We will work with a dataset of used cars from eBay Klieinanzeigen, a classifieds section of the German eBay website. The aim of the project is to clean the data and analyze the included used car listings.
The data dictionary provided with data as follows:
dateCrawled
- When this ad was first crawled. All field-values are taken from this date.name
- Name of the car.seller
- Whether the seller is private or a dealer.offerType
- The type of listingprice
- The price on the ad to sell the car.abtest
- Whether the listing is included in an A/B test.vehicleType
- The vehicle Type.yearOfRegistration
- The year in which the car was first registeredgearbox
- the transmission type.powerPS
- The power of the car in PS.model
- The car model name.kilometer
- How many kilometers the car has driven.monthofRegistration
- The month in which the car was first registered.fuelType
- What type of fuel the car uses.brand
- The brand of the car.notRepairedDamage
- if the car has a damage which is not yet repaired.dateCreated
- The date on which the eBay listing was created.nrOfPictures
- The number of pictures in the ad.postalCode
- The postal code for the location of the vehicle.lastSeenOnline
- When the crawler saw this ad last online.import pandas as pd
import numpy as np
autos = pd.read_csv('autos.csv', encoding='Latin-1')
autos
dateCrawled | name | seller | offerType | price | abtest | vehicleType | yearOfRegistration | gearbox | powerPS | model | odometer | monthOfRegistration | fuelType | brand | notRepairedDamage | dateCreated | nrOfPictures | postalCode | lastSeen | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2016-03-26 17:47:46 | Peugeot_807_160_NAVTECH_ON_BOARD | privat | Angebot | $5,000 | control | bus | 2004 | manuell | 158 | andere | 150,000km | 3 | lpg | peugeot | nein | 2016-03-26 00:00:00 | 0 | 79588 | 2016-04-06 06:45:54 |
1 | 2016-04-04 13:38:56 | BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik | privat | Angebot | $8,500 | control | limousine | 1997 | automatik | 286 | 7er | 150,000km | 6 | benzin | bmw | nein | 2016-04-04 00:00:00 | 0 | 71034 | 2016-04-06 14:45:08 |
2 | 2016-03-26 18:57:24 | Volkswagen_Golf_1.6_United | privat | Angebot | $8,990 | test | limousine | 2009 | manuell | 102 | golf | 70,000km | 7 | benzin | volkswagen | nein | 2016-03-26 00:00:00 | 0 | 35394 | 2016-04-06 20:15:37 |
3 | 2016-03-12 16:58:10 | Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan... | privat | Angebot | $4,350 | control | kleinwagen | 2007 | automatik | 71 | fortwo | 70,000km | 6 | benzin | smart | nein | 2016-03-12 00:00:00 | 0 | 33729 | 2016-03-15 03:16:28 |
4 | 2016-04-01 14:38:50 | Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg... | privat | Angebot | $1,350 | test | kombi | 2003 | manuell | 0 | focus | 150,000km | 7 | benzin | ford | nein | 2016-04-01 00:00:00 | 0 | 39218 | 2016-04-01 14:38:50 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
49995 | 2016-03-27 14:38:19 | Audi_Q5_3.0_TDI_qu._S_tr.__Navi__Panorama__Xenon | privat | Angebot | $24,900 | control | limousine | 2011 | automatik | 239 | q5 | 100,000km | 1 | diesel | audi | nein | 2016-03-27 00:00:00 | 0 | 82131 | 2016-04-01 13:47:40 |
49996 | 2016-03-28 10:50:25 | Opel_Astra_F_Cabrio_Bertone_Edition___TÜV_neu+... | privat | Angebot | $1,980 | control | cabrio | 1996 | manuell | 75 | astra | 150,000km | 5 | benzin | opel | nein | 2016-03-28 00:00:00 | 0 | 44807 | 2016-04-02 14:18:02 |
49997 | 2016-04-02 14:44:48 | Fiat_500_C_1.2_Dualogic_Lounge | privat | Angebot | $13,200 | test | cabrio | 2014 | automatik | 69 | 500 | 5,000km | 11 | benzin | fiat | nein | 2016-04-02 00:00:00 | 0 | 73430 | 2016-04-04 11:47:27 |
49998 | 2016-03-08 19:25:42 | Audi_A3_2.0_TDI_Sportback_Ambition | privat | Angebot | $22,900 | control | kombi | 2013 | manuell | 150 | a3 | 40,000km | 11 | diesel | audi | nein | 2016-03-08 00:00:00 | 0 | 35683 | 2016-04-05 16:45:07 |
49999 | 2016-03-14 00:42:12 | Opel_Vectra_1.6_16V | privat | Angebot | $1,250 | control | limousine | 1996 | manuell | 101 | vectra | 150,000km | 1 | benzin | opel | nein | 2016-03-13 00:00:00 | 0 | 45897 | 2016-04-06 21:18:48 |
50000 rows × 20 columns
autos.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 50000 entries, 0 to 49999 Data columns (total 20 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 dateCrawled 50000 non-null object 1 name 50000 non-null object 2 seller 50000 non-null object 3 offerType 50000 non-null object 4 price 50000 non-null object 5 abtest 50000 non-null object 6 vehicleType 44905 non-null object 7 yearOfRegistration 50000 non-null int64 8 gearbox 47320 non-null object 9 powerPS 50000 non-null int64 10 model 47242 non-null object 11 odometer 50000 non-null object 12 monthOfRegistration 50000 non-null int64 13 fuelType 45518 non-null object 14 brand 50000 non-null object 15 notRepairedDamage 40171 non-null object 16 dateCreated 50000 non-null object 17 nrOfPictures 50000 non-null int64 18 postalCode 50000 non-null int64 19 lastSeen 50000 non-null object dtypes: int64(5), object(15) memory usage: 7.6+ MB
#Check the missing values
missing_values = autos.isnull().sum()
missing_values.sort_values(ascending = False)
#Find percentage of missing values
percentage_missing = round((missing_values/len(autos)) * 100)
percentage_missing.sort_values(ascending = False)
notRepairedDamage 20.0 vehicleType 10.0 fuelType 9.0 model 6.0 gearbox 5.0 lastSeen 0.0 yearOfRegistration 0.0 name 0.0 seller 0.0 offerType 0.0 price 0.0 abtest 0.0 powerPS 0.0 postalCode 0.0 odometer 0.0 monthOfRegistration 0.0 brand 0.0 dateCreated 0.0 nrOfPictures 0.0 dateCrawled 0.0 dtype: float64
autos.head()
dateCrawled | name | seller | offerType | price | abtest | vehicleType | yearOfRegistration | gearbox | powerPS | model | odometer | monthOfRegistration | fuelType | brand | notRepairedDamage | dateCreated | nrOfPictures | postalCode | lastSeen | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2016-03-26 17:47:46 | Peugeot_807_160_NAVTECH_ON_BOARD | privat | Angebot | $5,000 | control | bus | 2004 | manuell | 158 | andere | 150,000km | 3 | lpg | peugeot | nein | 2016-03-26 00:00:00 | 0 | 79588 | 2016-04-06 06:45:54 |
1 | 2016-04-04 13:38:56 | BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik | privat | Angebot | $8,500 | control | limousine | 1997 | automatik | 286 | 7er | 150,000km | 6 | benzin | bmw | nein | 2016-04-04 00:00:00 | 0 | 71034 | 2016-04-06 14:45:08 |
2 | 2016-03-26 18:57:24 | Volkswagen_Golf_1.6_United | privat | Angebot | $8,990 | test | limousine | 2009 | manuell | 102 | golf | 70,000km | 7 | benzin | volkswagen | nein | 2016-03-26 00:00:00 | 0 | 35394 | 2016-04-06 20:15:37 |
3 | 2016-03-12 16:58:10 | Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan... | privat | Angebot | $4,350 | control | kleinwagen | 2007 | automatik | 71 | fortwo | 70,000km | 6 | benzin | smart | nein | 2016-03-12 00:00:00 | 0 | 33729 | 2016-03-15 03:16:28 |
4 | 2016-04-01 14:38:50 | Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg... | privat | Angebot | $1,350 | test | kombi | 2003 | manuell | 0 | focus | 150,000km | 7 | benzin | ford | nein | 2016-04-01 00:00:00 | 0 | 39218 | 2016-04-01 14:38:50 |
Observations
Vehicle Type
, Gear Box, Model
, Fuel Type
, Not Repaired Damage
have null values (no more than ~20% null values)autos.columns
Index(['dateCrawled', 'name', 'seller', 'offerType', 'price', 'abtest', 'vehicleType', 'yearOfRegistration', 'gearbox', 'powerPS', 'model', 'odometer', 'monthOfRegistration', 'fuelType', 'brand', 'notRepairedDamage', 'dateCreated', 'nrOfPictures', 'postalCode', 'lastSeen'], dtype='object')
#Change the column names to appropriate snakecase letters
autos.columns = ['date_crawled', 'name', 'seller', 'offer_type',
'price', 'abtest', 'vehicle_type', 'registeration_year',
'gearbox', 'power_ps', 'model', 'odometer',
'registeration_month', 'fuel_type', 'brand',
'unrepaired_damage', 'ad_created', 'nr_of_pictures',
'postal_code', 'last_seen']
autos.columns
Index(['date_crawled', 'name', 'seller', 'offer_type', 'price', 'abtest', 'vehicle_type', 'registeration_year', 'gearbox', 'power_ps', 'model', 'odometer', 'registeration_month', 'fuel_type', 'brand', 'unrepaired_damage', 'ad_created', 'nr_of_pictures', 'postal_code', 'last_seen'], dtype='object')
autos.head()
date_crawled | name | seller | offer_type | price | abtest | vehicle_type | registeration_year | gearbox | power_ps | model | odometer | registeration_month | fuel_type | brand | unrepaired_damage | ad_created | nr_of_pictures | postal_code | last_seen | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2016-03-26 17:47:46 | Peugeot_807_160_NAVTECH_ON_BOARD | privat | Angebot | $5,000 | control | bus | 2004 | manuell | 158 | andere | 150,000km | 3 | lpg | peugeot | nein | 2016-03-26 00:00:00 | 0 | 79588 | 2016-04-06 06:45:54 |
1 | 2016-04-04 13:38:56 | BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik | privat | Angebot | $8,500 | control | limousine | 1997 | automatik | 286 | 7er | 150,000km | 6 | benzin | bmw | nein | 2016-04-04 00:00:00 | 0 | 71034 | 2016-04-06 14:45:08 |
2 | 2016-03-26 18:57:24 | Volkswagen_Golf_1.6_United | privat | Angebot | $8,990 | test | limousine | 2009 | manuell | 102 | golf | 70,000km | 7 | benzin | volkswagen | nein | 2016-03-26 00:00:00 | 0 | 35394 | 2016-04-06 20:15:37 |
3 | 2016-03-12 16:58:10 | Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan... | privat | Angebot | $4,350 | control | kleinwagen | 2007 | automatik | 71 | fortwo | 70,000km | 6 | benzin | smart | nein | 2016-03-12 00:00:00 | 0 | 33729 | 2016-03-15 03:16:28 |
4 | 2016-04-01 14:38:50 | Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg... | privat | Angebot | $1,350 | test | kombi | 2003 | manuell | 0 | focus | 150,000km | 7 | benzin | ford | nein | 2016-04-01 00:00:00 | 0 | 39218 | 2016-04-01 14:38:50 |
autos.describe(include = 'all')
date_crawled | name | seller | offer_type | price | abtest | vehicle_type | registeration_year | gearbox | power_ps | model | odometer | registeration_month | fuel_type | brand | unrepaired_damage | ad_created | nr_of_pictures | postal_code | last_seen | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 50000 | 50000 | 50000 | 50000 | 50000 | 50000 | 44905 | 50000.000000 | 47320 | 50000.000000 | 47242 | 50000 | 50000.000000 | 45518 | 50000 | 40171 | 50000 | 50000.0 | 50000.000000 | 50000 |
unique | 48213 | 38754 | 2 | 2 | 2357 | 2 | 8 | NaN | 2 | NaN | 245 | 13 | NaN | 7 | 40 | 2 | 76 | NaN | NaN | 39481 |
top | 2016-03-09 11:54:38 | Ford_Fiesta | privat | Angebot | $0 | test | limousine | NaN | manuell | NaN | golf | 150,000km | NaN | benzin | volkswagen | nein | 2016-04-03 00:00:00 | NaN | NaN | 2016-04-07 06:17:27 |
freq | 3 | 78 | 49999 | 49999 | 1421 | 25756 | 12859 | NaN | 36993 | NaN | 4024 | 32424 | NaN | 30107 | 10687 | 35232 | 1946 | NaN | NaN | 8 |
mean | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2005.073280 | NaN | 116.355920 | NaN | NaN | 5.723360 | NaN | NaN | NaN | NaN | 0.0 | 50813.627300 | NaN |
std | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 105.712813 | NaN | 209.216627 | NaN | NaN | 3.711984 | NaN | NaN | NaN | NaN | 0.0 | 25779.747957 | NaN |
min | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1000.000000 | NaN | 0.000000 | NaN | NaN | 0.000000 | NaN | NaN | NaN | NaN | 0.0 | 1067.000000 | NaN |
25% | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1999.000000 | NaN | 70.000000 | NaN | NaN | 3.000000 | NaN | NaN | NaN | NaN | 0.0 | 30451.000000 | NaN |
50% | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2003.000000 | NaN | 105.000000 | NaN | NaN | 6.000000 | NaN | NaN | NaN | NaN | 0.0 | 49577.000000 | NaN |
75% | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2008.000000 | NaN | 150.000000 | NaN | NaN | 9.000000 | NaN | NaN | NaN | NaN | 0.0 | 71540.000000 | NaN |
max | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 9999.000000 | NaN | 17700.000000 | NaN | NaN | 12.000000 | NaN | NaN | NaN | NaN | 0.0 | 99998.000000 | NaN |
autos['price'].value_counts()
$0 1421 $500 781 $1,500 734 $2,500 643 $1,200 639 ... $14,321 1 $5,475 1 $33,777 1 $4,222 1 $889 1 Name: price, Length: 2357, dtype: int64
autos['odometer'].value_counts()
150,000km 32424 125,000km 5170 100,000km 2169 90,000km 1757 80,000km 1436 70,000km 1230 60,000km 1164 50,000km 1027 5,000km 967 40,000km 819 30,000km 789 20,000km 784 10,000km 264 Name: odometer, dtype: int64
autos['price'].head()
0 $5,000 1 $8,500 2 $8,990 3 $4,350 4 $1,350 Name: price, dtype: object
reg_year = autos['registeration_year'].unique()
np.sort(reg_year)
array([1000, 1001, 1111, 1500, 1800, 1910, 1927, 1929, 1931, 1934, 1937, 1938, 1939, 1941, 1943, 1948, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2800, 4100, 4500, 4800, 5000, 5911, 6200, 8888, 9000, 9996, 9999])
autos['nr_of_pictures'].value_counts()
0 50000 Name: nr_of_pictures, dtype: int64
price
and odometer
columns need to be converted to numeric types from text typesregisteration year
contains invalid years because it ranges from 1000 to 9999 yearsnr_of_pictures
can be dropped because it only contains 0 as its valueseller
and offer types
have almost the same values#Remove any non-numeric characters in the data
autos['price'] = autos['price'].str.replace('$', '')
autos['price'] = autos['price'].str.replace(',', '')
autos['odometer'] = autos['odometer'].str.replace('km', '')
autos['odometer'] = autos['odometer'].str.replace(',', '')
#Convert columns to numeric dtype
autos['price'] = autos['price'].astype(float)
autos['odometer'] = autos['odometer'].astype(float)
autos
date_crawled | name | seller | offer_type | price | abtest | vehicle_type | registeration_year | gearbox | power_ps | model | odometer | registeration_month | fuel_type | brand | unrepaired_damage | ad_created | nr_of_pictures | postal_code | last_seen | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2016-03-26 17:47:46 | Peugeot_807_160_NAVTECH_ON_BOARD | privat | Angebot | 5000.0 | control | bus | 2004 | manuell | 158 | andere | 150000.0 | 3 | lpg | peugeot | nein | 2016-03-26 00:00:00 | 0 | 79588 | 2016-04-06 06:45:54 |
1 | 2016-04-04 13:38:56 | BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik | privat | Angebot | 8500.0 | control | limousine | 1997 | automatik | 286 | 7er | 150000.0 | 6 | benzin | bmw | nein | 2016-04-04 00:00:00 | 0 | 71034 | 2016-04-06 14:45:08 |
2 | 2016-03-26 18:57:24 | Volkswagen_Golf_1.6_United | privat | Angebot | 8990.0 | test | limousine | 2009 | manuell | 102 | golf | 70000.0 | 7 | benzin | volkswagen | nein | 2016-03-26 00:00:00 | 0 | 35394 | 2016-04-06 20:15:37 |
3 | 2016-03-12 16:58:10 | Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan... | privat | Angebot | 4350.0 | control | kleinwagen | 2007 | automatik | 71 | fortwo | 70000.0 | 6 | benzin | smart | nein | 2016-03-12 00:00:00 | 0 | 33729 | 2016-03-15 03:16:28 |
4 | 2016-04-01 14:38:50 | Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg... | privat | Angebot | 1350.0 | test | kombi | 2003 | manuell | 0 | focus | 150000.0 | 7 | benzin | ford | nein | 2016-04-01 00:00:00 | 0 | 39218 | 2016-04-01 14:38:50 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
49995 | 2016-03-27 14:38:19 | Audi_Q5_3.0_TDI_qu._S_tr.__Navi__Panorama__Xenon | privat | Angebot | 24900.0 | control | limousine | 2011 | automatik | 239 | q5 | 100000.0 | 1 | diesel | audi | nein | 2016-03-27 00:00:00 | 0 | 82131 | 2016-04-01 13:47:40 |
49996 | 2016-03-28 10:50:25 | Opel_Astra_F_Cabrio_Bertone_Edition___TÜV_neu+... | privat | Angebot | 1980.0 | control | cabrio | 1996 | manuell | 75 | astra | 150000.0 | 5 | benzin | opel | nein | 2016-03-28 00:00:00 | 0 | 44807 | 2016-04-02 14:18:02 |
49997 | 2016-04-02 14:44:48 | Fiat_500_C_1.2_Dualogic_Lounge | privat | Angebot | 13200.0 | test | cabrio | 2014 | automatik | 69 | 500 | 5000.0 | 11 | benzin | fiat | nein | 2016-04-02 00:00:00 | 0 | 73430 | 2016-04-04 11:47:27 |
49998 | 2016-03-08 19:25:42 | Audi_A3_2.0_TDI_Sportback_Ambition | privat | Angebot | 22900.0 | control | kombi | 2013 | manuell | 150 | a3 | 40000.0 | 11 | diesel | audi | nein | 2016-03-08 00:00:00 | 0 | 35683 | 2016-04-05 16:45:07 |
49999 | 2016-03-14 00:42:12 | Opel_Vectra_1.6_16V | privat | Angebot | 1250.0 | control | limousine | 1996 | manuell | 101 | vectra | 150000.0 | 1 | benzin | opel | nein | 2016-03-13 00:00:00 | 0 | 45897 | 2016-04-06 21:18:48 |
50000 rows × 20 columns
#Rename column name - odometer to odometer_km
autos.rename({'odometer': 'odometer_km'}, axis=1, inplace=True)
autos.columns
Index(['date_crawled', 'name', 'seller', 'offer_type', 'price', 'abtest', 'vehicle_type', 'registeration_year', 'gearbox', 'power_ps', 'model', 'odometer_km', 'registeration_month', 'fuel_type', 'brand', 'unrepaired_damage', 'ad_created', 'nr_of_pictures', 'postal_code', 'last_seen'], dtype='object')
autos['seller'].value_counts()
privat 49999 gewerblich 1 Name: seller, dtype: int64
autos['offer_type'].value_counts()
Angebot 49999 Gesuch 1 Name: offer_type, dtype: int64
odometer_km
and price
values do not look rightautos['odometer_km'].unique().shape
(13,)
autos['odometer_km'].describe()
count 50000.000000 mean 125732.700000 std 40042.211706 min 5000.000000 25% 125000.000000 50% 150000.000000 75% 150000.000000 max 150000.000000 Name: odometer_km, dtype: float64
autos['odometer_km'].value_counts().sort_index(ascending=False)
150000.0 32424 125000.0 5170 100000.0 2169 90000.0 1757 80000.0 1436 70000.0 1230 60000.0 1164 50000.0 1027 40000.0 819 30000.0 789 20000.0 784 10000.0 264 5000.0 967 Name: odometer_km, dtype: int64
Based on our analysis, there seems to be no major outliers in the odometer_km
column
autos['price'].unique().shape
(2357,)
autos['price'].describe()
count 5.000000e+04 mean 9.840044e+03 std 4.811044e+05 min 0.000000e+00 25% 1.100000e+03 50% 2.950000e+03 75% 7.200000e+03 max 1.000000e+08 Name: price, dtype: float64
autos['price'].value_counts().sort_index(ascending=False)
99999999.0 1 27322222.0 1 12345678.0 3 11111111.0 2 10000000.0 1 ... 5.0 2 3.0 1 2.0 3 1.0 156 0.0 1421 Name: price, Length: 2357, dtype: int64
autos['price'].min(), autos['price'].max(), autos['price'].median(), autos['price'].mean()
(0.0, 99999999.0, 2950.0, 9840.04376)
In the price
column, we need to remove the numbers between 0 and 1, and any numbers above 10 million, because they are all outliers.
We should use boolean filter and filter index to remove these outliers
#Find all price values between $0 and $5
price_0to5_bool = autos["price"].between(0, 5)
price_0to5_bool = autos[price_0to5_bool]
price_0to5_bool.index.name = 'index_0to5'
price_0to5_bool
date_crawled | name | seller | offer_type | price | abtest | vehicle_type | registeration_year | gearbox | power_ps | model | odometer_km | registeration_month | fuel_type | brand | unrepaired_damage | ad_created | nr_of_pictures | postal_code | last_seen | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
index_0to5 | ||||||||||||||||||||
27 | 2016-03-27 18:45:01 | Hat_einer_Ahnung_mit_Ford_Galaxy_HILFE | privat | Angebot | 0.0 | control | NaN | 2005 | NaN | 0 | NaN | 150000.0 | 0 | NaN | ford | NaN | 2016-03-27 00:00:00 | 0 | 66701 | 2016-03-27 18:45:01 |
55 | 2016-03-07 02:47:54 | Mercedes_E320_AMG_zu_Tauschen! | privat | Angebot | 1.0 | test | NaN | 2017 | automatik | 224 | e_klasse | 125000.0 | 7 | benzin | mercedes_benz | nein | 2016-03-06 00:00:00 | 0 | 22111 | 2016-03-08 05:45:44 |
71 | 2016-03-28 19:39:35 | Suche_Opel_Astra_F__Corsa_oder_Kadett_E_mit_Re... | privat | Angebot | 0.0 | control | NaN | 1990 | manuell | 0 | NaN | 5000.0 | 0 | benzin | opel | NaN | 2016-03-28 00:00:00 | 0 | 4552 | 2016-04-07 01:45:48 |
80 | 2016-03-09 15:57:57 | Nissan_Primera_Hatchback_1_6_16v_73_Kw___99Ps_... | privat | Angebot | 0.0 | control | coupe | 1999 | manuell | 99 | primera | 150000.0 | 3 | benzin | nissan | ja | 2016-03-09 00:00:00 | 0 | 66903 | 2016-03-09 16:43:50 |
87 | 2016-03-29 23:37:22 | Bmw_520_e39_zum_ausschlachten | privat | Angebot | 0.0 | control | NaN | 2000 | NaN | 0 | 5er | 150000.0 | 0 | NaN | bmw | NaN | 2016-03-29 00:00:00 | 0 | 82256 | 2016-04-06 21:18:15 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
49884 | 2016-03-11 13:55:30 | Audi_a6_2.5l__Schnaeppchen_nur_heute | privat | Angebot | 0.0 | test | kombi | 1999 | manuell | 150 | a6 | 150000.0 | 11 | diesel | audi | NaN | 2016-03-11 00:00:00 | 0 | 27711 | 2016-03-12 03:17:08 |
49943 | 2016-03-16 20:46:08 | Opel_astra | privat | Angebot | 0.0 | control | NaN | 2016 | manuell | 101 | astra | 150000.0 | 8 | benzin | opel | NaN | 2016-03-16 00:00:00 | 0 | 89134 | 2016-03-17 19:44:20 |
49960 | 2016-03-25 22:51:55 | Ford_KA_zu_verschenken_***Reserviert*** | privat | Angebot | 0.0 | control | kleinwagen | 1999 | manuell | 60 | ka | 150000.0 | 6 | benzin | ford | NaN | 2016-03-25 00:00:00 | 0 | 34355 | 2016-03-25 22:51:55 |
49974 | 2016-03-20 10:52:31 | Golf_1_Cabrio_Tuev_Neu_viele_Extras_alles_eing... | privat | Angebot | 0.0 | control | cabrio | 1983 | manuell | 70 | golf | 150000.0 | 2 | benzin | volkswagen | nein | 2016-03-20 00:00:00 | 0 | 8209 | 2016-03-27 19:48:16 |
49984 | 2016-03-31 22:48:48 | Student_sucht_ein__Anfaengerauto___ab_2000_BJ_... | privat | Angebot | 0.0 | test | NaN | 2000 | NaN | 0 | NaN | 150000.0 | 0 | NaN | sonstige_autos | NaN | 2016-03-31 00:00:00 | 0 | 12103 | 2016-04-02 19:44:53 |
1583 rows × 20 columns
There are 1583 data values with price
ranging between 0 and 5.
#Remove the price values between $0 and $5, because they are outliers
autos.drop(axis = 0, labels = index_0to5)
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-129-a3625bcee24a> in <module> 1 #Remove the price values between $0 and $5, because they are outliers ----> 2 autos.drop(axis = 0, labels = index_0to5) NameError: name 'index_0to5' is not defined
date_crawled | name | seller | offer_type | price | abtest | vehicle_type | registeration_year | gearbox | power_ps | model | odometer_km | registeration_month | fuel_type | brand | unrepaired_damage | ad_created | nr_of_pictures | postal_code | last_seen | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2016-03-26 17:47:46 | Peugeot_807_160_NAVTECH_ON_BOARD | privat | Angebot | 5000.0 | control | bus | 2004 | manuell | 158 | andere | 150000.0 | 3 | lpg | peugeot | nein | 2016-03-26 00:00:00 | 0 | 79588 | 2016-04-06 06:45:54 |
1 | 2016-04-04 13:38:56 | BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik | privat | Angebot | 8500.0 | control | limousine | 1997 | automatik | 286 | 7er | 150000.0 | 6 | benzin | bmw | nein | 2016-04-04 00:00:00 | 0 | 71034 | 2016-04-06 14:45:08 |
2 | 2016-03-26 18:57:24 | Volkswagen_Golf_1.6_United | privat | Angebot | 8990.0 | test | limousine | 2009 | manuell | 102 | golf | 70000.0 | 7 | benzin | volkswagen | nein | 2016-03-26 00:00:00 | 0 | 35394 | 2016-04-06 20:15:37 |
3 | 2016-03-12 16:58:10 | Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan... | privat | Angebot | 4350.0 | control | kleinwagen | 2007 | automatik | 71 | fortwo | 70000.0 | 6 | benzin | smart | nein | 2016-03-12 00:00:00 | 0 | 33729 | 2016-03-15 03:16:28 |
4 | 2016-04-01 14:38:50 | Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg... | privat | Angebot | 1350.0 | test | kombi | 2003 | manuell | 0 | focus | 150000.0 | 7 | benzin | ford | nein | 2016-04-01 00:00:00 | 0 | 39218 | 2016-04-01 14:38:50 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
49995 | 2016-03-27 14:38:19 | Audi_Q5_3.0_TDI_qu._S_tr.__Navi__Panorama__Xenon | privat | Angebot | 24900.0 | control | limousine | 2011 | automatik | 239 | q5 | 100000.0 | 1 | diesel | audi | nein | 2016-03-27 00:00:00 | 0 | 82131 | 2016-04-01 13:47:40 |
49996 | 2016-03-28 10:50:25 | Opel_Astra_F_Cabrio_Bertone_Edition___TÜV_neu+... | privat | Angebot | 1980.0 | control | cabrio | 1996 | manuell | 75 | astra | 150000.0 | 5 | benzin | opel | nein | 2016-03-28 00:00:00 | 0 | 44807 | 2016-04-02 14:18:02 |
49997 | 2016-04-02 14:44:48 | Fiat_500_C_1.2_Dualogic_Lounge | privat | Angebot | 13200.0 | test | cabrio | 2014 | automatik | 69 | 500 | 5000.0 | 11 | benzin | fiat | nein | 2016-04-02 00:00:00 | 0 | 73430 | 2016-04-04 11:47:27 |
49998 | 2016-03-08 19:25:42 | Audi_A3_2.0_TDI_Sportback_Ambition | privat | Angebot | 22900.0 | control | kombi | 2013 | manuell | 150 | a3 | 40000.0 | 11 | diesel | audi | nein | 2016-03-08 00:00:00 | 0 | 35683 | 2016-04-05 16:45:07 |
49999 | 2016-03-14 00:42:12 | Opel_Vectra_1.6_16V | privat | Angebot | 1250.0 | control | limousine | 1996 | manuell | 101 | vectra | 150000.0 | 1 | benzin | opel | nein | 2016-03-13 00:00:00 | 0 | 45897 | 2016-04-06 21:18:48 |
48417 rows × 20 columns