Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells19654
Missing cells (%)11.6%
Duplicate rows17
Duplicate rows (%)0.2%
Total size in memory1.4 MiB
Average record size in memory148.0 B

Variable types

Categorical11
Text4
Numeric2

Dataset

Description국도상에 위치한 특수교량의 계측데이터를 관리하는 업무로 특수교통합계측관리시스템의 계측개요 및 장비 등 계측정보를 제공함
Author국토교통부
URLhttps://www.data.go.kr/data/15123646/fileData.do

Alerts

Dataset has 17 (0.2%) duplicate rowsDuplicates
센서사용단위 is highly overall correlated with 케이블길이 and 6 other fieldsHigh correlation
관리기준치 하한치 is highly overall correlated with 케이블길이 and 11 other fieldsHigh correlation
관리기준치 상한치 is highly overall correlated with 케이블길이 and 11 other fieldsHigh correlation
센서구분 is highly overall correlated with 케이블길이 and 7 other fieldsHigh correlation
특수교량명 is highly overall correlated with 케이블길이 and 8 other fieldsHigh correlation
로거타입 is highly overall correlated with 케이블길이 and 9 other fieldsHigh correlation
설치위치 is highly overall correlated with 케이블길이 and 6 other fieldsHigh correlation
센서설명 is highly overall correlated with 케이블길이 and 7 other fieldsHigh correlation
센서설치위치 is highly overall correlated with 초기값 and 4 other fieldsHigh correlation
로거타입명 is highly overall correlated with 케이블길이 and 9 other fieldsHigh correlation
특수교량 코드 is highly overall correlated with 케이블길이 and 8 other fieldsHigh correlation
케이블길이 is highly overall correlated with 특수교량 코드 and 9 other fieldsHigh correlation
초기값 is highly overall correlated with 특수교량 코드 and 10 other fieldsHigh correlation
관리기준치 하한치 is highly imbalanced (60.2%)Imbalance
관리기준치 상한치 is highly imbalanced (60.2%)Imbalance
로거명 has 1228 (12.3%) missing valuesMissing
케이블길이 has 9213 (92.1%) missing valuesMissing
초기값 has 9213 (92.1%) missing valuesMissing
초기값 has 106 (1.1%) zerosZeros

Reproduction

Analysis started2023-12-12 06:35:35.268027
Analysis finished2023-12-12 06:35:38.690638
Duration3.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

특수교량 코드
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
MPB
2321 
NH1
1795 
BYD
1408 
GGB
1040 
NRB
773 
Other values (8)
2663 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBYD
2nd rowNH1
3rd rowDSD
4th rowNH1
5th rowDGB

Common Values

ValueCountFrequency (%)
MPB 2321
23.2%
NH1 1795
17.9%
BYD 1408
14.1%
GGB 1040
10.4%
NRB 773
 
7.7%
SCP 761
 
7.6%
DSD 564
 
5.6%
JBB 330
 
3.3%
DGB 312
 
3.1%
SDB 259
 
2.6%
Other values (3) 437
 
4.4%

Length

2023-12-12T15:35:38.763095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mpb 2321
23.2%
nh1 1795
17.9%
byd 1408
14.1%
ggb 1040
10.4%
nrb 773
 
7.7%
scp 761
 
7.6%
dsd 564
 
5.6%
jbb 330
 
3.3%
dgb 312
 
3.1%
sdb 259
 
2.6%
Other values (3) 437
 
4.4%

특수교량명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
목포대교
2321 
남해대교
1795 
백야대교
1408 
거금대교
1040 
노량대교
773 
Other values (8)
2663 

Length

Max length5
Median length4
Mean length4.1014
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row백야대교
2nd row남해대교
3rd row돌산대교
4th row남해대교
5th row동강대교

Common Values

ValueCountFrequency (%)
목포대교 2321
23.2%
남해대교 1795
17.9%
백야대교 1408
14.1%
거금대교 1040
10.4%
노량대교 773
 
7.7%
삼천포대교 761
 
7.6%
돌산대교 564
 
5.6%
둔병대교 330
 
3.3%
동강대교 312
 
3.1%
삼도대교 259
 
2.6%
Other values (3) 437
 
4.4%

Length

2023-12-12T15:35:38.878842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
목포대교 2321
23.2%
남해대교 1795
17.9%
백야대교 1408
14.1%
거금대교 1040
10.4%
노량대교 773
 
7.7%
삼천포대교 761
 
7.6%
돌산대교 564
 
5.6%
둔병대교 330
 
3.3%
동강대교 312
 
3.1%
삼도대교 259
 
2.6%
Other values (3) 437
 
4.4%

센서코드
Text

Distinct741
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:35:39.171606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length7.824
Min length4

Characters and Unicode

Total characters78240
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.1%

Sample

1st rowSSG009
2nd rowTI_B1_Y
3rd rowEQK001_X
4th rowGP_T2T_01_Z
5th rowEQK_DGGN
ValueCountFrequency (%)
exp001 75
 
0.8%
wgd001_s 72
 
0.7%
wgd001_d 68
 
0.7%
eqk001_y 65
 
0.7%
bti001_x 65
 
0.7%
bti002_x 62
 
0.6%
dis001_y 61
 
0.6%
tmp001 61
 
0.6%
tmp005 60
 
0.6%
bti001_y 60
 
0.6%
Other values (731) 9351
93.5%
2023-12-12T15:35:39.643011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13396
17.1%
_ 10511
13.4%
S 4707
 
6.0%
1 4473
 
5.7%
C 4230
 
5.4%
T 4014
 
5.1%
2 3661
 
4.7%
G 3244
 
4.1%
P 3183
 
4.1%
M 2657
 
3.4%
Other values (28) 24164
30.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 40993
52.4%
Decimal Number 26732
34.2%
Connector Punctuation 10511
 
13.4%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 4707
11.5%
C 4230
10.3%
T 4014
 
9.8%
G 3244
 
7.9%
P 3183
 
7.8%
M 2657
 
6.5%
A 2263
 
5.5%
D 2211
 
5.4%
Q 2104
 
5.1%
E 1921
 
4.7%
Other values (16) 10459
25.5%
Decimal Number
ValueCountFrequency (%)
0 13396
50.1%
1 4473
 
16.7%
2 3661
 
13.7%
3 1578
 
5.9%
4 1108
 
4.1%
5 750
 
2.8%
6 684
 
2.6%
7 445
 
1.7%
8 373
 
1.4%
9 264
 
1.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10511
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40993
52.4%
Common 37247
47.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 4707
11.5%
C 4230
10.3%
T 4014
 
9.8%
G 3244
 
7.9%
P 3183
 
7.8%
M 2657
 
6.5%
A 2263
 
5.5%
D 2211
 
5.4%
Q 2104
 
5.1%
E 1921
 
4.7%
Other values (16) 10459
25.5%
Common
ValueCountFrequency (%)
0 13396
36.0%
_ 10511
28.2%
1 4473
 
12.0%
2 3661
 
9.8%
3 1578
 
4.2%
4 1108
 
3.0%
5 750
 
2.0%
6 684
 
1.8%
7 445
 
1.2%
8 373
 
1.0%
Other values (2) 268
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13396
17.1%
_ 10511
13.4%
S 4707
 
6.0%
1 4473
 
5.7%
C 4230
 
5.4%
T 4014
 
5.1%
2 3661
 
4.7%
G 3244
 
4.1%
P 3183
 
4.1%
M 2657
 
3.4%
Other values (28) 24164
30.9%

설치위치
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AA
2437 
CC
785 
DD
592 
중앙경간
 
516
GG
 
508
Other values (37)
5162 

Length

Max length7
Median length2
Mean length2.4876
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd row주탑남해
3rd rowAA
4th row주탑하동
5th rowZZ

Common Values

ValueCountFrequency (%)
AA 2437
24.4%
CC 785
 
7.8%
DD 592
 
5.9%
중앙경간 516
 
5.2%
GG 508
 
5.1%
PY2 486
 
4.9%
BB 469
 
4.7%
FF 451
 
4.5%
PY1 392
 
3.9%
EE 362
 
3.6%
Other values (32) 3002
30.0%

Length

2023-12-12T15:35:39.825497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aa 2437
24.2%
cc 785
 
7.8%
dd 592
 
5.9%
중앙경간 516
 
5.1%
gg 508
 
5.0%
py2 486
 
4.8%
bb 469
 
4.7%
ff 451
 
4.5%
py1 392
 
3.9%
ee 362
 
3.6%
Other values (32) 3087
30.6%

로거명
Text

MISSING 

Distinct60
Distinct (%)0.7%
Missing1228
Missing (%)12.3%
Memory size156.2 KiB
2023-12-12T15:35:40.110649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length5.9807342
Min length2

Characters and Unicode

Total characters52463
Distinct characters86
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[P-BB]
2nd row여수측 지반
3rd rowZZ
4th row중앙경간
5th rowPY1
ValueCountFrequency (%)
aa 1142
 
9.4%
주탑 1109
 
9.2%
py2 668
 
5.5%
py1 509
 
4.2%
cable 449
 
3.7%
서해안고속도로 406
 
3.4%
sg1 406
 
3.4%
중앙경간 405
 
3.4%
중앙경간(sg31 348
 
2.9%
p-ff 343
 
2.8%
Other values (54) 6303
52.1%
2023-12-12T15:35:40.592083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3316
 
6.3%
A 3263
 
6.2%
P 3030
 
5.8%
[ 2169
 
4.1%
- 2169
 
4.1%
] 2169
 
4.1%
1 1992
 
3.8%
2 1636
 
3.1%
Y 1622
 
3.1%
간 1567
 
3.0%
Other values (76) 29530
56.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16640
31.7%
Uppercase Letter 15372
29.3%
Decimal Number 5903
 
11.3%
Open Punctuation 3421
 
6.5%
Close Punctuation 3421
 
6.5%
Space Separator 3316
 
6.3%
Dash Punctuation 2169
 
4.1%
Lowercase Letter 1796
 
3.4%
Other Punctuation 425
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
간 1567
 
9.4%
경 1567
 
9.4%
주 1126
 
6.8%
탑 1109
 
6.7%
앙 753
 
4.5%
중 753
 
4.5%
이 698
 
4.2%
거 585
 
3.5%
더 585
 
3.5%
속 520
 
3.1%
Other values (43) 7377
44.3%
Uppercase Letter
ValueCountFrequency (%)
A 3263
21.2%
P 3030
19.7%
Y 1622
10.6%
C 1419
9.2%
G 1316
8.6%
S 1252
 
8.1%
B 820
 
5.3%
F 776
 
5.0%
E 626
 
4.1%
D 420
 
2.7%
Other values (3) 828
 
5.4%
Decimal Number
ValueCountFrequency (%)
1 1992
33.7%
2 1636
27.7%
3 1501
25.4%
4 428
 
7.3%
5 217
 
3.7%
7 60
 
1.0%
6 56
 
0.9%
9 13
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
b 449
25.0%
e 449
25.0%
l 449
25.0%
a 449
25.0%
Open Punctuation
ValueCountFrequency (%)
[ 2169
63.4%
( 1252
36.6%
Close Punctuation
ValueCountFrequency (%)
] 2169
63.4%
) 1252
36.6%
Other Punctuation
ValueCountFrequency (%)
/ 401
94.4%
, 24
 
5.6%
Space Separator
ValueCountFrequency (%)
3316
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18655
35.6%
Latin 17168
32.7%
Hangul 16640
31.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
간 1567
 
9.4%
경 1567
 
9.4%
주 1126
 
6.8%
탑 1109
 
6.7%
앙 753
 
4.5%
중 753
 
4.5%
이 698
 
4.2%
거 585
 
3.5%
더 585
 
3.5%
속 520
 
3.1%
Other values (43) 7377
44.3%
Latin
ValueCountFrequency (%)
A 3263
19.0%
P 3030
17.6%
Y 1622
9.4%
C 1419
8.3%
G 1316
7.7%
S 1252
 
7.3%
B 820
 
4.8%
F 776
 
4.5%
E 626
 
3.6%
b 449
 
2.6%
Other values (7) 2595
15.1%
Common
ValueCountFrequency (%)
3316
17.8%
[ 2169
11.6%
- 2169
11.6%
] 2169
11.6%
1 1992
10.7%
2 1636
8.8%
3 1501
8.0%
) 1252
 
6.7%
( 1252
 
6.7%
4 428
 
2.3%
Other values (6) 771
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35823
68.3%
Hangul 16640
31.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3316
 
9.3%
A 3263
 
9.1%
P 3030
 
8.5%
[ 2169
 
6.1%
- 2169
 
6.1%
] 2169
 
6.1%
1 1992
 
5.6%
2 1636
 
4.6%
Y 1622
 
4.5%
3 1501
 
4.2%
Other values (23) 12956
36.2%
Hangul
ValueCountFrequency (%)
간 1567
 
9.4%
경 1567
 
9.4%
주 1126
 
6.8%
탑 1109
 
6.7%
앙 753
 
4.5%
중 753
 
4.5%
이 698
 
4.2%
거 585
 
3.5%
더 585
 
3.5%
속 520
 
3.1%
Other values (43) 7377
44.3%

센서구분
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
TMP
2280 
ACC
1677 
CAC
1214 
DSG
934 
SSG
865 
Other values (13)
3030 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSSG
2nd rowBTI
3rd rowEQK
4th rowGPS
5th rowEQK

Common Values

ValueCountFrequency (%)
TMP 2280
22.8%
ACC 1677
16.8%
CAC 1214
12.1%
DSG 934
9.3%
SSG 865
 
8.6%
EQK 691
 
6.9%
BTI 609
 
6.1%
GPS 401
 
4.0%
WGD 390
 
3.9%
EXP 268
 
2.7%
Other values (8) 671
 
6.7%

Length

2023-12-12T15:35:40.716547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tmp 2280
22.8%
acc 1677
16.8%
cac 1214
12.1%
dsg 934
9.3%
ssg 865
 
8.6%
eqk 691
 
6.9%
bti 609
 
6.1%
gps 401
 
4.0%
wgd 390
 
3.9%
exp 268
 
2.7%
Other values (8) 671
 
6.7%

센서설명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
온도계
2280 
가속도계
1677 
케이블가속도계
1214 
동적변형율계
934 
정적변형율계
865 
Other values (13)
3030 

Length

Max length12
Median length8
Mean length4.9667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정적변형율계
2nd row2축경사계
3rd row지진계
4th rowGPS 센서
5th row지진계

Common Values

ValueCountFrequency (%)
온도계 2280
22.8%
가속도계 1677
16.8%
케이블가속도계 1214
12.1%
동적변형율계 934
9.3%
정적변형율계 865
 
8.6%
지진계 691
 
6.9%
2축경사계 609
 
6.1%
GPS 센서 401
 
4.0%
2D풍향풍속계 390
 
3.9%
신축변위계 268
 
2.7%
Other values (8) 671
 
6.7%

Length

2023-12-12T15:35:40.839408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
온도계 2280
21.9%
가속도계 1677
16.1%
케이블가속도계 1214
11.7%
동적변형율계 934
9.0%
정적변형율계 865
 
8.3%
지진계 691
 
6.6%
2축경사계 609
 
5.9%
gps 401
 
3.9%
센서 401
 
3.9%
2d풍향풍속계 390
 
3.7%
Other values (9) 939
9.0%

센서사용단위
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
g
3582 
°C
2280 
uε
1799 
mm
833 
° or rad
751 
Other values (5)
755 

Length

Max length8
Median length5
Mean length2.1688
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuε
2nd row° or rad
3rd rowg
4th rowmm
5th rowg

Common Values

ValueCountFrequency (%)
g 3582
35.8%
°C 2280
22.8%
uε 1799
18.0%
mm 833
 
8.3%
° or rad 751
 
7.5%
m/s 619
 
6.2%
ton.f 59
 
0.6%
m 52
 
0.5%
kN 15
 
0.1%
<NA> 10
 
0.1%

Length

2023-12-12T15:35:40.964384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:41.091713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 3582
31.1%
°c 2280
19.8%
uε 1799
15.6%
mm 833
 
7.2%
° 751
 
6.5%
or 751
 
6.5%
rad 751
 
6.5%
m/s 619
 
5.4%
ton.f 59
 
0.5%
m 52
 
0.5%
Other values (2) 25
 
0.2%

로거명.1
Text

Distinct67
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:35:41.345698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length4
Mean length5.0002
Min length4

Characters and Unicode

Total characters50002
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSL01
2nd rowDL001_1
3rd rowDL001_3
4th rowGNS_6
5th rowQL001_1
ValueCountFrequency (%)
sl01 1602
16.0%
dl01 1233
 
12.3%
dl001_2 754
 
7.5%
dl001_1 686
 
6.9%
dl02 671
 
6.7%
dl03 384
 
3.8%
dl04 354
 
3.5%
sl002_1 339
 
3.4%
sl04 337
 
3.4%
sl02 285
 
2.9%
Other values (57) 3355
33.6%
2023-12-12T15:35:41.735911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12170
24.3%
L 9440
18.9%
1 6609
13.2%
D 4990
10.0%
S 3488
 
7.0%
_ 3404
 
6.8%
2 3177
 
6.4%
3 1471
 
2.9%
Q 1212
 
2.4%
4 811
 
1.6%
Other values (16) 3230
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25029
50.1%
Uppercase Letter 20207
40.4%
Connector Punctuation 3404
 
6.8%
Lowercase Letter 1362
 
2.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 9440
46.7%
D 4990
24.7%
S 3488
 
17.3%
Q 1212
 
6.0%
G 657
 
3.3%
N 289
 
1.4%
M 58
 
0.3%
P 58
 
0.3%
O 15
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 12170
48.6%
1 6609
26.4%
2 3177
 
12.7%
3 1471
 
5.9%
4 811
 
3.2%
6 302
 
1.2%
5 286
 
1.1%
7 203
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
n 396
29.1%
r 198
14.5%
a 198
14.5%
e 198
14.5%
t 198
14.5%
l 58
 
4.3%
u 58
 
4.3%
s 58
 
4.3%
Connector Punctuation
ValueCountFrequency (%)
_ 3404
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28433
56.9%
Latin 21569
43.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 9440
43.8%
D 4990
23.1%
S 3488
 
16.2%
Q 1212
 
5.6%
G 657
 
3.0%
n 396
 
1.8%
N 289
 
1.3%
r 198
 
0.9%
a 198
 
0.9%
e 198
 
0.9%
Other values (7) 503
 
2.3%
Common
ValueCountFrequency (%)
0 12170
42.8%
1 6609
23.2%
_ 3404
 
12.0%
2 3177
 
11.2%
3 1471
 
5.2%
4 811
 
2.9%
6 302
 
1.1%
5 286
 
1.0%
7 203
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12170
24.3%
L 9440
18.9%
1 6609
13.2%
D 4990
10.0%
S 3488
 
7.0%
_ 3404
 
6.8%
2 3177
 
6.4%
3 1471
 
2.9%
Q 1212
 
2.4%
4 811
 
1.6%
Other values (16) 3230
 
6.5%

로거타입
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
D
6859 
F
2866 
S
 
275

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowD
3rd rowD
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
D 6859
68.6%
F 2866
28.7%
S 275
 
2.8%

Length

2023-12-12T15:35:41.869214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:41.955401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
d 6859
68.6%
f 2866
28.7%
s 275
 
2.8%

로거타입명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동적
6859 
정적
3141 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정적
2nd row동적
3rd row동적
4th row동적
5th row동적

Common Values

ValueCountFrequency (%)
동적 6859
68.6%
정적 3141
31.4%

Length

2023-12-12T15:35:42.046640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:42.132148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동적 6859
68.6%
정적 3141
31.4%

케이블길이
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct48
Distinct (%)6.1%
Missing9213
Missing (%)92.1%
Infinite0
Infinite (%)0.0%
Mean97.305809
Minimum22.839
Maximum243.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:35:42.249706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22.839
5-th percentile22.839
Q169.8905
median97.049
Q3117.97
95-th percentile240.58
Maximum243.46
Range220.621
Interquartile range (IQR)48.0795

Descriptive statistics

Standard deviation53.918463
Coefficient of variation (CV)0.5541135
Kurtosis1.1366479
Mean97.305809
Median Absolute Deviation (MAD)26.979
Skewness0.91964592
Sum76579.672
Variance2907.2007
MonotonicityNot monotonic
2023-12-12T15:35:42.401889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
22.839 56
 
0.6%
24.215 53
 
0.5%
70.07 39
 
0.4%
127.063 33
 
0.3%
72.96 32
 
0.3%
96.808 28
 
0.3%
111.576 27
 
0.3%
100.74 26
 
0.3%
26.59 25
 
0.2%
243.46 24
 
0.2%
Other values (38) 444
 
4.4%
(Missing) 9213
92.1%
ValueCountFrequency (%)
22.839 56
0.6%
24.215 53
0.5%
26.59 25
0.2%
30.0 9
 
0.1%
59.386 19
 
0.2%
65.747 11
 
0.1%
69.711 24
0.2%
70.07 39
0.4%
71.288 12
 
0.1%
72.96 32
0.3%
ValueCountFrequency (%)
243.46 24
0.2%
240.58 20
0.2%
216.462 6
 
0.1%
215.024 11
0.1%
156.298 2
 
< 0.1%
156.244 6
 
0.1%
156.182 4
 
< 0.1%
156.18 7
 
0.1%
149.032 10
0.1%
148.916 9
 
0.1%

초기값
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct33
Distinct (%)4.2%
Missing9213
Missing (%)92.1%
Infinite0
Infinite (%)0.0%
Mean384.35718
Minimum0
Maximum9982
Zeros106
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:35:42.521822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q161.2
median100
Q3121.5
95-th percentile642.9
Maximum9982
Range9982
Interquartile range (IQR)60.3

Descriptive statistics

Standard deviation1467.2288
Coefficient of variation (CV)3.8173577
Kurtosis31.811544
Mean384.35718
Median Absolute Deviation (MAD)21.8
Skewness5.6031458
Sum302489.1
Variance2152760.4
MonotonicityNot monotonic
2023-12-12T15:35:42.648620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
100.0 220
 
2.2%
0.0 106
 
1.1%
0.55 44
 
0.4%
150.0 40
 
0.4%
121.8 33
 
0.3%
114.4 28
 
0.3%
121.6 27
 
0.3%
63.0 24
 
0.2%
61.2 19
 
0.2%
121.5 19
 
0.2%
Other values (23) 227
 
2.3%
(Missing) 9213
92.1%
ValueCountFrequency (%)
0.0 106
1.1%
0.55 44
0.4%
53.4 14
 
0.1%
54.2 11
 
0.1%
57.1 15
 
0.1%
61.2 19
 
0.2%
61.8 13
 
0.1%
63.0 24
 
0.2%
94.8 12
 
0.1%
95.4 9
 
0.1%
ValueCountFrequency (%)
9982.0 7
0.1%
9840.0 4
 
< 0.1%
9744.0 2
 
< 0.1%
9652.0 2
 
< 0.1%
4542.0 9
0.1%
4251.0 5
0.1%
4201.0 2
 
< 0.1%
3993.0 1
 
< 0.1%
813.0 4
 
< 0.1%
642.9 11
0.1%

관리기준치 하한치
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9213 
0
 
787

Length

Max length4
Median length4
Mean length3.7639
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9213
92.1%
0 787
 
7.9%

Length

2023-12-12T15:35:42.771508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:42.857784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9213
92.1%
0 787
 
7.9%

관리기준치 상한치
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9213 
0
 
787

Length

Max length4
Median length4
Mean length3.7639
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9213
92.1%
0 787
 
7.9%

Length

2023-12-12T15:35:42.953116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:43.049025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9213
92.1%
0 787
 
7.9%

센서명
Text

Distinct552
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:35:43.360118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.2217
Min length4

Characters and Unicode

Total characters72217
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowTMP003
2nd rowTP_S2Q_01
3rd rowEQK_DSD
4th rowBTI006
5th rowSSG008
ValueCountFrequency (%)
acc002 94
 
0.9%
dis001 91
 
0.9%
acc003 85
 
0.9%
wgd001 83
 
0.8%
eqk001 75
 
0.8%
bti002 71
 
0.7%
bti001 68
 
0.7%
bti004 67
 
0.7%
acc001 67
 
0.7%
tmp005 65
 
0.7%
Other values (542) 9234
92.3%
2023-12-12T15:35:43.878403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13419
18.6%
_ 8308
11.5%
S 4808
 
6.7%
C 4573
 
6.3%
1 4472
 
6.2%
T 4266
 
5.9%
2 4131
 
5.7%
P 3373
 
4.7%
M 2857
 
4.0%
G 2662
 
3.7%
Other values (28) 19348
26.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 36094
50.0%
Decimal Number 27806
38.5%
Connector Punctuation 8308
 
11.5%
Dash Punctuation 9
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 4808
13.3%
C 4573
12.7%
T 4266
11.8%
P 3373
9.3%
M 2857
7.9%
G 2662
7.4%
A 2480
6.9%
D 1752
 
4.9%
Q 1734
 
4.8%
E 1442
 
4.0%
Other values (16) 6147
17.0%
Decimal Number
ValueCountFrequency (%)
0 13419
48.3%
1 4472
 
16.1%
2 4131
 
14.9%
3 1771
 
6.4%
4 1253
 
4.5%
5 809
 
2.9%
6 709
 
2.5%
7 479
 
1.7%
8 446
 
1.6%
9 317
 
1.1%
Connector Punctuation
ValueCountFrequency (%)
_ 8308
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36123
50.0%
Latin 36094
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 4808
13.3%
C 4573
12.7%
T 4266
11.8%
P 3373
9.3%
M 2857
7.9%
G 2662
7.4%
A 2480
6.9%
D 1752
 
4.9%
Q 1734
 
4.8%
E 1442
 
4.0%
Other values (16) 6147
17.0%
Common
ValueCountFrequency (%)
0 13419
37.1%
_ 8308
23.0%
1 4472
 
12.4%
2 4131
 
11.4%
3 1771
 
4.9%
4 1253
 
3.5%
5 809
 
2.2%
6 709
 
2.0%
7 479
 
1.3%
8 446
 
1.2%
Other values (2) 326
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72217
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13419
18.6%
_ 8308
11.5%
S 4808
 
6.7%
C 4573
 
6.3%
1 4472
 
6.2%
T 4266
 
5.9%
2 4131
 
5.7%
P 3373
 
4.7%
M 2857
 
4.0%
G 2662
 
3.7%
Other values (28) 19348
26.8%

센서설치위치
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AA
2365 
CC
767 
중앙경간
734 
DD
564 
GG
553 
Other values (37)
5017 

Length

Max length7
Median length2
Mean length2.5525
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAA
2nd row중앙경간
3rd rowZZ
4th rowCC
5th rowBB

Common Values

ValueCountFrequency (%)
AA 2365
23.6%
CC 767
 
7.7%
중앙경간 734
 
7.3%
DD 564
 
5.6%
GG 553
 
5.5%
BB 466
 
4.7%
주탑남해 453
 
4.5%
PY2 406
 
4.1%
FF 365
 
3.6%
보강거더 303
 
3.0%
Other values (32) 3024
30.2%

Length

2023-12-12T15:35:44.026254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aa 2365
23.5%
cc 767
 
7.6%
중앙경간 734
 
7.3%
dd 564
 
5.6%
gg 553
 
5.5%
bb 466
 
4.6%
주탑남해 453
 
4.5%
py2 406
 
4.0%
ff 365
 
3.6%
보강거더 303
 
3.0%
Other values (32) 3098
30.8%

Interactions

2023-12-12T15:35:37.974117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:37.736531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:38.094601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:37.848711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:35:44.172265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수교량 코드특수교량명설치위치로거명센서구분센서설명센서사용단위로거명.1로거타입로거타입명케이블길이초기값센서설치위치
특수교량 코드1.0001.0000.9510.9880.6140.6070.4730.9500.7270.5530.8960.9290.957
특수교량명1.0001.0000.9510.9880.6140.6070.4730.9500.7270.5530.8960.9290.957
설치위치0.9510.9511.0000.9990.8620.8560.8250.9760.8280.6090.9240.9900.918
로거명0.9880.9880.9991.0000.9090.9090.8620.9780.8980.8110.9320.9500.905
센서구분0.6140.6140.8620.9091.0001.0001.0000.9210.8520.943NaNNaN0.570
센서설명0.6070.6070.8560.9091.0001.0001.0000.9190.7690.862NaNNaN0.561
센서사용단위0.4730.4730.8250.8621.0001.0001.0000.8900.7780.657NaNNaN0.471
로거명.10.9500.9500.9760.9780.9210.9190.8901.0000.9431.0000.9060.8040.847
로거타입0.7270.7270.8280.8980.8520.7690.7780.9431.0001.000NaNNaN0.708
로거타입명0.5530.5530.6090.8110.9430.8620.6571.0001.0001.000NaNNaN0.449
케이블길이0.8960.8960.9240.932NaNNaNNaN0.906NaNNaN1.0000.9500.780
초기값0.9290.9290.9900.950NaNNaNNaN0.804NaNNaN0.9501.0000.872
센서설치위치0.9570.9570.9180.9050.5700.5610.4710.8470.7080.4490.7800.8721.000
2023-12-12T15:35:44.755368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
센서사용단위관리기준치 하한치관리기준치 상한치센서구분특수교량명로거타입설치위치센서설명센서설치위치로거타입명특수교량 코드
센서사용단위1.0001.0001.0001.0000.2250.4830.4721.0000.1890.6640.225
관리기준치 하한치1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
관리기준치 상한치1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
센서구분1.0001.0001.0001.0000.2620.6030.4111.0000.1800.8170.262
특수교량명0.2251.0001.0000.2621.0000.5590.6890.2610.7120.5181.000
로거타입0.4831.0001.0000.6030.5591.0000.5810.5890.4451.0000.559
설치위치0.4721.0001.0000.4110.6890.5811.0000.4150.3200.4900.689
센서설명1.0001.0001.0001.0000.2610.5890.4151.0000.1820.8170.261
센서설치위치0.1891.0001.0000.1800.7120.4450.3200.1821.0000.3580.712
로거타입명0.6641.0001.0000.8170.5181.0000.4900.8170.3581.0000.518
특수교량 코드0.2251.0001.0000.2621.0000.5590.6890.2610.7120.5181.000
2023-12-12T15:35:44.903015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
케이블길이초기값특수교량 코드특수교량명설치위치센서구분센서설명센서사용단위로거타입로거타입명관리기준치 하한치관리기준치 상한치센서설치위치
케이블길이1.0000.1340.6630.6630.5921.0001.0001.0001.0001.0001.0001.0000.411
초기값0.1341.0000.6980.6980.8741.0001.0001.0001.0001.0001.0001.0000.674
특수교량 코드0.6630.6981.0001.0000.6890.2620.2610.2250.5590.5181.0001.0000.712
특수교량명0.6630.6981.0001.0000.6890.2620.2610.2250.5590.5181.0001.0000.712
설치위치0.5920.8740.6890.6891.0000.4110.4150.4720.5810.4901.0001.0000.320
센서구분1.0001.0000.2620.2620.4111.0001.0001.0000.6030.8171.0001.0000.180
센서설명1.0001.0000.2610.2610.4151.0001.0001.0000.5890.8171.0001.0000.182
센서사용단위1.0001.0000.2250.2250.4721.0001.0001.0000.4830.6641.0001.0000.189
로거타입1.0001.0000.5590.5590.5810.6030.5890.4831.0001.0001.0001.0000.445
로거타입명1.0001.0000.5180.5180.4900.8170.8170.6641.0001.0001.0001.0000.358
관리기준치 하한치1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
관리기준치 상한치1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
센서설치위치0.4110.6740.7120.7120.3200.1800.1820.1890.4450.3581.0001.0001.000

Missing values

2023-12-12T15:35:38.234367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:35:38.434232image/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.
2023-12-12T15:35:38.587750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

특수교량 코드특수교량명센서코드설치위치로거명센서구분센서설명센서사용단위로거명.1로거타입로거타입명케이블길이초기값관리기준치 하한치관리기준치 상한치센서명센서설치위치
66246BYD백야대교SSG009BB[P-BB]SSG정적변형율계uεSL01F정적<NA><NA><NA><NA>TMP003AA
19327NH1남해대교TI_B1_Y주탑남해<NA>BTI2축경사계° or radDL001_1D동적<NA><NA><NA><NA>TP_S2Q_01중앙경간
35108DSD돌산대교EQK001_XAA여수측 지반EQK지진계gDL001_3D동적<NA><NA><NA><NA>EQK_DSDZZ
18959NH1남해대교GP_T2T_01_Z주탑하동<NA>GPSGPS 센서mmGNS_6D동적<NA><NA><NA><NA>BTI006CC
37429DGB동강대교EQK_DGGNZZZZEQK지진계gQL001_1D동적<NA><NA><NA><NA>SSG008BB
9308GBB거북선대교TMP001AA중앙경간TMP온도계°CDL01D동적<NA><NA><NA><NA>ACC001CC
40682DEB동이대교TMP_U01_1P1PY1TMP온도계°CSL01F정적<NA><NA><NA><NA>FBG3O1
80319SCP삼천포대교DIS001_YAE[경간-35]DIS처짐계(DIS)mmDL06D동적<NA><NA><NA><NA>EQK_SCQAA
55461MPB목포대교EQK_MPQZDD중앙경간(SG31)ACC가속도계gQL03D동적<NA><NA><NA><NA>CAC004_3GG
33605DSD돌산대교CAC014_YCE여수측 케이블CAC케이블가속도계gDL001_2D동적143.073169.800EQK_DSGZZ
특수교량 코드특수교량명센서코드설치위치로거명센서구분센서설명센서사용단위로거명.1로거타입로거타입명케이블길이초기값관리기준치 하한치관리기준치 상한치센서명센서설치위치
34899DSD돌산대교EQK_DSPXZZZZCAC케이블가속도계gDL001_2D동적<NA><NA><NA><NA>CAC005CE
70633BYD백야대교TMP011CC[P-CC]TMP온도계°CSL01F정적<NA><NA><NA><NA>CAC002BE
59733MPB목포대교TMP004_1CCPY2와 중앙경간 사이(SG23)TMP온도계°CSL03F정적<NA><NA><NA><NA>EXP002AA
54607MPB목포대교EQK_MPDXPY1PY1 주탑ACC가속도계gQL01D동적<NA><NA><NA><NA>WGD001PY2
35806DSD돌산대교SSG009CCC27SSG정적변형율계uεSL002_1F정적<NA><NA><NA><NA>CAC013CE
48230MPB목포대교CAC001_5GGCableCAC케이블가속도계gDL06D동적<NA><NA><NA><NA>DSG002_3DD
5465GGB거금대교TMP003AAAATMP온도계°CDL01D동적<NA><NA><NA><NA>TMP006AA
78773SCP삼천포대교CAC005AE[경간-35]CAC케이블가속도계gDL06D동적72.96100.000BTI003AD
64692BYD백야대교DSG006AA[P-AA]DSG동적변형율계uεDL02D동적<NA><NA><NA><NA>UTI002_TFF
41891JBB둔병대교EX_A1_01교대A1교대A1EXP신축변위계mmGantner_1D동적<NA><NA><NA><NA>EQK_JBAPY 주탑

Duplicate rows

Most frequently occurring

특수교량 코드특수교량명센서코드설치위치로거명센서구분센서설명센서사용단위로거명.1로거타입로거타입명케이블길이초기값관리기준치 하한치관리기준치 상한치센서명센서설치위치# duplicates
0DSD돌산대교ACC002_XBB여수측 주탑ACC가속도계gDL001_3D동적<NA><NA><NA><NA>ACC002BB2
1DSD돌산대교ACC002_ZBB여수측 주탑ACC가속도계gDL001_3D동적<NA><NA><NA><NA>ACC002BB2
2DSD돌산대교CAC001_XCE여수측 케이블CAC케이블가속도계gDL001_1D동적59.38661.200CAC014CE2
3DSD돌산대교CAC002_XCE여수측 케이블CAC케이블가속도계gDL001_1D동적69.71163.000CAC007CE2
4DSD돌산대교EQK001_YAA여수측 지반EQK지진계gDL001_3D동적<NA><NA><NA><NA>CAC011CE2
5DSD돌산대교EQK_DSPXZZZZCAC케이블가속도계gDL001_2D동적<NA><NA><NA><NA>CAC005CE2
6DSD돌산대교SSG003CCC27SSG정적변형율계uεSL002_1F정적<NA><NA><NA><NA>ACC002BB2
7DSD돌산대교SSG009CCC27SSG정적변형율계uεSL002_1F정적<NA><NA><NA><NA>ACC002BB2
8DSD돌산대교SSG010CCC27SSG정적변형율계uεSL002_1F정적<NA><NA><NA><NA>ACC002BB2
9DSD돌산대교TMP002BB여수측 주탑TMP온도계°CSL002_1F정적<NA><NA><NA><NA>ACC002BB2