Overview

Dataset statistics

Number of variables5
Number of observations231
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory41.6 B

Variable types

Numeric1
Categorical2
Text2

Dataset

Description국토안전관리원 특수시설관리실에서 유지관리 중인 일반 국도 상 특수교량 중 사장교에 설치된 케이블 가속도계 현황을 제공합니다.
Author국토안전관리원
URLhttps://www.data.go.kr/data/15125677/fileData.do

Alerts

교량코드 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
교량명 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
순번 is highly overall correlated with 교량명 and 1 other fieldsHigh correlation
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-23 07:20:14.010038
Analysis finished2023-12-23 07:20:15.607016
Duration1.6 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct231
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116
Minimum1
Maximum231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-23T07:20:16.108994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12.5
Q158.5
median116
Q3173.5
95-th percentile219.5
Maximum231
Range230
Interquartile range (IQR)115

Descriptive statistics

Standard deviation66.828138
Coefficient of variation (CV)0.57610464
Kurtosis-1.2
Mean116
Median Absolute Deviation (MAD)58
Skewness0
Sum26796
Variance4466
MonotonicityStrictly increasing
2023-12-23T07:20:16.604787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
160 1
 
0.4%
148 1
 
0.4%
149 1
 
0.4%
150 1
 
0.4%
151 1
 
0.4%
152 1
 
0.4%
153 1
 
0.4%
154 1
 
0.4%
155 1
 
0.4%
Other values (221) 221
95.7%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
231 1
0.4%
230 1
0.4%
229 1
0.4%
228 1
0.4%
227 1
0.4%
226 1
0.4%
225 1
0.4%
224 1
0.4%
223 1
0.4%
222 1
0.4%

교량명
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
삼천포대교
26 
목포대교
24 
제2진도대교
17 
진도대교
16 
화태대교
 
12
Other values (19)
136 

Length

Max length6
Median length4
Mean length4.5324675
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row거금대교
2nd row거금대교
3rd row거금대교
4th row거금대교
5th row거북선대교

Common Values

ValueCountFrequency (%)
삼천포대교 26
 
11.3%
목포대교 24
 
10.4%
제2진도대교 17
 
7.4%
진도대교 16
 
6.9%
화태대교 12
 
5.2%
영광대교 12
 
5.2%
세풍대교 11
 
4.8%
천사대교1 10
 
4.3%
삼도대교 8
 
3.5%
완도대교 8
 
3.5%
Other values (14) 87
37.7%

Length

2023-12-23T07:20:17.064471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
삼천포대교 26
 
11.3%
목포대교 24
 
10.4%
제2진도대교 17
 
7.4%
진도대교 16
 
6.9%
화태대교 12
 
5.2%
영광대교 12
 
5.2%
세풍대교 11
 
4.8%
천사대교1 10
 
4.3%
동이대교 8
 
3.5%
임자1대교 8
 
3.5%
Other values (14) 87
37.7%

교량코드
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
SCP
26 
MPB
24 
JD2
17 
JD1
16 
HTB
 
12
Other values (19)
136 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGGB
2nd rowGGB
3rd rowGGB
4th rowGGB
5th rowGBB

Common Values

ValueCountFrequency (%)
SCP 26
 
11.3%
MPB 24
 
10.4%
JD2 17
 
7.4%
JD1 16
 
6.9%
HTB 12
 
5.2%
YGB 12
 
5.2%
SPB 11
 
4.8%
CWB 10
 
4.3%
SDB 8
 
3.5%
WDB 8
 
3.5%
Other values (14) 87
37.7%

Length

2023-12-23T07:20:17.544433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
scp 26
 
11.3%
mpb 24
 
10.4%
jd2 17
 
7.4%
jd1 16
 
6.9%
htb 12
 
5.2%
ygb 12
 
5.2%
spb 11
 
4.8%
cwb 10
 
4.3%
deb 8
 
3.5%
iab 8
 
3.5%
Other values (14) 87
37.7%

센서명
Text

Distinct153
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-23T07:20:18.827592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length6.6839827
Min length6

Characters and Unicode

Total characters1544
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)52.8%

Sample

1st rowCAC001
2nd rowCAC002
3rd rowCAC004
4th rowEQK_GGPX
5th rowCAC001_X
ValueCountFrequency (%)
ca_r01 7
 
3.0%
cac004 6
 
2.6%
ca_l01 6
 
2.6%
cac001 6
 
2.6%
cac002 6
 
2.6%
cac003 5
 
2.2%
cac006 4
 
1.7%
cac_i03 4
 
1.7%
cac_i04 4
 
1.7%
cac008 4
 
1.7%
Other values (143) 179
77.5%
2023-12-23T07:20:20.795798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 371
24.0%
0 230
14.9%
A 210
13.6%
_ 172
11.1%
1 85
 
5.5%
2 44
 
2.8%
3 39
 
2.5%
4 36
 
2.3%
6 33
 
2.1%
R 31
 
2.0%
Other values (21) 293
19.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 840
54.4%
Decimal Number 532
34.5%
Connector Punctuation 172
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 371
44.2%
A 210
25.0%
R 31
 
3.7%
L 29
 
3.5%
I 28
 
3.3%
P 28
 
3.3%
E 24
 
2.9%
K 24
 
2.9%
Q 23
 
2.7%
Y 20
 
2.4%
Other values (10) 52
 
6.2%
Decimal Number
ValueCountFrequency (%)
0 230
43.2%
1 85
 
16.0%
2 44
 
8.3%
3 39
 
7.3%
4 36
 
6.8%
6 33
 
6.2%
5 22
 
4.1%
8 16
 
3.0%
7 16
 
3.0%
9 11
 
2.1%
Connector Punctuation
ValueCountFrequency (%)
_ 172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 840
54.4%
Common 704
45.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 371
44.2%
A 210
25.0%
R 31
 
3.7%
L 29
 
3.5%
I 28
 
3.3%
P 28
 
3.3%
E 24
 
2.9%
K 24
 
2.9%
Q 23
 
2.7%
Y 20
 
2.4%
Other values (10) 52
 
6.2%
Common
ValueCountFrequency (%)
0 230
32.7%
_ 172
24.4%
1 85
 
12.1%
2 44
 
6.2%
3 39
 
5.5%
4 36
 
5.1%
6 33
 
4.7%
5 22
 
3.1%
8 16
 
2.3%
7 16
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 371
24.0%
0 230
14.9%
A 210
13.6%
_ 172
11.1%
1 85
 
5.5%
2 44
 
2.8%
3 39
 
2.5%
4 36
 
2.3%
6 33
 
2.1%
R 31
 
2.0%
Other values (21) 293
19.0%

설치위치
Text

Distinct207
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-23T07:20:21.600930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length17
Min length4

Characters and Unicode

Total characters3927
Distinct characters103
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)81.0%

Sample

1st rowPY1 거금도 방향 최장케이블
2nd rowPY1 소록도 방향 최장케이블
3rd rowPY2 소록도 방향 최장케이블
4th rowPY2 거금도 방향 최장케이블
5th rowPY2 PY1 방면 최장케이블
ValueCountFrequency (%)
케이블 144
 
15.6%
시점에서 36
 
3.9%
좌측 33
 
3.6%
종점을 33
 
3.6%
바라보고 33
 
3.6%
우측 33
 
3.6%
py1 31
 
3.4%
py2 28
 
3.0%
방면 26
 
2.8%
동쪽 21
 
2.3%
Other values (154) 504
54.7%
2023-12-23T07:20:23.997086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
694
 
17.7%
블 222
 
5.7%
이 222
 
5.7%
케 222
 
5.7%
번 170
 
4.3%
1 143
 
3.6%
측 124
 
3.2%
P 85
 
2.2%
도 82
 
2.1%
서 79
 
2.0%
Other values (93) 1884
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2325
59.2%
Space Separator 694
 
17.7%
Decimal Number 469
 
11.9%
Uppercase Letter 308
 
7.8%
Other Punctuation 42
 
1.1%
Open Punctuation 28
 
0.7%
Close Punctuation 28
 
0.7%
Dash Punctuation 16
 
0.4%
Lowercase Letter 9
 
0.2%
Connector Punctuation 8
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
블 222
 
9.5%
이 222
 
9.5%
케 222
 
9.5%
번 170
 
7.3%
측 124
 
5.3%
도 82
 
3.5%
서 79
 
3.4%
쪽 78
 
3.4%
점 71
 
3.1%
방 50
 
2.2%
Other values (61) 1005
43.2%
Uppercase Letter
ValueCountFrequency (%)
P 85
27.6%
Y 59
19.2%
C 36
11.7%
N 28
 
9.1%
S 25
 
8.1%
E 19
 
6.2%
W 18
 
5.8%
J 9
 
2.9%
H 8
 
2.6%
A 6
 
1.9%
Other values (4) 15
 
4.9%
Decimal Number
ValueCountFrequency (%)
1 143
30.5%
2 64
13.6%
3 50
 
10.7%
0 44
 
9.4%
6 38
 
8.1%
7 29
 
6.2%
8 28
 
6.0%
5 27
 
5.8%
9 25
 
5.3%
4 21
 
4.5%
Other Punctuation
ValueCountFrequency (%)
: 33
78.6%
. 9
 
21.4%
Space Separator
ValueCountFrequency (%)
694
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 9
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2325
59.2%
Common 1285
32.7%
Latin 317
 
8.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
블 222
 
9.5%
이 222
 
9.5%
케 222
 
9.5%
번 170
 
7.3%
측 124
 
5.3%
도 82
 
3.5%
서 79
 
3.4%
쪽 78
 
3.4%
점 71
 
3.1%
방 50
 
2.2%
Other values (61) 1005
43.2%
Common
ValueCountFrequency (%)
694
54.0%
1 143
 
11.1%
2 64
 
5.0%
3 50
 
3.9%
0 44
 
3.4%
6 38
 
3.0%
: 33
 
2.6%
7 29
 
2.3%
( 28
 
2.2%
) 28
 
2.2%
Other values (7) 134
 
10.4%
Latin
ValueCountFrequency (%)
P 85
26.8%
Y 59
18.6%
C 36
11.4%
N 28
 
8.8%
S 25
 
7.9%
E 19
 
6.0%
W 18
 
5.7%
o 9
 
2.8%
J 9
 
2.8%
H 8
 
2.5%
Other values (5) 21
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2325
59.2%
ASCII 1602
40.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
694
43.3%
1 143
 
8.9%
P 85
 
5.3%
2 64
 
4.0%
Y 59
 
3.7%
3 50
 
3.1%
0 44
 
2.7%
6 38
 
2.4%
C 36
 
2.2%
: 33
 
2.1%
Other values (22) 356
22.2%
Hangul
ValueCountFrequency (%)
블 222
 
9.5%
이 222
 
9.5%
케 222
 
9.5%
번 170
 
7.3%
측 124
 
5.3%
도 82
 
3.5%
서 79
 
3.4%
쪽 78
 
3.4%
점 71
 
3.1%
방 50
 
2.2%
Other values (61) 1005
43.2%

Interactions

2023-12-23T07:20:14.464973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T07:20:24.632246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번교량명교량코드
순번1.0000.9780.978
교량명0.9781.0001.000
교량코드0.9781.0001.000
2023-12-23T07:20:25.123706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교량코드교량명
교량코드1.0001.000
교량명1.0001.000
2023-12-23T07:20:25.629773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번교량명교량코드
순번1.0000.8390.839
교량명0.8391.0001.000
교량코드0.8391.0001.000

Missing values

2023-12-23T07:20:14.948735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T07:20:15.427252image/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

순번교량명교량코드센서명설치위치
01거금대교GGBCAC001PY1 거금도 방향 최장케이블
12거금대교GGBCAC002PY1 소록도 방향 최장케이블
23거금대교GGBCAC004PY2 소록도 방향 최장케이블
34거금대교GGBEQK_GGPXPY2 거금도 방향 최장케이블
45거북선대교GBBCAC001_XPY2 PY1 방면 최장케이블
56거북선대교GBBEQK_TGPYPY2 PY1 방면 최장케이블
67목포대교MPBCAC004_6PY2 서해안 고속도로 방면 최장케이블
78목포대교MPBCAC001_1PY1 영암 방면 최장케이블
89목포대교MPBCAC002_3PY1 서해안 고속도로 방면 6번 케이블
910목포대교MPBCAC003_5PY2 영암 방면 최단케이블
순번교량명교량코드센서명설치위치
221222임자1대교IABCA_L34PY1 좌측 34번 케이블
222223임자1대교IABCA_R01PY1 우측 1번 케이블
223224임자2대교IBBCA_R26PY1 우측 26번 케이블
224225임자2대교IBBCA_R01PY1 우측 1번 케이블
225226임자2대교IBBCA_R27PY2 우측 27번 케이블
226227임자2대교IBBCA_L01PY1 좌측 1번 케이블
227228임자2대교IBBCA_L52PY2 좌측 52번 케이블
228229임자2대교IBBCA_R52PY2 우측 52번 케이블
229230임자2대교IBBCA_L27PY2 좌측 27번 케이블
230231임자2대교IBBEQK_IBP_YPY1 좌측 26번 케이블