Overview

Dataset statistics

Number of variables21
Number of observations189
Missing cells375
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.0 KiB
Average record size in memory178.7 B

Variable types

Categorical8
Text3
Numeric7
DateTime3

Dataset

Description지하개발 사업 수행 시 지반침하 등의 지하 안전사고 사전 예방을 위하여 지하안전관리에 관한 특별법에 의해 수행되는 지하안전평가 등의 지하안전 관련 행정업무 및 지반침하사고 정보 등의 지하안전정보 「지하안전관리에 관한 특별법」에 따라 지반침하로 인해 발생한 사고 및 피해, 안전조치 등에 대한 정보(지반침하란 지표면이 여러 요인에 의하여 일시에 붕괴되어 국부적으로 수직방향으로 꺼져 내려앉는 현상으로 도로, 건축물 및 주변 시설물에 대한 지반침하 사고 발생을 대상으로 함)
Author국토교통부
URLhttps://www.data.go.kr/data/15025448/standard.do

Alerts

피해부상자수 has constant value ""Constant
발생지역지질종류 is highly imbalanced (65.4%)Imbalance
피해사망자수 is highly imbalanced (95.2%)Imbalance
복구상태 is highly imbalanced (82.4%)Imbalance
발생규모면적 has 90 (47.6%) missing valuesMissing
복구방법 has 45 (23.8%) missing valuesMissing
복구비용 has 171 (90.5%) missing valuesMissing
복구완료일자 has 69 (36.5%) missing valuesMissing
발생규모연장 has 2 (1.1%) zerosZeros
복구비용 has 3 (1.6%) zerosZeros

Reproduction

Analysis started2024-04-17 10:32:23.827499
Analysis finished2024-04-17 10:32:24.220744
Duration0.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

Distinct11
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
울산광역시
42 
경기도
33 
충청북도
27 
서울특별시
23 
인천광역시
21 
Other values (6)
43 

Length

Max length5
Median length5
Mean length4.3544974
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
울산광역시 42
22.2%
경기도 33
17.5%
충청북도 27
14.3%
서울특별시 23
12.2%
인천광역시 21
11.1%
부산광역시 16
 
8.5%
전라북도 10
 
5.3%
전라남도 6
 
3.2%
경상남도 6
 
3.2%
경상북도 3
 
1.6%

Length

2024-04-17T19:32:24.279999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
울산광역시 42
22.2%
경기도 33
17.5%
충청북도 27
14.3%
서울특별시 23
12.2%
인천광역시 21
11.1%
부산광역시 16
 
8.5%
전라북도 10
 
5.3%
전라남도 6
 
3.2%
경상남도 6
 
3.2%
경상북도 3
 
1.6%

시군구명
Categorical

Distinct35
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
남구
19 
의정부시
14 
영등포구
14 
청주시
14 
충주시
 
11
Other values (30)
117 

Length

Max length4
Median length3
Mean length2.9312169
Min length2

Unique

Unique12 ?
Unique (%)6.3%

Sample

1st row남구
2nd row남구
3rd row남구
4th row동구
5th row북구

Common Values

ValueCountFrequency (%)
남구 19
 
10.1%
의정부시 14
 
7.4%
영등포구 14
 
7.4%
청주시 14
 
7.4%
충주시 11
 
5.8%
중구 10
 
5.3%
동구 10
 
5.3%
동래구 10
 
5.3%
수원시 10
 
5.3%
정읍시 9
 
4.8%
Other values (25) 68
36.0%

Length

2024-04-17T19:32:24.394428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남구 19
 
10.1%
청주시 14
 
7.4%
의정부시 14
 
7.4%
영등포구 14
 
7.4%
충주시 11
 
5.8%
중구 10
 
5.3%
동구 10
 
5.3%
동래구 10
 
5.3%
수원시 10
 
5.3%
여주시 9
 
4.8%
Other values (25) 68
36.0%

지반침하지역상세
Text

Distinct174
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-04-17T19:32:24.586344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length29
Mean length14.613757
Min length5

Characters and Unicode

Total characters2762
Distinct characters243
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique164 ?
Unique (%)86.8%

Sample

1st row삼산동 1488-17번지
2nd row용연사거리
3rd row삼산로 편안한 치과의원 일원
4th row오지벌 사거리 일원
5th row무룡로 동남정비공업사 앞
ValueCountFrequency (%)
앞 41
 
6.7%
일원 19
 
3.1%
영등포구 11
 
1.8%
팔달구 8
 
1.3%
사거리 8
 
1.3%
교차로 8
 
1.3%
도로 7
 
1.1%
여의도동 5
 
0.8%
온산읍 5
 
0.8%
수원시청역 5
 
0.8%
Other values (412) 496
80.9%
2024-04-17T19:32:24.922713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
425
 
15.4%
1 124
 
4.5%
동 105
 
3.8%
2 101
 
3.7%
- 77
 
2.8%
로 76
 
2.8%
3 64
 
2.3%
번 60
 
2.2%
지 57
 
2.1%
리 53
 
1.9%
Other values (233) 1620
58.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1623
58.8%
Decimal Number 564
 
20.4%
Space Separator 425
 
15.4%
Dash Punctuation 77
 
2.8%
Open Punctuation 26
 
0.9%
Close Punctuation 26
 
0.9%
Uppercase Letter 9
 
0.3%
Lowercase Letter 6
 
0.2%
Other Punctuation 5
 
0.2%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 105
 
6.5%
로 76
 
4.7%
번 60
 
3.7%
지 57
 
3.5%
리 53
 
3.3%
구 49
 
3.0%
앞 42
 
2.6%
원 41
 
2.5%
산 36
 
2.2%
거 29
 
1.8%
Other values (207) 1075
66.2%
Decimal Number
ValueCountFrequency (%)
1 124
22.0%
2 101
17.9%
3 64
11.3%
4 51
9.0%
8 48
 
8.5%
7 46
 
8.2%
5 45
 
8.0%
6 30
 
5.3%
9 28
 
5.0%
0 27
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
L 2
22.2%
G 2
22.2%
S 2
22.2%
I 1
11.1%
O 1
11.1%
R 1
11.1%
Lowercase Letter
ValueCountFrequency (%)
m 4
66.7%
u 1
 
16.7%
c 1
 
16.7%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
/ 1
 
20.0%
Space Separator
ValueCountFrequency (%)
425
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Math Symbol
ValueCountFrequency (%)
⇒ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1623
58.8%
Common 1124
40.7%
Latin 15
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 105
 
6.5%
로 76
 
4.7%
번 60
 
3.7%
지 57
 
3.5%
리 53
 
3.3%
구 49
 
3.0%
앞 42
 
2.6%
원 41
 
2.5%
산 36
 
2.2%
거 29
 
1.8%
Other values (207) 1075
66.2%
Common
ValueCountFrequency (%)
425
37.8%
1 124
 
11.0%
2 101
 
9.0%
- 77
 
6.9%
3 64
 
5.7%
4 51
 
4.5%
8 48
 
4.3%
7 46
 
4.1%
5 45
 
4.0%
6 30
 
2.7%
Other values (7) 113
 
10.1%
Latin
ValueCountFrequency (%)
m 4
26.7%
L 2
13.3%
G 2
13.3%
S 2
13.3%
I 1
 
6.7%
O 1
 
6.7%
u 1
 
6.7%
R 1
 
6.7%
c 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1623
58.8%
ASCII 1138
41.2%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
425
37.3%
1 124
 
10.9%
2 101
 
8.9%
- 77
 
6.8%
3 64
 
5.6%
4 51
 
4.5%
8 48
 
4.2%
7 46
 
4.0%
5 45
 
4.0%
6 30
 
2.6%
Other values (15) 127
 
11.2%
Hangul
ValueCountFrequency (%)
동 105
 
6.5%
로 76
 
4.7%
번 60
 
3.7%
지 57
 
3.5%
리 53
 
3.3%
구 49
 
3.0%
앞 42
 
2.6%
원 41
 
2.5%
산 36
 
2.2%
거 29
 
1.8%
Other values (207) 1075
66.2%
Arrows
ValueCountFrequency (%)
⇒ 1
100.0%

위도
Real number (ℝ)

Distinct182
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.502877
Minimum34.741239
Maximum39.393683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-17T19:32:25.282726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.741239
5-th percentile35.140953
Q135.521044
median36.646654
Q337.507173
95-th percentile37.741986
Maximum39.393683
Range4.652444
Interquartile range (IQR)1.9861295

Descriptive statistics

Standard deviation1.0318244
Coefficient of variation (CV)0.028266934
Kurtosis-1.3853819
Mean36.502877
Median Absolute Deviation (MAD)0.9553435
Skewness-0.065171157
Sum6899.0438
Variance1.0646617
MonotonicityNot monotonic
2024-04-17T19:32:25.398108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.507173 3
 
1.6%
37.345191 2
 
1.1%
35.507536 2
 
1.1%
35.204965 2
 
1.1%
35.438246 2
 
1.1%
37.404883 2
 
1.1%
35.548189 1
 
0.5%
35.428824 1
 
0.5%
35.521227 1
 
0.5%
37.5241289 1
 
0.5%
Other values (172) 172
91.0%
ValueCountFrequency (%)
34.741239 1
0.5%
34.747618 1
0.5%
34.7752 1
0.5%
34.829461 1
0.5%
34.834994 1
0.5%
34.869653 1
0.5%
34.886809 1
0.5%
34.932049 1
0.5%
35.007236 1
0.5%
35.134627 1
0.5%
ValueCountFrequency (%)
39.393683 1
0.5%
38.445274 1
0.5%
37.759047 1
0.5%
37.758426 1
0.5%
37.757491 1
0.5%
37.757438 1
0.5%
37.754537 1
0.5%
37.750069 1
0.5%
37.746349 1
0.5%
37.742882 1
0.5%

경도
Real number (ℝ)

Distinct181
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.87056
Minimum126.56717
Maximum129.43321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-17T19:32:25.520260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.56717
5-th percentile126.67525
Q1126.93078
median127.48912
Q3129.10201
95-th percentile129.37315
Maximum129.43321
Range2.866037
Interquartile range (IQR)2.171232

Descriptive statistics

Standard deviation1.0517314
Coefficient of variation (CV)0.0082249691
Kurtosis-1.549702
Mean127.87056
Median Absolute Deviation (MAD)0.6785541
Skewness0.40083496
Sum24167.536
Variance1.106139
MonotonicityNot monotonic
2024-04-17T19:32:25.635822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.72047 3
 
1.6%
127.493999 2
 
1.1%
129.334118 2
 
1.1%
129.312878 2
 
1.1%
129.058758 2
 
1.1%
129.351367 2
 
1.1%
127.443899 2
 
1.1%
129.330001 1
 
0.5%
129.305816 1
 
0.5%
129.372316 1
 
0.5%
Other values (171) 171
90.5%
ValueCountFrequency (%)
126.567172 1
0.5%
126.600551 1
0.5%
126.607975 1
0.5%
126.608408 1
0.5%
126.611485 1
0.5%
126.618748 1
0.5%
126.641234 1
0.5%
126.645331 1
0.5%
126.656624 1
0.5%
126.67073 1
0.5%
ValueCountFrequency (%)
129.433209 1
0.5%
129.433176 1
0.5%
129.431109 1
0.5%
129.43078 1
0.5%
129.430173 1
0.5%
129.428211 1
0.5%
129.426393 1
0.5%
129.420437 1
0.5%
129.414435 1
0.5%
129.3737048 1
0.5%

발생일자
Date

Distinct159
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Minimum2014-07-17 00:00:00
Maximum2019-12-26 00:00:00
2024-04-17T19:32:25.750094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:32:25.872175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

발생규모면적
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)22.2%
Missing90
Missing (%)47.6%
Infinite0
Infinite (%)0.0%
Mean2.7760606
Minimum0.08
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-17T19:32:25.976647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.2
Q10.5
median1
Q31.5
95-th percentile4
Maximum150
Range149.92
Interquartile range (IQR)1

Descriptive statistics

Standard deviation15.012574
Coefficient of variation (CV)5.4078698
Kurtosis97.243481
Mean2.7760606
Median Absolute Deviation (MAD)0.5
Skewness9.8215494
Sum274.83
Variance225.37739
MonotonicityNot monotonic
2024-04-17T19:32:26.081331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1.0 34
 
18.0%
0.5 12
 
6.3%
2.0 9
 
4.8%
1.5 7
 
3.7%
0.2 6
 
3.2%
0.3 5
 
2.6%
0.8 5
 
2.6%
3.0 3
 
1.6%
0.1 2
 
1.1%
0.6 2
 
1.1%
Other values (12) 14
 
7.4%
(Missing) 90
47.6%
ValueCountFrequency (%)
0.08 1
 
0.5%
0.1 2
 
1.1%
0.15 1
 
0.5%
0.2 6
 
3.2%
0.3 5
 
2.6%
0.5 12
 
6.3%
0.6 2
 
1.1%
0.7 1
 
0.5%
0.8 5
 
2.6%
1.0 34
18.0%
ValueCountFrequency (%)
150.0 1
 
0.5%
10.0 1
 
0.5%
7.0 1
 
0.5%
6.0 1
 
0.5%
4.0 2
 
1.1%
3.0 3
 
1.6%
2.5 1
 
0.5%
2.0 9
4.8%
1.5 7
3.7%
1.3 1
 
0.5%

발생규모연장
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2145503
Minimum0
Maximum40
Zeros2
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-17T19:32:26.185875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.5
median1
Q32
95-th percentile5.6
Maximum40
Range40
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation4.4596492
Coefficient of variation (CV)2.0137945
Kurtosis37.746609
Mean2.2145503
Median Absolute Deviation (MAD)0.5
Skewness5.6389838
Sum418.55
Variance19.888471
MonotonicityNot monotonic
2024-04-17T19:32:26.294560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1.0 41
21.7%
0.5 23
12.2%
2.0 19
10.1%
1.5 16
 
8.5%
0.3 13
 
6.9%
3.0 11
 
5.8%
5.0 9
 
4.8%
0.8 8
 
4.2%
0.1 7
 
3.7%
0.2 6
 
3.2%
Other values (18) 36
19.0%
ValueCountFrequency (%)
0.0 2
 
1.1%
0.1 7
 
3.7%
0.2 6
 
3.2%
0.25 1
 
0.5%
0.3 13
6.9%
0.4 2
 
1.1%
0.5 23
12.2%
0.6 6
 
3.2%
0.7 2
 
1.1%
0.8 8
 
4.2%
ValueCountFrequency (%)
40.0 1
 
0.5%
30.0 1
 
0.5%
22.0 1
 
0.5%
20.0 1
 
0.5%
15.0 1
 
0.5%
12.0 2
 
1.1%
10.0 1
 
0.5%
6.0 2
 
1.1%
5.0 9
4.8%
4.0 3
 
1.6%

발생규모깊이
Real number (ℝ)

Distinct24
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0378307
Minimum0.01
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-17T19:32:26.403768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.1
Q10.3
median1
Q31.5
95-th percentile2.68
Maximum5
Range4.99
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.94047354
Coefficient of variation (CV)0.90619169
Kurtosis4.7541389
Mean1.0378307
Median Absolute Deviation (MAD)0.5
Skewness1.8781381
Sum196.15
Variance0.88449048
MonotonicityNot monotonic
2024-04-17T19:32:26.503664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1.0 38
20.1%
0.5 28
14.8%
1.5 26
13.8%
0.1 23
12.2%
2.0 15
 
7.9%
0.2 13
 
6.9%
0.3 9
 
4.8%
0.7 5
 
2.6%
0.8 4
 
2.1%
2.5 4
 
2.1%
Other values (14) 24
12.7%
ValueCountFrequency (%)
0.01 1
 
0.5%
0.1 23
12.2%
0.14 1
 
0.5%
0.15 2
 
1.1%
0.2 13
6.9%
0.3 9
 
4.8%
0.4 1
 
0.5%
0.5 28
14.8%
0.6 1
 
0.5%
0.7 5
 
2.6%
ValueCountFrequency (%)
5.0 3
 
1.6%
4.0 3
 
1.6%
3.5 1
 
0.5%
3.0 2
 
1.1%
2.8 1
 
0.5%
2.5 4
 
2.1%
2.0 15
7.9%
1.8 1
 
0.5%
1.5 26
13.8%
1.3 1
 
0.5%

발생지역지질종류
Categorical

IMBALANCE 

Distinct5
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
<NA>
162 
화강암류
 
14
기타
 
10
편암류
 
2
충적층
 
1

Length

Max length4
Median length4
Mean length3.8783069
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 162
85.7%
화강암류 14
 
7.4%
기타 10
 
5.3%
편암류 2
 
1.1%
충적층 1
 
0.5%

Length

2024-04-17T19:32:26.613334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:32:26.711229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 162
85.7%
화강암류 14
 
7.4%
기타 10
 
5.3%
편암류 2
 
1.1%
충적층 1
 
0.5%

최초발생원인
Text

Distinct120
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-04-17T19:32:26.913287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length69
Median length31
Mean length14.322751
Min length4

Characters and Unicode

Total characters2707
Distinct characters199
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)51.9%

Sample

1st row노후 하수관 파손으로 인한 토사유실
2nd row관로파손
3rd row관로파손
4th row관로파손
5th row인접부 지반공사로 인한 침하
ValueCountFrequency (%)
하수관 37
 
5.2%
인한 33
 
4.6%
파손 24
 
3.4%
침하 22
 
3.1%
관로파손 20
 
2.8%
불량 19
 
2.7%
토사 18
 
2.5%
토사유실 18
 
2.5%
유실 16
 
2.3%
및 16
 
2.3%
Other values (239) 487
68.6%
2024-04-17T19:32:27.263524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
522
 
19.3%
수 143
 
5.3%
하 131
 
4.8%
관 122
 
4.5%
로 116
 
4.3%
손 96
 
3.5%
파 73
 
2.7%
사 71
 
2.6%
유 55
 
2.0%
인 54
 
2.0%
Other values (189) 1324
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2141
79.1%
Space Separator 522
 
19.3%
Close Punctuation 17
 
0.6%
Open Punctuation 17
 
0.6%
Other Punctuation 6
 
0.2%
Decimal Number 3
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 143
 
6.7%
하 131
 
6.1%
관 122
 
5.7%
로 116
 
5.4%
손 96
 
4.5%
파 73
 
3.4%
사 71
 
3.3%
유 55
 
2.6%
인 54
 
2.5%
상 51
 
2.4%
Other values (178) 1229
57.4%
Other Punctuation
ValueCountFrequency (%)
, 2
33.3%
. 2
33.3%
/ 1
16.7%
? 1
16.7%
Decimal Number
ValueCountFrequency (%)
9 1
33.3%
2 1
33.3%
8 1
33.3%
Space Separator
ValueCountFrequency (%)
522
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2141
79.1%
Common 566
 
20.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 143
 
6.7%
하 131
 
6.1%
관 122
 
5.7%
로 116
 
5.4%
손 96
 
4.5%
파 73
 
3.4%
사 71
 
3.3%
유 55
 
2.6%
인 54
 
2.5%
상 51
 
2.4%
Other values (178) 1229
57.4%
Common
ValueCountFrequency (%)
522
92.2%
) 17
 
3.0%
( 17
 
3.0%
, 2
 
0.4%
. 2
 
0.4%
/ 1
 
0.2%
~ 1
 
0.2%
9 1
 
0.2%
2 1
 
0.2%
8 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2141
79.1%
ASCII 566
 
20.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
522
92.2%
) 17
 
3.0%
( 17
 
3.0%
, 2
 
0.4%
. 2
 
0.4%
/ 1
 
0.2%
~ 1
 
0.2%
9 1
 
0.2%
2 1
 
0.2%
8 1
 
0.2%
Hangul
ValueCountFrequency (%)
수 143
 
6.7%
하 131
 
6.1%
관 122
 
5.7%
로 116
 
5.4%
손 96
 
4.5%
파 73
 
3.4%
사 71
 
3.3%
유 55
 
2.6%
인 54
 
2.5%
상 51
 
2.4%
Other values (178) 1229
57.4%

피해사망자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
188 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 188
99.5%
1 1
 
0.5%

Length

2024-04-17T19:32:27.374243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:32:27.455915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 188
99.5%
1 1
 
0.5%

피해부상자수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
189 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 189
100.0%

Length

2024-04-17T19:32:27.552710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:32:27.635509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 189
100.0%

피해차량대수
Categorical

Distinct4
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
146 
<NA>
30 
1
 
12
6
 
1

Length

Max length4
Median length1
Mean length1.4761905
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 146
77.2%
<NA> 30
 
15.9%
1 12
 
6.3%
6 1
 
0.5%

Length

2024-04-17T19:32:27.732251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:32:27.828736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 146
77.2%
na 30
 
15.9%
1 12
 
6.3%
6 1
 
0.5%

복구상태
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
복구완료
184 
복구중
 
5

Length

Max length4
Median length4
Mean length3.973545
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
복구완료 184
97.4%
복구중 5
 
2.6%

Length

2024-04-17T19:32:27.922558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:32:28.011965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
복구완료 184
97.4%
복구중 5
 
2.6%

복구방법
Text

MISSING 

Distinct58
Distinct (%)40.3%
Missing45
Missing (%)23.8%
Memory size1.6 KiB
2024-04-17T19:32:28.223361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length78
Median length47.5
Mean length12.013889
Min length4

Characters and Unicode

Total characters1730
Distinct characters128
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

Unique42 ?
Unique (%)29.2%

Sample

1st row침하 발견 즉시 파손부분 긴급복구 완료/ 노후관로 교체공사
2nd row관로보수
3rd row관로보수
4th row관로보수
5th row토사 되메우기
ValueCountFrequency (%)
및 45
 
9.9%
되메우기 31
 
6.8%
후 27
 
5.9%
관로보수 23
 
5.0%
포장 15
 
3.3%
조치 15
 
3.3%
포장완료 14
 
3.1%
하수관복구 11
 
2.4%
보수(포장 10
 
2.2%
확인굴착 10
 
2.2%
Other values (93) 255
55.9%
2024-04-17T19:32:28.588205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
312
 
18.0%
수 88
 
5.1%
관 70
 
4.0%
포 58
 
3.4%
장 57
 
3.3%
보 51
 
2.9%
및 45
 
2.6%
로 44
 
2.5%
우 43
 
2.5%
구 36
 
2.1%
Other values (118) 926
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1297
75.0%
Space Separator 312
 
18.0%
Decimal Number 37
 
2.1%
Other Punctuation 22
 
1.3%
Close Punctuation 21
 
1.2%
Open Punctuation 21
 
1.2%
Uppercase Letter 12
 
0.7%
Math Symbol 4
 
0.2%
Lowercase Letter 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 88
 
6.8%
관 70
 
5.4%
포 58
 
4.5%
장 57
 
4.4%
보 51
 
3.9%
및 45
 
3.5%
로 44
 
3.4%
우 43
 
3.3%
구 36
 
2.8%
후 34
 
2.6%
Other values (96) 771
59.4%
Decimal Number
ValueCountFrequency (%)
0 10
27.0%
1 8
21.6%
7 7
18.9%
2 6
16.2%
4 3
 
8.1%
6 2
 
5.4%
8 1
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
L 5
41.7%
T 2
 
16.7%
W 1
 
8.3%
R 1
 
8.3%
H 1
 
8.3%
D 1
 
8.3%
P 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
. 11
50.0%
, 6
27.3%
/ 5
22.7%
Space Separator
ValueCountFrequency (%)
312
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Math Symbol
ValueCountFrequency (%)
= 4
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1297
75.0%
Common 417
 
24.1%
Latin 16
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 88
 
6.8%
관 70
 
5.4%
포 58
 
4.5%
장 57
 
4.4%
보 51
 
3.9%
및 45
 
3.5%
로 44
 
3.4%
우 43
 
3.3%
구 36
 
2.8%
후 34
 
2.6%
Other values (96) 771
59.4%
Common
ValueCountFrequency (%)
312
74.8%
) 21
 
5.0%
( 21
 
5.0%
. 11
 
2.6%
0 10
 
2.4%
1 8
 
1.9%
7 7
 
1.7%
, 6
 
1.4%
2 6
 
1.4%
/ 5
 
1.2%
Other values (4) 10
 
2.4%
Latin
ValueCountFrequency (%)
L 5
31.2%
m 4
25.0%
T 2
 
12.5%
W 1
 
6.2%
R 1
 
6.2%
H 1
 
6.2%
D 1
 
6.2%
P 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1297
75.0%
ASCII 433
 
25.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
312
72.1%
) 21
 
4.8%
( 21
 
4.8%
. 11
 
2.5%
0 10
 
2.3%
1 8
 
1.8%
7 7
 
1.6%
, 6
 
1.4%
2 6
 
1.4%
L 5
 
1.2%
Other values (12) 26
 
6.0%
Hangul
ValueCountFrequency (%)
수 88
 
6.8%
관 70
 
5.4%
포 58
 
4.5%
장 57
 
4.4%
보 51
 
3.9%
및 45
 
3.5%
로 44
 
3.4%
우 43
 
3.3%
구 36
 
2.8%
후 34
 
2.6%
Other values (96) 771
59.4%

복구비용
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)72.2%
Missing171
Missing (%)90.5%
Infinite0
Infinite (%)0.0%
Mean5140555.6
Minimum0
Maximum30000000
Zeros3
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-17T19:32:28.692876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1500000
median2000000
Q36250000
95-th percentile21500000
Maximum30000000
Range30000000
Interquartile range (IQR)5750000

Descriptive statistics

Standard deviation8029707
Coefficient of variation (CV)1.562031
Kurtosis5.1791695
Mean5140555.6
Median Absolute Deviation (MAD)1985000
Skewness2.2813772
Sum92530000
Variance6.4476194 × 1013
MonotonicityNot monotonic
2024-04-17T19:32:28.785289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 3
 
1.6%
2000000 2
 
1.1%
1000000 2
 
1.1%
500000 2
 
1.1%
9000000 1
 
0.5%
2500000 1
 
0.5%
30000 1
 
0.5%
7000000 1
 
0.5%
30000000 1
 
0.5%
20000000 1
 
0.5%
Other values (3) 3
 
1.6%
(Missing) 171
90.5%
ValueCountFrequency (%)
0 3
1.6%
30000 1
 
0.5%
500000 2
1.1%
1000000 2
1.1%
2000000 2
1.1%
2500000 1
 
0.5%
3000000 1
 
0.5%
4000000 1
 
0.5%
7000000 1
 
0.5%
9000000 1
 
0.5%
ValueCountFrequency (%)
30000000 1
0.5%
20000000 1
0.5%
10000000 1
0.5%
9000000 1
0.5%
7000000 1
0.5%
4000000 1
0.5%
3000000 1
0.5%
2500000 1
0.5%
2000000 2
1.1%
1000000 2
1.1%

복구완료일자
Date

MISSING 

Distinct101
Distinct (%)84.2%
Missing69
Missing (%)36.5%
Memory size1.6 KiB
Minimum2014-07-17 00:00:00
Maximum2020-03-17 00:00:00
2024-04-17T19:32:28.893609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:32:29.035316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터기준일자
Date

Distinct32
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Minimum2017-11-16 00:00:00
Maximum2020-07-09 00:00:00
2024-04-17T19:32:29.150901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:32:29.240890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)

제공기관코드
Real number (ℝ)

Distinct32
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4754021.2
Minimum3130000
Maximum6310000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-17T19:32:29.338062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3130000
5-th percentile3180000
Q13700000
median4690000
Q36280000
95-th percentile6310000
Maximum6310000
Range3180000
Interquartile range (IQR)2580000

Descriptive statistics

Standard deviation1206857.1
Coefficient of variation (CV)0.25386026
Kurtosis-1.6100149
Mean4754021.2
Median Absolute Deviation (MAD)1020000
Skewness0.070927003
Sum8.9851 × 108
Variance1.456504 × 1012
MonotonicityNot monotonic
2024-04-17T19:32:29.438672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
6310000 34
18.0%
6280000 15
 
7.9%
5710000 14
 
7.4%
3820000 14
 
7.4%
3180000 14
 
7.4%
4390000 11
 
5.8%
3740000 10
 
5.3%
3300000 10
 
5.3%
4690000 9
 
4.8%
5700000 9
 
4.8%
Other values (22) 49
25.9%
ValueCountFrequency (%)
3130000 5
 
2.6%
3180000 14
7.4%
3210000 4
 
2.1%
3300000 10
5.3%
3310000 1
 
0.5%
3380000 5
 
2.6%
3490000 1
 
0.5%
3520000 1
 
0.5%
3550000 4
 
2.1%
3700000 6
3.2%
ValueCountFrequency (%)
6310000 34
18.0%
6280000 15
7.9%
5710000 14
7.4%
5700000 9
 
4.8%
5570000 1
 
0.5%
5440000 1
 
0.5%
5370000 4
 
2.1%
5340000 1
 
0.5%
5080000 1
 
0.5%
5020000 2
 
1.1%

제공기관명
Categorical

Distinct32
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
울산광역시
34 
인천광역시
15 
충청북도 청주시
14 
경기도 의정부시
14 
서울특별시 영등포구
14 
Other values (27)
98 

Length

Max length10
Median length9
Mean length7.4232804
Min length5

Unique

Unique12 ?
Unique (%)6.3%

Sample

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

Common Values

ValueCountFrequency (%)
울산광역시 34
18.0%
인천광역시 15
 
7.9%
충청북도 청주시 14
 
7.4%
경기도 의정부시 14
 
7.4%
서울특별시 영등포구 14
 
7.4%
충청북도 충주시 11
 
5.8%
경기도 수원시 10
 
5.3%
부산광역시 동래구 10
 
5.3%
전라북도 정읍시 9
 
4.8%
경기도 여주시 9
 
4.8%
Other values (22) 49
25.9%

Length

2024-04-17T19:32:29.549967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
울산광역시 42
 
12.8%
경기도 33
 
10.0%
충청북도 27
 
8.2%
서울특별시 23
 
7.0%
인천광역시 21
 
6.4%
부산광역시 16
 
4.9%
의정부시 14
 
4.3%
청주시 14
 
4.3%
영등포구 14
 
4.3%
충주시 11
 
3.3%
Other values (30) 114
34.7%

Sample

시도명시군구명지반침하지역상세위도경도발생일자발생규모면적발생규모연장발생규모깊이발생지역지질종류최초발생원인피해사망자수피해부상자수피해차량대수복구상태복구방법복구비용복구완료일자데이터기준일자제공기관코드제공기관명
0울산광역시남구삼산동 1488-17번지35.548189129.3300012017-04-23<NA>2.01.0<NA>노후 하수관 파손으로 인한 토사유실000복구완료침하 발견 즉시 파손부분 긴급복구 완료/ 노후관로 교체공사<NA>2017-04-232020-03-256310000울산광역시
1울산광역시남구용연사거리35.469121129.3599662016-10-28<NA>1.51.0<NA>관로파손000복구완료관로보수<NA><NA>2020-03-256310000울산광역시
2울산광역시남구삼산로 편안한 치과의원 일원35.532926129.3099822016-09-09<NA>2.00.5<NA>관로파손000복구완료관로보수<NA><NA>2020-03-256310000울산광역시
3울산광역시동구오지벌 사거리 일원35.513278129.4311092016-08-31<NA>2.00.5<NA>관로파손000복구완료관로보수<NA><NA>2020-03-256310000울산광역시
4울산광역시북구무룡로 동남정비공업사 앞35.580921129.3637512016-08-14<NA>1.00.2<NA>인접부 지반공사로 인한 침하000복구완료토사 되메우기<NA><NA>2020-03-256310000울산광역시
5울산광역시동구동부경찰서 앞 삼거리35.508637129.4301732016-08-08<NA>0.50.5<NA>상수관 누수로 인한 지반침하 발생000복구완료관로보수<NA><NA>2020-03-256310000울산광역시
6울산광역시남구무거동 대학로(신복R⇒울산대 100m지점)35.548984129.2628352016-08-07<NA>1.50.14<NA>관로파손000복구완료관로보수<NA><NA>2020-03-256310000울산광역시
7울산광역시중구병영성길 835.574803129.3405882016-06-24<NA>0.50.8<NA>관로파손000복구완료보수보강<NA><NA>2020-03-256310000울산광역시
8울산광역시남구번영로 남단 접속도로35.548028129.3234412016-03-23<NA>30.00.1<NA>지하터파기 중 지하수 유출000복구완료보수보강<NA><NA>2020-03-256310000울산광역시
9울산광역시중구유곡로 2735.55654129.306662016-02-18<NA>0.50.8<NA>관로파손000복구완료관로보수<NA><NA>2020-03-256310000울산광역시
시도명시군구명지반침하지역상세위도경도발생일자발생규모면적발생규모연장발생규모깊이발생지역지질종류최초발생원인피해사망자수피해부상자수피해차량대수복구상태복구방법복구비용복구완료일자데이터기준일자제공기관코드제공기관명
179경기도여주시창동 155 식당 앞 도로37.293631127.6347412018-05-230.10.10.1<NA>맨홀 접착부 토사 유실000복구완료토사유실부분 되메우기 재시공 및 포장<NA>2018-05-232019-10-235700000경기도 여주시
180경기도여주시흥문동 427-7 삼한아파트 진입로37.285688127.6344832018-06-201.01.00.5<NA>우수에 의한 토사유실000복구완료토사유실부분 되메우기 재시공 및 포장<NA>2018-06-202019-10-235700000경기도 여주시
181경기도여주시하동 138 하나빌라 앞 도로37.301355127.6277012018-04-151.01.00.5<NA>우수에 의한 토사유실000복구완료토사유실부분 되메우기 재시공 및 포장<NA>2018-04-152019-10-235700000경기도 여주시
182서울특별시영등포구문래동3가 4-29번지 주변37.515009126.9019962017-04-060.150.10.2화강암류하수관로 노후화에 의한 관파손 토사유입000복구완료아스팔트 콘크리트 포장완료40000002017-04-062019-05-093180000서울특별시 영등포구
183강원도고성군거진읍 거탄진로 116(호남식당 옆)38.445274128.4543722018-04-270.50.02.5<NA>노후하수관 손상000복구완료암거철거후 재설치<NA>2018-05-312018-08-204340000강원도 고성군
184경상북도포항시포항시 남구 이동 209-3번지36.033418129.3342612019-10-034.05.03.5기타배수관 이음부 누수로 인한 토사 유출로 추정000복구완료토사 유출부 되메우기 및 포장복구<NA>2019-10-032020-07-095020000경상북도 포항시
185인천광역시중구인천광역시 중구 항동7가 27-123 연안초등학교보도37.452841126.6084082019-12-260.30.30.2<NA>노후 하수관 손상000복구완료하수관 보수 후 포장복구 완료30000002020-01-152020-07-023490000인천광역시 중구
186부산광역시남구수영로(대연2구역 인접도로)일원 지하철 2호선 환기구 이설구역35.134627129.0822772018-03-097.022.00.1<NA>환기구 이설부 잔여침하 및 상수관 손상 누수로 침하발생000복구완료상수관 보수 및 추가침하 방지공사(보링그라우팅)<NA>2018-05-242020-06-253310000부산광역시 남구
187전라북도순창군팔덕면 청계리 산124-335.411279127.0775472017-05-100.21.01.0기타도로법면 유실로 인한 지반침하 발생000복구완료지반침하부 보수 후 복구완료100000002017-06-192020-07-024770000전라북도 순창군
188경상북도포항시경상북도 포항시 남구 해도동 고속버스터미널 앞36.027757129.3670672018-05-080.0820.00.5충적층오피스텔 기초 기하연속벽(슬러리월) 누수로 인한 주변 지하수 및 토사유출로 도로 침하 발생000복구완료지반보강(LW 8RHD) 그라우팅 시공포장(인도포함)복구 완료<NA>2018-09-182020-07-095020000경상북도 포항시