import pandas as pd
import numpy as np
import missingno as msno
import datetime as dt
from stringcase import snakecase
#with np.printoptions(threshold=np.inf):
# create a new class which makes it possible to format text in colour or bold etc.
class color:
PURPLE = '\033[95m'
CYAN = '\033[96m'
DARKCYAN = '\033[36m'
BLUE = '\033[94m'
GREEN = '\033[92m'
YELLOW = '\033[93m'
RED = '\033[91m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
END = '\033[0m'
autos = pd.read_csv("autos.csv",encoding='Latin-1')
This is an analysis of used car listings on eBay Kleinanzeigen, a classifieds section of the German eBay website.
During this analysis data will be cleaned and explored.
After cleaning and exploration the top ten car brands will be analysed in more detail (mean price and mean mileage).
Lastly the effect of mileage on price will be investigated.
The dataset was originally scraped and uploaded to Kaggle.
The version of the dataset that is used for this analysis is a sample of 50,000 data points that was prepared by Dataquest including simulating a less-cleaned version of the data.
autos.head(5)
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 |
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
Dataframe of 50,000 rows and 20 columns.
Column description can be seen below:
Column | Description |
---|---|
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 listing |
price | 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 registered |
gearbox | The transmission type |
powerPS | The power of the car in PS |
model | The car model name |
odometer | 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 |
The following columns seem to contain numeric data but are currently stored as object data:
There are five columns that have null values:
None of these columns have more than 20% null values.
msno.bar(autos)
<AxesSubplot:>
The column names use camelcase instead of Python's preferred snakecase.
In below chunk of code column names will be converted from camelcase to snakecase.
Furthermore, some of the column names will be renamed based on the data dictionary to be more descriptive.
#turning camelcase columns into snakecase columns
snakecase_col = []
for col in autos.columns:
col = snakecase(col)
snakecase_col.append(col)
autos.columns = snakecase_col
#renaming some columns by using a for loop with old and new values
rename_columns = [
['year_of_registration','registration_year'],
['month_of_registration','registration_month'],
['not_repaired_damage','unrepaired_damage'],
['date_created','ad_created'],
['power_p_s','power_PS']
]
for old_col, new_col in rename_columns:
autos.rename({old_col: new_col},axis=1,inplace=True)
autos.describe(include='all')
date_crawled | name | seller | offer_type | price | abtest | vehicle_type | registration_year | gearbox | power_PS | model | odometer | registration_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 | 2016-03-16 21:50:53 | Ford_Fiesta | privat | Angebot | $0 | test | limousine | NaN | manuell | NaN | golf | 150,000km | NaN | benzin | volkswagen | nein | 2016-04-03 00:00:00 | NaN | NaN | 2016-04-07 06:17:27 |
freq | 3 | 78 | 49999 | 49999 | 1421 | 25756 | 12859 | NaN | 36993 | NaN | 4024 | 32424 | NaN | 30107 | 10687 | 35232 | 1946 | NaN | NaN | 8 |
mean | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2005.073280 | NaN | 116.355920 | NaN | NaN | 5.723360 | NaN | NaN | NaN | NaN | 0.0 | 50813.627300 | NaN |
std | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 105.712813 | NaN | 209.216627 | NaN | NaN | 3.711984 | NaN | NaN | NaN | NaN | 0.0 | 25779.747957 | NaN |
min | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1000.000000 | NaN | 0.000000 | NaN | NaN | 0.000000 | NaN | NaN | NaN | NaN | 0.0 | 1067.000000 | NaN |
25% | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1999.000000 | NaN | 70.000000 | NaN | NaN | 3.000000 | NaN | NaN | NaN | NaN | 0.0 | 30451.000000 | NaN |
50% | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2003.000000 | NaN | 105.000000 | NaN | NaN | 6.000000 | NaN | NaN | NaN | NaN | 0.0 | 49577.000000 | NaN |
75% | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2008.000000 | NaN | 150.000000 | NaN | NaN | 9.000000 | NaN | NaN | NaN | NaN | 0.0 | 71540.000000 | NaN |
max | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 9999.000000 | NaN | 17700.000000 | NaN | NaN | 12.000000 | NaN | NaN | NaN | NaN | 0.0 | 99998.000000 | NaN |
What can be seen is that columns "seller" and "offer_type" consist of almost all the same values.
Therefore, these columns are deemed unuseful for further analysis and will be dropped from the dataframe.
#remove columns that provide unuseful data
autos.drop(['seller','offer_type'],axis=1,inplace=True)
As mentioned before, there are two columns which contain numeric data that need to be reformatted.
This will be done below. Afterwards they will be investigated for the presence of outliers.
#price
autos['price'] = (autos['price']
.str.replace("$","")
.str.replace(",","")
.astype(float)
#.apply('{:,}'.format) # I thought to use this to format a 1000 separator,
# but this changes the float into an object at the same time.
# Someone knows how to prevent this?
)
#odometer
autos['odometer'] = (autos['odometer'].
str.replace("km","").
str.replace(",","").
astype(int)
)
autos.rename({'odometer': 'odometer_km'},axis=1,inplace=True)
<ipython-input-10-1092ff9012d8>:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will*not* be treated as literal strings when regex=True. autos['price'] = (autos['price']
The columns 'price' and 'odometer_km' have now succesfully been transformed to integer values.
They will now be further analysed in order to check for outliers that might need to be removed.
def explore_series(dataframe, column, value_counts=False): #define a function to give detailed info about a column
# length of .unique()
template = (color.BOLD + "Number of unique values in column "
+ color.RED + column + color.END
+ color.BOLD + " is "
+ color.RED + "{:,} " + color.END)
print(template.format(len(dataframe[column].unique())))
print('\n')
# .describe()
print(color.BOLD + "Descriptive statistics of column "
+ color.RED + column + color.END)
print(dataframe[column].describe())
print('\n')
# .value_counts (put into a dataframe to calculate cumulative percentages as well)
if value_counts:
print(color.BOLD + "Unique values with their respective counts:" + color.END)
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
df = dataframe[column].value_counts().sort_index(ascending=True).to_frame()
df.columns = ['count'] #rename column to 'count'
df.index.name = column #rename index to column thats being explored
df['cum_sum'] = df['count'].cumsum() #calculating cumulative sum
df['cum_perc'] = round(100*df.cum_sum/df['count'].sum(),2) #calculating cumulative perc.
print(df)
explore_series(autos,"price", value_counts=True)
Number of unique values in column price is 2,357 Descriptive statistics of column price 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 Unique values with their respective counts: count cum_sum cum_perc price 0.0 1421 1421 2.84 1.0 156 1577 3.15 2.0 3 1580 3.16 3.0 1 1581 3.16 5.0 2 1583 3.17 8.0 1 1584 3.17 9.0 1 1585 3.17 10.0 7 1592 3.18 11.0 2 1594 3.19 12.0 3 1597 3.19 13.0 2 1599 3.20 14.0 1 1600 3.20 15.0 2 1602 3.20 17.0 3 1605 3.21 18.0 1 1606 3.21 20.0 4 1610 3.22 25.0 5 1615 3.23 29.0 1 1616 3.23 30.0 7 1623 3.25 35.0 1 1624 3.25 40.0 6 1630 3.26 45.0 4 1634 3.27 47.0 1 1635 3.27 49.0 4 1639 3.28 50.0 49 1688 3.38 55.0 2 1690 3.38 59.0 1 1691 3.38 60.0 9 1700 3.40 65.0 5 1705 3.41 66.0 1 1706 3.41 70.0 10 1716 3.43 75.0 5 1721 3.44 79.0 1 1722 3.44 80.0 15 1737 3.47 89.0 1 1738 3.48 90.0 5 1743 3.49 99.0 19 1762 3.52 100.0 134 1896 3.79 110.0 3 1899 3.80 111.0 2 1901 3.80 115.0 2 1903 3.81 117.0 1 1904 3.81 120.0 39 1943 3.89 122.0 1 1944 3.89 125.0 8 1952 3.90 129.0 1 1953 3.91 130.0 15 1968 3.94 135.0 1 1969 3.94 139.0 1 1970 3.94 140.0 9 1979 3.96 145.0 2 1981 3.96 149.0 7 1988 3.98 150.0 224 2212 4.42 156.0 2 2214 4.43 160.0 8 2222 4.44 170.0 7 2229 4.46 173.0 1 2230 4.46 175.0 12 2242 4.48 179.0 1 2243 4.49 180.0 35 2278 4.56 185.0 1 2279 4.56 188.0 1 2280 4.56 190.0 16 2296 4.59 193.0 1 2297 4.59 195.0 2 2299 4.60 198.0 1 2300 4.60 199.0 41 2341 4.68 200.0 266 2607 5.21 205.0 1 2608 5.22 210.0 1 2609 5.22 215.0 2 2611 5.22 217.0 1 2612 5.22 219.0 1 2613 5.23 220.0 33 2646 5.29 222.0 12 2658 5.32 225.0 8 2666 5.33 230.0 12 2678 5.36 235.0 2 2680 5.36 238.0 1 2681 5.36 240.0 3 2684 5.37 248.0 1 2685 5.37 249.0 13 2698 5.40 250.0 291 2989 5.98 251.0 1 2990 5.98 255.0 1 2991 5.98 260.0 5 2996 5.99 269.0 1 2997 5.99 270.0 6 3003 6.01 275.0 7 3010 6.02 277.0 1 3011 6.02 280.0 30 3041 6.08 285.0 1 3042 6.08 290.0 19 3061 6.12 295.0 1 3062 6.12 299.0 56 3118 6.24 300.0 384 3502 7.00 310.0 1 3503 7.01 320.0 12 3515 7.03 325.0 5 3520 7.04 329.0 2 3522 7.04 330.0 8 3530 7.06 333.0 17 3547 7.09 340.0 11 3558 7.12 349.0 15 3573 7.15 350.0 335 3908 7.82 356.0 1 3909 7.82 359.0 1 3910 7.82 360.0 8 3918 7.84 369.0 1 3919 7.84 370.0 21 3940 7.88 375.0 7 3947 7.89 378.0 1 3948 7.90 379.0 1 3949 7.90 380.0 29 3978 7.96 385.0 1 3979 7.96 388.0 1 3980 7.96 390.0 26 4006 8.01 395.0 2 4008 8.02 399.0 71 4079 8.16 400.0 321 4400 8.80 410.0 1 4401 8.80 414.0 1 4402 8.80 420.0 9 4411 8.82 425.0 4 4415 8.83 430.0 13 4428 8.86 435.0 1 4429 8.86 440.0 1 4430 8.86 444.0 8 4438 8.88 449.0 12 4450 8.90 450.0 265 4715 9.43 459.0 1 4716 9.43 460.0 5 4721 9.44 470.0 7 4728 9.46 475.0 4 4732 9.46 480.0 25 4757 9.51 485.0 2 4759 9.52 490.0 34 4793 9.59 495.0 4 4797 9.59 499.0 92 4889 9.78 500.0 781 5670 11.34 501.0 1 5671 11.34 510.0 2 5673 11.35 517.0 1 5674 11.35 520.0 8 5682 11.36 525.0 4 5686 11.37 530.0 8 5694 11.39 540.0 2 5696 11.39 549.0 13 5709 11.42 550.0 356 6065 12.13 554.0 1 6066 12.13 555.0 33 6099 12.20 560.0 4 6103 12.21 566.0 1 6104 12.21 570.0 7 6111 12.22 575.0 5 6116 12.23 578.0 1 6117 12.23 579.0 1 6118 12.24 580.0 20 6138 12.28 590.0 52 6190 12.38 595.0 2 6192 12.38 598.0 2 6194 12.39 599.0 122 6316 12.63 600.0 531 6847 13.69 606.0 1 6848 13.70 620.0 7 6855 13.71 625.0 5 6860 13.72 628.0 1 6861 13.72 630.0 3 6864 13.73 640.0 3 6867 13.73 644.0 1 6868 13.74 648.0 1 6869 13.74 649.0 14 6883 13.77 650.0 419 7302 14.60 655.0 1 7303 14.61 660.0 5 7308 14.62 666.0 14 7322 14.64 669.0 2 7324 14.65 670.0 6 7330 14.66 675.0 5 7335 14.67 679.0 1 7336 14.67 680.0 30 7366 14.73 686.0 1 7367 14.73 689.0 2 7369 14.74 690.0 68 7437 14.87 695.0 3 7440 14.88 699.0 127 7567 15.13 700.0 395 7962 15.92 710.0 1 7963 15.93 719.0 1 7964 15.93 720.0 7 7971 15.94 725.0 2 7973 15.95 729.0 1 7974 15.95 730.0 5 7979 15.96 740.0 3 7982 15.96 744.0 1 7983 15.97 745.0 2 7985 15.97 749.0 23 8008 16.02 750.0 433 8441 16.88 755.0 1 8442 16.88 760.0 8 8450 16.90 770.0 6 8456 16.91 777.0 20 8476 16.95 780.0 19 8495 16.99 785.0 1 8496 16.99 789.0 1 8497 16.99 790.0 45 8542 17.08 795.0 2 8544 17.09 799.0 120 8664 17.33 800.0 498 9162 18.32 810.0 3 9165 18.33 820.0 6 9171 18.34 825.0 4 9175 18.35 829.0 1 9176 18.35 830.0 6 9182 18.36 834.0 1 9183 18.37 839.0 1 9184 18.37 840.0 5 9189 18.38 846.0 1 9190 18.38 849.0 13 9203 18.41 850.0 410 9613 19.23 855.0 3 9616 19.23 860.0 1 9617 19.23 870.0 12 9629 19.26 875.0 3 9632 19.26 879.0 1 9633 19.27 880.0 14 9647 19.29 885.0 1 9648 19.30 887.0 1 9649 19.30 888.0 29 9678 19.36 889.0 1 9679 19.36 890.0 66 9745 19.49 895.0 3 9748 19.50 898.0 1 9749 19.50 899.0 129 9878 19.76 900.0 420 10298 20.60 910.0 1 10299 20.60 919.0 1 10300 20.60 920.0 1 10301 20.60 925.0 1 10302 20.60 930.0 4 10306 20.61 940.0 2 10308 20.62 945.0 2 10310 20.62 949.0 11 10321 20.64 950.0 379 10700 21.40 951.0 1 10701 21.40 958.0 1 10702 21.40 960.0 1 10703 21.41 965.0 2 10705 21.41 970.0 7 10712 21.42 975.0 2 10714 21.43 980.0 48 10762 21.52 985.0 4 10766 21.53 989.0 2 10768 21.54 990.0 147 10915 21.83 995.0 5 10920 21.84 996.0 1 10921 21.84 998.0 5 10926 21.85 999.0 434 11360 22.72 1000.0 639 11999 24.00 1039.0 1 12000 24.00 1040.0 1 12001 24.00 1049.0 6 12007 24.01 1050.0 95 12102 24.20 1059.0 1 12103 24.21 1070.0 1 12104 24.21 1080.0 6 12110 24.22 1090.0 4 12114 24.23 1095.0 3 12117 24.23 1098.0 1 12118 24.24 1099.0 44 12162 24.32 1100.0 376 12538 25.08 1111.0 39 12577 25.15 1112.0 1 12578 25.16 1119.0 1 12579 25.16 1120.0 2 12581 25.16 1149.0 10 12591 25.18 1150.0 226 12817 25.63 1169.0 1 12818 25.64 1170.0 1 12819 25.64 1180.0 4 12823 25.65 1189.0 1 12824 25.65 1190.0 37 12861 25.72 1195.0 1 12862 25.72 1199.0 126 12988 25.98 1200.0 639 13627 27.25 1201.0 2 13629 27.26 1209.0 1 13630 27.26 1212.0 2 13632 27.26 1221.0 1 13633 27.27 1222.0 3 13636 27.27 1234.0 3 13639 27.28 1240.0 2 13641 27.28 1247.0 1 13642 27.28 1249.0 4 13646 27.29 1250.0 335 13981 27.96 1265.0 1 13982 27.96 1270.0 4 13986 27.97 1275.0 1 13987 27.97 1280.0 12 13999 28.00 1285.0 3 14002 28.00 1290.0 38 14040 28.08 1295.0 1 14041 28.08 1299.0 135 14176 28.35 1300.0 371 14547 29.09 1310.0 1 14548 29.10 1325.0 1 14549 29.10 1330.0 1 14550 29.10 1333.0 3 14553 29.11 1340.0 2 14555 29.11 1345.0 2 14557 29.11 1349.0 5 14562 29.12 1350.0 276 14838 29.68 1355.0 1 14839 29.68 1370.0 3 14842 29.68 1375.0 3 14845 29.69 1379.0 1 14846 29.69 1380.0 10 14856 29.71 1385.0 1 14857 29.71 1390.0 67 14924 29.85 1395.0 5 14929 29.86 1398.0 2 14931 29.86 1399.0 95 15026 30.05 1400.0 292 15318 30.64 1414.0 1 15319 30.64 1420.0 1 15320 30.64 1425.0 2 15322 30.64 1430.0 3 15325 30.65 1432.0 1 15326 30.65 1440.0 2 15328 30.66 1444.0 4 15332 30.66 1448.0 2 15334 30.67 1449.0 8 15342 30.68 1450.0 252 15594 31.19 1466.0 1 15595 31.19 1470.0 2 15597 31.19 1475.0 2 15599 31.20 1480.0 8 15607 31.21 1485.0 1 15608 31.22 1490.0 88 15696 31.39 1494.0 1 15697 31.39 1495.0 3 15700 31.40 1497.0 1 15701 31.40 1498.0 1 15702 31.40 1499.0 157 15859 31.72 1500.0 734 16593 33.19 1520.0 2 16595 33.19 1525.0 2 16597 33.19 1530.0 2 16599 33.20 1545.0 1 16600 33.20 1549.0 5 16605 33.21 1550.0 150 16755 33.51 1555.0 7 16762 33.52 1560.0 1 16763 33.53 1570.0 2 16765 33.53 1575.0 2 16767 33.53 1579.0 1 16768 33.54 1580.0 5 16773 33.55 1590.0 43 16816 33.63 1595.0 3 16819 33.64 1599.0 92 16911 33.82 1600.0 327 17238 34.48 1630.0 2 17240 34.48 1640.0 1 17241 34.48 1642.0 1 17242 34.48 1645.0 1 17243 34.49 1649.0 8 17251 34.50 1650.0 238 17489 34.98 1659.0 1 17490 34.98 1660.0 2 17492 34.98 1666.0 3 17495 34.99 1670.0 2 17497 34.99 1675.0 2 17499 35.00 1680.0 8 17507 35.01 1689.0 1 17508 35.02 1690.0 43 17551 35.10 1695.0 2 17553 35.11 1698.0 1 17554 35.11 1699.0 85 17639 35.28 1700.0 268 17907 35.81 1730.0 1 17908 35.82 1749.0 5 17913 35.83 1750.0 238 18151 36.30 1755.0 1 18152 36.30 1759.0 1 18153 36.31 1760.0 1 18154 36.31 1765.0 1 18155 36.31 1770.0 1 18156 36.31 1775.0 1 18157 36.31 1777.0 4 18161 36.32 1780.0 1 18162 36.32 1790.0 42 18204 36.41 1795.0 1 18205 36.41 1797.0 1 18206 36.41 1798.0 1 18207 36.41 1799.0 73 18280 36.56 1800.0 355 18635 37.27 1820.0 1 18636 37.27 1840.0 4 18640 37.28 1845.0 1 18641 37.28 1849.0 5 18646 37.29 1850.0 216 18862 37.72 1856.0 1 18863 37.73 1860.0 1 18864 37.73 1870.0 1 18865 37.73 1875.0 2 18867 37.73 1880.0 6 18873 37.75 1888.0 3 18876 37.75 1890.0 38 18914 37.83 1895.0 1 18915 37.83 1899.0 53 18968 37.94 1900.0 239 19207 38.41 1906.0 1 19208 38.42 1925.0 1 19209 38.42 1930.0 1 19210 38.42 1933.0 1 19211 38.42 1935.0 1 19212 38.42 1945.0 2 19214 38.43 1949.0 1 19215 38.43 1950.0 208 19423 38.85 1955.0 1 19424 38.85 1960.0 1 19425 38.85 1970.0 2 19427 38.85 1975.0 1 19428 38.86 1980.0 15 19443 38.89 1981.0 1 19444 38.89 1984.0 1 19445 38.89 1985.0 1 19446 38.89 1990.0 117 19563 39.13 1995.0 4 19567 39.13 1996.0 1 19568 39.14 1998.0 4 19572 39.14 1999.0 322 19894 39.79 2000.0 460 20354 40.71 2001.0 1 20355 40.71 2004.0 1 20356 40.71 2033.0 1 20357 40.71 2035.0 1 20358 40.72 2050.0 27 20385 40.77 2070.0 1 20386 40.77 2090.0 2 20388 40.78 2095.0 1 20389 40.78 2099.0 21 20410 40.82 2100.0 208 20618 41.24 2111.0 2 20620 41.24 2128.0 1 20621 41.24 2134.0 1 20622 41.24 2140.0 1 20623 41.25 2149.0 2 20625 41.25 2150.0 95 20720 41.44 2175.0 1 20721 41.44 2180.0 1 20722 41.44 2190.0 29 20751 41.50 2195.0 1 20752 41.50 2199.0 52 20804 41.61 2200.0 382 21186 42.37 2210.0 6 21192 42.38 2222.0 27 21219 42.44 2225.0 1 21220 42.44 2241.0 1 21221 42.44 2245.0 1 21222 42.44 2249.0 2 21224 42.45 2250.0 147 21371 42.74 2265.0 2 21373 42.75 2270.0 1 21374 42.75 2280.0 6 21380 42.76 2288.0 1 21381 42.76 2290.0 29 21410 42.82 2295.0 2 21412 42.82 2299.0 38 21450 42.90 2300.0 290 21740 43.48 2321.0 2 21742 43.48 2333.0 5 21747 43.49 2340.0 3 21750 43.50 2349.0 2 21752 43.50 2350.0 127 21879 43.76 2359.0 1 21880 43.76 2380.0 2 21882 43.76 2390.0 31 21913 43.83 2398.0 2 21915 43.83 2399.0 55 21970 43.94 2400.0 193 22163 44.33 2410.0 1 22164 44.33 2430.0 1 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26660.0 1 48794 97.59 26700.0 2 48796 97.59 26725.0 1 48797 97.59 26750.0 2 48799 97.60 26800.0 5 48804 97.61 26849.0 1 48805 97.61 26850.0 3 48808 97.62 26899.0 2 48810 97.62 26900.0 24 48834 97.67 26950.0 3 48837 97.67 26990.0 5 48842 97.68 26999.0 12 48854 97.71 27000.0 23 48877 97.75 27020.0 1 48878 97.76 27250.0 1 48879 97.76 27280.0 1 48880 97.76 27299.0 1 48881 97.76 27300.0 5 48886 97.77 27350.0 1 48887 97.77 27400.0 2 48889 97.78 27450.0 2 48891 97.78 27490.0 4 48895 97.79 27499.0 6 48901 97.80 27500.0 27 48928 97.86 27600.0 1 48929 97.86 27650.0 1 48930 97.86 27690.0 1 48931 97.86 27700.0 3 48934 97.87 27750.0 2 48936 97.87 27777.0 3 48939 97.88 27800.0 5 48944 97.89 27850.0 2 48946 97.89 27900.0 23 48969 97.94 27950.0 3 48972 97.94 27990.0 7 48979 97.96 27999.0 8 48987 97.97 28000.0 23 49010 98.02 28200.0 3 49013 98.03 28290.0 1 49014 98.03 28300.0 1 49015 98.03 28399.0 1 49016 98.03 28400.0 1 49017 98.03 28450.0 5 49022 98.04 28470.0 1 49023 98.05 28490.0 2 49025 98.05 28499.0 3 49028 98.06 28500.0 21 49049 98.10 28600.0 3 49052 98.10 28700.0 1 49053 98.11 28750.0 2 49055 98.11 28800.0 4 49059 98.12 28850.0 1 49060 98.12 28900.0 16 49076 98.15 28950.0 3 49079 98.16 28990.0 5 49084 98.17 28999.0 5 49089 98.18 29000.0 7 49096 98.19 29200.0 3 49099 98.20 29250.0 1 49100 98.20 29300.0 1 49101 98.20 29333.0 2 49103 98.21 29350.0 2 49105 98.21 29380.0 1 49106 98.21 29400.0 1 49107 98.21 29445.0 1 49108 98.22 29450.0 1 49109 98.22 29490.0 3 49112 98.22 29499.0 2 49114 98.23 29500.0 20 49134 98.27 29550.0 1 49135 98.27 29600.0 1 49136 98.27 29699.0 1 49137 98.27 29700.0 2 49139 98.28 29750.0 2 49141 98.28 29777.0 1 49142 98.28 29800.0 4 49146 98.29 29850.0 2 49148 98.30 29890.0 1 49149 98.30 29900.0 15 49164 98.33 29950.0 5 49169 98.34 29970.0 1 49170 98.34 29980.0 1 49171 98.34 29989.0 1 49172 98.34 29990.0 9 49181 98.36 29999.0 15 49196 98.39 30000.0 10 49206 98.41 30200.0 1 49207 98.41 30400.0 1 49208 98.42 30499.0 2 49210 98.42 30500.0 7 49217 98.43 30570.0 1 49218 98.44 30650.0 1 49219 98.44 30700.0 2 49221 98.44 30800.0 1 49222 98.44 30888.0 1 49223 98.45 30899.0 1 49224 98.45 30900.0 5 49229 98.46 30933.0 1 49230 98.46 30950.0 1 49231 98.46 30987.0 1 49232 98.46 30990.0 2 49234 98.47 30999.0 2 49236 98.47 31000.0 8 49244 98.49 31111.0 1 49245 98.49 31199.0 2 49247 98.49 31200.0 1 49248 98.50 31313.0 1 49249 98.50 31400.0 1 49250 98.50 31450.0 1 49251 98.50 31490.0 3 49254 98.51 31499.0 2 49256 98.51 31500.0 13 49269 98.54 31600.0 1 49270 98.54 31799.0 2 49272 98.54 31800.0 5 49277 98.55 31900.0 12 49289 98.58 31950.0 2 49291 98.58 31990.0 4 49295 98.59 31999.0 8 49303 98.61 32000.0 10 49313 98.63 32150.0 1 49314 98.63 32222.0 2 49316 98.63 32400.0 2 49318 98.64 32490.0 1 49319 98.64 32500.0 16 49335 98.67 32700.0 2 49337 98.67 32800.0 1 49338 98.68 32900.0 7 49345 98.69 32949.0 1 49346 98.69 32950.0 2 49348 98.70 32990.0 3 49351 98.70 32999.0 4 49355 98.71 33000.0 9 49364 98.73 33200.0 1 49365 98.73 33300.0 1 49366 98.73 33449.0 1 49367 98.73 33490.0 2 49369 98.74 33499.0 1 49370 98.74 33500.0 8 49378 98.76 33600.0 1 49379 98.76 33650.0 1 49380 98.76 33700.0 2 49382 98.76 33750.0 1 49383 98.77 33777.0 1 49384 98.77 33800.0 3 49387 98.77 33850.0 1 49388 98.78 33900.0 10 49398 98.80 33950.0 2 49400 98.80 33980.0 1 49401 98.80 33990.0 3 49404 98.81 33999.0 3 49407 98.81 34000.0 8 49415 98.83 34150.0 1 49416 98.83 34490.0 1 49417 98.83 34499.0 2 49419 98.84 34500.0 15 49434 98.87 34550.0 1 49435 98.87 34650.0 1 49436 98.87 34750.0 1 49437 98.87 34800.0 4 49441 98.88 34850.0 1 49442 98.88 34890.0 1 49443 98.89 34900.0 12 49455 98.91 34940.0 1 49456 98.91 34950.0 2 49458 98.92 34980.0 1 49459 98.92 34990.0 3 49462 98.92 34996.0 1 49463 98.93 34999.0 4 49467 98.93 35000.0 10 49477 98.95 35222.0 1 49478 98.96 35370.0 1 49479 98.96 35450.0 1 49480 98.96 35460.0 1 49481 98.96 35499.0 2 49483 98.97 35500.0 3 49486 98.97 35555.0 1 49487 98.97 35700.0 1 49488 98.98 35790.0 2 49490 98.98 35800.0 1 49491 98.98 35890.0 2 49493 98.99 35900.0 9 49502 99.00 35911.0 1 49503 99.01 35950.0 1 49504 99.01 35980.0 1 49505 99.01 35990.0 1 49506 99.01 35999.0 2 49508 99.02 36000.0 12 49520 99.04 36500.0 4 49524 99.05 36675.0 1 49525 99.05 36800.0 1 49526 99.05 36850.0 1 49527 99.05 36900.0 4 49531 99.06 36999.0 2 49533 99.07 37000.0 4 49537 99.07 37300.0 1 49538 99.08 37400.0 1 49539 99.08 37450.0 1 49540 99.08 37480.0 1 49541 99.08 37499.0 1 49542 99.08 37500.0 4 49546 99.09 37700.0 1 49547 99.09 37750.0 1 49548 99.10 37800.0 4 49552 99.10 37850.0 2 49554 99.11 37900.0 9 49563 99.13 37950.0 1 49564 99.13 37999.0 3 49567 99.13 38000.0 4 49571 99.14 38200.0 1 49572 99.14 38400.0 1 49573 99.15 38450.0 1 49574 99.15 38500.0 8 49582 99.16 38700.0 1 49583 99.17 38760.0 1 49584 99.17 38800.0 2 49586 99.17 38850.0 1 49587 99.17 38900.0 8 49595 99.19 38950.0 1 49596 99.19 38990.0 2 49598 99.20 38999.0 2 49600 99.20 39000.0 3 49603 99.21 39300.0 2 49605 99.21 39500.0 9 49614 99.23 39700.0 1 49615 99.23 39800.0 1 49616 99.23 39890.0 1 49617 99.23 39900.0 12 49629 99.26 39911.0 1 49630 99.26 39979.0 1 49631 99.26 39990.0 1 49632 99.26 39999.0 6 49638 99.28 40000.0 3 49641 99.28 40400.0 1 49642 99.28 40499.0 1 49643 99.29 40500.0 2 49645 99.29 40800.0 1 49646 99.29 40850.0 1 49647 99.29 40900.0 3 49650 99.30 40990.0 1 49651 99.30 40999.0 2 49653 99.31 41000.0 3 49656 99.31 41500.0 6 49662 99.32 41800.0 1 49663 99.33 41850.0 1 49664 99.33 41900.0 6 49670 99.34 41999.0 1 49671 99.34 42000.0 3 49674 99.35 42222.0 1 49675 99.35 42500.0 3 49678 99.36 42750.0 1 49679 99.36 42760.0 1 49680 99.36 42800.0 1 49681 99.36 42900.0 7 49688 99.38 42990.0 2 49690 99.38 42996.0 2 49692 99.38 42999.0 1 49693 99.39 43000.0 2 49695 99.39 43461.0 1 49696 99.39 43500.0 2 49698 99.40 43900.0 5 49703 99.41 44000.0 4 49707 99.41 44200.0 2 49709 99.42 44444.0 1 49710 99.42 44497.0 1 49711 99.42 44499.0 2 49713 99.43 44500.0 1 49714 99.43 44777.0 1 49715 99.43 44900.0 5 49720 99.44 44990.0 1 49721 99.44 44996.0 1 49722 99.44 45000.0 5 49727 99.45 45500.0 4 49731 99.46 45800.0 1 49732 99.46 45900.0 3 49735 99.47 45949.0 1 49736 99.47 45950.0 1 49737 99.47 46000.0 2 49739 99.48 46200.0 1 49740 99.48 46500.0 1 49741 99.48 46800.0 1 49742 99.48 46900.0 3 49745 99.49 46911.0 1 49746 99.49 46990.0 1 49747 99.49 46999.0 1 49748 99.50 47000.0 4 49752 99.50 47499.0 1 49753 99.51 47500.0 3 49756 99.51 47800.0 1 49757 99.51 47900.0 3 49760 99.52 47950.0 1 49761 99.52 47997.0 1 49762 99.52 48000.0 4 49766 99.53 48300.0 1 49767 99.53 48490.0 1 49768 99.54 48500.0 4 49772 99.54 48600.0 1 49773 99.55 48700.0 2 49775 99.55 48850.0 1 49776 99.55 48888.0 1 49777 99.55 48900.0 3 49780 99.56 48999.0 2 49782 99.56 49000.0 3 49785 99.57 49500.0 4 49789 99.58 49900.0 5 49794 99.59 49999.0 4 49798 99.60 50000.0 2 49800 99.60 50500.0 2 49802 99.60 50900.0 1 49803 99.61 51000.0 1 49804 99.61 51500.0 1 49805 99.61 51900.0 2 49807 99.61 51990.0 1 49808 99.62 51999.0 1 49809 99.62 52000.0 1 49810 99.62 52500.0 3 49813 99.63 52900.0 5 49818 99.64 52911.0 1 49819 99.64 53000.0 4 49823 99.65 53500.0 2 49825 99.65 53900.0 2 49827 99.65 54500.0 2 49829 99.66 54990.0 1 49830 99.66 55000.0 5 49835 99.67 55500.0 1 49836 99.67 55555.0 1 49837 99.67 55800.0 1 49838 99.68 55900.0 1 49839 99.68 55996.0 1 49840 99.68 55999.0 1 49841 99.68 56000.0 4 49845 99.69 56500.0 2 49847 99.69 56800.0 1 49848 99.70 56900.0 1 49849 99.70 57800.0 1 49850 99.70 58500.0 2 49852 99.70 58700.0 1 49853 99.71 58900.0 1 49854 99.71 59000.0 3 49857 99.71 59500.0 3 49860 99.72 59850.0 1 49861 99.72 60000.0 3 49864 99.73 61500.0 1 49865 99.73 61900.0 1 49866 99.73 61950.0 1 49867 99.73 61999.0 1 49868 99.74 62000.0 2 49870 99.74 62900.0 2 49872 99.74 63000.0 1 49873 99.75 63499.0 1 49874 99.75 63999.0 2 49876 99.75 64280.0 1 49877 99.75 64500.0 2 49879 99.76 64600.0 1 49880 99.76 64900.0 3 49883 99.77 64990.0 1 49884 99.77 64999.0 1 49885 99.77 65000.0 1 49886 99.77 65699.0 1 49887 99.77 65700.0 1 49888 99.78 65990.0 1 49889 99.78 66500.0 1 49890 99.78 66964.0 1 49891 99.78 67000.0 1 49892 99.78 67911.0 1 49893 99.79 68000.0 1 49894 99.79 68300.0 1 49895 99.79 68500.0 1 49896 99.79 68750.0 1 49897 99.79 68900.0 1 49898 99.80 69500.0 1 49899 99.80 69900.0 1 49900 99.80 69993.0 1 49901 99.80 69997.0 1 49902 99.80 69999.0 1 49903 99.81 70000.0 1 49904 99.81 70850.0 1 49905 99.81 71000.0 1 49906 99.81 72500.0 1 49907 99.81 72600.0 1 49908 99.82 72900.0 1 49909 99.82 73500.0 1 49910 99.82 73900.0 1 49911 99.82 73996.0 1 49912 99.82 74900.0 3 49915 99.83 74999.0 2 49917 99.83 75000.0 1 49918 99.84 75900.0 1 49919 99.84 75997.0 1 49920 99.84 76997.0 1 49921 99.84 78911.0 1 49922 99.84 79500.0 1 49923 99.85 79933.0 1 49924 99.85 79980.0 1 49925 99.85 79999.0 1 49926 99.85 80000.0 3 49929 99.86 82987.0 1 49930 99.86 83000.0 1 49931 99.86 84000.0 1 49932 99.86 84997.0 1 49933 99.87 85000.0 1 49934 99.87 86500.0 1 49935 99.87 88900.0 1 49936 99.87 89000.0 1 49937 99.87 89900.0 1 49938 99.88 93000.0 2 49940 99.88 93911.0 1 49941 99.88 94999.0 1 49942 99.88 98500.0 1 49943 99.89 99000.0 2 49945 99.89 99900.0 2 49947 99.89 104900.0 1 49948 99.90 105000.0 2 49950 99.90 109999.0 1 49951 99.90 114400.0 1 49952 99.90 115000.0 1 49953 99.91 115991.0 1 49954 99.91 116000.0 1 49955 99.91 119500.0 1 49956 99.91 119900.0 1 49957 99.91 120000.0 2 49959 99.92 128000.0 1 49960 99.92 129000.0 1 49961 99.92 130000.0 1 49962 99.92 135000.0 1 49963 99.93 137999.0 1 49964 99.93 139997.0 1 49965 99.93 145000.0 1 49966 99.93 151990.0 1 49967 99.93 155000.0 1 49968 99.94 163500.0 1 49969 99.94 163991.0 1 49970 99.94 169000.0 1 49971 99.94 169999.0 1 49972 99.94 175000.0 1 49973 99.95 180000.0 1 49974 99.95 190000.0 1 49975 99.95 194000.0 1 49976 99.95 197000.0 1 49977 99.95 198000.0 1 49978 99.96 220000.0 1 49979 99.96 250000.0 1 49980 99.96 259000.0 1 49981 99.96 265000.0 1 49982 99.96 295000.0 1 49983 99.97 299000.0 1 49984 99.97 345000.0 1 49985 99.97 350000.0 1 49986 99.97 999990.0 1 49987 99.97 999999.0 2 49989 99.98 1234566.0 1 49990 99.98 1300000.0 1 49991 99.98 3890000.0 1 49992 99.98 10000000.0 1 49993 99.99 11111111.0 2 49995 99.99 12345678.0 3 49998 100.00 27322222.0 1 49999 100.00 99999999.0 1 50000 100.00
A mean of nearly ten thousand euro's for used cars of all types seems realistic.
The mean is only a fraction of the standard deviation (which is 481,000 euro's).
This suggests that there are outliers in the dataset.
The lowest price is zero, which has 1421 occurences in the dataset.
Price represents the starting value of an action. Since it is only the starting price it does not mean that a car will be sold for that price.
Therefore anything below 500 euro's (which seems like a realistic lower bound for used car prices) is not removed from the dataset.
There are also outliers at the top. When investigating the unique values with their respective counts the starting price increases slowly until 350,000.
Onwards the price increases rapidly. Occurences with a starting price higher than 350,000 will be investigated further.
price_above_350000 = autos[autos['price']>350000]
template = (color.BOLD + "There are "
+ color.RED + "{}" + color.END
+ color.BOLD + " instances with a starting price of 350,000 or higher" + color.END)
print(template.format(len(price_above_350000)))
price_above_350000
There are 14 instances with a starting price of 350,000 or higher
date_crawled | name | price | abtest | vehicle_type | registration_year | gearbox | power_PS | model | odometer_km | registration_month | fuel_type | brand | unrepaired_damage | ad_created | nr_of_pictures | postal_code | last_seen | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
514 | 2016-03-17 09:53:08 | Ford_Focus_Turnier_1.6_16V_Style | 999999.0 | test | kombi | 2009 | manuell | 101 | focus | 125000 | 4 | benzin | ford | nein | 2016-03-17 00:00:00 | 0 | 12205 | 2016-04-06 07:17:35 |
2897 | 2016-03-12 21:50:57 | Escort_MK_1_Hundeknochen_zum_umbauen_auf_RS_2000 | 11111111.0 | test | limousine | 1973 | manuell | 48 | escort | 50000 | 3 | benzin | ford | nein | 2016-03-12 00:00:00 | 0 | 94469 | 2016-03-12 22:45:27 |
7814 | 2016-04-04 11:53:31 | Ferrari_F40 | 1300000.0 | control | coupe | 1992 | NaN | 0 | NaN | 50000 | 12 | NaN | sonstige_autos | nein | 2016-04-04 00:00:00 | 0 | 60598 | 2016-04-05 11:34:11 |
11137 | 2016-03-29 23:52:57 | suche_maserati_3200_gt_Zustand_unwichtig_laufe... | 10000000.0 | control | coupe | 1960 | manuell | 368 | NaN | 100000 | 1 | benzin | sonstige_autos | nein | 2016-03-29 00:00:00 | 0 | 73033 | 2016-04-06 21:18:11 |
22947 | 2016-03-22 12:54:19 | Bmw_530d_zum_ausschlachten | 1234566.0 | control | kombi | 1999 | automatik | 190 | NaN | 150000 | 2 | diesel | bmw | NaN | 2016-03-22 00:00:00 | 0 | 17454 | 2016-04-02 03:17:32 |
24384 | 2016-03-21 13:57:51 | Schlachte_Golf_3_gt_tdi | 11111111.0 | test | NaN | 1995 | NaN | 0 | NaN | 150000 | 0 | NaN | volkswagen | NaN | 2016-03-21 00:00:00 | 0 | 18519 | 2016-03-21 14:40:18 |
27371 | 2016-03-09 15:45:47 | Fiat_Punto | 12345678.0 | control | NaN | 2017 | NaN | 95 | punto | 150000 | 0 | NaN | fiat | NaN | 2016-03-09 00:00:00 | 0 | 96110 | 2016-03-09 15:45:47 |
37585 | 2016-03-29 11:38:54 | Volkswagen_Jetta_GT | 999990.0 | test | limousine | 1985 | manuell | 111 | jetta | 150000 | 12 | benzin | volkswagen | ja | 2016-03-29 00:00:00 | 0 | 50997 | 2016-03-29 11:38:54 |
39377 | 2016-03-08 23:53:51 | Tausche_volvo_v40_gegen_van | 12345678.0 | control | NaN | 2018 | manuell | 95 | v40 | 150000 | 6 | NaN | volvo | nein | 2016-03-08 00:00:00 | 0 | 14542 | 2016-04-06 23:17:31 |
39705 | 2016-03-22 14:58:27 | Tausch_gegen_gleichwertiges | 99999999.0 | control | limousine | 1999 | automatik | 224 | s_klasse | 150000 | 9 | benzin | mercedes_benz | NaN | 2016-03-22 00:00:00 | 0 | 73525 | 2016-04-06 05:15:30 |
42221 | 2016-03-08 20:39:05 | Leasinguebernahme | 27322222.0 | control | limousine | 2014 | manuell | 163 | c4 | 40000 | 2 | diesel | citroen | NaN | 2016-03-08 00:00:00 | 0 | 76532 | 2016-03-08 20:39:05 |
43049 | 2016-03-21 19:53:52 | 2_VW_Busse_T3 | 999999.0 | test | bus | 1981 | manuell | 70 | transporter | 150000 | 1 | benzin | volkswagen | NaN | 2016-03-21 00:00:00 | 0 | 99880 | 2016-03-28 17:18:28 |
47598 | 2016-03-31 18:56:54 | Opel_Vectra_B_1_6i_16V_Facelift_Tuning_Showcar... | 12345678.0 | control | limousine | 2001 | manuell | 101 | vectra | 150000 | 3 | benzin | opel | nein | 2016-03-31 00:00:00 | 0 | 4356 | 2016-03-31 18:56:54 |
47634 | 2016-04-04 21:25:21 | Ferrari_FXX | 3890000.0 | test | coupe | 2006 | NaN | 799 | NaN | 5000 | 7 | NaN | sonstige_autos | nein | 2016-04-04 00:00:00 | 0 | 60313 | 2016-04-05 12:07:37 |
There are 14 occurences with a price higher than 350,000.
Since most of these cars are normal family cars (with brands like Ford, BMW, Fiat, Volkswagen) these prices are unrealistic.
The 14 instances will be removed from the dataset.
autos = autos[autos['price']<=350000]
explore_series(autos,'price')
Number of unique values in column price is 2,347 Descriptive statistics of column price count 49986.000000 mean 5721.525167 std 8983.617820 min 0.000000 25% 1100.000000 50% 2950.000000 75% 7200.000000 max 350000.000000 Name: price, dtype: float64
After removal there are 49,986 instances left in the dataset.
Removing the outliers at the top has had its impact on the mean starting price.
This has decreased from 9,840 to 5,721.
The standard deviation has decreased drastically as it is now less than 9,000.
The "odometer_km" will be explored next.
explore_series(autos, 'odometer_km', value_counts=True)
Number of unique values in column odometer_km is 13 Descriptive statistics of column odometer_km count 49986.000000 mean 125736.506222 std 40038.133399 min 5000.000000 25% 125000.000000 50% 150000.000000 75% 150000.000000 max 150000.000000 Name: odometer_km, dtype: float64 Unique values with their respective counts: count cum_sum cum_perc odometer_km 5000 966 966 1.93 10000 264 1230 2.46 20000 784 2014 4.03 30000 789 2803 5.61 40000 818 3621 7.24 50000 1025 4646 9.29 60000 1164 5810 11.62 70000 1230 7040 14.08 80000 1436 8476 16.96 90000 1757 10233 20.47 100000 2168 12401 24.81 125000 5169 17570 35.15 150000 32416 49986 100.00
There are only 13 different values of mileages in the dataset. This suggests that people have to use a preset of values when creating an auction on eBay.
The lowest value is 5,000 and the highest is 150,000 which are both plausible values for mileage on a used car.
Therefore there will be no correction on outliers for this column.
The mean is more than 120,000 km which suggests that most cars sold through eBay are likely to be a couple years old.
65% of the dataset has a mileage of 150,000 (or more).
This concludes the exploration of the columns "price" and "odometer_km".
There are five columns containing date information.
Column | Description |
---|---|
date_crawled | When this ad was first crawled, all field-values are taken from this date |
registration_year | The year in which the car was first registered |
registration_month | The month in which the car was first registered |
ad_created | The date on which the eBay listing was created |
last_seen | When the crawler saw this ad last online |
In order to explore the date columns more easily date_crawled, ad_created and last_seen have to be reformatted into a date type.
Once this has been done all columns will be explored shortly.
#creating a function to reformat the columns with string format into date format.
def formatDate(dataset,column):
newformat = []
for element in dataset[column]:
element = dt.datetime.strptime(element, "%Y-%m-%d %H:%M:%S")
element = element.date()
newformat.append(element)
dataset[column] = newformat
formatDate(autos,'date_crawled')
formatDate(autos,'ad_created')
formatDate(autos,'last_seen')
explore_series(autos,'date_crawled', value_counts=True)
Number of unique values in column date_crawled is 34 Descriptive statistics of column date_crawled count 49986 unique 34 top 2016-04-03 freq 1934 Name: date_crawled, dtype: object Unique values with their respective counts: count cum_sum cum_perc date_crawled 2016-03-05 1269 1269 2.54 2016-03-06 697 1966 3.93 2016-03-07 1798 3764 7.53 2016-03-08 1663 5427 10.86 2016-03-09 1660 7087 14.18 2016-03-10 1606 8693 17.39 2016-03-11 1624 10317 20.64 2016-03-12 1838 12155 24.32 2016-03-13 778 12933 25.87 2016-03-14 1831 14764 29.54 2016-03-15 1699 16463 32.94 2016-03-16 1475 17938 35.89 2016-03-17 1575 19513 39.04 2016-03-18 653 20166 40.34 2016-03-19 1745 21911 43.83 2016-03-20 1891 23802 47.62 2016-03-21 1874 25676 51.37 2016-03-22 1645 27321 54.66 2016-03-23 1619 28940 57.90 2016-03-24 1455 30395 60.81 2016-03-25 1587 31982 63.98 2016-03-26 1624 33606 67.23 2016-03-27 1552 35158 70.34 2016-03-28 1742 36900 73.82 2016-03-29 1707 38607 77.24 2016-03-30 1681 40288 80.60 2016-03-31 1595 41883 83.79 2016-04-01 1690 43573 87.17 2016-04-02 1770 45343 90.71 2016-04-03 1934 47277 94.58 2016-04-04 1824 49101 98.23 2016-04-05 655 49756 99.54 2016-04-06 159 49915 99.86 2016-04-07 71 49986 100.00
Looking at the data above it seems like the period over which the data has been crawled covers roughly one month (March-April 2016).
The distribution is more or less uniform.
explore_series(autos, 'ad_created', value_counts=True)
Number of unique values in column ad_created is 76 Descriptive statistics of column ad_created count 49986 unique 76 top 2016-04-03 freq 1946 Name: ad_created, dtype: object Unique values with their respective counts: count cum_sum cum_perc ad_created 2015-06-11 1 1 0.00 2015-08-10 1 2 0.00 2015-09-09 1 3 0.01 2015-11-10 1 4 0.01 2015-12-05 1 5 0.01 2015-12-30 1 6 0.01 2016-01-03 1 7 0.01 2016-01-07 1 8 0.02 2016-01-10 2 10 0.02 2016-01-13 1 11 0.02 2016-01-14 1 12 0.02 2016-01-16 1 13 0.03 2016-01-22 1 14 0.03 2016-01-27 3 17 0.03 2016-01-29 1 18 0.04 2016-02-01 1 19 0.04 2016-02-02 2 21 0.04 2016-02-05 2 23 0.05 2016-02-07 1 24 0.05 2016-02-08 1 25 0.05 2016-02-09 2 27 0.05 2016-02-11 1 28 0.06 2016-02-12 3 31 0.06 2016-02-14 2 33 0.07 2016-02-16 1 34 0.07 2016-02-17 1 35 0.07 2016-02-18 2 37 0.07 2016-02-19 3 40 0.08 2016-02-20 2 42 0.08 2016-02-21 3 45 0.09 2016-02-22 1 46 0.09 2016-02-23 4 50 0.10 2016-02-24 2 52 0.10 2016-02-25 3 55 0.11 2016-02-26 2 57 0.11 2016-02-27 6 63 0.13 2016-02-28 10 73 0.15 2016-02-29 8 81 0.16 2016-03-01 5 86 0.17 2016-03-02 5 91 0.18 2016-03-03 43 134 0.27 2016-03-04 72 206 0.41 2016-03-05 1152 1358 2.72 2016-03-06 756 2114 4.23 2016-03-07 1737 3851 7.70 2016-03-08 1665 5516 11.04 2016-03-09 1661 7177 14.36 2016-03-10 1593 8770 17.54 2016-03-11 1639 10409 20.82 2016-03-12 1830 12239 24.48 2016-03-13 846 13085 26.18 2016-03-14 1761 14846 29.70 2016-03-15 1687 16533 33.08 2016-03-16 1500 18033 36.08 2016-03-17 1559 19592 39.19 2016-03-18 686 20278 40.57 2016-03-19 1692 21970 43.95 2016-03-20 1893 23863 47.74 2016-03-21 1884 25747 51.51 2016-03-22 1638 27385 54.79 2016-03-23 1609 28994 58.00 2016-03-24 1454 30448 60.91 2016-03-25 1594 32042 64.10 2016-03-26 1628 33670 67.36 2016-03-27 1545 35215 70.45 2016-03-28 1748 36963 73.95 2016-03-29 1705 38668 77.36 2016-03-30 1672 40340 80.70 2016-03-31 1595 41935 83.89 2016-04-01 1690 43625 87.27 2016-04-02 1754 45379 90.78 2016-04-03 1946 47325 94.68 2016-04-04 1842 49167 98.36 2016-04-05 592 49759 99.55 2016-04-06 163 49922 99.87 2016-04-07 64 49986 100.00
The dates ads were created range from June 2015 until April of 2016.
The majority (+- 97%) of ads in the dataset were created after the date on which data was crawled for the first time.
This make sense as most auctions are only 'live' for a short period of time.
explore_series(autos, 'last_seen', value_counts=True)
Number of unique values in column last_seen is 34 Descriptive statistics of column last_seen count 49986 unique 34 top 2016-04-06 freq 11046 Name: last_seen, dtype: object Unique values with their respective counts: count cum_sum cum_perc last_seen 2016-03-05 54 54 0.11 2016-03-06 221 275 0.55 2016-03-07 268 543 1.09 2016-03-08 379 922 1.84 2016-03-09 492 1414 2.83 2016-03-10 538 1952 3.91 2016-03-11 626 2578 5.16 2016-03-12 1190 3768 7.54 2016-03-13 449 4217 8.44 2016-03-14 640 4857 9.72 2016-03-15 794 5651 11.31 2016-03-16 822 6473 12.95 2016-03-17 1396 7869 15.74 2016-03-18 371 8240 16.48 2016-03-19 787 9027 18.06 2016-03-20 1035 10062 20.13 2016-03-21 1036 11098 22.20 2016-03-22 1079 12177 24.36 2016-03-23 929 13106 26.22 2016-03-24 978 14084 28.18 2016-03-25 960 15044 30.10 2016-03-26 848 15892 31.79 2016-03-27 801 16693 33.40 2016-03-28 1042 17735 35.48 2016-03-29 1116 18851 37.71 2016-03-30 1242 20093 40.20 2016-03-31 1191 21284 42.58 2016-04-01 1155 22439 44.89 2016-04-02 1244 23683 47.38 2016-04-03 1268 24951 49.92 2016-04-04 1231 26182 52.38 2016-04-05 6212 32394 64.81 2016-04-06 11046 43440 86.90 2016-04-07 6546 49986 100.00
The last three days contain a disproportionate amount of 'last seen' values.
Given that these are 6-10x the values from the previous days, it's unlikely that there was a massive spike in sales.
It's more likely that these values are to do with the crawling period ending and don't indicate car sales.
explore_series(autos, 'registration_year', value_counts=True)
Number of unique values in column registration_year is 97 Descriptive statistics of column registration_year count 49986.000000 mean 2005.075721 std 105.727161 min 1000.000000 25% 1999.000000 50% 2003.000000 75% 2008.000000 max 9999.000000 Name: registration_year, dtype: float64 Unique values with their respective counts: count cum_sum cum_perc registration_year 1000 1 1 0.00 1001 1 2 0.00 1111 1 3 0.01 1500 1 4 0.01 1800 2 6 0.01 1910 9 15 0.03 1927 1 16 0.03 1929 1 17 0.03 1931 1 18 0.04 1934 2 20 0.04 1937 4 24 0.05 1938 1 25 0.05 1939 1 26 0.05 1941 2 28 0.06 1943 1 29 0.06 1948 1 30 0.06 1950 3 33 0.07 1951 2 35 0.07 1952 1 36 0.07 1953 1 37 0.07 1954 2 39 0.08 1955 2 41 0.08 1956 5 46 0.09 1957 2 48 0.10 1958 4 52 0.10 1959 7 59 0.12 1960 33 92 0.18 1961 6 98 0.20 1962 4 102 0.20 1963 9 111 0.22 1964 12 123 0.25 1965 17 140 0.28 1966 22 162 0.32 1967 27 189 0.38 1968 26 215 0.43 1969 19 234 0.47 1970 45 279 0.56 1971 27 306 0.61 1972 35 341 0.68 1973 25 366 0.73 1974 24 390 0.78 1975 19 409 0.82 1976 27 436 0.87 1977 22 458 0.92 1978 47 505 1.01 1979 35 540 1.08 1980 97 637 1.27 1981 30 667 1.33 1982 43 710 1.42 1983 53 763 1.53 1984 53 816 1.63 1985 104 920 1.84 1986 76 996 1.99 1987 75 1071 2.14 1988 142 1213 2.43 1989 181 1394 2.79 1990 395 1789 3.58 1991 356 2145 4.29 1992 390 2535 5.07 1993 445 2980 5.96 1994 660 3640 7.28 1995 1312 4952 9.91 1996 1444 6396 12.80 1997 2028 8424 16.85 1998 2453 10877 21.76 1999 2998 13875 27.76 2000 3354 17229 34.47 2001 2702 19931 39.87 2002 2533 22464 44.94 2003 2727 25191 50.40 2004 2737 27928 55.87 2005 3015 30943 61.90 2006 2707 33650 67.32 2007 2304 35954 71.93 2008 2231 38185 76.39 2009 2097 40282 80.59 2010 1597 41879 83.78 2011 1634 43513 87.05 2012 1323 44836 89.70 2013 806 45642 91.31 2014 665 46307 92.64 2015 399 46706 93.44 2016 1316 48022 96.07 2017 1452 49474 98.98 2018 491 49965 99.96 2019 3 49968 99.96 2800 1 49969 99.97 4100 1 49970 99.97 4500 1 49971 99.97 4800 1 49972 99.97 5000 4 49976 99.98 5911 1 49977 99.98 6200 1 49978 99.98 8888 1 49979 99.99 9000 2 49981 99.99 9996 1 49982 99.99 9999 4 49986 100.00
Both the minimum as maximum value of registration year seems strange.
The lowest registration year is 1,000 which must be incorrect as cars only started appearing in the late 1800's.
Due to this all occurences with a registration year before 1885 (first patented practical automobile) will be removed.
All registration years after 2016 must be incorrect as ads were created in 2015 & 2016.
These will be removed from the dataset as well.
autos = autos[autos['registration_year'].between(1885,2016)]
explore_series(autos, 'registration_year')
Number of unique values in column registration_year is 78 Descriptive statistics of column registration_year count 48016.000000 mean 2002.806002 std 7.306212 min 1910.000000 25% 1999.000000 50% 2003.000000 75% 2008.000000 max 2016.000000 Name: registration_year, dtype: float64
There is still a large variety in registration years of the cars.
The mean of 2002 with a small standard deviation indicate that most cars are approximately between 7 and 21 years old.
This concludes the exploration of date columns.
When working with data on cars, it's natural to explore variations across different car brands.
Aggregation can be used to understand the brand column.
explore_series(autos, 'brand', value_counts=True)
Number of unique values in column brand is 40 Descriptive statistics of column brand count 48016 unique 40 top volkswagen freq 10185 Name: brand, dtype: object Unique values with their respective counts: count cum_sum cum_perc brand alfa_romeo 318 318 0.66 audi 4149 4467 9.30 bmw 5283 9750 20.31 chevrolet 274 10024 20.88 chrysler 176 10200 21.24 citroen 668 10868 22.63 dacia 123 10991 22.89 daewoo 72 11063 23.04 daihatsu 123 11186 23.30 fiat 1242 12428 25.88 ford 3350 15778 32.86 honda 377 16155 33.65 hyundai 473 16628 34.63 jaguar 76 16704 34.79 jeep 108 16812 35.01 kia 341 17153 35.72 lada 29 17182 35.78 lancia 52 17234 35.89 land_rover 98 17332 36.10 mazda 727 18059 37.61 mercedes_benz 4579 22638 47.15 mini 415 23053 48.01 mitsubishi 391 23444 48.83 nissan 725 24169 50.34 opel 5194 29363 61.15 peugeot 1418 30781 64.11 porsche 293 31074 64.72 renault 2274 33348 69.45 rover 65 33413 69.59 saab 77 33490 69.75 seat 873 34363 71.57 skoda 770 35133 73.17 smart 668 35801 74.56 sonstige_autos 523 36324 75.65 subaru 105 36429 75.87 suzuki 284 36713 76.46 toyota 599 37312 77.71 trabant 75 37387 77.86 volkswagen 10185 47572 99.08 volvo 444 48016 100.00
There is a large variety of car brands in the dataset and data is not uniformly distributed.
For further analysis the dataset will be limited to the top 10 brands in terms of number of ads.
top_10_brands = (autos['brand']
.value_counts(normalize=True,dropna=False)
.head(10)
.index)
autos = autos[autos.brand.isin(top_10_brands)]
explore_series(autos, 'brand', value_counts=True)
Number of unique values in column brand is 10 Descriptive statistics of column brand count 38547 unique 10 top volkswagen freq 10185 Name: brand, dtype: object Unique values with their respective counts: count cum_sum cum_perc brand audi 4149 4149 10.76 bmw 5283 9432 24.47 fiat 1242 10674 27.69 ford 3350 14024 36.38 mercedes_benz 4579 18603 48.26 opel 5194 23797 61.74 peugeot 1418 25215 65.41 renault 2274 27489 71.31 seat 873 28362 73.58 volkswagen 10185 38547 100.00
There are still more then 38,000 entries left after limiting the dataset to the top 10 brands.
All brands left are European, except for Ford. Out of those 10, 5 are German which can be explained by the dataset being from the German section of eBay.
Now that the dataset contains only the top 10 brands it might be interesting to check whether there are differences in mean price between those brands.
mean_price_per_brand = {}
for brand in top_10_brands:
mean_price = autos.loc[autos['brand'] == brand,'price'].mean()
mean_price_per_brand[brand] = int(round(mean_price,0))
mean_price_per_brand = pd.Series(mean_price_per_brand, name='mean_price')
mean_price_per_brand.sort_values(ascending=False)
audi 9094 mercedes_benz 8485 bmw 8103 volkswagen 5231 seat 4296 ford 3652 peugeot 3039 opel 2877 fiat 2712 renault 2395 Name: mean_price, dtype: int64
Brands can be divided into three price categories:
Normally a car will become less valuable when the mileage of the car becomes higher.
It is interesting to see whether this principle can be found in the dataset.
For that the mean mileage per brand needs to be calculated.
mean_mileage_per_brand = {}
for brand in top_10_brands:
mean_mileage = autos.loc[autos['brand'] == brand,'odometer_km'].mean()
mean_mileage_per_brand[brand] = int(round(mean_mileage,0))
mean_mileage_per_brand = pd.Series(mean_mileage_per_brand,name='mean_mileage')
mean_mileage_per_brand
volkswagen 128724 bmw 132431 opel 129223 mercedes_benz 130856 audi 129288 ford 124069 renault 128184 peugeot 127137 fiat 116554 seat 121564 Name: mean_mileage, dtype: int64
price_mileage_per_brand = pd.concat([mean_price_per_brand,mean_mileage_per_brand],axis=1)
price_mileage_per_brand
mean_price | mean_mileage | |
---|---|---|
volkswagen | 5231 | 128724 |
bmw | 8103 | 132431 |
opel | 2877 | 129223 |
mercedes_benz | 8485 | 130856 |
audi | 9094 | 129288 |
ford | 3652 | 124069 |
renault | 2395 | 128184 |
peugeot | 3039 | 127137 |
fiat | 2712 | 116554 |
seat | 4296 | 121564 |
Above both mean price and mean mileage can be seen. It is however not possible to conclude whether higher mileage is affecting the price.
This is due to the fact that within a brand there are a lot of other variables affecting price (such as car type, engine type, registration year etc).
In order to confirm whether mileage affects the price a slice of the dataset is necessary where all those variables are kept the same as much as possible.
unique_brands = autos['brand'].unique()
brand_model = {}
brand_model_count = {}
for ub in unique_brands:
temp = autos.loc[autos['brand']==ub,'model'].value_counts().index[0]
temp2 = autos.loc[autos['brand']==ub,'model'].value_counts()[0]
brand_model[ub] = temp
brand_model_count[ub] = temp2
brand_model_series = pd.Series(brand_model,name = 'model')
brand_model_count_series = pd.Series(brand_model_count,name = 'count')
df = pd.concat([brand_model_series, brand_model_count_series], axis=1)
df.sort_values(by='count', ascending=False)
model | count | |
---|---|---|
volkswagen | golf | 3815 |
bmw | 3er | 2688 |
opel | corsa | 1645 |
audi | a4 | 1265 |
mercedes_benz | c_klasse | 1147 |
ford | focus | 776 |
renault | twingo | 636 |
peugeot | 2_reihe | 611 |
fiat | punto | 431 |
seat | ibiza | 335 |
Volkswagen Golf is the most occuring combination of brand and model.
#creating new dataset with only brand = volkswagen & model = golf
vw_golf = (autos.loc[(
autos['brand']=='volkswagen')&
(autos['model']=='golf')]
)
vw_golf['registration_year'].value_counts().head(5)
1998 277 1999 256 2000 243 1995 201 1997 175 Name: registration_year, dtype: int64
The top three registration years for Volkswagen Golf's in the dataset are 1998, 1999 and 2000.
#creating new data set only containing registration year 1998 - 2000
vw_golf_ry = (vw_golf[
vw_golf['registration_year'].isin([1998,1999,2000])])
vw_golf_ry['fuel_type'].value_counts().head(5)
benzin 585 diesel 117 lpg 5 cng 2 Name: fuel_type, dtype: int64
Benzine is by far the most occuring fuel type for Volkswagen Golf's with a registration year between 1998 and 2000.
#creating new data set only containing fuel type benzine
vw_golf_ry_benzine = (
vw_golf_ry[
vw_golf_ry['fuel_type']=='benzin'])
vw_golf_ry_benzine['gearbox'].value_counts().head(5)
manuell 534 automatik 36 Name: gearbox, dtype: int64
Almost all cars left in the dataset have a manual transmission
#creating new data set only containing manual gearboxes
vw_golf_ry_benzine_manual = (
vw_golf_ry_benzine[
vw_golf_ry_benzine['gearbox']=='manuell'])
vw_golf_ry_benzine_manual['unrepaired_damage'].value_counts()
nein 363 ja 65 Name: unrepaired_damage, dtype: int64
There are almost no cars with damage in the remaining dataset
#creating new data set only containing cars with no unrepaired damage
vw_golf_ry_benzine_manual_nd = (
vw_golf_ry_benzine_manual[
vw_golf_ry_benzine_manual['unrepaired_damage']=='nein'])
vw_golf_ry_benzine_manual_nd['power_PS'].value_counts().head(5)
75 165 101 98 105 21 150 20 116 15 Name: power_PS, dtype: int64
The amount of horsepowers for cars in the remainig dataset is more granular.
However, cars with 75 and 101 horsepower still cover almost 75% of the dataset.
Since the amount of horsepower is not so different between 75 and 101 both will be kept for further analysis.
#creating new data set only containing cars with 75 or 101 horsepower
vw_golf_ry_benzine_manual_nd_ps = (
vw_golf_ry_benzine_manual_nd[
vw_golf_ry_benzine_manual_nd['power_PS'].isin([75,101])])
#checking how distribution of odometer_km is within the remaining dataset
vw_golf_ry_benzine_manual_nd_ps['odometer_km'].value_counts()
150000 235 125000 13 90000 5 60000 3 100000 3 80000 1 30000 1 50000 1 5000 1 Name: odometer_km, dtype: int64
After filtering the dataset on several variables the distribution of odometer_km is skewered heavily towards 150,000.
There are so few occurences left for other values of odometer_km that it is not possible anymore to make conclusions about the effect of mileage on price.
Furthermore, price in this dataset is the starting price of an auction and not the actual selling price.
Some people might create an ad with an unrealistic starting price assuming that this will attract people and drive up the selling price.
It would therefore be better to investigate this effect further with a dataset that contains the selling price instead of the starting price of an auction.
#just because I went through all the trouble already I will calculate anyway. xD
df = vw_golf_ry_benzine_manual_nd_ps
odometer_km_unique = vw_golf_ry_benzine_manual_nd_ps['odometer_km'].unique()
price_odo = {}
for odo in odometer_km_unique:
temp = df.loc[df['odometer_km']==odo,'price'].mean()
price_odo[odo] = round(temp,0)
price_odo = pd.Series(price_odo, name='price')
price_odo.sort_index()
5000 1300.0 30000 1450.0 50000 3200.0 60000 3266.0 80000 2200.0 90000 2581.0 100000 2383.0 125000 1984.0 150000 1545.0 Name: price, dtype: float64