Overview

Dataset statistics

Number of variables7
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)2.4%
Total size in memory2.5 KiB
Average record size in memory62.0 B

Variable types

Text3
Numeric3
DateTime1

Dataset

Description창원시설공단에서 운영중인 업무용 보유 차량 현황 (차량번호, 차종 ,배기량, 승차인원, 구입년도, 구입금액, 운영부서)
Author창원시설공단
URLhttps://www.data.go.kr/data/15075267/fileData.do

Alerts

Dataset has 1 (2.4%) duplicate rowsDuplicates
배기량(cc) has 3 (7.1%) zerosZeros
구입금액(천원) has 1 (2.4%) zerosZeros

Reproduction

Analysis started2024-03-14 12:02:02.592311
Analysis finished2024-03-14 12:02:04.808659
Duration2.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

차량번호
Text

Distinct31
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Memory size464.0 B
2024-03-14T21:02:05.457391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length7.1904762
Min length7

Characters and Unicode

Total characters302
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)54.8%

Sample

1st row92모90**
2nd row67거43**
3rd row17하84**
4th row68거88**
5th row79노59**
ValueCountFrequency (%)
41머70 3
 
7.1%
경남71자 3
 
7.1%
32조84 3
 
7.1%
805주71 2
 
4.8%
67거43 2
 
4.8%
83조29 2
 
4.8%
49마27 2
 
4.8%
68구25 2
 
4.8%
97다37 1
 
2.4%
88러66 1
 
2.4%
Other values (21) 21
50.0%
2024-03-14T21:02:06.423189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 90
29.8%
8 25
 
8.3%
6 21
 
7.0%
7 21
 
7.0%
9 20
 
6.6%
2 18
 
6.0%
4 14
 
4.6%
3 14
 
4.6%
1 13
 
4.3%
0 10
 
3.3%
Other values (25) 56
18.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 164
54.3%
Other Punctuation 90
29.8%
Other Letter 48
 
15.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
조 5
 
10.4%
거 4
 
8.3%
경 3
 
6.2%
남 3
 
6.2%
자 3
 
6.2%
머 3
 
6.2%
마 3
 
6.2%
소 3
 
6.2%
부 2
 
4.2%
도 2
 
4.2%
Other values (14) 17
35.4%
Decimal Number
ValueCountFrequency (%)
8 25
15.2%
6 21
12.8%
7 21
12.8%
9 20
12.2%
2 18
11.0%
4 14
8.5%
3 14
8.5%
1 13
7.9%
0 10
 
6.1%
5 8
 
4.9%
Other Punctuation
ValueCountFrequency (%)
* 90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 254
84.1%
Hangul 48
 
15.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
조 5
 
10.4%
거 4
 
8.3%
경 3
 
6.2%
남 3
 
6.2%
자 3
 
6.2%
머 3
 
6.2%
마 3
 
6.2%
소 3
 
6.2%
부 2
 
4.2%
도 2
 
4.2%
Other values (14) 17
35.4%
Common
ValueCountFrequency (%)
* 90
35.4%
8 25
 
9.8%
6 21
 
8.3%
7 21
 
8.3%
9 20
 
7.9%
2 18
 
7.1%
4 14
 
5.5%
3 14
 
5.5%
1 13
 
5.1%
0 10
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 254
84.1%
Hangul 48
 
15.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 90
35.4%
8 25
 
9.8%
6 21
 
8.3%
7 21
 
8.3%
9 20
 
7.9%
2 18
 
7.1%
4 14
 
5.5%
3 14
 
5.5%
1 13
 
5.1%
0 10
 
3.9%
Hangul
ValueCountFrequency (%)
조 5
 
10.4%
거 4
 
8.3%
경 3
 
6.2%
남 3
 
6.2%
자 3
 
6.2%
머 3
 
6.2%
마 3
 
6.2%
소 3
 
6.2%
부 2
 
4.2%
도 2
 
4.2%
Other values (14) 17
35.4%

차종
Text

Distinct22
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size464.0 B
2024-03-14T21:02:07.160845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length5.8809524
Min length2

Characters and Unicode

Total characters247
Distinct characters65
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)33.3%

Sample

1st row봉고Ⅲ 1톤
2nd row마티즈 조이
3rd row그랜져
4th rowSM7
5th row그랜버드 47인승
ValueCountFrequency (%)
모닝 7
 
11.5%
1톤 7
 
11.5%
봉고ⅲ 6
 
9.8%
스타렉스3밴 4
 
6.6%
스타리아3밴 4
 
6.6%
스파크 3
 
4.9%
포터2 3
 
4.9%
조이 2
 
3.3%
2층버스 2
 
3.3%
유로시티 2
 
3.3%
Other values (18) 21
34.4%
2024-03-14T21:02:08.071966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 22
 
8.9%
19
 
7.7%
타 9
 
3.6%
3 9
 
3.6%
밴 9
 
3.6%
1 9
 
3.6%
톤 8
 
3.2%
모 7
 
2.8%
닝 7
 
2.8%
렉 7
 
2.8%
Other values (55) 141
57.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 186
75.3%
Decimal Number 26
 
10.5%
Space Separator 19
 
7.7%
Letter Number 6
 
2.4%
Uppercase Letter 4
 
1.6%
Open Punctuation 3
 
1.2%
Close Punctuation 3
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 22
 
11.8%
타 9
 
4.8%
밴 9
 
4.8%
톤 8
 
4.3%
모 7
 
3.8%
닝 7
 
3.8%
렉 7
 
3.8%
티 6
 
3.2%
아 6
 
3.2%
봉 6
 
3.2%
Other values (44) 99
53.2%
Decimal Number
ValueCountFrequency (%)
3 9
34.6%
1 9
34.6%
2 5
19.2%
7 2
 
7.7%
4 1
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
M 2
50.0%
S 2
50.0%
Space Separator
ValueCountFrequency (%)
19
100.0%
Letter Number
ValueCountFrequency (%)
Ⅲ 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 186
75.3%
Common 51
 
20.6%
Latin 10
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 22
 
11.8%
타 9
 
4.8%
밴 9
 
4.8%
톤 8
 
4.3%
모 7
 
3.8%
닝 7
 
3.8%
렉 7
 
3.8%
티 6
 
3.2%
아 6
 
3.2%
봉 6
 
3.2%
Other values (44) 99
53.2%
Common
ValueCountFrequency (%)
19
37.3%
3 9
17.6%
1 9
17.6%
2 5
 
9.8%
( 3
 
5.9%
) 3
 
5.9%
7 2
 
3.9%
4 1
 
2.0%
Latin
ValueCountFrequency (%)
Ⅲ 6
60.0%
M 2
 
20.0%
S 2
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 186
75.3%
ASCII 55
 
22.3%
Number Forms 6
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 22
 
11.8%
타 9
 
4.8%
밴 9
 
4.8%
톤 8
 
4.3%
모 7
 
3.8%
닝 7
 
3.8%
렉 7
 
3.8%
티 6
 
3.2%
아 6
 
3.2%
봉 6
 
3.2%
Other values (44) 99
53.2%
ASCII
ValueCountFrequency (%)
19
34.5%
3 9
16.4%
1 9
16.4%
2 5
 
9.1%
( 3
 
5.5%
) 3
 
5.5%
M 2
 
3.6%
S 2
 
3.6%
7 2
 
3.6%
4 1
 
1.8%
Number Forms
ValueCountFrequency (%)
Ⅲ 6
100.0%

배기량(cc)
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2382
Minimum0
Maximum12344
Zeros3
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T21:02:08.272635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39.8
Q1998
median2199
Q32497
95-th percentile7601.55
Maximum12344
Range12344
Interquartile range (IQR)1499

Descriptive statistics

Standard deviation2328.9936
Coefficient of variation (CV)0.97774709
Kurtosis8.2284551
Mean2382
Median Absolute Deviation (MAD)1000
Skewness2.6100889
Sum100044
Variance5424211.1
MonotonicityNot monotonic
2024-03-14T21:02:08.478785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2497 10
23.8%
998 7
16.7%
2199 5
11.9%
800 3
 
7.1%
0 3
 
7.1%
2902 3
 
7.1%
995 2
 
4.8%
7640 2
 
4.8%
2999 1
 
2.4%
2349 1
 
2.4%
Other values (5) 5
11.9%
ValueCountFrequency (%)
0 3
 
7.1%
796 1
 
2.4%
800 3
 
7.1%
995 2
 
4.8%
998 7
16.7%
999 1
 
2.4%
2199 5
11.9%
2349 1
 
2.4%
2359 1
 
2.4%
2497 10
23.8%
ValueCountFrequency (%)
12344 1
 
2.4%
7640 2
 
4.8%
6871 1
 
2.4%
2999 1
 
2.4%
2902 3
 
7.1%
2497 10
23.8%
2359 1
 
2.4%
2349 1
 
2.4%
2199 5
11.9%
999 1
 
2.4%

승차인원
Real number (ℝ)

Distinct8
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5714286
Minimum2
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T21:02:08.667988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q13
median5
Q35
95-th percentile46.9
Maximum71
Range69
Interquartile range (IQR)2

Descriptive statistics

Standard deviation16.633998
Coefficient of variation (CV)1.7378804
Kurtosis8.5909048
Mean9.5714286
Median Absolute Deviation (MAD)2
Skewness3.0813599
Sum402
Variance276.6899
MonotonicityNot monotonic
2024-03-14T21:02:08.951632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 18
42.9%
3 16
38.1%
11 2
 
4.8%
71 2
 
4.8%
47 1
 
2.4%
2 1
 
2.4%
45 1
 
2.4%
6 1
 
2.4%
ValueCountFrequency (%)
2 1
 
2.4%
3 16
38.1%
5 18
42.9%
6 1
 
2.4%
11 2
 
4.8%
45 1
 
2.4%
47 1
 
2.4%
71 2
 
4.8%
ValueCountFrequency (%)
71 2
 
4.8%
47 1
 
2.4%
45 1
 
2.4%
11 2
 
4.8%
6 1
 
2.4%
5 18
42.9%
3 16
38.1%
2 1
 
2.4%

구입년도
Date

Distinct30
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size464.0 B
Minimum2001-05-01 00:00:00
Maximum2021-07-01 00:00:00
2024-03-14T21:02:09.318471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:02:09.716129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

구입금액(천원)
Real number (ℝ)

ZEROS 

Distinct32
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47800
Minimum0
Maximum438370
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T21:02:10.103314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8848.5
Q110345
median17775
Q328110.25
95-th percentile272969.75
Maximum438370
Range438370
Interquartile range (IQR)17765.25

Descriptive statistics

Standard deviation99018.031
Coefficient of variation (CV)2.0715069
Kurtosis11.325386
Mean47800
Median Absolute Deviation (MAD)7866
Skewness3.4575552
Sum2007600
Variance9.8045704 × 109
MonotonicityNot monotonic
2024-03-14T21:02:10.703996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
10345 3
 
7.1%
9909 3
 
7.1%
438370 2
 
4.8%
9580 2
 
4.8%
29730 2
 
4.8%
11980 2
 
4.8%
27103 2
 
4.8%
42404 2
 
4.8%
16160 1
 
2.4%
280466 1
 
2.4%
Other values (22) 22
52.4%
ValueCountFrequency (%)
0 1
 
2.4%
7740 1
 
2.4%
8810 1
 
2.4%
9580 2
4.8%
9909 3
7.1%
10070 1
 
2.4%
10345 3
7.1%
10824 1
 
2.4%
11980 2
4.8%
13005 1
 
2.4%
ValueCountFrequency (%)
438370 2
4.8%
280466 1
2.4%
130541 1
2.4%
43300 1
2.4%
42404 2
4.8%
36500 1
2.4%
29730 2
4.8%
28446 1
2.4%
27103 2
4.8%
26260 1
2.4%

운영부서
Text

Distinct22
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size464.0 B
2024-03-14T21:02:11.432835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length6.8571429
Min length3

Characters and Unicode

Total characters288
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)33.3%

Sample

1st row안전팀
2nd row기획예산팀
3rd row인사노무팀
4th row재무회계팀
5th row재무회계팀
ValueCountFrequency (%)
환경사업관리소 9
21.4%
창원스포츠파크관리소 4
 
9.5%
재무회계팀 4
 
9.5%
창원축구센터 3
 
7.1%
시설정보팀 2
 
4.8%
교통사업관리소 2
 
4.8%
해양공원관리소 2
 
4.8%
장사시설관리소 2
 
4.8%
창원국제사격장관리소 1
 
2.4%
교통편의관리소 1
 
2.4%
Other values (12) 12
28.6%
2024-03-14T21:02:12.558095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
관 26
 
9.0%
리 22
 
7.6%
소 22
 
7.6%
사 15
 
5.2%
창 11
 
3.8%
업 11
 
3.8%
원 11
 
3.8%
환 9
 
3.1%
경 9
 
3.1%
팀 9
 
3.1%
Other values (52) 143
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 288
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 26
 
9.0%
리 22
 
7.6%
소 22
 
7.6%
사 15
 
5.2%
창 11
 
3.8%
업 11
 
3.8%
원 11
 
3.8%
환 9
 
3.1%
경 9
 
3.1%
팀 9
 
3.1%
Other values (52) 143
49.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 288
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 26
 
9.0%
리 22
 
7.6%
소 22
 
7.6%
사 15
 
5.2%
창 11
 
3.8%
업 11
 
3.8%
원 11
 
3.8%
환 9
 
3.1%
경 9
 
3.1%
팀 9
 
3.1%
Other values (52) 143
49.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 288
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
관 26
 
9.0%
리 22
 
7.6%
소 22
 
7.6%
사 15
 
5.2%
창 11
 
3.8%
업 11
 
3.8%
원 11
 
3.8%
환 9
 
3.1%
경 9
 
3.1%
팀 9
 
3.1%
Other values (52) 143
49.7%

Interactions

2024-03-14T21:02:03.960687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:02:03.018836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:02:03.509986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:02:04.106485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:02:03.213330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:02:03.663466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:02:04.345342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:02:03.370703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:02:03.820889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:02:12.826675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차량번호차종배기량(cc)승차인원구입년도구입금액(천원)운영부서
차량번호1.0000.9940.8290.7270.9650.0000.714
차종0.9941.0000.9931.0000.9481.0000.721
배기량(cc)0.8290.9931.0000.9140.6441.0000.000
승차인원0.7271.0000.9141.0000.6770.9780.000
구입년도0.9650.9480.6440.6771.0000.0000.839
구입금액(천원)0.0001.0001.0000.9780.0001.0000.000
운영부서0.7140.7210.0000.0000.8390.0001.000
2024-03-14T21:02:13.106293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배기량(cc)승차인원구입금액(천원)
배기량(cc)1.000-0.0190.399
승차인원-0.0191.0000.110
구입금액(천원)0.3990.1101.000

Missing values

2024-03-14T21:02:04.525944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:02:04.728353image/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

차량번호차종배기량(cc)승차인원구입년도구입금액(천원)운영부서
092모90**봉고Ⅲ 1톤249732016-0425760안전팀
167거43**마티즈 조이80052009-029580기획예산팀
217하84**그랜져299952016-120인사노무팀
368거88**SM7234952007-1225000재무회계팀
479노59**그랜버드 47인승12344472010-04130541재무회계팀
571버22**그랜드스타렉스2497112013-0326260재무회계팀
607소86**뉴마티즈80052006-067740재무회계팀
766우69**SM3052013-1143300시설정보팀
882도66**봉고Ⅲ 1톤 (방역방제)290232010-0336500시설정보팀
960더83**스파크99952015-0110070성산노인복지관
차량번호차종배기량(cc)승차인원구입년도구입금액(천원)운영부서
3297다37**포터2 1톤 더블캡249762009-1213005마산야구센터
3368구25**스파크99552007-0111980의창스포츠센터
3432조84**모닝99852013-1210345진해국민체육센터
3549마27**아이오닉 일렉트릭052011-0142404창원실내수영장
3649마27**아이오닉 일렉트릭052018-0542404해양공원관리소
3784소43**포터2249732018-0513822해양공원관리소
3856서92**모닝99852011-1110824교통사업관리소
3997가94**봉고Ⅲ 1톤290232006-0113935교통사업관리소
4067거43**마티즈 조이80052012-019580교통편의관리소
4141머70**모닝99852009-029909해양시설관리소

Duplicate rows

Most frequently occurring

차량번호차종배기량(cc)승차인원구입년도구입금액(천원)운영부서# duplicates
0805주71**스타리아3밴219932021-0729730환경사업관리소2