Overview

Dataset statistics

Number of variables18
Number of observations6846
Missing cells23068
Missing cells (%)18.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory996.3 KiB
Average record size in memory149.0 B

Variable types

Text8
Categorical2
DateTime3
Numeric5

Dataset

Description공공데이터 제공 표준데이터 속성정보(허용값, 표현형식/단위 등)는 [공공데이터 제공 표준] 전문을 참고하시기 바랍니다.(공공데이터포털>정보공유>자료실) 각 기관에서 등록한 표준데이터를 취합하여 제공하기 때문에 갱신주기는 개별 파일마다 다릅니다.(기관에서 등록한 데이터를 취합한 것으로 개별 파일별 갱신시점이 다름)
Author지방자치단체
URLhttps://www.data.go.kr/data/15021104/standard.do

Alerts

위도 is highly overall correlated with 제공기관코드 and 1 other fieldsHigh correlation
제공기관코드 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위험시설지정사유 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위험시설지정사유 is highly imbalanced (58.4%)Imbalance
소재지도로명주소 has 6164 (90.0%) missing valuesMissing
소재지지번주소 has 180 (2.6%) missing valuesMissing
위험시설지정고시번호 has 1313 (19.2%) missing valuesMissing
위험시설해제일자 has 6524 (95.3%) missing valuesMissing
연장 has 963 (14.1%) missing valuesMissing
폭 has 961 (14.0%) missing valuesMissing
시설부속물 has 6477 (94.6%) missing valuesMissing
관리기관전화번호 has 486 (7.1%) missing valuesMissing

Reproduction

Analysis started2024-05-11 07:50:59.254178
Analysis finished2024-05-11 07:51:11.746424
Duration12.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

Distinct5021
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size53.6 KiB
2024-05-11T16:51:12.303686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length7.6744084
Min length2

Characters and Unicode

Total characters52539
Distinct characters457
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

Unique3280 ?
Unique (%)47.9%

Sample

1st row하갈2교
2nd row하갈2소교량
3rd row하송 제1낙차보
4th row하송 제1진입로
5th row하송 제2진입로
ValueCountFrequency (%)
농로 135
 
1.6%
소교량 123
 
1.5%
세천 93
 
1.1%
1소교량 40
 
0.5%
안길 30
 
0.4%
마을진입로 30
 
0.4%
2소교량 26
 
0.3%
무명3교 22
 
0.3%
삼락천 21
 
0.3%
세천_0001 19
 
0.2%
Other values (5060) 7670
93.4%
2024-05-11T16:51:13.451843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 4609
 
8.8%
0 4142
 
7.9%
교 3564
 
6.8%
리 2982
 
5.7%
소 2372
 
4.5%
량 2346
 
4.5%
1 2104
 
4.0%
천 1810
 
3.4%
2 1544
 
2.9%
1365
 
2.6%
Other values (447) 25701
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34313
65.3%
Decimal Number 11780
 
22.4%
Connector Punctuation 4609
 
8.8%
Space Separator 1365
 
2.6%
Dash Punctuation 333
 
0.6%
Close Punctuation 65
 
0.1%
Open Punctuation 63
 
0.1%
Uppercase Letter 9
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 3564
 
10.4%
리 2982
 
8.7%
소 2372
 
6.9%
량 2346
 
6.8%
천 1810
 
5.3%
세 1137
 
3.3%
골 924
 
2.7%
로 845
 
2.5%
마 592
 
1.7%
농 567
 
1.7%
Other values (428) 17174
50.1%
Decimal Number
ValueCountFrequency (%)
0 4142
35.2%
1 2104
17.9%
2 1544
 
13.1%
3 855
 
7.3%
4 703
 
6.0%
5 608
 
5.2%
6 561
 
4.8%
7 499
 
4.2%
9 392
 
3.3%
8 372
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
B 3
33.3%
O 3
33.3%
X 3
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 4609
100.0%
Space Separator
ValueCountFrequency (%)
1365
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 333
100.0%
Close Punctuation
ValueCountFrequency (%)
) 65
100.0%
Open Punctuation
ValueCountFrequency (%)
( 63
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34313
65.3%
Common 18217
34.7%
Latin 9
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 3564
 
10.4%
리 2982
 
8.7%
소 2372
 
6.9%
량 2346
 
6.8%
천 1810
 
5.3%
세 1137
 
3.3%
골 924
 
2.7%
로 845
 
2.5%
마 592
 
1.7%
농 567
 
1.7%
Other values (428) 17174
50.1%
Common
ValueCountFrequency (%)
_ 4609
25.3%
0 4142
22.7%
1 2104
11.5%
2 1544
 
8.5%
1365
 
7.5%
3 855
 
4.7%
4 703
 
3.9%
5 608
 
3.3%
6 561
 
3.1%
7 499
 
2.7%
Other values (6) 1227
 
6.7%
Latin
ValueCountFrequency (%)
B 3
33.3%
O 3
33.3%
X 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34313
65.3%
ASCII 18226
34.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 4609
25.3%
0 4142
22.7%
1 2104
11.5%
2 1544
 
8.5%
1365
 
7.5%
3 855
 
4.7%
4 703
 
3.9%
5 608
 
3.3%
6 561
 
3.1%
7 499
 
2.7%
Other values (9) 1236
 
6.8%
Hangul
ValueCountFrequency (%)
교 3564
 
10.4%
리 2982
 
8.7%
소 2372
 
6.9%
량 2346
 
6.8%
천 1810
 
5.3%
세 1137
 
3.3%
골 924
 
2.7%
로 845
 
2.5%
마 592
 
1.7%
농 567
 
1.7%
Other values (428) 17174
50.1%

소재지도로명주소
Text

MISSING 

Distinct508
Distinct (%)74.5%
Missing6164
Missing (%)90.0%
Memory size53.6 KiB
2024-05-11T16:51:13.989664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length29
Mean length21.831378
Min length12

Characters and Unicode

Total characters14889
Distinct characters233
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

Unique431 ?
Unique (%)63.2%

Sample

1st row강원도 평창군 대관령면 차항서녘길 61-6
2nd row강원도 평창군 대관령면 차항서녘길 61-6
3rd row강원도 평창군 평창읍 고길천로 796-11
4th row강원도 평창군 진부면 방아다리로 513-28
5th row강원도 평창군 진부면 방아다리로 513-28
ValueCountFrequency (%)
강원도 209
 
6.5%
평창군 198
 
6.1%
강원특별자치도 188
 
5.8%
양양군 98
 
3.0%
양구군 91
 
2.8%
서산시 84
 
2.6%
충청남도 84
 
2.6%
사상구 54
 
1.7%
부산광역시 54
 
1.7%
삼락천로 53
 
1.6%
Other values (716) 2110
65.5%
2024-05-11T16:51:14.912039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2541
 
17.1%
도 666
 
4.5%
군 497
 
3.3%
면 464
 
3.1%
1 454
 
3.0%
강 435
 
2.9%
양 419
 
2.8%
원 418
 
2.8%
2 362
 
2.4%
길 323
 
2.2%
Other values (223) 8310
55.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9627
64.7%
Space Separator 2541
 
17.1%
Decimal Number 2282
 
15.3%
Dash Punctuation 309
 
2.1%
Open Punctuation 65
 
0.4%
Close Punctuation 65
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 666
 
6.9%
군 497
 
5.2%
면 464
 
4.8%
강 435
 
4.5%
양 419
 
4.4%
원 418
 
4.3%
길 323
 
3.4%
평 307
 
3.2%
창 293
 
3.0%
로 272
 
2.8%
Other values (209) 5533
57.5%
Decimal Number
ValueCountFrequency (%)
1 454
19.9%
2 362
15.9%
3 316
13.8%
5 199
8.7%
4 186
8.2%
6 175
 
7.7%
8 171
 
7.5%
0 156
 
6.8%
9 144
 
6.3%
7 119
 
5.2%
Space Separator
ValueCountFrequency (%)
2541
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 309
100.0%
Open Punctuation
ValueCountFrequency (%)
( 65
100.0%
Close Punctuation
ValueCountFrequency (%)
) 65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9627
64.7%
Common 5262
35.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 666
 
6.9%
군 497
 
5.2%
면 464
 
4.8%
강 435
 
4.5%
양 419
 
4.4%
원 418
 
4.3%
길 323
 
3.4%
평 307
 
3.2%
창 293
 
3.0%
로 272
 
2.8%
Other values (209) 5533
57.5%
Common
ValueCountFrequency (%)
2541
48.3%
1 454
 
8.6%
2 362
 
6.9%
3 316
 
6.0%
- 309
 
5.9%
5 199
 
3.8%
4 186
 
3.5%
6 175
 
3.3%
8 171
 
3.2%
0 156
 
3.0%
Other values (4) 393
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9627
64.7%
ASCII 5262
35.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2541
48.3%
1 454
 
8.6%
2 362
 
6.9%
3 316
 
6.0%
- 309
 
5.9%
5 199
 
3.8%
4 186
 
3.5%
6 175
 
3.3%
8 171
 
3.2%
0 156
 
3.0%
Other values (4) 393
 
7.5%
Hangul
ValueCountFrequency (%)
도 666
 
6.9%
군 497
 
5.2%
면 464
 
4.8%
강 435
 
4.5%
양 419
 
4.4%
원 418
 
4.3%
길 323
 
3.4%
평 307
 
3.2%
창 293
 
3.0%
로 272
 
2.8%
Other values (209) 5533
57.5%

소재지지번주소
Text

MISSING 

Distinct5699
Distinct (%)85.5%
Missing180
Missing (%)2.6%
Memory size53.6 KiB
2024-05-11T16:51:15.650080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length21.584308
Min length13

Characters and Unicode

Total characters143881
Distinct characters312
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

Unique4926 ?
Unique (%)73.9%

Sample

1st row경상북도 상주시 함창읍 하갈리 758
2nd row경상북도 상주시 함창읍 하갈리 717
3rd row경상북도 상주시 화서면 하송리 377
4th row경상북도 상주시 화서면 하송리 283-5
5th row경상북도 상주시 화서면 하송리 280-31
ValueCountFrequency (%)
강원특별자치도 1636
 
5.1%
강원도 1319
 
4.1%
홍천군 1016
 
3.1%
전라북도 1000
 
3.1%
장수군 676
 
2.1%
경상북도 669
 
2.1%
평창군 578
 
1.8%
충청남도 567
 
1.8%
영월군 407
 
1.3%
철원군 402
 
1.2%
Other values (5393) 24005
74.4%
2024-05-11T16:51:16.629344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25649
 
17.8%
도 6936
 
4.8%
리 6302
 
4.4%
군 5257
 
3.7%
면 5028
 
3.5%
1 4946
 
3.4%
원 3993
 
2.8%
- 3386
 
2.4%
강 3024
 
2.1%
2 2927
 
2.0%
Other values (302) 76433
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91205
63.4%
Space Separator 25649
 
17.8%
Decimal Number 23533
 
16.4%
Dash Punctuation 3386
 
2.4%
Close Punctuation 54
 
< 0.1%
Open Punctuation 54
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 6936
 
7.6%
리 6302
 
6.9%
군 5257
 
5.8%
면 5028
 
5.5%
원 3993
 
4.4%
강 3024
 
3.3%
북 2435
 
2.7%
천 2422
 
2.7%
남 2192
 
2.4%
자 1993
 
2.2%
Other values (288) 51623
56.6%
Decimal Number
ValueCountFrequency (%)
1 4946
21.0%
2 2927
12.4%
3 2437
10.4%
4 2216
9.4%
5 2118
9.0%
6 2054
8.7%
7 1833
 
7.8%
8 1730
 
7.4%
0 1661
 
7.1%
9 1611
 
6.8%
Space Separator
ValueCountFrequency (%)
25649
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3386
100.0%
Close Punctuation
ValueCountFrequency (%)
) 54
100.0%
Open Punctuation
ValueCountFrequency (%)
( 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91205
63.4%
Common 52676
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 6936
 
7.6%
리 6302
 
6.9%
군 5257
 
5.8%
면 5028
 
5.5%
원 3993
 
4.4%
강 3024
 
3.3%
북 2435
 
2.7%
천 2422
 
2.7%
남 2192
 
2.4%
자 1993
 
2.2%
Other values (288) 51623
56.6%
Common
ValueCountFrequency (%)
25649
48.7%
1 4946
 
9.4%
- 3386
 
6.4%
2 2927
 
5.6%
3 2437
 
4.6%
4 2216
 
4.2%
5 2118
 
4.0%
6 2054
 
3.9%
7 1833
 
3.5%
8 1730
 
3.3%
Other values (4) 3380
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91205
63.4%
ASCII 52676
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
25649
48.7%
1 4946
 
9.4%
- 3386
 
6.4%
2 2927
 
5.6%
3 2437
 
4.6%
4 2216
 
4.2%
5 2118
 
4.0%
6 2054
 
3.9%
7 1833
 
3.5%
8 1730
 
3.3%
Other values (4) 3380
 
6.4%
Hangul
ValueCountFrequency (%)
도 6936
 
7.6%
리 6302
 
6.9%
군 5257
 
5.8%
면 5028
 
5.5%
원 3993
 
4.4%
강 3024
 
3.3%
북 2435
 
2.7%
천 2422
 
2.7%
남 2192
 
2.4%
자 1993
 
2.2%
Other values (288) 51623
56.6%

시설유형
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size53.6 KiB
소교량
3757 
세천
1830 
농로
679 
마을진입로
402 
낙차공
 
125

Length

Max length5
Median length3
Mean length2.7509495
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소교량
2nd row소교량
3rd row낙차공
4th row소교량
5th row소교량

Common Values

ValueCountFrequency (%)
소교량 3757
54.9%
세천 1830
26.7%
농로 679
 
9.9%
마을진입로 402
 
5.9%
낙차공 125
 
1.8%
취입보 53
 
0.8%

Length

2024-05-11T16:51:17.020304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:51:17.318572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소교량 3757
54.9%
세천 1830
26.7%
농로 679
 
9.9%
마을진입로 402
 
5.9%
낙차공 125
 
1.8%
취입보 53
 
0.8%

위험시설지정고시번호
Text

MISSING 

Distinct171
Distinct (%)3.1%
Missing1313
Missing (%)19.2%
Memory size53.6 KiB
2024-05-11T16:51:17.843701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length9.1563347
Min length3

Characters and Unicode

Total characters50662
Distinct characters57
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

Unique39 ?
Unique (%)0.7%

Sample

1st row2016-1071
2nd row2016-1071
3rd row2018-600
4th row2018-600
5th row2018-600
ValueCountFrequency (%)
제2023-238 1016
 
14.3%
고시 730
 
10.3%
2021-222 574
 
8.1%
2019-67 482
 
6.8%
영월군 407
 
5.7%
제2023-57호 399
 
5.6%
2023-82 286
 
4.0%
남원시 228
 
3.2%
제2017-22호 142
 
2.0%
2021-100 139
 
2.0%
Other values (182) 2718
38.2%
2024-05-11T16:51:19.178597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 13028
25.7%
0 6258
12.4%
- 5471
10.8%
1 4980
 
9.8%
3 3560
 
7.0%
제 2221
 
4.4%
8 1852
 
3.7%
7 1846
 
3.6%
6 1665
 
3.3%
1588
 
3.1%
Other values (47) 8193
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35901
70.9%
Other Letter 7374
 
14.6%
Dash Punctuation 5471
 
10.8%
Space Separator 1588
 
3.1%
Lowercase Letter 176
 
0.3%
Uppercase Letter 88
 
0.2%
Other Punctuation 32
 
0.1%
Open Punctuation 16
 
< 0.1%
Close Punctuation 16
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 2221
30.1%
시 1088
14.8%
호 994
13.5%
고 822
 
11.1%
군 568
 
7.7%
월 407
 
5.5%
영 407
 
5.5%
남 230
 
3.1%
원 228
 
3.1%
성 69
 
0.9%
Other values (23) 340
 
4.6%
Decimal Number
ValueCountFrequency (%)
2 13028
36.3%
0 6258
17.4%
1 4980
 
13.9%
3 3560
 
9.9%
8 1852
 
5.2%
7 1846
 
5.1%
6 1665
 
4.6%
9 1251
 
3.5%
5 872
 
2.4%
4 589
 
1.6%
Lowercase Letter
ValueCountFrequency (%)
u 76
43.2%
g 76
43.2%
a 11
 
6.2%
n 11
 
6.2%
e 1
 
0.6%
b 1
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
A 76
86.4%
J 11
 
12.5%
F 1
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 5471
100.0%
Space Separator
ValueCountFrequency (%)
1588
100.0%
Other Punctuation
ValueCountFrequency (%)
. 32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43024
84.9%
Hangul 7374
 
14.6%
Latin 264
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 2221
30.1%
시 1088
14.8%
호 994
13.5%
고 822
 
11.1%
군 568
 
7.7%
월 407
 
5.5%
영 407
 
5.5%
남 230
 
3.1%
원 228
 
3.1%
성 69
 
0.9%
Other values (23) 340
 
4.6%
Common
ValueCountFrequency (%)
2 13028
30.3%
0 6258
14.5%
- 5471
12.7%
1 4980
 
11.6%
3 3560
 
8.3%
8 1852
 
4.3%
7 1846
 
4.3%
6 1665
 
3.9%
1588
 
3.7%
9 1251
 
2.9%
Other values (5) 1525
 
3.5%
Latin
ValueCountFrequency (%)
A 76
28.8%
u 76
28.8%
g 76
28.8%
J 11
 
4.2%
a 11
 
4.2%
n 11
 
4.2%
F 1
 
0.4%
e 1
 
0.4%
b 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43288
85.4%
Hangul 7374
 
14.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 13028
30.1%
0 6258
14.5%
- 5471
12.6%
1 4980
 
11.5%
3 3560
 
8.2%
8 1852
 
4.3%
7 1846
 
4.3%
6 1665
 
3.8%
1588
 
3.7%
9 1251
 
2.9%
Other values (14) 1789
 
4.1%
Hangul
ValueCountFrequency (%)
제 2221
30.1%
시 1088
14.8%
호 994
13.5%
고 822
 
11.1%
군 568
 
7.7%
월 407
 
5.5%
영 407
 
5.5%
남 230
 
3.1%
원 228
 
3.1%
성 69
 
0.9%
Other values (23) 340
 
4.6%

위험시설지정일자
Date

Distinct201
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size53.6 KiB
Minimum1900-01-01 00:00:00
Maximum2023-10-12 00:00:00
2024-05-11T16:51:19.610419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:19.962600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

위험시설지정사유
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct48
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size53.6 KiB
위험도평가결과 불량
3177 
<NA>
2068 
위험도평가결과 보통
475 
위험도 평가결과에 따른 위험시설 지정
 
286
재해위험성 급박
 
204
Other values (43)
636 

Length

Max length56
Median length10
Mean length8.5201578
Min length1

Unique

Unique17 ?
Unique (%)0.2%

Sample

1st row시설물 노후로 보수및개체
2nd row시설물 노후로 보수및개체
3rd row시설물 노후로 보수및개체
4th row시설물 노후로 보수및개체
5th row시설물 노후로 보수및개체

Common Values

ValueCountFrequency (%)
위험도평가결과 불량 3177
46.4%
<NA> 2068
30.2%
위험도평가결과 보통 475
 
6.9%
위험도 평가결과에 따른 위험시설 지정 286
 
4.2%
재해위험성 급박 204
 
3.0%
시설물 노후로 보수및개체 137
 
2.0%
위험도평가 불량 133
 
1.9%
소규모위험시설 56
 
0.8%
교량폭 협소+교량파손 및 균열 40
 
0.6%
재해위험 35
 
0.5%
Other values (38) 235
 
3.4%

Length

2024-05-11T16:51:20.366473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
위험도평가결과 3652
28.8%
불량 3325
26.2%
na 2068
16.3%
보통 487
 
3.8%
위험도 303
 
2.4%
따른 287
 
2.3%
지정 287
 
2.3%
평가결과에 286
 
2.3%
위험시설 286
 
2.3%
재해위험성 204
 
1.6%
Other values (94) 1502
11.8%

위험시설해제일자
Date

MISSING 

Distinct41
Distinct (%)12.7%
Missing6524
Missing (%)95.3%
Memory size53.6 KiB
Minimum2017-09-21 00:00:00
Maximum2023-07-17 00:00:00
2024-05-11T16:51:20.900950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:21.204481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct5469
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.721673
Minimum33.256653
Maximum38.309969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.3 KiB
2024-05-11T16:51:21.546066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.256653
5-th percentile35.002605
Q135.727843
median36.780588
Q337.703386
95-th percentile38.200084
Maximum38.309969
Range5.0533166
Interquartile range (IQR)1.9755423

Descriptive statistics

Standard deviation1.0746973
Coefficient of variation (CV)0.029266023
Kurtosis-1.1442096
Mean36.721673
Median Absolute Deviation (MAD)0.96670864
Skewness-0.23109535
Sum251396.58
Variance1.1549744
MonotonicityNot monotonic
2024-05-11T16:51:21.974411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1944167073 20
 
0.3%
37.3254243 12
 
0.2%
37.4413401335 12
 
0.2%
37.4309839723 10
 
0.1%
35.1950919668 10
 
0.1%
37.4341474675 10
 
0.1%
38.20186068 10
 
0.1%
37.6565744083 8
 
0.1%
38.13902028 8
 
0.1%
37.6911966666 8
 
0.1%
Other values (5459) 6738
98.4%
ValueCountFrequency (%)
33.25665253 1
< 0.1%
33.26461189 1
< 0.1%
33.26932641 1
< 0.1%
33.26969987 1
< 0.1%
33.27039812 1
< 0.1%
33.271237 1
< 0.1%
33.271819 1
< 0.1%
33.27257912 1
< 0.1%
33.27260226 1
< 0.1%
33.27451757 1
< 0.1%
ValueCountFrequency (%)
38.30996916 1
 
< 0.1%
38.30471164 1
 
< 0.1%
38.303106 1
 
< 0.1%
38.303082614 1
 
< 0.1%
38.3030694 1
 
< 0.1%
38.29988681 4
0.1%
38.299722 1
 
< 0.1%
38.29933775 2
< 0.1%
38.29805568 1
 
< 0.1%
38.29706447 2
< 0.1%

경도
Real number (ℝ)

Distinct5483
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.84028
Minimum125.40743
Maximum129.14567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.3 KiB
2024-05-11T16:51:22.298074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.40743
5-th percentile126.77564
Q1127.41603
median127.91095
Q3128.34033
95-th percentile128.73283
Maximum129.14567
Range3.7382424
Interquartile range (IQR)0.9242957

Descriptive statistics

Standard deviation0.61660251
Coefficient of variation (CV)0.0048232256
Kurtosis0.040746948
Mean127.84028
Median Absolute Deviation (MAD)0.4608965
Skewness-0.42939516
Sum875194.56
Variance0.38019866
MonotonicityNot monotonic
2024-05-11T16:51:22.688667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.9879630624 20
 
0.3%
128.4833596 12
 
0.2%
128.3607093317 12
 
0.2%
128.2956968743 10
 
0.1%
128.3409339046 10
 
0.1%
127.4067714 10
 
0.1%
128.9876220687 10
 
0.1%
128.5454475016 8
 
0.1%
128.450872442 8
 
0.1%
128.3324581869 8
 
0.1%
Other values (5473) 6738
98.4%
ValueCountFrequency (%)
125.4074315 1
< 0.1%
125.4449131 1
< 0.1%
125.9066697 1
< 0.1%
125.9072900375 1
< 0.1%
125.917736 1
< 0.1%
125.9193833 1
< 0.1%
125.9218196 1
< 0.1%
125.922961 1
< 0.1%
125.9264483 1
< 0.1%
125.9272829 2
< 0.1%
ValueCountFrequency (%)
129.1456739 1
< 0.1%
129.1201745471 1
< 0.1%
129.1201745 1
< 0.1%
129.1148611132 1
< 0.1%
129.1148611 1
< 0.1%
129.1033648136 1
< 0.1%
129.1033648 1
< 0.1%
129.0882646306 1
< 0.1%
129.0882646 1
< 0.1%
129.0882305 1
< 0.1%

연장
Real number (ℝ)

MISSING 

Distinct717
Distinct (%)12.2%
Missing963
Missing (%)14.1%
Infinite0
Infinite (%)0.0%
Mean218.76728
Minimum0
Maximum8870
Zeros15
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size60.3 KiB
2024-05-11T16:51:23.063828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median22
Q3300
95-th percentile900
Maximum8870
Range8870
Interquartile range (IQR)294

Descriptive statistics

Standard deviation432.93964
Coefficient of variation (CV)1.9789963
Kurtosis106.54261
Mean218.76728
Median Absolute Deviation (MAD)19.5
Skewness7.2155055
Sum1287007.9
Variance187436.73
MonotonicityNot monotonic
2024-05-11T16:51:23.355240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0 348
 
5.1%
4.0 342
 
5.0%
3.0 282
 
4.1%
6.0 252
 
3.7%
2.0 198
 
2.9%
10.0 181
 
2.6%
7.0 179
 
2.6%
8.0 144
 
2.1%
1.0 116
 
1.7%
20.0 105
 
1.5%
Other values (707) 3736
54.6%
(Missing) 963
 
14.1%
ValueCountFrequency (%)
0.0 15
 
0.2%
0.6 4
 
0.1%
1.0 116
1.7%
1.2 4
 
0.1%
1.5 5
 
0.1%
2.0 198
2.9%
2.1 2
 
< 0.1%
2.4 2
 
< 0.1%
2.5 10
 
0.1%
3.0 282
4.1%
ValueCountFrequency (%)
8870.0 2
< 0.1%
8370.0 2
< 0.1%
4000.0 1
< 0.1%
3940.0 2
< 0.1%
3830.0 2
< 0.1%
3400.0 2
< 0.1%
3372.0 2
< 0.1%
3357.0 1
< 0.1%
3200.0 2
< 0.1%
2800.0 2
< 0.1%

폭
Real number (ℝ)

MISSING 

Distinct131
Distinct (%)2.2%
Missing961
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean4.0228887
Minimum0
Maximum145
Zeros26
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size60.3 KiB
2024-05-11T16:51:23.709356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3.5
Q35
95-th percentile8
Maximum145
Range145
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.8744028
Coefficient of variation (CV)0.96308973
Kurtosis429.29399
Mean4.0228887
Median Absolute Deviation (MAD)1
Skewness15.92575
Sum23674.7
Variance15.010997
MonotonicityNot monotonic
2024-05-11T16:51:24.157111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 1309
19.1%
4.0 1016
14.8%
5.0 721
10.5%
2.0 513
 
7.5%
6.0 253
 
3.7%
1.0 252
 
3.7%
2.5 229
 
3.3%
3.5 180
 
2.6%
1.5 148
 
2.2%
8.0 107
 
1.6%
Other values (121) 1157
16.9%
(Missing) 961
14.0%
ValueCountFrequency (%)
0.0 26
0.4%
0.15 2
 
< 0.1%
0.2 1
 
< 0.1%
0.3 2
 
< 0.1%
0.4 8
 
0.1%
0.5 10
 
0.1%
0.6 13
0.2%
0.65 1
 
< 0.1%
0.7 9
 
0.1%
0.8 15
0.2%
ValueCountFrequency (%)
145.0 1
< 0.1%
94.0 1
< 0.1%
83.0 1
< 0.1%
80.0 1
< 0.1%
60.0 1
< 0.1%
55.0 2
< 0.1%
50.0 1
< 0.1%
43.0 1
< 0.1%
42.0 1
< 0.1%
38.0 1
< 0.1%

시설부속물
Text

MISSING 

Distinct51
Distinct (%)13.8%
Missing6477
Missing (%)94.6%
Memory size53.6 KiB
2024-05-11T16:51:24.529200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length1
Mean length3.1680217
Min length1

Characters and Unicode

Total characters1169
Distinct characters44
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)8.4%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 230
49.4%
도로 38
 
8.2%
소교량 36
 
7.7%
1개소 23
 
4.9%
낙차공 15
 
3.2%
소교량1개소 11
 
2.4%
2개소 11
 
2.4%
세천 9
 
1.9%
4개소 9
 
1.9%
복개교 8
 
1.7%
Other values (31) 76
 
16.3%
2024-05-11T16:51:25.113617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 235
20.1%
소 179
15.3%
개 118
10.1%
97
8.3%
교 79
 
6.8%
량 71
 
6.1%
1 49
 
4.2%
로 43
 
3.7%
도 42
 
3.6%
, 29
 
2.5%
Other values (34) 227
19.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 680
58.2%
Decimal Number 355
30.4%
Space Separator 97
 
8.3%
Other Punctuation 29
 
2.5%
Lowercase Letter 4
 
0.3%
Math Symbol 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 179
26.3%
개 118
17.4%
교 79
11.6%
량 71
 
10.4%
로 43
 
6.3%
도 42
 
6.2%
낙 25
 
3.7%
차 25
 
3.7%
공 25
 
3.7%
천 13
 
1.9%
Other values (20) 60
 
8.8%
Decimal Number
ValueCountFrequency (%)
0 235
66.2%
1 49
 
13.8%
3 18
 
5.1%
2 17
 
4.8%
4 14
 
3.9%
5 10
 
2.8%
7 5
 
1.4%
6 5
 
1.4%
9 1
 
0.3%
8 1
 
0.3%
Space Separator
ValueCountFrequency (%)
97
100.0%
Other Punctuation
ValueCountFrequency (%)
, 29
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 680
58.2%
Common 485
41.5%
Latin 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 179
26.3%
개 118
17.4%
교 79
11.6%
량 71
 
10.4%
로 43
 
6.3%
도 42
 
6.2%
낙 25
 
3.7%
차 25
 
3.7%
공 25
 
3.7%
천 13
 
1.9%
Other values (20) 60
 
8.8%
Common
ValueCountFrequency (%)
0 235
48.5%
97
20.0%
1 49
 
10.1%
, 29
 
6.0%
3 18
 
3.7%
2 17
 
3.5%
4 14
 
2.9%
5 10
 
2.1%
7 5
 
1.0%
6 5
 
1.0%
Other values (3) 6
 
1.2%
Latin
ValueCountFrequency (%)
m 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 680
58.2%
ASCII 489
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 235
48.1%
97
19.8%
1 49
 
10.0%
, 29
 
5.9%
3 18
 
3.7%
2 17
 
3.5%
4 14
 
2.9%
5 10
 
2.0%
7 5
 
1.0%
6 5
 
1.0%
Other values (4) 10
 
2.0%
Hangul
ValueCountFrequency (%)
소 179
26.3%
개 118
17.4%
교 79
11.6%
량 71
 
10.4%
로 43
 
6.3%
도 42
 
6.2%
낙 25
 
3.7%
차 25
 
3.7%
공 25
 
3.7%
천 13
 
1.9%
Other values (20) 60
 
8.8%

관리기관전화번호
Text

MISSING 

Distinct195
Distinct (%)3.1%
Missing486
Missing (%)7.1%
Memory size53.6 KiB
2024-05-11T16:51:25.592258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters76320
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

Unique47 ?
Unique (%)0.7%

Sample

1st row054-537-8019
2nd row054-537-8019
3rd row054-537-8912
4th row054-537-8912
5th row054-537-8353
ValueCountFrequency (%)
033-430-2890 1016
 
16.0%
063-350-2493 676
 
10.6%
033-450-5483 402
 
6.3%
033-480-7531 336
 
5.3%
041-750-2722 298
 
4.7%
041-521-5576 206
 
3.2%
054-370-2092 150
 
2.4%
033-330-2671 148
 
2.3%
055-639-3696 137
 
2.2%
055-570-3633 129
 
2.0%
Other values (185) 2862
45.0%
2024-05-11T16:51:26.481295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 15418
20.2%
0 13491
17.7%
- 12720
16.7%
2 6520
8.5%
4 5756
 
7.5%
5 5479
 
7.2%
7 4002
 
5.2%
6 3899
 
5.1%
8 3414
 
4.5%
9 3162
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63600
83.3%
Dash Punctuation 12720
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 15418
24.2%
0 13491
21.2%
2 6520
10.3%
4 5756
 
9.1%
5 5479
 
8.6%
7 4002
 
6.3%
6 3899
 
6.1%
8 3414
 
5.4%
9 3162
 
5.0%
1 2459
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 12720
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 76320
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 15418
20.2%
0 13491
17.7%
- 12720
16.7%
2 6520
8.5%
4 5756
 
7.5%
5 5479
 
7.2%
7 4002
 
5.2%
6 3899
 
5.1%
8 3414
 
4.5%
9 3162
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 15418
20.2%
0 13491
17.7%
- 12720
16.7%
2 6520
8.5%
4 5756
 
7.5%
5 5479
 
7.2%
7 4002
 
5.2%
6 3899
 
5.1%
8 3414
 
4.5%
9 3162
 
4.1%

관리기관명
Text

Distinct128
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size53.6 KiB
2024-05-11T16:51:27.155163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length9.8236927
Min length3

Characters and Unicode

Total characters67253
Distinct characters136
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

Unique15 ?
Unique (%)0.2%

Sample

1st row경상북도 상주시 함창읍
2nd row경상북도 상주시 함창읍
3rd row경상북도 상주시 화서면
4th row경상북도 상주시 화서면
5th row경상북도 상주시 화서면
ValueCountFrequency (%)
강원특별자치도 2144
 
15.3%
홍천군청 1016
 
7.2%
강원도 811
 
5.8%
장수군 676
 
4.8%
경상북도 669
 
4.8%
충청남도 651
 
4.6%
평창군청 578
 
4.1%
건설과 479
 
3.4%
영월군청 407
 
2.9%
경상남도 403
 
2.9%
Other values (133) 6220
44.3%
2024-05-11T16:51:28.068426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7208
 
10.7%
도 6018
 
8.9%
청 5431
 
8.1%
군 5186
 
7.7%
원 3591
 
5.3%
강 2968
 
4.4%
특 2360
 
3.5%
별 2360
 
3.5%
자 2360
 
3.5%
치 2360
 
3.5%
Other values (126) 27411
40.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 60026
89.3%
Space Separator 7208
 
10.7%
Close Punctuation 9
 
< 0.1%
Open Punctuation 9
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 6018
 
10.0%
청 5431
 
9.0%
군 5186
 
8.6%
원 3591
 
6.0%
강 2968
 
4.9%
특 2360
 
3.9%
별 2360
 
3.9%
자 2360
 
3.9%
치 2360
 
3.9%
시 1682
 
2.8%
Other values (122) 25710
42.8%
Space Separator
ValueCountFrequency (%)
7208
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 60026
89.3%
Common 7227
 
10.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 6018
 
10.0%
청 5431
 
9.0%
군 5186
 
8.6%
원 3591
 
6.0%
강 2968
 
4.9%
특 2360
 
3.9%
별 2360
 
3.9%
자 2360
 
3.9%
치 2360
 
3.9%
시 1682
 
2.8%
Other values (122) 25710
42.8%
Common
ValueCountFrequency (%)
7208
99.7%
) 9
 
0.1%
( 9
 
0.1%
. 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 60026
89.3%
ASCII 7227
 
10.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7208
99.7%
) 9
 
0.1%
( 9
 
0.1%
. 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
도 6018
 
10.0%
청 5431
 
9.0%
군 5186
 
8.6%
원 3591
 
6.0%
강 2968
 
4.9%
특 2360
 
3.9%
별 2360
 
3.9%
자 2360
 
3.9%
치 2360
 
3.9%
시 1682
 
2.8%
Other values (122) 25710
42.8%

데이터기준일자
Date

Distinct72
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size53.6 KiB
Minimum2017-11-23 00:00:00
Maximum2024-04-19 00:00:00
2024-05-11T16:51:28.406606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:28.770462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

제공기관코드
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4594373.2
Minimum3330000
Maximum6520000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.3 KiB
2024-05-11T16:51:29.075574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3330000
5-th percentile4250000
Q14271000
median4490000
Q34761000
95-th percentile5390000
Maximum6520000
Range3190000
Interquartile range (IQR)490000

Descriptive statistics

Standard deviation438815.86
Coefficient of variation (CV)0.095511583
Kurtosis0.63791726
Mean4594373.2
Median Absolute Deviation (MAD)239000
Skewness0.63114252
Sum3.1453079 × 1010
Variance1.9255936 × 1011
MonotonicityNot monotonic
2024-05-11T16:51:29.477042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4250000 508
 
7.4%
4251000 508
 
7.4%
4271000 399
 
5.8%
4751000 338
 
4.9%
4750000 338
 
4.9%
4321000 336
 
4.9%
4550000 298
 
4.4%
4280000 289
 
4.2%
4281000 289
 
4.2%
4490000 206
 
3.0%
Other values (80) 3337
48.7%
ValueCountFrequency (%)
3330000 1
 
< 0.1%
3390000 54
0.8%
3480000 3
 
< 0.1%
3620000 1
 
< 0.1%
3630000 1
 
< 0.1%
3640000 1
 
< 0.1%
3660000 121
1.8%
3980000 3
 
< 0.1%
4070000 4
 
0.1%
4140000 18
 
0.3%
ValueCountFrequency (%)
6520000 16
 
0.2%
5710000 15
 
0.2%
5700000 20
 
0.3%
5670000 1
 
< 0.1%
5600000 90
1.3%
5540000 96
1.4%
5480000 27
 
0.4%
5450000 8
 
0.1%
5430000 2
 
< 0.1%
5420000 10
 
0.1%

제공기관명
Text

Distinct90
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size53.6 KiB
2024-05-11T16:51:30.007097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length8.8829974
Min length7

Characters and Unicode

Total characters60813
Distinct characters78
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st row경상북도 상주시
2nd row경상북도 상주시
3rd row경상북도 상주시
4th row경상북도 상주시
5th row경상북도 상주시
ValueCountFrequency (%)
강원특별자치도 1841
 
13.4%
강원도 1170
 
8.5%
홍천군 1016
 
7.4%
장수군 676
 
4.9%
경상북도 661
 
4.8%
충청남도 651
 
4.8%
전북특별자치도 600
 
4.4%
전라북도 600
 
4.4%
평창군 578
 
4.2%
영월군 407
 
3.0%
Other values (76) 5492
40.1%
2024-05-11T16:51:30.911306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6846
 
11.3%
도 6838
 
11.2%
군 5256
 
8.6%
원 3642
 
6.0%
강 3024
 
5.0%
특 2457
 
4.0%
별 2457
 
4.0%
자 2457
 
4.0%
치 2457
 
4.0%
북 2027
 
3.3%
Other values (68) 23352
38.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53967
88.7%
Space Separator 6846
 
11.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 6838
 
12.7%
군 5256
 
9.7%
원 3642
 
6.7%
강 3024
 
5.6%
특 2457
 
4.6%
별 2457
 
4.6%
자 2457
 
4.6%
치 2457
 
4.6%
북 2027
 
3.8%
시 1663
 
3.1%
Other values (67) 21689
40.2%
Space Separator
ValueCountFrequency (%)
6846
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53967
88.7%
Common 6846
 
11.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 6838
 
12.7%
군 5256
 
9.7%
원 3642
 
6.7%
강 3024
 
5.6%
특 2457
 
4.6%
별 2457
 
4.6%
자 2457
 
4.6%
치 2457
 
4.6%
북 2027
 
3.8%
시 1663
 
3.1%
Other values (67) 21689
40.2%
Common
ValueCountFrequency (%)
6846
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53967
88.7%
ASCII 6846
 
11.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6846
100.0%
Hangul
ValueCountFrequency (%)
도 6838
 
12.7%
군 5256
 
9.7%
원 3642
 
6.7%
강 3024
 
5.6%
특 2457
 
4.6%
별 2457
 
4.6%
자 2457
 
4.6%
치 2457
 
4.6%
북 2027
 
3.8%
시 1663
 
3.1%
Other values (67) 21689
40.2%

Interactions

2024-05-11T16:51:08.850353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:04.032743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:05.337708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:06.568286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:07.665163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:09.081151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:04.252543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:05.571707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:06.822543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:07.899823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:09.365262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:04.475231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:05.833715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:07.055324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:08.178541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:09.557968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:04.681156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:06.055593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:07.251356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:08.370338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:09.890903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:04.936881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:06.324171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:07.457486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:51:08.597256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T16:51:31.251830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설유형위험시설지정사유위험시설해제일자위도경도연장폭시설부속물데이터기준일자제공기관코드제공기관명
시설유형1.0000.4870.6140.3490.3100.3500.0000.2230.7090.3960.710
위험시설지정사유0.4871.0000.9970.8420.8090.4570.0000.9850.9870.8990.992
위험시설해제일자0.6140.9971.0000.9970.9870.6650.0001.0000.9951.0000.994
위도0.3490.8420.9971.0000.7760.0970.1250.9740.9770.8460.991
경도0.3100.8090.9870.7761.0000.1620.0790.8730.9600.6560.971
연장0.3500.4570.6650.0970.1621.0000.0430.6930.4420.0550.436
폭0.0000.0000.0000.1250.0790.0431.0000.0000.0000.1700.000
시설부속물0.2230.9851.0000.9740.8730.6930.0001.0000.9440.9950.928
데이터기준일자0.7090.9870.9950.9770.9600.4420.0000.9441.0000.9971.000
제공기관코드0.3960.8991.0000.8460.6560.0550.1700.9950.9971.0001.000
제공기관명0.7100.9920.9940.9910.9710.4360.0000.9281.0001.0001.000
2024-05-11T16:51:31.650460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설유형위험시설지정사유
시설유형1.0000.230
위험시설지정사유0.2301.000
2024-05-11T16:51:31.998438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도연장폭제공기관코드시설유형위험시설지정사유
위도1.0000.152-0.1840.282-0.6230.1920.511
경도0.1521.000-0.0300.156-0.1980.1690.432
연장-0.184-0.0301.000-0.2610.1840.1330.214
폭0.2820.156-0.2611.000-0.2260.0000.000
제공기관코드-0.623-0.1980.184-0.2261.0000.2090.654
시설유형0.1920.1690.1330.0000.2091.0000.230
위험시설지정사유0.5110.4320.2140.0000.6540.2301.000

Missing values

2024-05-11T16:51:10.382206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T16:51:10.944586image/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.
2024-05-11T16:51:11.450315image/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

시설명소재지도로명주소소재지지번주소시설유형위험시설지정고시번호위험시설지정일자위험시설지정사유위험시설해제일자위도경도연장폭시설부속물관리기관전화번호관리기관명데이터기준일자제공기관코드제공기관명
0하갈2교<NA>경상북도 상주시 함창읍 하갈리 758소교량2016-10712016-12-20시설물 노후로 보수및개체<NA>36.51405128.23441410.03.0<NA>054-537-8019경상북도 상주시 함창읍2022-06-155110000경상북도 상주시
1하갈2소교량<NA>경상북도 상주시 함창읍 하갈리 717소교량2016-10712016-12-20시설물 노후로 보수및개체<NA>36.513584128.23479410.03.0<NA>054-537-8019경상북도 상주시 함창읍2022-06-155110000경상북도 상주시
2하송 제1낙차보<NA>경상북도 상주시 화서면 하송리 377낙차공2018-6002018-06-13시설물 노후로 보수및개체<NA>36.468953127.9710435.01.5<NA>054-537-8912경상북도 상주시 화서면2022-06-155110000경상북도 상주시
3하송 제1진입로<NA>경상북도 상주시 화서면 하송리 283-5소교량2018-6002018-06-14시설물 노후로 보수및개체<NA>36.485684127.95890835.03.0<NA>054-537-8912경상북도 상주시 화서면2022-06-155110000경상북도 상주시
4하송 제2진입로<NA>경상북도 상주시 화서면 하송리 280-31소교량2018-6002018-06-15시설물 노후로 보수및개체<NA>36.484063127.95852940.04.0<NA>054-537-8353경상북도 상주시 화서면2022-06-155110000경상북도 상주시
5하송 제3소교량<NA>경상북도 상주시 화서면 하송리 346-8소교량2018-6002018-06-16시설물 노후로 보수및개체<NA>36.477421127.9529420.03.0<NA>054-537-8912경상북도 상주시 화서면2022-06-155110000경상북도 상주시
6하송 제3진입로<NA>경상북도 상주시 화서면 하송리 372소교량2018-6002018-06-17시설물 노후로 보수및개체<NA>36.471326127.96398850.05.0<NA>054-537-8912경상북도 상주시 화서면2022-06-155110000경상북도 상주시
7하초 진입로<NA>경상북도 상주시 청리면 하초리 402-2마을진입로2018-6002018-06-05시설물 노후로 보수및개체<NA>36.313589128.09911300.02.5<NA>054-537-8142경상북도 상주시 청리면2022-06-155110000경상북도 상주시
8학하 세월교<NA>경상북도 상주시 청리면 학하리 447-1소교량2018-6002018-06-05시설물 노후로 보수및개체<NA>36.212834128.07503570.05.0<NA>054-537-8142경상북도 상주시 청리면2022-06-155110000경상북도 상주시
9헌신3교<NA>경상북도 상주시 헌신동 385소교량2017-4872017-04-26시설물 노후로 보수및개체<NA>36.418536128.2126615.04.0<NA>054-537-8652경상북도 상주시 동문동2022-06-155110000경상북도 상주시
시설명소재지도로명주소소재지지번주소시설유형위험시설지정고시번호위험시설지정일자위험시설지정사유위험시설해제일자위도경도연장폭시설부속물관리기관전화번호관리기관명데이터기준일자제공기관코드제공기관명
6836보현동1교충청남도 서산시 운산면 용현리 747-5<NA>소교량<NA>2019-03-29<NA><NA>36.768707126.572287<NA><NA><NA><NA>충청남도 서산시청 안전총괄과2022-06-144530000충청남도 서산시
6837(구)간상교 소교량<NA>경상북도 상주시 중동면 간상리 1559-205소교량2017-4872017-04-26시설물 노후로 보수및개체<NA>36.418522128.28171320.04.0<NA>054-537-8084경상북도 상주시 중동면2022-06-155110000경상북도 상주시
6838가곡리 소교량<NA>경상북도 상주시 외서면 가곡리 680-54소교량2017-4872017-04-26시설물 노후로 보수및개체<NA>36.477944128.10612320.06.5<NA>054-537-8414경상북도 상주시 외서면2022-06-155110000경상북도 상주시
6839가천2리 제2교<NA>경상북도 상주시 청리면 원장리 955-30소교량2018-6002018-06-05시설물 노후로 보수및개체<NA>36.336455128.12122129.05.8<NA>054-537-8142경상북도 상주시 청리면2022-06-155110000경상북도 상주시
6840개곡교<NA>경상북도 상주시 외서면 개곡리 647소교량2017-4872017-04-26시설물 노후로 보수및개체<NA>36.471903128.14965464.05.0<NA>054-537-8414경상북도 상주시 외서면2022-06-155110000경상북도 상주시
6841개운2교<NA>경상북도 상주시 개운동 605-38소교량2017-4872017-04-26시설물 노후로 보수및개체<NA>36.399765128.14847745.04.0<NA>054-537-8564경상북도 상주시 남원동2022-06-155110000경상북도 상주시
6842거동소교량<NA>경상북도 상주시 거동동 65-3소교량2017-4872017-04-26시설물 노후로 보수및개체<NA>36.386729128.17443910.05.0<NA>054-537-8683경상북도 상주시 동성동2022-06-155110000경상북도 상주시
6843고곡2 제1소교량<NA>경상북도 상주시 내서면 고곡리 804-8소교량2018-6002018-06-05시설물 노후로 보수및개체<NA>36.422114128.05911420.06.3<NA>054-537-8236경상북도 상주시 내서면2022-06-155110000경상북도 상주시
6844고곡2 제2소교량<NA>경상북도 상주시 내서면 고곡리 296-1소교량2018-6002018-06-05시설물 노후로 보수및개체<NA>36.424177128.05540112.03.9<NA>054-537-8236경상북도 상주시 내서면2022-06-155110000경상북도 상주시
6845고실1소교량<NA>경상북도 상주시 외남면 구서리 617소교량2018-6002018-06-05시설물 노후로 보수및개체<NA>36.34084128.08264419.05.4<NA>054-537-8204경상북도 상주시 외남면2022-06-155110000경상북도 상주시