td>116.310494</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>106.600653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                1995         1996  \\\n",
       "Industry                                                            \n",
       "Agriculture, Fishing and Forestry         359.649389   334.980277   \n",
       "Consumable Product                        376.594487   359.708621   \n",
       "Defence and Security                      153.550946   139.032819   \n",
       "Direct Industrial product                1197.997726  1205.307972   \n",
       "Energy sources                           6250.669630  6242.310383   \n",
       "Engineering, Machinery and Equipment      464.305545   440.316131   \n",
       "Health, Finance and Education             146.846622   138.464796   \n",
       "Housing and Entertainment Services        140.606581   133.392836   \n",
       "Mining Product and Activities             618.499748   570.048735   \n",
       "Other industry and services               246.796681   227.373793   \n",
       "Technology and Communication Activities   182.340372   171.112309   \n",
       "Transport                                 852.606143   818.231750   \n",
       "Waste                                     323.266921   304.748282   \n",
       "\n",
       "                                                1997         1998  \\\n",
       "Industry                                                            \n",
       "Agriculture, Fishing and Forestry         328.091114   339.236941   \n",
       "Consumable Product                        362.764414   341.825512   \n",
       "Defence and Security                      133.063370   141.093432   \n",
       "Direct Industrial product                1103.698933  1124.092125   \n",
       "Energy sources                           6071.495339  6697.178310   \n",
       "Engineering, Machinery and Equipment      426.238352   449.149721   \n",
       "Health, Finance and Education             130.259396   134.130024   \n",
       "Housing and Entertainment Services        128.082494   130.540106   \n",
       "Mining Product and Activities             509.945366   568.962337   \n",
       "Other industry and services               222.012290   235.296420   \n",
       "Technology and Communication Activities   162.990013   168.582750   \n",
       "Transport                                 816.327820   896.782990   \n",
       "Waste                                     299.660921   311.988641   \n",
       "\n",
       "                                                1999         2000  \\\n",
       "Industry                                                            \n",
       "Agriculture, Fishing and Forestry         366.202748   368.603839   \n",
       "Consumable Product                        363.612317   361.507763   \n",
       "Defence and Security                      141.314087   137.467924   \n",
       "Direct Industrial product                1069.130972   879.425689   \n",
       "Energy sources                           6673.562862  6557.548472   \n",
       "Engineering, Machinery and Equipment      435.552317   408.246487   \n",
       "Health, Finance and Education             135.006600   131.963029   \n",
       "Housing and Entertainment Services        133.385996   129.267971   \n",
       "Mining Product and Activities             547.795917   532.889079   \n",
       "Other industry and services               242.767217   235.365827   \n",
       "Technology and Communication Activities   171.891684   168.201025   \n",
       "Transport                                 911.028630   868.416759   \n",
       "Waste                                     335.749216   330.363784   \n",
       "\n",
       "                                                2001         2002  \\\n",
       "Industry                                                            \n",
       "Agriculture, Fishing and Forestry         358.760341   344.332024   \n",
       "Consumable Product                        346.782240   326.200426   \n",
       "Defence and Security                      138.174343   131.741926   \n",
       "Direct Industrial product                 904.238729   797.278256   \n",
       "Energy sources                           6331.377252  6173.987481   \n",
       "Engineering, Machinery and Equipment      405.047692   374.299824   \n",
       "Health, Finance and Education             128.789845   121.038531   \n",
       "Housing and Entertainment Services        128.111615   121.157878   \n",
       "Mining Product and Activities             517.003381   481.048194   \n",
       "Other industry and services               228.590973   216.841700   \n",
       "Technology and Communication Activities   163.360252   158.536545   \n",
       "Transport                                 886.384422   851.642525   \n",
       "Waste                                     301.871972   284.445490   \n",
       "\n",
       "                                                2003         2004  ...  \\\n",
       "Industry                                                           ...   \n",
       "Agriculture, Fishing and Forestry         314.954381   270.617646  ...   \n",
       "Consumable Product                        289.425875   243.726550  ...   \n",
       "Defence and Security                      117.192138   106.556505  ...   \n",
       "Direct Industrial product                 732.511219   642.185141  ...   \n",
       "Energy sources                           5512.703014  4682.492903  ...   \n",
       "Engineering, Machinery and Equipment      347.071884   293.171566  ...   \n",
       "Health, Finance and Education             108.883766    92.885297  ...   \n",
       "Housing and Entertainment Services        108.424134    92.938551  ...   \n",
       "Mining Product and Activities             417.399571   355.675892  ...   \n",
       "Other industry and services               190.880605   162.403600  ...   \n",
       "Technology and Communication Activities   140.280237   115.495576  ...   \n",
       "Transport                                 753.055069   662.901437  ...   \n",
       "Waste                                     253.743564   212.072910  ...   \n",
       "\n",
       "                                                2009         2010  \\\n",
       "Industry                                                            \n",
       "Agriculture, Fishing and Forestry         182.829938   169.696655   \n",
       "Consumable Product                        147.515245   131.215768   \n",
       "Defence and Security                       65.830928    61.473579   \n",
       "Direct Industrial product                 427.494768   407.437505   \n",
       "Energy sources                           2850.929763  2640.333719   \n",
       "Engineering, Machinery and Equipment      212.692403   178.093206   \n",
       "Health, Finance and Education              54.824459    49.500304   \n",
       "Housing and Entertainment Services         60.792025    55.192925   \n",
       "Mining Product and Activities             254.167017   233.729618   \n",
       "Other industry and services               103.185138    94.775075   \n",
       "Technology and Communication Activities    76.569330    70.241203   \n",
       "Transport                                 449.653700   412.421395   \n",
       "Waste                                     132.029432   125.307430   \n",
       "\n",
       "                                                2011         2012  \\\n",
       "Industry                                                            \n",
       "Agriculture, Fishing and Forestry         145.198324   145.865801   \n",
       "Consumable Product                        116.619381   116.986821   \n",
       "Defence and Security                       55.241614    52.319747   \n",
       "Direct Industrial product                 359.891472   370.122954   \n",
       "Energy sources                           2370.786162  2359.734416   \n",
       "Engineering, Machinery and Equipment      156.453897   146.702062   \n",
       "Health, Finance and Education              43.488931    42.677069   \n",
       "Housing and Entertainment Services         47.677940    45.598414   \n",
       "Mining Product and Activities             216.439464   233.085490   \n",
       "Other industry and services                83.250081    84.567723   \n",
       "Technology and Communication Activities    63.265801    59.328301   \n",
       "Transport                                 374.980184   357.440346   \n",
       "Waste                                     110.302630   103.229317   \n",
       "\n",
       "                                                2013         2014  \\\n",
       "Industry                                                            \n",
       "Agriculture, Fishing and Forestry         139.929996   138.083049   \n",
       "Consumable Product                        111.616723   113.106319   \n",
       "Defence and Security                       51.513848    52.560685   \n",
       "Direct Industrial product                 371.213096   342.656337   \n",
       "Energy sources                           2254.321774  2330.702165   \n",
       "Engineering, Machinery and Equipment      141.471801   139.786990   \n",
       "Health, Finance and Education              41.422701    40.240192   \n",
       "Housing and Entertainment Services         43.847905    42.280771   \n",
       "Mining Product and Activities             223.262295   233.047956   \n",
       "Other industry and services                81.801097    81.853067   \n",
       "Technology and Communication Activities    56.327284    57.049262   \n",
       "Transport                                 342.299632   340.047115   \n",
       "Waste                                     105.119309   103.524158   \n",
       "\n",
       "                                                2015         2016  \\\n",
       "Industry                                                            \n",
       "Agriculture, Fishing and Forestry         149.337663   149.322367   \n",
       "Consumable Product                        129.663254   133.308797   \n",
       "Defence and Security                       57.512771    60.289399   \n",
       "Direct Industrial product                 437.516546   665.604935   \n",
       "Energy sources                           2550.452487  2788.489017   \n",
       "Engineering, Machinery and Equipment      153.166442   146.927808   \n",
       "Health, Finance and Education              44.743888    45.978601   \n",
       "Housing and Entertainment Services         47.095921    48.103252   \n",
       "Mining Product and Activities             320.371506   358.125464   \n",
       "Other industry and services                91.792172    93.787896   \n",
       "Technology and Communication Activities    64.934389    65.775258   \n",
       "Transport                                 390.582084   407.626522   \n",
       "Waste                                     112.056034   116.310494   \n",
       "\n",
       "                                                2017         2018  \n",
       "Industry                                                           \n",
       "Agriculture, Fishing and Forestry         142.961477   138.063747  \n",
       "Consumable Product                        126.643680   119.594850  \n",
       "Defence and Security                       57.980549    55.259296  \n",
       "Direct Industrial product                 454.936748   323.766872  \n",
       "Energy sources                           2627.290346  2480.397014  \n",
       "Engineering, Machinery and Equipment      136.244412   126.871331  \n",
       "Health, Finance and Education              42.991922    39.600751  \n",
       "Housing and Entertainment Services         46.690589    44.364078  \n",
       "Mining Product and Activities             313.200166   268.396975  \n",
       "Other industry and services                91.073037    87.014558  \n",
       "Technology and Communication Activities    62.557984    59.250333  \n",
       "Transport                                 391.190304   375.469625  \n",
       "Waste                                     111.598195   106.600653  \n",
       "\n",
       "[13 rows x 24 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the dataframe by the 'Industry' columns separately\n",
    "df_itry_grouped = df_itry_grouped.groupby('Industry').mean() #.apply(list)\n",
    "\n",
    "df_itry_grouped\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transposed both the 'Country' and 'Industry' columns\n",
    "df_ctry_transposed = df_ctry_grouped.T\n",
    "df_itry_transposed = df_itry_grouped.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country   Argentina   Australia     Austria     Belgium      Brazil  \\\n",
      "Year                                                                  \n",
      "1995     199.113145  446.433185  180.212639  172.493900  245.805947   \n",
      "1996     210.710816  427.876937  167.727262  178.241313  252.176539   \n",
      "1997     208.617855  415.410754  188.146965  200.056064  247.418430   \n",
      "1998     212.023909  475.533920  196.844343  209.195320  264.846003   \n",
      "1999     220.759486  488.622471  188.694436  207.669814  378.061121   \n",
      "2000     206.627666  484.281380  185.233738  227.181705  345.734200   \n",
      "2001     211.711808  530.958697  192.258401  232.561387  397.416863   \n",
      "2002     478.350317  528.861985  176.966039  227.173179  445.025405   \n",
      "2003     419.929414  450.412324  160.306526  191.617581  380.881574   \n",
      "2004     403.561228  372.893350  166.099776  155.949615  330.972477   \n",
      "2005     355.476128  326.846594  141.961001  144.177434  254.589696   \n",
      "2006     327.082243  313.900374  124.835388  137.666201  207.531939   \n",
      "2007     276.703413  277.596435  104.392236  121.482214  180.531246   \n",
      "2008     236.562951  246.524619   79.986516  100.930787  156.052586   \n",
      "2009     251.040374  253.903552   80.943297  111.066166  158.749732   \n",
      "2010     223.501481  221.025659   84.300003  113.327968  129.600835   \n",
      "2011     201.515711  175.408749   74.822105   94.775016  110.912040   \n",
      "2012     194.543280  160.231771   80.044182   98.726428  122.584603   \n",
      "2013     192.453974  160.936181   81.195744  105.722343  131.427292   \n",
      "2014     208.970789  168.817016   78.201356  101.667696  140.194820   \n",
      "2015     198.419772  192.761417   92.761038  126.150311  180.244222   \n",
      "2016     205.726688  208.782794   96.180581  127.497498  172.488602   \n",
      "2017     170.645462  196.113395   96.250423  122.224762  153.381243   \n",
      "2018     184.428879  190.578131   83.110403  113.448047  153.663402   \n",
      "\n",
      "Country  Brunei Darussalam     Bulgaria    Cambodia      Canada       Chile  \\\n",
      "Year                                                                          \n",
      "1995           1161.168802  1413.677958  221.369710  485.899061  369.013682   \n",
      "1996           1141.278135  1831.430801  221.926143  477.257141  403.224863   \n",
      "1997           1269.122977  1833.181438  268.907092  476.909071  485.462776   \n",
      "1998           1391.401894  1413.285260  349.251750  537.388203  515.430180   \n",
      "1999           1201.106691  1342.625574  320.195953  512.709174  550.987359   \n",
      "2000            977.217852  1464.507138  306.306826  471.345866  465.239677   \n",
      "2001            958.329270  1382.468891  313.821286  490.378602  505.463471   \n",
      "2002           1013.936487  1171.759614  312.531886  496.355932  521.942704   \n",
      "2003           1039.587062   998.622532  307.231823  443.487036  501.235876   \n",
      "2004            882.685740   734.979638  268.661989  388.115416  421.462190   \n",
      "2005            696.063251   636.499190  261.942969  324.151348  345.842018   \n",
      "2006           1280.537227   560.089687  228.831405  286.875693  282.401959   \n",
      "2007           1156.698332   427.855334  242.444511  269.422498  267.459662   \n",
      "2008           1014.200624   336.185261  226.212799  241.098300  244.828340   \n",
      "2009            904.648140   317.183982  254.863258  272.830935  265.298752   \n",
      "2010            543.710953   364.927316  251.156031  223.729349  205.359271   \n",
      "2011            449.891644   307.125535  212.727143  198.919910  196.817355   \n",
      "2012            453.249193   287.960134  206.706334  194.329160  182.486182   \n",
      "2013            418.046881   253.665638  192.666897  193.726941  209.502091   \n",
      "2014            427.817113   258.517938  238.275429  195.869878  198.336754   \n",
      "2015            521.312967   305.534305  276.789396  225.303192  220.441814   \n",
      "2016            604.938776   277.922689  322.200875  238.363376  225.691300   \n",
      "2017            571.386829   277.308594  343.913916  216.853106  206.659481   \n",
      "2018            554.434339   252.464255  354.591501  208.677407  204.650268   \n",
      "\n",
      "Country  ...       Spain      Sweden  Switzerland      Taiwan    Thailand  \\\n",
      "Year     ...                                                                \n",
      "1995     ...  271.344373  149.339162    74.095599  449.340770  403.807959   \n",
      "1996     ...  260.831663  148.340569    75.320227  441.223850  442.959856   \n",
      "1997     ...  304.031196  147.416753    86.937930  471.538301  534.604807   \n",
      "1998     ...  309.472641  155.765910    89.215162  534.751767  642.625530   \n",
      "1999     ...  315.118817  156.863508    91.662411  496.653167  606.982157   \n",
      "2000     ...  354.232840  156.051393    97.600868  463.678365  568.553240   \n",
      "2001     ...  342.819442  154.487561    98.771818  513.469436  595.831861   \n",
      "2002     ...  326.035700  147.064594    88.915034  506.970730  567.848159   \n",
      "2003     ...  271.037874  124.675134    75.728550  490.633170  511.152031   \n",
      "2004     ...  244.651051  108.164028    68.183751  439.877178  485.159877   \n",
      "2005     ...  238.849967  103.873161    65.128069  416.249753  433.208396   \n",
      "2006     ...  283.409354   96.543296    60.183954  393.118412  411.606819   \n",
      "2007     ...  232.357798   81.111768    53.453052  368.094946  357.953702   \n",
      "2008     ...  193.884628   75.722244    45.787738  337.865364  322.867487   \n",
      "2009     ...  205.797164  100.506566    48.556796  359.857443  332.245224   \n",
      "2010     ...  214.069767   83.784531    47.820463  317.327370  287.928541   \n",
      "2011     ...  192.089252   67.491964    41.322201  301.629816  256.502838   \n",
      "2012     ...  237.773056   67.031546    42.515230  298.855197  260.809912   \n",
      "2013     ...  232.623584   67.488125    41.037970  285.294086  266.035335   \n",
      "2014     ...  231.477745   71.828955    37.250773  269.590689  271.102217   \n",
      "2015     ...  376.417768   73.736884    40.836833  301.834190  291.073833   \n",
      "2016     ...  349.406846   85.815007    43.220565  318.805433  293.461845   \n",
      "2017     ...  272.268599   84.011048    42.427808  306.521483  263.047007   \n",
      "2018     ...  240.036217   86.195501    40.180103  287.250183  233.852321   \n",
      "\n",
      "Country     Tunisia      Turkey  United Kingdom  United States     Vietnam  \n",
      "Year                                                                        \n",
      "1995     493.069907  359.423174      256.542513     483.567892  785.946890  \n",
      "1996     484.994329  371.162717      240.872475     467.103502  709.348114  \n",
      "1997     531.356527  376.125178      215.204153     472.643729  766.493743  \n",
      "1998     537.104540  369.083815      218.430018     470.013765  837.999053  \n",
      "1999     534.398261  391.154496      217.034848     439.219519  791.904043  \n",
      "2000     591.771776  412.803585      221.004876     411.103664  776.314563  \n",
      "2001     603.970463  498.561335      235.626650     383.511760  788.893024  \n",
      "2002     558.580410  438.428763      226.453607     407.001833  829.830253  \n",
      "2003     474.177835  359.671892      206.056279     379.590808  752.131536  \n",
      "2004     435.871230  286.558099      175.504989     374.104286  719.400432  \n",
      "2005     373.683796  237.049001      165.837781     349.084396  654.209448  \n",
      "2006     357.538364  246.182756      152.211210     322.349183  573.900487  \n",
      "2007     316.688130  220.134427      133.226568     312.705355  538.049649  \n",
      "2008     280.681484  176.178155      128.940222     281.523120  467.465611  \n",
      "2009     280.072713  232.975908      146.108957     277.990120  501.902895  \n",
      "2010     299.589390  192.474197      139.669050     270.626170  489.110715  \n",
      "2011     284.665878  181.792244      121.402202     262.424945  411.070200  \n",
      "2012     305.848446  179.876633      121.298774     251.092844  361.872472  \n",
      "2013     308.891294  168.246174      115.723751     245.943573  336.164594  \n",
      "2014     326.626802  181.244245      105.667450     233.376149  339.882335  \n",
      "2015     329.978075  228.525390      114.122354     231.169140  419.419856  \n",
      "2016     340.995302  248.481321      116.029686     233.094826  430.759758  \n",
      "2017     357.125736  254.145339      108.475918     218.877377  396.143133  \n",
      "2018     345.513016  259.232245       97.646616     205.894227  446.830924  \n",
      "\n",
      "[24 rows x 66 columns]\n",
      "      Fossil CO2 Emissions (tons) CO2 emisions change  \\\n",
      "Year                                                    \n",
      "1971                 1.567974e+10              -0.50%   \n",
      "1972                 1.646490e+10               5.01%   \n",
      "1973                 1.744179e+10               5.93%   \n",
      "1974                 1.739636e+10              -0.26%   \n",
      "1975                 1.734651e+10              -0.29%   \n",
      "1976                 1.832902e+10               5.66%   \n",
      "1977                 1.887432e+10               2.98%   \n",
      "1978                 1.947737e+10               3.20%   \n",
      "1979                 2.003192e+10               2.85%   \n",
      "1980                 1.980384e+10              -1.14%   \n",
      "1981                 1.943973e+10              -1.84%   \n",
      "1982                 1.918421e+10              -1.31%   \n",
      "1983                 1.930170e+10               0.61%   \n",
      "1984                 1.990465e+10               3.12%   \n",
      "1985                 2.017669e+10               1.37%   \n",
      "1986                 2.053026e+10               1.75%   \n",
      "1987                 2.119542e+10               3.24%   \n",
      "1988                 2.194569e+10               3.54%   \n",
      "1989                 2.234175e+10               1.80%   \n",
      "1990                 2.245044e+10               0.49%   \n",
      "1991                 2.259104e+10               0.63%   \n",
      "1992                 2.248860e+10              -0.45%   \n",
      "1993                 2.266435e+10               0.78%   \n",
      "1994                 2.289896e+10               1.04%   \n",
      "1995                 2.361914e+10               3.15%   \n",
      "1996                 2.407594e+10               1.93%   \n",
      "1997                 2.439828e+10               1.34%   \n",
      "1998                 2.454165e+10               0.59%   \n",
      "1999                 2.473371e+10               0.78%   \n",
      "2000                 2.559373e+10               3.48%   \n",
      "2001                 2.587790e+10               1.11%   \n",
      "2002                 2.635008e+10               1.82%   \n",
      "2003                 2.751393e+10               4.42%   \n",
      "2004                 2.872931e+10               4.42%   \n",
      "2005                 2.976901e+10               3.62%   \n",
      "2006                 3.075617e+10               3.32%   \n",
      "2007                 3.191650e+10               3.77%   \n",
      "2008                 3.212443e+10               0.65%   \n",
      "2009                 3.177052e+10              -1.10%   \n",
      "2010                 3.358779e+10               5.72%   \n",
      "2011                 3.457839e+10               2.95%   \n",
      "2012                 3.479056e+10               0.61%   \n",
      "2013                 3.541660e+10               1.80%   \n",
      "2014                 3.568678e+10               0.76%   \n",
      "2015                 3.563108e+10              -0.16%   \n",
      "2016                 3.575330e+10               0.34%   \n",
      "\n",
      "      CO2 emissions per capita     Population Pop. Change  \n",
      "Year                                                       \n",
      "1971                      4.15  3,775,759,617       2.04%  \n",
      "1972                      4.27  3,851,650,245       2.01%  \n",
      "1973                      4.44  3,927,780,238       1.98%  \n",
      "1974                      4.34  4,003,794,172       1.94%  \n",
      "1975                      4.25  4,079,480,606       1.89%  \n",
      "1976                      4.41  4,154,666,864       1.84%  \n",
      "1977                      4.46  4,229,506,060       1.80%  \n",
      "1978                      4.52  4,304,533,501       1.77%  \n",
      "1979                      4.57  4,380,506,100       1.76%  \n",
      "1980                      4.44  4,458,003,514       1.77%  \n",
      "1981                      4.28  4,536,996,762       1.77%  \n",
      "1982                      4.15  4,617,386,542       1.77%  \n",
      "1983                      4.11  4,699,569,304       1.78%  \n",
      "1984                      4.16  4,784,011,621       1.80%  \n",
      "1985                      4.14  4,870,921,740       1.82%  \n",
      "1986                      4.14  4,960,567,912       1.84%  \n",
      "1987                      4.20  5,052,522,147       1.85%  \n",
      "1988                      4.27  5,145,426,008       1.84%  \n",
      "1989                      4.27  5,237,441,558       1.79%  \n",
      "1990                      4.21  5,327,231,061       1.71%  \n",
      "1991                      4.17  5,414,289,444       1.63%  \n",
      "1992                      4.09  5,498,919,809       1.56%  \n",
      "1993                      4.06  5,581,597,546       1.50%  \n",
      "1994                      4.04  5,663,150,427       1.46%  \n",
      "1995                      4.11  5,744,212,979       1.43%  \n",
      "1996                      4.13  5,824,891,951       1.40%  \n",
      "1997                      4.13  5,905,045,788       1.38%  \n",
      "1998                      4.10  5,984,793,942       1.35%  \n",
      "1999                      4.08  6,064,239,055       1.33%  \n",
      "2000                      4.17  6,143,493,823       1.31%  \n",
      "2001                      4.16  6,222,626,606       1.29%  \n",
      "2002                      4.18  6,301,773,188       1.27%  \n",
      "2003                      4.31  6,381,185,114       1.26%  \n",
      "2004                      4.45  6,461,159,389       1.25%  \n",
      "2005                      4.55  6,541,907,027       1.25%  \n",
      "2006                      4.64  6,623,517,833       1.25%  \n",
      "2007                      4.76  6,705,946,610       1.24%  \n",
      "2008                      4.73  6,789,088,686       1.24%  \n",
      "2009                      4.62  6,872,767,093       1.23%  \n",
      "2010                      4.83  6,956,823,603       1.22%  \n",
      "2011                      4.91  7,041,194,301       1.21%  \n",
      "2012                      4.88  7,125,828,059       1.20%  \n",
      "2013                      4.91  7,210,581,976       1.19%  \n",
      "2014                      4.89  7,295,290,765       1.17%  \n",
      "2015                      4.83  7,379,797,139       1.16%  \n",
      "2016                      4.79  7,464,022,049       1.14%  \n",
      "      GDP (Billion US Dollars) Per Capita (in US Dollars)  Percentage Growth \n",
      "Year                                                                         \n",
      "1960                   1392.28                        459                 NaN\n",
      "1961                   1448.63                        471              0.0379\n",
      "1962                   1550.55                        496              0.0532\n",
      "1963                   1671.61                        523              0.0519\n",
      "1964                   1830.29                        561              0.0656\n",
      "...                        ...                        ...                 ...\n",
      "2017                  81400.67                     10,741              0.0339\n",
      "2018                  86456.89                     11,284              0.0329\n",
      "2019                  87645.26                     11,320              0.0259\n",
      "2020                  85105.60                     10,882             -0.0312\n",
      "2021                  96513.08                     12,235              0.0587\n",
      "\n",
      "[62 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Industry</th>\n",
       "      <th>Agriculture, Fishing and Forestry</th>\n",
       "      <th>Consumable Product</th>\n",
       "      <th>Defence and Security</th>\n",
       "      <th>Direct Industrial product</th>\n",
       "      <th>Energy sources</th>\n",
       "      <th>Engineering, Machinery and Equipment</th>\n",
       "      <th>Health, Finance and Education</th>\n",
       "      <th>Housing and Entertainment Services</th>\n",
       "      <th>Mining Product and Activities</th>\n",
       "      <th>Other industry and services</th>\n",
       "      <th>Technology and Communication Activities</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Waste</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>334.980277</td>\n",
       "      <td>359.708621</td>\n",
       "      <td>139.032819</td>\n",
       "      <td>1205.307972</td>\n",
       "      <td>6242.310383</td>\n",
       "      <td>440.316131</td>\n",
       "      <td>138.464796</td>\n",
       "      <td>133.392836</td>\n",
       "      <td>570.048735</td>\n",
       "      <td>227.373793</td>\n",
       "      <td>171.112309</td>\n",
       "      <td>818.231750</td>\n",
       "      <td>304.748282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>328.091114</td>\n",
       "      <td>362.764414</td>\n",
       "      <td>133.063370</td>\n",
       "      <td>1103.698933</td>\n",
       "      <td>6071.495339</td>\n",
       "      <td>426.238352</td>\n",
       "      <td>130.259396</td>\n",
       "      <td>128.082494</td>\n",
       "      <td>509.945366</td>\n",
       "      <td>222.012290</td>\n",
       "      <td>162.990013</td>\n",
       "      <td>816.327820</td>\n",
       "      <td>299.660921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>339.236941</td>\n",
       "      <td>341.825512</td>\n",
       "      <td>141.093432</td>\n",
       "      <td>1124.092125</td>\n",
       "      <td>6697.178310</td>\n",
       "      <td>449.149721</td>\n",
       "      <td>134.130024</td>\n",
       "      <td>130.540106</td>\n",
       "      <td>568.962337</td>\n",
       "      <td>235.296420</td>\n",
       "      <td>168.582750</td>\n",
       "      <td>896.782990</td>\n",
       "      <td>311.988641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>366.202748</td>\n",
       "      <td>363.612317</td>\n",
       "      <td>141.314087</td>\n",
       "      <td>1069.130972</td>\n",
       "      <td>6673.562862</td>\n",
       "      <td>435.552317</td>\n",
       "      <td>135.006600</td>\n",
       "      <td>133.385996</td>\n",
       "      <td>547.795917</td>\n",
       "      <td>242.767217</td>\n",
       "      <td>171.891684</td>\n",
       "      <td>911.028630</td>\n",
       "      <td>335.749216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>368.603839</td>\n",
       "      <td>361.507763</td>\n",
       "      <td>137.467924</td>\n",
       "      <td>879.425689</td>\n",
       "      <td>6557.548472</td>\n",
       "      <td>408.246487</td>\n",
       "      <td>131.963029</td>\n",
       "      <td>129.267971</td>\n",
       "      <td>532.889079</td>\n",
       "      <td>235.365827</td>\n",
       "      <td>168.201025</td>\n",
       "      <td>868.416759</td>\n",
       "      <td>330.363784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>358.760341</td>\n",
       "      <td>346.782240</td>\n",
       "      <td>138.174343</td>\n",
       "      <td>904.238729</td>\n",
       "      <td>6331.377252</td>\n",
       "      <td>405.047692</td>\n",
       "      <td>128.789845</td>\n",
       "      <td>128.111615</td>\n",
       "      <td>517.003381</td>\n",
       "      <td>228.590973</td>\n",
       "      <td>163.360252</td>\n",
       "      <td>886.384422</td>\n",
       "      <td>301.871972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>344.332024</td>\n",
       "      <td>326.200426</td>\n",
       "      <td>131.741926</td>\n",
       "      <td>797.278256</td>\n",
       "      <td>6173.987481</td>\n",
       "      <td>374.299824</td>\n",
       "      <td>121.038531</td>\n",
       "      <td>121.157878</td>\n",
       "      <td>481.048194</td>\n",
       "      <td>216.841700</td>\n",
       "      <td>158.536545</td>\n",
       "      <td>851.642525</td>\n",
       "      <td>284.445490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>314.954381</td>\n",
       "      <td>289.425875</td>\n",
       "      <td>117.192138</td>\n",
       "      <td>732.511219</td>\n",
       "      <td>5512.703014</td>\n",
       "      <td>347.071884</td>\n",
       "      <td>108.883766</td>\n",
       "      <td>108.424134</td>\n",
       "      <td>417.399571</td>\n",
       "      <td>190.880605</td>\n",
       "      <td>140.280237</td>\n",
       "      <td>753.055069</td>\n",
       "      <td>253.743564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>270.617646</td>\n",
       "      <td>243.726550</td>\n",
       "      <td>106.556505</td>\n",
       "      <td>642.185141</td>\n",
       "      <td>4682.492903</td>\n",
       "      <td>293.171566</td>\n",
       "      <td>92.885297</td>\n",
       "      <td>92.938551</td>\n",
       "      <td>355.675892</td>\n",
       "      <td>162.403600</td>\n",
       "      <td>115.495576</td>\n",
       "      <td>662.901437</td>\n",
       "      <td>212.072910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>247.594206</td>\n",
       "      <td>214.763248</td>\n",
       "      <td>91.823422</td>\n",
       "      <td>551.879876</td>\n",
       "      <td>4157.870173</td>\n",
       "      <td>258.898593</td>\n",
       "      <td>81.593105</td>\n",
       "      <td>80.855851</td>\n",
       "      <td>310.612663</td>\n",
       "      <td>142.071385</td>\n",
       "      <td>103.680813</td>\n",
       "      <td>591.188223</td>\n",
       "      <td>187.671161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>219.527723</td>\n",
       "      <td>190.043531</td>\n",
       "      <td>80.858914</td>\n",
       "      <td>896.308657</td>\n",
       "      <td>3658.172114</td>\n",
       "      <td>278.805835</td>\n",
       "      <td>73.165838</td>\n",
       "      <td>72.121117</td>\n",
       "      <td>288.710389</td>\n",
       "      <td>127.066199</td>\n",
       "      <td>94.468516</td>\n",
       "      <td>535.243058</td>\n",
       "      <td>168.353958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>188.892392</td>\n",
       "      <td>155.946054</td>\n",
       "      <td>72.536078</td>\n",
       "      <td>457.406719</td>\n",
       "      <td>3255.103986</td>\n",
       "      <td>248.410443</td>\n",
       "      <td>61.536946</td>\n",
       "      <td>62.328316</td>\n",
       "      <td>256.502031</td>\n",
       "      <td>107.480715</td>\n",
       "      <td>76.569650</td>\n",
       "      <td>466.923983</td>\n",
       "      <td>141.335460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>167.422759</td>\n",
       "      <td>134.763507</td>\n",
       "      <td>62.959064</td>\n",
       "      <td>415.001946</td>\n",
       "      <td>2850.600898</td>\n",
       "      <td>220.093843</td>\n",
       "      <td>54.803524</td>\n",
       "      <td>57.925567</td>\n",
       "      <td>222.748196</td>\n",
       "      <td>94.921721</td>\n",
       "      <td>71.419827</td>\n",
       "      <td>402.294598</td>\n",
       "      <td>124.410918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>182.829938</td>\n",
       "      <td>147.515245</td>\n",
       "      <td>65.830928</td>\n",
       "      <td>427.494768</td>\n",
       "      <td>2850.929763</td>\n",
       "      <td>212.692403</td>\n",
       "      <td>54.824459</td>\n",
       "      <td>60.792025</td>\n",
       "      <td>254.167017</td>\n",
       "      <td>103.185138</td>\n",
       "      <td>76.569330</td>\n",
       "      <td>449.653700</td>\n",
       "      <td>132.029432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>169.696655</td>\n",
       "      <td>131.215768</td>\n",
       "      <td>61.473579</td>\n",
       "      <td>407.437505</td>\n",
       "      <td>2640.333719</td>\n",
       "      <td>178.093206</td>\n",
       "      <td>49.500304</td>\n",
       "      <td>55.192925</td>\n",
       "      <td>233.729618</td>\n",
       "      <td>94.775075</td>\n",
       "      <td>70.241203</td>\n",
       "      <td>412.421395</td>\n",
       "      <td>125.307430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>145.198324</td>\n",
       "      <td>116.619381</td>\n",
       "      <td>55.241614</td>\n",
       "      <td>359.891472</td>\n",
       "      <td>2370.786162</td>\n",
       "      <td>156.453897</td>\n",
       "      <td>43.488931</td>\n",
       "      <td>47.677940</td>\n",
       "      <td>216.439464</td>\n",
       "      <td>83.250081</td>\n",
       "      <td>63.265801</td>\n",
       "      <td>374.980184</td>\n",
       "      <td>110.302630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>145.865801</td>\n",
       "      <td>116.986821</td>\n",
       "      <td>52.319747</td>\n",
       "      <td>370.122954</td>\n",
       "      <td>2359.734416</td>\n",
       "      <td>146.702062</td>\n",
       "      <td>42.677069</td>\n",
       "      <td>45.598414</td>\n",
       "      <td>233.085490</td>\n",
       "      <td>84.567723</td>\n",
       "      <td>59.328301</td>\n",
       "      <td>357.440346</td>\n",
       "      <td>103.229317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>139.929996</td>\n",
       "      <td>111.616723</td>\n",
       "      <td>51.513848</td>\n",
       "      <td>371.213096</td>\n",
       "      <td>2254.321774</td>\n",
       "      <td>141.471801</td>\n",
       "      <td>41.422701</td>\n",
       "      <td>43.847905</td>\n",
       "      <td>223.262295</td>\n",
       "      <td>81.801097</td>\n",
       "      <td>56.327284</td>\n",
       "      <td>342.299632</td>\n",
       "      <td>105.119309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>138.083049</td>\n",
       "      <td>113.106319</td>\n",
       "      <td>52.560685</td>\n",
       "      <td>342.656337</td>\n",
       "      <td>2330.702165</td>\n",
       "      <td>139.786990</td>\n",
       "      <td>40.240192</td>\n",
       "      <td>42.280771</td>\n",
       "      <td>233.047956</td>\n",
       "      <td>81.853067</td>\n",
       "      <td>57.049262</td>\n",
       "      <td>340.047115</td>\n",
       "      <td>103.524158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>149.337663</td>\n",
       "      <td>129.663254</td>\n",
       "      <td>57.512771</td>\n",
       "      <td>437.516546</td>\n",
       "      <td>2550.452487</td>\n",
       "      <td>153.166442</td>\n",
       "      <td>44.743888</td>\n",
       "      <td>47.095921</td>\n",
       "      <td>320.371506</td>\n",
       "      <td>91.792172</td>\n",
       "      <td>64.934389</td>\n",
       "      <td>390.582084</td>\n",
       "      <td>112.056034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>149.322367</td>\n",
       "      <td>133.308797</td>\n",
       "      <td>60.289399</td>\n",
       "      <td>665.604935</td>\n",
       "      <td>2788.489017</td>\n",
       "      <td>146.927808</td>\n",
       "      <td>45.978601</td>\n",
       "      <td>48.103252</td>\n",
       "      <td>358.125464</td>\n",
       "      <td>93.787896</td>\n",
       "      <td>65.775258</td>\n",
       "      <td>407.626522</td>\n",
       "      <td>116.310494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>138.063747</td>\n",
       "      <td>119.594850</td>\n",
       "      <td>55.259296</td>\n",
       "      <td>323.766872</td>\n",
       "      <td>2480.397014</td>\n",
       "      <td>126.871331</td>\n",
       "      <td>39.600751</td>\n",
       "      <td>44.364078</td>\n",
       "      <td>268.396975</td>\n",
       "      <td>87.014558</td>\n",
       "      <td>59.250333</td>\n",
       "      <td>375.469625</td>\n",
       "      <td>106.600653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Industry  Agriculture, Fishing and Forestry  Consumable Product  \\\n",
       "Year                                                              \n",
       "1995                             359.649389          376.594487   \n",
       "1996                             334.980277          359.708621   \n",
       "1997                             328.091114          362.764414   \n",
       "1998                             339.236941          341.825512   \n",
       "1999                             366.202748          363.612317   \n",
       "2000                             368.603839          361.507763   \n",
       "2001                             358.760341          346.782240   \n",
       "2002                             344.332024          326.200426   \n",
       "2003                             314.954381          289.425875   \n",
       "2004                             270.617646          243.726550   \n",
       "2005                             247.594206          214.763248   \n",
       "2006                             219.527723          190.043531   \n",
       "2007                             188.892392          155.946054   \n",
       "2008                             167.422759          134.763507   \n",
       "2009                             182.829938          147.515245   \n",
       "2010                             169.696655          131.215768   \n",
       "2011                             145.198324          116.619381   \n",
       "2012                             145.865801          116.986821   \n",
       "2013                             139.929996          111.616723   \n",
       "2014                             138.083049          113.106319   \n",
       "2015                             149.337663          129.663254   \n",
       "2016                             149.322367          133.308797   \n",
       "2017                             142.961477          126.643680   \n",
       "2018                             138.063747          119.594850   \n",
       "\n",
       "Industry  Defence and Security  Direct Industrial product  Energy sources  \\\n",
       "Year                                                                        \n",
       "1995                153.550946                1197.997726     6250.669630   \n",
       "1996                139.032819                1205.307972     6242.310383   \n",
       "1997                133.063370                1103.698933     6071.495339   \n",
       "1998                141.093432                1124.092125     6697.178310   \n",
       "1999                141.314087                1069.130972     6673.562862   \n",
       "2000                137.467924                 879.425689     6557.548472   \n",
       "2001                138.174343                 904.238729     6331.377252   \n",
       "2002                131.741926                 797.278256     6173.987481   \n",
       "2003                117.192138                 732.511219     5512.703014   \n",
       "2004                106.556505                 642.185141     4682.492903   \n",
       "2005                 91.823422                 551.879876     4157.870173   \n",
       "2006                 80.858914                 896.308657     3658.172114   \n",
       "2007                 72.536078                 457.406719     3255.103986   \n",
       "2008                 62.959064                 415.001946     2850.600898   \n",
       "2009                 65.830928                 427.494768     2850.929763   \n",
       "2010                 61.473579                 407.437505     2640.333719   \n",
       "2011                 55.241614                 359.891472     2370.786162   \n",
       "2012                 52.319747                 370.122954     2359.734416   \n",
       "2013                 51.513848                 371.213096     2254.321774   \n",
       "2014                 52.560685                 342.656337     2330.702165   \n",
       "2015                 57.512771                 437.516546     2550.452487   \n",
       "2016                 60.289399                 665.604935     2788.489017   \n",
       "2017                 57.980549                 454.936748     2627.290346   \n",
       "2018                 55.259296                 323.766872     2480.397014   \n",
       "\n",
       "Industry  Engineering, Machinery and Equipment  Health, Finance and Education  \\\n",
       "Year                                                                            \n",
       "1995                                464.305545                     146.846622   \n",
       "1996                                440.316131                     138.464796   \n",
       "1997                                426.238352                     130.259396   \n",
       "1998                                449.149721                     134.130024   \n",
       "1999                                435.552317                     135.006600   \n",
       "2000                                408.246487                     131.963029   \n",
       "2001                                405.047692                     128.789845   \n",
       "2002                                374.299824                     121.038531   \n",
       "2003                                347.071884                     108.883766   \n",
       "2004                                293.171566                      92.885297   \n",
       "2005                                258.898593                      81.593105   \n",
       "2006                                278.805835                      73.165838   \n",
       "2007                                248.410443                      61.536946   \n",
       "2008                                220.093843                      54.803524   \n",
       "2009                                212.692403                      54.824459   \n",
       "2010                                178.093206                      49.500304   \n",
       "2011                                156.453897                      43.488931   \n",
       "2012                                146.702062                      42.677069   \n",
       "2013                                141.471801                      41.422701   \n",
       "2014                                139.786990                      40.240192   \n",
       "2015                                153.166442                      44.743888   \n",
       "2016                                146.927808                      45.978601   \n",
       "2017                                136.244412                      42.991922   \n",
       "2018                                126.871331                      39.600751   \n",
       "\n",
       "Industry  Housing and Entertainment Services  Mining Product and Activities  \\\n",
       "Year                                                                          \n",
       "1995                              140.606581                     618.499748   \n",
       "1996                              133.392836                     570.048735   \n",
       "1997                              128.082494                     509.945366   \n",
       "1998                              130.540106                     568.962337   \n",
       "1999                              133.385996                     547.795917   \n",
       "2000                              129.267971                     532.889079   \n",
       "2001                              128.111615                     517.003381   \n",
       "2002                              121.157878                     481.048194   \n",
       "2003                              108.424134                     417.399571   \n",
       "2004                               92.938551                     355.675892   \n",
       "2005                               80.855851                     310.612663   \n",
       "2006                               72.121117                     288.710389   \n",
       "2007                               62.328316                     256.502031   \n",
       "2008                               57.925567                     222.748196   \n",
       "2009                               60.792025                     254.167017   \n",
       "2010                               55.192925                     233.729618   \n",
       "2011                               47.677940                     216.439464   \n",
       "2012                               45.598414                     233.085490   \n",
       "2013                               43.847905                     223.262295   \n",
       "2014                               42.280771                     233.047956   \n",
       "2015                               47.095921                     320.371506   \n",
       "2016                               48.103252                     358.125464   \n",
       "2017                               46.690589                     313.200166   \n",
       "2018                               44.364078                     268.396975   \n",
       "\n",
       "Industry  Other industry and services  \\\n",
       "Year                                    \n",
       "1995                       246.796681   \n",
       "1996                       227.373793   \n",
       "1997                       222.012290   \n",
       "1998                       235.296420   \n",
       "1999                       242.767217   \n",
       "2000                       235.365827   \n",
       "2001                       228.590973   \n",
       "2002                       216.841700   \n",
       "2003                       190.880605   \n",
       "2004                       162.403600   \n",
       "2005                       142.071385   \n",
       "2006                       127.066199   \n",
       "2007                       107.480715   \n",
       "2008                        94.921721   \n",
       "2009                       103.185138   \n",
       "2010                        94.775075   \n",
       "2011                        83.250081   \n",
       "2012                        84.567723   \n",
       "2013                        81.801097   \n",
       "2014                        81.853067   \n",
       "2015                        91.792172   \n",
       "2016                        93.787896   \n",
       "2017                        91.073037   \n",
       "2018                        87.014558   \n",
       "\n",
       "Industry  Technology and Communication Activities   Transport       Waste  \n",
       "Year                                                                       \n",
       "1995                                   182.340372  852.606143  323.266921  \n",
       "1996                                   171.112309  818.231750  304.748282  \n",
       "1997                                   162.990013  816.327820  299.660921  \n",
       "1998                                   168.582750  896.782990  311.988641  \n",
       "1999                                   171.891684  911.028630  335.749216  \n",
       "2000                                   168.201025  868.416759  330.363784  \n",
       "2001                                   163.360252  886.384422  301.871972  \n",
       "2002                                   158.536545  851.642525  284.445490  \n",
       "2003                                   140.280237  753.055069  253.743564  \n",
       "2004                                   115.495576  662.901437  212.072910  \n",
       "2005                                   103.680813  591.188223  187.671161  \n",
       "2006                                    94.468516  535.243058  168.353958  \n",
       "2007                                    76.569650  466.923983  141.335460  \n",
       "2008                                    71.419827  402.294598  124.410918  \n",
       "2009                                    76.569330  449.653700  132.029432  \n",
       "2010                                    70.241203  412.421395  125.307430  \n",
       "2011                                    63.265801  374.980184  110.302630  \n",
       "2012                                    59.328301  357.440346  103.229317  \n",
       "2013                                    56.327284  342.299632  105.119309  \n",
       "2014                                    57.049262  340.047115  103.524158  \n",
       "2015                                    64.934389  390.582084  112.056034  \n",
       "2016                                    65.775258  407.626522  116.310494  \n",
       "2017                                    62.557984  391.190304  111.598195  \n",
       "2018                                    59.250333  375.469625  106.600653  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ctry_transposed.index.name = 'Year'\n",
    "df_itry_transposed.index.name = 'Year'\n",
    "df_co2_date.index.name = 'Year'\n",
    "df_gdp_date.index.name = 'Year'\n",
    "print(df_ctry_transposed)\n",
    "print(df_co2_date)\n",
    "print(df_gdp_date)\n",
    "df_itry_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003',\n",
       "       '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012',\n",
       "       '2013', '2014', '2015', '2016', '2017', '2018'],\n",
       "      dtype='object', name='Year')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm the data type of the index\n",
    "df_itry_transposed.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968',\n",
       "       '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977',\n",
       "       '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',\n",
       "       '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',\n",
       "       '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004',\n",
       "       '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',\n",
       "       '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021'],\n",
       "      dtype='object', name='Year')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gdp_date.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979',\n",
       "       '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988',\n",
       "       '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997',\n",
       "       '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006',\n",
       "       '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015',\n",
       "       '2016'],\n",
       "      dtype='object', name='Year')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_co2_date.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the dataframes based on 'year' column\n",
    "df_itry_gdp_merged = pds.merge(df_itry_transposed, df_gdp_date[['GDP (Billion US Dollars)']], on='Year', how='left') \n",
    "df_ctry_gdp_merged = pds.merge(df_ctry_transposed, df_gdp_date[['GDP (Billion US Dollars)']], on='Year', how='left') \n",
    "df_itry_co2_merged = pds.merge(df_itry_transposed, df_co2_date[['Fossil CO2 Emissions (tons)']], on='Year', how='left') \n",
    "df_ctry_co2_merged = pds.merge(df_itry_transposed, df_co2_date[['Fossil CO2 Emissions (tons)']], on='Year', how='left') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert string index to datetime index\n",
    "df_itry_gdp_merged.index = pds.to_datetime(df_itry_gdp_merged.index)\n",
    "df_ctry_gdp_merged.index = pds.to_datetime(df_itry_gdp_merged.index)\n",
    "df_itry_co2_merged.index = pds.to_datetime(df_itry_co2_merged.index)\n",
    "df_ctry_co2_merged.index = pds.to_datetime(df_ctry_co2_merged.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: impyute in /usr/local/python/3.10.4/lib/python3.10/site-packages (0.0.8)\n",
      "Requirement already satisfied: scipy in /home/codespace/.local/lib/python3.10/site-packages (from impyute) (1.10.0)\n",
      "Requirement already satisfied: numpy in /home/codespace/.local/lib/python3.10/site-packages (from impyute) (1.24.2)\n",
      "Requirement already satisfied: scikit-learn in /home/codespace/.local/lib/python3.10/site-packages (from impyute) (1.2.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/codespace/.local/lib/python3.10/site-packages (from scikit-learn->impyute) (3.1.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /home/codespace/.local/lib/python3.10/site-packages (from scikit-learn->impyute) (1.2.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install impyute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Agriculture, Fishing and Forestry</th>\n",
       "      <th>Consumable Product</th>\n",
       "      <th>Defence and Security</th>\n",
       "      <th>Direct Industrial product</th>\n",
       "      <th>Energy sources</th>\n",
       "      <th>Engineering, Machinery and Equipment</th>\n",
       "      <th>Health, Finance and Education</th>\n",
       "      <th>Housing and Entertainment Services</th>\n",
       "      <th>Mining Product and Activities</th>\n",
       "      <th>Other industry and services</th>\n",
       "      <th>Technology and Communication Activities</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Waste</th>\n",
       "      <th>GDP (Billion US Dollars)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-02</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-03</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-04</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-05</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-30</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>138.063747</td>\n",
       "      <td>119.594850</td>\n",
       "      <td>55.259296</td>\n",
       "      <td>323.766872</td>\n",
       "      <td>2480.397014</td>\n",
       "      <td>126.871331</td>\n",
       "      <td>39.600751</td>\n",
       "      <td>44.364078</td>\n",
       "      <td>268.396975</td>\n",
       "      <td>87.014558</td>\n",
       "      <td>59.250333</td>\n",
       "      <td>375.469625</td>\n",
       "      <td>106.600653</td>\n",
       "      <td>86456.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8402 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Agriculture, Fishing and Forestry  Consumable Product  \\\n",
       "Year                                                                \n",
       "1995-01-01                         359.649389          376.594487   \n",
       "1995-01-02                         359.649389          376.594487   \n",
       "1995-01-03                         359.649389          376.594487   \n",
       "1995-01-04                         359.649389          376.594487   \n",
       "1995-01-05                         359.649389          376.594487   \n",
       "...                                       ...                 ...   \n",
       "2017-12-28                         142.961477          126.643680   \n",
       "2017-12-29                         142.961477          126.643680   \n",
       "2017-12-30                         142.961477          126.643680   \n",
       "2017-12-31                         142.961477          126.643680   \n",
       "2018-01-01                         138.063747          119.594850   \n",
       "\n",
       "            Defence and Security  Direct Industrial product  Energy sources  \\\n",
       "Year                                                                          \n",
       "1995-01-01            153.550946                1197.997726     6250.669630   \n",
       "1995-01-02            153.550946                1197.997726     6250.669630   \n",
       "1995-01-03            153.550946                1197.997726     6250.669630   \n",
       "1995-01-04            153.550946                1197.997726     6250.669630   \n",
       "1995-01-05            153.550946                1197.997726     6250.669630   \n",
       "...                          ...                        ...             ...   \n",
       "2017-12-28             57.980549                 454.936748     2627.290346   \n",
       "2017-12-29             57.980549                 454.936748     2627.290346   \n",
       "2017-12-30             57.980549                 454.936748     2627.290346   \n",
       "2017-12-31             57.980549                 454.936748     2627.290346   \n",
       "2018-01-01             55.259296                 323.766872     2480.397014   \n",
       "\n",
       "            Engineering, Machinery and Equipment  \\\n",
       "Year                                               \n",
       "1995-01-01                            464.305545   \n",
       "1995-01-02                            464.305545   \n",
       "1995-01-03                            464.305545   \n",
       "1995-01-04                            464.305545   \n",
       "1995-01-05                            464.305545   \n",
       "...                                          ...   \n",
       "2017-12-28                            136.244412   \n",
       "2017-12-29                            136.244412   \n",
       "2017-12-30                            136.244412   \n",
       "2017-12-31                            136.244412   \n",
       "2018-01-01                            126.871331   \n",
       "\n",
       "            Health, Finance and Education  Housing and Entertainment Services  \\\n",
       "Year                                                                            \n",
       "1995-01-01                     146.846622                          140.606581   \n",
       "1995-01-02                     146.846622                          140.606581   \n",
       "1995-01-03                     146.846622                          140.606581   \n",
       "1995-01-04                     146.846622                          140.606581   \n",
       "1995-01-05                     146.846622                          140.606581   \n",
       "...                                   ...                                 ...   \n",
       "2017-12-28                      42.991922                           46.690589   \n",
       "2017-12-29                      42.991922                           46.690589   \n",
       "2017-12-30                      42.991922                           46.690589   \n",
       "2017-12-31                      42.991922                           46.690589   \n",
       "2018-01-01                      39.600751                           44.364078   \n",
       "\n",
       "            Mining Product and Activities  Other industry and services  \\\n",
       "Year                                                                     \n",
       "1995-01-01                     618.499748                   246.796681   \n",
       "1995-01-02                     618.499748                   246.796681   \n",
       "1995-01-03                     618.499748                   246.796681   \n",
       "1995-01-04                     618.499748                   246.796681   \n",
       "1995-01-05                     618.499748                   246.796681   \n",
       "...                                   ...                          ...   \n",
       "2017-12-28                     313.200166                    91.073037   \n",
       "2017-12-29                     313.200166                    91.073037   \n",
       "2017-12-30                     313.200166                    91.073037   \n",
       "2017-12-31                     313.200166                    91.073037   \n",
       "2018-01-01                     268.396975                    87.014558   \n",
       "\n",
       "            Technology and Communication Activities   Transport       Waste  \\\n",
       "Year                                                                          \n",
       "1995-01-01                               182.340372  852.606143  323.266921   \n",
       "1995-01-02                               182.340372  852.606143  323.266921   \n",
       "1995-01-03                               182.340372  852.606143  323.266921   \n",
       "1995-01-04                               182.340372  852.606143  323.266921   \n",
       "1995-01-05                               182.340372  852.606143  323.266921   \n",
       "...                                             ...         ...         ...   \n",
       "2017-12-28                                62.557984  391.190304  111.598195   \n",
       "2017-12-29                                62.557984  391.190304  111.598195   \n",
       "2017-12-30                                62.557984  391.190304  111.598195   \n",
       "2017-12-31                                62.557984  391.190304  111.598195   \n",
       "2018-01-01                                59.250333  375.469625  106.600653   \n",
       "\n",
       "            GDP (Billion US Dollars)  \n",
       "Year                                  \n",
       "1995-01-01                  31048.48  \n",
       "1995-01-02                  31048.48  \n",
       "1995-01-03                  31048.48  \n",
       "1995-01-04                  31048.48  \n",
       "1995-01-05                  31048.48  \n",
       "...                              ...  \n",
       "2017-12-28                  81400.67  \n",
       "2017-12-29                  81400.67  \n",
       "2017-12-30                  81400.67  \n",
       "2017-12-31                  81400.67  \n",
       "2018-01-01                  86456.89  \n",
       "\n",
       "[8402 rows x 14 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using Multivariate Imputation by Chained Equation (MICE) method to fill the missing values in a dataset\n",
    "from impyute.imputation.cs import mice\n",
    "\n",
    "# X = data.drop('Survived', axis=1)\n",
    "# imputed = mice(df_itry_gdp_merged.values)\n",
    "# mice_ages = imputed[:, 2]\n",
    "\n",
    "# Resample to daily and forward fill missing values\n",
    "# df_itry_gdp_daily = df_itry_gdp_merged.resample('d').ffill()\n",
    "# df_ctry_gdp_daily = df_ctry_gdp_merged.resample('d').mice(df_ctry_gdp_merged.values)\n",
    "# df_itry_co2_daily = df_itry_co2_merged.resample('d').mice(df_itry_co2_merged.values)\n",
    "# df_ctry_co2_daily = df_ctry_co2_merged.resample('d').mice(df_ctry_co2_merged.values)\n",
    "\n",
    "df_itry_gdp_daily = df_itry_gdp_merged.resample('d').ffill()\n",
    "df_ctry_gdp_daily = df_ctry_gdp_merged.resample('d').ffill()\n",
    "df_itry_co2_daily = df_itry_co2_merged.resample('d').ffill()\n",
    "df_ctry_co2_daily = df_ctry_co2_merged.resample('d').ffill()\n",
    "\n",
    "df_itry_gdp_daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Agriculture, Fishing and Forestry</th>\n",
       "      <th>Consumable Product</th>\n",
       "      <th>Defence and Security</th>\n",
       "      <th>Direct Industrial product</th>\n",
       "      <th>Energy sources</th>\n",
       "      <th>Engineering, Machinery and Equipment</th>\n",
       "      <th>Health, Finance and Education</th>\n",
       "      <th>Housing and Entertainment Services</th>\n",
       "      <th>Mining Product and Activities</th>\n",
       "      <th>Other industry and services</th>\n",
       "      <th>Technology and Communication Activities</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Waste</th>\n",
       "      <th>GDP (Billion US Dollars)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-02</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-03</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-04</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-05</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-30</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8402 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Agriculture, Fishing and Forestry  Consumable Product  \\\n",
       "Year                                                                \n",
       "1995-01-01                         359.649389          376.594487   \n",
       "1995-01-02                         359.649389          376.594487   \n",
       "1995-01-03                         359.649389          376.594487   \n",
       "1995-01-04                         359.649389          376.594487   \n",
       "1995-01-05                         359.649389          376.594487   \n",
       "...                                       ...                 ...   \n",
       "2017-12-28                         142.961477          126.643680   \n",
       "2017-12-29                         142.961477          126.643680   \n",
       "2017-12-30                         142.961477          126.643680   \n",
       "2017-12-31                         142.961477          126.643680   \n",
       "2018-01-01                         142.961477          126.643680   \n",
       "\n",
       "            Defence and Security  Direct Industrial product  Energy sources  \\\n",
       "Year                                                                          \n",
       "1995-01-01            153.550946                1197.997726     6250.669630   \n",
       "1995-01-02            153.550946                1197.997726     6250.669630   \n",
       "1995-01-03            153.550946                1197.997726     6250.669630   \n",
       "1995-01-04            153.550946                1197.997726     6250.669630   \n",
       "1995-01-05            153.550946                1197.997726     6250.669630   \n",
       "...                          ...                        ...             ...   \n",
       "2017-12-28             57.980549                 454.936748     2627.290346   \n",
       "2017-12-29             57.980549                 454.936748     2627.290346   \n",
       "2017-12-30             57.980549                 454.936748     2627.290346   \n",
       "2017-12-31             57.980549                 454.936748     2627.290346   \n",
       "2018-01-01             57.980549                 454.936748     2627.290346   \n",
       "\n",
       "            Engineering, Machinery and Equipment  \\\n",
       "Year                                               \n",
       "1995-01-01                            464.305545   \n",
       "1995-01-02                            464.305545   \n",
       "1995-01-03                            464.305545   \n",
       "1995-01-04                            464.305545   \n",
       "1995-01-05                            464.305545   \n",
       "...                                          ...   \n",
       "2017-12-28                            136.244412   \n",
       "2017-12-29                            136.244412   \n",
       "2017-12-30                            136.244412   \n",
       "2017-12-31                            136.244412   \n",
       "2018-01-01                            136.244412   \n",
       "\n",
       "            Health, Finance and Education  Housing and Entertainment Services  \\\n",
       "Year                                                                            \n",
       "1995-01-01                     146.846622                          140.606581   \n",
       "1995-01-02                     146.846622                          140.606581   \n",
       "1995-01-03                     146.846622                          140.606581   \n",
       "1995-01-04                     146.846622                          140.606581   \n",
       "1995-01-05                     146.846622                          140.606581   \n",
       "...                                   ...                                 ...   \n",
       "2017-12-28                      42.991922                           46.690589   \n",
       "2017-12-29                      42.991922                           46.690589   \n",
       "2017-12-30                      42.991922                           46.690589   \n",
       "2017-12-31                      42.991922                           46.690589   \n",
       "2018-01-01                      42.991922                           46.690589   \n",
       "\n",
       "            Mining Product and Activities  Other industry and services  \\\n",
       "Year                                                                     \n",
       "1995-01-01                     618.499748                   246.796681   \n",
       "1995-01-02                     618.499748                   246.796681   \n",
       "1995-01-03                     618.499748                   246.796681   \n",
       "1995-01-04                     618.499748                   246.796681   \n",
       "1995-01-05                     618.499748                   246.796681   \n",
       "...                                   ...                          ...   \n",
       "2017-12-28                     313.200166                    91.073037   \n",
       "2017-12-29                     313.200166                    91.073037   \n",
       "2017-12-30                     313.200166                    91.073037   \n",
       "2017-12-31                     313.200166                    91.073037   \n",
       "2018-01-01                     313.200166                    91.073037   \n",
       "\n",
       "            Technology and Communication Activities   Transport       Waste  \\\n",
       "Year                                                                          \n",
       "1995-01-01                               182.340372  852.606143  323.266921   \n",
       "1995-01-02                               182.340372  852.606143  323.266921   \n",
       "1995-01-03                               182.340372  852.606143  323.266921   \n",
       "1995-01-04                               182.340372  852.606143  323.266921   \n",
       "1995-01-05                               182.340372  852.606143  323.266921   \n",
       "...                                             ...         ...         ...   \n",
       "2017-12-28                                62.557984  391.190304  111.598195   \n",
       "2017-12-29                                62.557984  391.190304  111.598195   \n",
       "2017-12-30                                62.557984  391.190304  111.598195   \n",
       "2017-12-31                                62.557984  391.190304  111.598195   \n",
       "2018-01-01                                62.557984  391.190304  111.598195   \n",
       "\n",
       "            GDP (Billion US Dollars)  \n",
       "Year                                  \n",
       "1995-01-01                  31048.48  \n",
       "1995-01-02                  31048.48  \n",
       "1995-01-03                  31048.48  \n",
       "1995-01-04                  31048.48  \n",
       "1995-01-05                  31048.48  \n",
       "...                              ...  \n",
       "2017-12-28                  81400.67  \n",
       "2017-12-29                  81400.67  \n",
       "2017-12-30                  81400.67  \n",
       "2017-12-31                  81400.67  \n",
       "2018-01-01                  81400.67  \n",
       "\n",
       "[8402 rows x 14 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Moving average\n",
    "df_itry_gdp_daily.rolling(window=7, min_periods=1).median() # No difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Agriculture, Fishing and Forestry</th>\n",
       "      <th>Consumable Product</th>\n",
       "      <th>Defence and Security</th>\n",
       "      <th>Direct Industrial product</th>\n",
       "      <th>Energy sources</th>\n",
       "      <th>Engineering, Machinery and Equipment</th>\n",
       "      <th>Health, Finance and Education</th>\n",
       "      <th>Housing and Entertainment Services</th>\n",
       "      <th>Mining Product and Activities</th>\n",
       "      <th>Other industry and services</th>\n",
       "      <th>Technology and Communication Activities</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Waste</th>\n",
       "      <th>GDP (Billion US Dollars)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
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       "    <tr>\n",
       "      <th>1995-01-02</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
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       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
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       "    <tr>\n",
       "      <th>1995-01-03</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
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       "      <td>464.305545</td>\n",
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       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-04</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-05</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
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       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
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       "    <tr>\n",
       "      <th>2017-12-30</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>138.063747</td>\n",
       "      <td>119.594850</td>\n",
       "      <td>55.259296</td>\n",
       "      <td>323.766872</td>\n",
       "      <td>2480.397014</td>\n",
       "      <td>126.871331</td>\n",
       "      <td>39.600751</td>\n",
       "      <td>44.364078</td>\n",
       "      <td>268.396975</td>\n",
       "      <td>87.014558</td>\n",
       "      <td>59.250333</td>\n",
       "      <td>375.469625</td>\n",
       "      <td>106.600653</td>\n",
       "      <td>86456.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8402 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Agriculture, Fishing and Forestry  Consumable Product  \\\n",
       "Year                                                                \n",
       "1995-01-01                         359.649389          376.594487   \n",
       "1995-01-02                         359.649389          376.594487   \n",
       "1995-01-03                         359.649389          376.594487   \n",
       "1995-01-04                         359.649389          376.594487   \n",
       "1995-01-05                         359.649389          376.594487   \n",
       "...                                       ...                 ...   \n",
       "2017-12-28                         142.961477          126.643680   \n",
       "2017-12-29                         142.961477          126.643680   \n",
       "2017-12-30                         142.961477          126.643680   \n",
       "2017-12-31                         142.961477          126.643680   \n",
       "2018-01-01                         138.063747          119.594850   \n",
       "\n",
       "            Defence and Security  Direct Industrial product  Energy sources  \\\n",
       "Year                                                                          \n",
       "1995-01-01            153.550946                1197.997726     6250.669630   \n",
       "1995-01-02            153.550946                1197.997726     6250.669630   \n",
       "1995-01-03            153.550946                1197.997726     6250.669630   \n",
       "1995-01-04            153.550946                1197.997726     6250.669630   \n",
       "1995-01-05            153.550946                1197.997726     6250.669630   \n",
       "...                          ...                        ...             ...   \n",
       "2017-12-28             57.980549                 454.936748     2627.290346   \n",
       "2017-12-29             57.980549                 454.936748     2627.290346   \n",
       "2017-12-30             57.980549                 454.936748     2627.290346   \n",
       "2017-12-31             57.980549                 454.936748     2627.290346   \n",
       "2018-01-01             55.259296                 323.766872     2480.397014   \n",
       "\n",
       "            Engineering, Machinery and Equipment  \\\n",
       "Year                                               \n",
       "1995-01-01                            464.305545   \n",
       "1995-01-02                            464.305545   \n",
       "1995-01-03                            464.305545   \n",
       "1995-01-04                            464.305545   \n",
       "1995-01-05                            464.305545   \n",
       "...                                          ...   \n",
       "2017-12-28                            136.244412   \n",
       "2017-12-29                            136.244412   \n",
       "2017-12-30                            136.244412   \n",
       "2017-12-31                            136.244412   \n",
       "2018-01-01                            126.871331   \n",
       "\n",
       "            Health, Finance and Education  Housing and Entertainment Services  \\\n",
       "Year                                                                            \n",
       "1995-01-01                     146.846622                          140.606581   \n",
       "1995-01-02                     146.846622                          140.606581   \n",
       "1995-01-03                     146.846622                          140.606581   \n",
       "1995-01-04                     146.846622                          140.606581   \n",
       "1995-01-05                     146.846622                          140.606581   \n",
       "...                                   ...                                 ...   \n",
       "2017-12-28                      42.991922                           46.690589   \n",
       "2017-12-29                      42.991922                           46.690589   \n",
       "2017-12-30                      42.991922                           46.690589   \n",
       "2017-12-31                      42.991922                           46.690589   \n",
       "2018-01-01                      39.600751                           44.364078   \n",
       "\n",
       "            Mining Product and Activities  Other industry and services  \\\n",
       "Year                                                                     \n",
       "1995-01-01                     618.499748                   246.796681   \n",
       "1995-01-02                     618.499748                   246.796681   \n",
       "1995-01-03                     618.499748                   246.796681   \n",
       "1995-01-04                     618.499748                   246.796681   \n",
       "1995-01-05                     618.499748                   246.796681   \n",
       "...                                   ...                          ...   \n",
       "2017-12-28                     313.200166                    91.073037   \n",
       "2017-12-29                     313.200166                    91.073037   \n",
       "2017-12-30                     313.200166                    91.073037   \n",
       "2017-12-31                     313.200166                    91.073037   \n",
       "2018-01-01                     268.396975                    87.014558   \n",
       "\n",
       "            Technology and Communication Activities   Transport       Waste  \\\n",
       "Year                                                                          \n",
       "1995-01-01                               182.340372  852.606143  323.266921   \n",
       "1995-01-02                               182.340372  852.606143  323.266921   \n",
       "1995-01-03                               182.340372  852.606143  323.266921   \n",
       "1995-01-04                               182.340372  852.606143  323.266921   \n",
       "1995-01-05                               182.340372  852.606143  323.266921   \n",
       "...                                             ...         ...         ...   \n",
       "2017-12-28                                62.557984  391.190304  111.598195   \n",
       "2017-12-29                                62.557984  391.190304  111.598195   \n",
       "2017-12-30                                62.557984  391.190304  111.598195   \n",
       "2017-12-31                                62.557984  391.190304  111.598195   \n",
       "2018-01-01                                59.250333  375.469625  106.600653   \n",
       "\n",
       "            GDP (Billion US Dollars)  \n",
       "Year                                  \n",
       "1995-01-01                  31048.48  \n",
       "1995-01-02                  31048.48  \n",
       "1995-01-03                  31048.48  \n",
       "1995-01-04                  31048.48  \n",
       "1995-01-05                  31048.48  \n",
       "...                              ...  \n",
       "2017-12-28                  81400.67  \n",
       "2017-12-29                  81400.67  \n",
       "2017-12-30                  81400.67  \n",
       "2017-12-31                  81400.67  \n",
       "2018-01-01                  86456.89  \n",
       "\n",
       "[8402 rows x 14 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_itry_gdp_daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There are missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5125/3309175590.py:2: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df_itry_gdp_daily = df_itry_gdp_daily.fillna(df.mean())\n",
      "/tmp/ipykernel_5125/3309175590.py:3: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df_ctry_gdp_daily = df_ctry_gdp_daily.fillna(df.mean())\n",
      "/tmp/ipykernel_5125/3309175590.py:4: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df_itry_co2_daily = df_itry_co2_daily.fillna(df.mean())\n",
      "/tmp/ipykernel_5125/3309175590.py:5: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df_ctry_co2_daily = df_ctry_co2_daily.fillna(df.mean())\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Agriculture, Fishing and Forestry</th>\n",
       "      <th>Consumable Product</th>\n",
       "      <th>Defence and Security</th>\n",
       "      <th>Direct Industrial product</th>\n",
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       "      <th>Transport</th>\n",
       "      <th>Waste</th>\n",
       "      <th>GDP (Billion US Dollars)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
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       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-02</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-03</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-04</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-05</th>\n",
       "      <td>359.649389</td>\n",
       "      <td>376.594487</td>\n",
       "      <td>153.550946</td>\n",
       "      <td>1197.997726</td>\n",
       "      <td>6250.669630</td>\n",
       "      <td>464.305545</td>\n",
       "      <td>146.846622</td>\n",
       "      <td>140.606581</td>\n",
       "      <td>618.499748</td>\n",
       "      <td>246.796681</td>\n",
       "      <td>182.340372</td>\n",
       "      <td>852.606143</td>\n",
       "      <td>323.266921</td>\n",
       "      <td>31048.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-30</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>142.961477</td>\n",
       "      <td>126.643680</td>\n",
       "      <td>57.980549</td>\n",
       "      <td>454.936748</td>\n",
       "      <td>2627.290346</td>\n",
       "      <td>136.244412</td>\n",
       "      <td>42.991922</td>\n",
       "      <td>46.690589</td>\n",
       "      <td>313.200166</td>\n",
       "      <td>91.073037</td>\n",
       "      <td>62.557984</td>\n",
       "      <td>391.190304</td>\n",
       "      <td>111.598195</td>\n",
       "      <td>81400.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>138.063747</td>\n",
       "      <td>119.594850</td>\n",
       "      <td>55.259296</td>\n",
       "      <td>323.766872</td>\n",
       "      <td>2480.397014</td>\n",
       "      <td>126.871331</td>\n",
       "      <td>39.600751</td>\n",
       "      <td>44.364078</td>\n",
       "      <td>268.396975</td>\n",
       "      <td>87.014558</td>\n",
       "      <td>59.250333</td>\n",
       "      <td>375.469625</td>\n",
       "      <td>106.600653</td>\n",
       "      <td>86456.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8402 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Agriculture, Fishing and Forestry  Consumable Product  \\\n",
       "Year                                                                \n",
       "1995-01-01                         359.649389          376.594487   \n",
       "1995-01-02                         359.649389          376.594487   \n",
       "1995-01-03                         359.649389          376.594487   \n",
       "1995-01-04                         359.649389          376.594487   \n",
       "1995-01-05                         359.649389          376.594487   \n",
       "...                                       ...                 ...   \n",
       "2017-12-28                         142.961477          126.643680   \n",
       "2017-12-29                         142.961477          126.643680   \n",
       "2017-12-30                         142.961477          126.643680   \n",
       "2017-12-31                         142.961477          126.643680   \n",
       "2018-01-01                         138.063747          119.594850   \n",
       "\n",
       "            Defence and Security  Direct Industrial product  Energy sources  \\\n",
       "Year                                                                          \n",
       "1995-01-01            153.550946                1197.997726     6250.669630   \n",
       "1995-01-02            153.550946                1197.997726     6250.669630   \n",
       "1995-01-03            153.550946                1197.997726     6250.669630   \n",
       "1995-01-04            153.550946                1197.997726     6250.669630   \n",
       "1995-01-05            153.550946                1197.997726     6250.669630   \n",
       "...                          ...                        ...             ...   \n",
       "2017-12-28             57.980549                 454.936748     2627.290346   \n",
       "2017-12-29             57.980549                 454.936748     2627.290346   \n",
       "2017-12-30             57.980549                 454.936748     2627.290346   \n",
       "2017-12-31             57.980549                 454.936748     2627.290346   \n",
       "2018-01-01             55.259296                 323.766872     2480.397014   \n",
       "\n",
       "            Engineering, Machinery and Equipment  \\\n",
       "Year                                               \n",
       "1995-01-01                            464.305545   \n",
       "1995-01-02                            464.305545   \n",
       "1995-01-03                            464.305545   \n",
       "1995-01-04                            464.305545   \n",
       "1995-01-05                            464.305545   \n",
       "...                                          ...   \n",
       "2017-12-28                            136.244412   \n",
       "2017-12-29                            136.244412   \n",
       "2017-12-30                            136.244412   \n",
       "2017-12-31                            136.244412   \n",
       "2018-01-01                            126.871331   \n",
       "\n",
       "            Health, Finance and Education  Housing and Entertainment Services  \\\n",
       "Year                                                                            \n",
       "1995-01-01                     146.846622                          140.606581   \n",
       "1995-01-02                     146.846622                          140.606581   \n",
       "1995-01-03                     146.846622                          140.606581   \n",
       "1995-01-04                     146.846622                          140.606581   \n",
       "1995-01-05                     146.846622                          140.606581   \n",
       "...                                   ...                                 ...   \n",
       "2017-12-28                      42.991922                           46.690589   \n",
       "2017-12-29                      42.991922                           46.690589   \n",
       "2017-12-30                      42.991922                           46.690589   \n",
       "2017-12-31                      42.991922                           46.690589   \n",
       "2018-01-01                      39.600751                           44.364078   \n",
       "\n",
       "            Mining Product and Activities  Other industry and services  \\\n",
       "Year                                                                     \n",
       "1995-01-01                     618.499748                   246.796681   \n",
       "1995-01-02                     618.499748                   246.796681   \n",
       "1995-01-03                     618.499748                   246.796681   \n",
       "1995-01-04                     618.499748                   246.796681   \n",
       "1995-01-05                     618.499748                   246.796681   \n",
       "...                                   ...                          ...   \n",
       "2017-12-28                     313.200166                    91.073037   \n",
       "2017-12-29                     313.200166                    91.073037   \n",
       "2017-12-30                     313.200166                    91.073037   \n",
       "2017-12-31                     313.200166                    91.073037   \n",
       "2018-01-01                     268.396975                    87.014558   \n",
       "\n",
       "            Technology and Communication Activities   Transport       Waste  \\\n",
       "Year                                                                          \n",
       "1995-01-01                               182.340372  852.606143  323.266921   \n",
       "1995-01-02                               182.340372  852.606143  323.266921   \n",
       "1995-01-03                               182.340372  852.606143  323.266921   \n",
       "1995-01-04                               182.340372  852.606143  323.266921   \n",
       "1995-01-05                               182.340372  852.606143  323.266921   \n",
       "...                                             ...         ...         ...   \n",
       "2017-12-28                                62.557984  391.190304  111.598195   \n",
       "2017-12-29                                62.557984  391.190304  111.598195   \n",
       "2017-12-30                                62.557984  391.190304  111.598195   \n",
       "2017-12-31                                62.557984  391.190304  111.598195   \n",
       "2018-01-01                                59.250333  375.469625  106.600653   \n",
       "\n",
       "            GDP (Billion US Dollars)  \n",
       "Year                                  \n",
       "1995-01-01                  31048.48  \n",
       "1995-01-02                  31048.48  \n",
       "1995-01-03                  31048.48  \n",
       "1995-01-04                  31048.48  \n",
       "1995-01-05                  31048.48  \n",
       "...                              ...  \n",
       "2017-12-28                  81400.67  \n",
       "2017-12-29                  81400.67  \n",
       "2017-12-30                  81400.67  \n",
       "2017-12-31                  81400.67  \n",
       "2018-01-01                  86456.89  \n",
       "\n",
       "[8402 rows x 14 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill missing values with the mean of each column\n",
    "df_itry_gdp_daily = df_itry_gdp_daily.fillna(df.mean())\n",
    "df_ctry_gdp_daily = df_ctry_gdp_daily.fillna(df.mean())\n",
    "df_itry_co2_daily = df_itry_co2_daily.fillna(df.mean())\n",
    "df_ctry_co2_daily = df_ctry_co2_daily.fillna(df.mean())\n",
    "df_itry_gdp_daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 8402 entries, 1995-01-01 to 2018-01-01\n",
      "Freq: D\n",
      "Data columns (total 14 columns):\n",
      " #   Column                                   Non-Null Count  Dtype  \n",
      "---  ------                                   --------------  -----  \n",
      " 0   Agriculture, Fishing and Forestry        8402 non-null   float64\n",
      " 1   Consumable Product                       8402 non-null   float64\n",
      " 2   Defence and Security                     8402 non-null   float64\n",
      " 3   Direct Industrial product                8402 non-null   float64\n",
      " 4   Energy sources                           8402 non-null   float64\n",
      " 5   Engineering, Machinery and Equipment     8402 non-null   float64\n",
      " 6   Health, Finance and Education            8402 non-null   float64\n",
      " 7   Housing and Entertainment Services       8402 non-null   float64\n",
      " 8   Mining Product and Activities            8402 non-null   float64\n",
      " 9   Other industry and services              8402 non-null   float64\n",
      " 10  Technology and Communication Activities  8402 non-null   float64\n",
      " 11  Transport                                8402 non-null   float64\n",
      " 12  Waste                                    8402 non-null   float64\n",
      " 13  GDP (Billion US Dollars)                 8402 non-null   float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 984.6 KB\n"
     ]
    }
   ],
   "source": [
    "df_itry_gdp_daily.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Plotting\n",
    "plt.figure(figsize=(20,6))\n",
    "\n",
    "sns.lineplot(data=df_itry_gdp_merged[['Agriculture, Fishing and Forestry','Engineering, Machinery and Equipment']])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Need rework (the quantity of data for each)\n",
    "plt.figure(figsize=(20,6))\n",
    "xis = sns.countplot(data=df_itry_gdp_daily)\n",
    "plt.xticks(x=df_itry_gdp_daily.index, rotation='vertical') \n",
    "# plt.legend(\n",
    "#     df_mod_ind.new_industry, \n",
    "#     title='Economic Sectors', \n",
    "#     title_fontsize=15, \n",
    "#     prop={'size':12}\n",
    "#     )\n",
    "plt.xlabel(\"Industry\")\n",
    "plt.title(\"Distribution of Economic Sectors Contribution to CO2 Emission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The CO2 emission time series dataset is ploted to visualize the dependency of the emission in the power generation with time.\n",
    "# fig, ax = plt.subplots()\n",
    "# for desc, group in Energy_sources:\n",
    "#     group.plot(x = \"Industry\", y='Value', label=desc,ax = ax, title='Carbon Emissions per Energy Source', fontsize = 20)\n",
    "#     ax.set_xlabel('Time(Monthly)')\n",
    "#     ax.set_ylabel('Carbon Emissions in MMT')\n",
    "#     ax.xaxis.label.set_size(20)\n",
    "#     ax.yaxis.label.set_size(20)\n",
    "#     ax.legend(fontsize = 16)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing Contd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Correlation of Features'}>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df_itry_gdp_daily.corr()\n",
    "# corr.style.background_gradient(cmap='YlOrRd')\n",
    "#Exploratory data analysis\n",
    "\n",
    "colormap = plt.cm.RdBu_r\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.title('Correlation of Features', y=1, size=15)\n",
    "sns.heatmap(corr, linewidths=0.15,vmin=-1.0, vmax=1.0, \n",
    "            square=True, cmap=colormap, linecolor='blue', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_itry_gdp_preprocessed = df_itry_gdp_daily.copy()\n",
    "df_ctry_gdp_preprocessed = df_ctry_gdp_daily.copy()\n",
    "df_itry_co2_preprocessed = df_itry_co2_daily.copy()\n",
    "df_ctry_co2_preprocessed = df_ctry_co2_daily.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export the preprocessed file to CSV\n",
    "# H:/My Drive/Python SQL and Tableau/python_code\n",
    "df_itry_gdp_preprocessed.to_csv('../dataset/global_CO2_emissions/world_gdp_with_focus_on_industries_preprocessed.csv', index=True)\n",
    "df_ctry_gdp_preprocessed.to_csv('../dataset/global_CO2_emissions/world_gdp_with_focus_on_countries_preprocessed.csv', index=True)\n",
    "df_itry_co2_preprocessed.to_csv('../dataset/global_CO2_emissions/global_CO2_emmissions_with_focus_on_industries_preprocessed.csv', index=True)\n",
    "df_ctry_co2_preprocessed.to_csv('../dataset/global_CO2_emissions/global_CO2_emmissions_with_focus_on_countries_preprocessed.csv', index=True)\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "3ad933181bd8a04b432d3370b9dc3b0662ad032c4dfaa4e4f1596c548f763858"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}