2020_Data_Cleaned.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from plotnine import ggplot, aes, geom_density, geom_line, geom_point, ggtitle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trading2020 \n",
    " - This file does not seem to seperate oil vs non-oil exports as in sheet 1-1 compared to sheet 1.1 in Trading20\n",
    "## Sheet 1-1\n",
    "- Over all Exports & Imports weight & value from 2011 - 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cleaned_Trading20= pd.read_csv('Cleaned_data/Trading2020_1-1_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cleaned_Trading20.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Added Difference in Weights & Difference in Value columns. (Exports - Imports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Imports Weight(1000 Tons)</th>\n",
       "      <th>Imports Vaule(Millions SAR)</th>\n",
       "      <th>Exports Weight(1000 Tons)</th>\n",
       "      <th>Exports Vaule(Millions SAR)</th>\n",
       "      <th>Difference in Weights</th>\n",
       "      <th>Difference in Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>63161</td>\n",
       "      <td>493449</td>\n",
       "      <td>449823</td>\n",
       "      <td>1367620</td>\n",
       "      <td>386662</td>\n",
       "      <td>874171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>74493</td>\n",
       "      <td>583473</td>\n",
       "      <td>467012</td>\n",
       "      <td>1456502</td>\n",
       "      <td>392519</td>\n",
       "      <td>873029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>82881</td>\n",
       "      <td>630582</td>\n",
       "      <td>462339</td>\n",
       "      <td>1409523</td>\n",
       "      <td>379458</td>\n",
       "      <td>778941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>82536</td>\n",
       "      <td>651876</td>\n",
       "      <td>456962</td>\n",
       "      <td>1284122</td>\n",
       "      <td>374426</td>\n",
       "      <td>632246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>84206</td>\n",
       "      <td>655033</td>\n",
       "      <td>466243</td>\n",
       "      <td>763313</td>\n",
       "      <td>382037</td>\n",
       "      <td>108280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>76731</td>\n",
       "      <td>525636</td>\n",
       "      <td>500277</td>\n",
       "      <td>688423</td>\n",
       "      <td>423546</td>\n",
       "      <td>162787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017</td>\n",
       "      <td>75998</td>\n",
       "      <td>504447</td>\n",
       "      <td>483767</td>\n",
       "      <td>831881</td>\n",
       "      <td>407769</td>\n",
       "      <td>327434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "      <td>75239</td>\n",
       "      <td>513993</td>\n",
       "      <td>509797</td>\n",
       "      <td>1103900</td>\n",
       "      <td>434558</td>\n",
       "      <td>589907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019</td>\n",
       "      <td>77512</td>\n",
       "      <td>574361</td>\n",
       "      <td>492262</td>\n",
       "      <td>981012</td>\n",
       "      <td>414750</td>\n",
       "      <td>406651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020</td>\n",
       "      <td>86137</td>\n",
       "      <td>517491</td>\n",
       "      <td>470531</td>\n",
       "      <td>651952</td>\n",
       "      <td>384394</td>\n",
       "      <td>134461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Imports Weight(1000 Tons)  Imports Vaule(Millions SAR)  \\\n",
       "0  2011                      63161                       493449   \n",
       "1  2012                      74493                       583473   \n",
       "2  2013                      82881                       630582   \n",
       "3  2014                      82536                       651876   \n",
       "4  2015                      84206                       655033   \n",
       "5  2016                      76731                       525636   \n",
       "6  2017                      75998                       504447   \n",
       "7  2018                      75239                       513993   \n",
       "8  2019                      77512                       574361   \n",
       "9  2020                      86137                       517491   \n",
       "\n",
       "   Exports Weight(1000 Tons)  Exports Vaule(Millions SAR)  \\\n",
       "0                     449823                      1367620   \n",
       "1                     467012                      1456502   \n",
       "2                     462339                      1409523   \n",
       "3                     456962                      1284122   \n",
       "4                     466243                       763313   \n",
       "5                     500277                       688423   \n",
       "6                     483767                       831881   \n",
       "7                     509797                      1103900   \n",
       "8                     492262                       981012   \n",
       "9                     470531                       651952   \n",
       "\n",
       "   Difference in Weights  Difference in Value  \n",
       "0                 386662               874171  \n",
       "1                 392519               873029  \n",
       "2                 379458               778941  \n",
       "3                 374426               632246  \n",
       "4                 382037               108280  \n",
       "5                 423546               162787  \n",
       "6                 407769               327434  \n",
       "7                 434558               589907  \n",
       "8                 414750               406651  \n",
       "9                 384394               134461  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cleaned_Trading20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Imports Weight(1000 Tons)</th>\n",
       "      <th>Imports Vaule(Millions SAR)</th>\n",
       "      <th>Exports Weight(1000 Tons)</th>\n",
       "      <th>Exports Vaule(Millions SAR)</th>\n",
       "      <th>Difference in Weights</th>\n",
       "      <th>Difference in Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2015.50000</td>\n",
       "      <td>77889.400000</td>\n",
       "      <td>565034.100000</td>\n",
       "      <td>475901.300000</td>\n",
       "      <td>1.053825e+06</td>\n",
       "      <td>398011.900000</td>\n",
       "      <td>488790.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.02765</td>\n",
       "      <td>6624.675576</td>\n",
       "      <td>62873.596867</td>\n",
       "      <td>19710.370373</td>\n",
       "      <td>3.121004e+05</td>\n",
       "      <td>20718.084894</td>\n",
       "      <td>301946.910181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2011.00000</td>\n",
       "      <td>63161.000000</td>\n",
       "      <td>493449.000000</td>\n",
       "      <td>449823.000000</td>\n",
       "      <td>6.519520e+05</td>\n",
       "      <td>374426.000000</td>\n",
       "      <td>108280.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2013.25000</td>\n",
       "      <td>75428.750000</td>\n",
       "      <td>514867.500000</td>\n",
       "      <td>463315.000000</td>\n",
       "      <td>7.804550e+05</td>\n",
       "      <td>382626.250000</td>\n",
       "      <td>203948.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015.50000</td>\n",
       "      <td>77121.500000</td>\n",
       "      <td>549998.500000</td>\n",
       "      <td>468771.500000</td>\n",
       "      <td>1.042456e+06</td>\n",
       "      <td>389590.500000</td>\n",
       "      <td>498279.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.75000</td>\n",
       "      <td>82794.750000</td>\n",
       "      <td>618804.750000</td>\n",
       "      <td>490138.250000</td>\n",
       "      <td>1.346746e+06</td>\n",
       "      <td>413004.750000</td>\n",
       "      <td>742267.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.00000</td>\n",
       "      <td>86137.000000</td>\n",
       "      <td>655033.000000</td>\n",
       "      <td>509797.000000</td>\n",
       "      <td>1.456502e+06</td>\n",
       "      <td>434558.000000</td>\n",
       "      <td>874171.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Year  Imports Weight(1000 Tons)  Imports Vaule(Millions SAR)  \\\n",
       "count    10.00000                  10.000000                    10.000000   \n",
       "mean   2015.50000               77889.400000                565034.100000   \n",
       "std       3.02765                6624.675576                 62873.596867   \n",
       "min    2011.00000               63161.000000                493449.000000   \n",
       "25%    2013.25000               75428.750000                514867.500000   \n",
       "50%    2015.50000               77121.500000                549998.500000   \n",
       "75%    2017.75000               82794.750000                618804.750000   \n",
       "max    2020.00000               86137.000000                655033.000000   \n",
       "\n",
       "       Exports Weight(1000 Tons)  Exports Vaule(Millions SAR)  \\\n",
       "count                  10.000000                 1.000000e+01   \n",
       "mean               475901.300000                 1.053825e+06   \n",
       "std                 19710.370373                 3.121004e+05   \n",
       "min                449823.000000                 6.519520e+05   \n",
       "25%                463315.000000                 7.804550e+05   \n",
       "50%                468771.500000                 1.042456e+06   \n",
       "75%                490138.250000                 1.346746e+06   \n",
       "max                509797.000000                 1.456502e+06   \n",
       "\n",
       "       Difference in Weights  Difference in Value  \n",
       "count              10.000000            10.000000  \n",
       "mean           398011.900000        488790.700000  \n",
       "std             20718.084894        301946.910181  \n",
       "min            374426.000000        108280.000000  \n",
       "25%            382626.250000        203948.750000  \n",
       "50%            389590.500000        498279.000000  \n",
       "75%            413004.750000        742267.250000  \n",
       "max            434558.000000        874171.000000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cleaned_Trading20.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(Cleaned_Trading20['Year'], Cleaned_Trading20['Imports Weight(1000 Tons)'], label= 'Imports')\n",
    "plt.bar(Cleaned_Trading20['Year'], Cleaned_Trading20['Exports Weight(1000 Tons)'], bottom=Cleaned_Trading20['Imports Weight(1000 Tons)'],\n",
    "label= 'Exports')\n",
    "plt.title('Imports & Exports from 2011-2020', fontsize= 14)\n",
    "plt.xlabel('Years', fontsize= 12)\n",
    "plt.ylabel('Weights in 1000 Tons', fontsize= 12)\n",
    "plt.legend(loc= 'upper left')\n",
    "plt.savefig('Plots/Imports & Exports from 2011-2020.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Not true! This would be a plot for China, not Saudi Arabia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exports - Imports\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.scatter(Cleaned_Trading20['Year'], Cleaned_Trading20['Difference in Weights'])\n",
    "plt.plot(Cleaned_Trading20[['Year']], Cleaned_Trading20[['Difference in Weights']], c='red')\n",
    "plt.xlabel('Years', fontsize= 12)\n",
    "plt.ylabel('Weights in 1000 Tons', fontsize= 12)\n",
    "plt.title('Difference in Trading Weights over the years', fontsize= 14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that exports are much higher from 2016-2019. But this is oil and non-oil exports, is it the same if oil exports where excluded? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare statistics from trading20 excel sheet with the one above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Difference in Trading Values over the years (including oil exports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.scatter(Cleaned_Trading20['Year'], Cleaned_Trading20['Difference in Value'])\n",
    "plt.plot(Cleaned_Trading20[['Year']], Cleaned_Trading20[['Difference in Value']], c='red')\n",
    "plt.title('Difference in Trading Values over the years')\n",
    "plt.xlabel('Years', fontsize= 12)\n",
    "plt.ylabel('Weights in 1000 Tons', fontsize= 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We see that with oil exports, income from over all exports where much higher from 2011 - 2014 than other years. \n",
    "- This can be related to the decrease of oil prices in that period compared to other years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trading20\n",
    "## Sheet 1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "Trading_Oil_non_Oil= pd.read_csv('Cleaned_data/Trading20_1-1_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Trading_Oil_non_Oil.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>Year</th>\n",
       "      <th>Oil Exports Value(Millions SAR)</th>\n",
       "      <th>Oil Percentage of Total Exports</th>\n",
       "      <th>Non-oil Exports Value(millions SAR)</th>\n",
       "      <th>Non-oil Percentage of Total Exports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>1191052.099</td>\n",
       "      <td>87.089</td>\n",
       "      <td>176567.732</td>\n",
       "      <td>12.911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>1265550.610</td>\n",
       "      <td>86.890</td>\n",
       "      <td>190951.554</td>\n",
       "      <td>13.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>1207080.084</td>\n",
       "      <td>85.637</td>\n",
       "      <td>202443.213</td>\n",
       "      <td>14.363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>1067091.642</td>\n",
       "      <td>83.099</td>\n",
       "      <td>217029.904</td>\n",
       "      <td>16.901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>573411.985</td>\n",
       "      <td>75.121</td>\n",
       "      <td>189901.078</td>\n",
       "      <td>24.879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>510729.487</td>\n",
       "      <td>74.188</td>\n",
       "      <td>177693.532</td>\n",
       "      <td>25.812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>638402.283</td>\n",
       "      <td>76.742</td>\n",
       "      <td>193479.004</td>\n",
       "      <td>23.258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>868442.402</td>\n",
       "      <td>78.670</td>\n",
       "      <td>235458.084</td>\n",
       "      <td>21.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>751828.129</td>\n",
       "      <td>76.638</td>\n",
       "      <td>229184.235</td>\n",
       "      <td>23.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>447599.225</td>\n",
       "      <td>68.655</td>\n",
       "      <td>204352.737</td>\n",
       "      <td>31.345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021.0</td>\n",
       "      <td>772750.031</td>\n",
       "      <td>64.430</td>\n",
       "      <td>274900.300</td>\n",
       "      <td>35.570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Oil Exports Value(Millions SAR)  Oil Percentage of Total Exports  \\\n",
       "0   2011.0                      1191052.099                           87.089   \n",
       "1   2012.0                      1265550.610                           86.890   \n",
       "2   2013.0                      1207080.084                           85.637   \n",
       "3   2014.0                      1067091.642                           83.099   \n",
       "4   2015.0                       573411.985                           75.121   \n",
       "5   2016.0                       510729.487                           74.188   \n",
       "6   2017.0                       638402.283                           76.742   \n",
       "7   2018.0                       868442.402                           78.670   \n",
       "8   2019.0                       751828.129                           76.638   \n",
       "9   2020.0                       447599.225                           68.655   \n",
       "10  2021.0                       772750.031                           64.430   \n",
       "\n",
       "    Non-oil Exports Value(millions SAR)  Non-oil Percentage of Total Exports  \n",
       "0                            176567.732                               12.911  \n",
       "1                            190951.554                               13.110  \n",
       "2                            202443.213                               14.363  \n",
       "3                            217029.904                               16.901  \n",
       "4                            189901.078                               24.879  \n",
       "5                            177693.532                               25.812  \n",
       "6                            193479.004                               23.258  \n",
       "7                            235458.084                               21.330  \n",
       "8                            229184.235                               23.362  \n",
       "9                            204352.737                               31.345  \n",
       "10                           274900.300                               35.570  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Trading_Oil_non_Oil.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5373596793377438"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "573412/1067091.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5953477929329857"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "447599.2/751828.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "772750.031172"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum of oil exports from 2021 Q4 excel file, sheet: 1\n",
    "240685.258109 + 206591.754939 + 172966.565216 + 152506.452908 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding the 2021 info to the data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "Trading_Oil_non_Oil.loc[10,'Year'] = 2021\n",
    "Trading_Oil_non_Oil.loc[10,'Non-oil Percentage of Total Exports'] = 35.57\n",
    "Trading_Oil_non_Oil.loc[10,'Oil Percentage of Total Exports'] = 64.43\n",
    "Trading_Oil_non_Oil.loc[10,'Oil Exports Value(Millions SAR)'] = 772750.031172\n",
    "Trading_Oil_non_Oil.loc[10,'Non-oil Exports Value(millions SAR)'] = 274900.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2291961.3716799994"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Trading_Oil_non_Oil['Non-oil Exports Value(millions SAR)'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9293937.977591999"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Trading_Oil_non_Oil['Oil Exports Value(Millions SAR)'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total value of exports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11585899.34928"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "9293937.9776 + 2291961.37168"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= Trading_Oil_non_Oil['Year']\n",
    "y= Trading_Oil_non_Oil['Oil Percentage of Total Exports']\n",
    "z= Trading_Oil_non_Oil['Non-oil Percentage of Total Exports']\n",
    "plt.figure(figsize = [10,6])\n",
    "plt.bar(x, y, 0.5, label= 'Oil Percentage of Total Exports')\n",
    "plt.bar(x, z, 0.5, bottom= y, label= 'Non-oil Percentage of Total Exports')\n",
    "plt.title('Oil Vs. Non-Oil Exports Percentage from 2011 - 2021', fontsize= 17)\n",
    "plt.xlabel('Year',fontsize= 15)\n",
    "plt.xticks(np.arange(2011, 2022, 1), fontsize= 13)\n",
    "plt.yticks(fontsize= 13)\n",
    "plt.ylabel('Percentage %', fontsize= 12)\n",
    "plt.legend(loc= 'best', fontsize= 12)\n",
    "plt.savefig('Plots/Oil Vs. Non-Oil Exports Percentage from 2011 - 2021.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "Trading_Oil_non_Oil.to_csv('Cleaned_data/Trading20_1-1_clean.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sheet 1-3\n",
    "- Kind of materials & if imported/exported\n",
    "- 2018 - 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "TD_materials=pd.read_csv('Cleaned_data/Trading2020_1-3_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "TD_materials.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>2018 Weight(1000 Tons)</th>\n",
       "      <th>2018 Value(Millions SAR)</th>\n",
       "      <th>2019 Weight(1000 Tons)</th>\n",
       "      <th>2019 Value(Millions SAR)</th>\n",
       "      <th>2020 Weight(1000 Tons)</th>\n",
       "      <th>2020 Value(Millions SAR)</th>\n",
       "      <th>Kind of material</th>\n",
       "      <th>Import/Export</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26661</td>\n",
       "      <td>26376</td>\n",
       "      <td>15299</td>\n",
       "      <td>19603</td>\n",
       "      <td>19426</td>\n",
       "      <td>22211</td>\n",
       "      <td>RAW MATERIAL</td>\n",
       "      <td>Imports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25868</td>\n",
       "      <td>123648</td>\n",
       "      <td>35094</td>\n",
       "      <td>127428</td>\n",
       "      <td>36109</td>\n",
       "      <td>124171</td>\n",
       "      <td>SEMI FINISHED PRODUCTS</td>\n",
       "      <td>Imports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22711</td>\n",
       "      <td>363968</td>\n",
       "      <td>27119</td>\n",
       "      <td>427330</td>\n",
       "      <td>30603</td>\n",
       "      <td>371109</td>\n",
       "      <td>FINISHED PRODUCTS</td>\n",
       "      <td>Imports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>371803</td>\n",
       "      <td>722108</td>\n",
       "      <td>357130</td>\n",
       "      <td>639256</td>\n",
       "      <td>342440</td>\n",
       "      <td>387861</td>\n",
       "      <td>RAW MATERIAL</td>\n",
       "      <td>Exports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63030</td>\n",
       "      <td>183069</td>\n",
       "      <td>67896</td>\n",
       "      <td>167011</td>\n",
       "      <td>69502</td>\n",
       "      <td>148533</td>\n",
       "      <td>SEMI FINISHED PRODUCTS</td>\n",
       "      <td>Exports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>74965</td>\n",
       "      <td>198724</td>\n",
       "      <td>67236</td>\n",
       "      <td>174745</td>\n",
       "      <td>58589</td>\n",
       "      <td>115558</td>\n",
       "      <td>FINISHED PRODUCTS</td>\n",
       "      <td>Exports</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2018 Weight(1000 Tons)  2018 Value(Millions SAR)  2019 Weight(1000 Tons)  \\\n",
       "0                   26661                     26376                   15299   \n",
       "1                   25868                    123648                   35094   \n",
       "2                   22711                    363968                   27119   \n",
       "3                  371803                    722108                  357130   \n",
       "4                   63030                    183069                   67896   \n",
       "5                   74965                    198724                   67236   \n",
       "\n",
       "   2019 Value(Millions SAR)  2020 Weight(1000 Tons)  2020 Value(Millions SAR)  \\\n",
       "0                     19603                   19426                     22211   \n",
       "1                    127428                   36109                    124171   \n",
       "2                    427330                   30603                    371109   \n",
       "3                    639256                  342440                    387861   \n",
       "4                    167011                   69502                    148533   \n",
       "5                    174745                   58589                    115558   \n",
       "\n",
       "         Kind of material Import/Export  \n",
       "0            RAW MATERIAL       Imports  \n",
       "1  SEMI FINISHED PRODUCTS       Imports  \n",
       "2       FINISHED PRODUCTS       Imports  \n",
       "3            RAW MATERIAL       Exports  \n",
       "4  SEMI FINISHED PRODUCTS       Exports  \n",
       "5       FINISHED PRODUCTS       Exports  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TD_materials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports Stats from 2018-2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>2018 Weight(1000 Tons)</th>\n",
       "      <th>2018 Value(Millions SAR)</th>\n",
       "      <th>2019 Weight(1000 Tons)</th>\n",
       "      <th>2019 Value(Millions SAR)</th>\n",
       "      <th>2020 Weight(1000 Tons)</th>\n",
       "      <th>2020 Value(Millions SAR)</th>\n",
       "      <th>Kind of material</th>\n",
       "      <th>Import/Export</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26661</td>\n",
       "      <td>26376</td>\n",
       "      <td>15299</td>\n",
       "      <td>19603</td>\n",
       "      <td>19426</td>\n",
       "      <td>22211</td>\n",
       "      <td>RAW MATERIAL</td>\n",
       "      <td>Imports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25868</td>\n",
       "      <td>123648</td>\n",
       "      <td>35094</td>\n",
       "      <td>127428</td>\n",
       "      <td>36109</td>\n",
       "      <td>124171</td>\n",
       "      <td>SEMI FINISHED PRODUCTS</td>\n",
       "      <td>Imports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22711</td>\n",
       "      <td>363968</td>\n",
       "      <td>27119</td>\n",
       "      <td>427330</td>\n",
       "      <td>30603</td>\n",
       "      <td>371109</td>\n",
       "      <td>FINISHED PRODUCTS</td>\n",
       "      <td>Imports</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2018 Weight(1000 Tons)  2018 Value(Millions SAR)  2019 Weight(1000 Tons)  \\\n",
       "0                   26661                     26376                   15299   \n",
       "1                   25868                    123648                   35094   \n",
       "2                   22711                    363968                   27119   \n",
       "\n",
       "   2019 Value(Millions SAR)  2020 Weight(1000 Tons)  2020 Value(Millions SAR)  \\\n",
       "0                     19603                   19426                     22211   \n",
       "1                    127428                   36109                    124171   \n",
       "2                    427330                   30603                    371109   \n",
       "\n",
       "         Kind of material Import/Export  \n",
       "0            RAW MATERIAL       Imports  \n",
       "1  SEMI FINISHED PRODUCTS       Imports  \n",
       "2       FINISHED PRODUCTS       Imports  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_Im = TD_materials.groupby('Import/Export')\n",
    "Imports_18_20 = grouped_Im.get_group('Imports')\n",
    "Imports_18_20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sum of imports\n",
    "- Imports_2018 = 363968 + 123648 + 26376 = 513992\n",
    "- Imports_2019 = 19603 + 127428 + 427330 = 574361\n",
    "- Imports_2020 = 22211 + 124171 + 371109 = 517491"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = ['2018', '2019', '2020']\n",
    "Raw = Imports_18_20[['2018 Weight(1000 Tons)','2019 Weight(1000 Tons)', '2020 Weight(1000 Tons)']].values[0]\n",
    "Semi= Imports_18_20[['2018 Weight(1000 Tons)','2019 Weight(1000 Tons)', '2020 Weight(1000 Tons)']].values[1]\n",
    "Finished=Imports_18_20[['2018 Weight(1000 Tons)','2019 Weight(1000 Tons)', '2020 Weight(1000 Tons)']].values[2]\n",
    "raw_semi = list(np.add(Raw,Semi))\n",
    "plt.figure(figsize = [8, 6])\n",
    "plt.bar(x, Raw, 0.4, label= 'Raw material')\n",
    "plt.bar(x, Semi, 0.4, bottom= Raw, label= 'semi finished products')\n",
    "plt.bar(x, Finished, 0.4, bottom= raw_semi, label= 'finished products')\n",
    "plt.title('Imported materials Weight from 2018 - 2020', fontsize= 17)\n",
    "plt.xlabel('Year',fontsize= 15)\n",
    "plt.xticks(fontsize= 13)\n",
    "plt.yticks(fontsize= 13)\n",
    "plt.ylabel('Weight(1000 Tons)', fontsize= 15)\n",
    "plt.legend(loc= 'upper left', fontsize= 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exports Stats from 2018-2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>2018 Weight(1000 Tons)</th>\n",
       "      <th>2018 Value(Millions SAR)</th>\n",
       "      <th>2019 Weight(1000 Tons)</th>\n",
       "      <th>2019 Value(Millions SAR)</th>\n",
       "      <th>2020 Weight(1000 Tons)</th>\n",
       "      <th>2020 Value(Millions SAR)</th>\n",
       "      <th>Kind of material</th>\n",
       "      <th>Import/Export</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>371803</td>\n",
       "      <td>722108</td>\n",
       "      <td>357130</td>\n",
       "      <td>639256</td>\n",
       "      <td>342440</td>\n",
       "      <td>387861</td>\n",
       "      <td>RAW MATERIAL</td>\n",
       "      <td>Exports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63030</td>\n",
       "      <td>183069</td>\n",
       "      <td>67896</td>\n",
       "      <td>167011</td>\n",
       "      <td>69502</td>\n",
       "      <td>148533</td>\n",
       "      <td>SEMI FINISHED PRODUCTS</td>\n",
       "      <td>Exports</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>74965</td>\n",
       "      <td>198724</td>\n",
       "      <td>67236</td>\n",
       "      <td>174745</td>\n",
       "      <td>58589</td>\n",
       "      <td>115558</td>\n",
       "      <td>FINISHED PRODUCTS</td>\n",
       "      <td>Exports</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2018 Weight(1000 Tons)  2018 Value(Millions SAR)  2019 Weight(1000 Tons)  \\\n",
       "3                  371803                    722108                  357130   \n",
       "4                   63030                    183069                   67896   \n",
       "5                   74965                    198724                   67236   \n",
       "\n",
       "   2019 Value(Millions SAR)  2020 Weight(1000 Tons)  2020 Value(Millions SAR)  \\\n",
       "3                    639256                  342440                    387861   \n",
       "4                    167011                   69502                    148533   \n",
       "5                    174745                   58589                    115558   \n",
       "\n",
       "         Kind of material Import/Export  \n",
       "3            RAW MATERIAL       Exports  \n",
       "4  SEMI FINISHED PRODUCTS       Exports  \n",
       "5       FINISHED PRODUCTS       Exports  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_Ex = TD_materials.groupby('Import/Export')\n",
    "Exports_18_20 = grouped_Ex.get_group('Exports')\n",
    "Exports_18_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = ['2018', '2019', '2020']\n",
    "Raw = Exports_18_20[['2018 Weight(1000 Tons)','2019 Weight(1000 Tons)', '2020 Weight(1000 Tons)']].values[0]\n",
    "Semi= Exports_18_20[['2018 Weight(1000 Tons)','2019 Weight(1000 Tons)', '2020 Weight(1000 Tons)']].values[1]\n",
    "Finished=Exports_18_20[['2018 Weight(1000 Tons)','2019 Weight(1000 Tons)', '2020 Weight(1000 Tons)']].values[2]\n",
    "raw_semi = list(np.add(Raw,Semi))\n",
    "plt.figure(figsize = [8, 6])\n",
    "plt.title('Exported materials Weight from 2018-2020', fontsize= 17)\n",
    "plt.bar(x, Raw, 0.4, label= 'Raw material')\n",
    "plt.bar(x, Semi, 0.4, bottom= Raw, label= 'semi finished products')\n",
    "plt.bar(x, Finished, 0.4, bottom= raw_semi, label= 'finished products')\n",
    "plt.xlabel('Year',fontsize= 15)\n",
    "plt.xticks(fontsize= 13)\n",
    "plt.yticks(fontsize= 13)\n",
    "plt.ylabel('Weight(1000 Tons)', fontsize= 15)\n",
    "plt.legend(loc= 'upper left', fontsize= 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hint: Exports include oil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sheet 1.2 from Trading20 excel sheet\n",
    "- from 2018-2020. Only values, no weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "Exports_bysection= pd.read_csv('Cleaned_data/Trading20_1-2_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "Exports_bysection.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>2018 Value(millions in SAR)</th>\n",
       "      <th>2019 Value(millions in SAR)</th>\n",
       "      <th>2020 Value(millions in SAR)</th>\n",
       "      <th>Section Description</th>\n",
       "      <th>Section Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6057.512578</td>\n",
       "      <td>5687.233568</td>\n",
       "      <td>5368.675468</td>\n",
       "      <td>Live animals; animal products</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1440.949608</td>\n",
       "      <td>1563.584641</td>\n",
       "      <td>1634.052079</td>\n",
       "      <td>Vegetable products</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>819.818876</td>\n",
       "      <td>849.966497</td>\n",
       "      <td>861.608808</td>\n",
       "      <td>Animal or vegetable fats and oils and their cl...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5470.373403</td>\n",
       "      <td>5782.133899</td>\n",
       "      <td>5846.126337</td>\n",
       "      <td>Prepared foodstuffs; beverages, spirits and vi...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>872029.853072</td>\n",
       "      <td>754960.758584</td>\n",
       "      <td>451092.776040</td>\n",
       "      <td>Mineral products</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>75455.289785</td>\n",
       "      <td>69551.508281</td>\n",
       "      <td>57873.453738</td>\n",
       "      <td>Products of the chemical or allied industries</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>79667.031342</td>\n",
       "      <td>72833.580298</td>\n",
       "      <td>62728.867805</td>\n",
       "      <td>Plastics and articles thereof; rubber and arti...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>270.407456</td>\n",
       "      <td>280.720176</td>\n",
       "      <td>169.362314</td>\n",
       "      <td>Raw hides and skins, leather, furskins and art...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>311.069498</td>\n",
       "      <td>364.556581</td>\n",
       "      <td>253.464176</td>\n",
       "      <td>Wood and articles of wood; wood charcoal; cork...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2777.208284</td>\n",
       "      <td>2556.065300</td>\n",
       "      <td>2482.395784</td>\n",
       "      <td>Pulp of wood or of other fibrous cellulosic ma...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2028.252427</td>\n",
       "      <td>1873.730113</td>\n",
       "      <td>2045.483365</td>\n",
       "      <td>Textiles and textile articles</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>80.914109</td>\n",
       "      <td>81.256700</td>\n",
       "      <td>51.349993</td>\n",
       "      <td>Footwear, headgear, umbrellas, sun umbrellas, ...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2161.095034</td>\n",
       "      <td>2058.660909</td>\n",
       "      <td>1677.223882</td>\n",
       "      <td>Articles of stone, plaster, cement, asbestos, ...</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3613.703033</td>\n",
       "      <td>5739.889889</td>\n",
       "      <td>8554.909788</td>\n",
       "      <td>Natural or cultured pearls, precious or semi-p...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20645.345694</td>\n",
       "      <td>16868.946345</td>\n",
       "      <td>16401.029776</td>\n",
       "      <td>Base metals and articles of base metal</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11901.193786</td>\n",
       "      <td>12103.530969</td>\n",
       "      <td>10966.047606</td>\n",
       "      <td>Machinery and mechanical appliances; electrica...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>15380.588908</td>\n",
       "      <td>22605.252704</td>\n",
       "      <td>19474.193307</td>\n",
       "      <td>Vehicles, aircraft, vessels and associated tra...</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1210.266146</td>\n",
       "      <td>1432.958403</td>\n",
       "      <td>1717.813006</td>\n",
       "      <td>Optical, photographic, cinematographic, measur...</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>122.270270</td>\n",
       "      <td>207.180405</td>\n",
       "      <td>212.344988</td>\n",
       "      <td>Arms and ammunition; parts and accessories the...</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1644.691169</td>\n",
       "      <td>1634.561353</td>\n",
       "      <td>1582.046995</td>\n",
       "      <td>Miscellaneous manufactured articles</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>812.651513</td>\n",
       "      <td>1976.287704</td>\n",
       "      <td>958.737430</td>\n",
       "      <td>Works of art, collectors' pieces and antiques</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    2018 Value(millions in SAR)  2019 Value(millions in SAR)  \\\n",
       "0                   6057.512578                  5687.233568   \n",
       "1                   1440.949608                  1563.584641   \n",
       "2                    819.818876                   849.966497   \n",
       "3                   5470.373403                  5782.133899   \n",
       "4                 872029.853072                754960.758584   \n",
       "5                  75455.289785                 69551.508281   \n",
       "6                  79667.031342                 72833.580298   \n",
       "7                    270.407456                   280.720176   \n",
       "8                    311.069498                   364.556581   \n",
       "9                   2777.208284                  2556.065300   \n",
       "10                  2028.252427                  1873.730113   \n",
       "11                    80.914109                    81.256700   \n",
       "12                  2161.095034                  2058.660909   \n",
       "13                  3613.703033                  5739.889889   \n",
       "14                 20645.345694                 16868.946345   \n",
       "15                 11901.193786                 12103.530969   \n",
       "16                 15380.588908                 22605.252704   \n",
       "17                  1210.266146                  1432.958403   \n",
       "18                   122.270270                   207.180405   \n",
       "19                  1644.691169                  1634.561353   \n",
       "20                   812.651513                  1976.287704   \n",
       "\n",
       "    2020 Value(millions in SAR)  \\\n",
       "0                   5368.675468   \n",
       "1                   1634.052079   \n",
       "2                    861.608808   \n",
       "3                   5846.126337   \n",
       "4                 451092.776040   \n",
       "5                  57873.453738   \n",
       "6                  62728.867805   \n",
       "7                    169.362314   \n",
       "8                    253.464176   \n",
       "9                   2482.395784   \n",
       "10                  2045.483365   \n",
       "11                    51.349993   \n",
       "12                  1677.223882   \n",
       "13                  8554.909788   \n",
       "14                 16401.029776   \n",
       "15                 10966.047606   \n",
       "16                 19474.193307   \n",
       "17                  1717.813006   \n",
       "18                   212.344988   \n",
       "19                  1582.046995   \n",
       "20                   958.737430   \n",
       "\n",
       "                                  Section Description  Section Number  \n",
       "0                       Live animals; animal products               1  \n",
       "1                                  Vegetable products               2  \n",
       "2   Animal or vegetable fats and oils and their cl...               3  \n",
       "3   Prepared foodstuffs; beverages, spirits and vi...               4  \n",
       "4                                    Mineral products               5  \n",
       "5       Products of the chemical or allied industries               6  \n",
       "6   Plastics and articles thereof; rubber and arti...               7  \n",
       "7   Raw hides and skins, leather, furskins and art...               8  \n",
       "8   Wood and articles of wood; wood charcoal; cork...               9  \n",
       "9   Pulp of wood or of other fibrous cellulosic ma...              10  \n",
       "10                      Textiles and textile articles              11  \n",
       "11  Footwear, headgear, umbrellas, sun umbrellas, ...              12  \n",
       "12  Articles of stone, plaster, cement, asbestos, ...              13  \n",
       "13  Natural or cultured pearls, precious or semi-p...              14  \n",
       "14             Base metals and articles of base metal              15  \n",
       "15  Machinery and mechanical appliances; electrica...              16  \n",
       "16  Vehicles, aircraft, vessels and associated tra...              17  \n",
       "17  Optical, photographic, cinematographic, measur...              18  \n",
       "18  Arms and ammunition; parts and accessories the...              19  \n",
       "19                Miscellaneous manufactured articles              20  \n",
       "20      Works of art, collectors' pieces and antiques              21  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Exports_bysection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1103900.485991"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Exports_bysection['2018 Value(millions in SAR)'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 5 exports in 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018 Value(millions in SAR)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15380.588908</th>\n",
       "      <td>Vehicles, aircraft, vessels and associated tra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20645.345694</th>\n",
       "      <td>Base metals and articles of base metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75455.289785</th>\n",
       "      <td>Products of the chemical or allied industries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79667.031342</th>\n",
       "      <td>Plastics and articles thereof; rubber and arti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872029.853072</th>\n",
       "      <td>Mineral products</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                           max\n",
       "2018 Value(millions in SAR)                                                   \n",
       "15380.588908                 Vehicles, aircraft, vessels and associated tra...\n",
       "20645.345694                            Base metals and articles of base metal\n",
       "75455.289785                     Products of the chemical or allied industries\n",
       "79667.031342                 Plastics and articles thereof; rubber and arti...\n",
       "872029.853072                                                 Mineral products"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_Exports_2018 =Exports_bysection.groupby(\"2018 Value(millions in SAR)\")['Section Description'].aggregate([\"max\"])\n",
    "Top_Exports_2018 = max_Exports_2018.tail()\n",
    "Top_Exports_2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 5 Exports in 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019 Value(millions in SAR)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16868.946345</th>\n",
       "      <td>Base metals and articles of base metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22605.252704</th>\n",
       "      <td>Vehicles, aircraft, vessels and associated tra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69551.508281</th>\n",
       "      <td>Products of the chemical or allied industries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72833.580298</th>\n",
       "      <td>Plastics and articles thereof; rubber and arti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754960.758584</th>\n",
       "      <td>Mineral products</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                           max\n",
       "2019 Value(millions in SAR)                                                   \n",
       "16868.946345                            Base metals and articles of base metal\n",
       "22605.252704                 Vehicles, aircraft, vessels and associated tra...\n",
       "69551.508281                     Products of the chemical or allied industries\n",
       "72833.580298                 Plastics and articles thereof; rubber and arti...\n",
       "754960.758584                                                 Mineral products"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_Exports_2019 =Exports_bysection.groupby(\"2019 Value(millions in SAR)\")['Section Description'].aggregate([\"max\"])\n",
    "Top_Exports_2019 = max_Exports_2019.tail()\n",
    "Top_Exports_2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 5 non-oil exports from 2018-2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018 Value(millions in SAR)</th>\n",
       "      <th>2019 Value(millions in SAR)</th>\n",
       "      <th>2020 Value(millions in SAR)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>872029.853072</th>\n",
       "      <th>754960.758584</th>\n",
       "      <th>451092.776040</th>\n",
       "      <td>Mineral products</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79667.031342</th>\n",
       "      <th>72833.580298</th>\n",
       "      <th>62728.867805</th>\n",
       "      <td>Plastics and articles thereof; rubber and arti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75455.289785</th>\n",
       "      <th>69551.508281</th>\n",
       "      <th>57873.453738</th>\n",
       "      <td>Products of the chemical or allied industries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20645.345694</th>\n",
       "      <th>16868.946345</th>\n",
       "      <th>16401.029776</th>\n",
       "      <td>Base metals and articles of base metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15380.588908</th>\n",
       "      <th>22605.252704</th>\n",
       "      <th>19474.193307</th>\n",
       "      <td>Vehicles, aircraft, vessels and associated tra...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                   max\n",
       "2018 Value(millions in SAR) 2019 Value(millions in SAR) 2020 Value(millions in SAR)                                                   \n",
       "872029.853072               754960.758584               451092.776040                                                 Mineral products\n",
       "79667.031342                72833.580298                62728.867805                 Plastics and articles thereof; rubber and arti...\n",
       "75455.289785                69551.508281                57873.453738                     Products of the chemical or allied industries\n",
       "20645.345694                16868.946345                16401.029776                            Base metals and articles of base metal\n",
       "15380.588908                22605.252704                19474.193307                 Vehicles, aircraft, vessels and associated tra..."
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max_Exports_18_20 =Exports_bysection.groupby([\"2018 Value(millions in SAR)\", \"2019 Value(millions in SAR)\", \n",
    "\"2020 Value(millions in SAR)\"])['Section Description'].aggregate([\"max\"])\n",
    "Max_Exports_18_20.sort_values(\"2018 Value(millions in SAR)\",ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Section Description</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Animal or vegetable fats and oils and their cleavage products; prepared edible fats; animal or vegetable waxes</th>\n",
       "      <td>849.966497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arms and ammunition; parts and accessories thereof</th>\n",
       "      <td>207.180405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Articles of stone, plaster, cement, asbestos, mica or similar materials; ceramic products; glass and glassware</th>\n",
       "      <td>2058.660909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Base metals and articles of base metal</th>\n",
       "      <td>16868.946345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Footwear, headgear, umbrellas, sun umbrellas, walking-sticks, seat-sticks, whips, riding-crops and parts thereof; prepared feathers and articles made therewith; artificial flowers; articles of human hair</th>\n",
       "      <td>81.256700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Live animals; animal products</th>\n",
       "      <td>5687.233568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machinery and mechanical appliances; electrical equipment; parts thereof; sound recorders and reproducers, television image and sound recorders and reproducers, and parts and accessories of such articles</th>\n",
       "      <td>12103.530969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mineral products</th>\n",
       "      <td>754960.758584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miscellaneous manufactured articles</th>\n",
       "      <td>1634.561353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Natural or cultured pearls, precious or semi-precious stones, precious metals, metals clad with precious metal and articles thereof; imitation jewellery; coin</th>\n",
       "      <td>5739.889889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optical, photographic, cinematographic, measuring, checking, precision, medical or surgical instruments and apparatus; clocks and watches; musical instruments; parts and accessories thereof</th>\n",
       "      <td>1432.958403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Plastics and articles thereof; rubber and articles thereof</th>\n",
       "      <td>72833.580298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prepared foodstuffs; beverages, spirits and vinegar; tobacco and manufactured tobacco substitutes</th>\n",
       "      <td>5782.133899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Products of the chemical or allied industries</th>\n",
       "      <td>69551.508281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulp of wood or of other fibrous cellulosic material; recovered (waste and scrap) paper or paperboard; paper and paperboard and articles thereof</th>\n",
       "      <td>2556.065300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raw hides and skins, leather, furskins and articles thereof; saddlery and harness; travel goods, handbags and similar containers; articles of animal gut (other than silk-worm gut)</th>\n",
       "      <td>280.720176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Textiles and textile articles</th>\n",
       "      <td>1873.730113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vegetable products</th>\n",
       "      <td>1563.584641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vehicles, aircraft, vessels and associated transport equipment</th>\n",
       "      <td>22605.252704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wood and articles of wood; wood charcoal; cork and articles of cork; manufactures of straw, of esparto or of other plaiting materials; basketware and wickerwork</th>\n",
       "      <td>364.556581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Works of art, collectors' pieces and antiques</th>\n",
       "      <td>1976.287704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             mean\n",
       "Section Description                                              \n",
       "Animal or vegetable fats and oils and their cle...     849.966497\n",
       "Arms and ammunition; parts and accessories thereof     207.180405\n",
       "Articles of stone, plaster, cement, asbestos, m...    2058.660909\n",
       "Base metals and articles of base metal               16868.946345\n",
       "Footwear, headgear, umbrellas, sun umbrellas, w...      81.256700\n",
       "Live animals; animal products                         5687.233568\n",
       "Machinery and mechanical appliances; electrical...   12103.530969\n",
       "Mineral products                                    754960.758584\n",
       "Miscellaneous manufactured articles                   1634.561353\n",
       "Natural or cultured pearls, precious or semi-pr...    5739.889889\n",
       "Optical, photographic, cinematographic, measuri...    1432.958403\n",
       "Plastics and articles thereof; rubber and artic...   72833.580298\n",
       "Prepared foodstuffs; beverages, spirits and vin...    5782.133899\n",
       "Products of the chemical or allied industries        69551.508281\n",
       "Pulp of wood or of other fibrous cellulosic mat...    2556.065300\n",
       "Raw hides and skins, leather, furskins and arti...     280.720176\n",
       "Textiles and textile articles                         1873.730113\n",
       "Vegetable products                                    1563.584641\n",
       "Vehicles, aircraft, vessels and associated tran...   22605.252704\n",
       "Wood and articles of wood; wood charcoal; cork ...     364.556581\n",
       "Works of art, collectors' pieces and antiques         1976.287704"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_Exports_20190 =Exports_bysection.groupby('Section Description')[\"2019 Value(millions in SAR)\"].aggregate([\"mean\"])\n",
    "max_Exports_20190"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 5 Exports in 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020 Value(millions in SAR)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16401.029776</th>\n",
       "      <td>Base metals and articles of base metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19474.193307</th>\n",
       "      <td>Vehicles, aircraft, vessels and associated tra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57873.453738</th>\n",
       "      <td>Products of the chemical or allied industries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62728.867805</th>\n",
       "      <td>Plastics and articles thereof; rubber and arti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451092.776040</th>\n",
       "      <td>Mineral products</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                           max\n",
       "2020 Value(millions in SAR)                                                   \n",
       "16401.029776                            Base metals and articles of base metal\n",
       "19474.193307                 Vehicles, aircraft, vessels and associated tra...\n",
       "57873.453738                     Products of the chemical or allied industries\n",
       "62728.867805                 Plastics and articles thereof; rubber and arti...\n",
       "451092.776040                                                 Mineral products"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_Exports_2020 =Exports_bysection.groupby(\"2020 Value(millions in SAR)\")['Section Description'].aggregate([\"max\"])\n",
    "Top_Exports_2020 = max_Exports_2020.tail()\n",
    "Top_Exports_2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15380.588908</th>\n",
       "      <td>Vehicles, aircraft, vessels and associated tra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20645.345694</th>\n",
       "      <td>Base metals and articles of base metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75455.289785</th>\n",
       "      <td>Products of the chemical or allied industries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79667.031342</th>\n",
       "      <td>Plastics and articles thereof; rubber and arti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872029.853072</th>\n",
       "      <td>Mineral products</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16868.946345</th>\n",
       "      <td>Base metals and articles of base metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22605.252704</th>\n",
       "      <td>Vehicles, aircraft, vessels and associated tra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69551.508281</th>\n",
       "      <td>Products of the chemical or allied industries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72833.580298</th>\n",
       "      <td>Plastics and articles thereof; rubber and arti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754960.758584</th>\n",
       "      <td>Mineral products</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16401.029776</th>\n",
       "      <td>Base metals and articles of base metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19474.193307</th>\n",
       "      <td>Vehicles, aircraft, vessels and associated tra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57873.453738</th>\n",
       "      <td>Products of the chemical or allied industries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62728.867805</th>\n",
       "      <td>Plastics and articles thereof; rubber and arti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451092.776040</th>\n",
       "      <td>Mineral products</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             max\n",
       "15380.588908   Vehicles, aircraft, vessels and associated tra...\n",
       "20645.345694              Base metals and articles of base metal\n",
       "75455.289785       Products of the chemical or allied industries\n",
       "79667.031342   Plastics and articles thereof; rubber and arti...\n",
       "872029.853072                                   Mineral products\n",
       "16868.946345              Base metals and articles of base metal\n",
       "22605.252704   Vehicles, aircraft, vessels and associated tra...\n",
       "69551.508281       Products of the chemical or allied industries\n",
       "72833.580298   Plastics and articles thereof; rubber and arti...\n",
       "754960.758584                                   Mineral products\n",
       "16401.029776              Base metals and articles of base metal\n",
       "19474.193307   Vehicles, aircraft, vessels and associated tra...\n",
       "57873.453738       Products of the chemical or allied industries\n",
       "62728.867805   Plastics and articles thereof; rubber and arti...\n",
       "451092.776040                                   Mineral products"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top_Exports_18_20 = pd.concat([Top_Exports_2018, Top_Exports_2019, Top_Exports_2020])\n",
    "Top_Exports_18_20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Top 5 section in exports from 2018-2020 combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2018</th>\n",
       "      <th>15380.588908</th>\n",
       "      <td>Vehicles, aircraft, vessels and associated tra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20645.345694</th>\n",
       "      <td>Base metals and articles of base metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75455.289785</th>\n",
       "      <td>Products of the chemical or allied industries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79667.031342</th>\n",
       "      <td>Plastics and articles thereof; rubber and arti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872029.853072</th>\n",
       "      <td>Mineral products</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2019</th>\n",
       "      <th>16868.946345</th>\n",
       "      <td>Base metals and articles of base metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22605.252704</th>\n",
       "      <td>Vehicles, aircraft, vessels and associated tra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69551.508281</th>\n",
       "      <td>Products of the chemical or allied industries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72833.580298</th>\n",
       "      <td>Plastics and articles thereof; rubber and arti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754960.758584</th>\n",
       "      <td>Mineral products</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2020</th>\n",
       "      <th>16401.029776</th>\n",
       "      <td>Base metals and articles of base metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19474.193307</th>\n",
       "      <td>Vehicles, aircraft, vessels and associated tra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57873.453738</th>\n",
       "      <td>Products of the chemical or allied industries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62728.867805</th>\n",
       "      <td>Plastics and articles thereof; rubber and arti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451092.776040</th>\n",
       "      <td>Mineral products</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  max\n",
       "2018 15380.588908   Vehicles, aircraft, vessels and associated tra...\n",
       "     20645.345694              Base metals and articles of base metal\n",
       "     75455.289785       Products of the chemical or allied industries\n",
       "     79667.031342   Plastics and articles thereof; rubber and arti...\n",
       "     872029.853072                                   Mineral products\n",
       "2019 16868.946345              Base metals and articles of base metal\n",
       "     22605.252704   Vehicles, aircraft, vessels and associated tra...\n",
       "     69551.508281       Products of the chemical or allied industries\n",
       "     72833.580298   Plastics and articles thereof; rubber and arti...\n",
       "     754960.758584                                   Mineral products\n",
       "2020 16401.029776              Base metals and articles of base metal\n",
       "     19474.193307   Vehicles, aircraft, vessels and associated tra...\n",
       "     57873.453738       Products of the chemical or allied industries\n",
       "     62728.867805   Plastics and articles thereof; rubber and arti...\n",
       "     451092.776040                                   Mineral products"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [Top_Exports_2018, Top_Exports_2019, Top_Exports_2020]\n",
    "Top_Exports_comb = pd.concat(frames, keys=['2018', '2019', '2020'])\n",
    "\n",
    "Top_Exports_comb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total exports for each year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2018 Value(millions in SAR)</th>\n",
       "      <th>2019 Value(millions in SAR)</th>\n",
       "      <th>2020 Value(millions in SAR)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>1.103900e+06</td>\n",
       "      <td>981012.363319</td>\n",
       "      <td>651951.962685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     2018 Value(millions in SAR)  2019 Value(millions in SAR)  \\\n",
       "sum                 1.103900e+06                981012.363319   \n",
       "\n",
       "     2020 Value(millions in SAR)  \n",
       "sum                651951.962685  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Exports_bysection[['2018 Value(millions in SAR)', '2019 Value(millions in SAR)', '2020 Value(millions in SAR)']].aggregate([\"sum\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2018 Value(millions in SAR)</th>\n",
       "      <th>2019 Value(millions in SAR)</th>\n",
       "      <th>2020 Value(millions in SAR)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>872029.853072</td>\n",
       "      <td>754960.758584</td>\n",
       "      <td>451092.77604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     2018 Value(millions in SAR)  2019 Value(millions in SAR)  \\\n",
       "max                872029.853072                754960.758584   \n",
       "\n",
       "     2020 Value(millions in SAR)  \n",
       "max                 451092.77604  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Exports_bysection[['2018 Value(millions in SAR)', '2019 Value(millions in SAR)', '2020 Value(millions in SAR)']].aggregate([\"max\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2018 Value(millions in SAR)</th>\n",
       "      <th>2019 Value(millions in SAR)</th>\n",
       "      <th>2020 Value(millions in SAR)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>52566.689809</td>\n",
       "      <td>46714.874444</td>\n",
       "      <td>31045.331556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      2018 Value(millions in SAR)  2019 Value(millions in SAR)  \\\n",
       "mean                 52566.689809                 46714.874444   \n",
       "\n",
       "      2020 Value(millions in SAR)  \n",
       "mean                 31045.331556  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_exports = Exports_bysection[['2018 Value(millions in SAR)', '2019 Value(millions in SAR)', '2020 Value(millions in SAR)']].aggregate([\"mean\"])\n",
    "mean_exports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2018 Value(millions in SAR)</th>\n",
       "      <th>2019 Value(millions in SAR)</th>\n",
       "      <th>2020 Value(millions in SAR)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>80.914109</td>\n",
       "      <td>81.2567</td>\n",
       "      <td>51.349993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     2018 Value(millions in SAR)  2019 Value(millions in SAR)  \\\n",
       "min                    80.914109                      81.2567   \n",
       "\n",
       "     2020 Value(millions in SAR)  \n",
       "min                    51.349993  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Exports_bysection[['2018 Value(millions in SAR)', '2019 Value(millions in SAR)', '2020 Value(millions in SAR)']].aggregate([\"min\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Exports_bysection[['2018 Value(millions in SAR)', '2019 Value(millions in SAR)', '2020 Value(millions in SAR)']].aggregate([\"min\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sheet 1-5\n",
    "- Imports by Transoprtation Type\n",
    "- Values & Weights\n",
    "- 2018 - 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Imports_byTransoprtType = pd.read_csv('Cleaned_data/Trading2020_1-5_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Imports_byTransoprtType.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight (1000 Tons) 2018</th>\n",
       "      <th>2018 Value (millions in SAR)</th>\n",
       "      <th>2019 Weight (1000 Tons)</th>\n",
       "      <th>2019 Value (millions in SAR)</th>\n",
       "      <th>2020 Weight (1000 Tons)</th>\n",
       "      <th>2020 Value (millions in SAR)</th>\n",
       "      <th>Importing Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66260.247914</td>\n",
       "      <td>310406.535978</td>\n",
       "      <td>66366.941453</td>\n",
       "      <td>352081.497419</td>\n",
       "      <td>75821.060302</td>\n",
       "      <td>320312.112449</td>\n",
       "      <td>Imports By Sea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8343.995809</td>\n",
       "      <td>76875.211899</td>\n",
       "      <td>10386.983890</td>\n",
       "      <td>83588.749138</td>\n",
       "      <td>9619.117143</td>\n",
       "      <td>75991.218771</td>\n",
       "      <td>Imports By Land</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>634.929403</td>\n",
       "      <td>126710.942322</td>\n",
       "      <td>757.717624</td>\n",
       "      <td>138691.208044</td>\n",
       "      <td>697.093139</td>\n",
       "      <td>121187.257807</td>\n",
       "      <td>Imports By Air</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Weight (1000 Tons) 2018  2018 Value (millions in SAR)  \\\n",
       "0             66260.247914                 310406.535978   \n",
       "1              8343.995809                  76875.211899   \n",
       "2               634.929403                 126710.942322   \n",
       "\n",
       "   2019 Weight (1000 Tons)  2019 Value (millions in SAR)  \\\n",
       "0             66366.941453                 352081.497419   \n",
       "1             10386.983890                  83588.749138   \n",
       "2               757.717624                 138691.208044   \n",
       "\n",
       "   2020 Weight (1000 Tons)  2020 Value (millions in SAR)   Importing Type  \n",
       "0             75821.060302                 320312.112449   Imports By Sea  \n",
       "1              9619.117143                  75991.218771  Imports By Land  \n",
       "2               697.093139                 121187.257807   Imports By Air  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Imports_byTransoprtType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = ['2018', '2019', '2020']\n",
    "sea = Imports_byTransoprtType[[\"Weight (1000 Tons) 2018\", \"2019 Weight (1000 Tons)\", \"2020 Weight (1000 Tons)\"]].values[0]\n",
    "land = Imports_byTransoprtType[[\"Weight (1000 Tons) 2018\", \"2019 Weight (1000 Tons)\", \"2020 Weight (1000 Tons)\"]].values[1]\n",
    "air = Imports_byTransoprtType[[\"Weight (1000 Tons) 2018\", \"2019 Weight (1000 Tons)\", \"2020 Weight (1000 Tons)\"]].values[2]\n",
    "sea_land = list(np.add(sea,land))\n",
    "plt.figure(figsize = [7,6])\n",
    "plt.bar(x, sea, 0.5, label= 'Sea')\n",
    "plt.bar(x, land, 0.5, bottom= sea, label= 'Land')\n",
    "plt.bar(x,air, 0.4, bottom= sea_land, label= 'Air')\n",
    "plt.title('Weight of Imports by Transportation Types', fontsize= 17)\n",
    "plt.xlabel('Year',fontsize= 15)\n",
    "plt.ylabel('Weights in 1000 Tons', fontsize= 15)\n",
    "plt.xticks(fontsize= 13)\n",
    "plt.yticks(fontsize= 13)\n",
    "plt.legend(loc= 'upper left', fontsize= 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try to do histogram?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sheet 2-1\n",
    "\n",
    "- Top 50 Import Commodities by value with 3 Major Trading Countries\n",
    "- Values & Weights\n",
    "- 2020 only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Imports_Top50_fromTop3= pd.read_csv(\"Cleaned_data/Trading2020_2-1_clean.csv\")\n",
    "Imports_Top50_fromTop3.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Country</th>\n",
       "      <th>Value(millions SAR)</th>\n",
       "      <th>Weight(Tons)</th>\n",
       "      <th>Commodity Name</th>\n",
       "      <th>Commodity Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHINA</td>\n",
       "      <td>12589.189336</td>\n",
       "      <td>5479.952</td>\n",
       "      <td>Telephones for cellular networks or for other...</td>\n",
       "      <td>85171200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VIETNAM</td>\n",
       "      <td>1857.808496</td>\n",
       "      <td>1205.218</td>\n",
       "      <td>Telephones for cellular networks or for other...</td>\n",
       "      <td>85171200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDIA</td>\n",
       "      <td>608.146995</td>\n",
       "      <td>353.263</td>\n",
       "      <td>Telephones for cellular networks or for other...</td>\n",
       "      <td>85171200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHINA</td>\n",
       "      <td>2758.417565</td>\n",
       "      <td>78927.376</td>\n",
       "      <td>PRIVATE CARS, CURRENT, SPARK-IGNITION, NOT OVE...</td>\n",
       "      <td>87032111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U.S.A</td>\n",
       "      <td>2386.783067</td>\n",
       "      <td>41189.769</td>\n",
       "      <td>PRIVATE CARS, CURRENT, SPARK-IGNITION, NOT OVE...</td>\n",
       "      <td>87032111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>GERMANY</td>\n",
       "      <td>128.801190</td>\n",
       "      <td>914.635</td>\n",
       "      <td>PARTS OF LIQUID PUMPS</td>\n",
       "      <td>84139100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>HONG KONG</td>\n",
       "      <td>664.766810</td>\n",
       "      <td>5755.388</td>\n",
       "      <td>FEMALE DRESSES, COTTON, NOT KNITTED</td>\n",
       "      <td>62044200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>UNITED KINGDOM</td>\n",
       "      <td>267.046152</td>\n",
       "      <td>598.895</td>\n",
       "      <td>FEMALE DRESSES, COTTON, NOT KNITTED</td>\n",
       "      <td>62044200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>CHINA</td>\n",
       "      <td>159.145744</td>\n",
       "      <td>1254.306</td>\n",
       "      <td>FEMALE DRESSES, COTTON, NOT KNITTED</td>\n",
       "      <td>62044200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>ARGENTINA</td>\n",
       "      <td>1271.459024</td>\n",
       "      <td>983473.960</td>\n",
       "      <td>OIL-CAKE AND OTHER SOLID RESIDUES, FROM SOYA-B...</td>\n",
       "      <td>23040000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>148 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Country  Value(millions SAR)  Weight(Tons)  \\\n",
       "0             CHINA         12589.189336      5479.952   \n",
       "1           VIETNAM          1857.808496      1205.218   \n",
       "2             INDIA           608.146995       353.263   \n",
       "3             CHINA          2758.417565     78927.376   \n",
       "4             U.S.A          2386.783067     41189.769   \n",
       "..              ...                  ...           ...   \n",
       "143         GERMANY           128.801190       914.635   \n",
       "144       HONG KONG           664.766810      5755.388   \n",
       "145  UNITED KINGDOM           267.046152       598.895   \n",
       "146           CHINA           159.145744      1254.306   \n",
       "147       ARGENTINA          1271.459024    983473.960   \n",
       "\n",
       "                                        Commodity Name  Commodity Code  \n",
       "0     Telephones for cellular networks or for other...      85171200.0  \n",
       "1     Telephones for cellular networks or for other...      85171200.0  \n",
       "2     Telephones for cellular networks or for other...      85171200.0  \n",
       "3    PRIVATE CARS, CURRENT, SPARK-IGNITION, NOT OVE...      87032111.0  \n",
       "4    PRIVATE CARS, CURRENT, SPARK-IGNITION, NOT OVE...      87032111.0  \n",
       "..                                                 ...             ...  \n",
       "143                              PARTS OF LIQUID PUMPS      84139100.0  \n",
       "144                FEMALE DRESSES, COTTON, NOT KNITTED      62044200.0  \n",
       "145                FEMALE DRESSES, COTTON, NOT KNITTED      62044200.0  \n",
       "146                FEMALE DRESSES, COTTON, NOT KNITTED      62044200.0  \n",
       "147  OIL-CAKE AND OTHER SOLID RESIDUES, FROM SOYA-B...      23040000.0  \n",
       "\n",
       "[148 rows x 5 columns]"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Imports_Top50_fromTop3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Imports_Top50_fromTop3.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Imports_Top50_fromTop3['Commodity Name'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                 39\n",
       "Value(millions SAR)    148\n",
       "Weight(Tons)           148\n",
       "Commodity Name          50\n",
       "Commodity Code          50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Imports_Top50_fromTop3.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CHINA                             20\n",
       "U.S.A                             17\n",
       "INDIA                             10\n",
       "GERMANY                            9\n",
       "UNITED ARAB EMIRATES               8\n",
       "JAPAN                              8\n",
       "SOUTH KOREA                        7\n",
       "FRANCE                             6\n",
       "BRAZIL                             4\n",
       "POLAND                             4\n",
       "RUSSIAN FEDERATION                 4\n",
       "SULTANATE OF OMAN                  3\n",
       "UNITED KINGDOM                     3\n",
       "EGYPT                              3\n",
       "UKRAINE                            3\n",
       "IRELAND                            3\n",
       "THAILAND                           3\n",
       "INDONESIA                          3\n",
       "SINGAPORE                          3\n",
       "BAHRAIN                            2\n",
       "SPAIN                              2\n",
       "ARGENTINA                          2\n",
       "NETHERLANDS                        2\n",
       "SWITZERLAND                        2\n",
       "VIETNAM                            2\n",
       "HONG KONG                          2\n",
       "JORDAN                             1\n",
       "CZECH REPUBLIC                     1\n",
       "PANAMA                             1\n",
       "MALAYSIA                           1\n",
       "LITHUANIA                          1\n",
       "SOUTH AFRICA                       1\n",
       "TURKEY                             1\n",
       "ZAMBIA                             1\n",
       "CONGO, THE DEMOCRATIC REPUBLIC     1\n",
       "SWEDEN                             1\n",
       "FINLAND                            1\n",
       "PAKISTAN                           1\n",
       "ITALY                              1\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Imports_Top50_fromTop3.Country.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the top commodities Saudi import from China?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Country</th>\n",
       "      <th>Value(millions SAR)</th>\n",
       "      <th>Weight(Tons)</th>\n",
       "      <th>Commodity Name</th>\n",
       "      <th>Commodity Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHINA</td>\n",
       "      <td>12589.189336</td>\n",
       "      <td>5479.952</td>\n",
       "      <td>Telephones for cellular networks or for other...</td>\n",
       "      <td>85171200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>CHINA</td>\n",
       "      <td>3627.451231</td>\n",
       "      <td>4529.742</td>\n",
       "      <td>Portable Auto Data Processing Machine with C.P.U</td>\n",
       "      <td>84713000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CHINA</td>\n",
       "      <td>3543.312001</td>\n",
       "      <td>4416.311</td>\n",
       "      <td>Other Machines for the reception, conversion a...</td>\n",
       "      <td>85176290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>CHINA</td>\n",
       "      <td>3507.547660</td>\n",
       "      <td>259580.617</td>\n",
       "      <td>Air-Conditioning Equipment (Freon)</td>\n",
       "      <td>84151010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHINA</td>\n",
       "      <td>2758.417565</td>\n",
       "      <td>78927.376</td>\n",
       "      <td>PRIVATE CARS, CURRENT, SPARK-IGNITION, NOT OVE...</td>\n",
       "      <td>87032111.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  Value(millions SAR)  Weight(Tons)  \\\n",
       "0    CHINA         12589.189336      5479.952   \n",
       "42   CHINA          3627.451231      4529.742   \n",
       "27   CHINA          3543.312001      4416.311   \n",
       "39   CHINA          3507.547660    259580.617   \n",
       "3    CHINA          2758.417565     78927.376   \n",
       "\n",
       "                                       Commodity Name  Commodity Code  \n",
       "0    Telephones for cellular networks or for other...      85171200.0  \n",
       "42   Portable Auto Data Processing Machine with C.P.U      84713000.0  \n",
       "27  Other Machines for the reception, conversion a...      85176290.0  \n",
       "39                 Air-Conditioning Equipment (Freon)      84151010.0  \n",
       "3   PRIVATE CARS, CURRENT, SPARK-IGNITION, NOT OVE...      87032111.0  "
      ]
     },
     "execution_count": 505,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CHINA = Imports_Top50_fromTop3[Imports_Top50_fromTop3.Country.isin(['CHINA'])].sort_values('Value(millions SAR)', ascending=False)\n",
    "CHINA0= CHINA.head()\n",
    "CHINA0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Country</th>\n",
       "      <th>Value(millions SAR)</th>\n",
       "      <th>Weight(Tons)</th>\n",
       "      <th>Commodity Name</th>\n",
       "      <th>Commodity Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>U.S.A</td>\n",
       "      <td>4570.528665</td>\n",
       "      <td>2717.278</td>\n",
       "      <td>OTHER PARTS OF AEROPLANES OR HELICOPTERS</td>\n",
       "      <td>88033000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U.S.A</td>\n",
       "      <td>2386.783067</td>\n",
       "      <td>41189.769</td>\n",
       "      <td>PRIVATE CARS, CURRENT, SPARK-IGNITION, NOT OVE...</td>\n",
       "      <td>87032111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>U.S.A</td>\n",
       "      <td>1724.467583</td>\n",
       "      <td>1352.670</td>\n",
       "      <td>OTHER MEDICINE, RETAIL</td>\n",
       "      <td>30049090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>U.S.A</td>\n",
       "      <td>1104.348292</td>\n",
       "      <td>289.565</td>\n",
       "      <td>AIRCRAFT ENGINES, SPARK OR ROTARY</td>\n",
       "      <td>84071000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>U.S.A</td>\n",
       "      <td>1064.144307</td>\n",
       "      <td>1724.271</td>\n",
       "      <td>COMPOSITE DIAGNOSTIC OR LABORATORY REAGENTS</td>\n",
       "      <td>38220000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  Value(millions SAR)  Weight(Tons)  \\\n",
       "18   U.S.A          4570.528665      2717.278   \n",
       "4    U.S.A          2386.783067     41189.769   \n",
       "7    U.S.A          1724.467583      1352.670   \n",
       "93   U.S.A          1104.348292       289.565   \n",
       "60   U.S.A          1064.144307      1724.271   \n",
       "\n",
       "                                       Commodity Name  Commodity Code  \n",
       "18           OTHER PARTS OF AEROPLANES OR HELICOPTERS      88033000.0  \n",
       "4   PRIVATE CARS, CURRENT, SPARK-IGNITION, NOT OVE...      87032111.0  \n",
       "7                              OTHER MEDICINE, RETAIL      30049090.0  \n",
       "93                  AIRCRAFT ENGINES, SPARK OR ROTARY      84071000.0  \n",
       "60        COMPOSITE DIAGNOSTIC OR LABORATORY REAGENTS      38220000.0  "
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "USA = Imports_Top50_fromTop3[Imports_Top50_fromTop3.Country.isin(['U.S.A'])].sort_values('Value(millions SAR)', ascending=False)\n",
    "USA0= USA.head()\n",
    "USA0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Country</th>\n",
       "      <th>Value(millions SAR)</th>\n",
       "      <th>Weight(Tons)</th>\n",
       "      <th>Commodity Name</th>\n",
       "      <th>Commodity Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>INDIA</td>\n",
       "      <td>4010.609925</td>\n",
       "      <td>1176050.164</td>\n",
       "      <td>SIMI-MILLED OR WHOLLY MILLED RICE WHETHER OR N...</td>\n",
       "      <td>10063000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>INDIA</td>\n",
       "      <td>1378.882034</td>\n",
       "      <td>721767.136</td>\n",
       "      <td>CYCLIC HYDROCARBONS (BENZENE)</td>\n",
       "      <td>29022000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>INDIA</td>\n",
       "      <td>1144.873209</td>\n",
       "      <td>1327038.124</td>\n",
       "      <td>OTHER UNGLAZED CERAMIC FLAGS AND TILES</td>\n",
       "      <td>69079090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>INDIA</td>\n",
       "      <td>746.312611</td>\n",
       "      <td>493344.080</td>\n",
       "      <td>ENGINE  GASOLINE</td>\n",
       "      <td>27101221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDIA</td>\n",
       "      <td>608.146995</td>\n",
       "      <td>353.263</td>\n",
       "      <td>Telephones for cellular networks or for other...</td>\n",
       "      <td>85171200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country  Value(millions SAR)  Weight(Tons)  \\\n",
       "24    INDIA          4010.609925   1176050.164   \n",
       "138   INDIA          1378.882034    721767.136   \n",
       "69    INDIA          1144.873209   1327038.124   \n",
       "46    INDIA           746.312611    493344.080   \n",
       "2     INDIA           608.146995       353.263   \n",
       "\n",
       "                                        Commodity Name  Commodity Code  \n",
       "24   SIMI-MILLED OR WHOLLY MILLED RICE WHETHER OR N...      10063000.0  \n",
       "138                      CYCLIC HYDROCARBONS (BENZENE)      29022000.0  \n",
       "69              OTHER UNGLAZED CERAMIC FLAGS AND TILES      69079090.0  \n",
       "46                                    ENGINE  GASOLINE      27101221.0  \n",
       "2     Telephones for cellular networks or for other...      85171200.0  "
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INDIA = Imports_Top50_fromTop3[Imports_Top50_fromTop3.Country.isin(['INDIA'])].sort_values('Value(millions SAR)', ascending=False)\n",
    "INDIA0= INDIA.head(5)\n",
    "INDIA0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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></th>\n",
       "      <th>Country</th>\n",
       "      <th>Value(millions SAR)</th>\n",
       "      <th>Weight(Tons)</th>\n",
       "      <th>Commodity Name</th>\n",
       "      <th>Commodity Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">CHINA</th>\n",
       "      <th>0</th>\n",
       "      <td>CHINA</td>\n",
       "      <td>12589.189336</td>\n",
       "      <td>5479.952</td>\n",
       "      <td>Telephones for cellular networks or for other...</td>\n",
       "      <td>85171200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>CHINA</td>\n",
       "      <td>3627.451231</td>\n",
       "      <td>4529.742</td>\n",
       "      <td>Portable Auto Data Processing Machine with C.P.U</td>\n",
       "      <td>84713000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CHINA</td>\n",
       "      <td>3543.312001</td>\n",
       "      <td>4416.311</td>\n",
       "      <td>Other Machines for the reception, conversion a...</td>\n",
       "      <td>85176290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>CHINA</td>\n",
       "      <td>3507.547660</td>\n",
       "      <td>259580.617</td>\n",
       "      <td>Air-Conditioning Equipment (Freon)</td>\n",
       "      <td>84151010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHINA</td>\n",
       "      <td>2758.417565</td>\n",
       "      <td>78927.376</td>\n",
       "      <td>PRIVATE CARS, CURRENT, SPARK-IGNITION, NOT OVE...</td>\n",
       "      <td>87032111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">USA</th>\n",
       "      <th>18</th>\n",
       "      <td>U.S.A</td>\n",
       "      <td>4570.528665</td>\n",
       "      <td>2717.278</td>\n",
       "      <td>OTHER PARTS OF AEROPLANES OR HELICOPTERS</td>\n",
       "      <td>88033000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U.S.A</td>\n",
       "      <td>2386.783067</td>\n",
       "      <td>41189.769</td>\n",
       "      <td>PRIVATE CARS, CURRENT, SPARK-IGNITION, NOT OVE...</td>\n",
       "      <td>87032111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>U.S.A</td>\n",
       "      <td>1724.467583</td>\n",
       "      <td>1352.670</td>\n",
       "      <td>OTHER MEDICINE, RETAIL</td>\n",
       "      <td>30049090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>U.S.A</td>\n",
       "      <td>1104.348292</td>\n",
       "      <td>289.565</td>\n",
       "      <td>AIRCRAFT ENGINES, SPARK OR ROTARY</td>\n",
       "      <td>84071000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>U.S.A</td>\n",
       "      <td>1064.144307</td>\n",
       "      <td>1724.271</td>\n",
       "      <td>COMPOSITE DIAGNOSTIC OR LABORATORY REAGENTS</td>\n",
       "      <td>38220000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">INDIA</th>\n",
       "      <th>24</th>\n",
       "      <td>INDIA</td>\n",
       "      <td>4010.609925</td>\n",
       "      <td>1176050.164</td>\n",
       "      <td>SIMI-MILLED OR WHOLLY MILLED RICE WHETHER OR N...</td>\n",
       "      <td>10063000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>INDIA</td>\n",
       "      <td>1378.882034</td>\n",
       "      <td>721767.136</td>\n",
       "      <td>CYCLIC HYDROCARBONS (BENZENE)</td>\n",
       "      <td>29022000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>INDIA</td>\n",
       "      <td>1144.873209</td>\n",
       "      <td>1327038.124</td>\n",
       "      <td>OTHER UNGLAZED CERAMIC FLAGS AND TILES</td>\n",
       "      <td>69079090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>INDIA</td>\n",
       "      <td>746.312611</td>\n",
       "      <td>493344.080</td>\n",
       "      <td>ENGINE  GASOLINE</td>\n",
       "      <td>27101221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDIA</td>\n",
       "      <td>608.146995</td>\n",
       "      <td>353.263</td>\n",
       "      <td>Telephones for cellular networks or for other...</td>\n",
       "      <td>85171200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Value(millions SAR)  Weight(Tons)  \\\n",
       "CHINA 0     CHINA         12589.189336      5479.952   \n",
       "      42    CHINA          3627.451231      4529.742   \n",
       "      27    CHINA          3543.312001      4416.311   \n",
       "      39    CHINA          3507.547660    259580.617   \n",
       "      3     CHINA          2758.417565     78927.376   \n",
       "USA   18    U.S.A          4570.528665      2717.278   \n",
       "      4     U.S.A          2386.783067     41189.769   \n",
       "      7     U.S.A          1724.467583      1352.670   \n",
       "      93    U.S.A          1104.348292       289.565   \n",
       "      60    U.S.A          1064.144307      1724.271   \n",
       "INDIA 24    INDIA          4010.609925   1176050.164   \n",
       "      138   INDIA          1378.882034    721767.136   \n",
       "      69    INDIA          1144.873209   1327038.124   \n",
       "      46    INDIA           746.312611    493344.080   \n",
       "      2     INDIA           608.146995       353.263   \n",
       "\n",
       "                                              Commodity Name  Commodity Code  \n",
       "CHINA 0     Telephones for cellular networks or for other...      85171200.0  \n",
       "      42    Portable Auto Data Processing Machine with C.P.U      84713000.0  \n",
       "      27   Other Machines for the reception, conversion a...      85176290.0  \n",
       "      39                  Air-Conditioning Equipment (Freon)      84151010.0  \n",
       "      3    PRIVATE CARS, CURRENT, SPARK-IGNITION, NOT OVE...      87032111.0  \n",
       "USA   18            OTHER PARTS OF AEROPLANES OR HELICOPTERS      88033000.0  \n",
       "      4    PRIVATE CARS, CURRENT, SPARK-IGNITION, NOT OVE...      87032111.0  \n",
       "      7                               OTHER MEDICINE, RETAIL      30049090.0  \n",
       "      93                   AIRCRAFT ENGINES, SPARK OR ROTARY      84071000.0  \n",
       "      60         COMPOSITE DIAGNOSTIC OR LABORATORY REAGENTS      38220000.0  \n",
       "INDIA 24   SIMI-MILLED OR WHOLLY MILLED RICE WHETHER OR N...      10063000.0  \n",
       "      138                      CYCLIC HYDROCARBONS (BENZENE)      29022000.0  \n",
       "      69              OTHER UNGLAZED CERAMIC FLAGS AND TILES      69079090.0  \n",
       "      46                                    ENGINE  GASOLINE      27101221.0  \n",
       "      2     Telephones for cellular networks or for other...      85171200.0  "
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top3_Im_cnts = [CHINA0, USA0, INDIA0]\n",
    "\n",
    "Top5_Imoprts_cnts = pd.concat(Top3_Im_cnts, keys=['CHINA', 'USA', 'INDIA'])\n",
    "\n",
    "Top5_Imoprts_cnts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sheet 3-4\n",
    "\n",
    "Imports by Commodities and Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Imports_commodityCountry = pd.read_csv('Cleaned_Data/Trading2020_3-4_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Imports_commodityCountry.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\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>Quantity/Units</th>\n",
       "      <th>Weights (Tons)</th>\n",
       "      <th>Value(1000 SAR)</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Description</th>\n",
       "      <th>Commodity Code</th>\n",
       "      <th>value in Millions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3292</td>\n",
       "      <td>368</td>\n",
       "      <td>3004</td>\n",
       "      <td>UNITED ARAB EMIRATES</td>\n",
       "      <td>120</td>\n",
       "      <td>OTHER BOVINE ANIMALS</td>\n",
       "      <td>1022900</td>\n",
       "      <td>3.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48652</td>\n",
       "      <td>19013</td>\n",
       "      <td>144297</td>\n",
       "      <td>BRAZIL</td>\n",
       "      <td>439</td>\n",
       "      <td>OTHER BOVINE ANIMALS</td>\n",
       "      <td>1022900</td>\n",
       "      <td>144.297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9228</td>\n",
       "      <td>4186</td>\n",
       "      <td>44258</td>\n",
       "      <td>SPAIN</td>\n",
       "      <td>526</td>\n",
       "      <td>OTHER BOVINE ANIMALS</td>\n",
       "      <td>1022900</td>\n",
       "      <td>44.258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17860</td>\n",
       "      <td>7035</td>\n",
       "      <td>75210</td>\n",
       "      <td>ROMANIA</td>\n",
       "      <td>540</td>\n",
       "      <td>OTHER BOVINE ANIMALS</td>\n",
       "      <td>1022900</td>\n",
       "      <td>75.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5069</td>\n",
       "      <td>2031</td>\n",
       "      <td>19504</td>\n",
       "      <td>UKRAINE</td>\n",
       "      <td>544</td>\n",
       "      <td>OTHER BOVINE ANIMALS</td>\n",
       "      <td>1022900</td>\n",
       "      <td>19.504</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24644</th>\n",
       "      <td>0</td>\n",
       "      <td>956</td>\n",
       "      <td>10700</td>\n",
       "      <td>U.S.A</td>\n",
       "      <td>410</td>\n",
       "      <td>For diplomatic corps, consulates</td>\n",
       "      <td>98020010</td>\n",
       "      <td>10.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24645</th>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>9341</td>\n",
       "      <td>GERMANY</td>\n",
       "      <td>517</td>\n",
       "      <td>For diplomatic corps, consulates</td>\n",
       "      <td>98020010</td>\n",
       "      <td>9.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24646</th>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>2483</td>\n",
       "      <td>FRANCE</td>\n",
       "      <td>522</td>\n",
       "      <td>For diplomatic corps, consulates</td>\n",
       "      <td>98020010</td>\n",
       "      <td>2.483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24647</th>\n",
       "      <td>0</td>\n",
       "      <td>525</td>\n",
       "      <td>6072</td>\n",
       "      <td>Other Countries</td>\n",
       "      <td>997</td>\n",
       "      <td>For diplomatic corps, consulates</td>\n",
       "      <td>98020010</td>\n",
       "      <td>6.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24648</th>\n",
       "      <td>0</td>\n",
       "      <td>1869997</td>\n",
       "      <td>28674503</td>\n",
       "      <td>Different Countries</td>\n",
       "      <td>996</td>\n",
       "      <td>Different Commodities</td>\n",
       "      <td>99999999</td>\n",
       "      <td>28674.503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24649 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quantity/Units  Weights (Tons)  Value(1000 SAR)          Country Name  \\\n",
       "0                3292             368             3004  UNITED ARAB EMIRATES   \n",
       "1               48652           19013           144297                BRAZIL   \n",
       "2                9228            4186            44258                 SPAIN   \n",
       "3               17860            7035            75210               ROMANIA   \n",
       "4                5069            2031            19504               UKRAINE   \n",
       "...               ...             ...              ...                   ...   \n",
       "24644               0             956            10700                 U.S.A   \n",
       "24645               0              86             9341               GERMANY   \n",
       "24646               0              36             2483                FRANCE   \n",
       "24647               0             525             6072       Other Countries   \n",
       "24648               0         1869997         28674503   Different Countries   \n",
       "\n",
       "       Country Code                       Description  Commodity Code  \\\n",
       "0               120              OTHER BOVINE ANIMALS         1022900   \n",
       "1               439              OTHER BOVINE ANIMALS         1022900   \n",
       "2               526              OTHER BOVINE ANIMALS         1022900   \n",
       "3               540              OTHER BOVINE ANIMALS         1022900   \n",
       "4               544              OTHER BOVINE ANIMALS         1022900   \n",
       "...             ...                               ...             ...   \n",
       "24644           410  For diplomatic corps, consulates        98020010   \n",
       "24645           517  For diplomatic corps, consulates        98020010   \n",
       "24646           522  For diplomatic corps, consulates        98020010   \n",
       "24647           997  For diplomatic corps, consulates        98020010   \n",
       "24648           996             Different Commodities        99999999   \n",
       "\n",
       "       value in Millions  \n",
       "0                  3.004  \n",
       "1                144.297  \n",
       "2                 44.258  \n",
       "3                 75.210  \n",
       "4                 19.504  \n",
       "...                  ...  \n",
       "24644             10.700  \n",
       "24645              9.341  \n",
       "24646              2.483  \n",
       "24647              6.072  \n",
       "24648          28674.503  \n",
       "\n",
       "[24649 rows x 8 columns]"
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Imports_commodityCountry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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></th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Description</th>\n",
       "      <th>Country Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">?Smart cards?</th>\n",
       "      <th>CHINA</th>\n",
       "      <td>24.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINLAND</th>\n",
       "      <td>1.648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRANCE</th>\n",
       "      <td>2.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GERMANY</th>\n",
       "      <td>3.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDIA</th>\n",
       "      <td>9.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">the personal effects and used household items brought into the co</th>\n",
       "      <th>SOUTH AFRICA</th>\n",
       "      <td>1.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TURKEY</th>\n",
       "      <td>26.154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U.S.A</th>\n",
       "      <td>676.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNITED ARAB EMIRATES</th>\n",
       "      <td>3669.899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNITED KINGDOM</th>\n",
       "      <td>33.546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24486 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                              max\n",
       "Description                                        Country Name                  \n",
       " ?Smart cards?                                     CHINA                   24.810\n",
       "                                                   FINLAND                  1.648\n",
       "                                                   FRANCE                   2.216\n",
       "                                                   GERMANY                  3.720\n",
       "                                                   INDIA                    9.281\n",
       "...                                                                           ...\n",
       "the personal effects and used household items b... SOUTH AFRICA             1.185\n",
       "                                                   TURKEY                  26.154\n",
       "                                                   U.S.A                  676.064\n",
       "                                                   UNITED ARAB EMIRATES  3669.899\n",
       "                                                   UNITED KINGDOM          33.546\n",
       "\n",
       "[24486 rows x 1 columns]"
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Imports_commodityCountry.groupby([ 'Description', 'Country Name'])['value in Millions'].aggregate(['max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Country Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CHINA</th>\n",
       "      <td>2122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U.S.A</th>\n",
       "      <td>1603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GERMANY</th>\n",
       "      <td>1348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ITALY</th>\n",
       "      <td>1168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDIA</th>\n",
       "      <td>1167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNITED ARAB EMIRATES</th>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNITED KINGDOM</th>\n",
       "      <td>867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TURKEY</th>\n",
       "      <td>830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRANCE</th>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOUTH KOREA</th>\n",
       "      <td>661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Country Name\n",
       "CHINA                         2122\n",
       "U.S.A                         1603\n",
       "GERMANY                       1348\n",
       "ITALY                         1168\n",
       "INDIA                         1167\n",
       "UNITED ARAB EMIRATES           932\n",
       "UNITED KINGDOM                 867\n",
       "TURKEY                         830\n",
       "FRANCE                         790\n",
       "SOUTH KOREA                    661"
      ]
     },
     "execution_count": 558,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top_countries = Imports_commodityCountry['Country Name'].value_counts().to_frame()\n",
    "Top_countries.drop('Other Countries', axis=0, inplace=True)\n",
    "Top_countries.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity/Units</th>\n",
       "      <th>Weights (Tons)</th>\n",
       "      <th>Value(1000 SAR)</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Description</th>\n",
       "      <th>Commodity Code</th>\n",
       "      <th>value in Millions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22123</th>\n",
       "      <td>0</td>\n",
       "      <td>2717</td>\n",
       "      <td>4570529</td>\n",
       "      <td>U.S.A</td>\n",
       "      <td>410</td>\n",
       "      <td>OTHER PARTS OF AEROPLANES OR HELICOPTERS</td>\n",
       "      <td>88033000</td>\n",
       "      <td>4570.529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21246</th>\n",
       "      <td>18749</td>\n",
       "      <td>41190</td>\n",
       "      <td>2386783</td>\n",
       "      <td>U.S.A</td>\n",
       "      <td>410</td>\n",
       "      <td>PRIVATE CARS, CURRENT, SPARK-IGNITION, NOT OVE...</td>\n",
       "      <td>87032111</td>\n",
       "      <td>2386.783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4914</th>\n",
       "      <td>0</td>\n",
       "      <td>1353</td>\n",
       "      <td>1724468</td>\n",
       "      <td>U.S.A</td>\n",
       "      <td>410</td>\n",
       "      <td>OTHER MEDICINE, RETAIL</td>\n",
       "      <td>30049090</td>\n",
       "      <td>1724.468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15064</th>\n",
       "      <td>1096</td>\n",
       "      <td>290</td>\n",
       "      <td>1104348</td>\n",
       "      <td>U.S.A</td>\n",
       "      <td>410</td>\n",
       "      <td>AIRCRAFT ENGINES, SPARK OR ROTARY</td>\n",
       "      <td>84071000</td>\n",
       "      <td>1104.348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6726</th>\n",
       "      <td>0</td>\n",
       "      <td>1724</td>\n",
       "      <td>1064144</td>\n",
       "      <td>U.S.A</td>\n",
       "      <td>410</td>\n",
       "      <td>COMPOSITE DIAGNOSTIC OR LABORATORY REAGENTS</td>\n",
       "      <td>38220000</td>\n",
       "      <td>1064.144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quantity/Units  Weights (Tons)  Value(1000 SAR) Country Name  \\\n",
       "22123               0            2717          4570529        U.S.A   \n",
       "21246           18749           41190          2386783        U.S.A   \n",
       "4914                0            1353          1724468        U.S.A   \n",
       "15064            1096             290          1104348        U.S.A   \n",
       "6726                0            1724          1064144        U.S.A   \n",
       "\n",
       "       Country Code                                        Description  \\\n",
       "22123           410           OTHER PARTS OF AEROPLANES OR HELICOPTERS   \n",
       "21246           410  PRIVATE CARS, CURRENT, SPARK-IGNITION, NOT OVE...   \n",
       "4914            410                             OTHER MEDICINE, RETAIL   \n",
       "15064           410                  AIRCRAFT ENGINES, SPARK OR ROTARY   \n",
       "6726            410        COMPOSITE DIAGNOSTIC OR LABORATORY REAGENTS   \n",
       "\n",
       "       Commodity Code  value in Millions  \n",
       "22123        88033000           4570.529  \n",
       "21246        87032111           2386.783  \n",
       "4914         30049090           1724.468  \n",
       "15064        84071000           1104.348  \n",
       "6726         38220000           1064.144  "
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "USA = Imports_commodityCountry[Imports_commodityCountry['Country Name'].isin(['U.S.A'])].sort_values('value in Millions', ascending=False)\n",
    "USA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity/Units</th>\n",
       "      <th>Weights (Tons)</th>\n",
       "      <th>Value(1000 SAR)</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Description</th>\n",
       "      <th>Commodity Code</th>\n",
       "      <th>value in Millions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24629</th>\n",
       "      <td>0</td>\n",
       "      <td>27081</td>\n",
       "      <td>3669899</td>\n",
       "      <td>UNITED ARAB EMIRATES</td>\n",
       "      <td>120</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>3669.899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24632</th>\n",
       "      <td>0</td>\n",
       "      <td>26823</td>\n",
       "      <td>2771625</td>\n",
       "      <td>HONG KONG</td>\n",
       "      <td>145</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>2771.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24636</th>\n",
       "      <td>0</td>\n",
       "      <td>8047</td>\n",
       "      <td>676064</td>\n",
       "      <td>U.S.A</td>\n",
       "      <td>410</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>676.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24631</th>\n",
       "      <td>0</td>\n",
       "      <td>6522</td>\n",
       "      <td>586772</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>142</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>586.772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24628</th>\n",
       "      <td>0</td>\n",
       "      <td>649</td>\n",
       "      <td>70642</td>\n",
       "      <td>BAHRAIN</td>\n",
       "      <td>116</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>70.642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24639</th>\n",
       "      <td>0</td>\n",
       "      <td>569</td>\n",
       "      <td>33546</td>\n",
       "      <td>UNITED KINGDOM</td>\n",
       "      <td>521</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>33.546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24642</th>\n",
       "      <td>0</td>\n",
       "      <td>344</td>\n",
       "      <td>28805</td>\n",
       "      <td>EUROPEAN UNION, N.E.S</td>\n",
       "      <td>950</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>28.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24641</th>\n",
       "      <td>0</td>\n",
       "      <td>209</td>\n",
       "      <td>26154</td>\n",
       "      <td>TURKEY</td>\n",
       "      <td>525</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>26.154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24643</th>\n",
       "      <td>0</td>\n",
       "      <td>793</td>\n",
       "      <td>8836</td>\n",
       "      <td>Other Countries</td>\n",
       "      <td>997</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>8.836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24637</th>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>8097</td>\n",
       "      <td>GERMANY</td>\n",
       "      <td>517</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>8.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24640</th>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>6004</td>\n",
       "      <td>FRANCE</td>\n",
       "      <td>522</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>6.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24633</th>\n",
       "      <td>0</td>\n",
       "      <td>1742</td>\n",
       "      <td>4150</td>\n",
       "      <td>EGYPT</td>\n",
       "      <td>214</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>4.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24638</th>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>3452</td>\n",
       "      <td>NETHERLANDS</td>\n",
       "      <td>518</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>3.452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24627</th>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>3331</td>\n",
       "      <td>KUWAIT</td>\n",
       "      <td>113</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>3.331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24630</th>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>2031</td>\n",
       "      <td>INDIA</td>\n",
       "      <td>131</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>2.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24635</th>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>1717</td>\n",
       "      <td>AUSTRALIA</td>\n",
       "      <td>310</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>1.717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24626</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>1480</td>\n",
       "      <td>JORDAN</td>\n",
       "      <td>112</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>1.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24634</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1185</td>\n",
       "      <td>SOUTH AFRICA</td>\n",
       "      <td>254</td>\n",
       "      <td>the personal effects and used household items ...</td>\n",
       "      <td>98010000</td>\n",
       "      <td>1.185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quantity/Units  Weights (Tons)  Value(1000 SAR)           Country Name  \\\n",
       "24629               0           27081          3669899   UNITED ARAB EMIRATES   \n",
       "24632               0           26823          2771625              HONG KONG   \n",
       "24636               0            8047           676064                  U.S.A   \n",
       "24631               0            6522           586772                  CHINA   \n",
       "24628               0             649            70642                BAHRAIN   \n",
       "24639               0             569            33546         UNITED KINGDOM   \n",
       "24642               0             344            28805  EUROPEAN UNION, N.E.S   \n",
       "24641               0             209            26154                 TURKEY   \n",
       "24643               0             793             8836        Other Countries   \n",
       "24637               0             109             8097                GERMANY   \n",
       "24640               0              85             6004                 FRANCE   \n",
       "24633               0            1742             4150                  EGYPT   \n",
       "24638               0              28             3452            NETHERLANDS   \n",
       "24627               0              84             3331                 KUWAIT   \n",
       "24630               0              41             2031                  INDIA   \n",
       "24635               0              99             1717              AUSTRALIA   \n",
       "24626               0             137             1480                 JORDAN   \n",
       "24634               0              10             1185           SOUTH AFRICA   \n",
       "\n",
       "       Country Code                                        Description  \\\n",
       "24629           120  the personal effects and used household items ...   \n",
       "24632           145  the personal effects and used household items ...   \n",
       "24636           410  the personal effects and used household items ...   \n",
       "24631           142  the personal effects and used household items ...   \n",
       "24628           116  the personal effects and used household items ...   \n",
       "24639           521  the personal effects and used household items ...   \n",
       "24642           950  the personal effects and used household items ...   \n",
       "24641           525  the personal effects and used household items ...   \n",
       "24643           997  the personal effects and used household items ...   \n",
       "24637           517  the personal effects and used household items ...   \n",
       "24640           522  the personal effects and used household items ...   \n",
       "24633           214  the personal effects and used household items ...   \n",
       "24638           518  the personal effects and used household items ...   \n",
       "24627           113  the personal effects and used household items ...   \n",
       "24630           131  the personal effects and used household items ...   \n",
       "24635           310  the personal effects and used household items ...   \n",
       "24626           112  the personal effects and used household items ...   \n",
       "24634           254  the personal effects and used household items ...   \n",
       "\n",
       "       Commodity Code  value in Millions  \n",
       "24629        98010000           3669.899  \n",
       "24632        98010000           2771.625  \n",
       "24636        98010000            676.064  \n",
       "24631        98010000            586.772  \n",
       "24628        98010000             70.642  \n",
       "24639        98010000             33.546  \n",
       "24642        98010000             28.805  \n",
       "24641        98010000             26.154  \n",
       "24643        98010000              8.836  \n",
       "24637        98010000              8.097  \n",
       "24640        98010000              6.004  \n",
       "24633        98010000              4.150  \n",
       "24638        98010000              3.452  \n",
       "24627        98010000              3.331  \n",
       "24630        98010000              2.031  \n",
       "24635        98010000              1.717  \n",
       "24626        98010000              1.480  \n",
       "24634        98010000              1.185  "
      ]
     },
     "execution_count": 527,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Imports_commodityCountry[Imports_commodityCountry['Description'].isin(['the personal effects and used household items brought into the co'])].sort_values('value in Millions', ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity/Units</th>\n",
       "      <th>Weights (Tons)</th>\n",
       "      <th>Value(1000 SAR)</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Description</th>\n",
       "      <th>Commodity Code</th>\n",
       "      <th>value in Millions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4929</th>\n",
       "      <td>0</td>\n",
       "      <td>2442</td>\n",
       "      <td>1952972</td>\n",
       "      <td>GERMANY</td>\n",
       "      <td>517</td>\n",
       "      <td>OTHER MEDICINE, RETAIL</td>\n",
       "      <td>30049090</td>\n",
       "      <td>1952.972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21186</th>\n",
       "      <td>11231</td>\n",
       "      <td>87242</td>\n",
       "      <td>786149</td>\n",
       "      <td>GERMANY</td>\n",
       "      <td>517</td>\n",
       "      <td>ROAD TRACTORS FOR SEMI-TRAILERS</td>\n",
       "      <td>87012000</td>\n",
       "      <td>786.149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4654</th>\n",
       "      <td>0</td>\n",
       "      <td>195</td>\n",
       "      <td>757409</td>\n",
       "      <td>GERMANY</td>\n",
       "      <td>517</td>\n",
       "      <td>ANTISERA AND OTHER BLOOD FRACTIONS</td>\n",
       "      <td>30021000</td>\n",
       "      <td>757.409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1464</th>\n",
       "      <td>0</td>\n",
       "      <td>728590</td>\n",
       "      <td>579291</td>\n",
       "      <td>GERMANY</td>\n",
       "      <td>517</td>\n",
       "      <td>BARLEY</td>\n",
       "      <td>10039000</td>\n",
       "      <td>579.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6648</th>\n",
       "      <td>0</td>\n",
       "      <td>194</td>\n",
       "      <td>504972</td>\n",
       "      <td>GERMANY</td>\n",
       "      <td>517</td>\n",
       "      <td>SUPPORTED CATALYSTS WITH PRECIOUS METAL</td>\n",
       "      <td>38151200</td>\n",
       "      <td>504.972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quantity/Units  Weights (Tons)  Value(1000 SAR) Country Name  \\\n",
       "4929                0            2442          1952972      GERMANY   \n",
       "21186           11231           87242           786149      GERMANY   \n",
       "4654                0             195           757409      GERMANY   \n",
       "1464                0          728590           579291      GERMANY   \n",
       "6648                0             194           504972      GERMANY   \n",
       "\n",
       "       Country Code                              Description  Commodity Code  \\\n",
       "4929            517                   OTHER MEDICINE, RETAIL        30049090   \n",
       "21186           517          ROAD TRACTORS FOR SEMI-TRAILERS        87012000   \n",
       "4654            517       ANTISERA AND OTHER BLOOD FRACTIONS        30021000   \n",
       "1464            517                                   BARLEY        10039000   \n",
       "6648            517  SUPPORTED CATALYSTS WITH PRECIOUS METAL        38151200   \n",
       "\n",
       "       value in Millions  \n",
       "4929            1952.972  \n",
       "21186            786.149  \n",
       "4654             757.409  \n",
       "1464             579.291  \n",
       "6648             504.972  "
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GERMANY = Imports_commodityCountry[Imports_commodityCountry['Country Name'].isin(['GERMANY'])].sort_values('value in Millions', ascending=False)\n",
    "GERMANY.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sheet 3-5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sheet 3-7\n",
    "- Exports by Country Groups and Major Commodities. Values & weights\n",
    "- 2020 only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Exports_commodityGroup = pd.read_csv('Cleaned_data/Trading2020_3-7_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Exports_commodityGroup.drop('Unnamed: 0', axis=1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Quantity/Units</th>\n",
       "      <th>Weight(Tons)</th>\n",
       "      <th>Value(1000 SAR)</th>\n",
       "      <th>Commodity Code</th>\n",
       "      <th>Commodity</th>\n",
       "      <th>Country Group</th>\n",
       "      <th>value in Millions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>15765.183</td>\n",
       "      <td>241949.257</td>\n",
       "      <td>2071100</td>\n",
       "      <td>MEAT, CONS. BWLS &amp; EXTREM. OF TURKEY/CHICKEN:G...</td>\n",
       "      <td>Gulf Cooperation Council</td>\n",
       "      <td>241.949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>290987.699</td>\n",
       "      <td>740453.872</td>\n",
       "      <td>4029110</td>\n",
       "      <td>OTHER MILK, UNSWEETENED</td>\n",
       "      <td>Gulf Cooperation Council</td>\n",
       "      <td>740.454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>83779.822</td>\n",
       "      <td>390287.427</td>\n",
       "      <td>4031000</td>\n",
       "      <td>YOGURT</td>\n",
       "      <td>Gulf Cooperation Council</td>\n",
       "      <td>390.287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>22805.576</td>\n",
       "      <td>414107.638</td>\n",
       "      <td>4063000</td>\n",
       "      <td>PROCESSED CHEESE, NOT GRATED OR POWDERED</td>\n",
       "      <td>Gulf Cooperation Council</td>\n",
       "      <td>414.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>45952.563</td>\n",
       "      <td>189859.825</td>\n",
       "      <td>8041021</td>\n",
       "      <td>STORED DATES</td>\n",
       "      <td>Gulf Cooperation Council</td>\n",
       "      <td>189.860</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>0</td>\n",
       "      <td>247.500</td>\n",
       "      <td>820.418</td>\n",
       "      <td>39021000</td>\n",
       "      <td>POLYPRO PYLENE</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>0</td>\n",
       "      <td>247.500</td>\n",
       "      <td>877.412</td>\n",
       "      <td>39023000</td>\n",
       "      <td>PROPYLENE COPOLYMERS</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>0</td>\n",
       "      <td>1.956</td>\n",
       "      <td>4210.834</td>\n",
       "      <td>84119900</td>\n",
       "      <td>OTHER PARTS FOR TURBO-JETS OR TURBO-PROPELLERS</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>4.211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>0</td>\n",
       "      <td>77.327</td>\n",
       "      <td>2789.056</td>\n",
       "      <td>84249090</td>\n",
       "      <td>OTHER PARTS OF MECHANICAL APPLIANCES FOR PRAJE...</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>2.789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>0</td>\n",
       "      <td>152.391</td>\n",
       "      <td>293.235</td>\n",
       "      <td>99999999</td>\n",
       "      <td>Other Commodities</td>\n",
       "      <td>Not Defined</td>\n",
       "      <td>0.293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>517 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Quantity/Units  Weight(Tons)  Value(1000 SAR)  Commodity Code  \\\n",
       "0                 0     15765.183       241949.257         2071100   \n",
       "1                 0    290987.699       740453.872         4029110   \n",
       "2                 0     83779.822       390287.427         4031000   \n",
       "3                 0     22805.576       414107.638         4063000   \n",
       "4                 0     45952.563       189859.825         8041021   \n",
       "..              ...           ...              ...             ...   \n",
       "512               0       247.500          820.418        39021000   \n",
       "513               0       247.500          877.412        39023000   \n",
       "514               0         1.956         4210.834        84119900   \n",
       "515               0        77.327         2789.056        84249090   \n",
       "516               0       152.391          293.235        99999999   \n",
       "\n",
       "                                             Commodity  \\\n",
       "0    MEAT, CONS. BWLS & EXTREM. OF TURKEY/CHICKEN:G...   \n",
       "1                              OTHER MILK, UNSWEETENED   \n",
       "2                                               YOGURT   \n",
       "3             PROCESSED CHEESE, NOT GRATED OR POWDERED   \n",
       "4                                         STORED DATES   \n",
       "..                                                 ...   \n",
       "512                                     POLYPRO PYLENE   \n",
       "513                               PROPYLENE COPOLYMERS   \n",
       "514     OTHER PARTS FOR TURBO-JETS OR TURBO-PROPELLERS   \n",
       "515  OTHER PARTS OF MECHANICAL APPLIANCES FOR PRAJE...   \n",
       "516                                  Other Commodities   \n",
       "\n",
       "                Country Group  value in Millions  \n",
       "0    Gulf Cooperation Council            241.949  \n",
       "1    Gulf Cooperation Council            740.454  \n",
       "2    Gulf Cooperation Council            390.287  \n",
       "3    Gulf Cooperation Council            414.108  \n",
       "4    Gulf Cooperation Council            189.860  \n",
       "..                        ...                ...  \n",
       "512               Not Defined              0.820  \n",
       "513               Not Defined              0.877  \n",
       "514               Not Defined              4.211  \n",
       "515               Not Defined              2.789  \n",
       "516               Not Defined              0.293  \n",
       "\n",
       "[517 rows x 7 columns]"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Exports_commodityGroup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other Commodities                                         11\n",
       "POLYPRO PYLENE                                            11\n",
       "POLYETHYLENE HAVING SPECIFIC GRAVITY OF 0.94 OR MORE      10\n",
       "POLYETHYLENE HAVING SPECIFIC GRAVITY OF LESS THAN 0.94    10\n",
       "CRUDE PETROLEUM OILS                                       9\n",
       "                                                          ..\n",
       "CLOTH AND TOWEL SMOOTHERS                                  1\n",
       "OTHER SELF-ADHESIVE PLATES, STRIPS ETC., OF PLASTICS       1\n",
       "OTHER PLASTIC PACKING GOODS                                1\n",
       "other of paper                                             1\n",
       "OTHER PARTS OF MECHANICAL APPLIANCES FOR PRAJEEDING ,      1\n",
       "Name: Commodity, Length: 238, dtype: int64"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Exports_commodityGroup['Commodity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Quantity/Units</th>\n",
       "      <th>Weight(Tons)</th>\n",
       "      <th>Value(1000 SAR)</th>\n",
       "      <th>Commodity Code</th>\n",
       "      <th>Commodity</th>\n",
       "      <th>Country Group</th>\n",
       "      <th>value in Millions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>9673478.0</td>\n",
       "      <td>11887264.0</td>\n",
       "      <td>27090000</td>\n",
       "      <td>CRUDE PETROLEUM OILS</td>\n",
       "      <td>Gulf Cooperation Council</td>\n",
       "      <td>11887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0</td>\n",
       "      <td>7337604.0</td>\n",
       "      <td>8345429.0</td>\n",
       "      <td>27090000</td>\n",
       "      <td>CRUDE PETROLEUM OILS</td>\n",
       "      <td>Other Arab League Countries</td>\n",
       "      <td>8345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>0</td>\n",
       "      <td>14504343.0</td>\n",
       "      <td>16916975.0</td>\n",
       "      <td>27090000</td>\n",
       "      <td>CRUDE PETROLEUM OILS</td>\n",
       "      <td>Islamic not Arabic countries</td>\n",
       "      <td>16917.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0</td>\n",
       "      <td>234013744.0</td>\n",
       "      <td>268799028.0</td>\n",
       "      <td>27090000</td>\n",
       "      <td>CRUDE PETROLEUM OILS</td>\n",
       "      <td>Asian not Arabic ,not Islamic</td>\n",
       "      <td>268799.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>0</td>\n",
       "      <td>4503281.0</td>\n",
       "      <td>5588933.0</td>\n",
       "      <td>27090000</td>\n",
       "      <td>CRUDE PETROLEUM OILS</td>\n",
       "      <td>African not Arabic,not Islamic</td>\n",
       "      <td>5589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>0</td>\n",
       "      <td>280648.0</td>\n",
       "      <td>325953.0</td>\n",
       "      <td>27090000</td>\n",
       "      <td>CRUDE PETROLEUM OILS</td>\n",
       "      <td>Australia and Oceania</td>\n",
       "      <td>326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>0</td>\n",
       "      <td>27624364.0</td>\n",
       "      <td>26785859.0</td>\n",
       "      <td>27090000</td>\n",
       "      <td>CRUDE PETROLEUM OILS</td>\n",
       "      <td>North America</td>\n",
       "      <td>26786.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>0</td>\n",
       "      <td>2579945.0</td>\n",
       "      <td>2954918.0</td>\n",
       "      <td>27090000</td>\n",
       "      <td>CRUDE PETROLEUM OILS</td>\n",
       "      <td>South America</td>\n",
       "      <td>2955.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>0</td>\n",
       "      <td>34592171.0</td>\n",
       "      <td>34830053.0</td>\n",
       "      <td>27090000</td>\n",
       "      <td>CRUDE PETROLEUM OILS</td>\n",
       "      <td>European Union</td>\n",
       "      <td>34830.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Quantity/Units  Weight(Tons)  Value(1000 SAR)  Commodity Code  \\\n",
       "10                0     9673478.0       11887264.0        27090000   \n",
       "61                0     7337604.0        8345429.0        27090000   \n",
       "107               0    14504343.0       16916975.0        27090000   \n",
       "155               0   234013744.0      268799028.0        27090000   \n",
       "209               0     4503281.0        5588933.0        27090000   \n",
       "258               0      280648.0         325953.0        27090000   \n",
       "307               0    27624364.0       26785859.0        27090000   \n",
       "361               0     2579945.0        2954918.0        27090000   \n",
       "411               0    34592171.0       34830053.0        27090000   \n",
       "\n",
       "                Commodity                   Country Group  value in Millions  \n",
       "10   CRUDE PETROLEUM OILS        Gulf Cooperation Council            11887.0  \n",
       "61   CRUDE PETROLEUM OILS     Other Arab League Countries             8345.0  \n",
       "107  CRUDE PETROLEUM OILS    Islamic not Arabic countries            16917.0  \n",
       "155  CRUDE PETROLEUM OILS   Asian not Arabic ,not Islamic           268799.0  \n",
       "209  CRUDE PETROLEUM OILS  African not Arabic,not Islamic             5589.0  \n",
       "258  CRUDE PETROLEUM OILS           Australia and Oceania              326.0  \n",
       "307  CRUDE PETROLEUM OILS                   North America            26786.0  \n",
       "361  CRUDE PETROLEUM OILS                   South America             2955.0  \n",
       "411  CRUDE PETROLEUM OILS                  European Union            34830.0  "
      ]
     },
     "execution_count": 562,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot_oil_ex_20 = Exports_commodityGroup[Exports_commodityGroup.Commodity.isin(['CRUDE PETROLEUM OILS'])]\n",
    "tot_oil_ex_20.round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "376434.412"
      ]
     },
     "execution_count": 563,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot_oil_ex_20['value in Millions'].sum()\n",
    "# Get the sum of all value in Millions, then substract 376434.412"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How much did the oil exports add to the Saudi economy in 2020? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "275517.5519999999"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Exports_commodityGroup['value in Millions'].sum() - tot_oil_ex_20['value in Millions'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 5 commodities exported from Saudi, other than oil products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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></th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Commodity</th>\n",
       "      <th>Country Group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>XYLOLE</th>\n",
       "      <th>North America</th>\n",
       "      <td>67550.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOGURT</th>\n",
       "      <th>Gulf Cooperation Council</th>\n",
       "      <td>83779.822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other of paper</th>\n",
       "      <th>African not Arabic,not Islamic</th>\n",
       "      <td>16338.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">the personal effects and used household items brought into the co</th>\n",
       "      <th>African not Arabic,not Islamic</th>\n",
       "      <td>5301.716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gulf Cooperation Council</th>\n",
       "      <td>1912.997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                         max\n",
       "Commodity                                          Country Group                            \n",
       "XYLOLE                                             North America                   67550.000\n",
       "YOGURT                                             Gulf Cooperation Council        83779.822\n",
       "other of paper                                     African not Arabic,not Islamic  16338.933\n",
       "the personal effects and used household items b... African not Arabic,not Islamic   5301.716\n",
       "                                                   Gulf Cooperation Council         1912.997"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Country_Group = Exports_commodityGroup.groupby(['Commodity','Country Group'])['Weight(Tons)'].aggregate([\"max\"])\n",
    "Country_Group.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trading20\n",
    "## Sheet 1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Trading_Oil_non_Oil= pd.read_csv('Cleaned_data/Trading20_1-1_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Trading_Oil_non_Oil.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Year</th>\n",
       "      <th>Oil Exports Value(Millions SAR)</th>\n",
       "      <th>Oil Percentage of Total Exports</th>\n",
       "      <th>Non-oil Exports Value(millions SAR)</th>\n",
       "      <th>Non-oil Percentage of Total Exports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>1.191052e+06</td>\n",
       "      <td>87.089414</td>\n",
       "      <td>176567.731649</td>\n",
       "      <td>12.910586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>1.265551e+06</td>\n",
       "      <td>86.889717</td>\n",
       "      <td>190951.553513</td>\n",
       "      <td>13.110283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>1.207080e+06</td>\n",
       "      <td>85.637470</td>\n",
       "      <td>202443.212959</td>\n",
       "      <td>14.362530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>1.067092e+06</td>\n",
       "      <td>83.098959</td>\n",
       "      <td>217029.903583</td>\n",
       "      <td>16.901041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>5.734120e+05</td>\n",
       "      <td>75.121469</td>\n",
       "      <td>189901.077563</td>\n",
       "      <td>24.878531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>5.107295e+05</td>\n",
       "      <td>74.188322</td>\n",
       "      <td>177693.532214</td>\n",
       "      <td>25.811678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017</td>\n",
       "      <td>6.384023e+05</td>\n",
       "      <td>76.741993</td>\n",
       "      <td>193479.004472</td>\n",
       "      <td>23.258007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "      <td>8.684424e+05</td>\n",
       "      <td>78.670352</td>\n",
       "      <td>235458.083665</td>\n",
       "      <td>21.329648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019</td>\n",
       "      <td>7.518281e+05</td>\n",
       "      <td>76.637987</td>\n",
       "      <td>229184.234637</td>\n",
       "      <td>23.362013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020</td>\n",
       "      <td>4.475992e+05</td>\n",
       "      <td>68.655246</td>\n",
       "      <td>204352.737425</td>\n",
       "      <td>31.344754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Oil Exports Value(Millions SAR)  Oil Percentage of Total Exports  \\\n",
       "0  2011                     1.191052e+06                        87.089414   \n",
       "1  2012                     1.265551e+06                        86.889717   \n",
       "2  2013                     1.207080e+06                        85.637470   \n",
       "3  2014                     1.067092e+06                        83.098959   \n",
       "4  2015                     5.734120e+05                        75.121469   \n",
       "5  2016                     5.107295e+05                        74.188322   \n",
       "6  2017                     6.384023e+05                        76.741993   \n",
       "7  2018                     8.684424e+05                        78.670352   \n",
       "8  2019                     7.518281e+05                        76.637987   \n",
       "9  2020                     4.475992e+05                        68.655246   \n",
       "\n",
       "   Non-oil Exports Value(millions SAR)  Non-oil Percentage of Total Exports  \n",
       "0                        176567.731649                            12.910586  \n",
       "1                        190951.553513                            13.110283  \n",
       "2                        202443.212959                            14.362530  \n",
       "3                        217029.903583                            16.901041  \n",
       "4                        189901.077563                            24.878531  \n",
       "5                        177693.532214                            25.811678  \n",
       "6                        193479.004472                            23.258007  \n",
       "7                        235458.083665                            21.329648  \n",
       "8                        229184.234637                            23.362013  \n",
       "9                        204352.737425                            31.344754  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Trading_Oil_non_Oil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= Trading_Oil_non_Oil['Year']\n",
    "y= Trading_Oil_non_Oil['Oil Percentage of Total Exports']\n",
    "z= Trading_Oil_non_Oil['Non-oil Percentage of Total Exports']\n",
    "plt.figure(figsize = [10,6])\n",
    "plt.bar(x, y, 0.5, label= 'Oil Percentage of Total Exports')\n",
    "plt.bar(x, z, 0.4, bottom= y, label= 'Non-oil Percentage of Total Exports')\n",
    "plt.title('Oil Vs. Non-Oil Exports Percentage from 2011 - 2020', fontsize= 17)\n",
    "plt.xlabel('Year',fontsize= 15)\n",
    "plt.xticks(fontsize= 13)\n",
    "plt.yticks(fontsize= 13)\n",
    "plt.ylabel('Percentage %', fontsize= 12)\n",
    "plt.legend(loc= 'best', fontsize= 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Non-oil Percentage of Total Exports', ylabel='Count'>"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RCYBlT0QkAJY9EZEAWPZERAJg2RMRCYBlT0QkAJY9EZEAWPZERAJg2RMRCYBlT0QkAJY9EZEAWPZERAJg2RMRCYBlT0QkAJUcKzUYDEhJScGPP/4IBwcHLF68GN27dzeOL168GEePHoWzszMAIDMzEy4uLnJEISIiyFT2e/bsQW1tLXJyclBYWIilS5di9erVxvHi4mJs2LABHTt2lGPzRER0C1ku4xw5cgQBAQEAgH79+qGoqMg4ZjAYUFZWhldeeQVhYWHIy8uTIwIRETUgy5m9VquFWq02fq1UKqHT6aBSqVBTU4PJkyfjueeeg16vR1RUFHr37o2ePXuarEOtbgeVStnsNpRKO7i6OjU7bu651qKwco7WbsvaOVujuYy2lNsa83i3628L+xowzWmreeWcS3Pd1lqylL1arUZ1dbXxa4PBAJXq5qYcHR0RFRUFR0dHAMCgQYNw8uTJRmWv1d4wuw1XVydoNDXNjut0+tbGtxiVSmnVHK3dlrVztkZzGW0ptzXm8W7X3xb2NWCa01bzyjmX5rrNHDe35n/3KctlnP79+6OgoAAAUFhYiIceesg49ssvvyA8PBx6vR51dXU4evQoHn30UTliEBHR72Q5s3/qqaewf/9+hIWFQZIkLFmyBO+++y48PT0RFBSEsWPHYuLEibC3t8fYsWPx4IMPyhGDiIh+J0vZ29nZYdGiRSbLfHx8jP+eNm0apk2bJsemiYioCfxQFRGRAFj2REQCYNkTEQmAZU9EJACWPRGRAFj2REQCYNkTEQmAZU9EJACWPRGRAFj2REQCYNkTEQmAZU9EJACWPRGRAFj2REQCYNkTEQmAZU9EJACWPRGRAFj2REQCYNkTEQmAZU9EJACWPRGRAFj2REQCYNkTEQmAZU9EJACWPRGRAFj2REQCkKXsDQYDXnnlFYSGhiIyMhJlZWUm47m5uRg3bhwmTpyIffv2yRGBiIgaUMmx0j179qC2thY5OTkoLCzE0qVLsXr1agDAxYsXkZ2djY8++gg3btxAREQEnnjiCTg4OMgRhYiIINOZ/ZEjRxAQEAAA6NevH4qKioxjx48fh6+vLxwcHODi4gJPT0+cPHlSjhhERPQ7Wc7stVot1Gq18WulUgmdTgeVSgWtVgsXFxfjmLOzM7RabaN1uLm5NFrW0sdIktSK1G3cTL7m//dEe70AX7MFyXJmr1arUV1dbfzaYDBApVI1OVZdXW1S/kREZHmylH3//v1RUFAAACgsLMRDDz1kHOvTpw+OHDmCGzdu4OrVqygtLTUZJyIiy1NIMlzzMBgMSElJwU8//QRJkrBkyRIUFBTA09MTQUFByM3NRU5ODiRJwvTp0zFs2DBLRyAiogZkKXtLOHbsGJYtW4bs7GycOHECqampUCqVcHBwQHp6Ojp16mTy+Geeecb4ewIPDw+kpaVZPWdJSQmmT5+OP/3pTwCA8PBwjBw50vjY69evIy4uDpcuXYKzszPS09PRsWNHq2acPXs2qqqqAADnzp1D3759sXLlSuNjJUnC4MGDja+hX79+mDt3rmzZ6urqkJSUhHPnzqG2thYvvvgievTogYSEBCgUCjz44IN49dVXYWf3v/+E3ot5bCpn165dbeq4bCqju7u7TR2TTWXcsWOHTR2TAKDX65GcnIwzZ85AoVBg4cKFaNeunc0dl3dEskHr1q2TRo8eLU2YMEGSJEmaNGmSVFJSIkmSJG3ZskVasmSJyeOvX78ujR071toxG+XMzc2V3nnnnWYfn5WVJb311luSJEnSjh07pNTUVKtnrKfRaKSnn35aqqysNFn+yy+/SNOnT5c9V728vDxp8eLFkiRJ0n//+19pyJAh0vTp06UDBw5IkiRJCxYskHbt2mXynHsxj03ltLXjsqmMtnZMNpWxnq0ck5IkSbt375YSEhIkSZKkAwcOSDNmzLDJ4/JO2OQnaD09PZGRkWH8esWKFejVqxeAmz9x27VrZ/L4kydP4tq1a5gyZQqioqJQWFh4T3IWFRXh3//+NyZNmoSkpKRG7zJq+JbUwYMH49tvv7V6xnoZGRmYPHkyHnjgAZPlxcXFqKysRGRkJJ5//nmcPn1a1nzDhw/Hyy+/DODmGZxSqURxcTEee+wxADfn6ZtvvjF5zr2Yx6Zy2tpx2VRGWzsmm8pYz1aOSQAYOnQoUlNTAQAVFRVo3769TR6Xd+Te/qxp3tmzZxudjR45ckQaPny4dOnSJZPlJ0+elHJyciSDwSCdPn1aCgoKkurq6qyeMy8vT/rhhx8kSZKkzMxMaenSpSaPjY6Oln7++WdJkiRJr9dLAQEBVs8oSZJUVVUljRw5UtLpdI0e+91330n5+fmSJEnSoUOHpHHjxlkl49WrV6XJkydL27dvl5544gnj8m+++UaaO3euyWPv1TzemrOerR2XDTPa6jF56zza4jEpSZIUHx8v+fr6Sl999ZVNH5ctYZNn9k3Jz8/Hq6++inXr1jW6Dubl5YWnn34aCoUCXl5ecHV1xcWLF62e8amnnkLv3r2N/y4pKTEZb/i20+rqarRv397qGQHgX//6F0aPHm1yVlWvd+/eCAoKAgAMGDAAFy5ckP1zC+fPn0dUVBTGjh2LMWPGmFwHbWqe7tU83poTsL3j8taMtnhMNjWPtnZM1ktPT8fnn3+OBQsW4MaNG8bltnRctlSbKPt//vOf+OCDD5CdnY1u3bo1Gs/Ly8PSpUsBAJWVldBqtXBzc7N2TEydOhXHjx8HAHz77bd49NFHTcb79++PL7/8EgBQUFAAPz8/q2cEbmYbPHhwk2OrVq3Ce++9B+DmZQh3d3coFArZslRVVWHKlCmIi4vD+PHjAQCPPPIIDh48CODmPA0YMMDkOfdiHpvKaWvHZVMZbe2YbCpjfTZbOSYB4JNPPsHatWsBAI6OjlAoFOjdu7fNHZd3wmbfjVNeXo45c+Zgy5Yt8Pf3h7u7u/En5cCBAxETE4P4+HjMmjULnTp1QmJiIioqKqBQKBAbG4v+/ftbNWdubi6Ki4uRmpoKe3t7dOrUCampqVCr1ZgyZQrWrFkDvV6PefPm4eLFi7C3t8fy5cut8kOpYUYAGDVqFLZs2WJy5lGf8dq1a4iLi0NNTQ2USiVeeeUV+Pj4yJZt8eLF2LlzJ7y9vY3L5s+fj8WLF6Ourg7e3t5YvHgxlErlPZ3HW3Pq9XqcOnUKXbt2tZnjsqm5nDVrFt544w2bOSabyrh+/XqEhITYzDEJADU1NUhMTERVVRV0Oh2ef/55+Pj4YMGCBTZ1XN4Jmy17IiKynDZxGYeIiO4Oy56ISAAseyIiAbDsiYgEwLInIhIAy15QBw8ehJ+fH86fP29ctmzZMmzbtk2W7c2ePRu1tbVISEgw3v66XkZGBoYNG4bIyEhERkYiLCzM+H5ma9i9ezcqKyuttr0PPvgAI0aMQH5+vnHZ3LlzERkZicDAQONc1H9c/1aHDh0y+9fdtm3bhmXLlpksO3jwIPz9/Y1zHBkZiZiYGMu8oFt88MEHsqyX7o4sf6mK2gYHBwckJibi3Xfflf1DKg3vYtiUZ599FuHh4QCA0tJSxMbG4uOPP5Y1U733338fKSkp6Ny5s1W2t2vXLrz55pt4+OGHjcuWL18O4OYPvk6dOhnnoikfffQRRo4ciZ49e97RdgcNGnTb/WAJq1evxuTJk2XfDt0Zlr3ABg0aBIPBgE2bNjX65szKysJnn30GlUqFAQMGIC4uDhkZGSgvL8elS5dQUVGBxMRE442f6l25cgVxcXHQarXQ6/V4+eWX4e/vj8DAQOzcubNFuTQaDZycnAAAO3fuxMaNG2FnZwc/Pz/ExsYiIyMD33//PWpqavDaa6/h888/x549e6DX6xEeHo6wsDBkZ2djx44dUCgUGDlyJKKiopCQkAAHBwecO3cOFy5cwNKlS3Hx4kWcOHEC8+bNw+bNm5GRkYGioiJoNBr07NkTaWlpuHz5MmJjY1FbWwsvLy8cOHAAu3fvxnfffYeVK1dCqVSiW7duWLRoEezt7Y2vo7y8HElJSdDr9VAoFEhOTsaxY8dQUlKC+fPnY+XKlU1+8tbcXLq4uOCrr75CcXExevTogb1792LXrl24du0aOnTogFWrVrV09wMAdDodJk+ejJkzZ6JXr16Ijo7Ghg0bEB8fDy8vL5w5cwaSJGHlypVwc3PD0qVLceTIEQDA6NGjER0djYSEBGg0Gmg0GgwZMgS//fYbUlJSEB0djcTERKhUKhgMBixfvhzu7u53lI8sh2UvuJSUFEyYMMGktH/88Ufs3LkTH374IVQqFf7+979j3759AG7+b2DDhg3Yv38/srKyGpX96tWr8fjjjyM6OhqVlZUIDw/HF198cdscGzduRH5+Puzs7NC+fXukpqZCo9EgIyMDH330ERwdHREXF4f9+/cDALy9vZGcnIySkhIUFBRg69at0Ov1WLFiBU6dOoX8/Hxs3rwZAPDcc8/hz3/+MwCga9euWLRokfEP6CxatAi9evVCSkoKamtr0b59e7z77rswGAwYNWoUKisr8c477yAoKAiTJk3C/v37sX//fkiShAULFmDz5s24//778eabb+Ljjz/GxIkTja/p9ddfR1RUFIYOHYoTJ04gKSkJ27Ztw44dO5CSkmK26M3NZUBAAEaOHIkuXbpAo9EYfxhOnToVP/zwQ7PrO3DgACIjI41fDxkyBNOmTcOyZcswY8YMuLm5IT4+3ljI/fv3x6JFi7Bp0yasXbsWTzzxBMrLy5GbmwudToeIiAgMGjQIwM0Th2effRbAzcs4KSkp2LRpE/r06YO4uDgcPnwYV69eZdnfQyx7wXXo0AFJSUmYN2+e8aP8p0+fRt++fY1nqQMGDMCpU6cAwHhL3y5duqC2thZlZWVITk4GADz99NMoLS013tyqc+fOUKvVuHTp0m1zNLyMU+/48eO4fPkyXnjhBQA3by71n//8B8DNm4wBwJkzZ9CnTx8olUoolUokJCQgPz8fFRUVxvL57bffUFZW1ij/0aNHTbbXrl07XL58GXPmzIGTkxNqampQV1eH0tJSPPPMM8a5AIDLly/jwoULmDVrFoCbf7ji8ccfN1lfaWkpBg4caNzur7/+ett5uPX55ubSzs4O9vb2xry//vordDpds+tr7jKOh4cH+vfvj8LCQpP709QXef/+/bF371506dIFAwYMgEKhgL29Pfr27YvS0lIA/9sfDY0fPx7r16/HtGnT4OLigtmzZ9/R6yfL4i9oCYGBgfDy8jJeI/f29sbx48eh0+kgSRIOHTpk/Ga+9dp+9+7dkZ2djezsbEyYMAE+Pj44fPgwgJs3/7py5QpcXV1blcvDwwPu7u7IyspCdnY2Jk+ejH79+gGA8c6Y3t7eKCkpgcFgQF1dHZ577jl4e3ujR48eeP/995GdnY1x48YZr4839bsJhUIBSZJQUFCA8+fPY8WKFZgzZw6uX78OSZLw0EMP4fvvvwcA4z3pO3TogC5duiAzMxPZ2dmYMWOGsRzrNZyLEydONPorVrfT3FzW5z158iT27NmDN998EwsWLIDBYGjV3SALCwtx6tQpDBw4EFlZWcblRUVFAICjR4+iR48e8PHxMV7Cqaurw/fff4/u3bsDMJ3X+gxffPEF/Pz88N5772H48OHYsGHDHWcjy+GZPQG4efOxAwcOAAAefvhhjBgxAuHh4TAYDPDz88PQoUPNvgOk3vTp05GUlITPP/8c169fx6JFi6BSte4w69ixI5599llERkZCr9fjj3/8I0aMGGHymF69eiEgIMCYNTw8HD179oS/vz/Cw8NRW1uLPn36mP3lq6+vL+Lj47F69WpkZmZi0qRJUCgU6NatGy5cuIDnn38e8fHx2LlzJx544AGoVCrY2dlh/vz5eOGFFyBJEpydnfH666+brDc+Ph4LFixAVlYWdDodXnvttTt6/c3NZd++fbFs2TKsWLECjo6OCAsLAwC4ubnhwoULza7v1ss4AJCZmYn58+dj1apV6Nq1KyZMmGD8Ax0ff/wxNm7cCEdHR7z++uvo0KEDvvvuO4SGhqKurg7Dhw9vdBdN4OYPqdjYWMTExGDevHlYvXo1DAYDEhMT7+j1k2XxRmhEt/Hll1+iQ4cO6NOnD7755husWbMG77///r2OJavIyEikpKTIfndJsh6e2RPdhoeHB5KSkqBUKmEwGDB//vx7HYnojvHMnohIAPwFLRGRAFj2REQCYNkTEQmAZU9EJACWPRGRAFj2REQC+D98zEuQhICRYQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= Trading_Oil_non_Oil['Year']\n",
    "y= Trading_Oil_non_Oil['Oil Percentage of Total Exports']\n",
    "z= Trading_Oil_non_Oil['Non-oil Percentage of Total Exports']\n",
    "sns.histplot(Trading_Oil_non_Oil, x=z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try to do histogram?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sheet 2.1\n",
    "Imports by section 2018-2020. Only values, no weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "Imports_bysection= pd.read_csv(\"Cleaned_data/Trading20_2-1_clean.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "Imports_bysection.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "Imports_bysection= Imports_bysection[['2018 Value(millions in SAR)', '2019 Value(millions in SAR)', '2020 Value(millions in SAR)',\n",
    "'2021 Value(millions in SAR)', 'Section Description', 'Section Number']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>2018 Value(millions in SAR)</th>\n",
       "      <th>2019 Value(millions in SAR)</th>\n",
       "      <th>2020 Value(millions in SAR)</th>\n",
       "      <th>2021 Value(millions in SAR)</th>\n",
       "      <th>Section Description</th>\n",
       "      <th>Section Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18095.450548</td>\n",
       "      <td>19507.619134</td>\n",
       "      <td>19013.048509</td>\n",
       "      <td>19001.093</td>\n",
       "      <td>Live animals; animal products</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32996.027181</td>\n",
       "      <td>31123.980544</td>\n",
       "      <td>34922.104079</td>\n",
       "      <td>32737.383</td>\n",
       "      <td>Vegetable products</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3521.849209</td>\n",
       "      <td>3338.453922</td>\n",
       "      <td>3363.130944</td>\n",
       "      <td>4262.998</td>\n",
       "      <td>Animal or vegetable fats and oils and their cl...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25635.233829</td>\n",
       "      <td>27399.318015</td>\n",
       "      <td>28417.779143</td>\n",
       "      <td>28601.617</td>\n",
       "      <td>Prepared foodstuffs; beverages, spirits and vi...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21090.437514</td>\n",
       "      <td>18021.236913</td>\n",
       "      <td>18612.873864</td>\n",
       "      <td>28683.826</td>\n",
       "      <td>Mineral products</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>51715.771619</td>\n",
       "      <td>53854.494181</td>\n",
       "      <td>53404.397121</td>\n",
       "      <td>60378.494</td>\n",
       "      <td>Products of the chemical or allied industries</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17486.718316</td>\n",
       "      <td>20567.953322</td>\n",
       "      <td>19462.287220</td>\n",
       "      <td>21745.130</td>\n",
       "      <td>Plastics and articles thereof; rubber and arti...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1872.681936</td>\n",
       "      <td>2295.584415</td>\n",
       "      <td>1650.055971</td>\n",
       "      <td>1746.679</td>\n",
       "      <td>Raw hides and skins, leather, furskins and art...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4271.516713</td>\n",
       "      <td>4750.736646</td>\n",
       "      <td>5169.182124</td>\n",
       "      <td>5668.442</td>\n",
       "      <td>Wood and articles of wood; wood charcoal; cork...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7260.801908</td>\n",
       "      <td>7150.673740</td>\n",
       "      <td>6547.644869</td>\n",
       "      <td>6890.310</td>\n",
       "      <td>Pulp of wood or of other fibrous cellulosic ma...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18115.072085</td>\n",
       "      <td>22732.145945</td>\n",
       "      <td>20018.080892</td>\n",
       "      <td>20972.063</td>\n",
       "      <td>Textiles and textile articles</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3341.486274</td>\n",
       "      <td>4099.506056</td>\n",
       "      <td>3023.086301</td>\n",
       "      <td>3929.500</td>\n",
       "      <td>Footwear, headgear, umbrellas, sun umbrellas, ...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6930.868170</td>\n",
       "      <td>7631.411464</td>\n",
       "      <td>8153.245459</td>\n",
       "      <td>7429.629</td>\n",
       "      <td>Articles of stone, plaster, cement, asbestos, ...</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15725.216602</td>\n",
       "      <td>13506.827734</td>\n",
       "      <td>7173.299797</td>\n",
       "      <td>20429.158</td>\n",
       "      <td>Natural or cultured pearls, precious or semi-p...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>43987.942217</td>\n",
       "      <td>48895.921336</td>\n",
       "      <td>49600.050179</td>\n",
       "      <td>53393.309</td>\n",
       "      <td>Base metals and articles of base metal</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>111167.035485</td>\n",
       "      <td>120291.009235</td>\n",
       "      <td>109093.738466</td>\n",
       "      <td>113849.744</td>\n",
       "      <td>Machinery and mechanical appliances; electrica...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>84651.755545</td>\n",
       "      <td>118264.134784</td>\n",
       "      <td>83009.123636</td>\n",
       "      <td>96963.158</td>\n",
       "      <td>Vehicles, aircraft, vessels and associated tra...</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15758.160291</td>\n",
       "      <td>16482.857929</td>\n",
       "      <td>16094.427194</td>\n",
       "      <td>15676.492</td>\n",
       "      <td>Optical, photographic, cinematographic, measur...</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13867.239072</td>\n",
       "      <td>9872.298193</td>\n",
       "      <td>9529.140691</td>\n",
       "      <td>12388.869</td>\n",
       "      <td>Arms and ammunition; parts and accessories the...</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12049.751506</td>\n",
       "      <td>13700.368640</td>\n",
       "      <td>13251.889371</td>\n",
       "      <td>14473.327</td>\n",
       "      <td>Miscellaneous manufactured articles</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4451.674179</td>\n",
       "      <td>10874.922456</td>\n",
       "      <td>7982.008440</td>\n",
       "      <td>8919.785</td>\n",
       "      <td>Works of art, collectors' pieces and antiques</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    2018 Value(millions in SAR)  2019 Value(millions in SAR)  \\\n",
       "0                  18095.450548                 19507.619134   \n",
       "1                  32996.027181                 31123.980544   \n",
       "2                   3521.849209                  3338.453922   \n",
       "3                  25635.233829                 27399.318015   \n",
       "4                  21090.437514                 18021.236913   \n",
       "5                  51715.771619                 53854.494181   \n",
       "6                  17486.718316                 20567.953322   \n",
       "7                   1872.681936                  2295.584415   \n",
       "8                   4271.516713                  4750.736646   \n",
       "9                   7260.801908                  7150.673740   \n",
       "10                 18115.072085                 22732.145945   \n",
       "11                  3341.486274                  4099.506056   \n",
       "12                  6930.868170                  7631.411464   \n",
       "13                 15725.216602                 13506.827734   \n",
       "14                 43987.942217                 48895.921336   \n",
       "15                111167.035485                120291.009235   \n",
       "16                 84651.755545                118264.134784   \n",
       "17                 15758.160291                 16482.857929   \n",
       "18                 13867.239072                  9872.298193   \n",
       "19                 12049.751506                 13700.368640   \n",
       "20                  4451.674179                 10874.922456   \n",
       "\n",
       "    2020 Value(millions in SAR)  2021 Value(millions in SAR)  \\\n",
       "0                  19013.048509                    19001.093   \n",
       "1                  34922.104079                    32737.383   \n",
       "2                   3363.130944                     4262.998   \n",
       "3                  28417.779143                    28601.617   \n",
       "4                  18612.873864                    28683.826   \n",
       "5                  53404.397121                    60378.494   \n",
       "6                  19462.287220                    21745.130   \n",
       "7                   1650.055971                     1746.679   \n",
       "8                   5169.182124                     5668.442   \n",
       "9                   6547.644869                     6890.310   \n",
       "10                 20018.080892                    20972.063   \n",
       "11                  3023.086301                     3929.500   \n",
       "12                  8153.245459                     7429.629   \n",
       "13                  7173.299797                    20429.158   \n",
       "14                 49600.050179                    53393.309   \n",
       "15                109093.738466                   113849.744   \n",
       "16                 83009.123636                    96963.158   \n",
       "17                 16094.427194                    15676.492   \n",
       "18                  9529.140691                    12388.869   \n",
       "19                 13251.889371                    14473.327   \n",
       "20                  7982.008440                     8919.785   \n",
       "\n",
       "                                  Section Description  Section Number  \n",
       "0                       Live animals; animal products               1  \n",
       "1                                  Vegetable products               2  \n",
       "2   Animal or vegetable fats and oils and their cl...               3  \n",
       "3   Prepared foodstuffs; beverages, spirits and vi...               4  \n",
       "4                                    Mineral products               5  \n",
       "5       Products of the chemical or allied industries               6  \n",
       "6   Plastics and articles thereof; rubber and arti...               7  \n",
       "7   Raw hides and skins, leather, furskins and art...               8  \n",
       "8   Wood and articles of wood; wood charcoal; cork...               9  \n",
       "9   Pulp of wood or of other fibrous cellulosic ma...              10  \n",
       "10                      Textiles and textile articles              11  \n",
       "11  Footwear, headgear, umbrellas, sun umbrellas, ...              12  \n",
       "12  Articles of stone, plaster, cement, asbestos, ...              13  \n",
       "13  Natural or cultured pearls, precious or semi-p...              14  \n",
       "14             Base metals and articles of base metal              15  \n",
       "15  Machinery and mechanical appliances; electrica...              16  \n",
       "16  Vehicles, aircraft, vessels and associated tra...              17  \n",
       "17  Optical, photographic, cinematographic, measur...              18  \n",
       "18  Arms and ammunition; parts and accessories the...              19  \n",
       "19                Miscellaneous manufactured articles              20  \n",
       "20      Works of art, collectors' pieces and antiques              21  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Imports_bysection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "Imports_bysection['2021 Value(millions in SAR)'] = Tot_21_Imports['Value(millions SAR)']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 5 imports from 2018 - 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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></th>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018 Value(millions in SAR)</th>\n",
       "      <th>2019 Value(millions in SAR)</th>\n",
       "      <th>2020 Value(millions in SAR)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>111167.035485</th>\n",
       "      <th>120291.009235</th>\n",
       "      <th>109093.738466</th>\n",
       "      <td>Machinery and mechanical appliances; electrica...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84651.755545</th>\n",
       "      <th>118264.134784</th>\n",
       "      <th>83009.123636</th>\n",
       "      <td>Vehicles, aircraft, vessels and associated tra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51715.771619</th>\n",
       "      <th>53854.494181</th>\n",
       "      <th>53404.397121</th>\n",
       "      <td>Products of the chemical or allied industries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43987.942217</th>\n",
       "      <th>48895.921336</th>\n",
       "      <th>49600.050179</th>\n",
       "      <td>Base metals and articles of base metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32996.027181</th>\n",
       "      <th>31123.980544</th>\n",
       "      <th>34922.104079</th>\n",
       "      <td>Vegetable products</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                   max\n",
       "2018 Value(millions in SAR) 2019 Value(millions in SAR) 2020 Value(millions in SAR)                                                   \n",
       "111167.035485               120291.009235               109093.738466                Machinery and mechanical appliances; electrica...\n",
       "84651.755545                118264.134784               83009.123636                 Vehicles, aircraft, vessels and associated tra...\n",
       "51715.771619                53854.494181                53404.397121                     Products of the chemical or allied industries\n",
       "43987.942217                48895.921336                49600.050179                            Base metals and articles of base metal\n",
       "32996.027181                31123.980544                34922.104079                                                Vegetable products"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "max_Imports =Imports_bysection.groupby([\"2018 Value(millions in SAR)\", \"2019 Value(millions in SAR)\", \n",
    "\"2020 Value(millions in SAR)\"])['Section Description'].aggregate([\"max\"])\n",
    "max_Imports.sort_values(\"2019 Value(millions in SAR)\",ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018 Value(millions in SAR)    513992.690199\n",
       "2019 Value(millions in SAR)    574361.454604\n",
       "2020 Value(millions in SAR)    517490.594270\n",
       "dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[['2018 Value(millions in SAR)', '2019 Value(millions in SAR)', '2020 Value(millions in SAR)']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018 Value(millions in SAR)    111167.035485\n",
       "2019 Value(millions in SAR)    120291.009235\n",
       "2020 Value(millions in SAR)    109093.738466\n",
       "dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Imports_bysection[['2018 Value(millions in SAR)', '2019 Value(millions in SAR)', '2020 Value(millions in SAR)']].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018 Value(millions in SAR)    24475.842390\n",
       "2019 Value(millions in SAR)    27350.545457\n",
       "2020 Value(millions in SAR)    24642.409251\n",
       "dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Imports_bysection[['2018 Value(millions in SAR)', '2019 Value(millions in SAR)', '2020 Value(millions in SAR)']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018 Value(millions in SAR)    1872.681936\n",
       "2019 Value(millions in SAR)    2295.584415\n",
       "2020 Value(millions in SAR)    1650.055971\n",
       "dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Imports_bysection[['2018 Value(millions in SAR)', '2019 Value(millions in SAR)', '2020 Value(millions in SAR)']].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Compare & get percentage of drop from 2019 and 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "Tot_21_Imports= pd.read_csv('Cleaned_data/tot_21_imports.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Im_2018= Imports_bysection['2018 Value(millions in SAR)'].max()\n",
    "Im_2019= Imports_bysection['2019 Value(millions in SAR)'].max()\n",
    "Im_2020= Imports_bysection['2020 Value(millions in SAR)'].max()\n",
    "Im_2021 = Tot_21_Imports['Value(millions SAR)'].max()\n",
    "Im = Imports_bysection[['2018 Value(millions in SAR)', '2019 Value(millions in SAR)', '2020 Value(millions in SAR)',\n",
    "'2021 Value(millions in SAR)']].max()\n",
    "x= ['2018', '2019', '2020', '2021']\n",
    "plt.bar(x, Im)\n",
    "plt.plot(x, Im, c='red')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To have a better analysis, make a scatter plot, best-fit-line, look up the outliers"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
  },
  "kernelspec": {
   "display_name": "Python 3.8.0 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}