Overview

Dataset statistics

Number of variables8
Number of observations94
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 KiB
Average record size in memory66.4 B

Variable types

Numeric1
Categorical4
Text2
DateTime1

Dataset

Description인천광역시 동구에서 보유한 공용차량 현황으로, 차량관리부서, 차량번호, 차명, 차종/차형, 차량등록일, 사용용도 등의 항목을 제공하고 있습니다.
Author인천광역시 동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15113813&srcSe=7661IVAWM27C61E190

Alerts

차종차형 is highly overall correlated with 사용용도 and 1 other fieldsHigh correlation
사용용도 is highly overall correlated with 차종차형High correlation
유류구분 is highly overall correlated with 차종차형High correlation
사용용도 is highly imbalanced (63.5%)Imbalance
연번 has unique valuesUnique
차량번호 has unique valuesUnique

Reproduction

Analysis started2024-05-17 20:59:00.888293
Analysis finished2024-05-17 20:59:02.895804
Duration2.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct94
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.5
Minimum1
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size978.0 B
2024-05-18T05:59:03.038955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.65
Q124.25
median47.5
Q370.75
95-th percentile89.35
Maximum94
Range93
Interquartile range (IQR)46.5

Descriptive statistics

Standard deviation27.279418
Coefficient of variation (CV)0.57430354
Kurtosis-1.2
Mean47.5
Median Absolute Deviation (MAD)23.5
Skewness0
Sum4465
Variance744.16667
MonotonicityStrictly increasing
2024-05-18T05:59:03.334705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
61 1
 
1.1%
70 1
 
1.1%
69 1
 
1.1%
68 1
 
1.1%
67 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
Other values (84) 84
89.4%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
94 1
1.1%
93 1
1.1%
92 1
1.1%
91 1
1.1%
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%
Distinct28
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size884.0 B
자치행정국 재무과
31 
복지환경국 자원순환과
복지경제국 노인장애인복지과
복지환경국 노인장애인복지과
보건소 보건행정과
Other values (23)
39 

Length

Max length14
Median length12
Mean length8.9361702
Min length3

Unique

Unique8 ?
Unique (%)8.5%

Sample

1st row자치행정국 일자리경제과
2nd row자치행정국 재무과
3rd row자치행정국 재무과
4th row자치행정국 재무과
5th row자치행정국 재무과

Common Values

ValueCountFrequency (%)
자치행정국 재무과 31
33.0%
복지환경국 자원순환과 8
 
8.5%
복지경제국 노인장애인복지과 6
 
6.4%
복지환경국 노인장애인복지과 5
 
5.3%
보건소 보건행정과 5
 
5.3%
자치행정국 일자리경제과 3
 
3.2%
송현1·2동 2
 
2.1%
화수1·화평동 2
 
2.1%
송현3동 2
 
2.1%
송림4동 2
 
2.1%
Other values (18) 28
29.8%

Length

2024-05-18T05:59:03.732656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자치행정국 36
22.0%
재무과 31
18.9%
복지환경국 15
9.1%
복지경제국 11
 
6.7%
노인장애인복지과 11
 
6.7%
자원순환과 9
 
5.5%
보건소 8
 
4.9%
보건행정과 5
 
3.0%
일자리경제과 4
 
2.4%
금창동 2
 
1.2%
Other values (19) 32
19.5%

차량번호
Text

UNIQUE 

Distinct94
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size884.0 B
2024-05-18T05:59:04.200284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.0319149
Min length7

Characters and Unicode

Total characters661
Distinct characters33
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)100.0%

Sample

1st row02구2395
2nd row02오0572
3rd row02오0977
4th row03머5323
5th row03머5531
ValueCountFrequency (%)
02구2395 1
 
1.1%
79마2211 1
 
1.1%
82수0670 1
 
1.1%
82수0632 1
 
1.1%
82수0625 1
 
1.1%
82수0624 1
 
1.1%
82수0623 1
 
1.1%
82수0602 1
 
1.1%
82수0601 1
 
1.1%
79마2317 1
 
1.1%
Other values (84) 84
89.4%
2024-05-18T05:59:05.085071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 82
12.4%
3 78
11.8%
2 73
11.0%
7 68
10.3%
1 63
9.5%
8 53
8.0%
6 45
6.8%
9 42
6.4%
5 35
 
5.3%
4 28
 
4.2%
Other values (23) 94
14.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 567
85.8%
Other Letter 94
 
14.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
15
16.0%
11
11.7%
10
10.6%
9
9.6%
6
 
6.4%
5
 
5.3%
5
 
5.3%
4
 
4.3%
4
 
4.3%
4
 
4.3%
Other values (13) 21
22.3%
Decimal Number
ValueCountFrequency (%)
0 82
14.5%
3 78
13.8%
2 73
12.9%
7 68
12.0%
1 63
11.1%
8 53
9.3%
6 45
7.9%
9 42
7.4%
5 35
6.2%
4 28
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 567
85.8%
Hangul 94
 
14.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
15
16.0%
11
11.7%
10
10.6%
9
9.6%
6
 
6.4%
5
 
5.3%
5
 
5.3%
4
 
4.3%
4
 
4.3%
4
 
4.3%
Other values (13) 21
22.3%
Common
ValueCountFrequency (%)
0 82
14.5%
3 78
13.8%
2 73
12.9%
7 68
12.0%
1 63
11.1%
8 53
9.3%
6 45
7.9%
9 42
7.4%
5 35
6.2%
4 28
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 567
85.8%
Hangul 94
 
14.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 82
14.5%
3 78
13.8%
2 73
12.9%
7 68
12.0%
1 63
11.1%
8 53
9.3%
6 45
7.9%
9 42
7.4%
5 35
6.2%
4 28
 
4.9%
Hangul
ValueCountFrequency (%)
15
16.0%
11
11.7%
10
10.6%
9
9.6%
6
 
6.4%
5
 
5.3%
5
 
5.3%
4
 
4.3%
4
 
4.3%
4
 
4.3%
Other values (13) 21
22.3%

차명
Text

Distinct51
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Memory size884.0 B
2024-05-18T05:59:05.560921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length6.287234
Min length2

Characters and Unicode

Total characters591
Distinct characters126
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)40.4%

Sample

1st row쏘울EV
2nd row카니발 리무진
3rd row싼타페
4th row니로EV
5th row니로EV
ValueCountFrequency (%)
니로ev 9
 
7.1%
봉고ⅲ1톤 8
 
6.3%
1톤 7
 
5.6%
모닝 6
 
4.8%
봉고ⅲ 6
 
4.8%
스타렉스 6
 
4.8%
그랜드스타렉스 5
 
4.0%
스타리아 5
 
4.0%
아이오닉 4
 
3.2%
일렉트릭 4
 
3.2%
Other values (50) 66
52.4%
2024-05-18T05:59:06.495470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36
 
6.1%
34
 
5.8%
22
 
3.7%
1 22
 
3.7%
22
 
3.7%
18
 
3.0%
E 18
 
3.0%
16
 
2.7%
16
 
2.7%
V 16
 
2.7%
Other values (116) 371
62.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 437
73.9%
Uppercase Letter 46
 
7.8%
Decimal Number 39
 
6.6%
Space Separator 34
 
5.8%
Letter Number 18
 
3.0%
Other Punctuation 9
 
1.5%
Lowercase Letter 3
 
0.5%
Dash Punctuation 2
 
0.3%
Other Symbol 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
36
 
8.2%
22
 
5.0%
22
 
5.0%
18
 
4.1%
16
 
3.7%
16
 
3.7%
13
 
3.0%
12
 
2.7%
12
 
2.7%
11
 
2.5%
Other values (84) 259
59.3%
Uppercase Letter
ValueCountFrequency (%)
E 18
39.1%
V 16
34.8%
I 2
 
4.3%
R 2
 
4.3%
U 1
 
2.2%
K 1
 
2.2%
S 1
 
2.2%
M 1
 
2.2%
Z 1
 
2.2%
T 1
 
2.2%
Other values (2) 2
 
4.3%
Decimal Number
ValueCountFrequency (%)
1 22
56.4%
9 4
 
10.3%
3 3
 
7.7%
2 3
 
7.7%
6 2
 
5.1%
8 2
 
5.1%
5 1
 
2.6%
0 1
 
2.6%
7 1
 
2.6%
Lowercase Letter
ValueCountFrequency (%)
c 1
33.3%
n 1
33.3%
g 1
33.3%
Letter Number
ValueCountFrequency (%)
14
77.8%
4
 
22.2%
Space Separator
ValueCountFrequency (%)
34
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 437
73.9%
Common 87
 
14.7%
Latin 67
 
11.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
36
 
8.2%
22
 
5.0%
22
 
5.0%
18
 
4.1%
16
 
3.7%
16
 
3.7%
13
 
3.0%
12
 
2.7%
12
 
2.7%
11
 
2.5%
Other values (84) 259
59.3%
Latin
ValueCountFrequency (%)
E 18
26.9%
V 16
23.9%
14
20.9%
4
 
6.0%
I 2
 
3.0%
R 2
 
3.0%
c 1
 
1.5%
n 1
 
1.5%
g 1
 
1.5%
U 1
 
1.5%
Other values (7) 7
 
10.4%
Common
ValueCountFrequency (%)
34
39.1%
1 22
25.3%
. 9
 
10.3%
9 4
 
4.6%
3 3
 
3.4%
2 3
 
3.4%
6 2
 
2.3%
- 2
 
2.3%
8 2
 
2.3%
5 1
 
1.1%
Other values (5) 5
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 437
73.9%
ASCII 135
 
22.8%
Number Forms 18
 
3.0%
CJK Compat 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
36
 
8.2%
22
 
5.0%
22
 
5.0%
18
 
4.1%
16
 
3.7%
16
 
3.7%
13
 
3.0%
12
 
2.7%
12
 
2.7%
11
 
2.5%
Other values (84) 259
59.3%
ASCII
ValueCountFrequency (%)
34
25.2%
1 22
16.3%
E 18
13.3%
V 16
11.9%
. 9
 
6.7%
9 4
 
3.0%
3 3
 
2.2%
2 3
 
2.2%
I 2
 
1.5%
6 2
 
1.5%
Other values (19) 22
16.3%
Number Forms
ValueCountFrequency (%)
14
77.8%
4
 
22.2%
CJK Compat
ValueCountFrequency (%)
1
100.0%

차종차형
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size884.0 B
중형승용차
23 
소형화물차
22 
중형승합차
18 
경형승용차
기타특수용으로 제작된 차량
Other values (10)
18 

Length

Max length14
Median length5
Mean length5.6170213
Min length3

Unique

Unique5 ?
Unique (%)5.3%

Sample

1st row중형승용차
2nd row지프형 및 다목적승용차
3rd row지프형 및 다목적승용차
4th row중형승용차
5th row중형승용차

Common Values

ValueCountFrequency (%)
중형승용차 23
24.5%
소형화물차 22
23.4%
중형승합차 18
19.1%
경형승용차 8
 
8.5%
기타특수용으로 제작된 차량 5
 
5.3%
지프형 및 다목적승용차 3
 
3.2%
중형화물차 3
 
3.2%
청소차 3
 
3.2%
소형승용차 2
 
2.1%
소형승합차 2
 
2.1%
Other values (5) 5
 
5.3%

Length

2024-05-18T05:59:06.908605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중형승용차 23
20.9%
소형화물차 22
20.0%
중형승합차 18
16.4%
경형승용차 8
 
7.3%
기타특수용으로 5
 
4.5%
제작된 5
 
4.5%
차량 5
 
4.5%
청소차 3
 
2.7%
중형화물차 3
 
2.7%
다목적승용차 3
 
2.7%
Other values (9) 15
13.6%
Distinct80
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Memory size884.0 B
Minimum2009-02-18 00:00:00
Maximum2024-04-24 00:00:00
2024-05-18T05:59:07.214247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T05:59:07.614850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사용용도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size884.0 B
업무용
80 
청소용
 
8
기타용
 
3
전용
 
2
의전용
 
1

Length

Max length3
Median length3
Mean length2.9787234
Min length2

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
업무용 80
85.1%
청소용 8
 
8.5%
기타용 3
 
3.2%
전용 2
 
2.1%
의전용 1
 
1.1%

Length

2024-05-18T05:59:08.017526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T05:59:08.335494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
업무용 80
85.1%
청소용 8
 
8.5%
기타용 3
 
3.2%
전용 2
 
2.1%
의전용 1
 
1.1%

유류구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size884.0 B
경유
51 
전기
23 
휘발유
11 
LPG
하이브리드
 
2

Length

Max length5
Median length2
Mean length2.2553191
Min length2

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
경유 51
54.3%
전기 23
24.5%
휘발유 11
 
11.7%
LPG 6
 
6.4%
하이브리드 2
 
2.1%
CNG 1
 
1.1%

Length

2024-05-18T05:59:08.723030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T05:59:08.995072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경유 51
54.3%
전기 23
24.5%
휘발유 11
 
11.7%
lpg 6
 
6.4%
하이브리드 2
 
2.1%
cng 1
 
1.1%

Interactions

2024-05-18T05:59:02.185467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T05:59:09.146044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/