Overview

Dataset statistics

Number of variables8
Number of observations73
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory68.8 B

Variable types

Numeric3
Categorical4
Text1

Dataset

Description한국동서발전의 차량보유 상세현황 정보를 제공합니다. 사업소, 차종, 차량명, 연식, 배기량, 연료 등의 항목을 나타냅니다.
URLhttps://www.data.go.kr/data/15117520/fileData.do

Alerts

번호 is highly overall correlated with 사업소High correlation
배기량 is highly overall correlated with 차종 and 1 other fieldsHigh correlation
사업소 is highly overall correlated with 번호High correlation
차종 is highly overall correlated with 배기량 and 1 other fieldsHigh correlation
사용목적 is highly overall correlated with 배기량 and 1 other fieldsHigh correlation
번호 has unique valuesUnique
배기량 has 6 (8.2%) zerosZeros

Reproduction

Analysis started2023-12-12 01:34:32.482698
Analysis finished2023-12-12 01:34:34.180491
Duration1.7 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37
Minimum1
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-12T10:34:34.290195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.6
Q119
median37
Q355
95-th percentile69.4
Maximum73
Range72
Interquartile range (IQR)36

Descriptive statistics

Standard deviation21.217131
Coefficient of variation (CV)0.57343598
Kurtosis-1.2
Mean37
Median Absolute Deviation (MAD)18
Skewness0
Sum2701
Variance450.16667
MonotonicityStrictly increasing
2023-12-12T10:34:34.467355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
56 1
 
1.4%
54 1
 
1.4%
53 1
 
1.4%
52 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
Other values (63) 63
86.3%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
73 1
1.4%
72 1
1.4%
71 1
1.4%
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%
64 1
1.4%

사업소
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size716.0 B
당진
27 
본사
13 
울산
8 
동해
8 
신재생개발본부
5 
Other values (4)
12 

Length

Max length8
Median length2
Mean length2.5479452
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row본사
2nd row본사
3rd row본사
4th row본사
5th row본사

Common Values

ValueCountFrequency (%)
당진 27
37.0%
본사 13
17.8%
울산 8
 
11.0%
동해 8
 
11.0%
신재생개발본부 5
 
6.8%
음성 4
 
5.5%
일산 3
 
4.1%
신호남 3
 
4.1%
기술전문연구센터 2
 
2.7%

Length

2023-12-12T10:34:35.036289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:34:35.237004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당진 27
37.0%
본사 13
17.8%
울산 8
 
11.0%
동해 8
 
11.0%
신재생개발본부 5
 
6.8%
음성 4
 
5.5%
일산 3
 
4.1%
신호남 3
 
4.1%
기술전문연구센터 2
 
2.7%

차종
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size716.0 B
승용
48 
화물
10 
소방차
5 
청소차
 
4
중형버스
 
3
Other values (2)
 
3

Length

Max length4
Median length2
Mean length2.2739726
Min length2

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row승용
2nd row승용
3rd row승용
4th row승용
5th row승용

Common Values

ValueCountFrequency (%)
승용 48
65.8%
화물 10
 
13.7%
소방차 5
 
6.8%
청소차 4
 
5.5%
중형버스 3
 
4.1%
대형버스 2
 
2.7%
구급차 1
 
1.4%

Length

2023-12-12T10:34:35.431381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:34:35.601242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
승용 48
65.8%
화물 10
 
13.7%
소방차 5
 
6.8%
청소차 4
 
5.5%
중형버스 3
 
4.1%
대형버스 2
 
2.7%
구급차 1
 
1.4%

차량(장비)명
Text

Distinct50
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Memory size716.0 B
2023-12-12T10:34:35.886186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.8630137
Min length2

Characters and Unicode

Total characters501
Distinct characters113
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)49.3%

Sample

1st row제네시스 G80
2nd row제네시스 G80
3rd row제네시스 G80
4th row제네시스 G80
5th row넥쏘(기획)
ValueCountFrequency (%)
하이브리드 7
 
6.4%
ev 6
 
5.5%
제네시스 5
 
4.6%
니로 5
 
4.6%
1톤 4
 
3.7%
g80 4
 
3.7%
ev6 4
 
3.7%
그랜져 3
 
2.8%
봉고ev 3
 
2.8%
일렉트릭 3
 
2.8%
Other values (50) 65
59.6%
2023-12-12T10:34:36.389507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36
 
7.2%
V 17
 
3.4%
E 17
 
3.4%
차 17
 
3.4%
) 16
 
3.2%
( 16
 
3.2%
이 16
 
3.2%
스 16
 
3.2%
소 13
 
2.6%
고 10
 
2.0%
Other values (103) 327
65.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 341
68.1%
Uppercase Letter 53
 
10.6%
Space Separator 36
 
7.2%
Decimal Number 33
 
6.6%
Close Punctuation 16
 
3.2%
Open Punctuation 16
 
3.2%
Letter Number 4
 
0.8%
Other Punctuation 1
 
0.2%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
차 17
 
5.0%
이 16
 
4.7%
스 16
 
4.7%
소 13
 
3.8%
고 10
 
2.9%
니 9
 
2.6%
타 8
 
2.3%
쏘 8
 
2.3%
리 8
 
2.3%
봉 7
 
2.1%
Other values (75) 229
67.2%
Uppercase Letter
ValueCountFrequency (%)
V 17
32.1%
E 17
32.1%
G 7
13.2%
F 3
 
5.7%
L 2
 
3.8%
C 1
 
1.9%
P 1
 
1.9%
T 1
 
1.9%
Y 1
 
1.9%
K 1
 
1.9%
Other values (2) 2
 
3.8%
Decimal Number
ValueCountFrequency (%)
1 8
24.2%
0 5
15.2%
6 5
15.2%
8 4
12.1%
2 3
 
9.1%
5 3
 
9.1%
9 2
 
6.1%
3 2
 
6.1%
7 1
 
3.0%
Letter Number
ValueCountFrequency (%)
Ⅲ 3
75.0%
Ⅱ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 341
68.1%
Common 103
 
20.6%
Latin 57
 
11.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
차 17
 
5.0%
이 16
 
4.7%
스 16
 
4.7%
소 13
 
3.8%
고 10
 
2.9%
니 9
 
2.6%
타 8
 
2.3%
쏘 8
 
2.3%
리 8
 
2.3%
봉 7
 
2.1%
Other values (75) 229
67.2%
Common
ValueCountFrequency (%)
36
35.0%
) 16
15.5%
( 16
15.5%
1 8
 
7.8%
0 5
 
4.9%
6 5
 
4.9%
8 4
 
3.9%
2 3
 
2.9%
5 3
 
2.9%
9 2
 
1.9%
Other values (4) 5
 
4.9%
Latin
ValueCountFrequency (%)
V 17
29.8%
E 17
29.8%
G 7
12.3%
Ⅲ 3
 
5.3%
F 3
 
5.3%
L 2
 
3.5%
C 1
 
1.8%
P 1
 
1.8%
T 1
 
1.8%
Y 1
 
1.8%
Other values (4) 4
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 341
68.1%
ASCII 156
31.1%
Number Forms 4
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36
23.1%
V 17
10.9%
E 17
10.9%
) 16
10.3%
( 16
10.3%
1 8
 
5.1%
G 7
 
4.5%
0 5
 
3.2%
6 5
 
3.2%
8 4
 
2.6%
Other values (16) 25
16.0%
Hangul
ValueCountFrequency (%)
차 17
 
5.0%
이 16
 
4.7%
스 16
 
4.7%
소 13
 
3.8%
고 10
 
2.9%
니 9
 
2.6%
타 8
 
2.3%
쏘 8
 
2.3%
리 8
 
2.3%
봉 7
 
2.1%
Other values (75) 229
67.2%
Number Forms
ValueCountFrequency (%)
Ⅲ 3
75.0%
Ⅱ 1
 
25.0%

연식
Real number (ℝ)

Distinct11
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.6849
Minimum2011
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-12T10:34:36.546917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2014.6
Q12017
median2019
Q32020
95-th percentile2023
Maximum2023
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8571388
Coefficient of variation (CV)0.0014153466
Kurtosis0.36194898
Mean2018.6849
Median Absolute Deviation (MAD)2
Skewness-0.68277675
Sum147364
Variance8.163242
MonotonicityNot monotonic
2023-12-12T10:34:36.698845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2020 18
24.7%
2018 12
16.4%
2022 9
12.3%
2016 7
 
9.6%
2015 6
 
8.2%
2019 5
 
6.8%
2023 5
 
6.8%
2017 4
 
5.5%
2021 3
 
4.1%
2011 3
 
4.1%
ValueCountFrequency (%)
2011 3
 
4.1%
2014 1
 
1.4%
2015 6
 
8.2%
2016 7
 
9.6%
2017 4
 
5.5%
2018 12
16.4%
2019 5
 
6.8%
2020 18
24.7%
2021 3
 
4.1%
2022 9
12.3%
ValueCountFrequency (%)
2023 5
 
6.8%
2022 9
12.3%
2021 3
 
4.1%
2020 18
24.7%
2019 5
 
6.8%
2018 12
16.4%
2017 4
 
5.5%
2016 7
 
9.6%
2015 6
 
8.2%
2014 1
 
1.4%

배기량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2542.863
Minimum0
Maximum12777
Zeros6
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-12T10:34:36.860639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1120
median1995
Q32497
95-th percentile12742
Maximum12777
Range12777
Interquartile range (IQR)2377

Descriptive statistics

Standard deviation3782.0982
Coefficient of variation (CV)1.4873386
Kurtosis2.2404935
Mean2542.863
Median Absolute Deviation (MAD)1815
Skewness1.8544767
Sum185629
Variance14304267
MonotonicityNot monotonic
2023-12-12T10:34:37.007473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
180 13
17.8%
2497 7
9.6%
0 6
 
8.2%
2359 6
 
8.2%
111 6
 
8.2%
167 4
 
5.5%
1999 4
 
5.5%
12742 4
 
5.5%
3933 3
 
4.1%
50 3
 
4.1%
Other values (13) 17
23.3%
ValueCountFrequency (%)
0 6
8.2%
50 3
 
4.1%
78 1
 
1.4%
80 2
 
2.7%
111 6
8.2%
120 1
 
1.4%
167 4
 
5.5%
180 13
17.8%
1995 2
 
2.7%
1998 1
 
1.4%
ValueCountFrequency (%)
12777 1
 
1.4%
12742 4
5.5%
10964 1
 
1.4%
10837 1
 
1.4%
9960 2
 
2.7%
8710 1
 
1.4%
3933 3
4.1%
3342 1
 
1.4%
2497 7
9.6%
2359 6
8.2%

연료
Categorical

Distinct6
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size716.0 B
전기
29 
경유
26 
수소
6 
휘발유+전기
6 
휘발유
5 

Length

Max length6
Median length2
Mean length2.4109589
Min length2

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row전기
2nd row전기
3rd row전기
4th row전기
5th row수소

Common Values

ValueCountFrequency (%)
전기 29
39.7%
경유 26
35.6%
수소 6
 
8.2%
휘발유+전기 6
 
8.2%
휘발유 5
 
6.8%
w전기 1
 
1.4%

Length

2023-12-12T10:34:37.187285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:34:37.333936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전기 29
39.7%
경유 26
35.6%
수소 6
 
8.2%
휘발유+전기 6
 
8.2%
휘발유 5
 
6.8%
w전기 1
 
1.4%

사용목적
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Memory size716.0 B
업무용
35 
설비순찰
7 
임원차량
4 
화재대응
 
3
행사용
 
3
Other values (17)
21 

Length

Max length16
Median length3
Mean length4.6986301
Min length3

Unique

Unique13 ?
Unique (%)17.8%

Sample

1st row임원차량
2nd row임원차량
3rd row임원차량
4th row임원차량
5th row업무용

Common Values

ValueCountFrequency (%)
업무용 35
47.9%
설비순찰 7
 
9.6%
임원차량 4
 
5.5%
화재대응 3
 
4.1%
행사용 3
 
4.1%
진공흡입용 2
 
2.7%
도로노면청소 2
 
2.7%
안전점검 및 순찰 2
 
2.7%
기술지원차량 2
 
2.7%
통합방재센터순찰용,당직순찰용 1
 
1.4%
Other values (12) 12
 
16.4%

Length

2023-12-12T10:34:37.489837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
업무용 35
39.8%
설비순찰 7
 
8.0%
및 4
 
4.5%
임원차량 4
 
4.5%
화재대응 3
 
3.4%
행사용 3
 
3.4%
순찰 3
 
3.4%
행사지원 2
 
2.3%
기술지원차량 2
 
2.3%
안전점검 2
 
2.3%
Other values (21) 23
26.1%

Interactions

2023-12-12T10:34:33.642481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:34:32.988781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:34:33.327569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:34:33.752432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:34:33.103682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:34:33.449309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:34:33.859452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:34:33.217876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:34:33.541548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:34:37.611039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호사업소차종차량(장비)명연식배기량연료사용목적
번호1.0000.8700.4770.9550.4280.2490.4510.771
사업소0.8701.0000.0000.8380.3270.1280.4580.753
차종0.4770.0001.0001.0000.3250.7690.2920.955
차량(장비)명0.9550.8381.0001.0000.9550.9910.8800.954
연식0.4280.3270.3250.9551.0000.7600.5850.580
배기량0.2490.1280.7690.9910.7601.0000.6630.902
연료0.4510.4580.2920.8800.5850.6631.0000.659
사용목적0.7710.7530.9550.9540.5800.9020.6591.000
2023-12-12T10:34:37.756938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업소사용목적연료차종
사업소1.0000.3640.2390.000
사용목적0.3641.0000.3210.711
연료0.2390.3211.0000.174
차종0.0000.7110.1741.000
2023-12-12T10:34:37.868418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호연식배기량사업소차종연료사용목적
번호1.000-0.0100.0250.6350.2560.2460.370
연식-0.0101.000-0.2210.1650.1760.3610.272
배기량0.025-0.2211.0000.0470.5470.4390.583
사업소0.6350.1650.0471.0000.0000.2390.364
차종0.2560.1760.5470.0001.0000.1740.711
연료0.2460.3610.4390.2390.1741.0000.321
사용목적0.3700.2720.5830.3640.7110.3211.000

Missing values

2023-12-12T10:34:33.979348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:34:34.129717image/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

번호사업소차종차량(장비)명연식배기량연료사용목적
01본사승용제네시스 G802022167전기임원차량
12본사승용제네시스 G802022167전기임원차량
23본사승용제네시스 G802022167전기임원차량
34본사승용제네시스 G802022167전기임원차량
45본사승용넥쏘(기획)20190수소업무용
56본사승용그랜져 하이브리드20152359휘발유+전기업무용
67본사승용그랜저IG20182359휘발유업무용
78본사승용LF쏘나타 하이브리드20161999휘발유+전기업무용
89본사승용코나 일렉트릭2020180전기업무용
910본사승용넥쏘20190수소업무용
번호사업소차종차량(장비)명연식배기량연료사용목적
6364신호남승용그랜저20112359휘발유업무용
6465신호남승용싼타페20151995경유업무용
6566신호남화물봉고Ⅲ 1톤 EV2020180전기업무용
6667신재생개발본부승용넥쏘20190수소업무용
6768신재생개발본부승용EV62023111전기업무용
6869신재생개발본부승용EV62023111전기업무용
6970신재생개발본부승용EV62023111전기업무용
7071신재생개발본부승용아이오닉62023111전기업무용
7172기술전문연구센터승용싼타페20151995경유기술지원차량
7273기술전문연구센터승용니로2021180전기기술지원차량