In this Guided Project we will work with a dataset of used cars from eBay Kleinanzeigen, a section of the German eBay website.
The aim of this project is to clean the data and analyze the included used car listings.
The data dictionary provided with data is as follows:
import numpy as np
import pandas as pd
autos = pd.read_csv('autos.csv', encoding='Windows-1252')
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 |
5 | 2016-03-21 13:47:45 | Chrysler_Grand_Voyager_2.8_CRD_Aut.Limited_Sto... | privat | Angebot | $7,900 | test | bus | 2006 | automatik | 150 | voyager | 150,000km | 4 | diesel | chrysler | NaN | 2016-03-21 00:00:00 | 0 | 22962 | 2016-04-06 09:45:21 |
6 | 2016-03-20 17:55:21 | VW_Golf_III_GT_Special_Electronic_Green_Metall... | privat | Angebot | $300 | test | limousine | 1995 | manuell | 90 | golf | 150,000km | 8 | benzin | volkswagen | NaN | 2016-03-20 00:00:00 | 0 | 31535 | 2016-03-23 02:48:59 |
7 | 2016-03-16 18:55:19 | Golf_IV_1.9_TDI_90PS | privat | Angebot | $1,990 | control | limousine | 1998 | manuell | 90 | golf | 150,000km | 12 | diesel | volkswagen | nein | 2016-03-16 00:00:00 | 0 | 53474 | 2016-04-07 03:17:32 |
8 | 2016-03-22 16:51:34 | Seat_Arosa | privat | Angebot | $250 | test | NaN | 2000 | manuell | 0 | arosa | 150,000km | 10 | NaN | seat | nein | 2016-03-22 00:00:00 | 0 | 7426 | 2016-03-26 18:18:10 |
9 | 2016-03-16 13:47:02 | Renault_Megane_Scenic_1.6e_RT_Klimaanlage | privat | Angebot | $590 | control | bus | 1997 | manuell | 90 | megane | 150,000km | 7 | benzin | renault | nein | 2016-03-16 00:00:00 | 0 | 15749 | 2016-04-06 10:46:35 |
10 | 2016-03-15 01:41:36 | VW_Golf_Tuning_in_siber/grau | privat | Angebot | $999 | test | NaN | 2017 | manuell | 90 | NaN | 150,000km | 4 | benzin | volkswagen | nein | 2016-03-14 00:00:00 | 0 | 86157 | 2016-04-07 03:16:21 |
11 | 2016-03-16 18:45:34 | Mercedes_A140_Motorschaden | privat | Angebot | $350 | control | NaN | 2000 | NaN | 0 | NaN | 150,000km | 0 | benzin | mercedes_benz | NaN | 2016-03-16 00:00:00 | 0 | 17498 | 2016-03-16 18:45:34 |
12 | 2016-03-31 19:48:22 | Smart_smart_fortwo_coupe_softouch_pure_MHD_Pan... | privat | Angebot | $5,299 | control | kleinwagen | 2010 | automatik | 71 | fortwo | 50,000km | 9 | benzin | smart | nein | 2016-03-31 00:00:00 | 0 | 34590 | 2016-04-06 14:17:52 |
13 | 2016-03-23 10:48:32 | Audi_A3_1.6_tuning | privat | Angebot | $1,350 | control | limousine | 1999 | manuell | 101 | a3 | 150,000km | 11 | benzin | audi | nein | 2016-03-23 00:00:00 | 0 | 12043 | 2016-04-01 14:17:13 |
14 | 2016-03-23 11:50:46 | Renault_Clio_3__Dynamique_1.2__16_V;_viele_Ver... | privat | Angebot | $3,999 | test | kleinwagen | 2007 | manuell | 75 | clio | 150,000km | 9 | benzin | renault | NaN | 2016-03-23 00:00:00 | 0 | 81737 | 2016-04-01 15:46:47 |
15 | 2016-04-01 12:06:20 | Corvette_C3_Coupe_T_Top_Crossfire_Injection | privat | Angebot | $18,900 | test | coupe | 1982 | automatik | 203 | NaN | 80,000km | 6 | benzin | sonstige_autos | nein | 2016-04-01 00:00:00 | 0 | 61276 | 2016-04-02 21:10:48 |
16 | 2016-03-16 14:59:02 | Opel_Vectra_B_Kombi | privat | Angebot | $350 | test | kombi | 1999 | manuell | 101 | vectra | 150,000km | 5 | benzin | opel | nein | 2016-03-16 00:00:00 | 0 | 57299 | 2016-03-18 05:29:37 |
17 | 2016-03-29 11:46:22 | Volkswagen_Scirocco_2_G60 | privat | Angebot | $5,500 | test | coupe | 1990 | manuell | 205 | scirocco | 150,000km | 6 | benzin | volkswagen | nein | 2016-03-29 00:00:00 | 0 | 74821 | 2016-04-05 20:46:26 |
18 | 2016-03-26 19:57:44 | Verkaufen_mein_bmw_e36_320_i_touring | privat | Angebot | $300 | control | bus | 1995 | manuell | 150 | 3er | 150,000km | 0 | benzin | bmw | NaN | 2016-03-26 00:00:00 | 0 | 54329 | 2016-04-02 12:16:41 |
19 | 2016-03-17 13:36:21 | mazda_tribute_2.0_mit_gas_und_tuev_neu_2018 | privat | Angebot | $4,150 | control | suv | 2004 | manuell | 124 | andere | 150,000km | 2 | lpg | mazda | nein | 2016-03-17 00:00:00 | 0 | 40878 | 2016-03-17 14:45:58 |
20 | 2016-03-05 19:57:31 | Audi_A4_Avant_1.9_TDI_*6_Gang*AHK*Klimatronik*... | privat | Angebot | $3,500 | test | kombi | 2003 | manuell | 131 | a4 | 150,000km | 5 | diesel | audi | NaN | 2016-03-05 00:00:00 | 0 | 53913 | 2016-03-07 05:46:46 |
21 | 2016-03-06 19:07:10 | Porsche_911_Carrera_4S_Cabrio | privat | Angebot | $41,500 | test | cabrio | 2004 | manuell | 320 | 911 | 150,000km | 4 | benzin | porsche | nein | 2016-03-06 00:00:00 | 0 | 65428 | 2016-04-05 23:46:19 |
22 | 2016-03-28 20:50:54 | MINI_Cooper_S_Cabrio | privat | Angebot | $25,450 | control | cabrio | 2015 | manuell | 184 | cooper | 10,000km | 1 | benzin | mini | nein | 2016-03-28 00:00:00 | 0 | 44789 | 2016-04-01 06:45:30 |
23 | 2016-03-10 19:55:34 | Peugeot_Boxer_2_2_HDi_120_Ps_9_Sitzer_inkl_Klima | privat | Angebot | $7,999 | control | bus | 2010 | manuell | 120 | NaN | 150,000km | 2 | diesel | peugeot | nein | 2016-03-10 00:00:00 | 0 | 30900 | 2016-03-17 08:45:17 |
24 | 2016-04-03 11:57:02 | BMW_535i_xDrive_Sport_Aut. | privat | Angebot | $48,500 | control | limousine | 2014 | automatik | 306 | 5er | 30,000km | 12 | benzin | bmw | nein | 2016-04-03 00:00:00 | 0 | 22547 | 2016-04-07 13:16:50 |
25 | 2016-03-21 21:56:18 | Ford_escort_kombi_an_bastler_mit_ghia_ausstattung | privat | Angebot | $90 | control | kombi | 1996 | manuell | 116 | NaN | 150,000km | 4 | benzin | ford | ja | 2016-03-21 00:00:00 | 0 | 27574 | 2016-04-01 05:16:49 |
26 | 2016-04-03 22:46:28 | Volkswagen_Polo_Fox | privat | Angebot | $777 | control | kleinwagen | 1992 | manuell | 54 | polo | 125,000km | 2 | benzin | volkswagen | nein | 2016-04-03 00:00:00 | 0 | 38110 | 2016-04-05 23:46:48 |
27 | 2016-03-27 18:45:01 | Hat_einer_Ahnung_mit_Ford_Galaxy_HILFE | privat | Angebot | $0 | control | NaN | 2005 | NaN | 0 | NaN | 150,000km | 0 | NaN | ford | NaN | 2016-03-27 00:00:00 | 0 | 66701 | 2016-03-27 18:45:01 |
28 | 2016-03-19 21:56:19 | MINI_Cooper_D | privat | Angebot | $5,250 | control | kleinwagen | 2007 | manuell | 110 | cooper | 150,000km | 7 | diesel | mini | ja | 2016-03-19 00:00:00 | 0 | 15745 | 2016-04-07 14:58:48 |
29 | 2016-04-02 12:45:44 | Mercedes_Benz_E_320_T_CDI_Avantgarde_DPF7_Sitz... | privat | Angebot | $4,999 | test | kombi | 2004 | automatik | 204 | e_klasse | 150,000km | 10 | diesel | mercedes_benz | nein | 2016-04-02 00:00:00 | 0 | 47638 | 2016-04-02 12:45:44 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
49970 | 2016-03-21 22:47:37 | c4_Grand_Picasso_mit_Automatik_Leder_Navi_Temp... | privat | Angebot | $15,800 | control | bus | 2010 | automatik | 136 | c4 | 60,000km | 4 | diesel | citroen | nein | 2016-03-21 00:00:00 | 0 | 14947 | 2016-04-07 04:17:34 |
49971 | 2016-03-29 14:54:12 | W.Lupo_1.0 | privat | Angebot | $950 | test | kleinwagen | 2001 | manuell | 50 | lupo | 150,000km | 4 | benzin | volkswagen | nein | 2016-03-29 00:00:00 | 0 | 65197 | 2016-03-29 20:41:51 |
49972 | 2016-03-26 22:25:23 | Mercedes_Benz_Vito_115_CDI_Extralang_Aut. | privat | Angebot | $3,300 | control | bus | 2004 | automatik | 150 | vito | 150,000km | 10 | diesel | mercedes_benz | ja | 2016-03-26 00:00:00 | 0 | 65326 | 2016-03-28 11:28:18 |
49973 | 2016-03-27 05:32:39 | Mercedes_Benz_SLK_200_Kompressor | privat | Angebot | $6,000 | control | cabrio | 2004 | manuell | 163 | slk | 150,000km | 11 | benzin | mercedes_benz | nein | 2016-03-27 00:00:00 | 0 | 53567 | 2016-03-27 08:25:24 |
49974 | 2016-03-20 10:52:31 | Golf_1_Cabrio_Tuev_Neu_viele_Extras_alles_eing... | privat | Angebot | $0 | control | cabrio | 1983 | manuell | 70 | golf | 150,000km | 2 | benzin | volkswagen | nein | 2016-03-20 00:00:00 | 0 | 8209 | 2016-03-27 19:48:16 |
49975 | 2016-03-27 20:51:39 | Honda_Jazz_1.3_DSi_i_VTEC_IMA_CVT_Comfort | privat | Angebot | $9,700 | control | kleinwagen | 2012 | automatik | 88 | jazz | 100,000km | 11 | hybrid | honda | nein | 2016-03-27 00:00:00 | 0 | 84385 | 2016-04-05 19:45:34 |
49976 | 2016-03-19 18:56:05 | Audi_80_Avant_2.6_E__Vollausstattung!!_Einziga... | privat | Angebot | $5,900 | test | kombi | 1992 | automatik | 150 | 80 | 150,000km | 12 | benzin | audi | nein | 2016-03-19 00:00:00 | 0 | 36100 | 2016-04-07 06:16:44 |
49977 | 2016-03-31 18:37:18 | Mercedes_Benz_C200_Cdi_W203 | privat | Angebot | $5,500 | control | limousine | 2003 | manuell | 116 | c_klasse | 150,000km | 2 | diesel | mercedes_benz | nein | 2016-03-31 00:00:00 | 0 | 33739 | 2016-04-06 12:16:11 |
49978 | 2016-04-04 10:37:14 | Mercedes_Benz_E_200_Classic | privat | Angebot | $900 | control | limousine | 1996 | automatik | 136 | e_klasse | 150,000km | 9 | benzin | mercedes_benz | ja | 2016-04-04 00:00:00 | 0 | 24405 | 2016-04-06 12:44:20 |
49979 | 2016-03-20 18:38:40 | Volkswagen_Polo_1.6_TDI_Style | privat | Angebot | $11,000 | test | kleinwagen | 2011 | manuell | 90 | polo | 70,000km | 11 | diesel | volkswagen | nein | 2016-03-20 00:00:00 | 0 | 48455 | 2016-04-07 01:45:12 |
49980 | 2016-03-12 10:55:54 | Ford_Escort_Turnier_16V | privat | Angebot | $400 | control | kombi | 1995 | manuell | 105 | escort | 125,000km | 3 | benzin | ford | NaN | 2016-03-12 00:00:00 | 0 | 56218 | 2016-04-06 17:16:49 |
49981 | 2016-03-15 09:38:21 | Opel_Astra_Kombi_mit_Anhaengerkupplung | privat | Angebot | $2,000 | control | kombi | 1998 | manuell | 115 | astra | 150,000km | 12 | benzin | opel | nein | 2016-03-15 00:00:00 | 0 | 86859 | 2016-04-05 17:21:46 |
49982 | 2016-03-29 18:51:08 | Skoda_Fabia_4_Tuerer_Bj:2004__85.000Tkm | privat | Angebot | $1,950 | control | kleinwagen | 2004 | manuell | 0 | fabia | 90,000km | 7 | benzin | skoda | NaN | 2016-03-29 00:00:00 | 0 | 45884 | 2016-03-29 18:51:08 |
49983 | 2016-03-06 12:43:04 | Ford_focus_99 | privat | Angebot | $600 | test | kleinwagen | 1999 | manuell | 101 | focus | 150,000km | 4 | benzin | ford | NaN | 2016-03-06 00:00:00 | 0 | 52477 | 2016-03-09 06:16:08 |
49984 | 2016-03-31 22:48:48 | Student_sucht_ein__Anfaengerauto___ab_2000_BJ_... | privat | Angebot | $0 | test | NaN | 2000 | NaN | 0 | NaN | 150,000km | 0 | NaN | sonstige_autos | NaN | 2016-03-31 00:00:00 | 0 | 12103 | 2016-04-02 19:44:53 |
49985 | 2016-04-02 16:38:23 | Verkaufe_meinen_vw_vento! | privat | Angebot | $1,000 | control | NaN | 1995 | automatik | 0 | NaN | 150,000km | 0 | benzin | volkswagen | NaN | 2016-04-02 00:00:00 | 0 | 30900 | 2016-04-06 15:17:52 |
49986 | 2016-04-04 20:46:02 | Chrysler_300C_3.0_CRD_DPF_Automatik_Voll_Ausst... | privat | Angebot | $15,900 | control | limousine | 2010 | automatik | 218 | 300c | 125,000km | 11 | diesel | chrysler | nein | 2016-04-04 00:00:00 | 0 | 73527 | 2016-04-06 23:16:00 |
49987 | 2016-03-22 20:47:27 | Audi_A3_Limousine_2.0_TDI_DPF_Ambition__NAVI__... | privat | Angebot | $21,990 | control | limousine | 2013 | manuell | 150 | a3 | 50,000km | 11 | diesel | audi | nein | 2016-03-22 00:00:00 | 0 | 94362 | 2016-03-26 22:46:06 |
49988 | 2016-03-28 19:49:51 | BMW_330_Ci | privat | Angebot | $9,550 | control | coupe | 2001 | manuell | 231 | 3er | 150,000km | 10 | benzin | bmw | nein | 2016-03-28 00:00:00 | 0 | 83646 | 2016-04-07 02:17:40 |
49989 | 2016-03-11 19:50:37 | VW_Polo_zum_Ausschlachten_oder_Wiederaufbau | privat | Angebot | $150 | test | kleinwagen | 1997 | manuell | 0 | polo | 150,000km | 5 | benzin | volkswagen | ja | 2016-03-11 00:00:00 | 0 | 21244 | 2016-03-12 10:17:55 |
49990 | 2016-03-21 19:54:19 | Mercedes_Benz_A_200__BlueEFFICIENCY__Urban | privat | Angebot | $17,500 | test | limousine | 2012 | manuell | 156 | a_klasse | 30,000km | 12 | benzin | mercedes_benz | nein | 2016-03-21 00:00:00 | 0 | 58239 | 2016-04-06 22:46:57 |
49991 | 2016-03-06 15:25:19 | Kleinwagen | privat | Angebot | $500 | control | NaN | 2016 | manuell | 0 | twingo | 150,000km | 0 | benzin | renault | NaN | 2016-03-06 00:00:00 | 0 | 61350 | 2016-03-06 18:24:19 |
49992 | 2016-03-10 19:37:38 | Fiat_Grande_Punto_1.4_T_Jet_16V_Sport | privat | Angebot | $4,800 | control | kleinwagen | 2009 | manuell | 120 | andere | 125,000km | 9 | lpg | fiat | nein | 2016-03-10 00:00:00 | 0 | 68642 | 2016-03-13 01:44:51 |
49993 | 2016-03-15 18:47:35 | Audi_A3__1_8l__Silber;_schoenes_Fahrzeug | privat | Angebot | $1,650 | control | kleinwagen | 1997 | manuell | 0 | NaN | 150,000km | 7 | benzin | audi | NaN | 2016-03-15 00:00:00 | 0 | 65203 | 2016-04-06 19:46:53 |
49994 | 2016-03-22 17:36:42 | Audi_A6__S6__Avant_4.2_quattro_eventuell_Tausc... | privat | Angebot | $5,000 | control | kombi | 2001 | automatik | 299 | a6 | 150,000km | 1 | benzin | audi | nein | 2016-03-22 00:00:00 | 0 | 46537 | 2016-04-06 08:16:39 |
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
We want to find out some details, thus we will look into the first 5 rows.
#print(autos.info())
#print(autos.head())
The dataset contains 20 columns, most of which are strings. Some columns have null values, but none have more than ~20% null values. The column names use camelcase instead of Python's preferred snakecase, which means we can't just replace spaces with underscores. The Class of the dataset is = 'pandas.core.frame.DataFrame'> It includeds 50000 entries ending with 2016-03-26 17:47:46 and beginning at 2016-03-14 00:42:12. The data is seperated between numbers (int64) and strings (objects) The dtypes of the indexes are: int64(5), object(14) The Brands of the car are directly connected via (_) with the description of the car in the column of the name, but the brand is shown in a seperated column. Also the price is not a number, more a string with a sign before it.
Now it is time to get all the column names from camelcase to snakecase.
autos.columns = ['date_crawled', 'name', 'seller', 'offer_type', 'price', 'ab_test',
'vehicle_type', 'registration_year', 'gearbox', 'power_ps', 'model',
'odometer', 'registration_month', 'fuel_type', 'brand',
'unrepaired_damage', 'ad_created', 'num_photos', 'postal_code','last_seen']
autos.head()
date_crawled | name | seller | offer_type | price | ab_test | vehicle_type | registration_year | gearbox | power_ps | model | odometer | registration_month | fuel_type | brand | unrepaired_damage | ad_created | num_photos | 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 |
The new autos Dateframe constist of the same old data, just with the exception of new Index Names which are written in Snakecase and not Camelecase
autos.describe(include='all')
date_crawled | name | seller | offer_type | price | ab_test | vehicle_type | registration_year | gearbox | power_ps | model | odometer | registration_month | fuel_type | brand | unrepaired_damage | ad_created | num_photos | 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 | 2016-03-08 10:40:35 | Ford_Fiesta | privat | Angebot | $0 | test | limousine | NaN | manuell | NaN | golf | 150,000km | NaN | benzin | volkswagen | nein | 2016-04-03 00:00:00 | NaN | NaN | 2016-04-07 06:17:27 |
freq | 3 | 78 | 49999 | 49999 | 1421 | 25756 | 12859 | NaN | 36993 | NaN | 4024 | 32424 | NaN | 30107 | 10687 | 35232 | 1946 | NaN | NaN | 8 |
mean | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2005.073280 | NaN | 116.355920 | NaN | NaN | 5.723360 | NaN | NaN | NaN | NaN | 0.0 | 50813.627300 | NaN |
std | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 105.712813 | NaN | 209.216627 | NaN | NaN | 3.711984 | NaN | NaN | NaN | NaN | 0.0 | 25779.747957 | NaN |
min | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1000.000000 | NaN | 0.000000 | NaN | NaN | 0.000000 | NaN | NaN | NaN | NaN | 0.0 | 1067.000000 | NaN |
25% | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1999.000000 | NaN | 70.000000 | NaN | NaN | 3.000000 | NaN | NaN | NaN | NaN | 0.0 | 30451.000000 | NaN |
50% | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2003.000000 | NaN | 105.000000 | NaN | NaN | 6.000000 | NaN | NaN | NaN | NaN | 0.0 | 49577.000000 | NaN |
75% | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2008.000000 | NaN | 150.000000 | NaN | NaN | 9.000000 | NaN | NaN | NaN | NaN | 0.0 | 71540.000000 | NaN |
max | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 9999.000000 | NaN | 17700.000000 | NaN | NaN | 12.000000 | NaN | NaN | NaN | NaN | 0.0 | 99998.000000 | NaN |
autos["num_photos"].value_counts()
0 50000 Name: num_photos, dtype: int64
It looks like the num_photos column has 0 for every column. We'll drop this column, plus the other two we noted as mostly one value.
autos = autos.drop(["num_photos", "seller", "offer_type"], axis=1)
The odometer includes numeric values as text. Thus, we have to remove any non.numeric characters and convert the column to a numeric dtype and we have to change the column name into odometer_km.
autos["odometer"] = (autos["odometer"].str.replace("km", "").str.replace(",", "").astype(int))
autos["price"] = (autos["price"].str.replace("$", "").str.replace(",", "").astype(int))
autos.rename(columns={'odometer': 'odometer_km'}, inplace=True)
autos.rename(columns={'price': 'price_€'}, inplace=True)
autos["odometer_km"].head()
autos["price_€"].head()
0 5000 1 8500 2 8990 3 4350 4 1350 Name: price_€, dtype: int64
We will analyze the columns using minimum and maximum values and look for any values that look unrealistically high or low (outliers) that we might want to remove.
autos["odometer_km"].unique().shape
(13,)
The Series Odometer_km has 13 unique values.
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
The total entries are 50.000 The smallest value in the odometer_km Series is 5000 and the highest 150000. The data type is float.
odo_unsorted = autos["odometer_km"].value_counts()
odo_sorted = odo_unsorted.sort_index(ascending=True)
print(odo_sorted)
5000 967 10000 264 20000 784 30000 789 40000 819 50000 1027 60000 1164 70000 1230 80000 1436 90000 1757 100000 2169 125000 5170 150000 32424 Name: odometer_km, dtype: int64
We can see that the values in this field are rounded, which might indicate that sellers had to choose from pre-set options for this field. Additionally, there are more high mileage than low mileage vehicles. But there are no strange values or data.
print(autos["price_€"].unique().shape)
print(autos["price_€"].describe())
autos["price_€"].value_counts().head(20)
(2357,) 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
0 1421 500 781 1500 734 2500 643 1000 639 1200 639 600 531 800 498 3500 498 2000 460 999 434 750 433 900 420 650 419 850 410 700 395 4500 394 300 384 2200 382 950 379 Name: price_€, dtype: int64
The description shows us, that we have many values are too small to be true, as well as some values which are too high. Thus, we will dropthos values.
Again, the prices in this column seem rounded, however given there are 2357 unique values in the column, that may just be people's tendency to round prices on the site.
There are 1,421 cars listed with $0 price - given that this is only 2Percent of the of the cars, we might consider removing these rows. The maximum price is one hundred million dollars, which seems a lot, let's look at the highest prices further.
autos["price_€"].value_counts().sort_index(ascending=False).head(20)
99999999 1 27322222 1 12345678 3 11111111 2 10000000 1 3890000 1 1300000 1 1234566 1 999999 2 999990 1 350000 1 345000 1 299000 1 295000 1 265000 1 259000 1 250000 1 220000 1 198000 1 197000 1 Name: price_€, dtype: int64
autos["price_€"].value_counts().sort_index(ascending=False).head(20)
99999999 1 27322222 1 12345678 3 11111111 2 10000000 1 3890000 1 1300000 1 1234566 1 999999 2 999990 1 350000 1 345000 1 299000 1 295000 1 265000 1 259000 1 250000 1 220000 1 198000 1 197000 1 Name: price_€, dtype: int64
There are a number of listings with prices below $30, including about 1,500 at $0. There are also a small number of listings with very high values, including 14 at around or over $1 million. Given that eBay is an auction site, there could legitimately be items where the opening bid is $1. We will keep the $1 items, but remove anything above $350,000, since it seems that prices increase steadily to that number and then jump up to less realistic numbers.
autos = autos[autos["price_€"].between(1,351000)]
autos["price_€"].describe()
count 48565.000000 mean 5888.935591 std 9059.854754 min 1.000000 25% 1200.000000 50% 3000.000000 75% 7490.000000 max 350000.000000 Name: price_€, dtype: float64
We removed around 1500cars from our list. Now the maximum price is 350.000€. We still include around 1500 entries with around 1 Euro, which most likely are open biddings.
Let's now move on to the date columns and understand the date range the data covers.
There are 5 columns that should represent date values. Some of these columns were created by the crawler, some came from the website itself
Right now, the date_crawled, last_seen, and ad_created columns are all identified as string values by pandas. Because these three columns are represented as strings, we need to convert the data into a numerical representation so we can understand it quantitatively.
Let's first understand how the values in the three string columns are formatted.
autos[['date_crawled','ad_created','last_seen']][0:5]
date_crawled | ad_created | last_seen | |
---|---|---|---|
0 | 2016-03-26 17:47:46 | 2016-03-26 00:00:00 | 2016-04-06 06:45:54 |
1 | 2016-04-04 13:38:56 | 2016-04-04 00:00:00 | 2016-04-06 14:45:08 |
2 | 2016-03-26 18:57:24 | 2016-03-26 00:00:00 | 2016-04-06 20:15:37 |
3 | 2016-03-12 16:58:10 | 2016-03-12 00:00:00 | 2016-03-15 03:16:28 |
4 | 2016-04-01 14:38:50 | 2016-04-01 00:00:00 | 2016-04-01 14:38:50 |
We notice that the first 10 characters represent the day (e.g. 2016-03-12). To understand the date range, we can extract just the date values, use Series.value_counts() to generate a distribution, and then sort by the index
autos["date_crawled"].describe()
print(autos["date_crawled"].str[:10].head(10))
0 2016-03-26 1 2016-04-04 2 2016-03-26 3 2016-03-12 4 2016-04-01 5 2016-03-21 6 2016-03-20 7 2016-03-16 8 2016-03-22 9 2016-03-16 Name: date_crawled, dtype: object
(autos["date_crawled"].
str[:10].
value_counts(normalize=True, dropna=False).
sort_index(ascending=False))
2016-04-07 0.001400 2016-04-06 0.003171 2016-04-05 0.013096 2016-04-04 0.036487 2016-04-03 0.038608 2016-04-02 0.035478 2016-04-01 0.033687 2016-03-31 0.031834 2016-03-30 0.033687 2016-03-29 0.034099 2016-03-28 0.034860 2016-03-27 0.031092 2016-03-26 0.032204 2016-03-25 0.031607 2016-03-24 0.029342 2016-03-23 0.032225 2016-03-22 0.032987 2016-03-21 0.037373 2016-03-20 0.037887 2016-03-19 0.034778 2016-03-18 0.012911 2016-03-17 0.031628 2016-03-16 0.029610 2016-03-15 0.034284 2016-03-14 0.036549 2016-03-13 0.015670 2016-03-12 0.036920 2016-03-11 0.032575 2016-03-10 0.032184 2016-03-09 0.033090 2016-03-08 0.033296 2016-03-07 0.036014 2016-03-06 0.014043 2016-03-05 0.025327 Name: date_crawled, dtype: float64
Looks like the site was crawled daily over roughly a one month period in March and April 2016. The distribution of listings crawled on each day is roughly uniform.
(autos["ad_created"].
str[:10].
value_counts(normalize=True, dropna=False).
sort_index(ascending=False))
2016-04-07 0.001256 2016-04-06 0.003253 2016-04-05 0.011819 2016-04-04 0.036858 2016-04-03 0.038855 2016-04-02 0.035149 2016-04-01 0.033687 2016-03-31 0.031875 2016-03-30 0.033501 2016-03-29 0.034037 2016-03-28 0.034984 2016-03-27 0.030989 2016-03-26 0.032266 2016-03-25 0.031751 2016-03-24 0.029280 2016-03-23 0.032060 2016-03-22 0.032801 2016-03-21 0.037579 2016-03-20 0.037949 2016-03-19 0.033687 2016-03-18 0.013590 2016-03-17 0.031278 2016-03-16 0.030125 2016-03-15 0.034016 2016-03-14 0.035190 2016-03-13 0.017008 2016-03-12 0.036755 2016-03-11 0.032904 2016-03-10 0.031895 2016-03-09 0.033151 ... 2016-02-21 0.000062 2016-02-20 0.000041 2016-02-19 0.000062 2016-02-18 0.000041 2016-02-17 0.000021 2016-02-16 0.000021 2016-02-14 0.000041 2016-02-12 0.000041 2016-02-11 0.000021 2016-02-09 0.000021 2016-02-08 0.000021 2016-02-07 0.000021 2016-02-05 0.000041 2016-02-02 0.000041 2016-02-01 0.000021 2016-01-29 0.000021 2016-01-27 0.000062 2016-01-22 0.000021 2016-01-16 0.000021 2016-01-14 0.000021 2016-01-13 0.000021 2016-01-10 0.000041 2016-01-07 0.000021 2016-01-03 0.000021 2015-12-30 0.000021 2015-12-05 0.000021 2015-11-10 0.000021 2015-09-09 0.000021 2015-08-10 0.000021 2015-06-11 0.000021 Name: ad_created, Length: 76, dtype: float64
There is a large variety of ad created dates. Most fall within 1-2 months of the listing date, but a few are quite old, with the oldest at around 9 months.
(autos["last_seen"].str[:10].
value_counts(normalize=True, dropna=False).
sort_index(ascending=False))
2016-04-07 0.131947 2016-04-06 0.221806 2016-04-05 0.124761 2016-04-04 0.024483 2016-04-03 0.025203 2016-04-02 0.024915 2016-04-01 0.022794 2016-03-31 0.023783 2016-03-30 0.024771 2016-03-29 0.022341 2016-03-28 0.020859 2016-03-27 0.015649 2016-03-26 0.016802 2016-03-25 0.019211 2016-03-24 0.019767 2016-03-23 0.018532 2016-03-22 0.021373 2016-03-21 0.020632 2016-03-20 0.020653 2016-03-19 0.015834 2016-03-18 0.007351 2016-03-17 0.028086 2016-03-16 0.016452 2016-03-15 0.015876 2016-03-14 0.012602 2016-03-13 0.008895 2016-03-12 0.023783 2016-03-11 0.012375 2016-03-10 0.010666 2016-03-09 0.009595 2016-03-08 0.007413 2016-03-07 0.005395 2016-03-06 0.004324 2016-03-05 0.001071 Name: last_seen, dtype: float64
The crawler recorded the date it last saw any listing, which allows us to determine on what day a listing was removed, presumably because the car was sold.
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, and more likely that these values are to do with the crawling period ending and don't indicate car sales.
autos["registration_year"].describe()
count 48565.000000 mean 2004.755421 std 88.643887 min 1000.000000 25% 1999.000000 50% 2004.000000 75% 2008.000000 max 9999.000000 Name: registration_year, dtype: float64
The registration_year is a float type value. It also includes some strange values.
Thus, we will drop those values.
The lowest acceptable value is 1900 and the highest is 2019.
autos["registration_year"].value_counts(normalize=True)
autos["registration_year"].describe()
count 48565.000000 mean 2004.755421 std 88.643887 min 1000.000000 25% 1999.000000 50% 2004.000000 75% 2008.000000 max 9999.000000 Name: registration_year, dtype: float64
(~autos["registration_year"].between(1900,2016)).sum() / autos.shape[0]
#autos["registration_year"].describe()
0.038793369710697
The total number of vales decreased from 48565 to 48545. Given the values out of the range are 4 percent, we will drop the values out of the range between 1910 and 2019. In total we droped 20 values that were out of that range.
autos = autos[autos["registration_year"].between(1900,2016)]
autos["registration_year"].value_counts(normalize=True).head(10)
2000 0.067608 2005 0.062895 1999 0.062060 2004 0.057904 2003 0.057818 2006 0.057197 2001 0.056468 2002 0.053255 1998 0.050620 2007 0.048778 Name: registration_year, dtype: float64
It appears that most of the vehicles were first registered in the past 20 years.
autos["brand"].value_counts(normalize=True)
volkswagen 0.211264 bmw 0.110045 opel 0.107581 mercedes_benz 0.096463 audi 0.086566 ford 0.069900 renault 0.047150 peugeot 0.029841 fiat 0.025642 seat 0.018273 skoda 0.016409 nissan 0.015274 mazda 0.015188 smart 0.014160 citroen 0.014010 toyota 0.012703 hyundai 0.010025 sonstige_autos 0.009811 volvo 0.009147 mini 0.008762 mitsubishi 0.008226 honda 0.007840 kia 0.007069 alfa_romeo 0.006641 porsche 0.006127 suzuki 0.005934 chevrolet 0.005698 chrysler 0.003513 dacia 0.002635 daihatsu 0.002506 jeep 0.002271 subaru 0.002142 land_rover 0.002099 saab 0.001649 jaguar 0.001564 daewoo 0.001500 trabant 0.001392 rover 0.001328 lancia 0.001071 lada 0.000578 Name: brand, dtype: float64
That list shows us all brands which are included in the Datafram autos. The 5 brands with the highest share are german manufacturers and represent around 60% of the total share. There are lots of brands that don't have a significant percentage of listings, so we will limit our analysis to brands representing more than 5% of total listings.
brand_counts = autos["brand"].value_counts(normalize=True)
common_brands = brand_counts[brand_counts > .05].index
print(common_brands)
Index(['volkswagen', 'bmw', 'opel', 'mercedes_benz', 'audi', 'ford'], dtype='object')
brand_mean_prices = {}
for brand in common_brands:
only_brand = autos[autos["brand"] == brand]
mean_price = only_brand["price_€"].mean()
brand_mean_prices[brand] = int(mean_price)
brand_mean_prices
{'audi': 9336, 'bmw': 8332, 'ford': 3749, 'mercedes_benz': 8628, 'opel': 2975, 'volkswagen': 5402}
brand_mean_power_ps = {}
for brand in common_brands:
brand_only = autos[autos["power_ps"] == brand]
mean_ps = autos["power_ps"].mean()
brand_power_ps_mean[brand]
brand_power_ps_mean
/dataquest/system/env/python3/lib/python3.4/site-packages/pandas/core/ops.py:816: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison result = getattr(x, name)(y)
TypeErrorTraceback (most recent call last) <ipython-input-32-f6bcd943c26a> in <module>() 2 3 for brand in common_brands: ----> 4 brand_only = autos[autos["power_ps"] == brand] 5 mean_ps = autos["power_ps"].mean() 6 brand_power_ps_mean[brand] /dataquest/system/env/python3/lib/python3.4/site-packages/pandas/core/ops.py in wrapper(self, other, axis) 877 878 with np.errstate(all='ignore'): --> 879 res = na_op(values, other) 880 if is_scalar(res): 881 raise TypeError('Could not compare {typ} type with Series' /dataquest/system/env/python3/lib/python3.4/site-packages/pandas/core/ops.py in na_op(x, y) 816 result = getattr(x, name)(y) 817 if result is NotImplemented: --> 818 raise TypeError("invalid type comparison") 819 except AttributeError: 820 result = op(x, y) TypeError: invalid type comparison