Overview

Dataset statistics

Number of variables10
Number of observations2658
Missing cells2741
Missing cells (%)10.3%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory215.6 KiB
Average record size in memory83.0 B

Variable types

Categorical4
Text3
Numeric3

Dataset

Description국토안전관리원에서 시설물을 대상으로 탄산화 시험 결과 데이터 입니다. 시설물은 교량, 터널, 댐, 상하수도, 하천, 항만으로 구분되어 있습니다. 탄산화깊이, 탄산화속도계수, 탄산화되는 시간, 평가등급 등 데이터를 제공 드립니다.데이터 공란사유 : 데이터 미집계(보고서 내 데이터 없음)
Author국토안전관리원
URLhttps://www.data.go.kr/data/15111011/fileData.do

Alerts

종별 has constant value ""Constant
Dataset has 1 (< 0.1%) duplicate rowsDuplicates
시설물종류 is highly overall correlated with 시설물구분High correlation
시설물구분 is highly overall correlated with 시설물종류High correlation
탄산화깊이(mm) is highly overall correlated with 탄산화속도계수(A)High correlation
탄산화속도계수(A) is highly overall correlated with 탄산화깊이(mm)High correlation
평가등급 is highly imbalanced (57.5%)Imbalance
탄산화깊이(mm) has 158 (5.9%) missing valuesMissing
탄산화속도계수(A) has 1345 (50.6%) missing valuesMissing
잔여피복두께가 모두 탄산화되는 시간 T(year) has 1224 (46.0%) missing valuesMissing
탄산화깊이(mm) has 106 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-12 09:30:44.127122
Analysis finished2023-12-12 09:30:46.450512
Duration2.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설물구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.9 KiB
상하수도
1258 
교량
618 
터널
567 
댐
137 
하천
 
42

Length

Max length4
Median length2
Mean length2.8950339
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상하수도 1258
47.3%
교량 618
23.3%
터널 567
21.3%
댐 137
 
5.2%
하천 42
 
1.6%
항만 36
 
1.4%

Length

2023-12-12T18:30:46.554268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:30:46.682890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상하수도 1258
47.3%
교량 618
23.3%
터널 567
21.3%
댐 137
 
5.2%
하천 42
 
1.6%
항만 36
 
1.4%

시설물종류
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size20.9 KiB
광역상수도
1258 
도로교량
533 
철도터널
316 
도로터널
251 
철도교량
 
85
Other values (5)
215 

Length

Max length5
Median length4
Mean length4.4725357
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광역상수도 1258
47.3%
도로교량 533
20.1%
철도터널 316
 
11.9%
도로터널 251
 
9.4%
철도교량 85
 
3.2%
다목적댐 61
 
2.3%
하구둑 42
 
1.6%
용수전용댐 40
 
1.5%
계류시설 36
 
1.4%
발전용댐 36
 
1.4%

Length

2023-12-12T18:30:46.836809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:30:47.003861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광역상수도 1258
47.3%
도로교량 533
20.1%
철도터널 316
 
11.9%
도로터널 251
 
9.4%
철도교량 85
 
3.2%
다목적댐 61
 
2.3%
하구둑 42
 
1.6%
용수전용댐 40
 
1.5%
계류시설 36
 
1.4%
발전용댐 36
 
1.4%

종별
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.9 KiB
1종
2658 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1종
2nd row1종
3rd row1종
4th row1종
5th row1종

Common Values

ValueCountFrequency (%)
1종 2658
100.0%

Length

2023-12-12T18:30:47.178737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:30:47.299478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1종 2658
100.0%

시설물번호
Text

Distinct74
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size20.9 KiB
2023-12-12T18:30:47.563565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters15948
Distinct characters21
Distinct categories2 ?
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 rowTU0009
2nd rowTU0009
3rd rowTU0009
4th rowTU0009
5th rowTU0009
ValueCountFrequency (%)
ws0009 389
 
14.6%
ws0002 244
 
9.2%
ws0003 230
 
8.7%
br0009 165
 
6.2%
ws0008 136
 
5.1%
ws0001 115
 
4.3%
br0007 114
 
4.3%
ws0005 114
 
4.3%
tu0009 46
 
1.7%
tu0022 42
 
1.6%
Other values (64) 1063
40.0%
2023-12-12T18:30:48.044217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7370
46.2%
W 1249
 
7.8%
S 1249
 
7.8%
B 630
 
4.0%
9 626
 
3.9%
2 620
 
3.9%
R 618
 
3.9%
1 611
 
3.8%
T 567
 
3.6%
U 567
 
3.6%
Other values (11) 1841
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10632
66.7%
Uppercase Letter 5316
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 1249
23.5%
S 1249
23.5%
B 630
11.9%
R 618
11.6%
T 567
10.7%
U 567
10.7%
D 176
 
3.3%
A 146
 
2.7%
E 42
 
0.8%
H 36
 
0.7%
Decimal Number
ValueCountFrequency (%)
0 7370
69.3%
9 626
 
5.9%
2 620
 
5.8%
1 611
 
5.7%
3 465
 
4.4%
5 259
 
2.4%
7 222
 
2.1%
8 184
 
1.7%
6 165
 
1.6%
4 110
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10632
66.7%
Latin 5316
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 1249
23.5%
S 1249
23.5%
B 630
11.9%
R 618
11.6%
T 567
10.7%
U 567
10.7%
D 176
 
3.3%
A 146
 
2.7%
E 42
 
0.8%
H 36
 
0.7%
Common
ValueCountFrequency (%)
0 7370
69.3%
9 626
 
5.9%
2 620
 
5.8%
1 611
 
5.7%
3 465
 
4.4%
5 259
 
2.4%
7 222
 
2.1%
8 184
 
1.7%
6 165
 
1.6%
4 110
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7370
46.2%
W 1249
 
7.8%
S 1249
 
7.8%
B 630
 
4.0%
9 626
 
3.9%
2 620
 
3.9%
R 618
 
3.9%
1 611
 
3.8%
T 567
 
3.6%
U 567
 
3.6%
Other values (11) 1841
 
11.5%

세부위치
Text

Distinct2287
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size20.9 KiB
2023-12-12T18:30:48.435783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length34
Mean length18.971031
Min length1

Characters and Unicode

Total characters50425
Distinct characters310
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2211 ?
Unique (%)83.2%

Sample

1st row20k415 LC
2nd row20k426 RW
3rd row20k633 RW
4th row20k896 LW
5th row20k911 LC
ValueCountFrequency (%)
전면벽체 154
 
1.6%
좌측벽체 149
 
1.6%
내부 147
 
1.5%
기둥 145
 
1.5%
바닥 143
 
1.5%
rw 134
 
1.4%
우측벽체 132
 
1.4%
후면벽체 127
 
1.3%
lw 121
 
1.3%
1공구 116
 
1.2%
Other values (2411) 8208
85.7%
2023-12-12T18:30:48.909223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6939
 
13.8%
1 2312
 
4.6%
2 1533
 
3.0%
( 1392
 
2.8%
) 1380
 
2.7%
0 1330
 
2.6%
수 1283
 
2.5%
- 1186
 
2.4%
지 1118
 
2.2%
3 1072
 
2.1%
Other values (300) 30880
61.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23243
46.1%
Decimal Number 9641
19.1%
Space Separator 6939
 
13.8%
Uppercase Letter 4444
 
8.8%
Open Punctuation 1396
 
2.8%
Close Punctuation 1396
 
2.8%
Dash Punctuation 1186
 
2.4%
Other Punctuation 916
 
1.8%
Math Symbol 752
 
1.5%
Lowercase Letter 495
 
1.0%
Other values (2) 17
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 1283
 
5.5%
지 1118
 
4.8%
장 903
 
3.9%
벽 828
 
3.6%
체 813
 
3.5%
정 791
 
3.4%
부 748
 
3.2%
면 540
 
2.3%
측 527
 
2.3%
구 500
 
2.2%
Other values (226) 15192
65.4%
Uppercase Letter
ValueCountFrequency (%)
C 621
14.0%
S 546
12.3%
R 440
9.9%
W 409
9.2%
B 320
7.2%
P 319
7.2%
L 303
 
6.8%
A 299
 
6.7%
G 230
 
5.2%
M 171
 
3.8%
Other values (13) 786
17.7%
Lowercase Letter
ValueCountFrequency (%)
k 183
37.0%
t 72
 
14.5%
a 71
 
14.3%
m 66
 
13.3%
b 39
 
7.9%
n 24
 
4.8%
l 10
 
2.0%
e 6
 
1.2%
y 5
 
1.0%
w 5
 
1.0%
Other values (6) 14
 
2.8%
Decimal Number
ValueCountFrequency (%)
1 2312
24.0%
2 1533
15.9%
0 1330
13.8%
3 1072
11.1%
5 931
9.7%
4 725
 
7.5%
6 559
 
5.8%
8 444
 
4.6%
7 405
 
4.2%
9 330
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 376
41.0%
/ 333
36.4%
# 80
 
8.7%
· 48
 
5.2%
, 43
 
4.7%
' 35
 
3.8%
。 1
 
0.1%
Other Number
ValueCountFrequency (%)
③ 4
26.7%
② 3
20.0%
④ 3
20.0%
⑤ 2
13.3%
① 2
13.3%
⑥ 1
 
6.7%
Math Symbol
ValueCountFrequency (%)
+ 431
57.3%
~ 289
38.4%
∼ 31
 
4.1%
= 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1392
99.7%
（ 4
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 1380
98.9%
） 16
 
1.1%
Other Symbol
ValueCountFrequency (%)
° 1
50.0%
® 1
50.0%
Space Separator
ValueCountFrequency (%)
6939
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23243
46.1%
Common 22243
44.1%
Latin 4939
 
9.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 1283
 
5.5%
지 1118
 
4.8%
장 903
 
3.9%
벽 828
 
3.6%
체 813
 
3.5%
정 791
 
3.4%
부 748
 
3.2%
면 540
 
2.3%
측 527
 
2.3%
구 500
 
2.2%
Other values (226) 15192
65.4%
Latin
ValueCountFrequency (%)
C 621
12.6%
S 546
11.1%
R 440
 
8.9%
W 409
 
8.3%
B 320
 
6.5%
P 319
 
6.5%
L 303
 
6.1%
A 299
 
6.1%
G 230
 
4.7%
k 183
 
3.7%
Other values (29) 1269
25.7%
Common
ValueCountFrequency (%)
6939
31.2%
1 2312
 
10.4%
2 1533
 
6.9%
( 1392
 
6.3%
) 1380
 
6.2%
0 1330
 
6.0%
- 1186
 
5.3%
3 1072
 
4.8%
5 931
 
4.2%
4 725
 
3.3%
Other values (25) 3443
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27065
53.7%
Hangul 23243
46.1%
None 71
 
0.1%
Math Operators 31
 
0.1%
Enclosed Alphanum 15
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6939
25.6%
1 2312
 
8.5%
2 1533
 
5.7%
( 1392
 
5.1%
) 1380
 
5.1%
0 1330
 
4.9%
- 1186
 
4.4%
3 1072
 
4.0%
5 931
 
3.4%
4 725
 
2.7%
Other values (51) 8265
30.5%
Hangul
ValueCountFrequency (%)
수 1283
 
5.5%
지 1118
 
4.8%
장 903
 
3.9%
벽 828
 
3.6%
체 813
 
3.5%
정 791
 
3.4%
부 748
 
3.2%
면 540
 
2.3%
측 527
 
2.3%
구 500
 
2.2%
Other values (226) 15192
65.4%
None
ValueCountFrequency (%)
· 48
67.6%
） 16
 
22.5%
（ 4
 
5.6%
。 1
 
1.4%
° 1
 
1.4%
® 1
 
1.4%
Math Operators
ValueCountFrequency (%)
∼ 31
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
③ 4
26.7%
② 3
20.0%
④ 3
20.0%
⑤ 2
13.3%
① 2
13.3%
⑥ 1
 
6.7%

철근피복두께(mm)
Real number (ℝ)

Distinct274
Distinct (%)10.4%
Missing14
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean74.066358
Minimum2
Maximum1160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.5 KiB
2023-12-12T18:30:49.088914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile27.045
Q146
median65
Q389
95-th percentile134
Maximum1160
Range1158
Interquartile range (IQR)43

Descriptive statistics

Standard deviation68.988063
Coefficient of variation (CV)0.93143588
Kurtosis131.30552
Mean74.066358
Median Absolute Deviation (MAD)21
Skewness9.9817591
Sum195831.45
Variance4759.3528
MonotonicityNot monotonic
2023-12-12T18:30:49.236278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.0 150
 
5.6%
100.0 105
 
4.0%
48.0 55
 
2.1%
46.0 42
 
1.6%
73.0 38
 
1.4%
71.0 37
 
1.4%
51.0 37
 
1.4%
38.0 35
 
1.3%
89.0 35
 
1.3%
41.0 34
 
1.3%
Other values (264) 2076
78.1%
ValueCountFrequency (%)
2.0 1
 
< 0.1%
3.0 3
0.1%
4.0 6
0.2%
6.0 1
 
< 0.1%
8.0 2
 
0.1%
10.0 4
0.2%
12.0 1
 
< 0.1%
14.0 4
0.2%
15.0 4
0.2%
16.0 1
 
< 0.1%
ValueCountFrequency (%)
1160.0 1
< 0.1%
1100.0 1
< 0.1%
1050.0 1
< 0.1%
1030.0 1
< 0.1%
1020.0 1
< 0.1%
1010.0 1
< 0.1%
1000.0 1
< 0.1%
980.0 1
< 0.1%
880.0 1
< 0.1%
700.0 1
< 0.1%

탄산화깊이(mm)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct499
Distinct (%)20.0%
Missing158
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean6.779588
Minimum0
Maximum40.3
Zeros106
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size23.5 KiB
2023-12-12T18:30:49.678372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q12.1
median5
Q39.4
95-th percentile19.305
Maximum40.3
Range40.3
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation6.2642116
Coefficient of variation (CV)0.92398116
Kurtosis2.5544522
Mean6.779588
Median Absolute Deviation (MAD)3
Skewness1.533561
Sum16948.97
Variance39.240347
MonotonicityNot monotonic
2023-12-12T18:30:49.847191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 172
 
6.5%
3.0 143
 
5.4%
2.0 134
 
5.0%
0.0 106
 
4.0%
4.0 83
 
3.1%
5.0 72
 
2.7%
6.0 42
 
1.6%
7.0 36
 
1.4%
8.0 31
 
1.2%
9.0 26
 
1.0%
Other values (489) 1655
62.3%
(Missing) 158
 
5.9%
ValueCountFrequency (%)
0.0 106
4.0%
0.1 20
 
0.8%
0.2 14
 
0.5%
0.26 1
 
< 0.1%
0.3 9
 
0.3%
0.4 5
 
0.2%
0.43 1
 
< 0.1%
0.5 13
 
0.5%
0.58 1
 
< 0.1%
0.6 5
 
0.2%
ValueCountFrequency (%)
40.3 1
< 0.1%
37.4 1
< 0.1%
35.7 1
< 0.1%
35.6 1
< 0.1%
35.1 2
0.1%
33.0 1
< 0.1%
32.43 1
< 0.1%
31.6 1
< 0.1%
31.2 1
< 0.1%
31.1 1
< 0.1%

탄산화속도계수(A)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct407
Distinct (%)31.0%
Missing1345
Missing (%)50.6%
Infinite0
Infinite (%)0.0%
Mean1.9210586
Minimum0
Maximum9.52
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size23.5 KiB
2023-12-12T18:30:49.997673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21
Q10.75
median1.6
Q32.71
95-th percentile4.7
Maximum9.52
Range9.52
Interquartile range (IQR)1.96

Descriptive statistics

Standard deviation1.4788066
Coefficient of variation (CV)0.76978732
Kurtosis1.8374337
Mean1.9210586
Median Absolute Deviation (MAD)0.94
Skewness1.2060511
Sum2522.35
Variance2.1868689
MonotonicityNot monotonic
2023-12-12T18:30:50.135482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 24
 
0.9%
0.4 22
 
0.8%
0.6 20
 
0.8%
2.4 20
 
0.8%
1.6 19
 
0.7%
0.2 19
 
0.7%
0.9 16
 
0.6%
0.23 15
 
0.6%
2.0 14
 
0.5%
0.02 13
 
0.5%
Other values (397) 1131
42.6%
(Missing) 1345
50.6%
ValueCountFrequency (%)
0.0 3
 
0.1%
0.02 13
0.5%
0.04 7
0.3%
0.05 3
 
0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.08 3
 
0.1%
0.11 2
 
0.1%
0.12 1
 
< 0.1%
0.14 2
 
0.1%
ValueCountFrequency (%)
9.52 1
< 0.1%
9.2 1
< 0.1%
9.1 1
< 0.1%
7.49 1
< 0.1%
7.48 1
< 0.1%
7.39 1
< 0.1%
7.32 1
< 0.1%
7.3 1
< 0.1%
7.2 1
< 0.1%
7.1 1
< 0.1%

잔여피복두께가 모두 탄산화되는 시간 T(year)
Text

MISSING 

Distinct267
Distinct (%)18.6%
Missing1224
Missing (%)46.0%
Memory size20.9 KiB
2023-12-12T18:30:50.612827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.4714086
Min length1

Characters and Unicode

Total characters7846
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique237 ?
Unique (%)16.5%

Sample

1st row309
2nd row216
3rd row112
4th row336
5th row455
ValueCountFrequency (%)
이상 755
31.2%
100년 548
22.6%
30년 272
 
11.2%
초과 234
 
9.7%
50년 117
 
4.8%
100 89
 
3.7%
∞ 69
 
2.8%
25년 50
 
2.1%
5985 5
 
0.2%
9360 3
 
0.1%
Other values (258) 281
 
11.6%
2023-12-12T18:30:51.259387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1749
22.3%
년 989
12.6%
989
12.6%
1 779
9.9%
이 755
9.6%
상 755
9.6%
3 378
 
4.8%
5 266
 
3.4%
과 234
 
3.0%
초 234
 
3.0%
Other values (8) 718
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3808
48.5%
Other Letter 2967
37.8%
Space Separator 989
 
12.6%
Math Symbol 69
 
0.9%
Other Punctuation 13
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1749
45.9%
1 779
20.5%
3 378
 
9.9%
5 266
 
7.0%
2 203
 
5.3%
8 96
 
2.5%
6 93
 
2.4%
4 87
 
2.3%
7 80
 
2.1%
9 77
 
2.0%
Other Letter
ValueCountFrequency (%)
년 989
33.3%
이 755
25.4%
상 755
25.4%
과 234
 
7.9%
초 234
 
7.9%
Space Separator
ValueCountFrequency (%)
989
100.0%
Math Symbol
ValueCountFrequency (%)
∞ 69
100.0%
Other Punctuation
ValueCountFrequency (%)
. 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4879
62.2%
Hangul 2967
37.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1749
35.8%
989
20.3%
1 779
16.0%
3 378
 
7.7%
5 266
 
5.5%
2 203
 
4.2%
8 96
 
2.0%
6 93
 
1.9%
4 87
 
1.8%
7 80
 
1.6%
Other values (3) 159
 
3.3%
Hangul
ValueCountFrequency (%)
년 989
33.3%
이 755
25.4%
상 755
25.4%
과 234
 
7.9%
초 234
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4810
61.3%
Hangul 2967
37.8%
Math Operators 69
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1749
36.4%
989
20.6%
1 779
16.2%
3 378
 
7.9%
5 266
 
5.5%
2 203
 
4.2%
8 96
 
2.0%
6 93
 
1.9%
4 87
 
1.8%
7 80
 
1.7%
Other values (2) 90
 
1.9%
Hangul
ValueCountFrequency (%)
년 989
33.3%
이 755
25.4%
상 755
25.4%
과 234
 
7.9%
초 234
 
7.9%
Math Operators
ValueCountFrequency (%)
∞ 69
100.0%

평가등급
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size20.9 KiB
a
1965 
<NA>
426 
b
207 
c
 
27
d
 
21
Other values (2)
 
12

Length

Max length4
Median length1
Mean length1.4823175
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowd
2nd rowa
3rd rowa
4th rowa
5th rowa

Common Values

ValueCountFrequency (%)
a 1965
73.9%
<NA> 426
 
16.0%
b 207
 
7.8%
c 27
 
1.0%
d 21
 
0.8%
e 10
 
0.4%
a~b 2
 
0.1%

Length

2023-12-12T18:30:51.464316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:30:51.631669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 1965
73.9%
na 426
 
16.0%
b 207
 
7.8%
c 27
 
1.0%
d 21
 
0.8%
e 10
 
0.4%
a~b 2
 
0.1%

Interactions

2023-12-12T18:30:45.587005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:44.813274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:45.240959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:45.700438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:44.954120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:45.353059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:45.821212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:45.120237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:30:45.479718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:30:51.749423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설물구분시설물종류시설물번호철근피복두께(mm)탄산화깊이(mm)탄산화속도계수(A)평가등급
시설물구분1.0001.0001.0000.5080.3640.3990.229
시설물종류1.0001.0001.0000.4750.4560.3710.176
시설물번호1.0001.0001.0000.7750.6430.6220.605
철근피복두께(mm)0.5080.4750.7751.0000.0000.2560.137
탄산화깊이(mm)0.3640.4560.6430.0001.0000.8470.418
탄산화속도계수(A)0.3990.3710.6220.2560.8471.0000.096
평가등급0.2290.1760.6050.1370.4180.0961.000
2023-12-12T18:30:51.882347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
평가등급시설물종류시설물구분
평가등급1.0000.0930.085
시설물종류0.0931.0000.999
시설물구분0.0850.9991.000
2023-12-12T18:30:52.015904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
철근피복두께(mm)탄산화깊이(mm)탄산화속도계수(A)시설물구분시설물종류평가등급
철근피복두께(mm)1.0000.0390.1680.2810.2390.057
탄산화깊이(mm)0.0391.0000.9360.2010.1550.235
탄산화속도계수(A)0.1680.9361.0000.2430.1920.061
시설물구분0.2810.2010.2431.0000.9990.085
시설물종류0.2390.1550.1920.9991.0000.093
평가등급0.0570.2350.0610.0850.0931.000

Missing values

2023-12-12T18:30:45.991904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:30:46.205725image/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.
2023-12-12T18:30:46.354207image/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

시설물구분시설물종류종별시설물번호세부위치철근피복두께(mm)탄산화깊이(mm)탄산화속도계수(A)잔여피복두께가 모두 탄산화되는 시간 T(year)평가등급
0터널철도터널1종TU000920k415 LC2.09.672.4<NA>d
1터널철도터널1종TU000920k426 RW50.08.272.1<NA>a
2터널철도터널1종TU000920k633 RW94.021.335.3<NA>a
3터널철도터널1종TU000920k896 LW126.010.472.6<NA>a
4터널철도터널1종TU000920k911 LC50.07.571.9<NA>a
5터널철도터널1종TU000921k140 LW70.07.01.8<NA>a
6터널철도터널1종TU000921k249 횡갱87.017.234.3<NA>a
7터널철도터널1종TU000921k249 LC70.014.23.6<NA>a
8터널철도터널1종TU000921k445 LC15.012.473.1<NA>c
9터널철도터널1종TU000921k515 LW110.09.42.4<NA>a
시설물구분시설물종류종별시설물번호세부위치철근피복두께(mm)탄산화깊이(mm)탄산화속도계수(A)잔여피복두께가 모두 탄산화되는 시간 T(year)평가등급
2648터널철도터널1종TU0015RW41.07.51.64100년 이상a
2649터널철도터널1종TU0015LW63.08.71.89100년 이상a
2650터널철도터널1종TU001833k350 하선 벽체67.09.182.11100년 이상a
2651터널철도터널1종TU001833k405 하선 벽체42.012.242.81100년 이상b
2652터널철도터널1종TU001833k445 상선 벽체62.015.123.47100년 이상a
2653터널철도터널1종TU001833k567 상선 벽체76.016.553.8100년 이상a
2654터널철도터널1종TU001833k570 상선 벽체64.017.03.9100년 이상a
2655터널철도터널1종TU001833k670 하선 벽체91.015.853.64100년 이상a
2656터널철도터널1종TU001833k770 하선 벽체77.018.164.17100년 이상a
2657터널철도터널1종TU001833k783 상선 벽체53.012.522.87100년 이상a

Duplicate rows

Most frequently occurring

시설물구분시설물종류종별시설물번호세부위치철근피복두께(mm)탄산화깊이(mm)탄산화속도계수(A)잔여피복두께가 모두 탄산화되는 시간 T(year)평가등급# duplicates
0터널철도터널1종TU0012RW50.05.9<NA><NA><NA>2