import numpy as np 
import pandas as pd

cars_org = pd.read_csv('autoscout24-germany-dataset.csv')

cars_org.head()

# CHECK: rows in original csv-file
rows0 = len(cars_org)
print (rows0)

46405

country = pd.read_json('cars_and_countries_2.json')

country.drop('index', axis=1, inplace=True)

print (country.columns)

Index(['make', 'country'], dtype='object')

cars_upd = pd.merge(cars_org, country, on='make', how='outer')

cars_upd.head()

# CHECK: rows in dataset after merging both files
rows1 = len(cars_upd)
print (rows1)

46405

cars_mod = cars_upd.drop_duplicates()

# CHECK: rows in dataset after deleting duplicates
rows2 = len(cars_mod)
print (rows2)

44265

# noch eine zusaetzliche Spalte mit dem Monat wo die Anzeige aufgegeben ist fuer eventuelle zusaetzliche Analyse (random generated data)
import random 
#month = [1,2,3,4,5,6,7,8,9,10,11,12]
month = ['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec']
cars_mod.loc[:, 'ad-month'] = [random.choice(month) for i in range(len(cars_mod))] 
cars_mod.sample(5)

C:\Users\mario\AppData\Local\Temp\ipykernel_21732\3370031157.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  cars_mod.loc[:, 'ad-month'] = [random.choice(month) for i in range(len(cars_mod))]

# Erstellung einer Kopie der Daten
cars_mod2 = cars_mod.copy()

# double CHECK: rows after adding randomized data
rows3 = len(cars_mod2)
print (rows3)

44265

NaN = cars_mod2.isnull().any(axis=1).sum()
print (NaN)

1266

cars_fin = cars_mod2.dropna()

# CHECK: rows after deleting the NaN's
rows4 = len(cars_fin)
print (rows4)

42999

cars_fin.head()

# CHECK: check if there are still some duplicates
dupl_chck = cars_fin[cars_fin.duplicated()]
print(dupl_chck)

Empty DataFrame
Columns: [mileage, make, model, fuel, gear, offerType, price, hp, year, country, ad-month]
Index: []

country_make = cars_fin.groupby('country')['make'].count()
country_make

country
China                 6
Czech Republic     2737
France             4785
Germany           19584
Italy              1710
Japan              3583
NaN                   1
Romania             661
Russia               31
South Korea        1773
Spain              1892
Sweden              779
UK                  801
USA                4656
Name: make, dtype: int64

# loeschen des Fehlerteufels "NaN" mit Lambda Funktion
cars_fin1 = cars_fin[~cars_fin.apply(lambda row: row.astype(str).str.contains('NaN', case=False).any(), axis=1)]

country_make1 = cars_fin1.groupby('country')['make'].count()
country_make1

country
China                 6
Czech Republic     2737
France             4785
Germany           19584
Italy              1710
Japan              3583
Romania             661
Russia               31
South Korea        1773
Spain              1892
Sweden              779
UK                  801
USA                4656
Name: make, dtype: int64

# CHECK: rows after update
rows5 = len(cars_fin1)
print (rows5)

42998

print(cars_fin1.describe(include='object'))

              make  model      fuel    gear offerType  country ad-month
count        42998  42998     42998   42998     42998    42998    42998
unique          69    817        11       3         5       13       12
top     Volkswagen   Golf  Gasoline  Manual      Used  Germany      aug
freq          6680   1450     26533   28031     37805    19584     3662

ge = 'gear' 
print(cars_fin1[ge].value_counts())

gear
Manual            28031
Automatic         14914
Semi-automatic       53
Name: count, dtype: int64

fin2_upd = cars_fin1[cars_fin1['gear'] != 'Semi-automatic']
print(fin2_upd[ge].value_counts())

gear
Manual       28031
Automatic    14914
Name: count, dtype: int64

fu = 'fuel' 
print(fin2_upd[fu].value_counts())

fuel
Gasoline             26494
Diesel               14470
Electric/Gasoline     1013
Electric               633
CNG                    113
LPG                    106
Electric/Diesel         48
Others                  45
-/- (Fuel)              20
Ethanol                  2
Hydrogen                 1
Name: count, dtype: int64

fin3_upd = fin2_upd[~fin2_upd['fuel'].isin(['Hydrogen', 'Ethanol', '-/- (Fuel)', 'Others'])]
print(fin3_upd[fu].value_counts())

fuel
Gasoline             26494
Diesel               14470
Electric/Gasoline     1013
Electric               633
CNG                    113
LPG                    106
Electric/Diesel         48
Name: count, dtype: int64

print(fin3_upd.describe(include='object'))

              make  model      fuel    gear offerType  country ad-month
count        42877  42877     42877   42877     42877    42877    42877
unique          69    816         7       2         5       13       12
top     Volkswagen   Golf  Gasoline  Manual      Used  Germany      aug
freq          6672   1450     26494   27979     37699    19533     3651

of = 'offerType' 
print(fin3_upd[of].value_counts())

offerType
Used              37699
Demonstration      2160
Pre-registered     1940
Employee's car     1068
New                  10
Name: count, dtype: int64

fin3_upd.to_csv('autoscout24-modified-x.csv', index=False)

import matplotlib.pyplot as plt

country_make1.plot.bar(figsize=(10,5), color='green', edgecolor='red')
plt.title('Makes per Country')
plt.xlabel('Country')
plt.ylabel('Make_number')

Text(0, 0.5, 'Make_number')

cars_fin1.country.hist(figsize=(10,5), color='blue', edgecolor='magenta')
plt.title('Anzahl pro Land')
plt.xlabel('Land')
plt.ylabel('Anzahl Autos')

Text(0, 0.5, 'Anzahl Autos')

# import: Seaborn
import seaborn as sns

cars24x = pd.read_csv('autoscout24-modified-x.csv')

cars24x.head()

print(cars24x.describe())

            mileage         price            hp          year
count  4.287700e+04  4.287700e+04  42877.000000  42877.000000
mean   7.266792e+04  1.663444e+04    134.054015   2015.931338
std    6.239793e+04  1.962369e+04     75.548088      3.113938
min    0.000000e+00  1.100000e+03      1.000000   2011.000000
25%    2.170000e+04  7.490000e+03     86.000000   2013.000000
50%    6.166100e+04  1.099000e+04    116.000000   2016.000000
75%    1.065500e+05  1.974500e+04    150.000000   2019.000000
max    1.111111e+06  1.199900e+06    850.000000   2021.000000

print(cars24x.describe(include='object'))

              make  model      fuel    gear offerType  country ad-month
count        42877  42877     42877   42877     42877    42877    42877
unique          69    816         7       2         5       13       12
top     Volkswagen   Golf  Gasoline  Manual      Used  Germany      aug
freq          6672   1450     26494   27979     37699    19533     3651

cars_per_country = cars24x.groupby('country')['make'].count()
print(cars_per_country)

country
China                 6
Czech Republic     2731
France             4764
Germany           19533
Italy              1702
Japan              3569
Romania             661
Russia               31
South Korea        1769
Spain              1889
Sweden              778
UK                  801
USA                4643
Name: make, dtype: int64

cars_per_country.plot.bar(figsize=(10,5), color='green', edgecolor='red')
plt.title('Makes per Country')
plt.xlabel('Country')
plt.ylabel('Make_number') 
plt.show()

plt.figure(figsize=(12,6))

# Countplot
sns.countplot(data=cars24x, x='year', hue='gear', palette='Set2')

plt.title('Distribution of gear by year of production')
plt.xlabel('year')
plt.ylabel('number of cars')

plt.xticks(rotation=45)

plt.show()

make = 'BMW'
#make = 'Porsche'
#make = 'Renault'
# ...

df_model = cars24x[cars24x['make'] == make]

# Top 20 models
top_20_models = df_model['model'].value_counts().head(20).index

# Filtering dataframe only for top 20 models
df_model_top_20 = df_model[df_model['model'].isin(top_20_models)]

plt.figure(figsize=(12, 16))

# Horizontal countplot for top 20 models
sns.countplot(
    data=df_model_top_20, 
    y='model', 
    hue='model',  
    order=top_20_models,  
    legend=False  
)

plt.title(f'Top 20 models for {make}', fontsize=10)
plt.xlabel('Number of models', fontsize=8)
plt.ylabel('Model', fontsize=8)

plt.show()

ads = cars24x['ad-month'].str.lower().str[:3].value_counts()\
              .reindex(['jan', 'feb', 'mar', 'apr', 'may', 'jun', 
                       'jul', 'aug', 'sep', 'oct', 'nov', 'dec'])

plt.figure(figsize=(12, 6))
sns.lineplot(
    x=ads.index,
    y=ads.values,
    marker='o',
    linewidth=3,
    color='#E53935',
    markersize=9
)

plt.title('Darstellung: Anzeigen pro Monat', pad=20, fontsize=14)
plt.xlabel('Monat', labelpad=10)
plt.ylabel('Anzeigen', labelpad=10)
plt.grid(axis='y', alpha=0.3)
y_min = max(0, ads.min() - 100)  
y_max = ads.max() + 100         
plt.ylim(y_min, y_max)

for x, y in zip(ads.index, ads.values):
    plt.text(x, y + (y_max - y_min)*0.02, f"{y:,}", ha='center', va='bottom', fontsize=9) 

plt.tight_layout()
plt.show()

new_cars = cars24x[cars24x['offerType'] == 'New']

pd.set_option('display.max_columns', None)  
pd.set_option('display.float_format', '{:.2f}'.format)  # Zahl formatierung

# interaktive visualisierung
from IPython.display import display

print(f"In der Datenbank wurden {len(new_cars)} GANZ NEUE Autos gefunden:")
display(new_cars.style
    .background_gradient(subset=['price'], cmap='YlOrRd')
    .format({'price': '€{:.2f}'})  # Preis formatierung
    .set_caption('Liste der neuen Autos')
    .set_properties(**{'text-align': 'left'})
)

In der Datenbank wurden 10 GANZ NEUE Autos gefunden:

avg_price_by_year = cars24x.groupby('year', as_index=False)['price'].mean()

plt.figure(figsize=(12, 6))
sns.lineplot(
    data=avg_price_by_year,
    x='year',
    y='price',
    marker='o',
    linewidth=2.5,
    color='crimson'
)
plt.title('price AVG according to production year')
plt.xlabel('year of production')
plt.ylabel('AVG price')
plt.grid(True)
plt.show()

plt.figure(figsize=(12, 6))
sns.boxplot(data=cars24x, x='make', y='price')
plt.title('Preisverteilung nach Marken')
plt.xlabel('Make')
plt.ylabel('Price')
plt.xticks(rotation=90)  
plt.tight_layout()
plt.show()

plt.figure(figsize=(12, 6))
sns.boxplot(
    data=cars24x,
    x='offerType',
    y='mileage',
    hue='offerType',
    showfliers=False,  
    palette='Set2'
)
plt.title('KM-Stand vs Angebotstyp', fontsize=14)
plt.xlabel('Angebotstyp', fontsize=12)
plt.ylabel('KM-Stand', fontsize=12)
plt.show()

plt.figure(figsize=(12, 6))
sns.boxplot(
    data=cars24x,
    x='offerType',
    y='price',
    hue='offerType',
    showfliers=False,  
    palette='Set2'
)
plt.title('Preis vs Angebotstyp', fontsize=14)
plt.xlabel('Angebotstyp', fontsize=12)
plt.ylabel('Preis', fontsize=12)
plt.show()

avg_hp = cars24x.groupby('make', as_index=False)['hp'].mean().sort_values('hp', ascending=False)

plt.figure(figsize=(14, 8))
sns.barplot(
    data=avg_hp,
    x='make',
    y='hp',
    hue='make',
    palette='rocket'
)
plt.title('hp avg according to make')
plt.xlabel('make')
plt.ylabel('hp avg')
plt.xticks(rotation=90, ha='right')  
plt.tight_layout()
plt.show()

plt.figure(figsize=(12, 6))
top_marke = cars24x['make'].value_counts().nlargest(10)
sns.barplot(x=top_marke.index, y=top_marke.values, palette='rainbow', hue=top_marke.index)
plt.title('Top 10 Automarken', fontsize=14)
plt.xlabel('Marke', fontsize=12)
plt.ylabel('Anzahl', fontsize=12)
plt.xticks(rotation=45)
plt.tight_layout()

for i, value in enumerate(top_marke.head(20).values):
    plt.text(i, value + 0.5, str(value), ha='center', va='bottom')

plt.show()

cars24x['make_model'] = cars24x['make'] + ' ' + cars24x['model']
top_model = cars24x['make_model'].value_counts().nlargest(20)

plt.figure(figsize=(12, 6))
sns.barplot(x=top_model.index, y=top_model.values, palette='Set3', hue=top_model.index)
plt.title('Top 20 Modele', fontsize=14)
plt.xlabel('Model', fontsize=12)
plt.ylabel('Anzahl', fontsize=12)
plt.xticks(rotation=80)
plt.tight_layout()

for i, value in enumerate(top_model.head(20).values):
    plt.text(i, value + 0.5, str(value), ha='center', va='bottom')

plt.show()

# Kategorie fuer KM:
bins = [0, 100000, 200000, 300000, 400000, float('inf')]
labels = ['<100k', '100k-200k', '200k-300k', '300k-400k', '>400k']
cars24x['km_category'] = pd.cut(cars24x['mileage'], bins=bins, labels=labels, right=False)

# groub by KM-kategorie & Kalkulation des durchschnittlichen Preises
avg_price_by_km = cars24x.groupby('km_category', as_index=False, observed=True)['price'].mean()

# Visualisierung
plt.figure(figsize=(12, 6))
sns.barplot(
    data=avg_price_by_km,
    x='km_category',
    y='price',
    hue='km_category',
    palette='viridis',
    order=labels,
    legend=False
)
plt.title('Preis nach KM-Stand')
plt.xlabel('KM-Stand')
plt.ylabel('Preis AVG')
plt.grid(True, axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

motor_tr = cars24x.groupby(['year', 'fuel'], as_index=False).size()

plt.figure(figsize=(14, 7))
sns.lineplot(
    data=motor_tr,
    x='year',
    y='size',
    hue='fuel',
    style='fuel',
    markers=True,
    dashes=False,
    linewidth=2.5,
    palette='viridis'
)
plt.title('Motor Trend durch die Jahre', fontsize=16)
plt.xlabel('Baujahr', fontsize=12)
plt.ylabel('Anzahl der Fahrzeuge', fontsize=12)
plt.grid(True)
plt.legend(title='Motor', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.show()

motor_pro = cars24x.groupby(['year', 'fuel']).size().unstack().fillna(0)
motor_pro = motor_pro.div(motor_pro.sum(axis=1), axis=0) * 100

plt.figure(figsize=(14, 7))
sns.lineplot(
    data=motor_pro.reset_index().melt(id_vars='year'),
    x='year',
    y='value',
    hue='fuel',
    style='fuel',
    linewidth=2.5
)
plt.title('Motor Trend durch die Jahre (%)', fontsize=16)
plt.xlabel('Baujahr', fontsize=12)
plt.ylabel('Prozentsatz (%)', fontsize=12)
plt.grid(True)
plt.legend(title='Motor', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.show()

plt.figure(figsize=(14, 7))
sns.lineplot(
    data=cars24x,
    x='year',
    y='hp',
    estimator='median',
    errorbar=None,
    color='red',
    linewidth=2.5,
    label='median'
)
sns.lineplot(
    data=cars24x,
    x='year',
    y='hp',
    estimator='mean',
    errorbar=None,
    color='blue',
    linewidth=2.5,
    label='average'
)
plt.title('PS Entwicklung durch Jahre', fontsize=16)
plt.xlabel('Produktionsjahr', fontsize=12)
plt.ylabel('PS', fontsize=12)
plt.grid(True)
plt.legend()
plt.show()

import dash
from dash import Dash, html, dcc, callback, Output, Input
import plotly.express as px

print("Anzahl der verschiedenen Automarken:", len(cars24x['make'].unique()))
print("Beispiel Automarken:", cars24x['make'].unique()[21:29])
print("Insgesammt Modele:", len(cars24x['model'].unique()))

Anzahl der verschiedenen Automarken: 69
Beispiel Automarken: ['Dodge' 'Estrima' 'FISKER' 'Ferrari' 'Fiat' 'Ford' 'Honda' 'Hyundai']
Insgesammt Modele: 816

cars24x = pd.read_csv('autoscout24-modified-x.csv')  

app = Dash(__name__)

app.layout = html.Div([
    html.H1(children='Analyse der Modele nach Marke', style={'textAlign':'center'}),
    dcc.Dropdown(
        id='dropdown-selection',
        options=[{'label': make, 'value': make} for make in sorted(cars24x['make'].unique())],  # sorted
        value=cars24x['make'].iloc[0]  # die erste AutoMarke als Startmarke
    ),
    dcc.Graph(id='graph-content')
])

@callback(
    Output('graph-content', 'figure'),
    Input('dropdown-selection', 'value')
)
def update_graph(selected_make):
    dff = cars24x[cars24x['make'] == selected_make]
    model_counts = dff['model'].value_counts().reset_index()
    model_counts.columns = ['model', 'count']
    
    fig = px.bar(
        model_counts,
        x='model',
        y='count',
        title=f'Anzahl der Modele fuer die Marke: {selected_make}',
        labels={'model': 'Model', 'count': 'Anzahl der Fahrzeuge'},
        height=600  
    )
    
    return fig

if __name__ == '__main__':
    app.run(debug=True, port=8088)

# Daten
cars24x = pd.read_csv('autoscout24-modified-x.csv')

app = Dash(__name__)

app.layout = html.Div([
    html.H1(children='Analyse der Modele nach Marke', style={'textAlign':'center'}),
    dcc.Dropdown(
        id='dropdown-selection',
        options=[{'label': make, 'value': make} for make in sorted(cars24x['make'].unique())],
        value=cars24x['make'].iloc[0]  # die erste AutoMarke als Startmarke
    ),
    dcc.Graph(id='graph-content')
])

@callback(
    Output('graph-content', 'figure'),
    Input('dropdown-selection', 'value')
)
def update_graph(selected_make):
    dff = cars24x[cars24x['make'] == selected_make]
    model_counts = dff['model'].value_counts().reset_index()
    model_counts.columns = ['model', 'count']
    
    fig = px.bar(
        model_counts,
        x='model',
        y='count',
        title=f'Anzahl der Modele fuer die Marke: {selected_make}',
        labels={'model': 'Model', 'count': 'Anzahl der Fahrzeuge'},
        color='model',  # color according model
        color_discrete_sequence=px.colors.qualitative.Plotly,  # color palette
        height=600
    )
    
    fig.update_layout(
        xaxis={'categoryorder': 'total descending'},
        showlegend=True  # show/hide legend
    )
    
    return fig

if __name__ == '__main__':
    app.run(debug=True, port=8087)

print(sorted(cars24x['country'].unique()))

['China', 'Czech Republic', 'France', 'Germany', 'Italy', 'Japan', 'Romania', 'Russia', 'South Korea', 'Spain', 'Sweden', 'UK', 'USA']

from cartopy.io import shapereader
from cartopy import crs

import matplotlib.pyplot as plt
import cartopy.crs as crs
import cartopy.io.shapereader as shapereader

only_eu = cars24x['country'].value_counts()

plt.figure(figsize=(12, 8))
ax = plt.axes(projection=crs.PlateCarree())
ax.set_extent([-11, 33, 33, 66])

reader = shapereader.Reader(
    shapereader.natural_earth(
        resolution='10m',
        category='cultural',
        name='admin_0_countries'
    )
)

for country in reader.records():
    country_name = country.attributes.get('NAME_LONG')
    if country_name in only_eu.index:
        ax.add_geometries(
            [country.geometry],
            crs.PlateCarree(),
            facecolor='blue',
            edgecolor='black',
            linewidth=0.5
        )

plt.title('Autohersteller – EU')
plt.show()

import matplotlib.pyplot as plt
import cartopy.crs as crs
import cartopy.io.shapereader as shapereader
import matplotlib.cm as cm
import matplotlib.colors as colors

# 1. podaci
countrycount = cars24x['country'].value_counts()

# 2. mapa
plt.figure(figsize=(16, 10))
ax = plt.axes(projection=crs.PlateCarree())
ax.set_global()

# 3. Natural Earth shapefile (AUTO download)
reader = shapereader.Reader(
    shapereader.natural_earth(
        resolution='10m',
        category='cultural',
        name='admin_0_countries'
    )
)

# 4. normalizacija boja
max_count = countrycount.max() if not countrycount.empty else 1
norm = colors.Normalize(vmin=0, vmax=max_count)
cmap = cm.Blues

# 5. crtanje drzava
for country in reader.records():
    countryname = (
        country.attributes.get('NAME_LONG')
        or country.attributes.get('ADMIN')
        or country.attributes.get('NAME')
        or country.attributes.get('SOVEREIGNT')
    )

    if countryname in countrycount.index:
        ax.add_geometries(
            [country.geometry],
            crs.PlateCarree(),
            facecolor=cmap(norm(countrycount[countryname])),
            edgecolor='black',
            linewidth=0.5
        )
    else:
        ax.add_geometries(
            [country.geometry],
            crs.PlateCarree(),
            facecolor='#f2f2f2',
            edgecolor='#cccccc',
            linewidth=0.3
        )

# 6. colorbar
sm = cm.ScalarMappable(norm=norm, cmap=cmap)
sm.set_array([])

cbar = plt.colorbar(sm, ax=ax, orientation='vertical', pad=0.02)
cbar.set_label('Broj vozila / proizvođača')

# 7. naslov
plt.title(
    'Zemlje proizvođača automobila\n(što tamnije – više vozila)',
    fontsize=14,
    pad=20
)

plt.show()

from dash import dash_table

app = Dash()

app.layout = [html.Div(children='My Cars'),
             dash_table.DataTable(data=cars24x.to_dict('records'), page_size=10),
             dcc.Graph(figure=px.histogram(cars24x, x='make', y='model', histfunc='count'))
             ]

if __name__ == '__main__':
    app.run(debug=True, port=8085)

app = Dash(__name__)

app.layout = html.Div([
    html.Div(children='My Cars'),
    html.Hr(),
    dcc.RadioItems(
        options=['mileage', 'hp'], 
        value='hp', 
        id='controls-and-radio-item'
    ),
    dash_table.DataTable(
        data=cars24x.to_dict('records'), 
        page_size=10
    ),
    dcc.Graph(
        id='controls-and-graph',  
        figure=px.histogram(cars24x, x='make', y='model', histfunc='count')
    )
])

@callback(
    Output(component_id='controls-and-graph', component_property='figure'),
    Input(component_id='controls-and-radio-item', component_property='value')
)
def update_graph(col_chosen):
    fig = px.histogram(cars24x, x='make', y=col_chosen, histfunc='count')
    return fig

if __name__ == '__main__':
    app.run(debug=True, port=8041)

Teil 1 :¶

Teil 2:¶

Teil 3 :¶

Teil 4 : A)¶

Teil 4 : B)¶

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Teil 8 :¶

	mileage	make	model	fuel	gear	offerType	price	hp	year
0	235000	BMW	316	Diesel	Manual	Used	6800	116.0	2011
1	92800	Volkswagen	Golf	Gasoline	Manual	Used	6877	122.0	2011
2	149300	SEAT	Exeo	Gasoline	Manual	Used	6900	160.0	2011
3	96200	Renault	Megane	Gasoline	Manual	Used	6950	110.0	2011
4	156000	Peugeot	308	Gasoline	Manual	Used	6950	156.0	2011

	mileage	make	model	fuel	gear	offerType	price	hp	year	country
0	39500	9ff	NaN	Gasoline	Manual	Used	7000	20.0	2018	Germany
1	21500	Abarth	500	Gasoline	Manual	Used	11850	160.0	2015	Italy
2	15000	Abarth	595 Competizione	Gasoline	Manual	Used	23975	179.0	2020	Italy
3	5	Abarth	500	Gasoline	Manual	Demonstration	24480	179.0	2020	Italy
4	5	Abarth	500	Gasoline	Manual	Demonstration	24480	179.0	2020	Italy

	mileage	make	model	fuel	gear	offerType	price	hp	year	country	ad-month
25186	158500	Opel	Corsa	Diesel	Manual	Used	3500	75.0	2011	Germany	feb
1679	28500	Audi	Q8	Gasoline	Automatic	Used	79889	340.0	2019	Germany	mar
36127	70000	Suzuki	Splash	Gasoline	Manual	Used	5199	86.0	2011	Japan	sep
3502	101900	BMW	316	Gasoline	Manual	Used	13099	136.0	2013	Germany	dec
5487	61000	Chevrolet	Spark	Gasoline	Manual	Used	3450	68.0	2011	USA	feb

	mileage	make	model	fuel	gear	offerType	price	hp	year	country	ad-month
5109	380	Bentley	Bentayga	Diesel	Automatic	New	€192780.00	435.000000	2021	UK	nov
7047	47	Ferrari	F8 Tributo	Gasoline	Automatic	New	€304900.00	721.000000	2021	Italy	mar
13156	0	Hyundai	i10	Gasoline	Manual	New	€12579.00	67.000000	2021	South Korea	apr
15063	17	Land	Rover Range Rover Sport	Gasoline	Automatic	New	€131980.00	575.000000	2021	UK	apr
30221	5	SEAT	Ibiza	Gasoline	Manual	New	€15990.00	80.000000	2021	Spain	oct
30222	5	SEAT	Ibiza	Gasoline	Manual	New	€16690.00	95.000000	2021	Spain	mar
30226	5	SEAT	Arona	Gasoline	Manual	New	€17990.00	95.000000	2021	Spain	jun
30227	5	SEAT	Arona	Gasoline	Automatic	New	€22690.00	110.000000	2021	Spain	may
32942	5	Skoda	Karoq	Gasoline	Automatic	New	€36490.00	150.000000	2021	Czech Republic	jun
32950	5	Skoda	Superb	Electric/Gasoline	Automatic	New	€52990.00	156.000000	2021	Czech Republic	may