EDA (Exploratory Data Analysis),也就是对数据进行探索性的分析,从而为之后的数据预处理和特征工程提供必要的结论。
通常我们用到pandas库和可视化工具如 matplotlib 和 seaborn 就可以完成了。主要的步骤是:理解问题;读取数据;单变量探索;多变量探索;数据预处理;建立假设,并检验。
本次对二手车价格数据EDA的整个过程我用代码记录了下来,下面是我代码的一些展示,不再用过多的语言描述。
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import missingno as msno
%matplotlib inline
Train_data = pd.read_csv('used_car_train_20200313.csv', sep=' ')#导入训练集
Test_data = pd.read_csv('used_car_testA_20200313.csv', sep=' ')#导入测试集
Train_data.head().append(Train_data.tail())#简单查看数据
SaleID | name | regDate | model | brand | bodyType | fuelType | gearbox | power | kilometer | ... | v_5 | v_6 | v_7 | v_8 | v_9 | v_10 | v_11 | v_12 | v_13 | v_14 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 736 | 20040402 | 30.0 | 6 | 1.0 | 0.0 | 0.0 | 60 | 12.5 | ... | 0.235676 | 0.101988 | 0.129549 | 0.022816 | 0.097462 | -2.881803 | 2.804097 | -2.420821 | 0.795292 | 0.914762 |
1 | 1 | 2262 | 20030301 | 40.0 | 1 | 2.0 | 0.0 | 0.0 | 0 | 15.0 | ... | 0.264777 | 0.121004 | 0.135731 | 0.026597 | 0.020582 | -4.900482 | 2.096338 | -1.030483 | -1.722674 | 0.245522 |
2 | 2 | 14874 | 20040403 | 115.0 | 15 | 1.0 | 0.0 | 0.0 | 163 | 12.5 | ... | 0.251410 | 0.114912 | 0.165147 | 0.062173 | 0.027075 | -4.846749 | 1.803559 | 1.565330 | -0.832687 | -0.229963 |
3 | 3 | 71865 | 19960908 | 109.0 | 10 | 0.0 | 0.0 | 1.0 | 193 | 15.0 | ... | 0.274293 | 0.110300 | 0.121964 | 0.033395 | 0.000000 | -4.509599 | 1.285940 | -0.501868 | -2.438353 | -0.478699 |
4 | 4 | 111080 | 20120103 | 110.0 | 5 | 1.0 | 0.0 | 0.0 | 68 | 5.0 | ... | 0.228036 | 0.073205 | 0.091880 | 0.078819 | 0.121534 | -1.896240 | 0.910783 | 0.931110 | 2.834518 | 1.923482 |
149995 | 149995 | 163978 | 20000607 | 121.0 | 10 | 4.0 | 0.0 | 1.0 | 163 | 15.0 | ... | 0.280264 | 0.000310 | 0.048441 | 0.071158 | 0.019174 | 1.988114 | -2.983973 | 0.589167 | -1.304370 | -0.302592 |
149996 | 149996 | 184535 | 20091102 | 116.0 | 11 | 0.0 | 0.0 | 0.0 | 125 | 10.0 | ... | 0.253217 | 0.000777 | 0.084079 | 0.099681 | 0.079371 | 1.839166 | -2.774615 | 2.553994 | 0.924196 | -0.272160 |
149997 | 149997 | 147587 | 20101003 | 60.0 | 11 | 1.0 | 1.0 | 0.0 | 90 | 6.0 | ... | 0.233353 | 0.000705 | 0.118872 | 0.100118 | 0.097914 | 2.439812 | -1.630677 | 2.290197 | 1.891922 | 0.414931 |
149998 | 149998 | 45907 | 20060312 | 34.0 | 10 | 3.0 | 1.0 | 0.0 | 156 | 15.0 | ... | 0.256369 | 0.000252 | 0.081479 | 0.083558 | 0.081498 | 2.075380 | -2.633719 | 1.414937 | 0.431981 | -1.659014 |
149999 | 149999 | 177672 | 19990204 | 19.0 | 28 | 6.0 | 0.0 | 1.0 | 193 | 12.5 | ... | 0.284475 | 0.000000 | 0.040072 | 0.062543 | 0.025819 | 1.978453 | -3.179913 | 0.031724 | -1.483350 | -0.342674 |
10 rows × 31 columns
Train_data.shape
(150000, 31)
Test_data.head().append(Test_data.tail())#简单查看测试集
SaleID | name | regDate | model | brand | bodyType | fuelType | gearbox | power | kilometer | ... | v_5 | v_6 | v_7 | v_8 | v_9 | v_10 | v_11 | v_12 | v_13 | v_14 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 150000 | 66932 | 20111212 | 222.0 | 4 | 5.0 | 1.0 | 1.0 | 313 | 15.0 | ... | 0.264405 | 0.121800 | 0.070899 | 0.106558 | 0.078867 | -7.050969 | -0.854626 | 4.800151 | 0.620011 | -3.664654 |
1 | 150001 | 174960 | 19990211 | 19.0 | 21 | 0.0 | 0.0 | 0.0 | 75 | 12.5 | ... | 0.261745 | 0.000000 | 0.096733 | 0.013705 | 0.052383 | 3.679418 | -0.729039 | -3.796107 | -1.541230 | -0.757055 |
2 | 150002 | 5356 | 20090304 | 82.0 | 21 | 0.0 | 0.0 | 0.0 | 109 | 7.0 | ... | 0.260216 | 0.112081 | 0.078082 | 0.062078 | 0.050540 | -4.926690 | 1.001106 | 0.826562 | 0.138226 | 0.754033 |
3 | 150003 | 50688 | 20100405 | 0.0 | 0 | 0.0 | 0.0 | 1.0 | 160 | 7.0 | ... | 0.260466 | 0.106727 | 0.081146 | 0.075971 | 0.048268 | -4.864637 | 0.505493 | 1.870379 | 0.366038 | 1.312775 |
4 | 150004 | 161428 | 19970703 | 26.0 | 14 | 2.0 | 0.0 | 0.0 | 75 | 15.0 | ... | 0.250999 | 0.000000 | 0.077806 | 0.028600 | 0.081709 | 3.616475 | -0.673236 | -3.197685 | -0.025678 | -0.101290 |
49995 | 199995 | 20903 | 19960503 | 4.0 | 4 | 4.0 | 0.0 | 0.0 | 116 | 15.0 | ... | 0.284664 | 0.130044 | 0.049833 | 0.028807 | 0.004616 | -5.978511 | 1.303174 | -1.207191 | -1.981240 | -0.357695 |
49996 | 199996 | 708 | 19991011 | 0.0 | 0 | 0.0 | 0.0 | 0.0 | 75 | 15.0 | ... | 0.268101 | 0.108095 | 0.066039 | 0.025468 | 0.025971 | -3.913825 | 1.759524 | -2.075658 | -1.154847 | 0.169073 |
49997 | 199997 | 6693 | 20040412 | 49.0 | 1 | 0.0 | 1.0 | 1.0 | 224 | 15.0 | ... | 0.269432 | 0.105724 | 0.117652 | 0.057479 | 0.015669 | -4.639065 | 0.654713 | 1.137756 | -1.390531 | 0.254420 |
49998 | 199998 | 96900 | 20020008 | 27.0 | 1 | 0.0 | 0.0 | 1.0 | 334 | 15.0 | ... | 0.261152 | 0.000490 | 0.137366 | 0.086216 | 0.051383 | 1.833504 | -2.828687 | 2.465630 | -0.911682 | -2.057353 |
49999 | 199999 | 193384 | 20041109 | 166.0 | 6 | 1.0 | NaN | 1.0 | 68 | 9.0 | ... | 0.228730 | 0.000300 | 0.103534 | 0.080625 | 0.124264 | 2.914571 | -1.135270 | 0.547628 | 2.094057 | -1.552150 |
10 rows × 30 columns
Test_data.shape
(50000, 30)
Train_data.columns
Index(['SaleID', 'name', 'regDate', 'model', 'brand', 'bodyType', 'fuelType',
'gearbox', 'power', 'kilometer', 'notRepairedDamage', 'regionCode',
'seller', 'offerType', 'creatDate', 'price', 'v_0', 'v_1', 'v_2', 'v_3',
'v_4', 'v_5', 'v_6', 'v_7', 'v_8', 'v_9', 'v_10', 'v_11', 'v_12',
'v_13', 'v_14'],
dtype='object')
Test_data.columns
Index(['SaleID', 'name', 'regDate', 'model', 'brand', 'bodyType', 'fuelType',
'gearbox', 'power', 'kilometer', 'notRepairedDamage', 'regionCode',
'seller', 'offerType', 'creatDate', 'v_0', 'v_1', 'v_2', 'v_3', 'v_4',
'v_5', 'v_6', 'v_7', 'v_8', 'v_9', 'v_10', 'v_11', 'v_12', 'v_13',
'v_14'],
dtype='object')
Train_data.describe()#熟悉数据的相关统计量
SaleID | name | regDate | model | brand | bodyType | fuelType | gearbox | power | kilometer | ... | v_5 | v_6 | v_7 | v_8 | v_9 | v_10 | v_11 | v_12 | v_13 | v_14 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 150000.000000 | 150000.000000 | 1.500000e+05 | 149999.000000 | 150000.000000 | 145494.000000 | 141320.000000 | 144019.000000 | 150000.000000 | 150000.000000 | ... | 150000.000000 | 150000.000000 | 150000.000000 | 150000.000000 | 150000.000000 | 150000.000000 | 150000.000000 | 150000.000000 | 150000.000000 | 150000.000000 |
mean | 74999.500000 | 68349.172873 | 2.003417e+07 | 47.129021 | 8.052733 | 1.792369 | 0.375842 | 0.224943 | 119.316547 | 12.597160 | ... | 0.248204 | 0.044923 | 0.124692 | 0.058144 | 0.061996 | -0.001000 | 0.009035 | 0.004813 | 0.000313 | -0.000688 |
std | 43301.414527 | 61103.875095 | 5.364988e+04 | 49.536040 | 7.864956 | 1.760640 | 0.548677 | 0.417546 | 177.168419 | 3.919576 | ... | 0.045804 | 0.051743 | 0.201410 | 0.029186 | 0.035692 | 3.772386 | 3.286071 | 2.517478 | 1.288988 | 1.038685 |
min | 0.000000 | 0.000000 | 1.991000e+07 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.500000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -9.168192 | -5.558207 | -9.639552 | -4.153899 | -6.546556 |
25% | 37499.750000 | 11156.000000 | 1.999091e+07 | 10.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 75.000000 | 12.500000 | ... | 0.243615 | 0.000038 | 0.062474 | 0.035334 | 0.033930 | -3.722303 | -1.951543 | -1.871846 | -1.057789 | -0.437034 |
50% | 74999.500000 | 51638.000000 | 2.003091e+07 | 30.000000 | 6.000000 | 1.000000 | 0.000000 | 0.000000 | 110.000000 | 15.000000 | ... | 0.257798 | 0.000812 | 0.095866 | 0.057014 | 0.058484 | 1.624076 | -0.358053 | -0.130753 | -0.036245 | 0.141246 |
75% | 112499.250000 | 118841.250000 | 2.007111e+07 | 66.000000 | 13.000000 | 3.000000 | 1.000000 | 0.000000 | 150.000000 | 15.000000 | ... | 0.265297 | 0.102009 | 0.125243 | 0.079382 | 0.087491 | 2.844357 | 1.255022 | 1.776933 | 0.942813 | 0.680378 |
max | 149999.000000 | 196812.000000 | 2.015121e+07 | 247.000000 | 39.000000 | 7.000000 | 6.000000 | 1.000000 | 19312.000000 | 15.000000 | ... | 0.291838 | 0.151420 | 1.404936 | 0.160791 | 0.222787 | 12.357011 | 18.819042 | 13.847792 | 11.147669 | 8.658418 |
8 rows × 30 columns
Test_data.describe()
SaleID | name | regDate | model | brand | bodyType | fuelType | gearbox | power | kilometer | ... | v_5 | v_6 | v_7 | v_8 | v_9 | v_10 | v_11 | v_12 | v_13 | v_14 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 50000.000000 | 50000.000000 | 5.000000e+04 | 50000.000000 | 50000.000000 | 48587.000000 | 47107.000000 | 48090.000000 | 50000.000000 | 50000.000000 | ... | 50000.000000 | 50000.000000 | 50000.000000 | 50000.000000 | 50000.000000 | 50000.000000 | 50000.000000 | 50000.000000 | 50000.000000 | 50000.000000 |
mean | 174999.500000 | 68542.223280 | 2.003393e+07 | 46.844520 | 8.056240 | 1.782185 | 0.373405 | 0.224350 | 119.883620 | 12.595580 | ... | 0.248669 | 0.045021 | 0.122744 | 0.057997 | 0.062000 | -0.017855 | -0.013742 | -0.013554 | -0.003147 | 0.001516 |
std | 14433.901067 | 61052.808133 | 5.368870e+04 | 49.469548 | 7.819477 | 1.760736 | 0.546442 | 0.417158 | 185.097387 | 3.908979 | ... | 0.044601 | 0.051766 | 0.195972 | 0.029211 | 0.035653 | 3.747985 | 3.231258 | 2.515962 | 1.286597 | 1.027360 |
min | 150000.000000 | 0.000000 | 1.991000e+07 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.500000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -9.160049 | -5.411964 | -8.916949 | -4.123333 | -6.112667 |
25% | 162499.750000 | 11203.500000 | 1.999091e+07 | 10.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 75.000000 | 12.500000 | ... | 0.243762 | 0.000044 | 0.062644 | 0.035084 | 0.033714 | -3.700121 | -1.971325 | -1.876703 | -1.060428 | -0.437920 |
50% | 174999.500000 | 52248.500000 | 2.003091e+07 | 29.000000 | 6.000000 | 1.000000 | 0.000000 | 0.000000 | 109.000000 | 15.000000 | ... | 0.257877 | 0.000815 | 0.095828 | 0.057084 | 0.058764 | 1.613212 | -0.355843 | -0.142779 | -0.035956 | 0.138799 |
75% | 187499.250000 | 118856.500000 | 2.007110e+07 | 65.000000 | 13.000000 | 3.000000 | 1.000000 | 0.000000 | 150.000000 | 15.000000 | ... | 0.265328 | 0.102025 | 0.125438 | 0.079077 | 0.087489 | 2.832708 | 1.262914 | 1.764335 | 0.941469 | 0.681163 |
max | 199999.000000 | 196805.000000 | 2.015121e+07 | 246.000000 | 39.000000 | 7.000000 | 6.000000 | 1.000000 | 20000.000000 | 15.000000 | ... | 0.291618 | 0.153265 | 1.358813 | 0.156355 | 0.214775 | 12.338872 | 18.856218 | 12.950498 | 5.913273 | 2.624622 |
8 rows × 29 columns
Train_data.info()#熟悉数据类型
RangeIndex: 150000 entries, 0 to 149999
Data columns (total 31 columns):
SaleID 150000 non-null int64
name 150000 non-null int64
regDate 150000 non-null int64
model 149999 non-null float64
brand 150000 non-null int64
bodyType 145494 non-null float64
fuelType 141320 non-null float64
gearbox 144019 non-null float64
power 150000 non-null int64
kilometer 150000 non-null float64
notRepairedDamage 150000 non-null object
regionCode 150000 non-null int64
seller 150000 non-null int64
offerType 150000 non-null int64
creatDate 150000 non-null int64
price 150000 non-null int64
v_0 150000 non-null float64
v_1 150000 non-null float64
v_2 150000 non-null float64
v_3 150000 non-null float64
v_4 150000 non-null float64
v_5 150000 non-null float64
v_6 150000 non-null float64
v_7 150000 non-null float64
v_8 150000 non-null float64
v_9 150000 non-null float64
v_10 150000 non-null float64
v_11 150000 non-null float64
v_12 150000 non-null float64
v_13 150000 non-null float64
v_14 150000 non-null float64
dtypes: float64(20), int64(10), object(1)
memory usage: 35.5+ MB
Test_data.info()
RangeIndex: 50000 entries, 0 to 49999
Data columns (total 30 columns):
SaleID 50000 non-null int64
name 50000 non-null int64
regDate 50000 non-null int64
model 50000 non-null float64
brand 50000 non-null int64
bodyType 48587 non-null float64
fuelType 47107 non-null float64
gearbox 48090 non-null float64
power 50000 non-null int64
kilometer 50000 non-null float64
notRepairedDamage 50000 non-null object
regionCode 50000 non-null int64
seller 50000 non-null int64
offerType 50000 non-null int64
creatDate 50000 non-null int64
v_0 50000 non-null float64
v_1 50000 non-null float64
v_2 50000 non-null float64
v_3 50000 non-null float64
v_4 50000 non-null float64
v_5 50000 non-null float64
v_6 50000 non-null float64
v_7 50000 non-null float64
v_8 50000 non-null float64
v_9 50000 non-null float64
v_10 50000 non-null float64
v_11 50000 non-null float64
v_12 50000 non-null float64
v_13 50000 non-null float64
v_14 50000 non-null float64
dtypes: float64(20), int64(9), object(1)
memory usage: 11.4+ MB
Train_data.isnull().sum()#查看缺失值情况
SaleID 0
name 0
regDate 0
model 1
brand 0
bodyType 4506
fuelType 8680
gearbox 5981
power 0
kilometer 0
notRepairedDamage 0
regionCode 0
seller 0
offerType 0
creatDate 0
price 0
v_0 0
v_1 0
v_2 0
v_3 0
v_4 0
v_5 0
v_6 0
v_7 0
v_8 0
v_9 0
v_10 0
v_11 0
v_12 0
v_13 0
v_14 0
dtype: int64
Test_data.isnull().sum()
SaleID 0
name 0
regDate 0
model 0
brand 0
bodyType 1413
fuelType 2893
gearbox 1910
power 0
kilometer 0
notRepairedDamage 0
regionCode 0
seller 0
offerType 0
creatDate 0
v_0 0
v_1 0
v_2 0
v_3 0
v_4 0
v_5 0
v_6 0
v_7 0
v_8 0
v_9 0
v_10 0
v_11 0
v_12 0
v_13 0
v_14 0
dtype: int64
miss=Train_data.isnull().sum()
miss=miss[miss>0].sort_values()
miss.plot.bar()
msno.matrix(Train_data.sample(250))#可视化看一下缺失值
msno.bar(Train_data.sample(1000))
msno.matrix(Test_data.sample(250))
msno.bar(Test_data.sample(1000))
Train_data.info()
RangeIndex: 150000 entries, 0 to 149999
Data columns (total 31 columns):
SaleID 150000 non-null int64
name 150000 non-null int64
regDate 150000 non-null int64
model 149999 non-null float64
brand 150000 non-null int64
bodyType 145494 non-null float64
fuelType 141320 non-null float64
gearbox 144019 non-null float64
power 150000 non-null int64
kilometer 150000 non-null float64
notRepairedDamage 150000 non-null object
regionCode 150000 non-null int64
seller 150000 non-null int64
offerType 150000 non-null int64
creatDate 150000 non-null int64
price 150000 non-null int64
v_0 150000 non-null float64
v_1 150000 non-null float64
v_2 150000 non-null float64
v_3 150000 non-null float64
v_4 150000 non-null float64
v_5 150000 non-null float64
v_6 150000 non-null float64
v_7 150000 non-null float64
v_8 150000 non-null float64
v_9 150000 non-null float64
v_10 150000 non-null float64
v_11 150000 non-null float64
v_12 150000 non-null float64
v_13 150000 non-null float64
v_14 150000 non-null float64
dtypes: float64(20), int64(10), object(1)
memory usage: 35.5+ MB
Train_data['notRepairedDamage'].value_counts()
0.0 111361
- 24324
1.0 14315
Name: notRepairedDamage, dtype: int64
Train_data['notRepairedDamage'].replace('-',np.nan,inplace=True)#将-替换为缺省值
Train_data['notRepairedDamage'].value_counts()
0.0 111361
1.0 14315
Name: notRepairedDamage, dtype: int64
Train_data.isnull().sum()#重新看一下缺省值多少
SaleID 0
name 0
regDate 0
model 1
brand 0
bodyType 4506
fuelType 8680
gearbox 5981
power 0
kilometer 0
notRepairedDamage 24324
regionCode 0
seller 0
offerType 0
creatDate 0
price 0
v_0 0
v_1 0
v_2 0
v_3 0
v_4 0
v_5 0
v_6 0
v_7 0
v_8 0
v_9 0
v_10 0
v_11 0
v_12 0
v_13 0
v_14 0
dtype: int64
Test_data['notRepairedDamage'].replace('-',np.nan,inplace=True)
Test_data['notRepairedDamage'].value_counts()
0.0 37249
1.0 4720
Name: notRepairedDamage, dtype: int64
Train_data['seller'].value_counts()#此项可删掉
0 149999
1 1
Name: seller, dtype: int64
Train_data['offerType'].value_counts()#此项可删掉
0 150000
Name: offerType, dtype: int64
del Train_data["seller"]
del Train_data["offerType"]
del Test_data["seller"]
del Test_data["offerType"]
Train_data['price']#了解结果分布情况
0 1850
1 3600
2 6222
3 2400
4 5200
5 8000
6 3500
7 1000
8 2850
9 650
10 3100
11 5450
12 1600
13 3100
14 6900
15 3200
16 10500
17 3700
18 790
19 1450
20 990
21 2800
22 350
23 599
24 9250
25 3650
26 2800
27 2399
28 4900
29 2999
...
149970 900
149971 3400
149972 999
149973 3500
149974 4500
149975 3990
149976 1200
149977 330
149978 3350
149979 5000
149980 4350
149981 9000
149982 2000
149983 12000
149984 6700
149985 4200
149986 2800
149987 3000
149988 7500
149989 1150
149990 450
149991 24950
149992 950
149993 4399
149994 14780
149995 5900
149996 9500
149997 7500
149998 4999
149999 4700
Name: price, Length: 150000, dtype: int64
Train_data['price'].value_counts()
500 2337
1500 2158
1200 1922
1000 1850
2500 1821
600 1535
3500 1533
800 1513
2000 1378
999 1356
750 1279
4500 1271
650 1257
1800 1223
2200 1201
850 1198
700 1174
900 1107
1300 1105
950 1104
3000 1098
1100 1079
5500 1079
1600 1074
300 1071
550 1042
350 1005
1250 1003
6500 973
1999 929
...
21560 1
7859 1
3120 1
2279 1
6066 1
6322 1
4275 1
10420 1
43300 1
305 1
1765 1
15970 1
44400 1
8885 1
2992 1
31850 1
15413 1
13495 1
9525 1
7270 1
13879 1
3760 1
24250 1
11360 1
10295 1
25321 1
8886 1
8801 1
37920 1
8188 1
Name: price, Length: 3763, dtype: int64
import scipy.stats as st
y = Train_data['price']
plt.figure(1); plt.title('Johnson SU')
sns.distplot(y, fit=st.johnsonsu)
plt.figure(2); plt.title('Johnson SU kde=false')
sns.distplot(y,kde=False, fit=st.johnsonsu)
plt.figure(3); plt.title('Normal')
sns.distplot(y, kde=False, fit=st.norm)
plt.figure(4); plt.title('Log Normal')
sns.distplot(y, kde=False, fit=st.lognorm)#查看价格的概率分布情况
plt.figure(1); plt.title('Johnson SU hist=false')
sns.distplot(y,kde=False,hist=False, fit=st.johnsonsu)
Train_data.skew(), Train_data.kurt()#看一下偏度和峰度
(SaleID 6.017846e-17
name 5.576058e-01
regDate 2.849508e-02
model 1.484388e+00
brand 1.150760e+00
bodyType 9.915299e-01
fuelType 1.595486e+00
gearbox 1.317514e+00
power 6.586318e+01
kilometer -1.525921e+00
notRepairedDamage 2.430640e+00
regionCode 6.888812e-01
creatDate -7.901331e+01
price 3.346487e+00
v_0 -1.316712e+00
v_1 3.594543e-01
v_2 4.842556e+00
v_3 1.062920e-01
v_4 3.679890e-01
v_5 -4.737094e+00
v_6 3.680730e-01
v_7 5.130233e+00
v_8 2.046133e-01
v_9 4.195007e-01
v_10 2.522046e-02
v_11 3.029146e+00
v_12 3.653576e-01
v_13 2.679152e-01
v_14 -1.186355e+00
dtype: float64, SaleID -1.200000
name -1.039945
regDate -0.697308
model 1.740483
brand 1.076201
bodyType 0.206937
fuelType 5.880049
gearbox -0.264161
power 5733.451054
kilometer 1.141934
notRepairedDamage 3.908072
regionCode -0.340832
creatDate 6881.080328
price 18.995183
v_0 3.993841
v_1 -1.753017
v_2 23.860591
v_3 -0.418006
v_4 -0.197295
v_5 22.934081
v_6 -1.742567
v_7 25.845489
v_8 -0.636225
v_9 -0.321491
v_10 -0.577935
v_11 12.568731
v_12 0.268937
v_13 -0.438274
v_14 2.393526
dtype: float64)
sns.distplot(Train_data.skew(),color='red',axlabel ='Skewness')
sns.distplot(Train_data.kurt(),color='black',axlabel ='Kurtness')
plt.hist(Train_data['price'], orientation = 'vertical',histtype = 'bar', color ='red')
plt.show()#查看价格数量
plt.hist(np.log(Train_data['price']), orientation = 'vertical',histtype = 'bar', color ='red')
plt.show()
# 分离label即预测值
Y_train = Train_data['price']
numeric_features = ['power', 'kilometer', 'v_0', 'v_1', 'v_2', 'v_3', 'v_4', 'v_5', 'v_6', 'v_7', 'v_8', 'v_9', 'v_10', 'v_11', 'v_12', 'v_13','v_14' ]
categorical_features = ['name', 'model', 'brand', 'bodyType', 'fuelType', 'gearbox', 'notRepairedDamage', 'regionCode',]
# 特征nunique分布
for cat_fea in categorical_features:
print(cat_fea + "的特征分布如下:")
print("{}特征有个{}不同的值".format(cat_fea, Train_data[cat_fea].nunique()))
print(Train_data[cat_fea].value_counts())
name的特征分布如下:
name特征有个99662不同的值
708 282
387 282
55 280
1541 263
203 233
53 221
713 217
290 197
1186 184
911 182
2044 176
1513 160
1180 158
631 157
893 153
2765 147
473 141
1139 137
1108 132
444 129
306 127
2866 123
2402 116
533 114
1479 113
422 113
4635 110
725 110
964 109
1373 104
...
89083 1
95230 1
164864 1
173060 1
179207 1
181256 1
185354 1
25564 1
19417 1
189324 1
162719 1
191373 1
193422 1
136082 1
140180 1
144278 1
146327 1
148376 1
158621 1
1404 1
15319 1
46022 1
64463 1
976 1
3025 1
5074 1
7123 1
11221 1
13270 1
174485 1
Name: name, Length: 99662, dtype: int64
model的特征分布如下:
model特征有个248不同的值
0.0 11762
19.0 9573
4.0 8445
1.0 6038
29.0 5186
48.0 5052
40.0 4502
26.0 4496
8.0 4391
31.0 3827
13.0 3762
17.0 3121
65.0 2730
49.0 2608
46.0 2454
30.0 2342
44.0 2195
5.0 2063
10.0 2004
21.0 1872
73.0 1789
11.0 1775
23.0 1696
22.0 1524
69.0 1522
63.0 1469
7.0 1460
16.0 1349
88.0 1309
66.0 1250
...
141.0 37
133.0 35
216.0 30
202.0 28
151.0 26
226.0 26
231.0 23
234.0 23
233.0 20
198.0 18
224.0 18
227.0 17
237.0 17
220.0 16
230.0 16
239.0 14
223.0 13
236.0 11
241.0 10
232.0 10
229.0 10
235.0 7
246.0 7
243.0 4
244.0 3
245.0 2
209.0 2
240.0 2
242.0 2
247.0 1
Name: model, Length: 248, dtype: int64
brand的特征分布如下:
brand特征有个40不同的值
0 31480
4 16737
14 16089
10 14249
1 13794
6 10217
9 7306
5 4665
13 3817
11 2945
3 2461
7 2361
16 2223
8 2077
25 2064
27 2053
21 1547
15 1458
19 1388
20 1236
12 1109
22 1085
26 966
30 940
17 913
24 772
28 649
32 592
29 406
37 333
2 321
31 318
18 316
36 228
34 227
33 218
23 186
35 180
38 65
39 9
Name: brand, dtype: int64
bodyType的特征分布如下:
bodyType特征有个8不同的值
0.0 41420
1.0 35272
2.0 30324
3.0 13491
4.0 9609
5.0 7607
6.0 6482
7.0 1289
Name: bodyType, dtype: int64
fuelType的特征分布如下:
fuelType特征有个7不同的值
0.0 91656
1.0 46991
2.0 2212
3.0 262
4.0 118
5.0 45
6.0 36
Name: fuelType, dtype: int64
gearbox的特征分布如下:
gearbox特征有个2不同的值
0.0 111623
1.0 32396
Name: gearbox, dtype: int64
notRepairedDamage的特征分布如下:
notRepairedDamage特征有个2不同的值
0.0 111361
1.0 14315
Name: notRepairedDamage, dtype: int64
regionCode的特征分布如下:
regionCode特征有个7905不同的值
419 369
764 258
125 137
176 136
462 134
428 132
24 130
1184 130
122 129
828 126
70 125
827 120
207 118
1222 117
2418 117
85 116
2615 115
2222 113
759 112
188 111
1757 110
1157 109
2401 107
1069 107
3545 107
424 107
272 107
451 106
450 105
129 105
...
6324 1
7372 1
7500 1
8107 1
2453 1
7942 1
5135 1
6760 1
8070 1
7220 1
8041 1
8012 1
5965 1
823 1
7401 1
8106 1
5224 1
8117 1
7507 1
7989 1
6505 1
6377 1
8042 1
7763 1
7786 1
6414 1
7063 1
4239 1
5931 1
7267 1
Name: regionCode, Length: 7905, dtype: int64
# 特征nunique分布
for cat_fea in categorical_features:
print(cat_fea + "的特征分布如下:")
print("{}特征有个{}不同的值".format(cat_fea, Test_data[cat_fea].nunique()))
print(Test_data[cat_fea].value_counts())
name的特征分布如下:
name特征有个37453不同的值
55 97
708 96
387 95
1541 88
713 74
53 72
1186 67
203 67
631 65
911 64
2044 62
2866 60
1139 57
893 54
1180 52
2765 50
1108 50
290 48
1513 47
691 45
473 44
299 43
444 41
422 39
964 39
1479 38
1273 38
306 36
725 35
4635 35
..
46786 1
48835 1
165572 1
68204 1
171719 1
59080 1
186062 1
11985 1
147155 1
134869 1
138967 1
173792 1
114403 1
59098 1
59144 1
40679 1
61161 1
128746 1
55022 1
143089 1
14066 1
147187 1
112892 1
46598 1
159481 1
22270 1
89855 1
42752 1
48899 1
11808 1
Name: name, Length: 37453, dtype: int64
model的特征分布如下:
model特征有个247不同的值
0.0 3896
19.0 3245
4.0 3007
1.0 1981
29.0 1742
48.0 1685
26.0 1525
40.0 1409
8.0 1397
31.0 1292
13.0 1210
17.0 1087
65.0 915
49.0 866
46.0 831
30.0 803
10.0 709
5.0 696
44.0 676
21.0 659
11.0 603
23.0 591
73.0 561
69.0 555
7.0 526
63.0 493
22.0 443
16.0 412
66.0 411
88.0 391
...
124.0 9
193.0 9
151.0 8
198.0 8
181.0 8
239.0 7
233.0 7
216.0 7
231.0 6
133.0 6
236.0 6
227.0 6
220.0 5
230.0 5
234.0 4
224.0 4
241.0 4
223.0 4
229.0 3
189.0 3
232.0 3
237.0 3
235.0 2
245.0 2
209.0 2
242.0 1
240.0 1
244.0 1
243.0 1
246.0 1
Name: model, Length: 247, dtype: int64
brand的特征分布如下:
brand特征有个40不同的值
0 10348
4 5763
14 5314
10 4766
1 4532
6 3502
9 2423
5 1569
13 1245
11 919
7 795
3 773
16 771
8 704
25 695
27 650
21 544
15 511
20 450
19 450
12 389
22 363
30 324
17 317
26 303
24 268
28 225
32 193
29 117
31 115
18 106
2 104
37 92
34 77
33 76
36 67
23 62
35 53
38 23
39 2
Name: brand, dtype: int64
bodyType的特征分布如下:
bodyType特征有个8不同的值
0.0 13985
1.0 11882
2.0 9900
3.0 4433
4.0 3303
5.0 2537
6.0 2116
7.0 431
Name: bodyType, dtype: int64
fuelType的特征分布如下:
fuelType特征有个7不同的值
0.0 30656
1.0 15544
2.0 774
3.0 72
4.0 37
6.0 14
5.0 10
Name: fuelType, dtype: int64
gearbox的特征分布如下:
gearbox特征有个2不同的值
0.0 37301
1.0 10789
Name: gearbox, dtype: int64
notRepairedDamage的特征分布如下:
notRepairedDamage特征有个2不同的值
0.0 37249
1.0 4720
Name: notRepairedDamage, dtype: int64
regionCode的特征分布如下:
regionCode特征有个6971不同的值
419 146
764 78
188 52
125 51
759 51
2615 50
462 49
542 44
85 44
1069 43
451 41
828 40
757 39
1688 39
2154 39
1947 39
24 39
2690 38
238 38
2418 38
827 38
1184 38
272 38
233 38
70 37
703 37
2067 37
509 37
360 37
176 37
...
5512 1
7465 1
1290 1
3717 1
1258 1
7401 1
7920 1
7925 1
5151 1
7527 1
7689 1
8114 1
3237 1
6003 1
7335 1
3984 1
7367 1
6001 1
8021 1
3691 1
4920 1
6035 1
3333 1
5382 1
6969 1
7753 1
7463 1
7230 1
826 1
112 1
Name: regionCode, Length: 6971, dtype: int64
numeric_features.append('price')
numeric_features
['power',
'kilometer',
'v_0',
'v_1',
'v_2',
'v_3',
'v_4',
'v_5',
'v_6',
'v_7',
'v_8',
'v_9',
'v_10',
'v_11',
'v_12',
'v_13',
'v_14',
'price']
## 1) 相关性分析
price_numeric = Train_data[numeric_features]
correlation = price_numeric.corr()
print(correlation['price'].sort_values(ascending = False),'\n')
price 1.000000
v_12 0.692823
v_8 0.685798
v_0 0.628397
power 0.219834
v_5 0.164317
v_2 0.085322
v_6 0.068970
v_1 0.060914
v_14 0.035911
v_13 -0.013993
v_7 -0.053024
v_4 -0.147085
v_9 -0.206205
v_10 -0.246175
v_11 -0.275320
kilometer -0.440519
v_3 -0.730946
Name: price, dtype: float64
f , ax = plt.subplots(figsize = (7, 7))
plt.title('Correlation of Numeric Features with Price',y=1,size=16)
sns.heatmap(correlation,square = True, vmax=0.8)
## 2) 查看几个特征得 偏度和峰值
for col in numeric_features:
print('{:15}'.format(col),
'Skewness: {:05.2f}'.format(Train_data[col].skew()) ,
' ' ,
'Kurtosis: {:06.2f}'.format(Train_data[col].kurt())
)
power Skewness: 65.86 Kurtosis: 5733.45
kilometer Skewness: -1.53 Kurtosis: 001.14
v_0 Skewness: -1.32 Kurtosis: 003.99
v_1 Skewness: 00.36 Kurtosis: -01.75
v_2 Skewness: 04.84 Kurtosis: 023.86
v_3 Skewness: 00.11 Kurtosis: -00.42
v_4 Skewness: 00.37 Kurtosis: -00.20
v_5 Skewness: -4.74 Kurtosis: 022.93
v_6 Skewness: 00.37 Kurtosis: -01.74
v_7 Skewness: 05.13 Kurtosis: 025.85
v_8 Skewness: 00.20 Kurtosis: -00.64
v_9 Skewness: 00.42 Kurtosis: -00.32
v_10 Skewness: 00.03 Kurtosis: -00.58
v_11 Skewness: 03.03 Kurtosis: 012.57
v_12 Skewness: 00.37 Kurtosis: 000.27
v_13 Skewness: 00.27 Kurtosis: -00.44
v_14 Skewness: -1.19 Kurtosis: 002.39
price Skewness: 03.35 Kurtosis: 019.00
## 3) 每个数字特征得分布可视化
f = pd.melt(Train_data, value_vars=numeric_features)
g = sns.FacetGrid(f, col="variable", col_wrap=2, sharex=False, sharey=False)
g = g.map(sns.distplot, "value")
## 4) 数字特征相互之间的关系可视化
sns.set()
columns = ['price', 'v_12', 'v_8' , 'v_0', 'power', 'v_5', 'v_2', 'v_6', 'v_1', 'v_14']
sns.pairplot(Train_data[columns],size = 2 ,kind ='scatter',diag_kind='kde')
plt.show()
## 5) 多变量互相回归关系可视化
fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6), (ax7, ax8), (ax9, ax10)) = plt.subplots(nrows=5, ncols=2, figsize=(24, 20))
# ['v_12', 'v_8' , 'v_0', 'power', 'v_5', 'v_2', 'v_6', 'v_1', 'v_14']
v_12_scatter_plot = pd.concat([Y_train,Train_data['v_12']],axis = 1)
sns.regplot(x='v_12',y = 'price', data = v_12_scatter_plot,scatter= True, fit_reg=True, ax=ax1)
v_8_scatter_plot = pd.concat([Y_train,Train_data['v_8']],axis = 1)
sns.regplot(x='v_8',y = 'price',data = v_8_scatter_plot,scatter= True, fit_reg=True, ax=ax2)
v_0_scatter_plot = pd.concat([Y_train,Train_data['v_0']],axis = 1)
sns.regplot(x='v_0',y = 'price',data = v_0_scatter_plot,scatter= True, fit_reg=True, ax=ax3)
power_scatter_plot = pd.concat([Y_train,Train_data['power']],axis = 1)
sns.regplot(x='power',y = 'price',data = power_scatter_plot,scatter= True, fit_reg=True, ax=ax4)
v_5_scatter_plot = pd.concat([Y_train,Train_data['v_5']],axis = 1)
sns.regplot(x='v_5',y = 'price',data = v_5_scatter_plot,scatter= True, fit_reg=True, ax=ax5)
v_2_scatter_plot = pd.concat([Y_train,Train_data['v_2']],axis = 1)
sns.regplot(x='v_2',y = 'price',data = v_2_scatter_plot,scatter= True, fit_reg=True, ax=ax6)
v_6_scatter_plot = pd.concat([Y_train,Train_data['v_6']],axis = 1)
sns.regplot(x='v_6',y = 'price',data = v_6_scatter_plot,scatter= True, fit_reg=True, ax=ax7)
v_1_scatter_plot = pd.concat([Y_train,Train_data['v_1']],axis = 1)
sns.regplot(x='v_1',y = 'price',data = v_1_scatter_plot,scatter= True, fit_reg=True, ax=ax8)
v_14_scatter_plot = pd.concat([Y_train,Train_data['v_14']],axis = 1)
sns.regplot(x='v_14',y = 'price',data = v_14_scatter_plot,scatter= True, fit_reg=True, ax=ax9)
v_13_scatter_plot = pd.concat([Y_train,Train_data['v_13']],axis = 1)
sns.regplot(x='v_13',y = 'price',data = v_13_scatter_plot,scatter= True, fit_reg=True, ax=ax10)
## 1) unique分布
for fea in categorical_features:
print(Train_data[fea].nunique())
99662
248
40
8
7
2
2
7905
categorical_features
['name',
'model',
'brand',
'bodyType',
'fuelType',
'gearbox',
'notRepairedDamage',
'regionCode']
## 2) 类别特征箱形图可视化
# 因为 name和 regionCode的类别太稀疏了,这里我们把不稀疏的几类画一下
categorical_features = ['model',
'brand',
'bodyType',
'fuelType',
'gearbox',
'notRepairedDamage']
for c in categorical_features:
Train_data[c] = Train_data[c].astype('category')
if Train_data[c].isnull().any():
Train_data[c] = Train_data[c].cat.add_categories(['MISSING'])
Train_data[c] = Train_data[c].fillna('MISSING')
def boxplot(x, y, **kwargs):
sns.boxplot(x=x, y=y)
x=plt.xticks(rotation=90)
f = pd.melt(Train_data, id_vars=['price'], value_vars=categorical_features)
g = sns.FacetGrid(f, col="variable", col_wrap=2, sharex=False, sharey=False, size=5)
g = g.map(boxplot, "value", "price")
## 3) 类别特征的小提琴图可视化
catg_list = categorical_features
target = 'price'
for catg in catg_list :
sns.violinplot(x=catg, y=target, data=Train_data)
plt.show()
categorical_features = ['model',
'brand',
'bodyType',
'fuelType',
'gearbox',
'notRepairedDamage']
## 4) 类别特征的柱形图可视化
def bar_plot(x, y, **kwargs):
sns.barplot(x=x, y=y)
x=plt.xticks(rotation=90)
f = pd.melt(Train_data, id_vars=['price'], value_vars=categorical_features)
g = sns.FacetGrid(f, col="variable", col_wrap=2, sharex=False, sharey=False, size=5)
g = g.map(bar_plot, "value", "price")
## 5) 类别特征的每个类别频数可视化(count_plot)
def count_plot(x, **kwargs):
sns.countplot(x=x)
x=plt.xticks(rotation=90)
f = pd.melt(Train_data, value_vars=categorical_features)
g = sns.FacetGrid(f, col="variable", col_wrap=2, sharex=False, sharey=False, size=5)
g = g.map(count_plot, "value")
import pandas_profiling
pfr = pandas_profiling.ProfileReport(Train_data)
pfr.to_file("./example.html")#用pandas_profiling生成数据报告