Notebook

About the Data: ¶

This rich data set provides a comprehensive window into Germany's used automobile market, spanning over 400,000 online sales listings from 2014-2016.

The contents of the data was in German, so some transalations and modifications (replacements of words) were made on the relevant fields.
Data Source is from Data World. However, the data was scrapped from eBay
Data was visualised using the Matplotlib library.
This data was from 2014 - 2016
Business problems intended to be analysed and identified from tyhis data were:
understanding the most used brands and their relationship with the vehicle types
undersatnding how prices affect the brands and vehicle types
understand consuer's preference for fuel type
Understand the sdvertisement trend on ebay (for cars)
identiffy the seller category with the most advertised cars on ebay.
Comparisms between high-end luxury vehicles versus their affordability
other metrics like pricing, brands, models, mileage, repairs and modifications were uncovered; revealling an intriguing demand patterns and shifts within this vast market.

A few major dimensions stand out as offering meaningful insights. These analyses revealed how the ebay platform performed and insights to help them understand conusmers' preferences.

Hopefully, this informs listings their sales team would be more on the lookout for (cosidering their increasing customer demands).

Data fields:

dateCrawled : when this ad was first crawled, all field-values are taken from this date
name : "name" of the car
seller : private or dealer
offerType (in German)
price : the price on the ad to sell the car
abtest
vehicleType (in German)
yearOfRegistration : at which year the car was first registered
gearbox (in German)
powerPS : power of the car in PS
model (in German)
kilometer : how many kilometers the car has driven
monthOfRegistration : at which month the car was first registered
fuelType (in German)
brand
notRepairedDamage : if the car has a damage which is not repaired yet (in German)
dateCreated : the date for which the ad at ebay was created
nrOfPictures : number of pictures in the ad
postalCode
lastSeenOnline : when the crawler saw this ad last online

Import the Data ¶

In [1]:

import pandas as pd

In [3]:

df = pd.read_csv(r"C:\Users\Teni\Desktop\Git-Github\Online Datasets\autos.csv")

In [4]:

df

# The original dataset

Out[4]:

	datecrawled	name	seller	offertype	price	abtest	vehicletype	yearofregistration	gearbox	powerps	model	kilometer	monthofregistration	fueltype	brand	notrepaireddamage	datecreated	nrofpictures	postalcode	lastseen
0	2016-03-24T11:52:17	Golf_3_1.6	privat	Angebot	480	test	NaN	1993	manuell	0	golf	150000	0	benzin	volkswagen	NaN	2016-03-24T00:00:00	False	70435	2016-04-07T03:16:57
1	2016-03-24T10:58:45	A5_Sportback_2.7_Tdi	privat	Angebot	18300	test	coupe	2011	manuell	190	NaN	125000	5	diesel	audi	ja	2016-03-24T00:00:00	False	66954	2016-04-07T01:46:50
2	2016-03-14T12:52:21	Jeep_Grand_Cherokee_"Overland"	privat	Angebot	9800	test	suv	2004	automatik	163	grand	125000	8	diesel	jeep	NaN	2016-03-14T00:00:00	False	90480	2016-04-05T12:47:46
3	2016-03-17T16:54:04	GOLF_4_1_4__3TÜRER	privat	Angebot	1500	test	kleinwagen	2001	manuell	75	golf	150000	6	benzin	volkswagen	nein	2016-03-17T00:00:00	False	91074	2016-03-17T17:40:17
4	2016-03-31T17:25:20	Skoda_Fabia_1.4_TDI_PD_Classic	privat	Angebot	3600	test	kleinwagen	2008	manuell	69	fabia	90000	7	diesel	skoda	nein	2016-03-31T00:00:00	False	60437	2016-04-06T10:17:21
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
371523	2016-03-14T17:48:27	Suche_t4___vito_ab_6_sitze	privat	Angebot	2200	test	NaN	2005	NaN	0	NaN	20000	1	NaN	sonstige_autos	NaN	2016-03-14T00:00:00	False	39576	2016-04-06T00:46:52
371524	2016-03-05T19:56:21	Smart_smart_leistungssteigerung_100ps	privat	Angebot	1199	test	cabrio	2000	automatik	101	fortwo	125000	3	benzin	smart	nein	2016-03-05T00:00:00	False	26135	2016-03-11T18:17:12
371525	2016-03-19T18:57:12	Volkswagen_Multivan_T4_TDI_7DC_UY2	privat	Angebot	9200	test	bus	1996	manuell	102	transporter	150000	3	diesel	volkswagen	nein	2016-03-19T00:00:00	False	87439	2016-04-07T07:15:26
371526	2016-03-20T19:41:08	VW_Golf_Kombi_1_9l_TDI	privat	Angebot	3400	test	kombi	2002	manuell	100	golf	150000	6	diesel	volkswagen	NaN	2016-03-20T00:00:00	False	40764	2016-03-24T12:45:21
371527	2016-03-07T19:39:19	BMW_M135i_vollausgestattet_NP_52.720____Euro	privat	Angebot	28990	control	limousine	2013	manuell	320	m_reihe	50000	8	benzin	bmw	nein	2016-03-07T00:00:00	False	73326	2016-03-22T03:17:10

371528 rows × 20 columns

In [5]:

# To get the summary, and datatype, of the fields.

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 371528 entries, 0 to 371527
Data columns (total 20 columns):
 #   Column               Non-Null Count   Dtype 
---  ------               --------------   ----- 
 0   datecrawled          371528 non-null  object
 1   name                 371528 non-null  object
 2   seller               371528 non-null  object
 3   offertype            371528 non-null  object
 4   price                371528 non-null  int64 
 5   abtest               371528 non-null  object
 6   vehicletype          333659 non-null  object
 7   yearofregistration   371528 non-null  int64 
 8   gearbox              351319 non-null  object
 9   powerps              371528 non-null  int64 
 10  model                351044 non-null  object
 11  kilometer            371528 non-null  int64 
 12  monthofregistration  371528 non-null  int64 
 13  fueltype             338142 non-null  object
 14  brand                371528 non-null  object
 15  notrepaireddamage    299468 non-null  object
 16  datecreated          371528 non-null  object
 17  nrofpictures         371528 non-null  bool  
 18  postalcode           371528 non-null  int64 
 19  lastseen             371528 non-null  object
dtypes: bool(1), int64(6), object(13)
memory usage: 54.2+ MB

Based on the highlighted business problems to answer:¶

To solve the business questions on:

most advertised brands and vehicle types
how the price affects the influx of cars advertised (are there more 'less priced' vehicles on the platform or more' pricey' vehicles?)
what type of sellers do we have on the platfomr the most
how does milleage and powerps of the vehicles relate with the brand and vehicle types? Perhaps there's a consumer trend outside ebay we're not paying attention to, yet

The needed fields are: seller, price, vehicletype, gearbox, powerups, kilometer, datecreated, fueltype

Clean the data ¶

Translate needed columns to the English Language

In [6]:

# summarizing the categorical data uder offertype. This is in German language; so the next code replaces the field values
df['offertype'].value_counts()

Out[6]:

Angebot    371516
Gesuch         12
Name: offertype, dtype: int64

In [7]:

df['offertype'] = df['offertype'].replace({'Angebot': 'Offer', 'Gesuch': 'Request'})

In [8]:

# updated field value replacement
df['offertype'].value_counts()

Out[8]:

Offer      371516
Request        12
Name: offertype, dtype: int64

In [9]:

# summarizing the categorical data uder fueltype. This is in German language; so the next code replaces the field values
df['fueltype'].value_counts()

Out[9]:

benzin     223857
diesel     107746
lpg          5378
cng           571
hybrid        278
andere        208
elektro       104
Name: fueltype, dtype: int64

In [10]:

df['fueltype']= df['fueltype'].replace({'benzin':'Petrol', 'diesel':'Diesel','lpg':'Liquefied Petroleum Gas', 'cng':'Compressed Natural Gas', 'hybrid': 'Hybrid', 'andere': 'Others', 'elektro':'Electric'})

In [11]:

# updated field replacement
df['fueltype'].value_counts()

Out[11]:

Petrol                     223857
Diesel                     107746
Liquefied Petroleum Gas      5378
Compressed Natural Gas        571
Hybrid                        278
Others                        208
Electric                      104
Name: fueltype, dtype: int64

In [12]:

df['gearbox']= df['gearbox'].replace({'manuell': 'Manual', 'automatik': 'Automatic'})

In [13]:

# updated field replacement f the gearbox attribute of the data
df['gearbox'].value_counts()

Out[13]:

Manual       274214
Automatic     77105
Name: gearbox, dtype: int64

In [14]:

df['abtest'].value_counts()

Out[14]:

test       192585
control    178943
Name: abtest, dtype: int64

In [15]:

df['vehicletype'].value_counts()

Out[15]:

limousine     95894
kleinwagen    80023
kombi         67564
bus           30201
cabrio        22898
coupe         19015
suv           14707
andere         3357
Name: vehicletype, dtype: int64

In [16]:

df['vehicletype']=df['vehicletype'].replace({'kleinwagen': 'small car', 'kombi': 'station wagon', 'cabrio': 'convertible', 'andere': 'other'})

In [17]:

df['seller'].value_counts()

Out[17]:

privat        371525
gewerblich         3
Name: seller, dtype: int64

In [18]:

df['seller'] = df['seller'].replace({'privat':'private', 'gewerblich':'dealer'})

df['seller'].value_counts()

Out[18]:

private    371525
dealer          3
Name: seller, dtype: int64

In [19]:

df['notrepaireddamage'].value_counts()

Out[19]:

nein    263182
ja       36286
Name: notrepaireddamage, dtype: int64

In [20]:

df['notrepaireddamage'] = df['notrepaireddamage'].replace({'nein': 'No', 'ja': 'Yes'})

In [21]:

df['model'].head(100)

Out[21]:

0            golf
1             NaN
2           grand
3            golf
4           fabia
         ...     
95             a1
96       insignia
97         passat
98            3er
99    transporter
Name: model, Length: 100, dtype: object

In [22]:

df['model']= df['model'].replace({'andere': 'other', })

In [23]:

df

Out[23]:

	datecrawled	name	seller	offertype	price	abtest	vehicletype	yearofregistration	gearbox	powerps	model	kilometer	monthofregistration	fueltype	brand	notrepaireddamage	datecreated	nrofpictures	postalcode	lastseen
0	2016-03-24T11:52:17	Golf_3_1.6	private	Offer	480	test	NaN	1993	Manual	0	golf	150000	0	Petrol	volkswagen	NaN	2016-03-24T00:00:00	False	70435	2016-04-07T03:16:57
1	2016-03-24T10:58:45	A5_Sportback_2.7_Tdi	private	Offer	18300	test	coupe	2011	Manual	190	NaN	125000	5	Diesel	audi	Yes	2016-03-24T00:00:00	False	66954	2016-04-07T01:46:50
2	2016-03-14T12:52:21	Jeep_Grand_Cherokee_"Overland"	private	Offer	9800	test	suv	2004	Automatic	163	grand	125000	8	Diesel	jeep	NaN	2016-03-14T00:00:00	False	90480	2016-04-05T12:47:46
3	2016-03-17T16:54:04	GOLF_4_1_4__3TÜRER	private	Offer	1500	test	small car	2001	Manual	75	golf	150000	6	Petrol	volkswagen	No	2016-03-17T00:00:00	False	91074	2016-03-17T17:40:17
4	2016-03-31T17:25:20	Skoda_Fabia_1.4_TDI_PD_Classic	private	Offer	3600	test	small car	2008	Manual	69	fabia	90000	7	Diesel	skoda	No	2016-03-31T00:00:00	False	60437	2016-04-06T10:17:21
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
371523	2016-03-14T17:48:27	Suche_t4___vito_ab_6_sitze	private	Offer	2200	test	NaN	2005	NaN	0	NaN	20000	1	NaN	sonstige_autos	NaN	2016-03-14T00:00:00	False	39576	2016-04-06T00:46:52
371524	2016-03-05T19:56:21	Smart_smart_leistungssteigerung_100ps	private	Offer	1199	test	convertible	2000	Automatic	101	fortwo	125000	3	Petrol	smart	No	2016-03-05T00:00:00	False	26135	2016-03-11T18:17:12
371525	2016-03-19T18:57:12	Volkswagen_Multivan_T4_TDI_7DC_UY2	private	Offer	9200	test	bus	1996	Manual	102	transporter	150000	3	Diesel	volkswagen	No	2016-03-19T00:00:00	False	87439	2016-04-07T07:15:26
371526	2016-03-20T19:41:08	VW_Golf_Kombi_1_9l_TDI	private	Offer	3400	test	station wagon	2002	Manual	100	golf	150000	6	Diesel	volkswagen	NaN	2016-03-20T00:00:00	False	40764	2016-03-24T12:45:21
371527	2016-03-07T19:39:19	BMW_M135i_vollausgestattet_NP_52.720____Euro	private	Offer	28990	control	limousine	2013	Manual	320	m_reihe	50000	8	Petrol	bmw	No	2016-03-07T00:00:00	False	73326	2016-03-22T03:17:10

371528 rows × 20 columns

Drop irrelevant columns

Some attributes are not needed in solving the business problem, so they'd all be dropped

In [24]:

df['nrofpictures'].value_counts()

Out[24]:

False    371528
Name: nrofpictures, dtype: int64

In [25]:

df = df.drop(['nrofpictures', 'lastseen', 'postalcode'], axis=1)

In [26]:

df

Out[26]:

	datecrawled	name	seller	offertype	price	abtest	vehicletype	yearofregistration	gearbox	powerps	model	kilometer	monthofregistration	fueltype	brand	notrepaireddamage	datecreated
0	2016-03-24T11:52:17	Golf_3_1.6	private	Offer	480	test	NaN	1993	Manual	0	golf	150000	0	Petrol	volkswagen	NaN	2016-03-24T00:00:00
1	2016-03-24T10:58:45	A5_Sportback_2.7_Tdi	private	Offer	18300	test	coupe	2011	Manual	190	NaN	125000	5	Diesel	audi	Yes	2016-03-24T00:00:00
2	2016-03-14T12:52:21	Jeep_Grand_Cherokee_"Overland"	private	Offer	9800	test	suv	2004	Automatic	163	grand	125000	8	Diesel	jeep	NaN	2016-03-14T00:00:00
3	2016-03-17T16:54:04	GOLF_4_1_4__3TÜRER	private	Offer	1500	test	small car	2001	Manual	75	golf	150000	6	Petrol	volkswagen	No	2016-03-17T00:00:00
4	2016-03-31T17:25:20	Skoda_Fabia_1.4_TDI_PD_Classic	private	Offer	3600	test	small car	2008	Manual	69	fabia	90000	7	Diesel	skoda	No	2016-03-31T00:00:00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
371523	2016-03-14T17:48:27	Suche_t4___vito_ab_6_sitze	private	Offer	2200	test	NaN	2005	NaN	0	NaN	20000	1	NaN	sonstige_autos	NaN	2016-03-14T00:00:00
371524	2016-03-05T19:56:21	Smart_smart_leistungssteigerung_100ps	private	Offer	1199	test	convertible	2000	Automatic	101	fortwo	125000	3	Petrol	smart	No	2016-03-05T00:00:00
371525	2016-03-19T18:57:12	Volkswagen_Multivan_T4_TDI_7DC_UY2	private	Offer	9200	test	bus	1996	Manual	102	transporter	150000	3	Diesel	volkswagen	No	2016-03-19T00:00:00
371526	2016-03-20T19:41:08	VW_Golf_Kombi_1_9l_TDI	private	Offer	3400	test	station wagon	2002	Manual	100	golf	150000	6	Diesel	volkswagen	NaN	2016-03-20T00:00:00
371527	2016-03-07T19:39:19	BMW_M135i_vollausgestattet_NP_52.720____Euro	private	Offer	28990	control	limousine	2013	Manual	320	m_reihe	50000	8	Petrol	bmw	No	2016-03-07T00:00:00

371528 rows × 17 columns

Trends to Uncover from the data :¶

Pricing Analysis: Investigate the price distribution for different vehicle types, brands, or models to understand pricing trends and identify outliers.
Trend Analysis over Time: Analyze the trends in the number of listings or sales over different months or years to understand seasonal patterns in the automotive market.
Brand Comparison: Compare the popularity or sales of different car brands to understand which brands are more preferred in the market and how they perform in terms of pricing or demand.
Vehicle Condition and Price Correlation: Examine how the condition of a vehicle (repaired damage or not) correlates with its pricing. Determine if repaired damage affects the selling price significantly.
Fuel Type Preference: Analyze the preference for different fuel types among different vehicle types or brands. Understand if there's a shift in preference towards more eco-friendly options like hybrid or electric vehicles.
Feature Importance: Analyze the impact of specific vehicle features (like power, gearbox type, vehicle type, etc.) on the pricing to identify which features drive higher prices or are more sought after by buyers.

Task 1.¶

Pricing Analysis: Investigate the price distribution for different vehicle types, brands, or models to understand pricing trends and identify outliers.

In [27]:

df

Out[27]:

	datecrawled	name	seller	offertype	price	abtest	vehicletype	yearofregistration	gearbox	powerps	model	kilometer	monthofregistration	fueltype	brand	notrepaireddamage	datecreated
0	2016-03-24T11:52:17	Golf_3_1.6	private	Offer	480	test	NaN	1993	Manual	0	golf	150000	0	Petrol	volkswagen	NaN	2016-03-24T00:00:00
1	2016-03-24T10:58:45	A5_Sportback_2.7_Tdi	private	Offer	18300	test	coupe	2011	Manual	190	NaN	125000	5	Diesel	audi	Yes	2016-03-24T00:00:00
2	2016-03-14T12:52:21	Jeep_Grand_Cherokee_"Overland"	private	Offer	9800	test	suv	2004	Automatic	163	grand	125000	8	Diesel	jeep	NaN	2016-03-14T00:00:00
3	2016-03-17T16:54:04	GOLF_4_1_4__3TÜRER	private	Offer	1500	test	small car	2001	Manual	75	golf	150000	6	Petrol	volkswagen	No	2016-03-17T00:00:00
4	2016-03-31T17:25:20	Skoda_Fabia_1.4_TDI_PD_Classic	private	Offer	3600	test	small car	2008	Manual	69	fabia	90000	7	Diesel	skoda	No	2016-03-31T00:00:00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
371523	2016-03-14T17:48:27	Suche_t4___vito_ab_6_sitze	private	Offer	2200	test	NaN	2005	NaN	0	NaN	20000	1	NaN	sonstige_autos	NaN	2016-03-14T00:00:00
371524	2016-03-05T19:56:21	Smart_smart_leistungssteigerung_100ps	private	Offer	1199	test	convertible	2000	Automatic	101	fortwo	125000	3	Petrol	smart	No	2016-03-05T00:00:00
371525	2016-03-19T18:57:12	Volkswagen_Multivan_T4_TDI_7DC_UY2	private	Offer	9200	test	bus	1996	Manual	102	transporter	150000	3	Diesel	volkswagen	No	2016-03-19T00:00:00
371526	2016-03-20T19:41:08	VW_Golf_Kombi_1_9l_TDI	private	Offer	3400	test	station wagon	2002	Manual	100	golf	150000	6	Diesel	volkswagen	NaN	2016-03-20T00:00:00
371527	2016-03-07T19:39:19	BMW_M135i_vollausgestattet_NP_52.720____Euro	private	Offer	28990	control	limousine	2013	Manual	320	m_reihe	50000	8	Petrol	bmw	No	2016-03-07T00:00:00

371528 rows × 17 columns

In [28]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 371528 entries, 0 to 371527
Data columns (total 17 columns):
 #   Column               Non-Null Count   Dtype 
---  ------               --------------   ----- 
 0   datecrawled          371528 non-null  object
 1   name                 371528 non-null  object
 2   seller               371528 non-null  object
 3   offertype            371528 non-null  object
 4   price                371528 non-null  int64 
 5   abtest               371528 non-null  object
 6   vehicletype          333659 non-null  object
 7   yearofregistration   371528 non-null  int64 
 8   gearbox              351319 non-null  object
 9   powerps              371528 non-null  int64 
 10  model                351044 non-null  object
 11  kilometer            371528 non-null  int64 
 12  monthofregistration  371528 non-null  int64 
 13  fueltype             338142 non-null  object
 14  brand                371528 non-null  object
 15  notrepaireddamage    299468 non-null  object
 16  datecreated          371528 non-null  object
dtypes: int64(5), object(12)
memory usage: 48.2+ MB

In [29]:

df['price'].isnull()

Out[29]:

0         False
1         False
2         False
3         False
4         False
          ...  
371523    False
371524    False
371525    False
371526    False
371527    False
Name: price, Length: 371528, dtype: bool

In [30]:

df = df.fillna('None')

# Since there's no Null value in Price, it's safe to fill all null values in the dataframe with the string 'None' than the integer '0'

Price and Brand Relationship ¶

In [31]:

df

Out[31]:

	datecrawled	name	seller	offertype	price	abtest	vehicletype	yearofregistration	gearbox	powerps	model	kilometer	monthofregistration	fueltype	brand	notrepaireddamage	datecreated
0	2016-03-24T11:52:17	Golf_3_1.6	private	Offer	480	test	None	1993	Manual	0	golf	150000	0	Petrol	volkswagen	None	2016-03-24T00:00:00
1	2016-03-24T10:58:45	A5_Sportback_2.7_Tdi	private	Offer	18300	test	coupe	2011	Manual	190	None	125000	5	Diesel	audi	Yes	2016-03-24T00:00:00
2	2016-03-14T12:52:21	Jeep_Grand_Cherokee_"Overland"	private	Offer	9800	test	suv	2004	Automatic	163	grand	125000	8	Diesel	jeep	None	2016-03-14T00:00:00
3	2016-03-17T16:54:04	GOLF_4_1_4__3TÜRER	private	Offer	1500	test	small car	2001	Manual	75	golf	150000	6	Petrol	volkswagen	No	2016-03-17T00:00:00
4	2016-03-31T17:25:20	Skoda_Fabia_1.4_TDI_PD_Classic	private	Offer	3600	test	small car	2008	Manual	69	fabia	90000	7	Diesel	skoda	No	2016-03-31T00:00:00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
371523	2016-03-14T17:48:27	Suche_t4___vito_ab_6_sitze	private	Offer	2200	test	None	2005	None	0	None	20000	1	None	sonstige_autos	None	2016-03-14T00:00:00
371524	2016-03-05T19:56:21	Smart_smart_leistungssteigerung_100ps	private	Offer	1199	test	convertible	2000	Automatic	101	fortwo	125000	3	Petrol	smart	No	2016-03-05T00:00:00
371525	2016-03-19T18:57:12	Volkswagen_Multivan_T4_TDI_7DC_UY2	private	Offer	9200	test	bus	1996	Manual	102	transporter	150000	3	Diesel	volkswagen	No	2016-03-19T00:00:00
371526	2016-03-20T19:41:08	VW_Golf_Kombi_1_9l_TDI	private	Offer	3400	test	station wagon	2002	Manual	100	golf	150000	6	Diesel	volkswagen	None	2016-03-20T00:00:00
371527	2016-03-07T19:39:19	BMW_M135i_vollausgestattet_NP_52.720____Euro	private	Offer	28990	control	limousine	2013	Manual	320	m_reihe	50000	8	Petrol	bmw	No	2016-03-07T00:00:00

371528 rows × 17 columns

In [32]:

import matplotlib.pyplot as plt
import pandas as pd


# Calculate the average price for each brand (top 10)
avg_prices = df.groupby('brand')['price'].mean().nlargest(10)

# Get the number of brands for each category (top 10)
brand_count = df['brand'].value_counts()[:10]

# Using the add_axes because I'd like to display 2 related information in one plot
fig = plt.figure(figsize=(22, 15))  # Adjust the figure size if needed
ax1 = fig.add_axes([0, 0, 1, 1])

# Plotting the Main graph for the Avg_prices for the top 10 brands
ax1.bar(avg_prices.index,avg_prices, color='blue')

# Defining the title of the main plot
ax1.set_title('Prices of the top 10 Brands advertised on the platform', fontsize=20, fontweight='bold',color='blue')

# Defining the x and y labels
ax1.set_xlabel('Brand', fontsize=20,fontweight='bold')
ax1.set_ylabel('Average Price', fontsize=20, fontweight='bold')

# # Adjusting the x and y items
ax1.tick_params(axis='x', rotation=45, labelsize=16) 
ax1.tick_params(axis='y', rotation=45, labelsize=16) 




# Plotting the inner graph for the count of the top 10 Brands
ax2 = fig.add_axes([0.5, 0.5, 0.25, 0.25])
ax2.bar(brand_count.index,brand_count, color='skyblue')

# Defining the title of the main plot
ax2.set_title('Top 10 advertised brands on the platform ', fontsize=20, fontweight='bold',color='skyblue')

# Defining the x and y labels
ax2.set_xlabel('Brand', fontsize=14,fontweight='bold')
ax2.set_ylabel('Count', fontsize=14, fontweight='bold')

# Adjusting the x and y items
ax2.tick_params(axis='x', rotation=45, labelsize=15) 
ax2.tick_params(axis='y', labelsize=15) 


# Display the plot
plt.show()

Possible reasons for the inverse relationship between the Brand price and the quantity:

The data reveals that the expensive cars do not meet the top 10 most sold or advertised cars on Ebay. Invariably, the less pricey cars are the most sold/advertised cars
- This tells us that either the demand and supply power is in play. The less pricey the car, the more they are in quantity, and vice-versa. This would nt be restricted to the platfomr alone, but the automobile industry in general.
- Most of the pricey cars are probably not sold on ebay (probaly there's a different market for those types of cars), and ebay is mostly popular for selling the less-pricey cars.
ebay doesn't have as much car sellers of whose who own/deal in luxury cars in comparism with those who own the less pricey cars (data sufficiency wouldn't allow us deterine this).

In [33]:

df['vehicletype'].value_counts()

Out[33]:

limousine        95894
small car        80023
station wagon    67564
None             37869
bus              30201
convertible      22898
coupe            19015
suv              14707
other             3357
Name: vehicletype, dtype: int64

In [34]:

import matplotlib.pyplot as plt
import pandas as pd

# Assuming df is your DataFrame

# Filter out rows where vehicletype is 'None' or 'Other'
filtered_df = df[~df['vehicletype'].isin(['None', 'other'])]

# Updated vehicle type and price data after filtering
vehicletype = filtered_df['vehicletype'].value_counts()
price_type = filtered_df.groupby('vehicletype')['price'].mean().sort_values(ascending=False)

# Create a figure and two subplots
fig, (axes1, axes2) = plt.subplots(1, 2, figsize=(12, 5))

# First subplot - Average prices based on vehicle types
axes1.bar(price_type.index, price_type, color='purple')
axes1.set_xlabel('Vehicle Types')
axes1.set_ylabel('Prices')
axes1.set_title('Top 7 Vehices per Price', fontweight='bold')
axes1.tick_params(axis='x', rotation=45, labelsize=10)  # Rotate x-axis labels for better reada6bility

# Second subplot - Count of vehicles based on types
axes2.bar(vehicletype.index, vehicletype, color='blue')
axes2.set_xlabel('Vehicle Type')
axes2.set_ylabel('Count')
axes2.set_title('Top 7 Vehicles', fontweight='bold')
axes2.tick_params(axis='x', rotation=45, labelsize=10)  # Rotate x-axis labels for better readability

plt.tight_layout()

plt.show()

Vehicle Type and Brand have the same trend; inverse relationship between the price and the vehicle type

As the brands, the trend is the same. The high-pricey vehicles are low in quantity, while the less-pricey vehicles are high in quantity.
The data reveals that the expensive vehicle types do not meet the top 7 most sold or advertised vehicle types on Ebay. Invariably, the less pricey vehicle types are the most sold/advertised vehicle types
In the automobile industry, either the limited quantity of the expensive vehicle types influenced the increased prices or because they're made with more quality-sourced resources, they're more expensive. Brands would also play a pivotal role in the pricing. The demand and supply power is in play. The less pricey the vehicle types , the more they are in quantity, and vice-versa. This would nt be restricted to the platfomr alone, but the automobile industry in general.
Most of the pricey vehicle types are probably not sold on eBay (probaly there's a different market for those types of cars), and ebay is mostly popular for selling the less-pricey vehicle types .
eBay doesn't have as much car sellers of whose who own/deal in luxury cars in comparism with those who own the less pricey cars (data sufficiency wouldn't allow us deterine this).
This gives an insight to the type of Private Dealers on the platfomr. Middle to low-inclome earners.

Trend Analysis over Time ¶

In [35]:

df

Out[35]:

	datecrawled	name	seller	offertype	price	abtest	vehicletype	yearofregistration	gearbox	powerps	model	kilometer	monthofregistration	fueltype	brand	notrepaireddamage	datecreated
0	2016-03-24T11:52:17	Golf_3_1.6	private	Offer	480	test	None	1993	Manual	0	golf	150000	0	Petrol	volkswagen	None	2016-03-24T00:00:00
1	2016-03-24T10:58:45	A5_Sportback_2.7_Tdi	private	Offer	18300	test	coupe	2011	Manual	190	None	125000	5	Diesel	audi	Yes	2016-03-24T00:00:00
2	2016-03-14T12:52:21	Jeep_Grand_Cherokee_"Overland"	private	Offer	9800	test	suv	2004	Automatic	163	grand	125000	8	Diesel	jeep	None	2016-03-14T00:00:00
3	2016-03-17T16:54:04	GOLF_4_1_4__3TÜRER	private	Offer	1500	test	small car	2001	Manual	75	golf	150000	6	Petrol	volkswagen	No	2016-03-17T00:00:00
4	2016-03-31T17:25:20	Skoda_Fabia_1.4_TDI_PD_Classic	private	Offer	3600	test	small car	2008	Manual	69	fabia	90000	7	Diesel	skoda	No	2016-03-31T00:00:00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
371523	2016-03-14T17:48:27	Suche_t4___vito_ab_6_sitze	private	Offer	2200	test	None	2005	None	0	None	20000	1	None	sonstige_autos	None	2016-03-14T00:00:00
371524	2016-03-05T19:56:21	Smart_smart_leistungssteigerung_100ps	private	Offer	1199	test	convertible	2000	Automatic	101	fortwo	125000	3	Petrol	smart	No	2016-03-05T00:00:00
371525	2016-03-19T18:57:12	Volkswagen_Multivan_T4_TDI_7DC_UY2	private	Offer	9200	test	bus	1996	Manual	102	transporter	150000	3	Diesel	volkswagen	No	2016-03-19T00:00:00
371526	2016-03-20T19:41:08	VW_Golf_Kombi_1_9l_TDI	private	Offer	3400	test	station wagon	2002	Manual	100	golf	150000	6	Diesel	volkswagen	None	2016-03-20T00:00:00
371527	2016-03-07T19:39:19	BMW_M135i_vollausgestattet_NP_52.720____Euro	private	Offer	28990	control	limousine	2013	Manual	320	m_reihe	50000	8	Petrol	bmw	No	2016-03-07T00:00:00

371528 rows × 17 columns

In [36]:

df['datecreated'] = pd.to_datetime(df['datecreated'])

year = df['datecreated'].dt.year
year_count = year.value_counts()

year_count

Out[36]:

2016    371498
2015        29
2014         1
Name: datecreated, dtype: int64

In [37]:

import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.ticker as ticker

df['datecreated'] = pd.to_datetime(df['datecreated'])
year_created = df['datecreated'].dt.year
year_count = year_created.value_counts()

fig = plt.figure()

axes1 = fig.add_axes([0, 0, 1, 1])
axes1.bar(year_count.index, year_count, color='blue')
axes1.set_xlabel('Year')
axes1.set_ylabel('Count')
axes1.set_title('Sales Year Trend', fontweight='bold')
axes1.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))  # Ensure integer values on x-axis



axes2 = fig.add_axes([0.25, 0.25, 0.25, 0.25])
axes2.bar(year_count.index,year_count, color='purple')
axes2.set_xlim(2014, 2015)
axes2.set_ylim(0, 35)
axes2.set_xlabel('Year', fontsize=8)
axes2.set_ylabel('Count', fontsize=8)
axes2.set_title('Zoomed_In View for years 2014 and 2015', fontsize=10)
axes2.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))  # Ensure integer values on x-axis


plt.show()

plt.tight_layout()

<Figure size 640x480 with 0 Axes>

Analyzing the reasons for the spike in Car Advertisements in 2016 ¶

More Ads were put out in 2016 than ever advertised. What was the cause for this?

Firstly, most of our top advertised brands released new models in 2016. As a matter of fact, they topped the list of top 25 bestmodels and car sales in year 2016
The most cars were sold in 2016, than in previous year. This was a general market trend.
A speculation could be due to the. From this report, 2016 increased by 4.51% to almost 3.4 million cars sold; the best since 2009.
This general rise were attributed to environmental bonus (which must have been promoted since 2016) but was introducted in 2020.

In [38]:

import matplotlib.pyplot as plt

sellers = df['seller'].value_counts()

fig = plt.figure()

ax1 = fig.add_axes([0, 0, 1, 1])
ax1.bar(sellers.index, sellers)
ax1.set_title("Sellers' Index", fontweight='bold')

ax2=fig.add_axes([0.5, 0.5, 0.25, 0.25])
ax2.bar(sellers.index, sellers)
ax2.set_ylim(0, 30)
ax2.set_title('Zoomed_In view', size=10)

Out[38]:

Text(0.5, 1.0, 'Zoomed_In view')

Why do we have more Privtate Sellers?

Firstly, the introduction of the environmental bonus tilted to favour individual ownership of vehicles. It's safe to assume most of the private sellers took the most of this bonus by plunging fulltime in car businesses.
Other questions revealed from thsi question is;
- is our marketing content selling our plaform more to private sellers car dealers?
- Could this mean, dealers have other marketing platforms or means they use in selling or advertising, that we're not aware of?

Brand Comparism ¶

In [39]:

df

Out[39]:

	datecrawled	name	seller	offertype	price	abtest	vehicletype	yearofregistration	gearbox	powerps	model	kilometer	monthofregistration	fueltype	brand	notrepaireddamage	datecreated
0	2016-03-24T11:52:17	Golf_3_1.6	private	Offer	480	test	None	1993	Manual	0	golf	150000	0	Petrol	volkswagen	None	2016-03-24
1	2016-03-24T10:58:45	A5_Sportback_2.7_Tdi	private	Offer	18300	test	coupe	2011	Manual	190	None	125000	5	Diesel	audi	Yes	2016-03-24
2	2016-03-14T12:52:21	Jeep_Grand_Cherokee_"Overland"	private	Offer	9800	test	suv	2004	Automatic	163	grand	125000	8	Diesel	jeep	None	2016-03-14
3	2016-03-17T16:54:04	GOLF_4_1_4__3TÜRER	private	Offer	1500	test	small car	2001	Manual	75	golf	150000	6	Petrol	volkswagen	No	2016-03-17
4	2016-03-31T17:25:20	Skoda_Fabia_1.4_TDI_PD_Classic	private	Offer	3600	test	small car	2008	Manual	69	fabia	90000	7	Diesel	skoda	No	2016-03-31
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
371523	2016-03-14T17:48:27	Suche_t4___vito_ab_6_sitze	private	Offer	2200	test	None	2005	None	0	None	20000	1	None	sonstige_autos	None	2016-03-14
371524	2016-03-05T19:56:21	Smart_smart_leistungssteigerung_100ps	private	Offer	1199	test	convertible	2000	Automatic	101	fortwo	125000	3	Petrol	smart	No	2016-03-05
371525	2016-03-19T18:57:12	Volkswagen_Multivan_T4_TDI_7DC_UY2	private	Offer	9200	test	bus	1996	Manual	102	transporter	150000	3	Diesel	volkswagen	No	2016-03-19
371526	2016-03-20T19:41:08	VW_Golf_Kombi_1_9l_TDI	private	Offer	3400	test	station wagon	2002	Manual	100	golf	150000	6	Diesel	volkswagen	None	2016-03-20
371527	2016-03-07T19:39:19	BMW_M135i_vollausgestattet_NP_52.720____Euro	private	Offer	28990	control	limousine	2013	Manual	320	m_reihe	50000	8	Petrol	bmw	No	2016-03-07

371528 rows × 17 columns

In [40]:

df['brand']

Out[40]:

0             volkswagen
1                   audi
2                   jeep
3             volkswagen
4                  skoda
               ...      
371523    sonstige_autos
371524             smart
371525        volkswagen
371526        volkswagen
371527               bmw
Name: brand, Length: 371528, dtype: object

In [41]:

import matplotlib.pyplot as plt
import pandas as pd


filtered_df = df[~df['vehicletype'].isin(['None', 'other'])]

vehicle_type = filtered_df['vehicletype'].value_counts()
brand = df['brand'].value_counts().nlargest(15)



fig = plt.figure(figsize=(24, 16))


axes1 = fig.add_axes([0, 0, 1, 1])

axes1.bar(brand.index, brand, color='#50ABC7')
axes1.set_title('Brand', fontweight='bold', fontsize=20)
axes1.set_xlabel('brands')
axes1.set_ylabel('brand count')
axes1.tick_params(axis='x', rotation=45, labelsize=13)


axes2 = fig.add_axes([0.5, 0.5, 0.25, 0.25])
axes2.bar(vehicle_type.index,vehicle_type)
axes2.set_title('Types of Vehicle', fontweight='bold')
# axes2.set_xlabel('vehicle types')
axes2.set_ylabel('count')
axes2.tick_params(axis='x', rotation=45, labelsize=10)


plt.show()

Why do we have more Privtate Sellers?

Firstly, the introduction of the environmental bonus tilted to favour individual ownership of vehicles. It's safe to assume most of the private sellers took the most of this bonus by plunging fulltime in car businesses.
Other questions revealed from thsi question is;
- is our marketing content selling our plaform more to private sellers car dealers?
- Could this mean, dealers have other marketing platforms or means they use in selling or advertising, that we're not aware of?

Vehicle Condition and Price Correlation ¶

In [42]:

df

Out[42]:

	datecrawled	name	seller	offertype	price	abtest	vehicletype	yearofregistration	gearbox	powerps	model	kilometer	monthofregistration	fueltype	brand	notrepaireddamage	datecreated
0	2016-03-24T11:52:17	Golf_3_1.6	private	Offer	480	test	None	1993	Manual	0	golf	150000	0	Petrol	volkswagen	None	2016-03-24
1	2016-03-24T10:58:45	A5_Sportback_2.7_Tdi	private	Offer	18300	test	coupe	2011	Manual	190	None	125000	5	Diesel	audi	Yes	2016-03-24
2	2016-03-14T12:52:21	Jeep_Grand_Cherokee_"Overland"	private	Offer	9800	test	suv	2004	Automatic	163	grand	125000	8	Diesel	jeep	None	2016-03-14
3	2016-03-17T16:54:04	GOLF_4_1_4__3TÜRER	private	Offer	1500	test	small car	2001	Manual	75	golf	150000	6	Petrol	volkswagen	No	2016-03-17
4	2016-03-31T17:25:20	Skoda_Fabia_1.4_TDI_PD_Classic	private	Offer	3600	test	small car	2008	Manual	69	fabia	90000	7	Diesel	skoda	No	2016-03-31
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
371523	2016-03-14T17:48:27	Suche_t4___vito_ab_6_sitze	private	Offer	2200	test	None	2005	None	0	None	20000	1	None	sonstige_autos	None	2016-03-14
371524	2016-03-05T19:56:21	Smart_smart_leistungssteigerung_100ps	private	Offer	1199	test	convertible	2000	Automatic	101	fortwo	125000	3	Petrol	smart	No	2016-03-05
371525	2016-03-19T18:57:12	Volkswagen_Multivan_T4_TDI_7DC_UY2	private	Offer	9200	test	bus	1996	Manual	102	transporter	150000	3	Diesel	volkswagen	No	2016-03-19
371526	2016-03-20T19:41:08	VW_Golf_Kombi_1_9l_TDI	private	Offer	3400	test	station wagon	2002	Manual	100	golf	150000	6	Diesel	volkswagen	None	2016-03-20
371527	2016-03-07T19:39:19	BMW_M135i_vollausgestattet_NP_52.720____Euro	private	Offer	28990	control	limousine	2013	Manual	320	m_reihe	50000	8	Petrol	bmw	No	2016-03-07

371528 rows × 17 columns

In [43]:

df['notrepaireddamage'] = df['notrepaireddamage'].replace({'Yes': 'repaired', 'No': 'never_repaired'})
filtered_damages = df['notrepaireddamage'][~df['notrepaireddamage'].isin(['None'])].value_counts()

filtered_damages

Out[43]:

never_repaired    263182
repaired           36286
Name: notrepaireddamage, dtype: int64

In [44]:

gearbox_counts = df[df['gearbox'] !='None']['gearbox'].value_counts()
filtered_df = df[df['gearbox'] != 'None']
gear_box = round(filtered_df.groupby('gearbox')['kilometer'].mean(), 2)

gear_box

Out[44]:

gearbox
Automatic    122592.96
Manual       127043.68
Name: kilometer, dtype: float64

In [ ]:

In [45]:

import matplotlib.pyplot as plt
import pandas as pd

filtered_damages = df['notrepaireddamage'][df['notrepaireddamage'].isin(['Yes', 'No'])].value_counts()

filtered_damages = df[~df['notrepaireddamage'].isin(['None'])]['notrepaireddamage'].value_counts()
prices = df['price'].value_counts()

gearbox_counts = df[df['gearbox'] !='None']['gearbox'].value_counts()
filtered_df = df[df['gearbox'] != 'None']
gear_box = round(filtered_df.groupby('gearbox')['kilometer'].mean(), 2)


   
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(38, 15))

ax1.bar(gearbox_counts.index, gearbox_counts, label='Gearbox')
ax1.set_xlabel('Gear_Box', size=22, weight='bold')
ax1.set_ylabel('Count', size=22, weight='bold')
ax1.tick_params(axis='x', labelsize=20)  
ax1.tick_params(axis='y', labelsize=20)  

ax2.bar(filtered_damages.index, filtered_damages, label='Repair Status', color='skyblue')
ax2.set_xlabel('Repair Status', size=22, weight='bold')
ax2.set_ylabel('Count', size=22, weight='bold')
ax2.tick_params(axis='x', labelsize=20)  
ax2.tick_params(axis='y', labelsize=20)  


ax3.bar(gear_box.index, gear_box, label='Avg kilomenter per Gear_Type')
ax3.set_xlabel('Gear_Type', size=22, weight='bold')
ax3.set_ylabel('Kilometer', size=22, weight='bold')
ax3.tick_params(axis='x', labelsize=20)  
ax3.tick_params(axis='y', labelsize=20)  


ax1.legend(fontsize=26)
ax2.legend(loc='center right', fontsize=26)
ax3.legend(loc='lower left', fontsize=26)

# plt.tight_layout()

plt.show()

Vehicle conditions sold on the platform.

The data reveals that manual cars are the stongest and they make up cars which haven't been repaired.
- If we are able to analyse the sales data, we can determine if the vehicle conditions have imporved sales.

Fuel Type Preference ¶

In [46]:

df

Out[46]:

	datecrawled	name	seller	offertype	price	abtest	vehicletype	yearofregistration	gearbox	powerps	model	kilometer	monthofregistration	fueltype	brand	notrepaireddamage	datecreated
0	2016-03-24T11:52:17	Golf_3_1.6	private	Offer	480	test	None	1993	Manual	0	golf	150000	0	Petrol	volkswagen	None	2016-03-24
1	2016-03-24T10:58:45	A5_Sportback_2.7_Tdi	private	Offer	18300	test	coupe	2011	Manual	190	None	125000	5	Diesel	audi	repaired	2016-03-24
2	2016-03-14T12:52:21	Jeep_Grand_Cherokee_"Overland"	private	Offer	9800	test	suv	2004	Automatic	163	grand	125000	8	Diesel	jeep	None	2016-03-14
3	2016-03-17T16:54:04	GOLF_4_1_4__3TÜRER	private	Offer	1500	test	small car	2001	Manual	75	golf	150000	6	Petrol	volkswagen	never_repaired	2016-03-17
4	2016-03-31T17:25:20	Skoda_Fabia_1.4_TDI_PD_Classic	private	Offer	3600	test	small car	2008	Manual	69	fabia	90000	7	Diesel	skoda	never_repaired	2016-03-31
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
371523	2016-03-14T17:48:27	Suche_t4___vito_ab_6_sitze	private	Offer	2200	test	None	2005	None	0	None	20000	1	None	sonstige_autos	None	2016-03-14
371524	2016-03-05T19:56:21	Smart_smart_leistungssteigerung_100ps	private	Offer	1199	test	convertible	2000	Automatic	101	fortwo	125000	3	Petrol	smart	never_repaired	2016-03-05
371525	2016-03-19T18:57:12	Volkswagen_Multivan_T4_TDI_7DC_UY2	private	Offer	9200	test	bus	1996	Manual	102	transporter	150000	3	Diesel	volkswagen	never_repaired	2016-03-19
371526	2016-03-20T19:41:08	VW_Golf_Kombi_1_9l_TDI	private	Offer	3400	test	station wagon	2002	Manual	100	golf	150000	6	Diesel	volkswagen	None	2016-03-20
371527	2016-03-07T19:39:19	BMW_M135i_vollausgestattet_NP_52.720____Euro	private	Offer	28990	control	limousine	2013	Manual	320	m_reihe	50000	8	Petrol	bmw	never_repaired	2016-03-07

371528 rows × 17 columns

In [47]:

import matplotlib.pyplot as plt


fuel_type = df['fueltype'][~df['fueltype'].isin(['None', 'Others'])].value_counts()

plt.figure(figsize=(12, 8))
plt.bar(fuel_type.index, fuel_type, label='Fuel Type Preference', color='#C79F50')
plt.tick_params(axis='x', rotation = 45, labelsize=8)
plt.tick_params(axis='y', labelsize=8)

plt.legend();

Vehicle Conditions

From research, this is a general trend across the country

Feature Importances ¶

In [48]:

df

Out[48]:

	datecrawled	name	seller	offertype	price	abtest	vehicletype	yearofregistration	gearbox	powerps	model	kilometer	monthofregistration	fueltype	brand	notrepaireddamage	datecreated
0	2016-03-24T11:52:17	Golf_3_1.6	private	Offer	480	test	None	1993	Manual	0	golf	150000	0	Petrol	volkswagen	None	2016-03-24
1	2016-03-24T10:58:45	A5_Sportback_2.7_Tdi	private	Offer	18300	test	coupe	2011	Manual	190	None	125000	5	Diesel	audi	repaired	2016-03-24
2	2016-03-14T12:52:21	Jeep_Grand_Cherokee_"Overland"	private	Offer	9800	test	suv	2004	Automatic	163	grand	125000	8	Diesel	jeep	None	2016-03-14
3	2016-03-17T16:54:04	GOLF_4_1_4__3TÜRER	private	Offer	1500	test	small car	2001	Manual	75	golf	150000	6	Petrol	volkswagen	never_repaired	2016-03-17
4	2016-03-31T17:25:20	Skoda_Fabia_1.4_TDI_PD_Classic	private	Offer	3600	test	small car	2008	Manual	69	fabia	90000	7	Diesel	skoda	never_repaired	2016-03-31
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
371523	2016-03-14T17:48:27	Suche_t4___vito_ab_6_sitze	private	Offer	2200	test	None	2005	None	0	None	20000	1	None	sonstige_autos	None	2016-03-14
371524	2016-03-05T19:56:21	Smart_smart_leistungssteigerung_100ps	private	Offer	1199	test	convertible	2000	Automatic	101	fortwo	125000	3	Petrol	smart	never_repaired	2016-03-05
371525	2016-03-19T18:57:12	Volkswagen_Multivan_T4_TDI_7DC_UY2	private	Offer	9200	test	bus	1996	Manual	102	transporter	150000	3	Diesel	volkswagen	never_repaired	2016-03-19
371526	2016-03-20T19:41:08	VW_Golf_Kombi_1_9l_TDI	private	Offer	3400	test	station wagon	2002	Manual	100	golf	150000	6	Diesel	volkswagen	None	2016-03-20
371527	2016-03-07T19:39:19	BMW_M135i_vollausgestattet_NP_52.720____Euro	private	Offer	28990	control	limousine	2013	Manual	320	m_reihe	50000	8	Petrol	bmw	never_repaired	2016-03-07

371528 rows × 17 columns

In [49]:

import matplotlib.pyplot as plt

powerps = df['powerps'].value_counts()

# plt.bar(powerps.index, powerps)
powerps 

# power, gearbox type, vehicle type,

Out[49]:

0       40820
75      24035
60      15907
150     15442
140     13585
        ...  
1339        1
780         1
6920        1
1659        1
564         1
Name: powerps, Length: 794, dtype: int64

In [50]:

filtered_gearbox = df[~df['gearbox'].isin(['None'])]

avg_power_gear = round(filtered_gearbox.groupby('gearbox')['powerps'].mean(), 2)

In [51]:

filtered_vehicles = df[df['vehicletype']!='None']

avg_power_veh = round(filtered_vehicles.groupby('vehicletype')['powerps'].mean())

avg_power_veh

Out[51]:

vehicletype
bus              114.0
convertible      145.0
coupe            173.0
limousine        132.0
other            102.0
small car         69.0
station wagon    136.0
suv              166.0
Name: powerps, dtype: float64

In [52]:

import matplotlib.pyplot as plt

filtered_gearbox = df[~df['gearbox'].isin(['None'])]
avg_power_gear = round(filtered_gearbox.groupby('gearbox')['powerps'].mean(), 2)
filtered_vehicles = df[df['vehicletype']!='None']
avg_power_veh = round(filtered_vehicles.groupby('vehicletype')['powerps'].mean())

fig, (axis1, axis2) = plt.subplots(1, 2, figsize=(20, 14), gridspec_kw={'width_ratios': [1, 2]})

axis1.bar(avg_power_gear.index, avg_power_gear)

axis1.set_xlabel('Gearbox', fontweight='bold')
axis1.set_ylabel('Powerps', fontweight='bold')

axis1.set_title('Avg Powerups per GearBox', fontweight='bold', fontsize=10)

# Axis2
axis2.bar(avg_power_veh.index, avg_power_veh)

axis2.set_title('Avg Powerups per Vehicle Type', fontweight='bold', fontsize=10)

plt.show()

Powerps of Advertised Vehicles

The data reveals that automatic cars have more Powerps than manual Cars; even though previous graph showed that manual cars are sttronger, with less repairs. This simply means Automatic cars focus on efficiency and speed, rather than the durability and effectiveness of namual cars.

Since majority of the cars sold on the platform are manual cars, this would help us understand customer preference

Conclusion:

Automatic cars have more horsepower than manual cars, showing a focus on efficiency and speed for automatics versus the durability of manuals. Despite this, most cars sold are manuals, reflecting customer preference.
Expensive cars aren't in the top-selling list on eBay; cheaper cars dominate. Likely, demand and supply dynamics favor lower-priced cars, possibly due to a different market for pricier vehicles.
Similar trends exist among brands and vehicle types: high-priced ones are less common, suggesting limited quantities or higher-quality resources contributing to their cost.
More ads were posted in 2016, likely due to top brands releasing new models, a general market trend of increased car sales, and factors like the environmental bonus introduced in 2020.
The environmental bonus likely encouraged individual ownership, possibly driving more private sellers into the car business.
Questions arise about the effectiveness of the platform's marketing to private sellers and whether dealers use alternative marketing channels we're not aware of.