1_Kick_Off_Lecture.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1-vAOcKer2v1"
   },
   "source": [
    "![Checkars](https://raw.githubusercontent.com/juliandnl/redi_ss20/master/checkars/grafik.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "dapAaqR5UUui"
   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "id": "o-S6RzsRK1c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly==5.1.0 in /Applications/anaconda3/lib/python3.8/site-packages (5.1.0)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in /Applications/anaconda3/lib/python3.8/site-packages (from plotly==5.1.0) (7.0.0)\n",
      "Requirement already satisfied: six in /Applications/anaconda3/lib/python3.8/site-packages (from plotly==5.1.0) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "# https://plotly.com/python/pandas-backend/\n",
    "!pip install plotly==5.1.0\n",
    "#pip install plotly==5.1.3\n",
    "pd.options.plotting.backend = \"plotly\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "0NXmNVB37rkT"
   },
   "outputs": [],
   "source": [
    "# read in the data\n",
    "cars = pd.read_csv(\"https://raw.githubusercontent.com/juliandnl/redi_ss20/master/cars.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BeECOGFfCRFB"
   },
   "source": [
    "## 1. What kind of information do we have?\n",
    "\n",
    "We can split this question up into some sub questions:\n",
    "- how many cars are in the dataset?\n",
    "- what information do we have per car?\n",
    "- how do the information look like? (numbers, words?)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0R-YyDkHCxuI",
    "outputId": "b9085429-9ec4-46dc-d3de-84abd185c4b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rows and columns\n",
    "cars.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "YGKS9U8xE6WQ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 9 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Make      10000 non-null  object \n",
      " 1   Model     10000 non-null  object \n",
      " 2   Year      10000 non-null  int64  \n",
      " 3   Variant   10000 non-null  object \n",
      " 4   Kms       10000 non-null  int64  \n",
      " 5   Price     10000 non-null  int64  \n",
      " 6   Doors     10000 non-null  float64\n",
      " 7   Kind      10000 non-null  object \n",
      " 8   Location  10000 non-null  object \n",
      "dtypes: float64(1), int64(3), object(5)\n",
      "memory usage: 703.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Overview of the dataset\n",
    "cars.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "jwfYTSl38HfC"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>Variant</th>\n",
       "      <th>Kms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Kind</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Vento</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.5 Luxury 170cv</td>\n",
       "      <td>99950</td>\n",
       "      <td>360000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Sedán</td>\n",
       "      <td>Córdoba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Ranger</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.3 Cd Xl Plus 4x2</td>\n",
       "      <td>140000</td>\n",
       "      <td>320000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Pick-Up</td>\n",
       "      <td>Entre Ríos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Fox</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.6 Trendline</td>\n",
       "      <td>132000</td>\n",
       "      <td>209980</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Bs.as. G.b.a. Sur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Ranger</td>\n",
       "      <td>2017</td>\n",
       "      <td>3.2 Cd Xls Tdci 200cv Automática</td>\n",
       "      <td>13000</td>\n",
       "      <td>798000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Pick-Up</td>\n",
       "      <td>Neuquén</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Gol</td>\n",
       "      <td>2013</td>\n",
       "      <td>1.4 Power 83cv 3 p</td>\n",
       "      <td>107000</td>\n",
       "      <td>146000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Córdoba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Amarok</td>\n",
       "      <td>2014</td>\n",
       "      <td>2.0 Cd Tdi 180cv 4x4 Highline C34</td>\n",
       "      <td>115000</td>\n",
       "      <td>790000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Pick-Up</td>\n",
       "      <td>Buenos Aires Interior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Ka</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.0 Fly Viral 63cv</td>\n",
       "      <td>95243</td>\n",
       "      <td>142000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Bs.as. G.b.a. Sur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Ka</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.6 Fly Viral 95cv</td>\n",
       "      <td>110000</td>\n",
       "      <td>148000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Buenos Aires Interior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Focus III</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.6 S</td>\n",
       "      <td>17500</td>\n",
       "      <td>367000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Buenos Aires Interior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Fiesta Kinetic Design</td>\n",
       "      <td>2014</td>\n",
       "      <td>1.6 Sedan Titanium Powershift 120cv</td>\n",
       "      <td>43000</td>\n",
       "      <td>287000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Sedán</td>\n",
       "      <td>Bs.as. G.b.a. Norte</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Make                  Model  Year  \\\n",
       "0  Volkswagen                  Vento  2012   \n",
       "1        Ford                 Ranger  2012   \n",
       "2  Volkswagen                    Fox  2011   \n",
       "3        Ford                 Ranger  2017   \n",
       "4  Volkswagen                    Gol  2013   \n",
       "5  Volkswagen                 Amarok  2014   \n",
       "6        Ford                     Ka  2012   \n",
       "7        Ford                     Ka  2012   \n",
       "8        Ford              Focus III  2017   \n",
       "9        Ford  Fiesta Kinetic Design  2014   \n",
       "\n",
       "                               Variant     Kms   Price  Doors       Kind  \\\n",
       "0                     2.5 Luxury 170cv   99950  360000    4.0      Sedán   \n",
       "1                   2.3 Cd Xl Plus 4x2  140000  320000    2.0    Pick-Up   \n",
       "2                        1.6 Trendline  132000  209980    5.0  Hatchback   \n",
       "3     3.2 Cd Xls Tdci 200cv Automática   13000  798000    4.0    Pick-Up   \n",
       "4                   1.4 Power 83cv 3 p  107000  146000    3.0  Hatchback   \n",
       "5    2.0 Cd Tdi 180cv 4x4 Highline C34  115000  790000    4.0    Pick-Up   \n",
       "6                   1.0 Fly Viral 63cv   95243  142000    3.0  Hatchback   \n",
       "7                   1.6 Fly Viral 95cv  110000  148000    3.0  Hatchback   \n",
       "8                                1.6 S   17500  367000    5.0  Hatchback   \n",
       "9  1.6 Sedan Titanium Powershift 120cv   43000  287000    4.0      Sedán   \n",
       "\n",
       "                Location  \n",
       "0                Córdoba  \n",
       "1             Entre Ríos  \n",
       "2      Bs.as. G.b.a. Sur  \n",
       "3                Neuquén  \n",
       "4                Córdoba  \n",
       "5  Buenos Aires Interior  \n",
       "6      Bs.as. G.b.a. Sur  \n",
       "7  Buenos Aires Interior  \n",
       "8  Buenos Aires Interior  \n",
       "9    Bs.as. G.b.a. Norte  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First rows\n",
    "cars.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "ZG7yknjsFTbY"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Kms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Doors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.293800</td>\n",
       "      <td>74483.338400</td>\n",
       "      <td>3.495455e+05</td>\n",
       "      <td>4.295800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.816043</td>\n",
       "      <td>35150.279998</td>\n",
       "      <td>1.773396e+05</td>\n",
       "      <td>0.889821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2011.000000</td>\n",
       "      <td>5094.000000</td>\n",
       "      <td>6.300000e+03</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>49500.000000</td>\n",
       "      <td>2.290000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2013.000000</td>\n",
       "      <td>73000.000000</td>\n",
       "      <td>3.000000e+05</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>97000.000000</td>\n",
       "      <td>4.300000e+05</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>218000.000000</td>\n",
       "      <td>2.800000e+06</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Year            Kms         Price         Doors\n",
       "count  10000.000000   10000.000000  1.000000e+04  10000.000000\n",
       "mean    2013.293800   74483.338400  3.495455e+05      4.295800\n",
       "std        1.816043   35150.279998  1.773396e+05      0.889821\n",
       "min     2011.000000    5094.000000  6.300000e+03      2.000000\n",
       "25%     2012.000000   49500.000000  2.290000e+05      4.000000\n",
       "50%     2013.000000   73000.000000  3.000000e+05      5.000000\n",
       "75%     2015.000000   97000.000000  4.300000e+05      5.000000\n",
       "max     2018.000000  218000.000000  2.800000e+06      5.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the numerical columns\n",
    "cars.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>Variant</th>\n",
       "      <th>Kms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Doors</th>\n",
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       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Vento</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.5 Luxury 170cv</td>\n",
       "      <td>99950</td>\n",
       "      <td>360000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Sedán</td>\n",
       "      <td>Córdoba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Ranger</td>\n",
       "      <td>2012</td>\n",
       "      <td>2.3 Cd Xl Plus 4x2</td>\n",
       "      <td>140000</td>\n",
       "      <td>320000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Pick-Up</td>\n",
       "      <td>Entre Ríos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Fox</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.6 Trendline</td>\n",
       "      <td>132000</td>\n",
       "      <td>209980</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Bs.as. G.b.a. Sur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Ranger</td>\n",
       "      <td>2017</td>\n",
       "      <td>3.2 Cd Xls Tdci 200cv Automática</td>\n",
       "      <td>13000</td>\n",
       "      <td>798000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Pick-Up</td>\n",
       "      <td>Neuquén</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Gol</td>\n",
       "      <td>2013</td>\n",
       "      <td>1.4 Power 83cv 3 p</td>\n",
       "      <td>107000</td>\n",
       "      <td>146000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Córdoba</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Focus III</td>\n",
       "      <td>2016</td>\n",
       "      <td>2.0 Se</td>\n",
       "      <td>67000</td>\n",
       "      <td>399000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Bs.as. G.b.a. Oeste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>Bora</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.9 Trendline I 100cv</td>\n",
       "      <td>120000</td>\n",
       "      <td>240000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Sedán</td>\n",
       "      <td>Buenos Aires Interior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Fiesta Kinetic Design</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.6 Design 120cv Titanium</td>\n",
       "      <td>89000</td>\n",
       "      <td>250000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Tucumán</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Fiesta Kinetic Design</td>\n",
       "      <td>2013</td>\n",
       "      <td>1.6 Design 120cv Titanium</td>\n",
       "      <td>76000</td>\n",
       "      <td>295000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Buenos Aires Interior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>Ford</td>\n",
       "      <td>Focus III</td>\n",
       "      <td>2014</td>\n",
       "      <td>2.0 Titanium At6</td>\n",
       "      <td>32000</td>\n",
       "      <td>380000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Bs.as. G.b.a. Sur</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Make                  Model  Year  \\\n",
       "0     Volkswagen                  Vento  2012   \n",
       "1           Ford                 Ranger  2012   \n",
       "2     Volkswagen                    Fox  2011   \n",
       "3           Ford                 Ranger  2017   \n",
       "4     Volkswagen                    Gol  2013   \n",
       "...          ...                    ...   ...   \n",
       "9995        Ford              Focus III  2016   \n",
       "9996  Volkswagen                   Bora  2012   \n",
       "9997        Ford  Fiesta Kinetic Design  2012   \n",
       "9998        Ford  Fiesta Kinetic Design  2013   \n",
       "9999        Ford              Focus III  2014   \n",
       "\n",
       "                               Variant     Kms   Price  Doors       Kind  \\\n",
       "0                     2.5 Luxury 170cv   99950  360000    4.0      Sedán   \n",
       "1                   2.3 Cd Xl Plus 4x2  140000  320000    2.0    Pick-Up   \n",
       "2                        1.6 Trendline  132000  209980    5.0  Hatchback   \n",
       "3     3.2 Cd Xls Tdci 200cv Automática   13000  798000    4.0    Pick-Up   \n",
       "4                   1.4 Power 83cv 3 p  107000  146000    3.0  Hatchback   \n",
       "...                                ...     ...     ...    ...        ...   \n",
       "9995                            2.0 Se   67000  399000    5.0  Hatchback   \n",
       "9996             1.9 Trendline I 100cv  120000  240000    4.0      Sedán   \n",
       "9997         1.6 Design 120cv Titanium   89000  250000    5.0  Hatchback   \n",
       "9998         1.6 Design 120cv Titanium   76000  295000    5.0  Hatchback   \n",
       "9999                  2.0 Titanium At6   32000  380000    5.0  Hatchback   \n",
       "\n",
       "                   Location  \n",
       "0                   Córdoba  \n",
       "1                Entre Ríos  \n",
       "2         Bs.as. G.b.a. Sur  \n",
       "3                   Neuquén  \n",
       "4                   Córdoba  \n",
       "...                     ...  \n",
       "9995    Bs.as. G.b.a. Oeste  \n",
       "9996  Buenos Aires Interior  \n",
       "9997                Tucumán  \n",
       "9998  Buenos Aires Interior  \n",
       "9999      Bs.as. G.b.a. Sur  \n",
       "\n",
       "[10000 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Make', 'Model', 'Year', 'Variant', 'Kms', 'Price', 'Doors', 'Kind',\n",
       "       'Location'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make        0\n",
       "Model       0\n",
       "Year        0\n",
       "Variant     0\n",
       "Kms         0\n",
       "Price       0\n",
       "Doors       0\n",
       "Kind        0\n",
       "Location    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZcD-AnFDFlTy"
   },
   "source": [
    "## Conclusion:\n",
    "- we have 10.000 cars in the dataset\n",
    "- we have 8 columns\n",
    "- we have the following information for each car: Make, Model, year, Variant, Kms, Price, Doors, Kind, Location\n",
    "- Kms, Year, Price and Doors are numerical, the rest is text."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GNxP36gGIeRV"
   },
   "source": [
    "## 2. What kind of cars are sold?\n",
    "\n",
    "How can we split this question up? \n",
    "\n",
    "What kind of:\n",
    "- Makes \n",
    "- Kinds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Volkswagen\n",
       "1             Ford\n",
       "2       Volkswagen\n",
       "3             Ford\n",
       "4       Volkswagen\n",
       "           ...    \n",
       "9995          Ford\n",
       "9996    Volkswagen\n",
       "9997          Ford\n",
       "9998          Ford\n",
       "9999          Ford\n",
       "Name: Make, Length: 10000, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars[\"Make\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1ew-x24zUnQy",
    "outputId": "45a261a3-139c-4a53-a371-bd90d9df9f66"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Chrysler', 'Ford', 'Honda', 'Mercedes Benz', 'Volkswagen'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select column from pandas can be done with a . or with [\"\"]\n",
    "set(cars[\"Make\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Volkswagen',\n",
       " 'Ford',\n",
       " 'Volkswagen',\n",
       " 'Ford',\n",
       " 'Volkswagen',\n",
       " 'Volkswagen',\n",
       " 'Ford',\n",
       " 'Ford',\n",
       " 'Ford',\n",
       " 'Ford']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(cars[\"Make\"])[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Ford', 'Mercedes Benz', 'Chrysler', 'Honda', 'Volkswagen']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(set(cars[\"Make\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9xkm-39HAHuj",
    "outputId": "abef57a5-42e9-4b5f-ce32-9c51c449f845"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Volkswagen\n",
       "1             Ford\n",
       "2       Volkswagen\n",
       "3             Ford\n",
       "4       Volkswagen\n",
       "           ...    \n",
       "9995          Ford\n",
       "9996    Volkswagen\n",
       "9997          Ford\n",
       "9998          Ford\n",
       "9999          Ford\n",
       "Name: Make, Length: 10000, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZXhGujCyzyM7",
    "outputId": "922ba555-74c1-4c79-fb54-3fab31cccac4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Volkswagen', 'Ford', 'Mercedes Benz', 'Honda', 'Chrysler'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Make.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Volkswagen', 'Ford', 'Mercedes Benz', 'Honda', 'Chrysler'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['Make'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "waQ146Ue0Ku_",
    "outputId": "bd39ccb2-bb5e-42a8-b090-c597d98569fd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Volkswagen       5173\n",
       "Ford             4025\n",
       "Honda             522\n",
       "Mercedes Benz     256\n",
       "Chrysler           24\n",
       "Name: Make, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Make.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "VHgOfFiBVKBq",
    "outputId": "7154b352-dcdc-4d81-d6d7-5b31f6862e51"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "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": [
    "makes = cars.Make.value_counts()\n",
    "makes.plot.bar()\n",
    "# here we assign the result of cars.Make.value_counts() to a new variable which \n",
    "# we called makes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(makes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vmhbUbGcV75Y",
    "outputId": "3a671428-692a-4f32-c0b0-be34d0b1e26a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hatchback      4056\n",
       "Sedán          2173\n",
       "Pick-Up        1547\n",
       "SUV            1305\n",
       "Monovolumen     705\n",
       "Coupé            90\n",
       "Rural            59\n",
       "Furgón           53\n",
       "Cabriolet        11\n",
       "Minivan           1\n",
       "Name: Kind, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Kind.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "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": [
    "cars.Kind.value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y8fzO1o_WR3_"
   },
   "source": [
    "## Conclusion:\n",
    "- There are 5 different car brands in the dataset\n",
    "- Most cars which are sold are Volkswagen, followed by Ford. \n",
    "- The most common car kind sold is a hatchback, followed by Sedán and Pick-up. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pbupavq70LVX"
   },
   "source": [
    "# 3. Where are the cars sold?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Make', 'Model', 'Year', 'Variant', 'Kms', 'Price', 'Doors', 'Kind',\n",
       "       'Location'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RhZvGw_p0See",
    "outputId": "9300a2a4-d7ae-449f-df4a-a41da5d51f78"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Córdoba', 'Entre Ríos', 'Bs.as. G.b.a. Sur', 'Neuquén',\n",
       "       'Buenos Aires Interior', 'Bs.as. G.b.a. Norte', 'Capital Federal',\n",
       "       'Bs.as. G.b.a. Oeste', 'Tierra Del Fuego', 'Santa Fe', 'Mendoza',\n",
       "       'Misiones', 'La Pampa', 'Chaco', 'Salta', 'Bs.as. Costa Atlántica',\n",
       "       'Santa Cruz', 'Corrientes', 'Tucumán', 'San Juan', 'Catamarca',\n",
       "       'San Luis', 'Río Negro', 'Chubut', 'Jujuy', 'Formosa',\n",
       "       'Santiago Del Estero', 'La Rioja'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Location.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Bs.as. Costa Atlántica',\n",
       " 'Bs.as. G.b.a. Norte',\n",
       " 'Bs.as. G.b.a. Oeste',\n",
       " 'Bs.as. G.b.a. Sur',\n",
       " 'Buenos Aires Interior',\n",
       " 'Capital Federal',\n",
       " 'Catamarca',\n",
       " 'Chaco',\n",
       " 'Chubut',\n",
       " 'Corrientes',\n",
       " 'Córdoba',\n",
       " 'Entre Ríos',\n",
       " 'Formosa',\n",
       " 'Jujuy',\n",
       " 'La Pampa',\n",
       " 'La Rioja',\n",
       " 'Mendoza',\n",
       " 'Misiones',\n",
       " 'Neuquén',\n",
       " 'Río Negro',\n",
       " 'Salta',\n",
       " 'San Juan',\n",
       " 'San Luis',\n",
       " 'Santa Cruz',\n",
       " 'Santa Fe',\n",
       " 'Santiago Del Estero',\n",
       " 'Tierra Del Fuego',\n",
       " 'Tucumán'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(cars.Location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iahj70GfoxL4",
    "outputId": "f9c5d3c8-4e0a-4ec8-ec58-92b37d0ee8ff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Location.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(cars.Location))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mg7t6A6nDNJy",
    "outputId": "b6b1b1cf-d538-4be0-b426-e49d99f39d58"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Volkswagen', 'Ford', 'Mercedes Benz', 'Honda', 'Chrysler'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Make.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AbtktSxUDVmN",
    "outputId": "1a988395-d12b-4520-d02c-a0597812a8e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Make.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "AVeA3g7uVeov"
   },
   "outputs": [],
   "source": [
    "locations = cars.Location.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wy61TFwPo5sM",
    "outputId": "b5895136-1a65-4add-dc51-0175186d90da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Capital Federal           2013\n",
       "Bs.as. G.b.a. Sur         1748\n",
       "Bs.as. G.b.a. Norte       1726\n",
       "Bs.as. G.b.a. Oeste       1339\n",
       "Córdoba                    785\n",
       "Buenos Aires Interior      768\n",
       "Santa Fe                   359\n",
       "Bs.as. Costa Atlántica     268\n",
       "Mendoza                    254\n",
       "Entre Ríos                 116\n",
       "Neuquén                     96\n",
       "Tucumán                     84\n",
       "Río Negro                   67\n",
       "Corrientes                  58\n",
       "Chaco                       53\n",
       "Salta                       51\n",
       "Chubut                      36\n",
       "Misiones                    35\n",
       "San Luis                    24\n",
       "Santa Cruz                  20\n",
       "La Pampa                    20\n",
       "Formosa                     15\n",
       "Tierra Del Fuego            13\n",
       "Santiago Del Estero         12\n",
       "San Juan                    11\n",
       "La Rioja                    10\n",
       "Jujuy                       10\n",
       "Catamarca                    9\n",
       "Name: Location, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "pjjjBSo-UFZu",
    "outputId": "f6e5f896-ce49-4d89-b338-ee8d70aaf4d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "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": [
    "locations.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XFR9HPhmotiB"
   },
   "source": [
    "## Conclusion:\n",
    "- There are 28 different regions.\n",
    "- The biggest region is Capital Federal.\n",
    "- There are a few regions where lots of cars are.\n",
    "- There are lots of regions where a few cars are."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oQb7I4z31tJp"
   },
   "source": [
    "# BONUS: 4. How expensive are the cars?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Make', 'Model', 'Year', 'Variant', 'Kms', 'Price', 'Doors', 'Kind',\n",
       "       'Location'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kTTFLPdG5Q8L",
    "outputId": "b0a79d65-e299-41d0-9e24-baca42d5f610"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       360000\n",
       "1       320000\n",
       "2       209980\n",
       "3       798000\n",
       "4       146000\n",
       "         ...  \n",
       "9995    399000\n",
       "9996    240000\n",
       "9997    250000\n",
       "9998    295000\n",
       "9999    380000\n",
       "Name: Price, Length: 10000, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GkG2E8y-ECZe",
    "outputId": "f828c832-ae81-4b94-a9c5-0c371d72f17f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2800000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Price.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RmnKhVh3EUwY",
    "outputId": "141a9e1a-59b5-4f7e-c185-1b1de2b8f6ea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6300"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Price.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(cars.Price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([360000, 320000, 209980, ..., 250000, 295000, 380000])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.array(cars.Price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "id": "Rt7wWAhDEsA_",
    "outputId": "b2626f1c-6d8c-473b-9b8d-96256098fba9"
   },
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>Variant</th>\n",
       "      <th>Kms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Kind</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8007</th>\n",
       "      <td>Mercedes Benz</td>\n",
       "      <td>Clase GLC</td>\n",
       "      <td>2017</td>\n",
       "      <td>2.0 Glc250 300 4matic Atomático</td>\n",
       "      <td>18000</td>\n",
       "      <td>2800000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Bs.as. G.b.a. Sur</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Make      Model  Year                          Variant    Kms  \\\n",
       "8007  Mercedes Benz  Clase GLC  2017  2.0 Glc250 300 4matic Atomático  18000   \n",
       "\n",
       "        Price  Doors Kind           Location  \n",
       "8007  2800000    5.0  SUV  Bs.as. G.b.a. Sur  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars[ cars['Price']==cars['Price'].max() ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8007    Mercedes Benz\n",
       "Name: Make, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Make[ cars.Price == cars.Price.max() ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XCazoZrdEsRi",
    "outputId": "30f3d1fa-937a-4229-f728-1570e40ec815"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8007    SUV\n",
       "Name: Kind, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.Kind[ cars.Price == cars.Price.max() ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "id": "nMpsvZWeHZx7",
    "outputId": "ff16ccb4-451d-430f-a612-efc911a737c8"
   },
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>Variant</th>\n",
       "      <th>Kms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Kind</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2965</th>\n",
       "      <td>Chrysler</td>\n",
       "      <td>PT Cruiser</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.4 Touring</td>\n",
       "      <td>71000</td>\n",
       "      <td>6300</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Hatchback</td>\n",
       "      <td>Santa Fe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8007</th>\n",
       "      <td>Mercedes Benz</td>\n",
       "      <td>Clase GLC</td>\n",
       "      <td>2017</td>\n",
       "      <td>2.0 Glc250 300 4matic Atomático</td>\n",
       "      <td>18000</td>\n",
       "      <td>2800000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Bs.as. G.b.a. Sur</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Make       Model  Year                          Variant    Kms  \\\n",
       "2965       Chrysler  PT Cruiser  2011                      2.4 Touring  71000   \n",
       "8007  Mercedes Benz   Clase GLC  2017  2.0 Glc250 300 4matic Atomático  18000   \n",
       "\n",
       "        Price  Doors       Kind           Location  \n",
       "2965     6300    5.0  Hatchback           Santa Fe  \n",
       "8007  2800000    5.0        SUV  Bs.as. G.b.a. Sur  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine two conditions\n",
    "cars[ (cars['Price'] == cars['Price'].min()) | (cars['Price'] == cars['Price'].max()) ] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "349545.4545"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars['Price'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x04i1kTlWyQ1"
   },
   "source": [
    "--- \n",
    "# Review\n",
    "\n",
    "This section is only for the reviewing team!\n",
    "\n",
    "Guideline how to review:\n",
    "https://docs.google.com/presentation/d/1YORFwlfVQo9ogj7jR9t6_pqxmGIlpBSGubbp1UdtcBQ/edit?usp=sharing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "id": "b-AdRPmP3u5y",
    "outputId": "80b1bccc-080b-4a08-f33d-50830a4ed454"
   },
   "source": [
    "#@title Review Criteria:\n",
    "%%html\n",
    "<h3><input type=\"checkbox\"> 1. Are all questions answered? <br></h3> \n",
    "<h3><input type=\"checkbox\"> 2. Does all code run through? <br></h3> \n",
    "<h3><input type=\"checkbox\"> 3. Are the conclusions understandable?  <br></h3> \n",
    "<h3><input type=\"checkbox\"> 4. Are the bonus question answered?  <br></h3> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x09QWAIP4UyI"
   },
   "source": [
    "input(\"Do you have any feedback?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dWB9-gCBxr_j"
   },
   "source": [
    "# Further material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H1LZOyLlzgF3"
   },
   "source": [
    "**Further summarization methods:**\n",
    "\n",
    "```\n",
    ".head()\n",
    ".info()\n",
    ".columns\n",
    ".unique()\n",
    ".min()\n",
    ".max()\n",
    ".mean()\n",
    ".count()\n",
    ".value_counts()\n",
    "```\n",
    "\n",
    "\n",
    "Where to find further methods from pandas: \n",
    "- https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html\n",
    "- https://www.dataquest.io/blog/pandas-cheat-sheet/\n",
    "- https://towardsdatascience.com/an-introduction-to-pandas-29d15a7da6d\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tobCydLW7bTA"
   },
   "outputs": [],
   "source": []
  }
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