Toyota_car_price_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "#importing the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Toyota_worldwide_sales</th>\n",
       "      <th>Toyota_sales_inside_japan</th>\n",
       "      <th>Toyota_sales_outside_japan</th>\n",
       "      <th>Daihatsu_worldwide_sales</th>\n",
       "      <th>Daihatsu_sales_inside_japan</th>\n",
       "      <th>Daihatsu_sales_outside_japan</th>\n",
       "      <th>Hino_worldwide_sales</th>\n",
       "      <th>Hino_sales_inside_japan</th>\n",
       "      <th>Hino_sales_outside_japan</th>\n",
       "      <th>Total_worldwide_sales</th>\n",
       "      <th>Total_sales_inside_japan</th>\n",
       "      <th>Total_sales_outside_japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>6979552</td>\n",
       "      <td>1375509</td>\n",
       "      <td>5604043</td>\n",
       "      <td>754406</td>\n",
       "      <td>596229</td>\n",
       "      <td>158177</td>\n",
       "      <td>79242</td>\n",
       "      <td>24436</td>\n",
       "      <td>54806</td>\n",
       "      <td>7813200</td>\n",
       "      <td>1996174</td>\n",
       "      <td>5817026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>7527738</td>\n",
       "      <td>1566157</td>\n",
       "      <td>5961581</td>\n",
       "      <td>783292</td>\n",
       "      <td>608528</td>\n",
       "      <td>174764</td>\n",
       "      <td>107104</td>\n",
       "      <td>29164</td>\n",
       "      <td>77940</td>\n",
       "      <td>8418134</td>\n",
       "      <td>2203849</td>\n",
       "      <td>6214285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>7096853</td>\n",
       "      <td>1200976</td>\n",
       "      <td>5895877</td>\n",
       "      <td>730498</td>\n",
       "      <td>548301</td>\n",
       "      <td>182197</td>\n",
       "      <td>121606</td>\n",
       "      <td>34244</td>\n",
       "      <td>87362</td>\n",
       "      <td>7948957</td>\n",
       "      <td>1783521</td>\n",
       "      <td>6165436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>8717314</td>\n",
       "      <td>1692228</td>\n",
       "      <td>7025086</td>\n",
       "      <td>875662</td>\n",
       "      <td>677200</td>\n",
       "      <td>198462</td>\n",
       "      <td>154795</td>\n",
       "      <td>42462</td>\n",
       "      <td>112333</td>\n",
       "      <td>9747771</td>\n",
       "      <td>2411890</td>\n",
       "      <td>7335881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>8947756</td>\n",
       "      <td>1584316</td>\n",
       "      <td>7363440</td>\n",
       "      <td>869875</td>\n",
       "      <td>662718</td>\n",
       "      <td>207157</td>\n",
       "      <td>162460</td>\n",
       "      <td>48188</td>\n",
       "      <td>114272</td>\n",
       "      <td>9980091</td>\n",
       "      <td>2295222</td>\n",
       "      <td>7684869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  Toyota_worldwide_sales  Toyota_sales_inside_japan  \\\n",
       "0  2009                 6979552                    1375509   \n",
       "1  2010                 7527738                    1566157   \n",
       "2  2011                 7096853                    1200976   \n",
       "3  2012                 8717314                    1692228   \n",
       "4  2013                 8947756                    1584316   \n",
       "\n",
       "   Toyota_sales_outside_japan  Daihatsu_worldwide_sales  \\\n",
       "0                     5604043                    754406   \n",
       "1                     5961581                    783292   \n",
       "2                     5895877                    730498   \n",
       "3                     7025086                    875662   \n",
       "4                     7363440                    869875   \n",
       "\n",
       "   Daihatsu_sales_inside_japan  Daihatsu_sales_outside_japan  \\\n",
       "0                       596229                        158177   \n",
       "1                       608528                        174764   \n",
       "2                       548301                        182197   \n",
       "3                       677200                        198462   \n",
       "4                       662718                        207157   \n",
       "\n",
       "   Hino_worldwide_sales  Hino_sales_inside_japan  Hino_sales_outside_japan  \\\n",
       "0                 79242                    24436                     54806   \n",
       "1                107104                    29164                     77940   \n",
       "2                121606                    34244                     87362   \n",
       "3                154795                    42462                    112333   \n",
       "4                162460                    48188                    114272   \n",
       "\n",
       "   Total_worldwide_sales  Total_sales_inside_japan  Total_sales_outside_japan  \n",
       "0                7813200                   1996174                    5817026  \n",
       "1                8418134                   2203849                    6214285  \n",
       "2                7948957                   1783521                    6165436  \n",
       "3                9747771                   2411890                    7335881  \n",
       "4                9980091                   2295222                    7684869  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales = pd.read_csv('data/toyota_financial_data_latest.csv')\n",
    "sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Toyota_worldwide_sales</th>\n",
       "      <th>Toyota_sales_inside_japan</th>\n",
       "      <th>Toyota_sales_outside_japan</th>\n",
       "      <th>Daihatsu_worldwide_sales</th>\n",
       "      <th>Daihatsu_sales_inside_japan</th>\n",
       "      <th>Daihatsu_sales_outside_japan</th>\n",
       "      <th>Hino_worldwide_sales</th>\n",
       "      <th>Hino_sales_inside_japan</th>\n",
       "      <th>Hino_sales_outside_japan</th>\n",
       "      <th>Total_worldwide_sales</th>\n",
       "      <th>Total_sales_inside_japan</th>\n",
       "      <th>Total_sales_outside_japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>1.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>8.678966e+06</td>\n",
       "      <td>1.532715e+06</td>\n",
       "      <td>7.146251e+06</td>\n",
       "      <td>818753.181818</td>\n",
       "      <td>630487.454545</td>\n",
       "      <td>188265.727273</td>\n",
       "      <td>155596.363636</td>\n",
       "      <td>51706.545455</td>\n",
       "      <td>103889.818182</td>\n",
       "      <td>9.653315e+06</td>\n",
       "      <td>2.214909e+06</td>\n",
       "      <td>7.438406e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.316625</td>\n",
       "      <td>9.939283e+05</td>\n",
       "      <td>1.360421e+05</td>\n",
       "      <td>9.012974e+05</td>\n",
       "      <td>56289.117307</td>\n",
       "      <td>45868.936561</td>\n",
       "      <td>14990.716008</td>\n",
       "      <td>37827.935374</td>\n",
       "      <td>16916.310019</td>\n",
       "      <td>22130.124920</td>\n",
       "      <td>1.067327e+06</td>\n",
       "      <td>1.806163e+05</td>\n",
       "      <td>9.308611e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2009.000000</td>\n",
       "      <td>6.979552e+06</td>\n",
       "      <td>1.200976e+06</td>\n",
       "      <td>5.604043e+06</td>\n",
       "      <td>730498.000000</td>\n",
       "      <td>548301.000000</td>\n",
       "      <td>158177.000000</td>\n",
       "      <td>79242.000000</td>\n",
       "      <td>24436.000000</td>\n",
       "      <td>54806.000000</td>\n",
       "      <td>7.813200e+06</td>\n",
       "      <td>1.783521e+06</td>\n",
       "      <td>5.817026e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2011.500000</td>\n",
       "      <td>8.122526e+06</td>\n",
       "      <td>1.526094e+06</td>\n",
       "      <td>6.493334e+06</td>\n",
       "      <td>781838.000000</td>\n",
       "      <td>602378.500000</td>\n",
       "      <td>180626.000000</td>\n",
       "      <td>138200.500000</td>\n",
       "      <td>38353.000000</td>\n",
       "      <td>97261.000000</td>\n",
       "      <td>9.082952e+06</td>\n",
       "      <td>2.186659e+06</td>\n",
       "      <td>6.775083e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>9.147342e+06</td>\n",
       "      <td>1.566157e+06</td>\n",
       "      <td>7.593024e+06</td>\n",
       "      <td>816997.000000</td>\n",
       "      <td>630902.000000</td>\n",
       "      <td>186095.000000</td>\n",
       "      <td>168348.000000</td>\n",
       "      <td>57422.000000</td>\n",
       "      <td>110926.000000</td>\n",
       "      <td>1.015108e+07</td>\n",
       "      <td>2.281991e+06</td>\n",
       "      <td>7.910982e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.500000</td>\n",
       "      <td>9.303754e+06</td>\n",
       "      <td>1.597242e+06</td>\n",
       "      <td>7.720654e+06</td>\n",
       "      <td>859028.500000</td>\n",
       "      <td>660826.500000</td>\n",
       "      <td>199885.000000</td>\n",
       "      <td>177797.000000</td>\n",
       "      <td>65542.500000</td>\n",
       "      <td>115810.500000</td>\n",
       "      <td>1.030842e+07</td>\n",
       "      <td>2.325893e+06</td>\n",
       "      <td>8.017838e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>9.714253e+06</td>\n",
       "      <td>1.692228e+06</td>\n",
       "      <td>8.104084e+06</td>\n",
       "      <td>915239.000000</td>\n",
       "      <td>708207.000000</td>\n",
       "      <td>207157.000000</td>\n",
       "      <td>203768.000000</td>\n",
       "      <td>70808.000000</td>\n",
       "      <td>132960.000000</td>\n",
       "      <td>1.074212e+07</td>\n",
       "      <td>2.411890e+06</td>\n",
       "      <td>8.403229e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year  Toyota_worldwide_sales  Toyota_sales_inside_japan  \\\n",
       "count    11.000000            1.100000e+01               1.100000e+01   \n",
       "mean   2014.000000            8.678966e+06               1.532715e+06   \n",
       "std       3.316625            9.939283e+05               1.360421e+05   \n",
       "min    2009.000000            6.979552e+06               1.200976e+06   \n",
       "25%    2011.500000            8.122526e+06               1.526094e+06   \n",
       "50%    2014.000000            9.147342e+06               1.566157e+06   \n",
       "75%    2016.500000            9.303754e+06               1.597242e+06   \n",
       "max    2019.000000            9.714253e+06               1.692228e+06   \n",
       "\n",
       "       Toyota_sales_outside_japan  Daihatsu_worldwide_sales  \\\n",
       "count                1.100000e+01                 11.000000   \n",
       "mean                 7.146251e+06             818753.181818   \n",
       "std                  9.012974e+05              56289.117307   \n",
       "min                  5.604043e+06             730498.000000   \n",
       "25%                  6.493334e+06             781838.000000   \n",
       "50%                  7.593024e+06             816997.000000   \n",
       "75%                  7.720654e+06             859028.500000   \n",
       "max                  8.104084e+06             915239.000000   \n",
       "\n",
       "       Daihatsu_sales_inside_japan  Daihatsu_sales_outside_japan  \\\n",
       "count                    11.000000                     11.000000   \n",
       "mean                 630487.454545                 188265.727273   \n",
       "std                   45868.936561                  14990.716008   \n",
       "min                  548301.000000                 158177.000000   \n",
       "25%                  602378.500000                 180626.000000   \n",
       "50%                  630902.000000                 186095.000000   \n",
       "75%                  660826.500000                 199885.000000   \n",
       "max                  708207.000000                 207157.000000   \n",
       "\n",
       "       Hino_worldwide_sales  Hino_sales_inside_japan  \\\n",
       "count             11.000000                11.000000   \n",
       "mean          155596.363636             51706.545455   \n",
       "std            37827.935374             16916.310019   \n",
       "min            79242.000000             24436.000000   \n",
       "25%           138200.500000             38353.000000   \n",
       "50%           168348.000000             57422.000000   \n",
       "75%           177797.000000             65542.500000   \n",
       "max           203768.000000             70808.000000   \n",
       "\n",
       "       Hino_sales_outside_japan  Total_worldwide_sales  \\\n",
       "count                 11.000000           1.100000e+01   \n",
       "mean              103889.818182           9.653315e+06   \n",
       "std                22130.124920           1.067327e+06   \n",
       "min                54806.000000           7.813200e+06   \n",
       "25%                97261.000000           9.082952e+06   \n",
       "50%               110926.000000           1.015108e+07   \n",
       "75%               115810.500000           1.030842e+07   \n",
       "max               132960.000000           1.074212e+07   \n",
       "\n",
       "       Total_sales_inside_japan  Total_sales_outside_japan  \n",
       "count              1.100000e+01               1.100000e+01  \n",
       "mean               2.214909e+06               7.438406e+06  \n",
       "std                1.806163e+05               9.308611e+05  \n",
       "min                1.783521e+06               5.817026e+06  \n",
       "25%                2.186659e+06               6.775083e+06  \n",
       "50%                2.281991e+06               7.910982e+06  \n",
       "75%                2.325893e+06               8.017838e+06  \n",
       "max                2.411890e+06               8.403229e+06  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11 entries, 0 to 10\n",
      "Data columns (total 13 columns):\n",
      " #   Column                        Non-Null Count  Dtype\n",
      "---  ------                        --------------  -----\n",
      " 0   year                          11 non-null     int64\n",
      " 1   Toyota_worldwide_sales        11 non-null     int64\n",
      " 2   Toyota_sales_inside_japan     11 non-null     int64\n",
      " 3   Toyota_sales_outside_japan    11 non-null     int64\n",
      " 4   Daihatsu_worldwide_sales      11 non-null     int64\n",
      " 5   Daihatsu_sales_inside_japan   11 non-null     int64\n",
      " 6   Daihatsu_sales_outside_japan  11 non-null     int64\n",
      " 7   Hino_worldwide_sales          11 non-null     int64\n",
      " 8   Hino_sales_inside_japan       11 non-null     int64\n",
      " 9   Hino_sales_outside_japan      11 non-null     int64\n",
      " 10  Total_worldwide_sales         11 non-null     int64\n",
      " 11  Total_sales_inside_japan      11 non-null     int64\n",
      " 12  Total_sales_outside_japan     11 non-null     int64\n",
      "dtypes: int64(13)\n",
      "memory usage: 1.2 KB\n"
     ]
    }
   ],
   "source": [
    "sales.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+mkmTJgHwve99j/PPP7/VjPPnz2fYsGGcdtppjBw5kmefXfMTygsvvJDdd9+dI444gj/96U8ALFq0iP322w+A++67j4hg0aLiMKMhQ4awYsUKzjvvPL7zne8AcO+997L33nszZswYrrzyyuZ5r1q1is997nOMHj2avffem6uuuqrVx/fMM89w0EEHMWLECIYNG8Y999zT/LhHjRrFO9/5Ti688MIW73v77bczZswYRo4cyfHHH8/y5csB+MIXvsDQoUPZe++9Ofvss1tddntlOze1JKnYRX399dcze/ZsVq9ezejRoznkkEM45ZRTmDBhAqeffjqrV6/mpptu4r777uOmm25i3rx5/OEPf2Dx4sXsu+++jB07lsmTJ3PppZdyyy23APDyyy/zy1/+kh49evDoo4/ysY99rNVCHjt2LDNnzuSoo47i+eefZ8mSJQDcfffdnHzyya1m7NmzJ/PmzeOaa65ZoyibHtfNN9/MAw88wMqVKxkxYgRjxoxh++2358UXX2T58uXMnDmTUaNGNf87cOBAevToscZ8Tj75ZKZMmcKBBx7IWWed1Tx8ypQpbLPNNsyePZtXX32V/fffnyOPPJJBgwa96fFdd911HH300Zx99tmsXr2aV155BYDJkyez9dZbs2rVKt7znvcwfvx4hg4d2ny/F154gcmTJ3PnnXfSs2dPvva1r3HxxRczceJEpk2bxsMPP0xEsGzZsrfwzK/JLWNJymjmzJkcd9xx9OzZkz59+vCBD3yAu+++m1122YU+ffowd+5cbr/9dkaPHs1WW23F3XffzYc//GG6devGdtttx0EHHdTij+68+uqrTJw4kWHDhjFhwgTmzZvXaoaDDz6YGTNmMHfuXPbee2/69evHCy+8wKxZs9h///1bzQiwyy67sO+++75pnjNmzOC4445j8803Z8stt+Too49uHjdmzBjuueceZs6cyRe/+EVmzJjBzJkzOfjgg9eYx5IlS3jllVc48MADAfjIRz7SPG769Olcc801jBgxgv32249ly5Y1b33X23fffbnqqqu44IILeOihh+jduzcAN9xwAyNHjmTkyJE88sgjb1pH99xzD/PmzeOAAw5gxIgRXH/99TzxxBNsvfXWbLLJJnziE5/gpz/9Kb169Wp13baXW8aSlNHaPmedOHEi1157LU888QSnnnpqm9PX+ta3vsWOO+7Iddddx2uvvdZcQC3ZaaedeOGFF5g+fTpjx45l0aJFTJ06lX79+tGrV6+1LnNtRdTa13uayv+ZZ57h6KOP5pvf/Cavvvoq48ePb/c8UkpcfvnlzQfFrc2hhx7KXXfdxc9//nNOPPFEzj33XEaPHs3FF1/M7Nmz6du3LyeddNKbvhecUmLcuHH813/915vmOWfOHH75y18ydepUrrjiCqZPn95mjrWpfBk3+ohHaPuoR0naWMaOHcupp57KF77wBVavXs2tt97KjTfeCMBxxx3HBRdcwOrVqzn88MObp7/22ms58cQTWbJkCb/5zW+4+OKLefzxx/nb3/7WPN8XX3yRXXfdlYjgBz/4QZslvt9++3HJJZc0l+SECROaDwZbW8b2PK6VK1fys5/9jDPPPLN53Pnnn8+hhx5K9+7d6dOnD9OnT+eiiy5aYx79+/enR48e/Pa3v2XMmDFcf/31zePe+973cvnll3PIIYfQvXt3HnvsMQYNGsTmm2/+pixPPvkkAwcOZNKkSbz00kvcf//97LHHHvTp04ctttiCZ599ljvuuINx49b8GYYDDjiAz3zmMyxYsICdd96Z5cuXs2jRIrbbbjtWrFjB+9//fvbbb781dm2/VZUvY0naEKrypnz06NGccMIJzbt6P/nJT7LXXnsBxfmNx44dy3bbbccmmxSfKo4fP55Zs2YxfPhwIoKLLrqIbbbZhr59+7J69WqGDx/OxIkTOeOMMxg/fjw33HADhx9+OG9729vWmqNpa3Xw4MFsv/32LFmypHm3cWsZ58+fv9bHdeyxxzJ8+HAGDx7M2LFjm8ftuuuurFq1qnnYgQceyOLFi9liiy3eNJ9rrrmGU045hV69enHkkUc2Dz/11FN56qmnmr9+tM0223Drrbe2mOXOO+/koosuYtNNN6V3795cd911DBo0iKFDhzJs2DB23nnn5l3htbbddlu+//3vc/zxx7Ny5UoAvv71r7P55pvzwQ9+kFdffZXXX3/9TW8i3opo1KHo62rUqFGppc856rllLKkRHnnkEfbcc8/cMdbJ66+/zogRI7jlllvYeeedc8fROmrpNRcR96WURtVP6wFcklRBc+fOZZdddmHcuHEWcRfgbmpJqqC99tqLxx9/vKHzfOCBBzj55JPXGNazZ8/m7912dB358VnGDbCxT6snSW/FiBEjNvipJHPqyI/P3dSSuoxcx8io61nX15plLKlL6NGjB0uXLrWQtcGllFi6dOmbzia2Nu6mltQlDBw4kIULF7J48eLcUdQF9OjRg4EDB7Z7estYUpew6aabMmTIkNwxpBZZxl2EB5lJUnX5mbEkSZlZxpIkZWYZS5KUmWUsSVJmHsClSvAAM0ldmWUstZNvGCRtKO6mliQpM7eMpU7C3/6WOi63jCVJyswyliQpM8tYkqTMLGNJkjKzjCVJysyjqSVtNH5XW2qZW8aSJGVmGUuSlJllLElSZpaxJEmZWcaSJGVmGUuSlJllLElSZn7PWJJK/vKVcnHLWJKkzCxjSZIys4wlScrMMpYkKTPLWJKkzCxjSZIys4wlScrMMpYkKTPLWJKkzCxjSZIys4wlScrMMpYkKTPLWJKkzCxjSZIys4wlScrMMpYkKTPLWJKkzCxjSZIya1cZR8S4iHgsIuZHxDktjN8yIv4nIv4QEQ9HxMcbH1WSpM6pzTKOiG7AZcBRwFDghIgYWjfZ6cC8lNJw4N3AtyJiswZnlSSpU2rPlvFoYH5KaUFKaSUwFTimbpoE9ImIAHoDfwFWNTSpJEmdVHvKeAfg6ZrbC8thtS4F9gQWAXOBz6SUXq+fUURMiog5ETFn8eLFbzGyJEmdS3vKOFoYlupuvxd4ANgeGAFcGhFbvOlOKU1JKY1KKY0aMGDAOoeVJKkzak8ZLwR2rLk9kGILuNbHgZ+kwnzgcWCPxkSUJKlza08Z3wvsFhFDyoOyJgC31U3zFHAYQERsC+wOLGhkUEmSOqvubU2QUloVEWcAdwDdgKtTSg9HxGnl+CuBrwLXRsRcit3aZ6eUlmzA3JIkdRptljFASmkaMK1u2JU11xcBRzY2miRJXYNn4JIkKTPLWJKkzCxjSZIys4wlScrMMpYkKTPLWJKkzCxjSZIys4wlScrMMpYkKTPLWJKkzNp1OkxJUjUMPufnDZ3fE5Pf19D56a1xy1iSpMwsY0mSMrOMJUnKzDKWJCkzy1iSpMwsY0mSMrOMJUnKzDKWJCkzy1iSpMwsY0mSMrOMJUnKzDKWJCkzfyhCktRQ/pjFunPLWJKkzCxjSZIys4wlScrMz4wlSV1KFT/TdstYkqTMLGNJkjKzjCVJyswyliQpM8tYkqTMLGNJkjKzjCVJyswyliQpM8tYkqTMLGNJkjKzjCVJyswyliQpM8tYkqTMLGNJkjKzjCVJyswyliQpM8tYkqTMLGNJkjKzjCVJyswyliQpM8tYkqTMLGNJkjKzjCVJyswyliQpM8tYkqTMLGNJkjKzjCVJyqxdZRwR4yLisYiYHxHntDLNuyPigYh4OCJ+3diYkiR1Xt3bmiAiugGXAUcAC4F7I+K2lNK8mmn6ApcD41JKT0XENhsqsCRJnU17toxHA/NTSgtSSiuBqcAxddN8GPhJSukpgJTSC42NKUlS59WeMt4BeLrm9sJyWK13AFtFxF0RcV9EfLSlGUXEpIiYExFzFi9e/NYSS5LUybSnjKOFYanudndgH+B9wHuBf42Id7zpTilNSSmNSimNGjBgwDqHlSSpM2rzM2OKLeEda24PBBa1MM2SlNJyYHlEzACGA39sSEpJkjqx9mwZ3wvsFhFDImIzYAJwW900twIHR0T3iOgJ7Ac80tiokiR1Tm1uGaeUVkXEGcAdQDfg6pTSwxFxWjn+ypTSIxHxC+BB4HXgqpTSQxsyuCRJnUV7dlOTUpoGTKsbdmXd7W8C32xcNEmSugbPwCVJUmaWsSRJmVnGkiRlZhlLkpSZZSxJUmaWsSRJmVnGkiRlZhlLkpSZZSxJUmaWsSRJmVnGkiRlZhlLkpSZZSxJUmaWsSRJmVnGkiRlZhlLkpSZZSxJUmaWsSRJmVnGkiRlZhlLkpSZZSxJUmaWsSRJmVnGkiRlZhlLkpSZZSxJUmaWsSRJmVnGkiRlZhlLkpSZZSxJUmaWsSRJmVnGkiRlZhlLkpSZZSxJUmaWsSRJmVnGkiRlZhlLkpSZZSxJUmaWsSRJmVnGkiRlZhlLkpSZZSxJUmaWsSRJmVnGkiRlZhlLkpSZZSxJUmaWsSRJmVnGkiRlZhlLkpSZZSxJUmaWsSRJmVnGkiRlZhlLkpSZZSxJUmaWsSRJmbWrjCNiXEQ8FhHzI+KctUy3b0SsjojxjYsoSVLn1mYZR0Q34DLgKGAocEJEDG1luv8A7mh0SEmSOrP2bBmPBuanlBaklFYCU4FjWpju08DNwAsNzCdJUqfXnjLeAXi65vbCcliziNgBOBa4snHRJEnqGtpTxtHCsFR3+zvA2Sml1WudUcSkiJgTEXMWL17c3oySJHVq3dsxzUJgx5rbA4FFddOMAqZGBEB/4B8iYlVK6ZbaiVJKU4ApAKNGjaovdEmSuqT2lPG9wG4RMQR4BpgAfLh2gpTSkKbrEXEt8LP6IpYkSS1rs4xTSqsi4gyKo6S7AVenlB6OiNPK8X5OLEnSemjPljEppWnAtLphLZZwSunk9Y8lSVLX4Rm4JEnKzDKWJCkzy1iSpMwsY0mSMrOMJUnKzDKWJCkzy1iSpMwsY0mSMrOMJUnKzDKWJCkzy1iSpMwsY0mSMrOMJUnKzDKWJCkzy1iSpMwsY0mSMrOMJUnKzDKWJCkzy1iSpMwsY0mSMrOMJUnKzDKWJCkzy1iSpMwsY0mSMrOMJUnKzDKWJCkzy1iSpMwsY0mSMrOMJUnKzDKWJCkzy1iSpMwsY0mSMrOMJUnKzDKWJCkzy1iSpMwsY0mSMrOMJUnKzDKWJCkzy1iSpMwsY0mSMrOMJUnKzDKWJCkzy1iSpMwsY0mSMrOMJUnKzDKWJCkzy1iSpMwsY0mSMrOMJUnKzDKWJCkzy1iSpMwsY0mSMrOMJUnKzDKWJCkzy1iSpMzaVcYRMS4iHouI+RFxTgvjT4yIB8vLPRExvPFRJUnqnNos44joBlwGHAUMBU6IiKF1kz0OHJJS2hv4KjCl0UElSeqs2rNlPBqYn1JakFJaCUwFjqmdIKV0T0rpr+XNWcDAxsaUJKnzak8Z7wA8XXN7YTmsNROB29cnlCRJXUn3dkwTLQxLLU4Y8R6KMj6olfGTgEkAgwYNamdESZI6t/ZsGS8Edqy5PRBYVD9RROwNXAUck1Ja2tKMUkpTUkqjUkqjBgwY8FbySpLU6bSnjO8FdouIIRGxGTABuK12gogYBPwE+EhK6Y+NjylJUufV5m7qlNKqiDgDuAPoBlydUno4Ik4rx18JfBnoB1weEQCrUkqjNlxsSZI6j/Z8ZkxKaRowrW7YlTXXTwFOaWw0SZK6Bs/AJUlSZpaxJEmZWcaSJGVmGUuSlJllLElSZpaxJEmZWcaSJGVmGUuSlJllLElSZpaxJEmZWcaSJGVmGUuSlJllLElSZpaxJEmZWcaSJGVmGUuSlJllLElSZpaxJEmZWcaSJGVmGUuSlJllLElSZpaxJEmZWcaSJGVmGUuSlJllLElSZpaxJEmZWcaSJGVmGUuSlJllLElSZpaxJEmZWcaSJGVmGUuSlJllLElSZpaxJEmZWcaSJGVmGUuSlJllLElSZpaxJEmZWcaSJGVmGUuSlJllLElSZpaxJEmZWcaSJGVmGUuSlJllLElSZpaxJEmZWcaSJGVmGUuSlJllLElSZpaxJEmZWcaSJGVmGUuSlJllLElSZpaxJEmZWcaSJGXWrjKOiHER8VhEzI+Ic1oYHxFxSTn+wfn0IbQAAApySURBVIgY2fiokiR1Tm2WcUR0Ay4DjgKGAidExNC6yY4Cdisvk4ArGpxTkqROqz1bxqOB+SmlBSmllcBU4Ji6aY4BfpgKs4C+EfH2BmeVJKlTak8Z7wA8XXN7YTlsXaeRJEktiJTS2ieI+BDw3pTSKeXtjwCjU0qfrpnm58C/p5TuLm/fCfxLSum+unlNotiNDbA78FijHgjQH1jSwPltCGZcf1XPB9XPWPV8YMZGqHo+qH7GDZFvp5TSgPqB3dtxx4XAjjW3BwKL3sI0pJSmAFPascx1FhFzUkqjNsS8G8WM66/q+aD6GaueD8zYCFXPB9XPuDHztWc39b3AbhExJCI2AyYAt9VNcxvw0fKo6v2BF1NKzzY4qyRJnVKbW8YppVURcQZwB9ANuDql9HBEnFaOvxKYBvwDMB94Gfj4hossSVLn0p7d1KSUplEUbu2wK2uuJ+D0xkZbZxtk93eDmXH9VT0fVD9j1fOBGRuh6vmg+hk3Wr42D+CSJEkblqfDlCQpM8tYkqTMLGNJkjKzjCVJyqzDlnFEvCciLo2IWyPi5oiYHBG75s7VJCK2jogvR8Qp5fevvxQRP4uIb0bEVhXId1FEHJg7x9pExLERsXV5fUBE/DAi5kbEjRExMHc+gIjoX3f7pPIXzCZFROTKtTYR8b+5M9Qq/3/8U0R8qLx+WLkOPxURlfgbFRHvjYiJETG4bvg/50n0ZmXGKyLitvLv4hURMS53rrZExJdzZ2iSs1c65NHUETEZ2Ba4E/gA8DjwR+BTwNdTSjdljAdAREwD5gJbAHuW138EHAEMTynV/9jGRhURi4EngQHAjcANKaX7c2aqFxHzUkpDy+s3ArOAm4DDgRNTSkfkzAcQEb9PKY0sr58HHAz8N/B+YGFK6azM+R6sHwS8g/JUtCmlvTd6qDoRcTmwDbAZ8BLwNuB/KM5d8HxK6TMZ4xERXwcOAn4PHA18J6X03XJc8/OfU0R8h+J5/SHFGRGhOBPiR4E/5V6HaxMRT6WUBlUgR95eSSl1uAswt+Z6d+A35fWtgIdy5yuzPFD+G8AzLY3LnO/+8t/dgH8FHgYeBb4CvCN3vjLbYzXX76vaOqxdj+X13wO9yuub1r5OM+a7DbgO2APYCRhM8aMuO1GcI7cK63BuzTpbCmxW3u5ekXU4F+heXu9Lcc6Fb9c//5kz/rGV4UFRxrnzvdTK5W/Aqtz5mp7nmusbvVcqsQvoLXi9afclsD3FmcFIKf2V4sVXBZuUu6N3BHo37d6KiH4UWwC5JYCU0p9SSl9NKb0T+CegB3UneMnoroi4MCI2L69/AIpdScCLeaM12zwi3hUR+wDdUkrLAVJKrwGr80aDlNI/AjdTnLxgeErpCeC1lNKTKaUns4Z7wypoXmf3puKnWkkpraIC65CiiJsyLqPYOt4iIm6iGv+XAVZExOgWhu8LrNjYYVqwDNgtpbRF3aUPUJVTJ2ftlXadgauCvg7cHxGPUbzj/yQUnysCf8gZrMa/U2xpAvwzcFVEJGAocEG2VG9404srpfQg8CBw7saP06IzgC/xxq97nRURyyl2YX4kW6o1PQtcVF7/S0S8PaX0bPmma1XGXM1SSj+NiOnAVyPiFKpTIE2ei4jeKaW/p5SaP+OMiO2AlRlzNflzRBySUvo1QEppNTAxIv4NOC5vtGYnA1dERB/e2E29I8XW58mZMtX6IcXemOdbGPffGzlLa7L2Sof8zBiKA6SAnYH55bvVyomIbhTreFVEdAdGUOyyzv5OsOmPX+4c7RURW1JsoSzNnaU9yuf+bSmll3NnqRURw4ExqeZ0tlUVEb0odvu/kDnH5gAppVdaGLdDSumZjZ+qZeUbmB0o3mwvTCk9lzlSh5KzVzpsGbcmIvZIKT3a9pT5VD1j1fOBGRuh6vmg+hmrng+qn7FK+cpvQIymeEOTKH4KeHbaCEXZGcu4EkfmrU3VM1Y9H5ixEaqeD6qfser5oPoZq5IvIo4ELgf+BDTt7RgI7Ap8KqU0fUMuv0N+ZhwRl7Q2iuJox+yqnrHq+cCMjVD1fFD9jFXPB9XPWPV8pYuBw8uDHJtFxBCKg1r33JAL75BbxhHxN+D/AK+2MPpbKaX+LQzfqKqeser5wIyNUPV8UP2MVc8H1c9Y9XwAEfEnYM+mI+drhm8GzEspbdCTf3TILWPgXorvfd1TPyIizt/4cVpU9YxVzwdmbISq54PqZ6x6Pqh+xqrnA7gauDciplJ8Fx+KI9InAN/f0AvvqFvGWwMrqnakaq2qZ6x6PjBjI1Q9H1Q/Y9XzQfUzVj1fk4gYCvwjNUekA7ellOZt8GV3xDKWJKkz6ZBn4IqILcsTeD8aEUvLyyPlsEocDFD1jFXPB2bsCvmg+hmrng+qn7Hq+SB/xg5ZxhQ/uPBX4N0ppX4ppX7Ae8ph2X8kolT1jFXPB2ZshKrng+pnrHo+qH7GqueD1jMuYyNk7JC7qSPisZTS7us6bmOqesaq52srhxnbp+r52spRhYxVz9dWjipkrHq+tnJsjIwddcv4yYj4l4jYtmlARGwbEWfzxlFwuVU9Y9XzgRkboer5oPoZq54Pqp+x6vkgc8aOWsbHA/2AX0fEXyPiL8BdwNYUvzxUBVXPWPV8YMZGqHo+qH7GqueD6mesej7InLFD7qaG4nymFKcqm1X7gwcRMS6l9It8yd5Q9YxVzwdmbISq54PqZ6x6Pqh+xqrng8wZUwV+1HldL8CZFD+rdwvwBHBMzbjf587XETJWPZ8Zu0a+jpCx6vk6Qsaq56tCxuwr4C2utLlA7/L6YGAO8Jny9v2583WEjFXPZ8auka8jZKx6vo6Qser5qpCxo54Os1sqdyGklJ6IiHcDP46InSjOmlIFVc9Y9Xxgxkaoej6ofsaq54PqZ6x6PsicsaMewPVcRIxoulGuwPcD/YG9sqVaU9UzVj0fmLERqp4Pqp+x6vmg+hmrng8yZ+yQB3BFxEBgVUrpuRbGHZhS+k2GWPU5Kp2x6vnKHGZcT1XPV+aodMaq5ytzVDpj1fOVObJm7JBlLElSZ9JRd1NLktRpWMaSJGVmGUuSlJllLKlVEdEtdwapK7CMpU4iIr4aEZ+puf21iDgzIr4QEfdGxIMRcUHN+Fsi4r6IeDgiJtUM/3tEXBgRvwPGbOSHIXVJlrHUeXwf+BhARGwCTACeB3YDRgMjgH0iYmw5/T+nlPYBRgFnRkS/cngv4KGU0n4ppbs35gOQuqqOegYuSXXKswYtjYh3AdsC9wP7AkeW1wF6U5TzDIoCPrYcvmM5fCmwGrh5Y2aXujrLWOpcrgJOBrYDrgYOA/49pfS92onKU/0dDoxJKb0cEXcBPcrRK1JKqzdWYEnuppY6m58C4yi2iO8oL/8cEb0BImKHiNgG2BL4a1nEewD75wosyS1jqVNJKa2MiF8By8qt2+kRsSfw24gA+DtwEvAL4LSIeJDiZ+Nm5cosydNhSp1KeeDW74EPpZT+lDuPpPZxN7XUSUTEUGA+cKdFLHUsbhlLkpSZW8aSJGVmGUuSlJllLElSZpaxJEmZWcaSJGVmGUuSlNn/B981W++NoERUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(sales.groupby(['year'])['Toyota_worldwide_sales'].mean().sort_values(ascending = False))\n",
    "df.plot.bar(figsize=(8,6))\n",
    "plt.title('Toyota Worldwide Sales around one decade')\n",
    "plt.show()\n",
    "\n",
    "df = pd.DataFrame(sales.groupby(['year'])['Toyota_sales_inside_japan'].mean().sort_values(ascending = False))\n",
    "df.plot.bar(figsize=(8,6))\n",
    "plt.title('Toyota Worldwide Sales around one decade')\n",
    "plt.show()\n",
    "\n",
    "df = pd.DataFrame(sales.groupby(['year'])['Toyota_sales_outside_japan'].mean().sort_values(ascending = False))\n",
    "df.plot.bar(figsize=(8,6))\n",
    "plt.title('Toyota Worldwide Sales around one decade')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(sales.groupby(['year'])['Hino_worldwide_sales'].mean().sort_values(ascending = False))\n",
    "df.plot.bar(figsize=(8,6))\n",
    "plt.title('Hino Worldwide Sales around one decade')\n",
    "plt.show()\n",
    "\n",
    "df = pd.DataFrame(sales.groupby(['year'])['Hino_sales_inside_japan'].mean().sort_values(ascending = False))\n",
    "df.plot.bar(figsize=(8,6))\n",
    "plt.title('Hino Worldwide Sales around one decade')\n",
    "plt.show()\n",
    "\n",
    "df = pd.DataFrame(sales.groupby(['year'])['Hino_sales_outside_japan'].mean().sort_values(ascending = False))\n",
    "df.plot.bar(figsize=(8,6))\n",
    "plt.title('Hino Worldwide Sales around one decade')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(sales.groupby(['year'])['Daihatsu_worldwide_sales'].mean().sort_values(ascending = False))\n",
    "df.plot.bar(figsize=(8,6))\n",
    "plt.title('Daihatsu Worldwide Sales around one decade')\n",
    "plt.show()\n",
    "\n",
    "df = pd.DataFrame(sales.groupby(['year'])['Daihatsu_sales_inside_japan'].mean().sort_values(ascending = False))\n",
    "df.plot.bar(figsize=(8,6))\n",
    "plt.title('Daihatsu Worldwide Sales around one decade')\n",
    "plt.show()\n",
    "\n",
    "df = pd.DataFrame(sales.groupby(['year'])['Daihatsu_sales_outside_japan'].mean().sort_values(ascending = False))\n",
    "df.plot.bar(figsize=(8,6))\n",
    "plt.title('Daihatsu Worldwide Sales around one decade')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(sales.groupby(['year'])['Total_worldwide_sales'].mean().sort_values(ascending = False))\n",
    "df.plot.bar(figsize=(8,6))\n",
    "plt.title('Total Wordwide Sales around one decade')\n",
    "plt.show()\n",
    "\n",
    "df = pd.DataFrame(sales.groupby(['year'])['Total_sales_inside_japan'].mean().sort_values(ascending = False))\n",
    "df.plot.bar(figsize=(8,6))\n",
    "plt.title('Total Wordwide Sales around one decade')\n",
    "plt.show()\n",
    "\n",
    "df = pd.DataFrame(sales.groupby(['year'])['Total_sales_outside_japan'].mean().sort_values(ascending = False))\n",
    "df.plot.bar(figsize=(8,6))\n",
    "plt.title('Total Wordwide Sales around one decade')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total_sales_outside_japan    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if data has some NAN values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_columns = ['year', 'Daihatsu_sales_inside_japan', 'Daihatsu_sales_outside_japan']\n",
    "label_column = 'Daihatsu_worldwide_sales'\n",
    "\n",
    "X = sales[feature_columns]\n",
    "y = sales[label_column]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "plt.hist(predictions)\n",
    "plt.xlabel('Sales')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.38620646e-12, 1.00000000e+00, 1.00000000e+00])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sales['year'],sales['Daihatsu_sales_inside_japan'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sales['year'],sales['Daihatsu_sales_outside_japan'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sales['year'],sales['Daihatsu_worldwide_sales'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_Daihatsu_sales_inside_japan = sales['Daihatsu_sales_inside_japan'].mean()\n",
    "mean_Daihatsu_sales_outside_japan = sales['Daihatsu_sales_outside_japan'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction for the sales on 2030\n",
    "typical_car_sales_2030 = np.array([2030, mean_Daihatsu_sales_inside_japan, mean_Daihatsu_sales_outside_japan ]).reshape(1,3)\n",
    "prediction_2030 = model.predict(typical_car_sales_2030)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([818753.18181818])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sales prediction for world wide sales for Daihatsu is:\n",
    "prediction_2030"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_sales = sales.append({'year' : '2030', 'Daihatsu_worldwide_sales': int(prediction_2030)}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>Toyota_worldwide_sales</th>\n",
       "      <th>Toyota_sales_inside_japan</th>\n",
       "      <th>Toyota_sales_outside_japan</th>\n",
       "      <th>Daihatsu_worldwide_sales</th>\n",
       "      <th>Daihatsu_sales_inside_japan</th>\n",
       "      <th>Daihatsu_sales_outside_japan</th>\n",
       "      <th>Hino_worldwide_sales</th>\n",
       "      <th>Hino_sales_inside_japan</th>\n",
       "      <th>Hino_sales_outside_japan</th>\n",
       "      <th>Total_worldwide_sales</th>\n",
       "      <th>Total_sales_inside_japan</th>\n",
       "      <th>Total_sales_outside_japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>9223727.0</td>\n",
       "      <td>1580851.0</td>\n",
       "      <td>7642876.0</td>\n",
       "      <td>780384</td>\n",
       "      <td>587042.0</td>\n",
       "      <td>193342.0</td>\n",
       "      <td>170469.0</td>\n",
       "      <td>63309.0</td>\n",
       "      <td>107160.0</td>\n",
       "      <td>10174580.0</td>\n",
       "      <td>2231202.0</td>\n",
       "      <td>7943378.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017</td>\n",
       "      <td>9383780.0</td>\n",
       "      <td>1633161.0</td>\n",
       "      <td>7750619.0</td>\n",
       "      <td>816997</td>\n",
       "      <td>630902.0</td>\n",
       "      <td>186095.0</td>\n",
       "      <td>185125.0</td>\n",
       "      <td>67776.0</td>\n",
       "      <td>117349.0</td>\n",
       "      <td>10385902.0</td>\n",
       "      <td>2331839.0</td>\n",
       "      <td>8054063.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>9541748.0</td>\n",
       "      <td>1564309.0</td>\n",
       "      <td>7977439.0</td>\n",
       "      <td>848182</td>\n",
       "      <td>646874.0</td>\n",
       "      <td>201308.0</td>\n",
       "      <td>203768.0</td>\n",
       "      <td>70808.0</td>\n",
       "      <td>132960.0</td>\n",
       "      <td>10593698.0</td>\n",
       "      <td>2281991.0</td>\n",
       "      <td>8311707.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019</td>\n",
       "      <td>9714253.0</td>\n",
       "      <td>1610169.0</td>\n",
       "      <td>8104084.0</td>\n",
       "      <td>837990</td>\n",
       "      <td>658935.0</td>\n",
       "      <td>179055.0</td>\n",
       "      <td>189879.0</td>\n",
       "      <td>69789.0</td>\n",
       "      <td>120090.0</td>\n",
       "      <td>10742122.0</td>\n",
       "      <td>2338893.0</td>\n",
       "      <td>8403229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>818753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  Toyota_worldwide_sales  Toyota_sales_inside_japan  \\\n",
       "7   2016               9223727.0                  1580851.0   \n",
       "8   2017               9383780.0                  1633161.0   \n",
       "9   2018               9541748.0                  1564309.0   \n",
       "10  2019               9714253.0                  1610169.0   \n",
       "11  2030                     NaN                        NaN   \n",
       "\n",
       "    Toyota_sales_outside_japan  Daihatsu_worldwide_sales  \\\n",
       "7                    7642876.0                    780384   \n",
       "8                    7750619.0                    816997   \n",
       "9                    7977439.0                    848182   \n",
       "10                   8104084.0                    837990   \n",
       "11                         NaN                    818753   \n",
       "\n",
       "    Daihatsu_sales_inside_japan  Daihatsu_sales_outside_japan  \\\n",
       "7                      587042.0                      193342.0   \n",
       "8                      630902.0                      186095.0   \n",
       "9                      646874.0                      201308.0   \n",
       "10                     658935.0                      179055.0   \n",
       "11                          NaN                           NaN   \n",
       "\n",
       "    Hino_worldwide_sales  Hino_sales_inside_japan  Hino_sales_outside_japan  \\\n",
       "7               170469.0                  63309.0                  107160.0   \n",
       "8               185125.0                  67776.0                  117349.0   \n",
       "9               203768.0                  70808.0                  132960.0   \n",
       "10              189879.0                  69789.0                  120090.0   \n",
       "11                   NaN                      NaN                       NaN   \n",
       "\n",
       "    Total_worldwide_sales  Total_sales_inside_japan  Total_sales_outside_japan  \n",
       "7              10174580.0                 2231202.0                  7943378.0  \n",
       "8              10385902.0                 2331839.0                  8054063.0  \n",
       "9              10593698.0                 2281991.0                  8311707.0  \n",
       "10             10742122.0                 2338893.0                  8403229.0  \n",
       "11                    NaN                       NaN                        NaN  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_sales.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dZD2XlvTePZJuAp5Mx63K1WVmNuFtaO9ixfqOw4/s7trXy4r1HQDDlmSURqgmvHK5HG1tbbVuhtVY0X/RmY0Wl9yyha5+Hs3dWF/ipzd+eMD1SNoWEeX+9g34eTCS/i1wDnByJRYRqwbcCrNRbiT+ojMbLXb3k1yqxYdioN/k/w5wFdnKMQGfBN47bK0wGwVWt3YeTi4VvQcOsbq1s0YtMivOzPrSoOJDMdBlyhdHxNVk35r/c+APyVZvmY0bI/EXndlosby5idLkuj6x0uQ6ljc3Ddt7DDTBVP6FvSlpJtmk/Zxha4XZKDASf9GZjRaL5zVy85LzaKwvIbK5l5uXnDeiq8gq/j7dlPLrwLYU+5tha4XZKLC8uanPHAwM/190ZqPJ4nmNhc4vHu97MH8AvBIRN6Xf3wV0AL8gu7mk2bhR+YfmVWRmw6PqMmVJTwGXpe+VfAj4PtlE//nA70XElSPTzOJ5mbKZ2eCdyDLlutwXFK8C1kTED4EfSnp6OBtpZmbjy/Em+evSPb4gu9XLlty+AX+HxszMJp7jJYn7gUcl/ZpsJdn/BpB0Fse5Xb+ZmU1sVRNMRPyFpEeA04GH48iEzTs4zu36zcxsYjvuMFdEPN5P7JfFNMfMzMaLgX7R0szMbFCcYMzMrBBOMGZmVggnGDMzK4QTjJmZFaLQBCPpy5K2S3pG0v2STpY0XdJmSTvSz2m541dI2impU1JzLn6BpI6073alZy1LOknS2hTfKml2rszS9B47JC0t8jzNzOztCkswkhqBLwLliDgXqANagBuBRyJiLvBI+h1JZ6f95wCLgG9Lqjys4A5gGTA3bYtS/BqyZ9ScRXbzzVtTXdOBlcBFwIXAynwiMzOz4hU9RDYJKKXbzUwBdgOXA3en/XcDi9Pry4HvR8RbEfECsBO4UNLpwNSIeCx90fOeo8pU6loHLEi9m2Zgc0T0RMReYDNHkpKZmY2AwhJMRHQB3wBeBvYA+yPiYeA9EbEnHbMHeHcq0gi8kqtiV4o1ptdHx/uUiYiDZLevmVGlLjMzGyFFDpFNI+thzAFmAqdI+ky1Iv3Eokp8qGXybVwmqU1SW3d3d5WmmZnZYBU5RHYZ8EJEdEfEAWA9cDHwahr2Iv38VTp+F3BGrvwssiG1Xen10fE+ZdIw3KlAT5W6+oiINRFRjohyQ0PDCZyqmZkdrcgE8zIwX9KUNC+yAHgW2AhUVnUtBR5MrzcCLWll2Byyyfwn0jDa65Lmp3quPqpMpa4rgS1pnqYVWChpWupJLUwxMzMbIYU90yUitkpaBzwFHATagTXAu4AHJF1DloQ+mY7fLukB4Ofp+OsjovJw9OuAu4ASsCltAHcC90raSdZzaUl19Ui6CXgyHbcq9+A0GyM2tHf58cVmY1jVRyZPJH5k8uiyob2LFes76D1w6HCsNLmOm5ec5yRjNopUe2Syv8lvo9Lq1s4+yQWg98AhVrd21qhFZjZYTjA2Ku3e1zuouJmNPk4wNirNrC8NKm5mo48TjI1Ky5ubKE2u6xMrTa5jeXNTjVpkZoNV2CoysxNRmcj3KjKzscsJxkatxfManVDMxjAPkZmZWSGcYMzMrBBOMGZmVgjPwdSIb4NiZuOdE0wNHH0blK59vaxY3wHgJGNm44aHyGrAt0Exs4nACaYGfBsUM5sIPERWAzPrS3T1k0x8G5Th4fkts9HBPZga8G1QilOZ3+ra10twZH5rQ3tXrZtmNuE4wdTA4nmN3LzkPBrrSwhorC/5OSfDxPNbZqOHh8hqxLdBKYbnt8xGD/dgbFzxbf7NRo/CEoykJklP57bXJN0gaW0u9qKkp9PxsyX15vZ9J1fXBZI6JO2UdLskpfhJqb6dkrZKmp0rs1TSjrQtLeo8bXTx/JbZ6FHYEFlEdALnA0iqA7qAH0XEX1aOkfRNYH+u2HMRcX4/1d0BLAMeBx4CFgGbgGuAvRFxlqQW4FbgKknTgZVAGQhgm6SNEbF3mE/TRhnf5t9s9BipOZgFZMnjpUog9UI+BXy4WkFJpwNTI+Kx9Ps9wGKyBHM58LV06DrgW6neZmBzRPSkMpvJktL9w3hONkp5fstsdBipOZgW3v4/90uBVyNiRy42R1K7pEclXZpijcCu3DG7Uqyy7xWAiDhI1huakY/3U+YwScsktUlq6+7uHtqZmZlZvwpPMJLeCXwC+MFRuz5N36SzBzgzIuYBXwHukzQVUD/VRqX6Y+yrVuZIIGJNRJQjotzQ0FD9RMzMbFBGogfzUeCpiHi1EpA0CVgCrK3EIuKtiPhNer0NeA74AFnvY1auvlnA7vR6F3BGrs5TgZ58vJ8yZmY2AkYiwRzdUwG4DPhFRBwe+pLUkBYDIOl9wFzg+YjYA7wuaX6aX7kaeDAV2whUVohdCWyJiABagYWSpkmaBixMMTMzGyGFTvJLmgJ8BPj8Ubv6m5P5ELBK0kHgEHBtZZIeuA64CyiRTe5vSvE7gXsl7STrubQARESPpJuAJ9Nxq3J1mZnZCFD2B7+Vy+Voa2urdTPMzMYUSdsiotzfPn+T38zMCuEEY2ZmhXCCMTOzQjjBmJlZIZxgzMysEE4wZmZWCCcYMzMrhBOMmZkVwgnGzMwK4QRjZmaFcIIxM7NCOMGYmVkhnGDMzKwQhd6ufyLY0N7F6tZOdu/rZWZ9ieXNTX4evJkZTjAnZEN7FyvWd9B74BAAXft6WbG+A8BJxswmPA+RnYDVrZ2Hk0tF74FDrG7trFGLzMxGDyeYE7B7X++g4mZmE4kTzAmYWV8aVNzMbCIpLMFIapL0dG57TdINkr4mqSsX/1iuzApJOyV1SmrOxS+Q1JH23S5JKX6SpLUpvlXS7FyZpZJ2pG1pEee4vLmJ0uS6PrHS5DqWNzcV8XZmZmNKYQkmIjoj4vyIOB+4AHgT+FHafVtlX0Q8BCDpbKAFOAdYBHxbUuX/3ncAy4C5aVuU4tcAeyPiLOA24NZU13RgJXARcCGwUtK04T7HxfMauXnJeTTWlxDQWF/i5iXneYLfzIyRW0W2AHguIl5KnY/+XA58PyLeAl6QtBO4UNKLwNSIeAxA0j3AYmBTKvO1VH4d8K3Uu2kGNkdETyqzmSwp3T/cJ7Z4XqMTiplZP0ZqDqaFvv9z/4Kkn0n6bq5n0Qi8kjtmV4o1ptdHx/uUiYiDwH5gRpW6+pC0TFKbpLbu7u6hnpuZmfWj8AQj6Z3AJ4AfpNAdwPuB84E9wDcrh/ZTPKrEh1rmSCBiTUSUI6Lc0NBwzHMwM7PBG4kezEeBpyLiVYCIeDUiDkXE74C/JpsjgayXcUau3Cxgd4rP6ifep4ykScCpQE+VuszMbISMRIL5NLnhMUmn5/ZdATyTXm8EWtLKsDlkk/lPRMQe4HVJ89P8ytXAg7kylRViVwJbIiKAVmChpGlpCG5hipmZ2QgpdJJf0hTgI8Dnc+GvSzqfbMjqxcq+iNgu6QHg58BB4PqIqHxN/jrgLqBENrm/KcXvBO5NCwJ6yOZ6iIgeSTcBT6bjVlUm/M3MbGQo+4PfyuVytLW11boZZmZjiqRtEVHub5+/yW9mZoVwgjEzs0I4wZiZWSGcYMzMrBBOMGZmVggnGDMzK4QTjJmZFcIJxszMCuEEY2ZmhXCCMTOzQjjBmJlZIZxgzMysECP1yGQbRhvau1jd2snufb3MrC+xvLnJj202s1HHCWaM2dDexYr1HfQeyJ5k0LWvlxXrOwCcZMxsVPEQ2RizurXzcHKp6D1wiNWtnTVqkZlZ/5xgxpjd+3oHFTczqxUnmDFmZn1pUHEzs1pxghljljc3UZpc1ydWmlzH8uamGrXIzKx/hSUYSU2Sns5tr0m6QdJqSb+Q9DNJP5JUn46fLak3d/x3cnVdIKlD0k5Jt0tSip8kaW2Kb5U0O1dmqaQdaVta1HmOtMXzGrl5yXk01pcQ0Fhf4uYl53mC38xGHUVE8W8i1QFdwEVAE7AlIg5KuhUgIr6aksPfR8S5/ZR/AvgS8DjwEHB7RGyS9B+B34+IayW1AFdExFWSpgNtQBkIYBtwQUTsPVYby+VytLW1Dd9Jm5lNAJK2RUS5v30jNUS2AHguIl6KiIcj4mCKPw7MqlZQ0unA1Ih4LLJseA+wOO2+HLg7vV4HLEi9m2Zgc0T0pKSyGVg0vKdkZmbVjFSCaQHu7yf+OWBT7vc5ktolPSrp0hRrBHbljtmVYpV9rwCkpLUfmJGP91PmMEnLJLVJauvu7h78WZmZ2TEVnmAkvRP4BPCDo+J/ChwEvpdCe4AzI2Ie8BXgPklTAfVTbWVc71j7qpU5EohYExHliCg3NDQM5HTMzGyARqIH81HgqYh4tRJIk+4fB/59GvYiIt6KiN+k19uA54APkPU+8sNos4Dd6fUu4IxU5yTgVKAnH++njJmZjYCRSDCfJjc8JmkR8FXgExHxZi7ekBYDIOl9wFzg+YjYA7wuaX6aX7kaeDAV2whUVohdSbZ4IIBWYKGkaZKmAQtTzMzMRkihq8gkTSGbC3lfROxPsZ3AScBv0mGPp1VgfwysIhs2OwSsjIj/kcqUgbuAEtmczZ9EREg6GbgXmEfWc2mJiOdTmc8B/ym9x19ExN8ep63dwEvDcuKDcxrw6xq871jia1Sdr091vj7HdyLX6L0R0e8cw4gsU7Zjk9R2rCV+lvE1qs7Xpzpfn+Mr6hr5m/xmZlYIJxgzMyuEE0ztral1A8YAX6PqfH2q8/U5vkKukedgzMysEO7BmJlZIZxgzMysEE4ww0zSGZL+UdKzkrZL+lKKT5e0OT0+YHP6AiiSZqTjfyvpW0fV1e9jCsa6Yb5GP5HUmXvMw7trcU7DaQjX5yOStqXPyjZJH87VNe4+Q8N8fcbd5weGdI0uzF2Df5Z0Ra6uoX+GIsLbMG7A6cAH0+t/AfwSOBv4OnBjit8I3JpenwL8K+Ba4FtH1fUE8Idk91bbBHy01uc3Cq/RT4Byrc+pxtdnHjAzvT4X6BrPn6Fhvj7j7vMzxGs0BZiUK/ur3O9D/gy5BzPMImJPRDyVXr8OPEt2J+f8owXuJj1yICLeiIh/Av5vvh5Vf0zBmDZc12i8GsL1aY+Iyr32tgMnK3sY37j8DA3X9RnZVo+sIVyjN+PIY1ROJt0c+EQ/Q04wBVL2ELV5wFbgPZHdV43083hd8WqPKRg3TvAaVfxt6tr/2XgYAsobwvX5Y6A9It5iAnyGTvD6VIzbzw8M/BpJukjSdqADuDYlnBP6DDnBFETSu4AfAjdExGtDqaKf2LhaUz4M1wiyO3KfB1yats8OV/tqbbDXR9I5wK3A5yuhfg4bN5+hYbg+MI4/PzC4axQRWyPiHOAPgBXK7vV4Qp8hJ5gCSJpM9h/1exGxPoVfTd3NSrfzV8epptpjCsa8YbpGRERX+vk6cB9wYTEtHlmDvT6SZgE/Aq6OiOdSeNx+hobp+ozbzw8M/d9YRDwLvEE2X3VCnyEnmGGWuth3As9GxH/N7co/WmApRx450K+o/piCMW24rpGkSZJOS68nkz1j6Jnhb/HIGuz1kVQP/BhYERE/rRw8Xj9Dw3V9xuvnB4Z0jeYoe6YWkt4LNAEvnvBnqNarHcbbRrbaKYCfAU+n7WNkj3J+BNiRfk7PlXmR7HEDvyX7i+HsFC+TfeCfA75FuvPCWN+G6xqRrS7blurZDvw3oK7W5zfS1wf4z2R/cT6d2949Xj9Dw3V9xuvnZ4jX6LPpGjwNPAUsztU15M+QbxVjZmaF8BCZmZkVwgnGzMwK4QRjZmaFcIIxM7NCOMGYmVkhnGDMakSZf5L00VzsU5L+Zy3bZTZcvEzZrIYknQv8gOxeUXVk30NYFLlvmw+hzklx5MaFZjXjBGNWY5K+TvZFwFOA1yPiJklLgeuBdwL/B/hCRPxO0hrgg0AJWBsRq1Idu4C/AhYBfxkRP6jBqZj1ManWDTAz/pzs29P/DyinXs0VwMURcTAllRaye2XdGBE96bYe/yhpXUT8PNXzRkRcUosTMOuPE4xZjUXEG5LWAr+NiLckXUZ2R9u2dPf4EvBKOvzTkq4h+7c7k+yWOZUEs3ZkW25WnROM2ejwu7RBdov070bEn+UPkDQX+BJwYUTsk/R3ZA+HqnhjRFpqNkBeRan6OCsAAAB1SURBVGY2+vwD8KncnX5nSDoTmAq8DryWbrXeXMM2mh2XezBmo0xEdEj6c+AfJL0DOABcC7SRDYc9AzwP/PTYtZjVnleRmZlZITxEZmZmhXCCMTOzQjjBmJlZIZxgzMysEE4wZmZWCCcYMzMrhBOMmZkV4v8DU7GCdtbwxKwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(predicted_sales['year'],predicted_sales['Daihatsu_worldwide_sales'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>North America</th>\n",
       "      <th>U.S. (incl. Hawaii)</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Mexico</th>\n",
       "      <th>Other</th>\n",
       "      <th>Latin America</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Other.1</th>\n",
       "      <th>...</th>\n",
       "      <th>Other.4</th>\n",
       "      <th>Middle East</th>\n",
       "      <th>GCC*</th>\n",
       "      <th>Other.5</th>\n",
       "      <th>Africa</th>\n",
       "      <th>South Africa</th>\n",
       "      <th>Other.6</th>\n",
       "      <th>Others</th>\n",
       "      <th>Worldwide sales</th>\n",
       "      <th>Sales outside of Japan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007</td>\n",
       "      <td>2922823</td>\n",
       "      <td>2620826</td>\n",
       "      <td>201326</td>\n",
       "      <td>66209</td>\n",
       "      <td>34462</td>\n",
       "      <td>278730</td>\n",
       "      <td>72313</td>\n",
       "      <td>28581</td>\n",
       "      <td>177836</td>\n",
       "      <td>...</td>\n",
       "      <td>31023</td>\n",
       "      <td>484778</td>\n",
       "      <td>422265</td>\n",
       "      <td>62513</td>\n",
       "      <td>295117</td>\n",
       "      <td>152821</td>\n",
       "      <td>142296</td>\n",
       "      <td>4097</td>\n",
       "      <td>8429268</td>\n",
       "      <td>6841933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>2536741</td>\n",
       "      <td>2217662</td>\n",
       "      <td>224158</td>\n",
       "      <td>63305</td>\n",
       "      <td>31616</td>\n",
       "      <td>275321</td>\n",
       "      <td>80572</td>\n",
       "      <td>31168</td>\n",
       "      <td>163581</td>\n",
       "      <td>...</td>\n",
       "      <td>32080</td>\n",
       "      <td>590941</td>\n",
       "      <td>515898</td>\n",
       "      <td>75043</td>\n",
       "      <td>274700</td>\n",
       "      <td>126499</td>\n",
       "      <td>148201</td>\n",
       "      <td>3846</td>\n",
       "      <td>7996102</td>\n",
       "      <td>6526059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>2053172</td>\n",
       "      <td>1770253</td>\n",
       "      <td>205115</td>\n",
       "      <td>51991</td>\n",
       "      <td>25813</td>\n",
       "      <td>215810</td>\n",
       "      <td>94709</td>\n",
       "      <td>28482</td>\n",
       "      <td>92619</td>\n",
       "      <td>...</td>\n",
       "      <td>24388</td>\n",
       "      <td>484324</td>\n",
       "      <td>433043</td>\n",
       "      <td>51281</td>\n",
       "      <td>191530</td>\n",
       "      <td>88663</td>\n",
       "      <td>102867</td>\n",
       "      <td>3707</td>\n",
       "      <td>6979552</td>\n",
       "      <td>5604043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>2008089</td>\n",
       "      <td>1763532</td>\n",
       "      <td>171972</td>\n",
       "      <td>46769</td>\n",
       "      <td>25816</td>\n",
       "      <td>269513</td>\n",
       "      <td>99474</td>\n",
       "      <td>32859</td>\n",
       "      <td>137180</td>\n",
       "      <td>...</td>\n",
       "      <td>28448</td>\n",
       "      <td>555495</td>\n",
       "      <td>489612</td>\n",
       "      <td>65883</td>\n",
       "      <td>190227</td>\n",
       "      <td>98068</td>\n",
       "      <td>92159</td>\n",
       "      <td>2034</td>\n",
       "      <td>7527738</td>\n",
       "      <td>5961581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>1880472</td>\n",
       "      <td>1644661</td>\n",
       "      <td>162260</td>\n",
       "      <td>48589</td>\n",
       "      <td>24962</td>\n",
       "      <td>259963</td>\n",
       "      <td>99552</td>\n",
       "      <td>36355</td>\n",
       "      <td>124056</td>\n",
       "      <td>...</td>\n",
       "      <td>27883</td>\n",
       "      <td>525670</td>\n",
       "      <td>478450</td>\n",
       "      <td>47220</td>\n",
       "      <td>205749</td>\n",
       "      <td>106311</td>\n",
       "      <td>99438</td>\n",
       "      <td>1347</td>\n",
       "      <td>7096853</td>\n",
       "      <td>5895877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  North America  U.S. (incl. Hawaii)  Canada  Mexico  Other  \\\n",
       "0  2007        2922823              2620826  201326   66209  34462   \n",
       "1  2008        2536741              2217662  224158   63305  31616   \n",
       "2  2009        2053172              1770253  205115   51991  25813   \n",
       "3  2010        2008089              1763532  171972   46769  25816   \n",
       "4  2011        1880472              1644661  162260   48589  24962   \n",
       "\n",
       "   Latin America  Brazil  Argentina  Other.1  ...  Other.4  Middle East  \\\n",
       "0         278730   72313      28581   177836  ...    31023       484778   \n",
       "1         275321   80572      31168   163581  ...    32080       590941   \n",
       "2         215810   94709      28482    92619  ...    24388       484324   \n",
       "3         269513   99474      32859   137180  ...    28448       555495   \n",
       "4         259963   99552      36355   124056  ...    27883       525670   \n",
       "\n",
       "     GCC*  Other.5  Africa  South Africa  Other.6  Others  Worldwide sales  \\\n",
       "0  422265    62513  295117        152821   142296    4097          8429268   \n",
       "1  515898    75043  274700        126499   148201    3846          7996102   \n",
       "2  433043    51281  191530         88663   102867    3707          6979552   \n",
       "3  489612    65883  190227         98068    92159    2034          7527738   \n",
       "4  478450    47220  205749        106311    99438    1347          7096853   \n",
       "\n",
       "   Sales outside of Japan  \n",
       "0                 6841933  \n",
       "1                 6526059  \n",
       "2                 5604043  \n",
       "3                 5961581  \n",
       "4                 5895877  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_region = pd.read_csv('data/toyota_sales_of_each_region.csv')\n",
    "sales_region.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_profiling import ProfileReport\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas_profiling import ProfileReport\n",
    "\n",
    "df_Sales_ALL = pd.read_csv('data/toyota_sales_of_each_region.csv')\n",
    "df_US_WorldWide = df_Sales_ALL[['Year','U.S. (incl. Hawaii)','Worldwide sales']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>U.S. (incl. Hawaii)</th>\n",
       "      <th>Worldwide sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007</td>\n",
       "      <td>2620826</td>\n",
       "      <td>8429268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>2217662</td>\n",
       "      <td>7996102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>1770253</td>\n",
       "      <td>6979552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>1763532</td>\n",
       "      <td>7527738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>1644661</td>\n",
       "      <td>7096853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  U.S. (incl. Hawaii)  Worldwide sales\n",
       "0  2007              2620826          8429268\n",
       "1  2008              2217662          7996102\n",
       "2  2009              1770253          6979552\n",
       "3  2010              1763532          7527738\n",
       "4  2011              1644661          7096853"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_US_WorldWide.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile = ProfileReport(df_US_WorldWide, title='US and WorldWide Sales Report', html={'style':{'full_width':True}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile.to_file(output_file=\"Sales_US_WorldWide_Profiling.html\")"
   ]
  },
  {
   "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>Year</th>\n",
       "      <th>U.S._(incl._Hawaii)</th>\n",
       "      <th>Worldwide_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007</td>\n",
       "      <td>2620826</td>\n",
       "      <td>8429268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>2217662</td>\n",
       "      <td>7996102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>1770253</td>\n",
       "      <td>6979552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>1763532</td>\n",
       "      <td>7527738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>1644661</td>\n",
       "      <td>7096853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012</td>\n",
       "      <td>2082504</td>\n",
       "      <td>8717314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>2236042</td>\n",
       "      <td>8947756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>2373767</td>\n",
       "      <td>9147342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015</td>\n",
       "      <td>2499263</td>\n",
       "      <td>9188559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016</td>\n",
       "      <td>2449629</td>\n",
       "      <td>9223727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017</td>\n",
       "      <td>2434516</td>\n",
       "      <td>9383780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018</td>\n",
       "      <td>2426735</td>\n",
       "      <td>9541748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019</td>\n",
       "      <td>2383348</td>\n",
       "      <td>9714253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  U.S._(incl._Hawaii)  Worldwide_sales\n",
       "0   2007              2620826          8429268\n",
       "1   2008              2217662          7996102\n",
       "2   2009              1770253          6979552\n",
       "3   2010              1763532          7527738\n",
       "4   2011              1644661          7096853\n",
       "5   2012              2082504          8717314\n",
       "6   2013              2236042          8947756\n",
       "7   2014              2373767          9147342\n",
       "8   2015              2499263          9188559\n",
       "9   2016              2449629          9223727\n",
       "10  2017              2434516          9383780\n",
       "11  2018              2426735          9541748\n",
       "12  2019              2383348          9714253"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_US_WorldWide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_columns = ['Year', 'U.S._(incl._Hawaii)']\n",
    "label_column = 'Worldwide_sales'\n",
    "\n",
    "X = df_US_WorldWide[feature_columns]\n",
    "y = df_US_WorldWide[label_column]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "Linear_model = LinearRegression()\n",
    "Linear_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2468003983882059"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Linear_model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = Linear_model.predict(X_test)\n",
    "plt.hist(predictions)\n",
    "plt.xlabel('Sales')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "worldwide_car_sales_2030 = np.array([2030, mean_US_sale, mean_worldwide_sale]).reshape(1,3)\n",
    "prediction_2030 = model.predict(worldwide_car_sales_2030)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10830517.69230768])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_2030"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_worldwide_sales_2030 = df_US_WorldWide.append({'Year' : '2030', 'Worldwide_sales': int(prediction_2030)}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>U.S._(incl._Hawaii)</th>\n",
       "      <th>Worldwide_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007</td>\n",
       "      <td>2620826.0</td>\n",
       "      <td>8429268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>2217662.0</td>\n",
       "      <td>7996102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>1770253.0</td>\n",
       "      <td>6979552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>1763532.0</td>\n",
       "      <td>7527738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>1644661.0</td>\n",
       "      <td>7096853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012</td>\n",
       "      <td>2082504.0</td>\n",
       "      <td>8717314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>2236042.0</td>\n",
       "      <td>8947756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>2373767.0</td>\n",
       "      <td>9147342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015</td>\n",
       "      <td>2499263.0</td>\n",
       "      <td>9188559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016</td>\n",
       "      <td>2449629.0</td>\n",
       "      <td>9223727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017</td>\n",
       "      <td>2434516.0</td>\n",
       "      <td>9383780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018</td>\n",
       "      <td>2426735.0</td>\n",
       "      <td>9541748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019</td>\n",
       "      <td>2383348.0</td>\n",
       "      <td>9714253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10830517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  U.S._(incl._Hawaii)  Worldwide_sales\n",
       "0   2007            2620826.0          8429268\n",
       "1   2008            2217662.0          7996102\n",
       "2   2009            1770253.0          6979552\n",
       "3   2010            1763532.0          7527738\n",
       "4   2011            1644661.0          7096853\n",
       "5   2012            2082504.0          8717314\n",
       "6   2013            2236042.0          8947756\n",
       "7   2014            2373767.0          9147342\n",
       "8   2015            2499263.0          9188559\n",
       "9   2016            2449629.0          9223727\n",
       "10  2017            2434516.0          9383780\n",
       "11  2018            2426735.0          9541748\n",
       "12  2019            2383348.0          9714253\n",
       "13  2030                  NaN         10830517"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_worldwide_sales_2030"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(predicted_worldwide_sales_2030['Year'],predicted_worldwide_sales_2030['Worldwide_sales'])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}