transmission (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kmodes.kmodes import KModes\n",
    "from kmodes.kprototypes import KPrototypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Medians by model_family\n",
    "#work_dataset.groupby('model_family').median().apply(lambda s: s.apply(lambda x: format(round(x,1))))\n",
    "#merged.groupby(['model_family','year','seller_country']).agg(np.random.choice).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "df=pd.read_csv(\"out.c_testing_bucket.price_comparison.csv.gz\",compression='gzip')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1648859, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['seller_country','model_family','car_style','fuel_type','transmission','drive','year','cubic_capacity', 'power','mileage','equipment_position','days_on_stock','price_with_vat_czk']]\n",
    "df=df.dropna()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "#All dataset\n",
    "SEMI=df[df['transmission']=='TRANSMISSION_SEMI_AUTOMATIC']#Semiautomatic\n",
    "AUTO=df[df['transmission']=='TRANSMISSION_AUTOMATIC']#Automatic\n",
    "MAN=df[df['transmission']=='TRANSMISSION_MANUAL']#Manual\n",
    "\n",
    "#By country\n",
    "#DE\n",
    "DE_SEMI=df[(df['transmission']=='TRANSMISSION_SEMI_AUTOMATIC') & (df['seller_country']=='DE')]\n",
    "DE_AUTO=df[(df['transmission']=='TRANSMISSION_AUTOMATIC') & (df['seller_country']=='DE')]\n",
    "DE_MAN=df[(df['transmission']=='TRANSMISSION_MANUAL') & (df['seller_country']=='DE')]\n",
    "\n",
    "\n",
    "#CZ\n",
    "CZ_SEMI=df[(df['transmission']=='TRANSMISSION_SEMI_AUTOMATIC') & (df['seller_country']=='CZ')]\n",
    "CZ_AUTO=df[(df['transmission']=='TRANSMISSION_AUTOMATIC') & (df['seller_country']=='CZ')]\n",
    "CZ_MAN=df[(df['transmission']=='TRANSMISSION_MANUAL') & (df['seller_country']=='CZ')]\n",
    "\n",
    "# give condition of price change to AUTO and MAN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "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>cubic_capacity</th>\n",
       "      <th>power</th>\n",
       "      <th>mileage</th>\n",
       "      <th>equipment_position</th>\n",
       "      <th>days_on_stock</th>\n",
       "      <th>price_with_vat_czk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2012.6</td>\n",
       "      <td>2042.5</td>\n",
       "      <td>119.9</td>\n",
       "      <td>129968.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>49.6</td>\n",
       "      <td>350873.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.7</td>\n",
       "      <td>1192.8</td>\n",
       "      <td>79.9</td>\n",
       "      <td>97717.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>68.0</td>\n",
       "      <td>405466.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2006.0</td>\n",
       "      <td>998.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>96000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>1598.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>95000.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>13.0</td>\n",
       "      <td>170000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>200498.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>55.0</td>\n",
       "      <td>430900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2021.0</td>\n",
       "      <td>6417.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>2296000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year cubic_capacity  power   mileage equipment_position  \\\n",
       "count    45.0           45.0   45.0      45.0               45.0   \n",
       "mean   2012.6         2042.5  119.9  129968.6                0.6   \n",
       "std       4.7         1192.8   79.9   97717.2                0.3   \n",
       "min    2006.0          998.0   44.0       1.0                0.0   \n",
       "25%    2009.0         1332.0   68.0   55000.0                0.3   \n",
       "50%    2012.0         1598.0  100.0   95000.0                0.6   \n",
       "75%    2017.0         1995.0  140.0  200498.0                0.8   \n",
       "max    2021.0         6417.0  373.0  350000.0                1.0   \n",
       "\n",
       "      days_on_stock price_with_vat_czk  \n",
       "count          45.0               45.0  \n",
       "mean           49.6           350873.4  \n",
       "std            68.0           405466.6  \n",
       "min             2.0            44990.0  \n",
       "25%             9.0            96000.0  \n",
       "50%            13.0           170000.0  \n",
       "75%            55.0           430900.0  \n",
       "max           288.0          2296000.0  "
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CZ_SEMI.describe().apply(lambda s: s.apply(lambda x: format(round(x,1))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='transmission'>"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "pd.crosstab(df['transmission'],df['transmission']).plot.bar(stacked=True)\n",
    "#plt.yscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "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>transmission</th>\n",
       "      <th>TRANSMISSION_AUTOMATIC</th>\n",
       "      <th>TRANSMISSION_MANUAL</th>\n",
       "      <th>TRANSMISSION_SEMI_AUTOMATIC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transmission</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TRANSMISSION_AUTOMATIC</th>\n",
       "      <td>795730</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRANSMISSION_MANUAL</th>\n",
       "      <td>0</td>\n",
       "      <td>851631</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRANSMISSION_SEMI_AUTOMATIC</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "transmission                 TRANSMISSION_AUTOMATIC  TRANSMISSION_MANUAL  \\\n",
       "transmission                                                               \n",
       "TRANSMISSION_AUTOMATIC                       795730                    0   \n",
       "TRANSMISSION_MANUAL                               0               851631   \n",
       "TRANSMISSION_SEMI_AUTOMATIC                       0                    0   \n",
       "\n",
       "transmission                 TRANSMISSION_SEMI_AUTOMATIC  \n",
       "transmission                                              \n",
       "TRANSMISSION_AUTOMATIC                                 0  \n",
       "TRANSMISSION_MANUAL                                    0  \n",
       "TRANSMISSION_SEMI_AUTOMATIC                         1498  "
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['transmission'],df['transmission'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "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>transmission</th>\n",
       "      <th>TRANSMISSION_AUTOMATIC</th>\n",
       "      <th>TRANSMISSION_MANUAL</th>\n",
       "      <th>TRANSMISSION_SEMI_AUTOMATIC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transmission</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TRANSMISSION_AUTOMATIC</th>\n",
       "      <td>48.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRANSMISSION_MANUAL</th>\n",
       "      <td>0.00</td>\n",
       "      <td>51.65</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRANSMISSION_SEMI_AUTOMATIC</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "transmission                 TRANSMISSION_AUTOMATIC  TRANSMISSION_MANUAL  \\\n",
       "transmission                                                               \n",
       "TRANSMISSION_AUTOMATIC                        48.26                 0.00   \n",
       "TRANSMISSION_MANUAL                            0.00                51.65   \n",
       "TRANSMISSION_SEMI_AUTOMATIC                    0.00                 0.00   \n",
       "\n",
       "transmission                 TRANSMISSION_SEMI_AUTOMATIC  \n",
       "transmission                                              \n",
       "TRANSMISSION_AUTOMATIC                              0.00  \n",
       "TRANSMISSION_MANUAL                                 0.00  \n",
       "TRANSMISSION_SEMI_AUTOMATIC                         0.09  "
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#in percentage\n",
    "pd.crosstab(df['transmission'],df['transmission']).apply(lambda r: round(((r/len(df))*100),2), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>year</th>\n",
       "      <th>cubic_capacity</th>\n",
       "      <th>power</th>\n",
       "      <th>mileage</th>\n",
       "      <th>equipment_position</th>\n",
       "      <th>days_on_stock</th>\n",
       "      <th>price_with_vat_czk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1498.0</td>\n",
       "      <td>1498.0</td>\n",
       "      <td>1498.0</td>\n",
       "      <td>1498.0</td>\n",
       "      <td>1498.0</td>\n",
       "      <td>1498.0</td>\n",
       "      <td>1498.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2012.1</td>\n",
       "      <td>1542.2</td>\n",
       "      <td>101.9</td>\n",
       "      <td>81211.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>67.3</td>\n",
       "      <td>595392.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.3</td>\n",
       "      <td>1062.3</td>\n",
       "      <td>108.6</td>\n",
       "      <td>57324.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>70.1</td>\n",
       "      <td>1597495.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2006.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23521.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>36125.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>14.2</td>\n",
       "      <td>90299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>79325.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>42.0</td>\n",
       "      <td>139068.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1598.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>115774.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>97.0</td>\n",
       "      <td>377579.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2021.0</td>\n",
       "      <td>6498.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>21390808.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year cubic_capacity   power   mileage equipment_position  \\\n",
       "count  1498.0         1498.0  1498.0    1498.0             1498.0   \n",
       "mean   2012.1         1542.2   101.9   81211.5                0.5   \n",
       "std       4.3         1062.3   108.6   57324.5                0.3   \n",
       "min    2006.0            2.0     6.0       0.0                0.0   \n",
       "25%    2008.0          999.0    51.0   36125.0                0.2   \n",
       "50%    2011.0          999.0    62.0   79325.0                0.5   \n",
       "75%    2016.0         1598.0   103.0  115774.0                0.8   \n",
       "max    2021.0         6498.0   588.0  350000.0                1.0   \n",
       "\n",
       "      days_on_stock price_with_vat_czk  \n",
       "count        1498.0             1498.0  \n",
       "mean           67.3           595392.2  \n",
       "std            70.1          1597495.2  \n",
       "min             1.0            23521.0  \n",
       "25%            14.2            90299.0  \n",
       "50%            42.0           139068.0  \n",
       "75%            97.0           377579.5  \n",
       "max           360.0         21390808.0  "
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SEMI.describe().apply(lambda s: s.apply(lambda x: format((round(x,1)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "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>cubic_capacity</th>\n",
       "      <th>power</th>\n",
       "      <th>mileage</th>\n",
       "      <th>equipment_position</th>\n",
       "      <th>days_on_stock</th>\n",
       "      <th>price_with_vat_czk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>795730.0</td>\n",
       "      <td>795730.0</td>\n",
       "      <td>795730.0</td>\n",
       "      <td>795730.0</td>\n",
       "      <td>795730.0</td>\n",
       "      <td>795730.0</td>\n",
       "      <td>795730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.5</td>\n",
       "      <td>2140.5</td>\n",
       "      <td>150.2</td>\n",
       "      <td>66693.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>78.1</td>\n",
       "      <td>798341.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.6</td>\n",
       "      <td>805.2</td>\n",
       "      <td>69.7</td>\n",
       "      <td>70127.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>74.7</td>\n",
       "      <td>1872599.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2006.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>1598.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>12030.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>423464.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>45323.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>55.0</td>\n",
       "      <td>659413.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>2191.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>111.0</td>\n",
       "      <td>947578.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2021.0</td>\n",
       "      <td>49980.0</td>\n",
       "      <td>3005.0</td>\n",
       "      <td>9000050.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>812487349.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           year cubic_capacity     power    mileage equipment_position  \\\n",
       "count  795730.0       795730.0  795730.0   795730.0           795730.0   \n",
       "mean     2016.5         2140.5     150.2    66693.2                0.6   \n",
       "std         3.6          805.2      69.7    70127.8                0.3   \n",
       "min      2006.0            0.0       0.0        0.0                0.0   \n",
       "25%      2015.0         1598.0     110.0    12030.0                0.4   \n",
       "50%      2017.0         1984.0     135.0    45323.0                0.7   \n",
       "75%      2019.0         2191.0     173.0   100000.0                0.9   \n",
       "max      2021.0        49980.0    3005.0  9000050.0                1.0   \n",
       "\n",
       "      days_on_stock price_with_vat_czk  \n",
       "count      795730.0           795730.0  \n",
       "mean           78.1           798341.5  \n",
       "std            74.7          1872599.4  \n",
       "min             1.0               26.0  \n",
       "25%            21.0           423464.2  \n",
       "50%            55.0           659413.5  \n",
       "75%           111.0           947578.2  \n",
       "max           364.0        812487349.0  "
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AUTO.describe().apply(lambda s: s.apply(lambda x: format((round(x,1)))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ADDITION (UPDATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "CZ=pd.concat([CZ_SEMI, CZ_AUTO], ignore_index=True)['transmission'].value_counts()\n",
    "DE=pd.concat([DE_SEMI, DE_AUTO], ignore_index=True)['transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Semi automatic (DE)\n",
    "fig = plt.figure(figsize=(15,15))\n",
    "\n",
    "ax1 = plt.subplot2grid((1,2),(0,0))\n",
    "plt.pie(CZ, autopct='%1.1f%%',labels=CZ.index,\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax1.legend(loc=5, labels=CZ.index)\n",
    "plt.title('CZ: \\n {}'.format(CZ.to_string()))\n",
    "\n",
    "ax2 = plt.subplot2grid((1,2),(0,1))\n",
    "plt.pie(DE, autopct='%1.1f%%',labels=DE.index,\n",
    "        shadow=True, startangle=90)\n",
    "ax2.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax2.legend(loc=5, labels=DE.index)\n",
    "plt.title('DE: \\n {}'.format(DE.to_string()))\n",
    "\n",
    "plt.subplots_adjust(left=0.1,wspace=0.1, hspace=1, right=None, top=0.5, bottom=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRANSMISSION_AUTOMATIC         23282\n",
      "TRANSMISSION_SEMI_AUTOMATIC       45\n",
      "Name: transmission, dtype: int64\n",
      "TRANSMISSION_AUTOMATIC         772448\n",
      "TRANSMISSION_SEMI_AUTOMATIC      1453\n",
      "Name: transmission, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for x in [CZ,DE]: print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#RANDOM SAMPLES creation WITH SAME SIZE OF SEMI AUTOMATIC SAMPLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "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>seller_country</th>\n",
       "      <th>model_family</th>\n",
       "      <th>transmission_x</th>\n",
       "      <th>transmission_y</th>\n",
       "      <th>year</th>\n",
       "      <th>car_style_x</th>\n",
       "      <th>car_style_y</th>\n",
       "      <th>drive_x</th>\n",
       "      <th>drive_y</th>\n",
       "      <th>fuel_type_x</th>\n",
       "      <th>...</th>\n",
       "      <th>cubic_capacity_x</th>\n",
       "      <th>cubic_capacity_y</th>\n",
       "      <th>power_x</th>\n",
       "      <th>power_y</th>\n",
       "      <th>price_with_vat_czk_x</th>\n",
       "      <th>price_with_vat_czk_y</th>\n",
       "      <th>mileage_diff</th>\n",
       "      <th>price_diff</th>\n",
       "      <th>power_diff</th>\n",
       "      <th>cubic_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_AUDI-MODELFAMILY_Q5</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2017</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>783264</td>\n",
       "      <td>1148511</td>\n",
       "      <td>63486.0</td>\n",
       "      <td>-365247</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_AUDI-MODELFAMILY_Q5</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2017</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>783264</td>\n",
       "      <td>1062684</td>\n",
       "      <td>20513.0</td>\n",
       "      <td>-279420</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>-16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_AUDI-MODELFAMILY_Q5</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2017</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>783264</td>\n",
       "      <td>964801</td>\n",
       "      <td>45551.0</td>\n",
       "      <td>-181537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_AUDI-MODELFAMILY_Q5</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2017</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>783264</td>\n",
       "      <td>1062684</td>\n",
       "      <td>20513.0</td>\n",
       "      <td>-279420</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>-16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_AUDI-MODELFAMILY_Q5</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2017</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>783264</td>\n",
       "      <td>1149090</td>\n",
       "      <td>61727.0</td>\n",
       "      <td>-365826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527537</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_SKODA-MODELFAMILY_CITIGO</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2013</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_PETROL</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>180406</td>\n",
       "      <td>177040</td>\n",
       "      <td>-28000.0</td>\n",
       "      <td>3366</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527538</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_SKODA-MODELFAMILY_CITIGO</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2013</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_PETROL</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>180406</td>\n",
       "      <td>156263</td>\n",
       "      <td>-51550.0</td>\n",
       "      <td>24143</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527539</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_SKODA-MODELFAMILY_CITIGO</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2013</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_PETROL</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>180406</td>\n",
       "      <td>130110</td>\n",
       "      <td>-37000.0</td>\n",
       "      <td>50296</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527540</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_SKODA-MODELFAMILY_CITIGO</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2013</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_PETROL</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>180406</td>\n",
       "      <td>156770</td>\n",
       "      <td>-11000.0</td>\n",
       "      <td>23636</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527541</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_SKODA-MODELFAMILY_CITIGO</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2013</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_PETROL</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>180406</td>\n",
       "      <td>136227</td>\n",
       "      <td>-31000.0</td>\n",
       "      <td>44179</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>527542 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       seller_country                   model_family  \\\n",
       "0                  DE       MAKE_AUDI-MODELFAMILY_Q5   \n",
       "1                  DE       MAKE_AUDI-MODELFAMILY_Q5   \n",
       "2                  DE       MAKE_AUDI-MODELFAMILY_Q5   \n",
       "3                  DE       MAKE_AUDI-MODELFAMILY_Q5   \n",
       "4                  DE       MAKE_AUDI-MODELFAMILY_Q5   \n",
       "...               ...                            ...   \n",
       "527537             DE  MAKE_SKODA-MODELFAMILY_CITIGO   \n",
       "527538             DE  MAKE_SKODA-MODELFAMILY_CITIGO   \n",
       "527539             DE  MAKE_SKODA-MODELFAMILY_CITIGO   \n",
       "527540             DE  MAKE_SKODA-MODELFAMILY_CITIGO   \n",
       "527541             DE  MAKE_SKODA-MODELFAMILY_CITIGO   \n",
       "\n",
       "                     transmission_x          transmission_y  year  \\\n",
       "0       TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2017   \n",
       "1       TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2017   \n",
       "2       TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2017   \n",
       "3       TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2017   \n",
       "4       TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2017   \n",
       "...                             ...                     ...   ...   \n",
       "527537  TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2013   \n",
       "527538  TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2013   \n",
       "527539  TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2013   \n",
       "527540  TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2013   \n",
       "527541  TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2013   \n",
       "\n",
       "                    car_style_x              car_style_y    drive_x  \\\n",
       "0       CARSTYLE_SEDANS_SALOONS  CARSTYLE_SEDANS_SALOONS  DRIVE_4X4   \n",
       "1       CARSTYLE_SEDANS_SALOONS  CARSTYLE_SEDANS_SALOONS  DRIVE_4X4   \n",
       "2       CARSTYLE_SEDANS_SALOONS  CARSTYLE_SEDANS_SALOONS  DRIVE_4X4   \n",
       "3       CARSTYLE_SEDANS_SALOONS  CARSTYLE_SEDANS_SALOONS  DRIVE_4X4   \n",
       "4       CARSTYLE_SEDANS_SALOONS  CARSTYLE_SEDANS_SALOONS  DRIVE_4X4   \n",
       "...                         ...                      ...        ...   \n",
       "527537         CARSTYLE_COMPACT         CARSTYLE_COMPACT  DRIVE_4X2   \n",
       "527538         CARSTYLE_COMPACT         CARSTYLE_COMPACT  DRIVE_4X2   \n",
       "527539         CARSTYLE_COMPACT         CARSTYLE_COMPACT  DRIVE_4X2   \n",
       "527540         CARSTYLE_COMPACT         CARSTYLE_COMPACT  DRIVE_4X2   \n",
       "527541         CARSTYLE_COMPACT         CARSTYLE_COMPACT  DRIVE_4X2   \n",
       "\n",
       "          drive_y      fuel_type_x  ... cubic_capacity_x  cubic_capacity_y  \\\n",
       "0       DRIVE_4X4  FUELTYPE_DIESEL  ...           1968.0            1968.0   \n",
       "1       DRIVE_4X4  FUELTYPE_DIESEL  ...           1968.0            1984.0   \n",
       "2       DRIVE_4X4  FUELTYPE_DIESEL  ...           1968.0            1968.0   \n",
       "3       DRIVE_4X4  FUELTYPE_DIESEL  ...           1968.0            1984.0   \n",
       "4       DRIVE_4X4  FUELTYPE_DIESEL  ...           1968.0            1968.0   \n",
       "...           ...              ...  ...              ...               ...   \n",
       "527537  DRIVE_4X2  FUELTYPE_PETROL  ...            999.0             999.0   \n",
       "527538  DRIVE_4X2  FUELTYPE_PETROL  ...            999.0             999.0   \n",
       "527539  DRIVE_4X2  FUELTYPE_PETROL  ...            999.0             999.0   \n",
       "527540  DRIVE_4X2  FUELTYPE_PETROL  ...            999.0             999.0   \n",
       "527541  DRIVE_4X2  FUELTYPE_PETROL  ...            999.0             999.0   \n",
       "\n",
       "        power_x  power_y  price_with_vat_czk_x  price_with_vat_czk_y  \\\n",
       "0         140.0    140.0                783264               1148511   \n",
       "1         140.0    185.0                783264               1062684   \n",
       "2         140.0    140.0                783264                964801   \n",
       "3         140.0    185.0                783264               1062684   \n",
       "4         140.0    140.0                783264               1149090   \n",
       "...         ...      ...                   ...                   ...   \n",
       "527537     55.0     44.0                180406                177040   \n",
       "527538     55.0     44.0                180406                156263   \n",
       "527539     55.0     55.0                180406                130110   \n",
       "527540     55.0     44.0                180406                156770   \n",
       "527541     55.0     55.0                180406                136227   \n",
       "\n",
       "        mileage_diff  price_diff  power_diff  cubic_diff  \n",
       "0            63486.0     -365247         0.0         0.0  \n",
       "1            20513.0     -279420       -45.0       -16.0  \n",
       "2            45551.0     -181537         0.0         0.0  \n",
       "3            20513.0     -279420       -45.0       -16.0  \n",
       "4            61727.0     -365826         0.0         0.0  \n",
       "...              ...         ...         ...         ...  \n",
       "527537      -28000.0        3366        11.0         0.0  \n",
       "527538      -51550.0       24143        11.0         0.0  \n",
       "527539      -37000.0       50296         0.0         0.0  \n",
       "527540      -11000.0       23636        11.0         0.0  \n",
       "527541      -31000.0       44179         0.0         0.0  \n",
       "\n",
       "[527542 rows x 27 columns]"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#One option to compare cars with automatic and semiautomatic transmissions is to check for similarities in characteristics \n",
    "#by merging two tables (AUTO and SEMI) on \"model family\" and \"year\"\n",
    "merged=pd.merge(SEMI,AUTO,how='inner',on=['model_family','year','seller_country'])\n",
    "merged=merged[[\"seller_country\",\"model_family\",\"transmission_x\",\"transmission_y\",\"year\",'car_style_x','car_style_y','drive_x','drive_y','fuel_type_x','fuel_type_y','mileage_x','mileage_y','days_on_stock_x','days_on_stock_y','equipment_position_x','equipment_position_y','cubic_capacity_x','cubic_capacity_y','power_x','power_y','price_with_vat_czk_x','price_with_vat_czk_y']]\n",
    "merged['mileage_diff']=merged['mileage_x']-merged['mileage_y']\n",
    "merged['price_diff']=merged['price_with_vat_czk_x']-merged['price_with_vat_czk_y']\n",
    "merged['power_diff']=merged['power_x']-merged['power_y']\n",
    "merged['cubic_diff']=merged['cubic_capacity_x']-merged['cubic_capacity_y']\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random sample for \"automat\"\n",
    "merged[[\"seller_country\", \"model_family\",\"transmission_y\",\"car_style_y\",\"drive_y\",\"fuel_type_y\",\"mileage_y\",\"days_on_stock_y\",\"equipment_position_y\",\"cubic_capacity_y\",\"power_y\",\"price_with_vat_czk_y\"]].sample(n=len(SEMI), random_state=77)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random sample for \"man\"\n",
    "merged=pd.merge(SEMI,MAN,how='inner',on=['model_family','year','seller_country'])\n",
    "merged=merged[[\"seller_country\",\"model_family\",\"transmission_x\",\"transmission_y\",\"year\",'car_style_x','car_style_y','drive_x','drive_y','fuel_type_x','fuel_type_y','mileage_x','mileage_y','days_on_stock_x','days_on_stock_y','equipment_position_x','equipment_position_y','cubic_capacity_x','cubic_capacity_y','power_x','power_y','price_with_vat_czk_x','price_with_vat_czk_y']]\n",
    "merged['mileage_diff']=merged['mileage_x']-merged['mileage_y']\n",
    "merged['price_diff']=merged['price_with_vat_czk_x']-merged['price_with_vat_czk_y']\n",
    "merged['power_diff']=merged['power_x']-merged['power_y']\n",
    "merged['cubic_diff']=merged['cubic_capacity_x']-merged['cubic_capacity_y']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MAN random\n",
    "\n",
    "merged[[\"seller_country\", \"model_family\",\"transmission_y\",\"car_style_y\",\"drive_y\",\"fuel_type_y\",\"mileage_y\",\"days_on_stock_y\",\"equipment_position_y\",\"cubic_capacity_y\",\"power_y\",\"price_with_vat_czk_y\"]].sample(n=len(SEMI), random_state=55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "########################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>seller_country</th>\n",
       "      <th>model_family</th>\n",
       "      <th>transmission_x</th>\n",
       "      <th>transmission_y</th>\n",
       "      <th>year</th>\n",
       "      <th>car_style_x</th>\n",
       "      <th>car_style_y</th>\n",
       "      <th>drive_x</th>\n",
       "      <th>drive_y</th>\n",
       "      <th>fuel_type_x</th>\n",
       "      <th>...</th>\n",
       "      <th>cubic_capacity_x</th>\n",
       "      <th>cubic_capacity_y</th>\n",
       "      <th>power_x</th>\n",
       "      <th>power_y</th>\n",
       "      <th>price_with_vat_czk_x</th>\n",
       "      <th>price_with_vat_czk_y</th>\n",
       "      <th>mileage_diff</th>\n",
       "      <th>price_diff</th>\n",
       "      <th>power_diff</th>\n",
       "      <th>cubic_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CZ</td>\n",
       "      <td>MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPAC...</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2008</td>\n",
       "      <td>CARSTYLE_OTHER</td>\n",
       "      <td>CARSTYLE_OTHER</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>98990</td>\n",
       "      <td>121386.0</td>\n",
       "      <td>-38990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CZ</td>\n",
       "      <td>MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPAC...</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2008</td>\n",
       "      <td>CARSTYLE_OTHER</td>\n",
       "      <td>CARSTYLE_OTHER</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>119000</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>-59000</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CZ</td>\n",
       "      <td>MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPAC...</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2008</td>\n",
       "      <td>CARSTYLE_OTHER</td>\n",
       "      <td>CARSTYLE_OTHER</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>65000</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>-5000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CZ</td>\n",
       "      <td>MAKE_PEUGEOT-MODELFAMILY_208</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2016</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_PETROL</td>\n",
       "      <td>...</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>170000</td>\n",
       "      <td>230000</td>\n",
       "      <td>68902.0</td>\n",
       "      <td>-60000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CZ</td>\n",
       "      <td>MAKE_PEUGEOT-MODELFAMILY_208</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2016</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_PETROL</td>\n",
       "      <td>...</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>170000</td>\n",
       "      <td>269000</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>-99000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>CZ</td>\n",
       "      <td>MAKE_SKODA-MODELFAMILY_KODIAQ</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2017</td>\n",
       "      <td>CARSTYLE_SUV_OFFROAD</td>\n",
       "      <td>CARSTYLE_SUV_OFFROAD</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>749000</td>\n",
       "      <td>743000</td>\n",
       "      <td>14150.0</td>\n",
       "      <td>6000</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>CZ</td>\n",
       "      <td>MAKE_SKODA-MODELFAMILY_KODIAQ</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2017</td>\n",
       "      <td>CARSTYLE_SUV_OFFROAD</td>\n",
       "      <td>CARSTYLE_SUV_OFFROAD</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>749000</td>\n",
       "      <td>729900</td>\n",
       "      <td>-36000.0</td>\n",
       "      <td>19100</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>CZ</td>\n",
       "      <td>MAKE_SKODA-MODELFAMILY_KODIAQ</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2017</td>\n",
       "      <td>CARSTYLE_SUV_OFFROAD</td>\n",
       "      <td>CARSTYLE_SUV_OFFROAD</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>749000</td>\n",
       "      <td>659000</td>\n",
       "      <td>10800.0</td>\n",
       "      <td>90000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>CZ</td>\n",
       "      <td>MAKE_SKODA-MODELFAMILY_KODIAQ</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2017</td>\n",
       "      <td>CARSTYLE_SUV_OFFROAD</td>\n",
       "      <td>CARSTYLE_SUV_OFFROAD</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>749000</td>\n",
       "      <td>699000</td>\n",
       "      <td>7471.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>CZ</td>\n",
       "      <td>MAKE_SKODA-MODELFAMILY_KODIAQ</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2017</td>\n",
       "      <td>CARSTYLE_SUV_OFFROAD</td>\n",
       "      <td>CARSTYLE_SUV_OFFROAD</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>749000</td>\n",
       "      <td>619900</td>\n",
       "      <td>-24215.0</td>\n",
       "      <td>129100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>754 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    seller_country                                       model_family  \\\n",
       "0               CZ  MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPAC...   \n",
       "1               CZ  MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPAC...   \n",
       "2               CZ  MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPAC...   \n",
       "3               CZ                       MAKE_PEUGEOT-MODELFAMILY_208   \n",
       "4               CZ                       MAKE_PEUGEOT-MODELFAMILY_208   \n",
       "..             ...                                                ...   \n",
       "749             CZ                      MAKE_SKODA-MODELFAMILY_KODIAQ   \n",
       "750             CZ                      MAKE_SKODA-MODELFAMILY_KODIAQ   \n",
       "751             CZ                      MAKE_SKODA-MODELFAMILY_KODIAQ   \n",
       "752             CZ                      MAKE_SKODA-MODELFAMILY_KODIAQ   \n",
       "753             CZ                      MAKE_SKODA-MODELFAMILY_KODIAQ   \n",
       "\n",
       "                  transmission_x          transmission_y  year  \\\n",
       "0    TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2008   \n",
       "1    TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2008   \n",
       "2    TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2008   \n",
       "3    TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2016   \n",
       "4    TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2016   \n",
       "..                           ...                     ...   ...   \n",
       "749  TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2017   \n",
       "750  TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2017   \n",
       "751  TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2017   \n",
       "752  TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2017   \n",
       "753  TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2017   \n",
       "\n",
       "              car_style_x           car_style_y    drive_x    drive_y  \\\n",
       "0          CARSTYLE_OTHER        CARSTYLE_OTHER  DRIVE_4X2  DRIVE_4X2   \n",
       "1          CARSTYLE_OTHER        CARSTYLE_OTHER  DRIVE_4X2  DRIVE_4X2   \n",
       "2          CARSTYLE_OTHER        CARSTYLE_OTHER  DRIVE_4X2  DRIVE_4X2   \n",
       "3        CARSTYLE_COMPACT      CARSTYLE_COMPACT  DRIVE_4X2  DRIVE_4X2   \n",
       "4        CARSTYLE_COMPACT      CARSTYLE_COMPACT  DRIVE_4X2  DRIVE_4X2   \n",
       "..                    ...                   ...        ...        ...   \n",
       "749  CARSTYLE_SUV_OFFROAD  CARSTYLE_SUV_OFFROAD  DRIVE_4X2  DRIVE_4X4   \n",
       "750  CARSTYLE_SUV_OFFROAD  CARSTYLE_SUV_OFFROAD  DRIVE_4X2  DRIVE_4X4   \n",
       "751  CARSTYLE_SUV_OFFROAD  CARSTYLE_SUV_OFFROAD  DRIVE_4X2  DRIVE_4X2   \n",
       "752  CARSTYLE_SUV_OFFROAD  CARSTYLE_SUV_OFFROAD  DRIVE_4X2  DRIVE_4X4   \n",
       "753  CARSTYLE_SUV_OFFROAD  CARSTYLE_SUV_OFFROAD  DRIVE_4X2  DRIVE_4X2   \n",
       "\n",
       "         fuel_type_x  ... cubic_capacity_x  cubic_capacity_y  power_x  \\\n",
       "0    FUELTYPE_DIESEL  ...           1560.0            1560.0     80.0   \n",
       "1    FUELTYPE_DIESEL  ...           1560.0            1997.0     80.0   \n",
       "2    FUELTYPE_DIESEL  ...           1560.0            1560.0     80.0   \n",
       "3    FUELTYPE_PETROL  ...           1199.0            1200.0     60.0   \n",
       "4    FUELTYPE_PETROL  ...           1199.0            1199.0     60.0   \n",
       "..               ...  ...              ...               ...      ...   \n",
       "749  FUELTYPE_DIESEL  ...           1968.0            1968.0    110.0   \n",
       "750  FUELTYPE_DIESEL  ...           1968.0            1968.0    110.0   \n",
       "751  FUELTYPE_DIESEL  ...           1968.0            1968.0    110.0   \n",
       "752  FUELTYPE_DIESEL  ...           1968.0            1968.0    110.0   \n",
       "753  FUELTYPE_DIESEL  ...           1968.0            1968.0    110.0   \n",
       "\n",
       "     power_y  price_with_vat_czk_x  price_with_vat_czk_y  mileage_diff  \\\n",
       "0       80.0                 60000                 98990      121386.0   \n",
       "1      100.0                 60000                119000      101000.0   \n",
       "2       80.0                 60000                 65000       60000.0   \n",
       "3       60.0                170000                230000       68902.0   \n",
       "4       60.0                170000                269000       75000.0   \n",
       "..       ...                   ...                   ...           ...   \n",
       "749    140.0                749000                743000       14150.0   \n",
       "750    140.0                749000                729900      -36000.0   \n",
       "751    110.0                749000                659000       10800.0   \n",
       "752    110.0                749000                699000        7471.0   \n",
       "753    110.0                749000                619900      -24215.0   \n",
       "\n",
       "     price_diff  power_diff  cubic_diff  \n",
       "0        -38990         0.0         0.0  \n",
       "1        -59000       -20.0      -437.0  \n",
       "2         -5000         0.0         0.0  \n",
       "3        -60000         0.0        -1.0  \n",
       "4        -99000         0.0         0.0  \n",
       "..          ...         ...         ...  \n",
       "749        6000       -30.0         0.0  \n",
       "750       19100       -30.0         0.0  \n",
       "751       90000         0.0         0.0  \n",
       "752       50000         0.0         0.0  \n",
       "753      129100         0.0         0.0  \n",
       "\n",
       "[754 rows x 27 columns]"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged=pd.merge(CZ_SEMI,CZ_AUTO,how='inner',on=['model_family','year','seller_country'])\n",
    "merged=merged[[\"seller_country\",\"model_family\",\"transmission_x\",\"transmission_y\",\"year\",'car_style_x','car_style_y','drive_x','drive_y','fuel_type_x','fuel_type_y','mileage_x','mileage_y','days_on_stock_x','days_on_stock_y','equipment_position_x','equipment_position_y','cubic_capacity_x','cubic_capacity_y','power_x','power_y','price_with_vat_czk_x','price_with_vat_czk_y']]\n",
    "merged['mileage_diff']=merged['mileage_x']-merged['mileage_y']\n",
    "merged['price_diff']=merged['price_with_vat_czk_x']-merged['price_with_vat_czk_y']\n",
    "merged['power_diff']=merged['power_x']-merged['power_y']\n",
    "merged['cubic_diff']=merged['cubic_capacity_x']-merged['cubic_capacity_y']\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "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",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>754.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-55007.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>191676.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1464792.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-119000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-38995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>559000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price_diff\n",
       "count       754.0\n",
       "mean     -55007.9\n",
       "std      191676.7\n",
       "min    -1464792.0\n",
       "25%     -119000.0\n",
       "50%      -38995.0\n",
       "75%       38325.0\n",
       "max      559000.0"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CZ:AVERAGE DIFFERENCE IN PRICE BETWEEN SEMI_AUTOMATIC AND AUTOMATIC \n",
    "merged[['price_diff']].describe().apply(lambda s: s.apply(lambda x: format(round(x,1))))\n",
    "#In average, cars with automatic transmission are on 50k more expensive than with semiautomatic (Czech market)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "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></th>\n",
       "      <th>mileage_x</th>\n",
       "      <th>mileage_y</th>\n",
       "      <th>days_on_stock_x</th>\n",
       "      <th>days_on_stock_y</th>\n",
       "      <th>equipment_position_x</th>\n",
       "      <th>equipment_position_y</th>\n",
       "      <th>cubic_capacity_x</th>\n",
       "      <th>cubic_capacity_y</th>\n",
       "      <th>power_x</th>\n",
       "      <th>power_y</th>\n",
       "      <th>price_with_vat_czk_x</th>\n",
       "      <th>price_with_vat_czk_y</th>\n",
       "      <th>mileage_diff</th>\n",
       "      <th>price_diff</th>\n",
       "      <th>power_diff</th>\n",
       "      <th>cubic_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_family</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAKE_AUDI-MODELFAMILY_A4</th>\n",
       "      <th>2006</th>\n",
       "      <td>350000.0</td>\n",
       "      <td>217542.125000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>98.125000</td>\n",
       "      <td>0.779310</td>\n",
       "      <td>0.373552</td>\n",
       "      <td>2967.0</td>\n",
       "      <td>2094.875000</td>\n",
       "      <td>171.0</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>96000.0</td>\n",
       "      <td>1.043500e+05</td>\n",
       "      <td>132457.875000</td>\n",
       "      <td>-8350.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>872.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAKE_AUDI-MODELFAMILY_A7</th>\n",
       "      <th>2013</th>\n",
       "      <td>165600.0</td>\n",
       "      <td>200060.600000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>97.200000</td>\n",
       "      <td>0.077376</td>\n",
       "      <td>0.229941</td>\n",
       "      <td>2967.0</td>\n",
       "      <td>2971.400000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>540000.0</td>\n",
       "      <td>5.235398e+05</td>\n",
       "      <td>-34460.600000</td>\n",
       "      <td>16460.200000</td>\n",
       "      <td>-50.000000</td>\n",
       "      <td>-4.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAKE_BMW-MODELFAMILY_335</th>\n",
       "      <th>2006</th>\n",
       "      <td>216912.0</td>\n",
       "      <td>170000.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.977349</td>\n",
       "      <td>0.000839</td>\n",
       "      <td>2979.0</td>\n",
       "      <td>2993.000000</td>\n",
       "      <td>225.0</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>295000.0</td>\n",
       "      <td>2.350000e+05</td>\n",
       "      <td>46912.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>-14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAKE_BMW-MODELFAMILY_520</th>\n",
       "      <th>2015</th>\n",
       "      <td>59800.0</td>\n",
       "      <td>131203.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.753305</td>\n",
       "      <td>0.454212</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>579000.0</td>\n",
       "      <td>4.713798e+05</td>\n",
       "      <td>-71403.000000</td>\n",
       "      <td>107620.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAKE_BMW-MODELFAMILY_X6</th>\n",
       "      <th>2012</th>\n",
       "      <td>300000.0</td>\n",
       "      <td>157502.600000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>65.200000</td>\n",
       "      <td>0.819880</td>\n",
       "      <td>0.584923</td>\n",
       "      <td>2993.0</td>\n",
       "      <td>2993.000000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>399000.0</td>\n",
       "      <td>5.205178e+05</td>\n",
       "      <td>142497.400000</td>\n",
       "      <td>-121517.800000</td>\n",
       "      <td>-40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAKE_BMW-MODELFAMILY_X7</th>\n",
       "      <th>2020</th>\n",
       "      <td>8900.0</td>\n",
       "      <td>5413.954545</td>\n",
       "      <td>49.0</td>\n",
       "      <td>53.363636</td>\n",
       "      <td>0.251042</td>\n",
       "      <td>0.298390</td>\n",
       "      <td>2998.0</td>\n",
       "      <td>3248.681818</td>\n",
       "      <td>250.0</td>\n",
       "      <td>296.227273</td>\n",
       "      <td>2296000.0</td>\n",
       "      <td>3.071860e+06</td>\n",
       "      <td>3486.045455</td>\n",
       "      <td>-775860.409091</td>\n",
       "      <td>-46.227273</td>\n",
       "      <td>-250.681818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">MAKE_CITROEN-MODELFAMILY_C4_PICASSO</th>\n",
       "      <th>2008</th>\n",
       "      <td>265000.0</td>\n",
       "      <td>204908.800000</td>\n",
       "      <td>13.0</td>\n",
       "      <td>71.600000</td>\n",
       "      <td>0.957036</td>\n",
       "      <td>0.875716</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>1865.900000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>94.900000</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>7.919000e+04</td>\n",
       "      <td>60091.200000</td>\n",
       "      <td>-11690.000000</td>\n",
       "      <td>-14.900000</td>\n",
       "      <td>-305.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>112650.0</td>\n",
       "      <td>162274.333333</td>\n",
       "      <td>24.5</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.940282</td>\n",
       "      <td>0.779531</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>1572.666667</td>\n",
       "      <td>82.0</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>152500.0</td>\n",
       "      <td>1.230000e+05</td>\n",
       "      <td>-49624.333333</td>\n",
       "      <td>29500.000000</td>\n",
       "      <td>-11.000000</td>\n",
       "      <td>-12.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>194125.0</td>\n",
       "      <td>205000.000000</td>\n",
       "      <td>88.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.620990</td>\n",
       "      <td>0.562806</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>119900.0</td>\n",
       "      <td>1.240000e+05</td>\n",
       "      <td>-10875.000000</td>\n",
       "      <td>-4100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACETOURER</th>\n",
       "      <th>2008</th>\n",
       "      <td>265000.0</td>\n",
       "      <td>170871.333333</td>\n",
       "      <td>13.0</td>\n",
       "      <td>22.666667</td>\n",
       "      <td>0.836645</td>\n",
       "      <td>0.806475</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>1705.666667</td>\n",
       "      <td>80.0</td>\n",
       "      <td>86.666667</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>9.433000e+04</td>\n",
       "      <td>94128.666667</td>\n",
       "      <td>-34330.000000</td>\n",
       "      <td>-6.666667</td>\n",
       "      <td>-145.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         mileage_x  \\\n",
       "model_family                                       year              \n",
       "MAKE_AUDI-MODELFAMILY_A4                           2006   350000.0   \n",
       "MAKE_AUDI-MODELFAMILY_A7                           2013   165600.0   \n",
       "MAKE_BMW-MODELFAMILY_335                           2006   216912.0   \n",
       "MAKE_BMW-MODELFAMILY_520                           2015    59800.0   \n",
       "MAKE_BMW-MODELFAMILY_X6                            2012   300000.0   \n",
       "MAKE_BMW-MODELFAMILY_X7                            2020     8900.0   \n",
       "MAKE_CITROEN-MODELFAMILY_C4_PICASSO                2008   265000.0   \n",
       "                                                   2011   112650.0   \n",
       "                                                   2013   194125.0   \n",
       "MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACE... 2008   265000.0   \n",
       "\n",
       "                                                             mileage_y  \\\n",
       "model_family                                       year                  \n",
       "MAKE_AUDI-MODELFAMILY_A4                           2006  217542.125000   \n",
       "MAKE_AUDI-MODELFAMILY_A7                           2013  200060.600000   \n",
       "MAKE_BMW-MODELFAMILY_335                           2006  170000.000000   \n",
       "MAKE_BMW-MODELFAMILY_520                           2015  131203.000000   \n",
       "MAKE_BMW-MODELFAMILY_X6                            2012  157502.600000   \n",
       "MAKE_BMW-MODELFAMILY_X7                            2020    5413.954545   \n",
       "MAKE_CITROEN-MODELFAMILY_C4_PICASSO                2008  204908.800000   \n",
       "                                                   2011  162274.333333   \n",
       "                                                   2013  205000.000000   \n",
       "MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACE... 2008  170871.333333   \n",
       "\n",
       "                                                         days_on_stock_x  \\\n",
       "model_family                                       year                    \n",
       "MAKE_AUDI-MODELFAMILY_A4                           2006             13.0   \n",
       "MAKE_AUDI-MODELFAMILY_A7                           2013              9.0   \n",
       "MAKE_BMW-MODELFAMILY_335                           2006              6.0   \n",
       "MAKE_BMW-MODELFAMILY_520                           2015              6.0   \n",
       "MAKE_BMW-MODELFAMILY_X6                            2012             13.0   \n",
       "MAKE_BMW-MODELFAMILY_X7                            2020             49.0   \n",
       "MAKE_CITROEN-MODELFAMILY_C4_PICASSO                2008             13.0   \n",
       "                                                   2011             24.5   \n",
       "                                                   2013             88.0   \n",
       "MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACE... 2008             13.0   \n",
       "\n",
       "                                                         days_on_stock_y  \\\n",
       "model_family                                       year                    \n",
       "MAKE_AUDI-MODELFAMILY_A4                           2006        98.125000   \n",
       "MAKE_AUDI-MODELFAMILY_A7                           2013        97.200000   \n",
       "MAKE_BMW-MODELFAMILY_335                           2006        47.000000   \n",
       "MAKE_BMW-MODELFAMILY_520                           2015        50.000000   \n",
       "MAKE_BMW-MODELFAMILY_X6                            2012        65.200000   \n",
       "MAKE_BMW-MODELFAMILY_X7                            2020        53.363636   \n",
       "MAKE_CITROEN-MODELFAMILY_C4_PICASSO                2008        71.600000   \n",
       "                                                   2011        31.000000   \n",
       "                                                   2013        13.000000   \n",
       "MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACE... 2008        22.666667   \n",
       "\n",
       "                                                         equipment_position_x  \\\n",
       "model_family                                       year                         \n",
       "MAKE_AUDI-MODELFAMILY_A4                           2006              0.779310   \n",
       "MAKE_AUDI-MODELFAMILY_A7                           2013              0.077376   \n",
       "MAKE_BMW-MODELFAMILY_335                           2006              0.977349   \n",
       "MAKE_BMW-MODELFAMILY_520                           2015              0.753305   \n",
       "MAKE_BMW-MODELFAMILY_X6                            2012              0.819880   \n",
       "MAKE_BMW-MODELFAMILY_X7                            2020              0.251042   \n",
       "MAKE_CITROEN-MODELFAMILY_C4_PICASSO                2008              0.957036   \n",
       "                                                   2011              0.940282   \n",
       "                                                   2013              0.620990   \n",
       "MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACE... 2008              0.836645   \n",
       "\n",
       "                                                         equipment_position_y  \\\n",
       "model_family                                       year                         \n",
       "MAKE_AUDI-MODELFAMILY_A4                           2006              0.373552   \n",
       "MAKE_AUDI-MODELFAMILY_A7                           2013              0.229941   \n",
       "MAKE_BMW-MODELFAMILY_335                           2006              0.000839   \n",
       "MAKE_BMW-MODELFAMILY_520                           2015              0.454212   \n",
       "MAKE_BMW-MODELFAMILY_X6                            2012              0.584923   \n",
       "MAKE_BMW-MODELFAMILY_X7                            2020              0.298390   \n",
       "MAKE_CITROEN-MODELFAMILY_C4_PICASSO                2008              0.875716   \n",
       "                                                   2011              0.779531   \n",
       "                                                   2013              0.562806   \n",
       "MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACE... 2008              0.806475   \n",
       "\n",
       "                                                         cubic_capacity_x  \\\n",
       "model_family                                       year                     \n",
       "MAKE_AUDI-MODELFAMILY_A4                           2006            2967.0   \n",
       "MAKE_AUDI-MODELFAMILY_A7                           2013            2967.0   \n",
       "MAKE_BMW-MODELFAMILY_335                           2006            2979.0   \n",
       "MAKE_BMW-MODELFAMILY_520                           2015            1995.0   \n",
       "MAKE_BMW-MODELFAMILY_X6                            2012            2993.0   \n",
       "MAKE_BMW-MODELFAMILY_X7                            2020            2998.0   \n",
       "MAKE_CITROEN-MODELFAMILY_C4_PICASSO                2008            1560.0   \n",
       "                                                   2011            1560.0   \n",
       "                                                   2013            1560.0   \n",
       "MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACE... 2008            1560.0   \n",
       "\n",
       "                                                         cubic_capacity_y  \\\n",
       "model_family                                       year                     \n",
       "MAKE_AUDI-MODELFAMILY_A4                           2006       2094.875000   \n",
       "MAKE_AUDI-MODELFAMILY_A7                           2013       2971.400000   \n",
       "MAKE_BMW-MODELFAMILY_335                           2006       2993.000000   \n",
       "MAKE_BMW-MODELFAMILY_520                           2015       1995.000000   \n",
       "MAKE_BMW-MODELFAMILY_X6                            2012       2993.000000   \n",
       "MAKE_BMW-MODELFAMILY_X7                            2020       3248.681818   \n",
       "MAKE_CITROEN-MODELFAMILY_C4_PICASSO                2008       1865.900000   \n",
       "                                                   2011       1572.666667   \n",
       "                                                   2013       1560.000000   \n",
       "MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACE... 2008       1705.666667   \n",
       "\n",
       "                                                         power_x     power_y  \\\n",
       "model_family                                       year                        \n",
       "MAKE_AUDI-MODELFAMILY_A4                           2006    171.0  117.000000   \n",
       "MAKE_AUDI-MODELFAMILY_A7                           2013    180.0  230.000000   \n",
       "MAKE_BMW-MODELFAMILY_335                           2006    225.0  210.000000   \n",
       "MAKE_BMW-MODELFAMILY_520                           2015    140.0  140.000000   \n",
       "MAKE_BMW-MODELFAMILY_X6                            2012    180.0  220.000000   \n",
       "MAKE_BMW-MODELFAMILY_X7                            2020    250.0  296.227273   \n",
       "MAKE_CITROEN-MODELFAMILY_C4_PICASSO                2008     80.0   94.900000   \n",
       "                                                   2011     82.0   93.000000   \n",
       "                                                   2013     82.0   82.000000   \n",
       "MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACE... 2008     80.0   86.666667   \n",
       "\n",
       "                                                         price_with_vat_czk_x  \\\n",
       "model_family                                       year                         \n",
       "MAKE_AUDI-MODELFAMILY_A4                           2006               96000.0   \n",
       "MAKE_AUDI-MODELFAMILY_A7                           2013              540000.0   \n",
       "MAKE_BMW-MODELFAMILY_335                           2006              295000.0   \n",
       "MAKE_BMW-MODELFAMILY_520                           2015              579000.0   \n",
       "MAKE_BMW-MODELFAMILY_X6                            2012              399000.0   \n",
       "MAKE_BMW-MODELFAMILY_X7                            2020             2296000.0   \n",
       "MAKE_CITROEN-MODELFAMILY_C4_PICASSO                2008               67500.0   \n",
       "                                                   2011              152500.0   \n",
       "                                                   2013              119900.0   \n",
       "MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACE... 2008               60000.0   \n",
       "\n",
       "                                                         price_with_vat_czk_y  \\\n",
       "model_family                                       year                         \n",
       "MAKE_AUDI-MODELFAMILY_A4                           2006          1.043500e+05   \n",
       "MAKE_AUDI-MODELFAMILY_A7                           2013          5.235398e+05   \n",
       "MAKE_BMW-MODELFAMILY_335                           2006          2.350000e+05   \n",
       "MAKE_BMW-MODELFAMILY_520                           2015          4.713798e+05   \n",
       "MAKE_BMW-MODELFAMILY_X6                            2012          5.205178e+05   \n",
       "MAKE_BMW-MODELFAMILY_X7                            2020          3.071860e+06   \n",
       "MAKE_CITROEN-MODELFAMILY_C4_PICASSO                2008          7.919000e+04   \n",
       "                                                   2011          1.230000e+05   \n",
       "                                                   2013          1.240000e+05   \n",
       "MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACE... 2008          9.433000e+04   \n",
       "\n",
       "                                                          mileage_diff  \\\n",
       "model_family                                       year                  \n",
       "MAKE_AUDI-MODELFAMILY_A4                           2006  132457.875000   \n",
       "MAKE_AUDI-MODELFAMILY_A7                           2013  -34460.600000   \n",
       "MAKE_BMW-MODELFAMILY_335                           2006   46912.000000   \n",
       "MAKE_BMW-MODELFAMILY_520                           2015  -71403.000000   \n",
       "MAKE_BMW-MODELFAMILY_X6                            2012  142497.400000   \n",
       "MAKE_BMW-MODELFAMILY_X7                            2020    3486.045455   \n",
       "MAKE_CITROEN-MODELFAMILY_C4_PICASSO                2008   60091.200000   \n",
       "                                                   2011  -49624.333333   \n",
       "                                                   2013  -10875.000000   \n",
       "MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACE... 2008   94128.666667   \n",
       "\n",
       "                                                            price_diff  \\\n",
       "model_family                                       year                  \n",
       "MAKE_AUDI-MODELFAMILY_A4                           2006   -8350.000000   \n",
       "MAKE_AUDI-MODELFAMILY_A7                           2013   16460.200000   \n",
       "MAKE_BMW-MODELFAMILY_335                           2006   60000.000000   \n",
       "MAKE_BMW-MODELFAMILY_520                           2015  107620.200000   \n",
       "MAKE_BMW-MODELFAMILY_X6                            2012 -121517.800000   \n",
       "MAKE_BMW-MODELFAMILY_X7                            2020 -775860.409091   \n",
       "MAKE_CITROEN-MODELFAMILY_C4_PICASSO                2008  -11690.000000   \n",
       "                                                   2011   29500.000000   \n",
       "                                                   2013   -4100.000000   \n",
       "MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACE... 2008  -34330.000000   \n",
       "\n",
       "                                                         power_diff  \\\n",
       "model_family                                       year               \n",
       "MAKE_AUDI-MODELFAMILY_A4                           2006   54.000000   \n",
       "MAKE_AUDI-MODELFAMILY_A7                           2013  -50.000000   \n",
       "MAKE_BMW-MODELFAMILY_335                           2006   15.000000   \n",
       "MAKE_BMW-MODELFAMILY_520                           2015    0.000000   \n",
       "MAKE_BMW-MODELFAMILY_X6                            2012  -40.000000   \n",
       "MAKE_BMW-MODELFAMILY_X7                            2020  -46.227273   \n",
       "MAKE_CITROEN-MODELFAMILY_C4_PICASSO                2008  -14.900000   \n",
       "                                                   2011  -11.000000   \n",
       "                                                   2013    0.000000   \n",
       "MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACE... 2008   -6.666667   \n",
       "\n",
       "                                                         cubic_diff  \n",
       "model_family                                       year              \n",
       "MAKE_AUDI-MODELFAMILY_A4                           2006  872.125000  \n",
       "MAKE_AUDI-MODELFAMILY_A7                           2013   -4.400000  \n",
       "MAKE_BMW-MODELFAMILY_335                           2006  -14.000000  \n",
       "MAKE_BMW-MODELFAMILY_520                           2015    0.000000  \n",
       "MAKE_BMW-MODELFAMILY_X6                            2012    0.000000  \n",
       "MAKE_BMW-MODELFAMILY_X7                            2020 -250.681818  \n",
       "MAKE_CITROEN-MODELFAMILY_C4_PICASSO                2008 -305.900000  \n",
       "                                                   2011  -12.666667  \n",
       "                                                   2013    0.000000  \n",
       "MAKE_CITROEN-MODELFAMILY_GRAND_C4_PICASSO_SPACE... 2008 -145.666667  "
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CZ: Each model, by each year\n",
    "merged.groupby(['model_family','year']).mean('price_diff').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#######################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "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>seller_country</th>\n",
       "      <th>model_family</th>\n",
       "      <th>transmission_x</th>\n",
       "      <th>transmission_y</th>\n",
       "      <th>year</th>\n",
       "      <th>car_style_x</th>\n",
       "      <th>car_style_y</th>\n",
       "      <th>drive_x</th>\n",
       "      <th>drive_y</th>\n",
       "      <th>fuel_type_x</th>\n",
       "      <th>...</th>\n",
       "      <th>cubic_capacity_x</th>\n",
       "      <th>cubic_capacity_y</th>\n",
       "      <th>power_x</th>\n",
       "      <th>power_y</th>\n",
       "      <th>price_with_vat_czk_x</th>\n",
       "      <th>price_with_vat_czk_y</th>\n",
       "      <th>mileage_diff</th>\n",
       "      <th>price_diff</th>\n",
       "      <th>power_diff</th>\n",
       "      <th>cubic_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_AUDI-MODELFAMILY_Q5</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2017</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>783264</td>\n",
       "      <td>1148511</td>\n",
       "      <td>63486.0</td>\n",
       "      <td>-365247</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_AUDI-MODELFAMILY_Q5</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2017</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>783264</td>\n",
       "      <td>1062684</td>\n",
       "      <td>20513.0</td>\n",
       "      <td>-279420</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>-16.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_AUDI-MODELFAMILY_Q5</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2017</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>783264</td>\n",
       "      <td>964801</td>\n",
       "      <td>45551.0</td>\n",
       "      <td>-181537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_AUDI-MODELFAMILY_Q5</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2017</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>783264</td>\n",
       "      <td>1062684</td>\n",
       "      <td>20513.0</td>\n",
       "      <td>-279420</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>-16.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>MAKE_AUDI-MODELFAMILY_Q5</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2017</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>CARSTYLE_SEDANS_SALOONS</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>DRIVE_4X4</td>\n",
       "      <td>FUELTYPE_DIESEL</td>\n",
       "      <td>...</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>783264</td>\n",
       "      <td>1149090</td>\n",
       "      <td>61727.0</td>\n",
       "      <td>-365826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>526783</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_SKODA-MODELFAMILY_CITIGO</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2013</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_PETROL</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>180406</td>\n",
       "      <td>177040</td>\n",
       "      <td>-28000.0</td>\n",
       "      <td>3366</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526784</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_SKODA-MODELFAMILY_CITIGO</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2013</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_PETROL</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>180406</td>\n",
       "      <td>156263</td>\n",
       "      <td>-51550.0</td>\n",
       "      <td>24143</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>526785</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_SKODA-MODELFAMILY_CITIGO</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2013</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_PETROL</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>180406</td>\n",
       "      <td>130110</td>\n",
       "      <td>-37000.0</td>\n",
       "      <td>50296</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>526786</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_SKODA-MODELFAMILY_CITIGO</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2013</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_PETROL</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>180406</td>\n",
       "      <td>156770</td>\n",
       "      <td>-11000.0</td>\n",
       "      <td>23636</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>526787</th>\n",
       "      <td>DE</td>\n",
       "      <td>MAKE_SKODA-MODELFAMILY_CITIGO</td>\n",
       "      <td>TRANSMISSION_SEMI_AUTOMATIC</td>\n",
       "      <td>TRANSMISSION_AUTOMATIC</td>\n",
       "      <td>2013</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>CARSTYLE_COMPACT</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>DRIVE_4X2</td>\n",
       "      <td>FUELTYPE_PETROL</td>\n",
       "      <td>...</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>180406</td>\n",
       "      <td>136227</td>\n",
       "      <td>-31000.0</td>\n",
       "      <td>44179</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>526788 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       seller_country                   model_family  \\\n",
       "0                  DE       MAKE_AUDI-MODELFAMILY_Q5   \n",
       "1                  DE       MAKE_AUDI-MODELFAMILY_Q5   \n",
       "2                  DE       MAKE_AUDI-MODELFAMILY_Q5   \n",
       "3                  DE       MAKE_AUDI-MODELFAMILY_Q5   \n",
       "4                  DE       MAKE_AUDI-MODELFAMILY_Q5   \n",
       "...               ...                            ...   \n",
       "526783             DE  MAKE_SKODA-MODELFAMILY_CITIGO   \n",
       "526784             DE  MAKE_SKODA-MODELFAMILY_CITIGO   \n",
       "526785             DE  MAKE_SKODA-MODELFAMILY_CITIGO   \n",
       "526786             DE  MAKE_SKODA-MODELFAMILY_CITIGO   \n",
       "526787             DE  MAKE_SKODA-MODELFAMILY_CITIGO   \n",
       "\n",
       "                     transmission_x          transmission_y  year  \\\n",
       "0       TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2017   \n",
       "1       TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2017   \n",
       "2       TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2017   \n",
       "3       TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2017   \n",
       "4       TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2017   \n",
       "...                             ...                     ...   ...   \n",
       "526783  TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2013   \n",
       "526784  TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2013   \n",
       "526785  TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2013   \n",
       "526786  TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2013   \n",
       "526787  TRANSMISSION_SEMI_AUTOMATIC  TRANSMISSION_AUTOMATIC  2013   \n",
       "\n",
       "                    car_style_x              car_style_y    drive_x  \\\n",
       "0       CARSTYLE_SEDANS_SALOONS  CARSTYLE_SEDANS_SALOONS  DRIVE_4X4   \n",
       "1       CARSTYLE_SEDANS_SALOONS  CARSTYLE_SEDANS_SALOONS  DRIVE_4X4   \n",
       "2       CARSTYLE_SEDANS_SALOONS  CARSTYLE_SEDANS_SALOONS  DRIVE_4X4   \n",
       "3       CARSTYLE_SEDANS_SALOONS  CARSTYLE_SEDANS_SALOONS  DRIVE_4X4   \n",
       "4       CARSTYLE_SEDANS_SALOONS  CARSTYLE_SEDANS_SALOONS  DRIVE_4X4   \n",
       "...                         ...                      ...        ...   \n",
       "526783         CARSTYLE_COMPACT         CARSTYLE_COMPACT  DRIVE_4X2   \n",
       "526784         CARSTYLE_COMPACT         CARSTYLE_COMPACT  DRIVE_4X2   \n",
       "526785         CARSTYLE_COMPACT         CARSTYLE_COMPACT  DRIVE_4X2   \n",
       "526786         CARSTYLE_COMPACT         CARSTYLE_COMPACT  DRIVE_4X2   \n",
       "526787         CARSTYLE_COMPACT         CARSTYLE_COMPACT  DRIVE_4X2   \n",
       "\n",
       "          drive_y      fuel_type_x  ... cubic_capacity_x  cubic_capacity_y  \\\n",
       "0       DRIVE_4X4  FUELTYPE_DIESEL  ...           1968.0            1968.0   \n",
       "1       DRIVE_4X4  FUELTYPE_DIESEL  ...           1968.0            1984.0   \n",
       "2       DRIVE_4X4  FUELTYPE_DIESEL  ...           1968.0            1968.0   \n",
       "3       DRIVE_4X4  FUELTYPE_DIESEL  ...           1968.0            1984.0   \n",
       "4       DRIVE_4X4  FUELTYPE_DIESEL  ...           1968.0            1968.0   \n",
       "...           ...              ...  ...              ...               ...   \n",
       "526783  DRIVE_4X2  FUELTYPE_PETROL  ...            999.0             999.0   \n",
       "526784  DRIVE_4X2  FUELTYPE_PETROL  ...            999.0             999.0   \n",
       "526785  DRIVE_4X2  FUELTYPE_PETROL  ...            999.0             999.0   \n",
       "526786  DRIVE_4X2  FUELTYPE_PETROL  ...            999.0             999.0   \n",
       "526787  DRIVE_4X2  FUELTYPE_PETROL  ...            999.0             999.0   \n",
       "\n",
       "        power_x  power_y  price_with_vat_czk_x  price_with_vat_czk_y  \\\n",
       "0         140.0    140.0                783264               1148511   \n",
       "1         140.0    185.0                783264               1062684   \n",
       "2         140.0    140.0                783264                964801   \n",
       "3         140.0    185.0                783264               1062684   \n",
       "4         140.0    140.0                783264               1149090   \n",
       "...         ...      ...                   ...                   ...   \n",
       "526783     55.0     44.0                180406                177040   \n",
       "526784     55.0     44.0                180406                156263   \n",
       "526785     55.0     55.0                180406                130110   \n",
       "526786     55.0     44.0                180406                156770   \n",
       "526787     55.0     55.0                180406                136227   \n",
       "\n",
       "        mileage_diff  price_diff  power_diff  cubic_diff  \n",
       "0            63486.0     -365247         0.0         0.0  \n",
       "1            20513.0     -279420       -45.0       -16.0  \n",
       "2            45551.0     -181537         0.0         0.0  \n",
       "3            20513.0     -279420       -45.0       -16.0  \n",
       "4            61727.0     -365826         0.0         0.0  \n",
       "...              ...         ...         ...         ...  \n",
       "526783      -28000.0        3366        11.0         0.0  \n",
       "526784      -51550.0       24143        11.0         0.0  \n",
       "526785      -37000.0       50296         0.0         0.0  \n",
       "526786      -11000.0       23636        11.0         0.0  \n",
       "526787      -31000.0       44179         0.0         0.0  \n",
       "\n",
       "[526788 rows x 27 columns]"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged=pd.merge(DE_SEMI,DE_AUTO,how='inner',on=['model_family','year','seller_country'])\n",
    "merged=merged[[\"seller_country\",\"model_family\",\"transmission_x\",\"transmission_y\",\"year\",'car_style_x','car_style_y','drive_x','drive_y','fuel_type_x','fuel_type_y','mileage_x','mileage_y','days_on_stock_x','days_on_stock_y','equipment_position_x','equipment_position_y','cubic_capacity_x','cubic_capacity_y','power_x','power_y','price_with_vat_czk_x','price_with_vat_czk_y']]\n",
    "merged['mileage_diff']=merged['mileage_x']-merged['mileage_y']\n",
    "merged['price_diff']=merged['price_with_vat_czk_x']-merged['price_with_vat_czk_y']\n",
    "merged['power_diff']=merged['power_x']-merged['power_y']\n",
    "merged['cubic_diff']=merged['cubic_capacity_x']-merged['cubic_capacity_y']\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>price_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>526788.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-10993.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>548489.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-364590569.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-49527.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-13099.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16196769.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         price_diff\n",
       "count      526788.0\n",
       "mean       -10993.8\n",
       "std        548489.9\n",
       "min    -364590569.0\n",
       "25%        -49527.0\n",
       "50%        -13099.0\n",
       "75%         23348.0\n",
       "max      16196769.0"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DE:AVERAGE DIFFERENCE IN PRICE BETWEEN SEMI_AUTOMATIC AND AUTOMATIC \n",
    "merged[['price_diff']].describe().apply(lambda s: s.apply(lambda x: format(round(x,1))))\n",
    "#In average, cars with automatic transmission are on 11k more expensive than with semiautomatic (German market)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "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></th>\n",
       "      <th>mileage_x</th>\n",
       "      <th>mileage_y</th>\n",
       "      <th>days_on_stock_x</th>\n",
       "      <th>days_on_stock_y</th>\n",
       "      <th>equipment_position_x</th>\n",
       "      <th>equipment_position_y</th>\n",
       "      <th>cubic_capacity_x</th>\n",
       "      <th>cubic_capacity_y</th>\n",
       "      <th>power_x</th>\n",
       "      <th>power_y</th>\n",
       "      <th>price_with_vat_czk_x</th>\n",
       "      <th>price_with_vat_czk_y</th>\n",
       "      <th>mileage_diff</th>\n",
       "      <th>price_diff</th>\n",
       "      <th>power_diff</th>\n",
       "      <th>cubic_diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_family</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">MAKE_ABARTH-MODELFAMILY_500C</th>\n",
       "      <th>2010</th>\n",
       "      <td>156500.00</td>\n",
       "      <td>126150.000000</td>\n",
       "      <td>62.00</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.460981</td>\n",
       "      <td>0.076087</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1368.000000</td>\n",
       "      <td>103.0</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>221649.0</td>\n",
       "      <td>233373.000000</td>\n",
       "      <td>30350.000000</td>\n",
       "      <td>-11724.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>32000.00</td>\n",
       "      <td>91629.777778</td>\n",
       "      <td>75.00</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.426966</td>\n",
       "      <td>0.509886</td>\n",
       "      <td>1368.0</td>\n",
       "      <td>1380.444444</td>\n",
       "      <td>118.0</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>325976.0</td>\n",
       "      <td>261057.888889</td>\n",
       "      <td>-59629.777778</td>\n",
       "      <td>64918.111111</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>-12.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>79650.00</td>\n",
       "      <td>77880.545455</td>\n",
       "      <td>64.00</td>\n",
       "      <td>94.727273</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.667722</td>\n",
       "      <td>1368.0</td>\n",
       "      <td>1368.000000</td>\n",
       "      <td>103.0</td>\n",
       "      <td>110.090909</td>\n",
       "      <td>262340.0</td>\n",
       "      <td>302795.000000</td>\n",
       "      <td>1769.454545</td>\n",
       "      <td>-40455.000000</td>\n",
       "      <td>-7.090909</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MAKE_ABARTH-MODELFAMILY_595C</th>\n",
       "      <th>2015</th>\n",
       "      <td>37500.00</td>\n",
       "      <td>52450.000000</td>\n",
       "      <td>251.00</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>0.792593</td>\n",
       "      <td>0.531481</td>\n",
       "      <td>1368.0</td>\n",
       "      <td>1368.000000</td>\n",
       "      <td>118.0</td>\n",
       "      <td>114.250000</td>\n",
       "      <td>394933.0</td>\n",
       "      <td>365945.000000</td>\n",
       "      <td>-14950.000000</td>\n",
       "      <td>28988.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>16.25</td>\n",
       "      <td>197.551724</td>\n",
       "      <td>123.75</td>\n",
       "      <td>72.068966</td>\n",
       "      <td>0.611473</td>\n",
       "      <td>0.764014</td>\n",
       "      <td>1368.0</td>\n",
       "      <td>1368.000000</td>\n",
       "      <td>123.0</td>\n",
       "      <td>127.655172</td>\n",
       "      <td>707932.0</td>\n",
       "      <td>731163.241379</td>\n",
       "      <td>-181.301724</td>\n",
       "      <td>-23231.241379</td>\n",
       "      <td>-4.655172</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAKE_ABARTH-MODELFAMILY_595_COMPETIZIONE</th>\n",
       "      <th>2014</th>\n",
       "      <td>18700.00</td>\n",
       "      <td>60800.000000</td>\n",
       "      <td>184.00</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>1368.0</td>\n",
       "      <td>1368.000000</td>\n",
       "      <td>118.0</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>442700.0</td>\n",
       "      <td>362360.000000</td>\n",
       "      <td>-42100.000000</td>\n",
       "      <td>80340.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MAKE_ABARTH-MODELFAMILY_595_TURISMO</th>\n",
       "      <th>2012</th>\n",
       "      <td>11996.00</td>\n",
       "      <td>77000.000000</td>\n",
       "      <td>15.00</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>1368.0</td>\n",
       "      <td>1368.000000</td>\n",
       "      <td>118.0</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>329477.0</td>\n",
       "      <td>289913.000000</td>\n",
       "      <td>-65004.000000</td>\n",
       "      <td>39564.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>44000.00</td>\n",
       "      <td>50993.000000</td>\n",
       "      <td>87.00</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>0.392157</td>\n",
       "      <td>1368.0</td>\n",
       "      <td>1368.000000</td>\n",
       "      <td>118.0</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>261482.0</td>\n",
       "      <td>359001.000000</td>\n",
       "      <td>-6993.000000</td>\n",
       "      <td>-97519.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAKE_ABARTH-MODELFAMILY_695</th>\n",
       "      <th>2011</th>\n",
       "      <td>48788.00</td>\n",
       "      <td>101550.000000</td>\n",
       "      <td>108.00</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>1368.0</td>\n",
       "      <td>1368.000000</td>\n",
       "      <td>158.0</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>616758.0</td>\n",
       "      <td>527982.000000</td>\n",
       "      <td>-52762.000000</td>\n",
       "      <td>88776.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAKE_ALFA_ROMEO-MODELFAMILY_GIULIA</th>\n",
       "      <th>2018</th>\n",
       "      <td>17500.00</td>\n",
       "      <td>54195.308824</td>\n",
       "      <td>4.00</td>\n",
       "      <td>86.161765</td>\n",
       "      <td>0.320051</td>\n",
       "      <td>0.647641</td>\n",
       "      <td>2143.0</td>\n",
       "      <td>2203.058824</td>\n",
       "      <td>110.0</td>\n",
       "      <td>196.073529</td>\n",
       "      <td>629987.0</td>\n",
       "      <td>850105.602941</td>\n",
       "      <td>-36695.308824</td>\n",
       "      <td>-220118.602941</td>\n",
       "      <td>-86.073529</td>\n",
       "      <td>-60.058824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               mileage_x      mileage_y  \\\n",
       "model_family                             year                             \n",
       "MAKE_ABARTH-MODELFAMILY_500C             2010  156500.00  126150.000000   \n",
       "                                         2011   32000.00   91629.777778   \n",
       "                                         2012   79650.00   77880.545455   \n",
       "MAKE_ABARTH-MODELFAMILY_595C             2015   37500.00   52450.000000   \n",
       "                                         2020      16.25     197.551724   \n",
       "MAKE_ABARTH-MODELFAMILY_595_COMPETIZIONE 2014   18700.00   60800.000000   \n",
       "MAKE_ABARTH-MODELFAMILY_595_TURISMO      2012   11996.00   77000.000000   \n",
       "                                         2014   44000.00   50993.000000   \n",
       "MAKE_ABARTH-MODELFAMILY_695              2011   48788.00  101550.000000   \n",
       "MAKE_ALFA_ROMEO-MODELFAMILY_GIULIA       2018   17500.00   54195.308824   \n",
       "\n",
       "                                               days_on_stock_x  \\\n",
       "model_family                             year                    \n",
       "MAKE_ABARTH-MODELFAMILY_500C             2010            62.00   \n",
       "                                         2011            75.00   \n",
       "                                         2012            64.00   \n",
       "MAKE_ABARTH-MODELFAMILY_595C             2015           251.00   \n",
       "                                         2020           123.75   \n",
       "MAKE_ABARTH-MODELFAMILY_595_COMPETIZIONE 2014           184.00   \n",
       "MAKE_ABARTH-MODELFAMILY_595_TURISMO      2012            15.00   \n",
       "                                         2014            87.00   \n",
       "MAKE_ABARTH-MODELFAMILY_695              2011           108.00   \n",
       "MAKE_ALFA_ROMEO-MODELFAMILY_GIULIA       2018             4.00   \n",
       "\n",
       "                                               days_on_stock_y  \\\n",
       "model_family                             year                    \n",
       "MAKE_ABARTH-MODELFAMILY_500C             2010        36.000000   \n",
       "                                         2011        57.000000   \n",
       "                                         2012        94.727273   \n",
       "MAKE_ABARTH-MODELFAMILY_595C             2015        53.000000   \n",
       "                                         2020        72.068966   \n",
       "MAKE_ABARTH-MODELFAMILY_595_COMPETIZIONE 2014       180.000000   \n",
       "MAKE_ABARTH-MODELFAMILY_595_TURISMO      2012        23.000000   \n",
       "                                         2014        28.000000   \n",
       "MAKE_ABARTH-MODELFAMILY_695              2011        93.000000   \n",
       "MAKE_ALFA_ROMEO-MODELFAMILY_GIULIA       2018        86.161765   \n",
       "\n",
       "                                               equipment_position_x  \\\n",
       "model_family                             year                         \n",
       "MAKE_ABARTH-MODELFAMILY_500C             2010              0.460981   \n",
       "                                         2011              0.426966   \n",
       "                                         2012              0.533333   \n",
       "MAKE_ABARTH-MODELFAMILY_595C             2015              0.792593   \n",
       "                                         2020              0.611473   \n",
       "MAKE_ABARTH-MODELFAMILY_595_COMPETIZIONE 2014              0.680000   \n",
       "MAKE_ABARTH-MODELFAMILY_595_TURISMO      2012              0.733333   \n",
       "                                         2014              0.019608   \n",
       "MAKE_ABARTH-MODELFAMILY_695              2011              0.142857   \n",
       "MAKE_ALFA_ROMEO-MODELFAMILY_GIULIA       2018              0.320051   \n",
       "\n",
       "                                               equipment_position_y  \\\n",
       "model_family                             year                         \n",
       "MAKE_ABARTH-MODELFAMILY_500C             2010              0.076087   \n",
       "                                         2011              0.509886   \n",
       "                                         2012              0.667722   \n",
       "MAKE_ABARTH-MODELFAMILY_595C             2015              0.531481   \n",
       "                                         2020              0.764014   \n",
       "MAKE_ABARTH-MODELFAMILY_595_COMPETIZIONE 2014              0.040000   \n",
       "MAKE_ABARTH-MODELFAMILY_595_TURISMO      2012              0.066667   \n",
       "                                         2014              0.392157   \n",
       "MAKE_ABARTH-MODELFAMILY_695              2011              0.428571   \n",
       "MAKE_ALFA_ROMEO-MODELFAMILY_GIULIA       2018              0.647641   \n",
       "\n",
       "                                               cubic_capacity_x  \\\n",
       "model_family                             year                     \n",
       "MAKE_ABARTH-MODELFAMILY_500C             2010            1400.0   \n",
       "                                         2011            1368.0   \n",
       "                                         2012            1368.0   \n",
       "MAKE_ABARTH-MODELFAMILY_595C             2015            1368.0   \n",
       "                                         2020            1368.0   \n",
       "MAKE_ABARTH-MODELFAMILY_595_COMPETIZIONE 2014            1368.0   \n",
       "MAKE_ABARTH-MODELFAMILY_595_TURISMO      2012            1368.0   \n",
       "                                         2014            1368.0   \n",
       "MAKE_ABARTH-MODELFAMILY_695              2011            1368.0   \n",
       "MAKE_ALFA_ROMEO-MODELFAMILY_GIULIA       2018            2143.0   \n",
       "\n",
       "                                               cubic_capacity_y  power_x  \\\n",
       "model_family                             year                              \n",
       "MAKE_ABARTH-MODELFAMILY_500C             2010       1368.000000    103.0   \n",
       "                                         2011       1380.444444    118.0   \n",
       "                                         2012       1368.000000    103.0   \n",
       "MAKE_ABARTH-MODELFAMILY_595C             2015       1368.000000    118.0   \n",
       "                                         2020       1368.000000    123.0   \n",
       "MAKE_ABARTH-MODELFAMILY_595_COMPETIZIONE 2014       1368.000000    118.0   \n",
       "MAKE_ABARTH-MODELFAMILY_595_TURISMO      2012       1368.000000    118.0   \n",
       "                                         2014       1368.000000    118.0   \n",
       "MAKE_ABARTH-MODELFAMILY_695              2011       1368.000000    158.0   \n",
       "MAKE_ALFA_ROMEO-MODELFAMILY_GIULIA       2018       2203.058824    110.0   \n",
       "\n",
       "                                                  power_y  \\\n",
       "model_family                             year               \n",
       "MAKE_ABARTH-MODELFAMILY_500C             2010  103.000000   \n",
       "                                         2011  108.000000   \n",
       "                                         2012  110.090909   \n",
       "MAKE_ABARTH-MODELFAMILY_595C             2015  114.250000   \n",
       "                                         2020  127.655172   \n",
       "MAKE_ABARTH-MODELFAMILY_595_COMPETIZIONE 2014  118.000000   \n",
       "MAKE_ABARTH-MODELFAMILY_595_TURISMO      2012  118.000000   \n",
       "                                         2014  118.000000   \n",
       "MAKE_ABARTH-MODELFAMILY_695              2011  132.000000   \n",
       "MAKE_ALFA_ROMEO-MODELFAMILY_GIULIA       2018  196.073529   \n",
       "\n",
       "                                               price_with_vat_czk_x  \\\n",
       "model_family                             year                         \n",
       "MAKE_ABARTH-MODELFAMILY_500C             2010              221649.0   \n",
       "                                         2011              325976.0   \n",
       "                                         2012              262340.0   \n",
       "MAKE_ABARTH-MODELFAMILY_595C             2015              394933.0   \n",
       "                                         2020              707932.0   \n",
       "MAKE_ABARTH-MODELFAMILY_595_COMPETIZIONE 2014              442700.0   \n",
       "MAKE_ABARTH-MODELFAMILY_595_TURISMO      2012              329477.0   \n",
       "                                         2014              261482.0   \n",
       "MAKE_ABARTH-MODELFAMILY_695              2011              616758.0   \n",
       "MAKE_ALFA_ROMEO-MODELFAMILY_GIULIA       2018              629987.0   \n",
       "\n",
       "                                               price_with_vat_czk_y  \\\n",
       "model_family                             year                         \n",
       "MAKE_ABARTH-MODELFAMILY_500C             2010         233373.000000   \n",
       "                                         2011         261057.888889   \n",
       "                                         2012         302795.000000   \n",
       "MAKE_ABARTH-MODELFAMILY_595C             2015         365945.000000   \n",
       "                                         2020         731163.241379   \n",
       "MAKE_ABARTH-MODELFAMILY_595_COMPETIZIONE 2014         362360.000000   \n",
       "MAKE_ABARTH-MODELFAMILY_595_TURISMO      2012         289913.000000   \n",
       "                                         2014         359001.000000   \n",
       "MAKE_ABARTH-MODELFAMILY_695              2011         527982.000000   \n",
       "MAKE_ALFA_ROMEO-MODELFAMILY_GIULIA       2018         850105.602941   \n",
       "\n",
       "                                               mileage_diff     price_diff  \\\n",
       "model_family                             year                                \n",
       "MAKE_ABARTH-MODELFAMILY_500C             2010  30350.000000  -11724.000000   \n",
       "                                         2011 -59629.777778   64918.111111   \n",
       "                                         2012   1769.454545  -40455.000000   \n",
       "MAKE_ABARTH-MODELFAMILY_595C             2015 -14950.000000   28988.000000   \n",
       "                                         2020   -181.301724  -23231.241379   \n",
       "MAKE_ABARTH-MODELFAMILY_595_COMPETIZIONE 2014 -42100.000000   80340.000000   \n",
       "MAKE_ABARTH-MODELFAMILY_595_TURISMO      2012 -65004.000000   39564.000000   \n",
       "                                         2014  -6993.000000  -97519.000000   \n",
       "MAKE_ABARTH-MODELFAMILY_695              2011 -52762.000000   88776.000000   \n",
       "MAKE_ALFA_ROMEO-MODELFAMILY_GIULIA       2018 -36695.308824 -220118.602941   \n",
       "\n",
       "                                               power_diff  cubic_diff  \n",
       "model_family                             year                          \n",
       "MAKE_ABARTH-MODELFAMILY_500C             2010    0.000000   32.000000  \n",
       "                                         2011   10.000000  -12.444444  \n",
       "                                         2012   -7.090909    0.000000  \n",
       "MAKE_ABARTH-MODELFAMILY_595C             2015    3.750000    0.000000  \n",
       "                                         2020   -4.655172    0.000000  \n",
       "MAKE_ABARTH-MODELFAMILY_595_COMPETIZIONE 2014    0.000000    0.000000  \n",
       "MAKE_ABARTH-MODELFAMILY_595_TURISMO      2012    0.000000    0.000000  \n",
       "                                         2014    0.000000    0.000000  \n",
       "MAKE_ABARTH-MODELFAMILY_695              2011   26.000000    0.000000  \n",
       "MAKE_ALFA_ROMEO-MODELFAMILY_GIULIA       2018  -86.073529  -60.058824  "
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DE: Each model, by each year\n",
    "merged.groupby(['model_family','year']).mean('price_diff').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Graphical representations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c3100699a0>"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#CZ price comparison\n",
    "xy = merged.groupby(['model_family']).agg(np.random.choice)\n",
    "\n",
    "x = xy['price_with_vat_czk_x'].values#semi_automatic\n",
    "y = xy['price_with_vat_czk_y'].values#automatic\n",
    "z=range(1,27)\n",
    "\n",
    "plt.plot(z, x,'mo-', label ='Price of car model(semi_automatic)' )\n",
    "plt.plot(z, y,'go-', label ='Price of car model(automatic)' )\n",
    "plt.title(\"CZ\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DE price comparison\n",
    "xy = merged.groupby(['model_family']).agg(np.random.choice)\n",
    "\n",
    "x = xy['price_with_vat_czk_x'].values#semi_automatic\n",
    "y = xy['price_with_vat_czk_y'].values#automatic\n",
    "z=range(1,209)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c30966aaf0>"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.plot(z, x,'mo-', label ='Price of car model(semi_automatic)' )\n",
    "plt.plot(z, y,'go-', label ='Price of car model(automatic)' )\n",
    "plt.title(\"DE\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = merged.groupby(['model_family',]).agg(np.random.choice)['price_diff'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=np.zeros((1,191))\n",
    "i=1\n",
    "for i in range(100):\n",
    "    a = merged.groupby(['model_family',]).agg(np.random.choice)['price_diff'].values\n",
    "    b=np.concatenate((b,np.reshape(a,(1,191))))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "      <th>100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>82710.0</td>\n",
       "      <td>-1872.0</td>\n",
       "      <td>82710.0</td>\n",
       "      <td>-234740.0</td>\n",
       "      <td>11149.0</td>\n",
       "      <td>24519.0</td>\n",
       "      <td>24519.0</td>\n",
       "      <td>-234740.0</td>\n",
       "      <td>45604.0</td>\n",
       "      <td>24519.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11149.0</td>\n",
       "      <td>-5968.0</td>\n",
       "      <td>-5968.0</td>\n",
       "      <td>45604.0</td>\n",
       "      <td>-234740.0</td>\n",
       "      <td>-1872.0</td>\n",
       "      <td>24519.0</td>\n",
       "      <td>11149.0</td>\n",
       "      <td>45604.0</td>\n",
       "      <td>-1872.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>103943.0</td>\n",
       "      <td>278921.0</td>\n",
       "      <td>105634.0</td>\n",
       "      <td>241875.0</td>\n",
       "      <td>13770.0</td>\n",
       "      <td>-44928.0</td>\n",
       "      <td>84361.0</td>\n",
       "      <td>100675.0</td>\n",
       "      <td>-721.0</td>\n",
       "      <td>97179.0</td>\n",
       "      <td>...</td>\n",
       "      <td>116427.0</td>\n",
       "      <td>-67788.0</td>\n",
       "      <td>-91060.0</td>\n",
       "      <td>139415.0</td>\n",
       "      <td>27529.0</td>\n",
       "      <td>101209.0</td>\n",
       "      <td>-61897.0</td>\n",
       "      <td>4647.0</td>\n",
       "      <td>111723.0</td>\n",
       "      <td>-26578.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>136744.0</td>\n",
       "      <td>177751.0</td>\n",
       "      <td>177751.0</td>\n",
       "      <td>132632.0</td>\n",
       "      <td>177751.0</td>\n",
       "      <td>177751.0</td>\n",
       "      <td>132632.0</td>\n",
       "      <td>132632.0</td>\n",
       "      <td>177751.0</td>\n",
       "      <td>177751.0</td>\n",
       "      <td>...</td>\n",
       "      <td>177751.0</td>\n",
       "      <td>177751.0</td>\n",
       "      <td>92894.0</td>\n",
       "      <td>92894.0</td>\n",
       "      <td>132632.0</td>\n",
       "      <td>136744.0</td>\n",
       "      <td>132632.0</td>\n",
       "      <td>177751.0</td>\n",
       "      <td>132632.0</td>\n",
       "      <td>132632.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28396.0</td>\n",
       "      <td>-45976.0</td>\n",
       "      <td>-34944.0</td>\n",
       "      <td>-34944.0</td>\n",
       "      <td>-89779.0</td>\n",
       "      <td>-51296.0</td>\n",
       "      <td>28396.0</td>\n",
       "      <td>-34944.0</td>\n",
       "      <td>-45976.0</td>\n",
       "      <td>-45976.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-6979.0</td>\n",
       "      <td>-45976.0</td>\n",
       "      <td>28396.0</td>\n",
       "      <td>-45976.0</td>\n",
       "      <td>28396.0</td>\n",
       "      <td>-89779.0</td>\n",
       "      <td>-89779.0</td>\n",
       "      <td>28396.0</td>\n",
       "      <td>-51296.0</td>\n",
       "      <td>-89779.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14000.0</td>\n",
       "      <td>6100.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>9010.0</td>\n",
       "      <td>6001.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>-9000.0</td>\n",
       "      <td>9010.0</td>\n",
       "      <td>6010.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-9000.0</td>\n",
       "      <td>-9000.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>6100.0</td>\n",
       "      <td>-6000.0</td>\n",
       "      <td>6100.0</td>\n",
       "      <td>6001.0</td>\n",
       "      <td>-6000.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>6001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>217873.0</td>\n",
       "      <td>283072.0</td>\n",
       "      <td>39574.0</td>\n",
       "      <td>63963.0</td>\n",
       "      <td>59202.0</td>\n",
       "      <td>236522.0</td>\n",
       "      <td>174082.0</td>\n",
       "      <td>23489.0</td>\n",
       "      <td>232860.0</td>\n",
       "      <td>141916.0</td>\n",
       "      <td>...</td>\n",
       "      <td>181415.0</td>\n",
       "      <td>50128.0</td>\n",
       "      <td>215652.0</td>\n",
       "      <td>-3553.0</td>\n",
       "      <td>253630.0</td>\n",
       "      <td>125885.0</td>\n",
       "      <td>93866.0</td>\n",
       "      <td>108691.0</td>\n",
       "      <td>-4823.0</td>\n",
       "      <td>184671.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>-85726.0</td>\n",
       "      <td>-26371.0</td>\n",
       "      <td>-28189.0</td>\n",
       "      <td>54152.0</td>\n",
       "      <td>-50282.0</td>\n",
       "      <td>23385.0</td>\n",
       "      <td>-15729.0</td>\n",
       "      <td>-8364.0</td>\n",
       "      <td>70954.0</td>\n",
       "      <td>162832.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-19209.0</td>\n",
       "      <td>25675.0</td>\n",
       "      <td>86757.0</td>\n",
       "      <td>70954.0</td>\n",
       "      <td>54955.0</td>\n",
       "      <td>27430.0</td>\n",
       "      <td>103819.0</td>\n",
       "      <td>101830.0</td>\n",
       "      <td>-19498.0</td>\n",
       "      <td>16144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>-25050.0</td>\n",
       "      <td>112919.0</td>\n",
       "      <td>50202.0</td>\n",
       "      <td>155322.0</td>\n",
       "      <td>52235.0</td>\n",
       "      <td>-43789.0</td>\n",
       "      <td>34406.0</td>\n",
       "      <td>163676.0</td>\n",
       "      <td>15301.0</td>\n",
       "      <td>77394.0</td>\n",
       "      <td>...</td>\n",
       "      <td>235593.0</td>\n",
       "      <td>117011.0</td>\n",
       "      <td>30381.0</td>\n",
       "      <td>77394.0</td>\n",
       "      <td>235593.0</td>\n",
       "      <td>152550.0</td>\n",
       "      <td>9247.0</td>\n",
       "      <td>40778.0</td>\n",
       "      <td>104083.0</td>\n",
       "      <td>155797.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>62205.0</td>\n",
       "      <td>77065.0</td>\n",
       "      <td>50237.0</td>\n",
       "      <td>60076.0</td>\n",
       "      <td>-10762.0</td>\n",
       "      <td>18328.0</td>\n",
       "      <td>71869.0</td>\n",
       "      <td>-41010.0</td>\n",
       "      <td>43236.0</td>\n",
       "      <td>52919.0</td>\n",
       "      <td>...</td>\n",
       "      <td>57853.0</td>\n",
       "      <td>30921.0</td>\n",
       "      <td>-34461.0</td>\n",
       "      <td>105053.0</td>\n",
       "      <td>21850.0</td>\n",
       "      <td>9805.0</td>\n",
       "      <td>53589.0</td>\n",
       "      <td>-8251.0</td>\n",
       "      <td>53013.0</td>\n",
       "      <td>66969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>1512.0</td>\n",
       "      <td>20377.0</td>\n",
       "      <td>44025.0</td>\n",
       "      <td>11333.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>-35406.0</td>\n",
       "      <td>22724.0</td>\n",
       "      <td>1512.0</td>\n",
       "      <td>6296.0</td>\n",
       "      <td>160051.0</td>\n",
       "      <td>...</td>\n",
       "      <td>79490.0</td>\n",
       "      <td>-60919.0</td>\n",
       "      <td>-110189.0</td>\n",
       "      <td>269450.0</td>\n",
       "      <td>20377.0</td>\n",
       "      <td>-73229.0</td>\n",
       "      <td>25763.0</td>\n",
       "      <td>-19761.0</td>\n",
       "      <td>17598.0</td>\n",
       "      <td>211244.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>191 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          1         2         3         4         5         6         7    \\\n",
       "0     82710.0   -1872.0   82710.0 -234740.0   11149.0   24519.0   24519.0   \n",
       "1    103943.0  278921.0  105634.0  241875.0   13770.0  -44928.0   84361.0   \n",
       "2    136744.0  177751.0  177751.0  132632.0  177751.0  177751.0  132632.0   \n",
       "3     28396.0  -45976.0  -34944.0  -34944.0  -89779.0  -51296.0   28396.0   \n",
       "4     14000.0    6100.0   14000.0    9010.0    6001.0   11000.0   14000.0   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "186  217873.0  283072.0   39574.0   63963.0   59202.0  236522.0  174082.0   \n",
       "187  -85726.0  -26371.0  -28189.0   54152.0  -50282.0   23385.0  -15729.0   \n",
       "188  -25050.0  112919.0   50202.0  155322.0   52235.0  -43789.0   34406.0   \n",
       "189   62205.0   77065.0   50237.0   60076.0  -10762.0   18328.0   71869.0   \n",
       "190    1512.0   20377.0   44025.0   11333.0   36000.0  -35406.0   22724.0   \n",
       "\n",
       "          8         9         10   ...       91        92        93   \\\n",
       "0   -234740.0   45604.0   24519.0  ...   11149.0   -5968.0   -5968.0   \n",
       "1    100675.0    -721.0   97179.0  ...  116427.0  -67788.0  -91060.0   \n",
       "2    132632.0  177751.0  177751.0  ...  177751.0  177751.0   92894.0   \n",
       "3    -34944.0  -45976.0  -45976.0  ...   -6979.0  -45976.0   28396.0   \n",
       "4     -9000.0    9010.0    6010.0  ...   -9000.0   -9000.0   11000.0   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "186   23489.0  232860.0  141916.0  ...  181415.0   50128.0  215652.0   \n",
       "187   -8364.0   70954.0  162832.0  ...  -19209.0   25675.0   86757.0   \n",
       "188  163676.0   15301.0   77394.0  ...  235593.0  117011.0   30381.0   \n",
       "189  -41010.0   43236.0   52919.0  ...   57853.0   30921.0  -34461.0   \n",
       "190    1512.0    6296.0  160051.0  ...   79490.0  -60919.0 -110189.0   \n",
       "\n",
       "          94        95        96        97        98        99        100  \n",
       "0     45604.0 -234740.0   -1872.0   24519.0   11149.0   45604.0   -1872.0  \n",
       "1    139415.0   27529.0  101209.0  -61897.0    4647.0  111723.0  -26578.0  \n",
       "2     92894.0  132632.0  136744.0  132632.0  177751.0  132632.0  132632.0  \n",
       "3    -45976.0   28396.0  -89779.0  -89779.0   28396.0  -51296.0  -89779.0  \n",
       "4      6100.0   -6000.0    6100.0    6001.0   -6000.0   11000.0    6001.0  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "186   -3553.0  253630.0  125885.0   93866.0  108691.0   -4823.0  184671.0  \n",
       "187   70954.0   54955.0   27430.0  103819.0  101830.0  -19498.0   16144.0  \n",
       "188   77394.0  235593.0  152550.0    9247.0   40778.0  104083.0  155797.0  \n",
       "189  105053.0   21850.0    9805.0   53589.0   -8251.0   53013.0   66969.0  \n",
       "190  269450.0   20377.0  -73229.0   25763.0  -19761.0   17598.0  211244.0  \n",
       "\n",
       "[191 rows x 100 columns]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(b.T).iloc[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#From this approach it is noticed that some patterns between two auto groups does exist.Nonetheless, the decision of replacement\n",
    "#is rather subjective, because it depends on circumstances on which the decisionmaker this replacement would apply.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#------------------------- UPDATE FINISH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    397911\n",
       "True     147158\n",
       "dtype: int64"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Then using the table above, each category will be compared for similarity and output will be percentage of similarities.\n",
    "#Based on this comparison, we can say if percentage of similarities between of two transmission characteristics is high,\n",
    "#then it is reasonable to conclude that two transmission categories could be set as one category.\n",
    "#For example:\n",
    "((merged['car_style_x']==merged['car_style_y'])&(merged['fuel_type_x']==merged['fuel_type_y'])&   \n",
    "(merged['drive_x']==merged['drive_y'])&(merged['power_x']==merged['power_y'])).value_counts()\n",
    "#However, since this method is manual and distributions of \"AUTO\" and \"SEMI\" are quite different, \n",
    "#using this method is not efficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#One of the efficient ways to determine the similarities is by using scatter plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "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": [
    "#scatter plot of car prices by semiautomatic and automatic transmission\n",
    "#green dots: automatic transmission\n",
    "#red dots: semiautomatic transmission\n",
    "ax1 = merged.plot(kind='scatter', x='year', y='price_with_vat_czk_y', color='g')#automatic\n",
    "ax2 = merged.plot(kind='scatter', x='year', y='price_with_vat_czk_x', color='r', ax=ax1)  #semi-automatic\n",
    "plt.yscale(\"log\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "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": [
    "#scatter plot of car engine power\n",
    "ax1 = merged.plot(kind='scatter', x='year', y='power_y', color='g')#automatic\n",
    "ax2 = merged.plot(kind='scatter', x='year', y='power_x', color='r', ax=ax1)  #semi-automatic\n",
    "plt.yscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "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": [
    "ax1 = merged.plot(kind='scatter', x='power_y', y='price_with_vat_czk_y', color='g')#automatic\n",
    "ax2 = merged.plot(kind='scatter', x='power_x', y='price_with_vat_czk_x', color='r', ax=ax1)  #semi-automatic\n",
    "plt.yscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "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": [
    "ax1 = merged.plot(kind='scatter', x='power_y', y='cubic_capacity_y', color='g')#automatic\n",
    "ax2 = merged.plot(kind='scatter', x='power_x', y='cubic_capacity_x', color='r', ax=ax1)  #semi-automatic\n",
    "plt.yscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The scatter plots above shows that such car features (points) like price by year, power by price, cubic capasity by power\n",
    "# of semiautomatic transmission car are merging(fitting) with corresponding points of automatic transmission cars.\n",
    "# This indicates the similarity of two transmissions according to given dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2. Cluster analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The cluster approach can be also employed as another method. The purpose of cluster analysis is to figure out, whether \"AUTO\" \n",
    "# and \"SEMI\" are in the same cluster or in different clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clustering by categorical variables: model_family, power, fuel, etc.\n",
    "#The first step is to \"equalize\" the sample size by setting the same size of each \"AUTO\", \"SEMI\", \"MAN\"\n",
    "#Since \"SEMI\" has the lowest sample size, same sample sizes for \"AUTO\" and \"MAN\" will be generated randomly.\n",
    "\n",
    "AUTO=AUTO.sample(n=len(SEMI), random_state=77)\n",
    "MAN=MAN.sample(n=len(SEMI), random_state=11)\n",
    "dataset=pd.concat([AUTO,MAN,SEMI]).sample(frac=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4494"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [],
   "source": [
    "#coding categories (0,1)\n",
    "cat_code=pd.get_dummies(dataset[['model_family','car_style','fuel_type','transmission','drive']],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "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": [
    "from scipy.cluster.hierarchy import dendrogram, linkage, fcluster\n",
    "link = linkage(cat_code,'ward','euclidean')\n",
    "dn=dendrogram(link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#On the dendrogram it seems that there are 2 (or 3) clusters of categorical data.\n",
    "#Similarly we can build dandrogram of continuos variables and then check if the number of clusters will be the same as for cat.data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dendogram of hierarchical approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAD7CAYAAACYLnSTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAV/ElEQVR4nO3dXYxcZ33H8d/fa+eF2A5xsn5JwDHQ4JDi4KBtIA1toTTUDReBlojSCiKXygg1EkjcUC7aVKpULgqoFxXFNDG+AApJQImKFWpFtBFtSNkEx05iO27dJIpZ25tsXuw42N71vxfzHO/x8XmfmZ3Zx9+PNJqZc57zPP9zzjP/Oa8z5u4CAMxvCwYdAACgeyRzAIgAyRwAIkAyB4AIkMwBIAIkcwCIwMK5bOyyyy7zNWvWzGWTADDvPfrooy+4+2hZmTlN5mvWrNH4+PhcNgkA856ZPVtVhsMsABABkjkARIBkDgARIJkDQARI5gAQAZI5AESAZA4AEZjT68zn2nceeU737Tgw6DBwDrhl/RX6k/esHnQYOIdFvWV+344Demri1UGHgcg9NfEqGw0YuKi3zCXpmlVL9b3P3DDoMBCxj3/j4UGHAMS9ZQ4A5wqSOQBEgGQOABEgmQNABEjmABABkjkARKAymZvZBWb232b2uJk9aWZ/E4YvM7PtZrYvPF/S/3ABAHnqbJkfl/S77v4uSeslbTCz90r6oqQH3f0qSQ+G9wCAAahM5t5xNLxdFB4u6RZJW8PwrZI+0o8AAQDVah0zN7MRM9sh6bCk7e7+iKQV7j4hSeF5ed+iBACUqpXM3X3G3ddLepOk683snXUbMLNNZjZuZuOTk5MtwwQAlGl0NYu7vyzp3yVtkHTIzFZJUng+XDDNZncfc/ex0dHR7qIFAOSqczXLqJm9Mby+UNLvSdoj6X5Jt4Vit0m6r08xAgAq1PnVxFWStprZiDrJ//vu/q9m9rCk75vZpyU9J+nWPsYJAChRmczdfaek63KGvyjpg/0ICgDQDHeAAkAESOYAEAGSOQBEgGQOABEgmQNABEjmABABkjkARIBkDgARIJkDQARI5gAQAZI5AESAZA4AESCZA0AESOYAEAGSOQBEgGQOABEgmQNABEjmABABkjkARIBkDgARIJkDQARI5gAQAZI5AESAZA4AEahM5mb2ZjP7iZntNrMnzexzYfgdZnbAzHaEx839DxcAkGdhjTLTkr7g7o+Z2RJJj5rZ9jDua+7+9/0LDwBQR2Uyd/cJSRPh9REz2y3pin4HBgCor9ExczNbI+k6SY+EQbeb2U4zu8vMLimYZpOZjZvZ+OTkZHfRAgBy1U7mZrZY0r2SPu/ur0r6uqS3SVqvzpb7V/Kmc/fN7j7m7mOjo6PdRwwAOEutZG5mi9RJ5N929x9IkrsfcvcZdz8l6ZuSru9fmACAMnWuZjFJd0ra7e5fTQ1flSr2UUlP9D48AEAdda5muVHSJyXtMrMdYdiXJH3CzNZLcknPSPpMH+IDANRQ52qWn0qynFHbeh8OAKAN7gAFgAiQzAEgAiRzAIgAyRwAIkAyB4AIkMwBIAIkcwCIAMkcACJAMgeACJDMASACJHMAiADJHAAiQDIHgAiQzAEgAiRzAIgAyRwAIkAyB4AIkMwBIAIkcwCIAMkcACJAMgeACJDMASACJHMAiEBlMjezN5vZT8xst5k9aWafC8OXmdl2M9sXni/pf7gAgDx1tsynJX3B3d8h6b2S/sLMrpH0RUkPuvtVkh4M7wEAA1CZzN19wt0fC6+PSNot6QpJt0jaGoptlfSRPsUIAKjQ6Ji5ma2RdJ2kRyStcPcJqZPwJS3veXQAgFpqJ3MzWyzpXkmfd/dXG0y3yczGzWx8cnKyTYwAgAq1krmZLVInkX/b3X8QBh8ys1Vh/CpJh/OmdffN7j7m7mOjo6O9iBkAkFHnahaTdKek3e7+1dSo+yXdFl7fJum+3ocHAKhjYY0yN0r6pKRdZrYjDPuSpC9L+r6ZfVrSc5Ju7UuEAIBKlcnc3X8qyQpGf7C34QAA2uAOUACIAMkcACJAMgeACJDMASACJHMAiADJHAAiQDIHgAiQzAEgAiRzAIgAyRwAIkAyB4AIkMwBIAIkcwCIAMkcACJAMgeACJDMASACJHMAiADJHAAiQDIHgAiQzAEgAiRzAIgAyRwAIkAyB4AIkMwBIAKVydzM7jKzw2b2RGrYHWZ2wMx2hMfN/Q0TAFCmzpb5tyRtyBn+NXdfHx7behsWAKCJymTu7g9JmpqDWAAALXVzzPx2M9sZDsNc0rOIAACNtU3mX5f0NknrJU1I+kpRQTPbZGbjZjY+OTnZsjkAQJlWydzdD7n7jLufkvRNSdeXlN3s7mPuPjY6Oto2TgBAiVbJ3MxWpd5+VNITRWUBAP23sKqAmX1X0vslXWZmz0v6a0nvN7P1klzSM5I+078QAQBVKpO5u38iZ/CdfYgFANASd4ACQARI5gAQAZI5AESAZA4AESCZA0AESOYAEAGSOQBEgGQOABEgmQNABEjmABABkjkARIBkDgARIJkDQARI5gAQAZI5AESAZA4AESCZA0AEKv9pCChz99N3a9v+bYMOY6D2Tv2OJGnjA5sHHMng3fzWm3Xr228ddBjnJJI5urJt/zbtndqrtcvWDjqUgbnuuv8YdAhDYe/UXkkimQ8IyRxdW7tsrbZs2DLoMDBgGx/YOOgQzmkcMweACJDMASACJHMAiEBlMjezu8zssJk9kRq2zMy2m9m+8HxJf8MEAJSps2X+LUkbMsO+KOlBd79K0oPhPQBgQCqTubs/JGkqM/gWSVvD662SPtLbsAAATbQ9Zr7C3SckKTwv711IAICm+n4C1Mw2mdm4mY1PTk72uzkAOCe1TeaHzGyVJIXnw0UF3X2zu4+5+9jo6GjL5gAAZdom8/sl3RZe3ybpvt6EAwBoo86lid+V9LCktWb2vJl9WtKXJd1kZvsk3RTeAwAGpPK3Wdz9EwWjPtjjWAAALXEHKABEgGQOABEYnp/AHd8i7bqnt3UevKXzvOVve1fnuo9JY/zUJ4DhMjzJfNc90sFd0sp1Pavye6t7fJHNwV2dZ5I5gCEzPMlc6iTyjT8adBTFtnx40BEAQC6OmQNABEjmABABkjkARIBkDgARIJkDQARI5gAQAZI5AESAZA4AESCZA0AESOYAEAGSOQBEgGQOABEgmQNABEjmABABkjkARIBkDgARIJkDQARI5gAQAZI5AESgq/8ANbNnJB2RNCNp2t3HehEUAKCZXvyh8wfc/YUe1AMAaKkXyXx+Gd8i7bqn3bQHd3aet3y4ffvrPiaNbWw/PQDk6PaYuUv6NzN71Mw29SKgvtt1j3RwV7tpV17bebR1cFf7LxIAKNHtlvmN7v5LM1suabuZ7XH3h9IFQpLfJEmrV6/usrkeWblO2vijuW+3my16ACjRVTJ391+G58Nm9kNJ10t6KFNms6TNkjQ2NubdtAfE6O6n79a2/dsGHUbX9kztkSRtfGD+H0a8+a0369a33zroMBppfZjFzC4ysyXJa0kfkvRErwIDzhXb9m/T3qm9gw6ja1cvu1pXL7t60GF0be/U3nn55drNlvkKST80s6Se77j7Az2JCjjHrF22Vls2bBl0GND83bNonczdfb+kd/UwFgBAS9wBCgARIJkDQARI5gAQAZI5AESAZA4AESCZA0AESOYAEIFz71cTe63JrzA2/dXFOf6FxTa3lXdzC/d8vGUaGFZsmXerya8wNvnVxQH8wmKb28rb3sI9X2+ZBoYVW+a90I9fYRzQLyzO1W3l8/WWaWBYsWUOABEgmQNABEjmABCBwRwzz7sCpOxKD/43EwBKDWbLPO8KkKIrPfjfTACoNLirWepeAcL/ZgJAJS5NRM/Vufmozs1G3FSEbrT9b9W2N8INur+SzAep7O7RqrtFh/g8QnLz0dplawvLVN1olNy81I8Px7D9gfKw/RHyoJNSr9Tph3na3gQn9ae/1kUyH6Tk3MHKdWePK7tTNDnfMKTJXOr+5qN+Jra2H/J+GaY/QR6GpNRL59JNcMOVzOte5TLEW6WNtbl7lPMIXeMPlPMNQ1JCO8OVzPO2VLNbqHW3SosOYRQdvojpCwLAOWe4krlUvaVad6u06BBG0eWPUr1knv2SiH3PAcC8MHzJvJf6cflj9kui7Z5DEz3+Aik6AVh2Iq6fJ8Xy4imKJZaTc231++TtXJyMnW/rsFdXZ0n9nfe5TebHXugkoCQZjW9pvjWcTWSD2Aou+5Lox/HsHn+BFJ0ALDoRV+ekWLrDZzt2VQfOiycvlthOzrXR75O3/T4ZO5dXKfVqg6AXV2dJ/e+/XSVzM9sg6R8kjUj6Z3f/cukEx14KSena2Ts7xzbOJut0ok4n6XQySyeyvCSWV5d0dtIv+4LIK1+lah7qtFvWZvYL5Iytde/U1eALruoEYPqD4XLtmdpTmpzTHT7dset24DonJJtsLZZtTVVtRQ37luN8Pnk7l1cp9XKDoMkyL+p72c9RWi/6XOtkbmYjkv5R0k2Snpf0czO7392fKpxo+nVJSzvJ5shEJwGdToIhyR+ZkJ79aec5SfRJ0pNmE1UyPKnjrMR/7Wwbx1+draPsC+LIhPTaZH752l84qXk4uLMzPB3z9r/q1H/+UumiUWnJqk75qf/NL59uM3c+11XPq5pvOTdJznc/fffpOtP1JcOTDpxtJ4mpLJ5s3CdPndQN37nhdExFH4Kyran0/Ey+PqkXX3/x9PujJ49qz9SeMz6Mw57ch8WgbhbL9qOifiadmVCL2qmzhV8WY50933S/y/a5tv3N3L3xRJJkZjdIusPdfz+8/0tJcve/K5pm7PIRH9+0OFQwIvlMTsWp4UVlzl/aSVppV76v8/zcw51pzl8qnXjtzOmTYdKZw21EWn3D7LRXvq+TjJNx5110ZnvZGLPTJq/T7UqzdaTjL5rfdAzZdvPiy85rsPG9f6jxQ+On3y+wBTrlp84qJ0mLFy3W0ZNHzyiXfn7Dwjfo6MmjWmCzP+mTDJOksRVjGj80frrsseljZ7RVNlzSWXGl48l7nx6eyI7Pm9/0vGSlY8yLqUg2tuzya6PutG3bWLxo8VnroqgPPP6px7Vu67rT4/PK9TK+9DRl6z0bf3rapF8UxVmnjTqSON69/N1nfNbS9SZtja0Y02OHH8vt63nzkszDz/70Z4+6+1hZHN0k849J2uDufx7ef1LSe9z99ky5TZI2hbdrJTX7XzIAwJXuPlpWoJtj5pYz7KxvBnffLGlzF+0AACp08xO4z0t6c+r9myT9srtwAABtdJPMfy7pKjN7i5mdJ+mPJd3fm7AAAE20Pszi7tNmdrukH6tzaeJd7v5kzyIDANTW+gQoAGB48IfOABABkjkARIBkDgAR6PsPbZnZ5ZI+K+njkiYkvV2d69HfKOlldb5QRiTtl7Rc0q8k/aek35J0QtI+SVdJekbSCknjktZJukzSKUkn1bkkcq2kpyX9Zhi+X9LxUMcVoc1Foa2Fkl6TtETSdOr1y5KOSbpQ0uuSXgxxLlHnuvpFkl4J018Uyv6PpItDO9dIekmdyzbPl7RS0i9C/ddJekHSr0s6GuI+L9R3MsR6StLlYTktCeWuCXH9X4jlNUnvlPSG0P5LYZlNSPq10O7BEM9bwjgPdV0q6ZIwfia0MRLeXxjieCXEuF+dG7x2SvpQWDevhWW5RNIBSavCPO0LbSfDj0m6VtLhEPeR0M6KUG4mxHOxpKVhOS+QNCnpUFhuR8O6uUjSlZIuCDE9FYatCe2dkPRfod5TIca3hrottJXM/5Ewf5eHsslNGK+EMtNheb4U2jgg6bfV6V8Xh+WxLMRyVLP9YHeo8zlJN4b5eVNYR8dDHL+S9HhYBxeH5bE6tHk8PL8Q6nwlrMc3htdXhPmcCnXuC8trRVifF0h6Vp1+cmlYj2tDHAfC8jwSxs2E8aNhvlydfrg4xGTh/auhjVfCsrogxDAdyh4L87EylEnKLwwxWhg+GZbZTKj3wjBvx0O8y9T5rJ0I5V8Ocd8QYl4alvul6vTLk2H46+r0r29I+qMQyztC+xbaHZd0fWh7oWZzzl51+ksS85Iwj1Jn3V8Y2k3m87Ew/wfV6Vsrw7p6LdSZtLkqxHRpWFcXa/ZzPB3auygs8xNh3GJJT6rTFxTqOhIeFtr5lLtPqULfT4CaGWdYAaA7/+Tuny0rwGEWABh+z1YVmIvfM79DnUMsV6izm+Xq7M4tUGeXaVF4PROeXbO7PKfCsEXhfbLbPBNeJ1v9M6FMsjt1PAxPDqskdc+kyie7lwkL042EdkdSsYyk6pFmvwSnw3QLdeZPGZxK1TMdxi9IzU9Sr6uzy3kqPCyUT6Y3zf5sgofHwkx5Sy2vdPtJvTOpsuelyifzkNQ7nVpmyTQLU+NHUsNHQvmkbDLvSZ3Jek3Hk4xP5ttS5dPPJ1N1LMyMT+Yn2a1N4j8e5i1pZ1FqnEKsyXJPYs7+HEWyjJL1tiBVbkGqjmSZJ+/TsaaXk8Kw6dQ0x9XZXU/3LU/Vk16XM5r9nCRlk7iS5ZDMc7YPpPtF0l+SZZ4uk57P7IZdsg5PhDpGUstJml3/Sdlk2SzIlEvamk7VoUx7J1PLJDn8mP58J8s6eSTzkyzz9PCk7eMhvqQPeWpc0qfTfX1BangSc1LnSc0uu5HM62T5Jes927eyn7O0RTpz/SefQUs9Tkj6F0n3qsJcHGZJOhsAoJ0/c/fSH1Sfi8MsJHIA6M4fVBWYi2S+X7O7D9Ls7kb2kR6viuFF45PHqcz4ursfRW0Wtd+kzmz5ounLlklV+9l5znvfVjd15c170frPTlM0v3WnyVseRfEUabPu69ZXFVPZMioqWxVLkz5W9bkriqdqeNnnv2odVX0GqtrIqzM9LJ0/yvJVm75UFENZXYcl3VlV0VwcM3+Lzj5WV8Yyz0XDi8ZXjavTdlmdRe3XrbPN8LI4qspVtVdXk3VYNm3d6Zus7zrTlLXZtE82ib9OfW1ialK2ybJLv67b7+rGU2d9NJm26WegTltWMLzJ9E3U6QPL1bnk9cdlFXE1CwAMr2Rr/fyqgnORzNNXO9TZZetmN67O+LxdtCa7YdlxVfF3c3ijTT1Vu7hVhyLq1F+2y9vkkEUvlkvZ+ybrOjt9neVYtgya1JVXtqrfVdXVRNXybDtdN+s4W1/ZsqkaXlZvneF1y5at96Z9LnmfXNXzXFVAc5HM05fy5D3n7QK22T2qO75oF7LpLpcVjM8Oa7LbltdG03qqdlPL1kHd+vOmKVunRXH0YrmUvW+yrrPT11mOZcugSV15Zav6XVVdTfTjsGTyvldxlS2bJofi2gyvW7ZsvTftc9n5/VBlQHNwaeIv1LmF/4JkkGa/bZLGk+s5k+Hp54Rnpktfv6nUuHQb6etys2Vzw82pL2mrqN3sdEm5EZ357Zq0mb4GPTt9dr6L5i2vTF49ReWz8eZ9CLIdI2+avPWWvv69LJbsusjOf7Ks0+tugc6Oq2jes/OQrjNvCy5vHopizpu2aHxVfFX1Z8eXtdekTil/vaSl77co6u/Z91XLIdt+UV1FdVaVz66/opjKllFZny2b96L32XktqzP7OZM6Rzd+w913qgS/Zw4AEeAEKABEgGQOABEgmQNABEjmABABkjkAROD/Ac9ChUMOuaEFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.cluster.hierarchy as sch\n",
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "cont_code=dataset[['year','cubic_capacity', 'power','mileage','equipment_position','days_on_stock','price_with_vat_czk']]\n",
    "\n",
    "data_scaled=normalize(cont_code)\n",
    "data_scaled=pd.DataFrame(data_scaled, columns=cont_code.columns)\n",
    "dendogram= sch.dendrogram(sch.linkage(data_scaled, method=\"ward\")) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clusters (2 clusters will be chosen as proposed by dendrogram)\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "hc=AgglomerativeClustering(n_clusters=2, affinity='euclidean', linkage='ward')\n",
    "y_hc=hc.fit_predict(data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRANSMISSION_AUTOMATIC         1363\n",
       "TRANSMISSION_MANUAL            1064\n",
       "TRANSMISSION_SEMI_AUTOMATIC     812\n",
       "Name: transmission, dtype: int64"
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First cluster\n",
    "dataset['clusters']=y_hc\n",
    "dataset[dataset['clusters']==0]['transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRANSMISSION_SEMI_AUTOMATIC    686\n",
       "TRANSMISSION_MANUAL            434\n",
       "TRANSMISSION_AUTOMATIC         135\n",
       "Name: transmission, dtype: int64"
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Second cluster\n",
    "dataset[dataset['clusters']==1]['transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#K means clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "km=KMeans(n_clusters=2)\n",
    "y_km=km.fit_predict(data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRANSMISSION_AUTOMATIC         1334\n",
       "TRANSMISSION_MANUAL            1005\n",
       "TRANSMISSION_SEMI_AUTOMATIC     768\n",
       "Name: transmission, dtype: int64"
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['clusters']=y_km\n",
    "dataset[dataset['clusters']==0]['transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRANSMISSION_SEMI_AUTOMATIC    730\n",
       "TRANSMISSION_MANUAL            493\n",
       "TRANSMISSION_AUTOMATIC         164\n",
       "Name: transmission, dtype: int64"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset['clusters']==1]['transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['model_family', 'car_style', 'fuel_type', 'transmission', 'drive',\n",
       "       'year', 'cubic_capacity', 'power', 'mileage', 'equipment_position',\n",
       "       'days_on_stock', 'price_with_vat_czk', 'clusters'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Clustering on mixed data(cat.and cont.)\n",
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [],
   "source": [
    "mark_array=dataset.values\n",
    "mark_array[:, 5]=mark_array[:, 5].astype(float)\n",
    "mark_array[:, 10]=mark_array[:, 10].astype(float)\n",
    "mark_array[:, 11]=mark_array[:, 11].astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kproto = KPrototypes(n_clusters=2, max_iter=1000,verbose=2)\n",
    "clusters=kproto.fit_predict(mark_array, categorical=[0,1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRANSMISSION_MANUAL            1498\n",
       "TRANSMISSION_AUTOMATIC         1493\n",
       "TRANSMISSION_SEMI_AUTOMATIC    1438\n",
       "Name: transmission, dtype: int64"
      ]
     },
     "execution_count": 496,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['clusters']=clusters\n",
    "dataset[dataset['clusters']==0]['transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRANSMISSION_SEMI_AUTOMATIC    60\n",
       "TRANSMISSION_AUTOMATIC          5\n",
       "Name: transmission, dtype: int64"
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['clusters']=clusters\n",
    "dataset[dataset['clusters']==1]['transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Conclusion: Scatter plots and cluster analysis results provide information that based on available dataset technical car features\n",
    "#and price do not depend on transmission type. Cars with different transmissions may have similar feautures and can be included in one cluster(or group)\n"
   ]
  }
 ],
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