Overview

Dataset statistics

Number of variables19
Number of observations109
Missing cells301
Missing cells (%)14.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.7 KiB
Average record size in memory157.2 B

Variable types

Numeric4
Text8
Categorical6
DateTime1

Dataset

Description재해위험지구 현황(제공표준)
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=QAGA6BFEHE6PMLHXXR1Q13349172&infSeq=1

Alerts

재해위험지구관리번호 is highly overall correlated with 수계명 and 1 other fieldsHigh correlation
지정일자 is highly overall correlated with 해제일자 and 2 other fieldsHigh correlation
해제일자 is highly overall correlated with 지정일자 and 3 other fieldsHigh correlation
지정면적 is highly overall correlated with 지정일자 and 1 other fieldsHigh correlation
재해위험유형 is highly overall correlated with 관리기관명High correlation
시설물유형 is highly overall correlated with 관리기관명High correlation
수계명 is highly overall correlated with 재해위험지구관리번호 and 2 other fieldsHigh correlation
추진사항 is highly overall correlated with 지정일자 and 2 other fieldsHigh correlation
관리기관명 is highly overall correlated with 재해위험지구관리번호 and 4 other fieldsHigh correlation
재해위험지역상세주소 has 88 (80.7%) missing valuesMissing
해제일자 has 36 (33.0%) missing valuesMissing
정보수정일자 has 12 (11.0%) missing valuesMissing
해제사유 has 57 (52.3%) missing valuesMissing
위험요인 has 79 (72.5%) missing valuesMissing
관리기관전화번호 has 29 (26.6%) missing valuesMissing
재해위험지구관리번호 has unique valuesUnique
지정면적 has 49 (45.0%) zerosZeros

Reproduction

Analysis started2024-04-20 18:30:26.140158
Analysis finished2024-04-20 18:30:30.709381
Duration4.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

재해위험지구관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct109
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1581247 × 1011
Minimum4.1113201 × 1011
Maximum4.1830202 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-04-21T03:30:30.770669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1113201 × 1011
5-th percentile4.1220202 × 1011
Q14.1480201 × 1011
median4.1610202 × 1011
Q34.1730201 × 1011
95-th percentile4.1800202 × 1011
Maximum4.1830202 × 1011
Range7.17001 × 109
Interquartile range (IQR)2.499995 × 109

Descriptive statistics

Standard deviation1.9588297 × 109
Coefficient of variation (CV)0.0047108489
Kurtosis-0.35117625
Mean4.1581247 × 1011
Median Absolute Deviation (MAD)1.199986 × 109
Skewness-0.82083943
Sum4.5323559 × 1013
Variance3.837014 × 1018
MonotonicityNot monotonic
2024-04-21T03:30:30.887626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
416102013004 1
 
0.9%
418002001005 1
 
0.9%
418002021011 1
 
0.9%
418002021010 1
 
0.9%
418002023013 1
 
0.9%
418002024015 1
 
0.9%
418002024017 1
 
0.9%
418002011007 1
 
0.9%
418002011006 1
 
0.9%
418002014009 1
 
0.9%
Other values (99) 99
90.8%
ValueCountFrequency (%)
411132013003 1
0.9%
411151999001 1
0.9%
411332013004 1
0.9%
411952007001 1
0.9%
411972007001 1
0.9%
412202018005 1
0.9%
412202021006 1
0.9%
412502012002 1
0.9%
412502012003 1
0.9%
412502012004 1
0.9%
ValueCountFrequency (%)
418302023003 1
0.9%
418202023004 1
0.9%
418202022003 1
0.9%
418202011002 1
0.9%
418202011001 1
0.9%
418002024017 1
0.9%
418002024016 1
0.9%
418002024015 1
0.9%
418002024014 1
0.9%
418002023013 1
0.9%

재해위험지구명
Text

Distinct95
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Memory size1004.0 B
2024-04-21T03:30:31.111132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length4
Mean length4.412844
Min length2

Characters and Unicode

Total characters481
Distinct characters115
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)74.3%

Sample

1st row경안지구
2nd row무갑지구
3rd row지월지구
4th row곤지암지구
5th row정지지구
ValueCountFrequency (%)
지구 3
 
2.6%
경안지구 2
 
1.7%
서원지구 2
 
1.7%
금당지구 2
 
1.7%
덕평지구 2
 
1.7%
장안지구 2
 
1.7%
장풍지구 2
 
1.7%
홍문지구 2
 
1.7%
상교지구 2
 
1.7%
상패지구 2
 
1.7%
Other values (90) 95
81.9%
2024-04-21T03:30:31.440691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 94
19.5%
구 86
 
17.9%
리 14
 
2.9%
상 12
 
2.5%
장 12
 
2.5%
천 10
 
2.1%
산 10
 
2.1%
수 8
 
1.7%
평 8
 
1.7%
7
 
1.5%
Other values (105) 220
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 466
96.9%
Space Separator 7
 
1.5%
Decimal Number 4
 
0.8%
Open Punctuation 2
 
0.4%
Close Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 94
20.2%
구 86
18.5%
리 14
 
3.0%
상 12
 
2.6%
장 12
 
2.6%
천 10
 
2.1%
산 10
 
2.1%
수 8
 
1.7%
평 8
 
1.7%
원 7
 
1.5%
Other values (100) 205
44.0%
Decimal Number
ValueCountFrequency (%)
2 3
75.0%
4 1
 
25.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 466
96.9%
Common 15
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 94
20.2%
구 86
18.5%
리 14
 
3.0%
상 12
 
2.6%
장 12
 
2.6%
천 10
 
2.1%
산 10
 
2.1%
수 8
 
1.7%
평 8
 
1.7%
원 7
 
1.5%
Other values (100) 205
44.0%
Common
ValueCountFrequency (%)
7
46.7%
2 3
20.0%
( 2
 
13.3%
) 2
 
13.3%
4 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 466
96.9%
ASCII 15
 
3.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 94
20.2%
구 86
18.5%
리 14
 
3.0%
상 12
 
2.6%
장 12
 
2.6%
천 10
 
2.1%
산 10
 
2.1%
수 8
 
1.7%
평 8
 
1.7%
원 7
 
1.5%
Other values (100) 205
44.0%
ASCII
ValueCountFrequency (%)
7
46.7%
2 3
20.0%
( 2
 
13.3%
) 2
 
13.3%
4 1
 
6.7%

재해위험지역
Text

Distinct65
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Memory size1004.0 B
2024-04-21T03:30:31.654602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length10.247706
Min length7

Characters and Unicode

Total characters1117
Distinct characters101
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)43.1%

Sample

1st row경기도 광주시 경안동
2nd row경기도 광주시 초월읍
3rd row경기도 광주시 초월읍
4th row경기도 광주시
5th row경기도 광주시
ValueCountFrequency (%)
경기도 109
35.9%
연천군 16
 
5.3%
여주시 12
 
3.9%
여주군 12
 
3.9%
광주시 10
 
3.3%
포천시 9
 
3.0%
동두천시 8
 
2.6%
김포시 8
 
2.6%
여주읍 5
 
1.6%
가평군 4
 
1.3%
Other values (78) 111
36.5%
2024-04-21T03:30:31.954666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
195
17.5%
경 112
 
10.0%
도 109
 
9.8%
기 109
 
9.8%
시 76
 
6.8%
주 42
 
3.8%
천 41
 
3.7%
군 36
 
3.2%
동 35
 
3.1%
여 29
 
2.6%
Other values (91) 333
29.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 919
82.3%
Space Separator 195
 
17.5%
Decimal Number 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 112
 
12.2%
도 109
 
11.9%
기 109
 
11.9%
시 76
 
8.3%
주 42
 
4.6%
천 41
 
4.5%
군 36
 
3.9%
동 35
 
3.8%
여 29
 
3.2%
면 23
 
2.5%
Other values (88) 307
33.4%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
2 1
33.3%
Space Separator
ValueCountFrequency (%)
195
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 919
82.3%
Common 198
 
17.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 112
 
12.2%
도 109
 
11.9%
기 109
 
11.9%
시 76
 
8.3%
주 42
 
4.6%
천 41
 
4.5%
군 36
 
3.9%
동 35
 
3.8%
여 29
 
3.2%
면 23
 
2.5%
Other values (88) 307
33.4%
Common
ValueCountFrequency (%)
195
98.5%
1 2
 
1.0%
2 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 919
82.3%
ASCII 198
 
17.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
195
98.5%
1 2
 
1.0%
2 1
 
0.5%
Hangul
ValueCountFrequency (%)
경 112
 
12.2%
도 109
 
11.9%
기 109
 
11.9%
시 76
 
8.3%
주 42
 
4.6%
천 41
 
4.5%
군 36
 
3.9%
동 35
 
3.8%
여 29
 
3.2%
면 23
 
2.5%
Other values (88) 307
33.4%

재해위험지역상세주소
Text

MISSING 

Distinct20
Distinct (%)95.2%
Missing88
Missing (%)80.7%
Memory size1004.0 B
2024-04-21T03:30:32.116337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length15
Mean length10.714286
Min length2

Characters and Unicode

Total characters225
Distinct characters70
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)90.5%

Sample

1st row453-48번지, 167-8번지 일원
2nd row오학리
3rd row187
4th row여주군 대신면 천서리 215
5th row여주군 대신면 장풍리 112
ValueCountFrequency (%)
일원 10
 
18.5%
여주군 4
 
7.4%
대신면 2
 
3.7%
점동면 2
 
3.7%
경기도 1
 
1.9%
보산1펌프장 1
 
1.9%
안흥교 1
 
1.9%
도솔암 1
 
1.9%
중앙2,3펌프장 1
 
1.9%
처리유역 1
 
1.9%
Other values (30) 30
55.6%
2024-04-21T03:30:32.413606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
33
 
14.7%
원 11
 
4.9%
1 11
 
4.9%
일 10
 
4.4%
리 8
 
3.6%
5 7
 
3.1%
3 7
 
3.1%
지 7
 
3.1%
- 6
 
2.7%
2 6
 
2.7%
Other values (60) 119
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 133
59.1%
Decimal Number 49
 
21.8%
Space Separator 33
 
14.7%
Dash Punctuation 6
 
2.7%
Other Punctuation 3
 
1.3%
Math Symbol 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 11
 
8.3%
일 10
 
7.5%
리 8
 
6.0%
지 7
 
5.3%
장 5
 
3.8%
면 5
 
3.8%
군 5
 
3.8%
동 5
 
3.8%
번 5
 
3.8%
여 4
 
3.0%
Other values (46) 68
51.1%
Decimal Number
ValueCountFrequency (%)
1 11
22.4%
5 7
14.3%
3 7
14.3%
2 6
12.2%
6 6
12.2%
8 5
10.2%
4 3
 
6.1%
7 2
 
4.1%
9 1
 
2.0%
0 1
 
2.0%
Space Separator
ValueCountFrequency (%)
33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 133
59.1%
Common 92
40.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 11
 
8.3%
일 10
 
7.5%
리 8
 
6.0%
지 7
 
5.3%
장 5
 
3.8%
면 5
 
3.8%
군 5
 
3.8%
동 5
 
3.8%
번 5
 
3.8%
여 4
 
3.0%
Other values (46) 68
51.1%
Common
ValueCountFrequency (%)
33
35.9%
1 11
 
12.0%
5 7
 
7.6%
3 7
 
7.6%
- 6
 
6.5%
2 6
 
6.5%
6 6
 
6.5%
8 5
 
5.4%
, 3
 
3.3%
4 3
 
3.3%
Other values (4) 5
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 133
59.1%
ASCII 92
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
33
35.9%
1 11
 
12.0%
5 7
 
7.6%
3 7
 
7.6%
- 6
 
6.5%
2 6
 
6.5%
6 6
 
6.5%
8 5
 
5.4%
, 3
 
3.3%
4 3
 
3.3%
Other values (4) 5
 
5.4%
Hangul
ValueCountFrequency (%)
원 11
 
8.3%
일 10
 
7.5%
리 8
 
6.0%
지 7
 
5.3%
장 5
 
3.8%
면 5
 
3.8%
군 5
 
3.8%
동 5
 
3.8%
번 5
 
3.8%
여 4
 
3.0%
Other values (46) 68
51.1%

재해위험유형
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size1004.0 B
침수위험
74 
유실위험
17 
취약방재시설
10 
붕괴시설
 
7
고립위험
 
1

Length

Max length6
Median length4
Mean length4.1834862
Min length4

Unique

Unique1 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
침수위험 74
67.9%
유실위험 17
 
15.6%
취약방재시설 10
 
9.2%
붕괴시설 7
 
6.4%
고립위험 1
 
0.9%

Length

2024-04-21T03:30:32.524590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:30:32.619017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
침수위험 74
67.9%
유실위험 17
 
15.6%
취약방재시설 10
 
9.2%
붕괴시설 7
 
6.4%
고립위험 1
 
0.9%

재해위험등급
Categorical

Distinct3
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size1004.0 B
나 등급
51 
가 등급
31 
다 등급
27 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row나 등급
2nd row나 등급
3rd row가 등급
4th row나 등급
5th row가 등급

Common Values

ValueCountFrequency (%)
나 등급 51
46.8%
가 등급 31
28.4%
다 등급 27
24.8%

Length

2024-04-21T03:30:32.712506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:30:32.789544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등급 109
50.0%
나 51
23.4%
가 31
 
14.2%
다 27
 
12.4%

시설명
Text

Distinct84
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Memory size1004.0 B
2024-04-21T03:30:32.992131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length5.6422018
Min length2

Characters and Unicode

Total characters615
Distinct characters130
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

Unique77 ?
Unique (%)70.6%

Sample

1st row경안천
2nd row경안천
3rd row경안천
4th row곤지암천, 노곡천, 신촌천
5th row경안천
ValueCountFrequency (%)
배수펌프장 10
 
7.4%
경안천 8
 
5.9%
소하천 7
 
5.1%
하천 4
 
2.9%
자연재해위험개선지구 4
 
2.9%
등 3
 
2.2%
도로 3
 
2.2%
급경사지 2
 
1.5%
제방 2
 
1.5%
곤지암천 2
 
1.5%
Other values (89) 91
66.9%
2024-04-21T03:30:33.512217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 48
 
7.8%
지 46
 
7.5%
수 29
 
4.7%
27
 
4.4%
구 27
 
4.4%
장 24
 
3.9%
펌 21
 
3.4%
프 21
 
3.4%
배 19
 
3.1%
하 15
 
2.4%
Other values (120) 338
55.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 552
89.8%
Space Separator 27
 
4.4%
Other Punctuation 14
 
2.3%
Decimal Number 13
 
2.1%
Open Punctuation 4
 
0.7%
Close Punctuation 4
 
0.7%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 48
 
8.7%
지 46
 
8.3%
수 29
 
5.3%
구 27
 
4.9%
장 24
 
4.3%
펌 21
 
3.8%
프 21
 
3.8%
배 19
 
3.4%
하 15
 
2.7%
경 12
 
2.2%
Other values (110) 290
52.5%
Decimal Number
ValueCountFrequency (%)
2 4
30.8%
1 4
30.8%
3 3
23.1%
9 1
 
7.7%
4 1
 
7.7%
Space Separator
ValueCountFrequency (%)
27
100.0%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 552
89.8%
Common 63
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 48
 
8.7%
지 46
 
8.3%
수 29
 
5.3%
구 27
 
4.9%
장 24
 
4.3%
펌 21
 
3.8%
프 21
 
3.8%
배 19
 
3.4%
하 15
 
2.7%
경 12
 
2.2%
Other values (110) 290
52.5%
Common
ValueCountFrequency (%)
27
42.9%
, 14
22.2%
2 4
 
6.3%
( 4
 
6.3%
) 4
 
6.3%
1 4
 
6.3%
3 3
 
4.8%
- 1
 
1.6%
9 1
 
1.6%
4 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 552
89.8%
ASCII 63
 
10.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 48
 
8.7%
지 46
 
8.3%
수 29
 
5.3%
구 27
 
4.9%
장 24
 
4.3%
펌 21
 
3.8%
프 21
 
3.8%
배 19
 
3.4%
하 15
 
2.7%
경 12
 
2.2%
Other values (110) 290
52.5%
ASCII
ValueCountFrequency (%)
27
42.9%
, 14
22.2%
2 4
 
6.3%
( 4
 
6.3%
) 4
 
6.3%
1 4
 
6.3%
3 3
 
4.8%
- 1
 
1.6%
9 1
 
1.6%
4 1
 
1.6%

시설물유형
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size1004.0 B
지방관리
28 
기타
27 
국가하천
16 
지방2급하천
15 
지방1급하천
12 
Other values (8)
11 

Length

Max length6
Median length5
Mean length3.9357798
Min length2

Unique

Unique5 ?
Unique (%)4.6%

Sample

1st row국가하천
2nd row국가하천
3rd row국가하천
4th row지방2급하천
5th row국가하천

Common Values

ValueCountFrequency (%)
지방관리 28
25.7%
기타 27
24.8%
국가하천 16
14.7%
지방2급하천 15
13.8%
지방1급하천 12
11.0%
사유림 2
 
1.8%
국가관리 2
 
1.8%
<NA> 2
 
1.8%
국유림 1
 
0.9%
군도 1
 
0.9%
Other values (3) 3
 
2.8%

Length

2024-04-21T03:30:33.626966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지방관리 28
25.7%
기타 27
24.8%
국가하천 16
14.7%
지방2급하천 15
13.8%
지방1급하천 12
11.0%
사유림 2
 
1.8%
국가관리 2
 
1.8%
na 2
 
1.8%
국유림 1
 
0.9%
군도 1
 
0.9%
Other values (3) 3
 
2.8%

수계명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size1004.0 B
한강
47 
<NA>
28 
기타
15 
임진강
14 
안성천
5 

Length

Max length4
Median length2
Mean length2.6880734
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한강 47
43.1%
<NA> 28
25.7%
기타 15
 
13.8%
임진강 14
 
12.8%
안성천 5
 
4.6%

Length

2024-04-21T03:30:33.734352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:30:33.825505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한강 47
43.1%
na 28
25.7%
기타 15
 
13.8%
임진강 14
 
12.8%
안성천 5
 
4.6%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)58.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20101738
Minimum19960711
Maximum20240319
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-04-21T03:30:33.922335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19960711
5-th percentile19980303
Q120060224
median20100910
Q320150331
95-th percentile20230880
Maximum20240319
Range279608
Interquartile range (IQR)90107

Descriptive statistics

Standard deviation82873.908
Coefficient of variation (CV)0.0041227236
Kurtosis-1.0192402
Mean20101738
Median Absolute Deviation (MAD)49421
Skewness0.18285766
Sum2.1910894 × 109
Variance6.8680847 × 109
MonotonicityNot monotonic
2024-04-21T03:30:34.063463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20060224 18
 
16.5%
20131108 5
 
4.6%
20120402 4
 
3.7%
20000701 3
 
2.8%
20240319 3
 
2.8%
20120507 2
 
1.8%
20000408 2
 
1.8%
20000623 2
 
1.8%
20070723 2
 
1.8%
20100910 2
 
1.8%
Other values (54) 66
60.6%
ValueCountFrequency (%)
19960711 1
0.9%
19960712 1
0.9%
19960716 1
0.9%
19970710 1
0.9%
19980101 2
1.8%
19980607 2
1.8%
19980616 1
0.9%
19990708 1
0.9%
19990910 2
1.8%
20000408 2
1.8%
ValueCountFrequency (%)
20240319 3
2.8%
20240315 1
 
0.9%
20240307 1
 
0.9%
20231120 1
 
0.9%
20230519 2
1.8%
20230412 2
1.8%
20230329 1
 
0.9%
20230315 2
1.8%
20230110 1
 
0.9%
20220531 1
 
0.9%

해제일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)57.5%
Missing36
Missing (%)33.0%
Infinite0
Infinite (%)0.0%
Mean20112918
Minimum19980925
Maximum20220928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-04-21T03:30:34.183020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19980925
5-th percentile20011141
Q120081229
median20130104
Q320160408
95-th percentile20194115
Maximum20220928
Range240003
Interquartile range (IQR)79179

Descriptive statistics

Standard deviation58512.26
Coefficient of variation (CV)0.002909188
Kurtosis-0.63421148
Mean20112918
Median Absolute Deviation (MAD)31102
Skewness-0.47408058
Sum1.468243 × 109
Variance3.4236845 × 109
MonotonicityNot monotonic
2024-04-21T03:30:34.295924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
20130104 12
 
11.0%
20090630 5
 
4.6%
20140723 5
 
4.6%
20120710 3
 
2.8%
20100402 3
 
2.8%
20190121 3
 
2.8%
20160502 2
 
1.8%
20160610 2
 
1.8%
20161206 2
 
1.8%
20011009 2
 
1.8%
Other values (32) 34
31.2%
(Missing) 36
33.0%
ValueCountFrequency (%)
19980925 1
0.9%
20011006 1
0.9%
20011009 2
1.8%
20011229 1
0.9%
20011231 2
1.8%
20021125 1
0.9%
20021127 2
1.8%
20021128 1
0.9%
20030619 1
0.9%
20040214 1
0.9%
ValueCountFrequency (%)
20220928 1
 
0.9%
20210712 1
 
0.9%
20200203 1
 
0.9%
20200106 1
 
0.9%
20190121 3
2.8%
20181210 1
 
0.9%
20180726 1
 
0.9%
20170428 1
 
0.9%
20170106 1
 
0.9%
20161206 2
1.8%

정보수정일자
Date

MISSING 

Distinct97
Distinct (%)100.0%
Missing12
Missing (%)11.0%
Memory size1004.0 B
Minimum2011-11-07 14:15:57
Maximum2024-04-16 11:02:53
2024-04-21T03:30:34.423918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:34.549447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

지정사유
Text

Distinct86
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Memory size1004.0 B
2024-04-21T03:30:34.787426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length95
Median length67
Mean length24.027523
Min length2

Characters and Unicode

Total characters2619
Distinct characters204
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)65.1%

Sample

1st row경안천 홍수위 보다 낮음
2nd row내수배제의 불량 및 경안천의 정체로 인한 하천의 역류로 침수피해 발생
3rd row집중호우시 인명피해 및 주택침수
4th row곤지암천 및 노곡천·신촌천 수위상승에 따라 저지대 내수배제 불가
5th row집중호우 시 주거지, 농경지 등의 침수로 인한 인명 및 재산피해 발생 위험
ValueCountFrequency (%)
및 32
 
5.0%
따른 15
 
2.3%
발생 15
 
2.3%
수위 14
 
2.2%
내수배제 12
 
1.9%
인한 12
 
1.9%
집중호우시 11
 
1.7%
농경지 11
 
1.7%
11
 
1.7%
집중호우 11
 
1.7%
Other values (259) 500
77.6%
2024-04-21T03:30:35.171179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
535
 
20.4%
수 153
 
5.8%
지 61
 
2.3%
로 60
 
2.3%
하 57
 
2.2%
침 56
 
2.1%
위 54
 
2.1%
천 52
 
2.0%
우 40
 
1.5%
상 37
 
1.4%
Other values (194) 1514
57.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2009
76.7%
Space Separator 535
 
20.4%
Decimal Number 28
 
1.1%
Other Punctuation 18
 
0.7%
Dash Punctuation 13
 
0.5%
Open Punctuation 7
 
0.3%
Close Punctuation 7
 
0.3%
Math Symbol 1
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 153
 
7.6%
지 61
 
3.0%
로 60
 
3.0%
하 57
 
2.8%
침 56
 
2.8%
위 54
 
2.7%
천 52
 
2.6%
우 40
 
2.0%
상 37
 
1.8%
으 36
 
1.8%
Other values (175) 1403
69.8%
Decimal Number
ValueCountFrequency (%)
1 8
28.6%
2 5
17.9%
0 5
17.9%
7 2
 
7.1%
5 2
 
7.1%
3 2
 
7.1%
8 1
 
3.6%
6 1
 
3.6%
9 1
 
3.6%
4 1
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 12
66.7%
. 5
27.8%
· 1
 
5.6%
Space Separator
ValueCountFrequency (%)
535
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2009
76.7%
Common 610
 
23.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 153
 
7.6%
지 61
 
3.0%
로 60
 
3.0%
하 57
 
2.8%
침 56
 
2.8%
위 54
 
2.7%
천 52
 
2.6%
우 40
 
2.0%
상 37
 
1.8%
으 36
 
1.8%
Other values (175) 1403
69.8%
Common
ValueCountFrequency (%)
535
87.7%
- 13
 
2.1%
, 12
 
2.0%
1 8
 
1.3%
( 7
 
1.1%
) 7
 
1.1%
. 5
 
0.8%
2 5
 
0.8%
0 5
 
0.8%
7 2
 
0.3%
Other values (9) 11
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2009
76.7%
ASCII 609
 
23.3%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
535
87.8%
- 13
 
2.1%
, 12
 
2.0%
1 8
 
1.3%
( 7
 
1.1%
) 7
 
1.1%
. 5
 
0.8%
2 5
 
0.8%
0 5
 
0.8%
7 2
 
0.3%
Other values (8) 10
 
1.6%
Hangul
ValueCountFrequency (%)
수 153
 
7.6%
지 61
 
3.0%
로 60
 
3.0%
하 57
 
2.8%
침 56
 
2.8%
위 54
 
2.7%
천 52
 
2.6%
우 40
 
2.0%
상 37
 
1.8%
으 36
 
1.8%
Other values (175) 1403
69.8%
None
ValueCountFrequency (%)
· 1
100.0%

해제사유
Text

MISSING 

Distinct32
Distinct (%)61.5%
Missing57
Missing (%)52.3%
Memory size1004.0 B
2024-04-21T03:30:35.380953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length91
Median length26.5
Mean length15.096154
Min length4

Characters and Unicode

Total characters785
Distinct characters106
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

Unique25 ?
Unique (%)48.1%

Sample

1st row정비사업 완료에 따른 재해위험요소 해소
2nd row정비사업완료에 따른 재해위험요소 해소
3rd row정비사업 완료에 따른 재해위험요소 해소
4th row봉성2호펌프장 공사 완료
5th row정비완료
ValueCountFrequency (%)
해소 19
 
10.3%
완료 19
 
10.3%
따른 16
 
8.6%
정비사업 14
 
7.6%
완료에 12
 
6.5%
재해위험요소 8
 
4.3%
보수.보강공사 6
 
3.2%
공사 6
 
3.2%
보수.보강 4
 
2.2%
정비완료 4
 
2.2%
Other values (64) 77
41.6%
2024-04-21T03:30:35.690742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
133
 
16.9%
해 42
 
5.4%
완 41
 
5.2%
료 41
 
5.2%
소 37
 
4.7%
사 35
 
4.5%
정 28
 
3.6%
비 25
 
3.2%
위 24
 
3.1%
보 22
 
2.8%
Other values (96) 357
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 622
79.2%
Space Separator 133
 
16.9%
Other Punctuation 11
 
1.4%
Decimal Number 7
 
0.9%
Open Punctuation 4
 
0.5%
Close Punctuation 4
 
0.5%
Lowercase Letter 2
 
0.3%
Uppercase Letter 1
 
0.1%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
해 42
 
6.8%
완 41
 
6.6%
료 41
 
6.6%
소 37
 
5.9%
사 35
 
5.6%
정 28
 
4.5%
비 25
 
4.0%
위 24
 
3.9%
보 22
 
3.5%
험 21
 
3.4%
Other values (85) 306
49.2%
Decimal Number
ValueCountFrequency (%)
0 3
42.9%
2 2
28.6%
4 2
28.6%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
m 1
50.0%
Space Separator
ValueCountFrequency (%)
133
100.0%
Other Punctuation
ValueCountFrequency (%)
. 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
L 1
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 622
79.2%
Common 160
 
20.4%
Latin 3
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
해 42
 
6.8%
완 41
 
6.6%
료 41
 
6.6%
소 37
 
5.9%
사 35
 
5.6%
정 28
 
4.5%
비 25
 
4.0%
위 24
 
3.9%
보 22
 
3.5%
험 21
 
3.4%
Other values (85) 306
49.2%
Common
ValueCountFrequency (%)
133
83.1%
. 11
 
6.9%
( 4
 
2.5%
) 4
 
2.5%
0 3
 
1.9%
2 2
 
1.2%
4 2
 
1.2%
= 1
 
0.6%
Latin
ValueCountFrequency (%)
L 1
33.3%
k 1
33.3%
m 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 622
79.2%
ASCII 163
 
20.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
133
81.6%
. 11
 
6.7%
( 4
 
2.5%
) 4
 
2.5%
0 3
 
1.8%
2 2
 
1.2%
4 2
 
1.2%
L 1
 
0.6%
= 1
 
0.6%
k 1
 
0.6%
Hangul
ValueCountFrequency (%)
해 42
 
6.8%
완 41
 
6.6%
료 41
 
6.6%
소 37
 
5.9%
사 35
 
5.6%
정 28
 
4.5%
비 25
 
4.0%
위 24
 
3.9%
보 22
 
3.5%
험 21
 
3.4%
Other values (85) 306
49.2%

추진사항
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1004.0 B
정비완료
76 
미착수
19 
정비중(응급조치완료)
11 
정비중(응급조치중)
 
3

Length

Max length11
Median length4
Mean length4.6972477
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정비완료
2nd row정비완료
3rd row정비완료
4th row정비완료
5th row정비중(응급조치완료)

Common Values

ValueCountFrequency (%)
정비완료 76
69.7%
미착수 19
 
17.4%
정비중(응급조치완료) 11
 
10.1%
정비중(응급조치중) 3
 
2.8%

Length

2024-04-21T03:30:35.815192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:30:35.909575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정비완료 76
69.7%
미착수 19
 
17.4%
정비중(응급조치완료 11
 
10.1%
정비중(응급조치중 3
 
2.8%

위험요인
Text

MISSING 

Distinct19
Distinct (%)63.3%
Missing79
Missing (%)72.5%
Memory size1004.0 B
2024-04-21T03:30:36.059093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.8333333
Min length2

Characters and Unicode

Total characters115
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)36.7%

Sample

1st row침수위험
2nd row침수위험
3rd row농경지침수
4th row침수
5th row교량유실
ValueCountFrequency (%)
내수침수 3
 
9.7%
침수위험 3
 
9.7%
농경지침수 3
 
9.7%
내수 2
 
6.5%
저지대침수 2
 
6.5%
산사태 2
 
6.5%
저수지 2
 
6.5%
침수 2
 
6.5%
소하천 1
 
3.2%
유실위험 1
 
3.2%
Other values (10) 10
32.3%
2024-04-21T03:30:36.326920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 24
20.9%
침 15
13.0%
지 9
 
7.8%
위 7
 
6.1%
험 7
 
6.1%
내 5
 
4.3%
저 4
 
3.5%
농 3
 
2.6%
경 3
 
2.6%
사 3
 
2.6%
Other values (25) 35
30.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 113
98.3%
Other Punctuation 1
 
0.9%
Space Separator 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 24
21.2%
침 15
13.3%
지 9
 
8.0%
위 7
 
6.2%
험 7
 
6.2%
내 5
 
4.4%
저 4
 
3.5%
농 3
 
2.7%
경 3
 
2.7%
사 3
 
2.7%
Other values (23) 33
29.2%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 113
98.3%
Common 2
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 24
21.2%
침 15
13.3%
지 9
 
8.0%
위 7
 
6.2%
험 7
 
6.2%
내 5
 
4.4%
저 4
 
3.5%
농 3
 
2.7%
경 3
 
2.7%
사 3
 
2.7%
Other values (23) 33
29.2%
Common
ValueCountFrequency (%)
, 1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 113
98.3%
ASCII 2
 
1.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 24
21.2%
침 15
13.3%
지 9
 
8.0%
위 7
 
6.2%
험 7
 
6.2%
내 5
 
4.4%
저 4
 
3.5%
농 3
 
2.7%
경 3
 
2.7%
사 3
 
2.7%
Other values (23) 33
29.2%
ASCII
ValueCountFrequency (%)
, 1
50.0%
1
50.0%

지정면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277804.04
Minimum0
Maximum16080000
Zeros49
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-04-21T03:30:36.451114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7039
Q3106000
95-th percentile755920
Maximum16080000
Range16080000
Interquartile range (IQR)106000

Descriptive statistics

Standard deviation1558403.4
Coefficient of variation (CV)5.6097219
Kurtosis100.4128
Mean277804.04
Median Absolute Deviation (MAD)7039
Skewness9.8503538
Sum30280640
Variance2.4286211 × 1012
MonotonicityNot monotonic
2024-04-21T03:30:36.571465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49
45.0%
30797 2
 
1.8%
1763455 1
 
0.9%
146200 1
 
0.9%
182515 1
 
0.9%
567128 1
 
0.9%
53700 1
 
0.9%
53600 1
 
0.9%
294033 1
 
0.9%
89510 1
 
0.9%
Other values (50) 50
45.9%
ValueCountFrequency (%)
0 49
45.0%
2008 1
 
0.9%
3200 1
 
0.9%
3500 1
 
0.9%
4500 1
 
0.9%
5275 1
 
0.9%
7039 1
 
0.9%
7294 1
 
0.9%
9750 1
 
0.9%
11000 1
 
0.9%
ValueCountFrequency (%)
16080000 1
0.9%
1763455 1
0.9%
1655000 1
0.9%
1335858 1
0.9%
1162868 1
0.9%
767600 1
0.9%
738400 1
0.9%
692600 1
0.9%
567128 1
0.9%
419853 1
0.9%

관리기관명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Memory size1004.0 B
<NA>
29 
여주시 안전총괄과
11 
김포시청
8 
연천군청
7 
포천시
7 
Other values (26)
47 

Length

Max length11
Median length4
Mean length5.5321101
Min length3

Unique

Unique15 ?
Unique (%)13.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 29
26.6%
여주시 안전총괄과 11
 
10.1%
김포시청 8
 
7.3%
연천군청 7
 
6.4%
포천시 7
 
6.4%
여주군청 6
 
5.5%
동두천시 안전총괄과 4
 
3.7%
농업정책과 3
 
2.8%
안전총괄과 3
 
2.8%
고양시 재난대응담당관 3
 
2.8%
Other values (21) 28
25.7%

Length

2024-04-21T03:30:36.695473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 29
20.7%
안전총괄과 22
15.7%
여주시 11
 
7.9%
김포시청 8
 
5.7%
연천군청 7
 
5.0%
포천시 7
 
5.0%
여주군청 6
 
4.3%
동두천시 6
 
4.3%
연천군 5
 
3.6%
고양시 4
 
2.9%
Other values (21) 35
25.0%

관리기관전화번호
Text

MISSING 

Distinct40
Distinct (%)50.0%
Missing29
Missing (%)26.6%
Memory size1004.0 B
2024-04-21T03:30:36.885586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.3125
Min length7

Characters and Unicode

Total characters905
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)31.2%

Sample

1st row0317608693
2nd row031-760-4803
3rd row031-980-2924
4th row031-980-5427
5th row031-980-2797
ValueCountFrequency (%)
0318872552 10
 
12.5%
031-887-2552 8
 
10.0%
031-538-2494 6
 
7.5%
031-980-2924 4
 
5.0%
031-839-2412 4
 
5.0%
031-8075-2994 4
 
5.0%
0318602331 3
 
3.8%
839-2411 2
 
2.5%
031-860-2334 2
 
2.5%
031-839-2454 2
 
2.5%
Other values (30) 35
43.8%
2024-04-21T03:30:37.162073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 121
13.4%
- 115
12.7%
0 113
12.5%
2 107
11.8%
8 101
11.2%
1 100
11.0%
5 65
7.2%
4 57
6.3%
9 55
6.1%
7 43
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 790
87.3%
Dash Punctuation 115
 
12.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 121
15.3%
0 113
14.3%
2 107
13.5%
8 101
12.8%
1 100
12.7%
5 65
8.2%
4 57
7.2%
9 55
7.0%
7 43
 
5.4%
6 28
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 115
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 905
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 121
13.4%
- 115
12.7%
0 113
12.5%
2 107
11.8%
8 101
11.2%
1 100
11.0%
5 65
7.2%
4 57
6.3%
9 55
6.1%
7 43
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 121
13.4%
- 115
12.7%
0 113
12.5%
2 107
11.8%
8 101
11.2%
1 100
11.0%
5 65
7.2%
4 57
6.3%
9 55
6.1%
7 43
 
4.8%

Interactions

2024-04-21T03:30:29.942591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:28.960796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:29.310086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:29.633440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:30.021427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:29.085349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:29.385907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:29.706521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:30.096398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:29.161568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:29.460447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:29.798189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:30.170258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:29.229370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:29.539333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:30:29.870327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T03:30:37.256747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재해위험지구관리번호재해위험지구명재해위험지역재해위험지역상세주소재해위험유형재해위험등급시설명시설물유형수계명지정일자해제일자정보수정일자지정사유해제사유추진사항위험요인지정면적관리기관명관리기관전화번호
재해위험지구관리번호1.0000.9471.0000.8240.5420.5190.9980.5920.7740.4250.5081.0000.9880.9700.5140.9440.6090.9940.995
재해위험지구명0.9471.0000.9841.0000.9770.9330.9930.9661.0000.9820.9981.0000.9850.9740.9140.9481.0000.9950.990
재해위험지역1.0000.9841.0000.9440.9110.7770.9940.8910.9600.7660.7921.0000.9860.9840.6120.9440.6310.9780.971
재해위험지역상세주소0.8241.0000.9441.0001.0001.0000.9820.3040.7190.8270.9461.0000.9320.9111.0000.821NaN1.0001.000
재해위험유형0.5420.9770.9111.0001.0000.5590.9810.6470.3480.5670.4811.0000.9810.6260.0000.9810.0000.9240.970
재해위험등급0.5190.9330.7771.0000.5591.0000.8370.4220.2440.7690.5321.0000.9480.6980.2500.8570.0000.8380.789
시설명0.9980.9930.9940.9820.9810.8371.0000.9880.9750.8490.9301.0000.9690.9850.9400.9581.0000.9960.991
시설물유형0.5920.9660.8910.3040.6470.4220.9881.0000.4310.3630.5601.0000.8890.7550.0000.9530.0000.9460.987
수계명0.7741.0000.9600.7190.3480.2440.9750.4311.0000.6210.7191.0000.9350.9900.5350.9330.4200.9360.992
지정일자0.4250.9820.7660.8270.5670.7690.8490.3630.6211.0000.7561.0000.9950.9400.6990.3300.5020.8190.893
해제일자0.5080.9980.7920.9460.4810.5320.9300.5600.7190.7561.0001.0000.9740.916NaN0.9180.0000.8670.950
정보수정일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지정사유0.9880.9850.9860.9320.9810.9480.9690.8890.9350.9950.9741.0001.0000.9870.9260.9820.0000.9900.992
해제사유0.9700.9740.9840.9110.6260.6980.9850.7550.9900.9400.9161.0000.9871.000NaN0.9400.0000.9810.978
추진사항0.5140.9140.6121.0000.0000.2500.9400.0000.5350.699NaN1.0000.926NaN1.0000.0000.0420.8840.801
위험요인0.9440.9480.9440.8210.9810.8570.9580.9530.9330.3300.9181.0000.9820.9400.0001.000NaN0.9711.000
지정면적0.6091.0000.631NaN0.0000.0001.0000.0000.4200.5020.0001.0000.0000.0000.042NaN1.0000.5660.138
관리기관명0.9940.9950.9781.0000.9240.8380.9960.9460.9360.8190.8671.0000.9900.9810.8840.9710.5661.0000.993
관리기관전화번호0.9950.9900.9711.0000.9700.7890.9910.9870.9920.8930.9501.0000.9920.9780.8011.0000.1380.9931.000
2024-04-21T03:30:37.398946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수계명재해위험등급추진사항재해위험유형시설물유형관리기관명
수계명1.0000.2300.2310.2870.2580.633
재해위험등급0.2301.0000.2370.4970.1990.475
추진사항0.2310.2371.0000.0000.0000.550
재해위험유형0.2870.4970.0001.0000.4130.552
시설물유형0.2580.1990.0000.4131.0000.546
관리기관명0.6330.4750.5500.5520.5461.000
2024-04-21T03:30:37.486950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재해위험지구관리번호지정일자해제일자지정면적재해위험유형재해위험등급시설물유형수계명추진사항관리기관명
재해위험지구관리번호1.0000.010-0.112-0.1750.2400.3520.2730.5900.3250.815
지정일자0.0101.0000.9150.7110.3640.4590.1560.4350.5200.394
해제일자-0.1120.9151.0000.5340.2710.2470.2340.5201.0000.424
지정면적-0.1750.7110.5341.0000.0000.0000.0000.2780.0360.358
재해위험유형0.2400.3640.2710.0001.0000.4970.4130.2870.0000.552
재해위험등급0.3520.4590.2470.0000.4971.0000.1990.2300.2370.475
시설물유형0.2730.1560.2340.0000.4130.1991.0000.2580.0000.546
수계명0.5900.4350.5200.2780.2870.2300.2581.0000.2310.633
추진사항0.3250.5201.0000.0360.0000.2370.0000.2311.0000.550
관리기관명0.8150.3940.4240.3580.5520.4750.5460.6330.5501.000

Missing values

2024-04-21T03:30:30.296173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T03:30:30.477426image/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.
2024-04-21T03:30:30.615873image/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

재해위험지구관리번호재해위험지구명재해위험지역재해위험지역상세주소재해위험유형재해위험등급시설명시설물유형수계명지정일자해제일자정보수정일자지정사유해제사유추진사항위험요인지정면적관리기관명관리기관전화번호
0416102013004경안지구경기도 광주시 경안동<NA>침수위험나 등급경안천국가하천기타20120507201407232016-07-12 09:13:43경안천 홍수위 보다 낮음정비사업 완료에 따른 재해위험요소 해소정비완료침수위험0<NA><NA>
1416102008002무갑지구경기도 광주시 초월읍<NA>침수위험나 등급경안천국가하천<NA>20070723201604082016-07-12 08:57:11내수배제의 불량 및 경안천의 정체로 인한 하천의 역류로 침수피해 발생정비사업완료에 따른 재해위험요소 해소정비완료<NA>106000<NA><NA>
2416102013008지월지구경기도 광주시 초월읍<NA>침수위험가 등급경안천국가하천<NA>20120426201407232016-07-12 09:39:20집중호우시 인명피해 및 주택침수정비사업 완료에 따른 재해위험요소 해소정비완료<NA>0<NA><NA>
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