Overview

Dataset statistics

Number of variables6
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.0 KiB
Average record size in memory50.1 B

Variable types

Numeric2
Categorical4

Dataset

Description국내에 등록되어 도로를 운행 중인 자동차 중 자동차 검사를 받은 차량의 실 누적 주행거리를 활용한 일평균 주행거리 자료입니다.
Author한국교통안전공단
URLhttps://www.data.go.kr/data/15088739/fileData.do

Alerts

용도 is highly overall correlated with 차종High correlation
차종 is highly overall correlated with 용도 and 1 other fieldsHigh correlation
유종 is highly overall correlated with 차종High correlation
용도 is highly imbalanced (76.2%)Imbalance
차종 is highly imbalanced (59.3%)Imbalance
유종 is highly imbalanced (55.3%)Imbalance
구분 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:30:32.710114
Analysis finished2023-12-12 08:30:33.872726
Duration1.16 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.5
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T17:30:33.982401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.95
Q1250.75
median500.5
Q3750.25
95-th percentile950.05
Maximum1000
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.81944
Coefficient of variation (CV)0.57706181
Kurtosis-1.2
Mean500.5
Median Absolute Deviation (MAD)250
Skewness0
Sum500500
Variance83416.667
MonotonicityStrictly increasing
2023-12-12T17:30:34.157656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
673 1
 
0.1%
660 1
 
0.1%
661 1
 
0.1%
662 1
 
0.1%
663 1
 
0.1%
664 1
 
0.1%
665 1
 
0.1%
666 1
 
0.1%
667 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1000 1
0.1%
999 1
0.1%
998 1
0.1%
997 1
0.1%
996 1
0.1%
995 1
0.1%
994 1
0.1%
993 1
0.1%
992 1
0.1%
991 1
0.1%

시도
Categorical

Distinct17
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
경기
279 
서울
258 
부산
80 
인천
44 
대구
40 
Other values (12)
299 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기
2nd row강원
3rd row서울
4th row서울
5th row충북

Common Values

ValueCountFrequency (%)
경기 279
27.9%
서울 258
25.8%
부산 80
 
8.0%
인천 44
 
4.4%
대구 40
 
4.0%
경남 39
 
3.9%
강원 35
 
3.5%
충남 34
 
3.4%
경북 34
 
3.4%
전북 33
 
3.3%
Other values (7) 124
12.4%

Length

2023-12-12T17:30:34.307712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 279
27.9%
서울 258
25.8%
부산 80
 
8.0%
인천 44
 
4.4%
대구 40
 
4.0%
경남 39
 
3.9%
강원 35
 
3.5%
경북 34
 
3.4%
충남 34
 
3.4%
전북 33
 
3.3%
Other values (7) 124
12.4%

용도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
비사업용
961 
사업용
 
39

Length

Max length4
Median length4
Mean length3.961
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row비사업용
2nd row사업용
3rd row비사업용
4th row비사업용
5th row비사업용

Common Values

ValueCountFrequency (%)
비사업용 961
96.1%
사업용 39
 
3.9%

Length

2023-12-12T17:30:34.490920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:30:34.629916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비사업용 961
96.1%
사업용 39
 
3.9%

차종
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
승용차 일반형
771 
화물차 특수용도형
 
66
승용차 다목적형
 
42
특수차 특수작업형
 
27
특수차 구난형
 
22
Other values (7)
 
72

Length

Max length9
Median length7
Mean length7.256
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row화물차 일반형
2nd row화물차 일반형
3rd row승용차 일반형
4th row승용차 일반형
5th row승용차 일반형

Common Values

ValueCountFrequency (%)
승용차 일반형 771
77.1%
화물차 특수용도형 66
 
6.6%
승용차 다목적형 42
 
4.2%
특수차 특수작업형 27
 
2.7%
특수차 구난형 22
 
2.2%
특수차 견인형 15
 
1.5%
화물차 덤프형 15
 
1.5%
승합차 특수용도형 15
 
1.5%
화물차 일반형 14
 
1.4%
승용차 기타형 7
 
0.7%
Other values (2) 6
 
0.6%

Length

2023-12-12T17:30:34.767786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
승용차 820
41.0%
일반형 789
39.5%
화물차 97
 
4.9%
특수용도형 81
 
4.0%
특수차 64
 
3.2%
다목적형 42
 
2.1%
특수작업형 27
 
1.4%
구난형 22
 
1.1%
승합차 19
 
0.9%
견인형 15
 
0.8%
Other values (3) 24
 
1.2%

유종
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
휘발유
793 
경유
173 
기타
 
27
LPG
 
7

Length

Max length3
Median length3
Mean length2.8
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경유
2nd row경유
3rd row휘발유
4th row기타
5th row휘발유

Common Values

ValueCountFrequency (%)
휘발유 793
79.3%
경유 173
 
17.3%
기타 27
 
2.7%
LPG 7
 
0.7%

Length

2023-12-12T17:30:34.931362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:30:35.069138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
휘발유 793
79.3%
경유 173
 
17.3%
기타 27
 
2.7%
lpg 7
 
0.7%

일평균주행거리
Real number (ℝ)

Distinct999
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.411208
Minimum0.091286307
Maximum232.19559
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T17:30:35.261993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.091286307
5-th percentile1.7328082
Q19.8161832
median18.35901
Q332.135361
95-th percentile56.558382
Maximum232.19559
Range232.10431
Interquartile range (IQR)22.319178

Descriptive statistics

Standard deviation20.54925
Coefficient of variation (CV)0.87775265
Kurtosis21.677999
Mean23.411208
Median Absolute Deviation (MAD)10.154035
Skewness3.1964919
Sum23411.208
Variance422.27168
MonotonicityNot monotonic
2023-12-12T17:30:35.447464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.433802817 2
 
0.2%
7.375328084 1
 
0.1%
47.55342466 1
 
0.1%
37.02663116 1
 
0.1%
34.53217158 1
 
0.1%
25.05080935 1
 
0.1%
15.6128591 1
 
0.1%
24.45013477 1
 
0.1%
12.71008403 1
 
0.1%
33.80446927 1
 
0.1%
Other values (989) 989
98.9%
ValueCountFrequency (%)
0.091286307 1
0.1%
0.103448276 1
0.1%
0.109090909 1
0.1%
0.122994652 1
0.1%
0.123411978 1
0.1%
0.135734072 1
0.1%
0.151785714 1
0.1%
0.17791411 1
0.1%
0.18630137 1
0.1%
0.199453552 1
0.1%
ValueCountFrequency (%)
232.1955923 1
0.1%
214.7628866 1
0.1%
159.324159 1
0.1%
147.7120823 1
0.1%
128.6005587 1
0.1%
115.2442748 1
0.1%
96.07062147 1
0.1%
87.15994437 1
0.1%
86.4962406 1
0.1%
85.18052014 1
0.1%

Interactions

2023-12-12T17:30:33.364618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:30:33.093637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:30:33.507900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:30:33.232149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:30:35.570562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분시도용도차종유종일평균주행거리
구분1.0000.2610.3610.5000.5280.265
시도0.2611.0000.3230.3720.3720.203
용도0.3610.3231.0000.7070.6120.478
차종0.5000.3720.7071.0000.8820.469
유종0.5280.3720.6120.8821.0000.298
일평균주행거리0.2650.2030.4780.4690.2981.000
2023-12-12T17:30:35.708678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도유종시도차종
용도1.0000.4230.2870.557
유종0.4231.0000.2130.591
시도0.2870.2131.0000.143
차종0.5570.5910.1431.000
2023-12-12T17:30:35.846956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분일평균주행거리시도용도차종유종
구분1.0000.3510.1040.2760.2360.343
일평균주행거리0.3511.0000.0860.3590.2190.137
시도0.1040.0861.0000.2870.1430.213
용도0.2760.3590.2871.0000.5570.423
차종0.2360.2190.1430.5571.0000.591
유종0.3430.1370.2130.4230.5911.000

Missing values

2023-12-12T17:30:33.673811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:30:33.816968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

구분시도용도차종유종일평균주행거리
01경기비사업용화물차 일반형경유32.189474
12강원사업용화물차 일반형경유20.775342
23서울비사업용승용차 일반형휘발유44.561905
34서울비사업용승용차 일반형기타3.054577
45충북비사업용승용차 일반형휘발유6.11031
56전남비사업용특수차 견인형경유1.395939
67경기비사업용특수차 견인형경유1.184
78전북비사업용승용차 기타형경유43.987446
89서울비사업용특수차 특수작업형경유0.873995
910서울비사업용승용차 일반형휘발유11.123077
구분시도용도차종유종일평균주행거리
990991서울비사업용승용차 일반형휘발유37.551539
991992서울비사업용승용차 일반형휘발유35.682825
992993경기비사업용승용차 일반형휘발유11.19403
993994경기비사업용승용차 일반형휘발유22.166441
994995경기비사업용승용차 일반형휘발유32.249645
995996서울비사업용승용차 일반형휘발유13.299301
996997전남비사업용승용차 일반형휘발유18.219645
997998서울비사업용승용차 일반형휘발유8.260989
998999인천비사업용승용차 일반형휘발유22.637228
9991000서울비사업용승용차 일반형휘발유37.668387