Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells15342
Missing cells (%)11.8%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory1.1 MiB
Average record size in memory117.0 B

Variable types

Text6
Numeric5
Categorical1
DateTime1

Dataset

Description공공데이터 제공 표준데이터 속성정보(허용값, 표현형식/단위 등)는 [공공데이터 제공 표준] 전문을 참고하시기 바랍니다.(공공데이터포털&gt;정보공유&gt;자료실) 각 기관에서 등록한 표준데이터를 취합하여 제공하기 때문에 갱신주기는 개별 파일마다 다릅니다.(기관에서 등록한 데이터를 취합한 것으로 개별 파일별 갱신시점이 다름) 전국데이터의 경우 API 서비스를 이용해주시기 바랍니다.
Author지방자치단체
URLhttps://www.data.go.kr/data/15017320/standard.do

Alerts

Dataset has 2 (< 0.1%) duplicate rowsDuplicates
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
제공기관코드 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
소재지도로명주소 has 8051 (80.5%) missing valuesMissing
소재지지번주소 has 301 (3.0%) missing valuesMissing
위도 has 558 (5.6%) missing valuesMissing
경도 has 558 (5.6%) missing valuesMissing
설치연도 has 5874 (58.7%) missing valuesMissing
설치개수 is highly skewed (γ1 = 34.05627484)Skewed

Reproduction

Analysis started2024-05-18 08:54:44.602859
Analysis finished2024-05-18 08:54:55.780978
Duration11.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

보안등위치명
Text

Distinct9384
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T17:54:56.436554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length25
Mean length7.8025
Min length2

Characters and Unicode

Total characters78025
Distinct characters335
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

Unique9312 ?
Unique (%)93.1%

Sample

1st row월영동074
2nd row방배2동056
3rd row자산동040
4th row소답동105
5th row논현로31길 24-7
ValueCountFrequency (%)
강원도 182
 
1.3%
태백시 182
 
1.3%
진안읍 166
 
1.2%
전라북도 160
 
1.2%
익산시 160
 
1.2%
1 121
 
0.9%
마령면 98
 
0.7%
ts 87
 
0.6%
오류2동 63
 
0.5%
광덕면 61
 
0.4%
Other values (9321) 12605
90.8%
2024-05-18T17:54:58.807842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6300
 
8.1%
동 4668
 
6.0%
2 4460
 
5.7%
0 4422
 
5.7%
3889
 
5.0%
3 3745
 
4.8%
4 3167
 
4.1%
5 2749
 
3.5%
6 2556
 
3.3%
면 2456
 
3.1%
Other values (325) 39613
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 35242
45.2%
Decimal Number 34041
43.6%
Space Separator 3889
 
5.0%
Dash Punctuation 2386
 
3.1%
Close Punctuation 857
 
1.1%
Open Punctuation 856
 
1.1%
Uppercase Letter 549
 
0.7%
Other Punctuation 198
 
0.3%
Connector Punctuation 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 4668
 
13.2%
면 2456
 
7.0%
로 1490
 
4.2%
길 1320
 
3.7%
산 1127
 
3.2%
리 936
 
2.7%
읍 657
 
1.9%
진 613
 
1.7%
북 593
 
1.7%
도 593
 
1.7%
Other values (286) 20789
59.0%
Uppercase Letter
ValueCountFrequency (%)
S 102
18.6%
T 88
16.0%
B 77
14.0%
L 54
9.8%
N 28
 
5.1%
E 26
 
4.7%
D 24
 
4.4%
C 23
 
4.2%
M 17
 
3.1%
O 15
 
2.7%
Other values (10) 95
17.3%
Decimal Number
ValueCountFrequency (%)
1 6300
18.5%
2 4460
13.1%
0 4422
13.0%
3 3745
11.0%
4 3167
9.3%
5 2749
8.1%
6 2556
7.5%
7 2366
 
7.0%
8 2231
 
6.6%
9 2045
 
6.0%
Close Punctuation
ValueCountFrequency (%)
) 766
89.4%
] 91
 
10.6%
Open Punctuation
ValueCountFrequency (%)
( 765
89.4%
[ 91
 
10.6%
Other Punctuation
ValueCountFrequency (%)
/ 166
83.8%
. 32
 
16.2%
Space Separator
ValueCountFrequency (%)
3889
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2386
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 42234
54.1%
Hangul 35242
45.2%
Latin 549
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 4668
 
13.2%
면 2456
 
7.0%
로 1490
 
4.2%
길 1320
 
3.7%
산 1127
 
3.2%
리 936
 
2.7%
읍 657
 
1.9%
진 613
 
1.7%
북 593
 
1.7%
도 593
 
1.7%
Other values (286) 20789
59.0%
Latin
ValueCountFrequency (%)
S 102
18.6%
T 88
16.0%
B 77
14.0%
L 54
9.8%
N 28
 
5.1%
E 26
 
4.7%
D 24
 
4.4%
C 23
 
4.2%
M 17
 
3.1%
O 15
 
2.7%
Other values (10) 95
17.3%
Common
ValueCountFrequency (%)
1 6300
14.9%
2 4460
10.6%
0 4422
10.5%
3889
9.2%
3 3745
8.9%
4 3167
7.5%
5 2749
6.5%
6 2556
6.1%
- 2386
 
5.6%
7 2366
 
5.6%
Other values (9) 6194
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42783
54.8%
Hangul 35242
45.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6300
14.7%
2 4460
10.4%
0 4422
10.3%
3889
9.1%
3 3745
8.8%
4 3167
7.4%
5 2749
6.4%
6 2556
 
6.0%
- 2386
 
5.6%
7 2366
 
5.5%
Other values (29) 6743
15.8%
Hangul
ValueCountFrequency (%)
동 4668
 
13.2%
면 2456
 
7.0%
로 1490
 
4.2%
길 1320
 
3.7%
산 1127
 
3.2%
리 936
 
2.7%
읍 657
 
1.9%
진 613
 
1.7%
북 593
 
1.7%
도 593
 
1.7%
Other values (286) 20789
59.0%

설치개수
Real number (ℝ)

SKEWED 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0136
Minimum0
Maximum14
Zeros22
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T17:54:59.777506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2453998
Coefficient of variation (CV)0.24210714
Kurtosis1568.9919
Mean1.0136
Median Absolute Deviation (MAD)0
Skewness34.056275
Sum10136
Variance0.060221062
MonotonicityNot monotonic
2024-05-18T17:55:00.502899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 9870
98.7%
2 95
 
0.9%
0 22
 
0.2%
4 4
 
< 0.1%
3 4
 
< 0.1%
10 1
 
< 0.1%
14 1
 
< 0.1%
5 1
 
< 0.1%
13 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 22
 
0.2%
1 9870
98.7%
2 95
 
0.9%
3 4
 
< 0.1%
4 4
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
13 1
 
< 0.1%
10 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 4
 
< 0.1%
3 4
 
< 0.1%
2 95
 
0.9%
1 9870
98.7%
0 22
 
0.2%

소재지도로명주소
Text

MISSING 

Distinct1833
Distinct (%)94.0%
Missing8051
Missing (%)80.5%
Memory size156.2 KiB
2024-05-18T17:55:01.488097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length30
Mean length20.922524
Min length12

Characters and Unicode

Total characters40778
Distinct characters339
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1755 ?
Unique (%)90.0%

Sample

1st row강원도 태백시 장성1길 66
2nd row서울특별시 구로구 부일로1길 51
3rd row서울특별시 구로구 남부순환로105라길 15
4th row강원도 태백시 청솔길 40
5th row충청북도 제천시 세거리로5길 112
ValueCountFrequency (%)
서울특별시 464
 
5.4%
구로구 328
 
3.8%
강원도 282
 
3.3%
충청남도 239
 
2.8%
부산광역시 223
 
2.6%
서산시 198
 
2.3%
태백시 183
 
2.1%
사하구 178
 
2.1%
운산면 140
 
1.6%
전라북도 128
 
1.5%
Other values (2068) 6234
72.5%
2024-05-18T17:55:03.376128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6648
 
16.3%
1 1709
 
4.2%
로 1511
 
3.7%
길 1478
 
3.6%
시 1462
 
3.6%
도 1415
 
3.5%
구 1261
 
3.1%
2 938
 
2.3%
3 819
 
2.0%
서 788
 
1.9%
Other values (329) 22749
55.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25750
63.1%
Decimal Number 6991
 
17.1%
Space Separator 6648
 
16.3%
Dash Punctuation 733
 
1.8%
Open Punctuation 221
 
0.5%
Close Punctuation 221
 
0.5%
Other Punctuation 209
 
0.5%
Uppercase Letter 3
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 1511
 
5.9%
길 1478
 
5.7%
시 1462
 
5.7%
도 1415
 
5.5%
구 1261
 
4.9%
서 788
 
3.1%
산 730
 
2.8%
면 653
 
2.5%
별 627
 
2.4%
특 625
 
2.4%
Other values (310) 15200
59.0%
Decimal Number
ValueCountFrequency (%)
1 1709
24.4%
2 938
13.4%
3 819
11.7%
4 617
 
8.8%
5 583
 
8.3%
6 565
 
8.1%
7 486
 
7.0%
8 472
 
6.8%
0 426
 
6.1%
9 376
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
A 2
66.7%
C 1
33.3%
Lowercase Letter
ValueCountFrequency (%)
d 1
50.0%
n 1
50.0%
Space Separator
ValueCountFrequency (%)
6648
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 733
100.0%
Open Punctuation
ValueCountFrequency (%)
( 221
100.0%
Close Punctuation
ValueCountFrequency (%)
) 221
100.0%
Other Punctuation
ValueCountFrequency (%)
, 209
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25750
63.1%
Common 15023
36.8%
Latin 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 1511
 
5.9%
길 1478
 
5.7%
시 1462
 
5.7%
도 1415
 
5.5%
구 1261
 
4.9%
서 788
 
3.1%
산 730
 
2.8%
면 653
 
2.5%
별 627
 
2.4%
특 625
 
2.4%
Other values (310) 15200
59.0%
Common
ValueCountFrequency (%)
6648
44.3%
1 1709
 
11.4%
2 938
 
6.2%
3 819
 
5.5%
- 733
 
4.9%
4 617
 
4.1%
5 583
 
3.9%
6 565
 
3.8%
7 486
 
3.2%
8 472
 
3.1%
Other values (5) 1453
 
9.7%
Latin
ValueCountFrequency (%)
A 2
40.0%
C 1
20.0%
d 1
20.0%
n 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25749
63.1%
ASCII 15028
36.9%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6648
44.2%
1 1709
 
11.4%
2 938
 
6.2%
3 819
 
5.4%
- 733
 
4.9%
4 617
 
4.1%
5 583
 
3.9%
6 565
 
3.8%
7 486
 
3.2%
8 472
 
3.1%
Other values (9) 1458
 
9.7%
Hangul
ValueCountFrequency (%)
로 1511
 
5.9%
길 1478
 
5.7%
시 1462
 
5.7%
도 1415
 
5.5%
구 1261
 
4.9%
서 788
 
3.1%
산 730
 
2.8%
면 653
 
2.5%
별 627
 
2.4%
특 625
 
2.4%
Other values (309) 15199
59.0%
Compat Jamo
ValueCountFrequency (%)
ㅣ 1
100.0%

소재지지번주소
Text

MISSING 

Distinct8858
Distinct (%)91.3%
Missing301
Missing (%)3.0%
Memory size156.2 KiB
2024-05-18T17:55:04.318406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length30
Mean length21.638107
Min length12

Characters and Unicode

Total characters209868
Distinct characters311
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

Unique8318 ?
Unique (%)85.8%

Sample

1st row경상남도 창원시 마산합포구 월영동 614-303
2nd row서울특별시 서초구 방배동 산 73-157
3rd row경상남도 창원시 마산합포구 자산동 39-8
4th row경상남도 창원시 의창구 소답동 169
5th row서울특별시 서초구 양재동 6-27
ValueCountFrequency (%)
경상남도 3089
 
6.6%
창원시 2949
 
6.3%
서울특별시 2157
 
4.6%
서초구 1636
 
3.5%
마산합포구 1331
 
2.9%
의창구 1065
 
2.3%
전라북도 1016
 
2.2%
성산구 549
 
1.2%
경기도 522
 
1.1%
충청남도 510
 
1.1%
Other values (8096) 31779
68.2%
2024-05-18T17:55:05.846063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36926
 
17.6%
1 8037
 
3.8%
시 7824
 
3.7%
- 7488
 
3.6%
도 7410
 
3.5%
구 6782
 
3.2%
동 6575
 
3.1%
2 5133
 
2.4%
서 4855
 
2.3%
산 4685
 
2.2%
Other values (301) 114153
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 126870
60.5%
Decimal Number 38563
 
18.4%
Space Separator 36926
 
17.6%
Dash Punctuation 7488
 
3.6%
Close Punctuation 9
 
< 0.1%
Open Punctuation 9
 
< 0.1%
Other Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 7824
 
6.2%
도 7410
 
5.8%
구 6782
 
5.3%
동 6575
 
5.2%
서 4855
 
3.8%
산 4685
 
3.7%
남 4535
 
3.6%
리 4158
 
3.3%
창 4147
 
3.3%
경 3873
 
3.1%
Other values (284) 72026
56.8%
Decimal Number
ValueCountFrequency (%)
1 8037
20.8%
2 5133
13.3%
3 4245
11.0%
4 3598
9.3%
5 3525
9.1%
6 3133
 
8.1%
7 2886
 
7.5%
8 2715
 
7.0%
0 2698
 
7.0%
9 2593
 
6.7%
Space Separator
ValueCountFrequency (%)
36926
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7488
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 126870
60.5%
Common 82997
39.5%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 7824
 
6.2%
도 7410
 
5.8%
구 6782
 
5.3%
동 6575
 
5.2%
서 4855
 
3.8%
산 4685
 
3.7%
남 4535
 
3.6%
리 4158
 
3.3%
창 4147
 
3.3%
경 3873
 
3.1%
Other values (284) 72026
56.8%
Common
ValueCountFrequency (%)
36926
44.5%
1 8037
 
9.7%
- 7488
 
9.0%
2 5133
 
6.2%
3 4245
 
5.1%
4 3598
 
4.3%
5 3525
 
4.2%
6 3133
 
3.8%
7 2886
 
3.5%
8 2715
 
3.3%
Other values (6) 5311
 
6.4%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 126870
60.5%
ASCII 82998
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36926
44.5%
1 8037
 
9.7%
- 7488
 
9.0%
2 5133
 
6.2%
3 4245
 
5.1%
4 3598
 
4.3%
5 3525
 
4.2%
6 3133
 
3.8%
7 2886
 
3.5%
8 2715
 
3.3%
Other values (7) 5312
 
6.4%
Hangul
ValueCountFrequency (%)
시 7824
 
6.2%
도 7410
 
5.8%
구 6782
 
5.3%
동 6575
 
5.2%
서 4855
 
3.8%
산 4685
 
3.7%
남 4535
 
3.6%
리 4158
 
3.3%
창 4147
 
3.3%
경 3873
 
3.1%
Other values (284) 72026
56.8%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8850
Distinct (%)93.7%
Missing558
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean36.19318
Minimum33.264518
Maximum38.154968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T17:55:06.361709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.264518
5-th percentile35.081589
Q135.227476
median35.964257
Q337.367585
95-th percentile37.499497
Maximum38.154968
Range4.8904504
Interquartile range (IQR)2.1401088

Descriptive statistics

Standard deviation1.0018576
Coefficient of variation (CV)0.02768084
Kurtosis-1.2203036
Mean36.19318
Median Absolute Deviation (MAD)0.78738541
Skewness0.069566956
Sum341736
Variance1.0037187
MonotonicityNot monotonic
2024-05-18T17:55:06.836846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.47125229 17
 
0.2%
37.47737326 16
 
0.2%
36.78753163 13
 
0.1%
37.46936451 13
 
0.1%
37.47741782 12
 
0.1%
37.47975543 9
 
0.1%
37.49844254 7
 
0.1%
35.973975 7
 
0.1%
37.49074049 7
 
0.1%
37.23652734 7
 
0.1%
Other values (8840) 9334
93.3%
(Missing) 558
 
5.6%
ValueCountFrequency (%)
33.2645175602 1
< 0.1%
33.2654953041 1
< 0.1%
33.26566628 1
< 0.1%
33.26569881 1
< 0.1%
33.26575731 1
< 0.1%
33.26576139 1
< 0.1%
33.26581986 1
< 0.1%
33.26585987 1
< 0.1%
33.26595441 1
< 0.1%
33.26606422 1
< 0.1%
ValueCountFrequency (%)
38.154968 1
< 0.1%
38.15442834 1
< 0.1%
38.1537462 2
< 0.1%
38.12884484 1
< 0.1%
38.12484421 1
< 0.1%
38.12205498 1
< 0.1%
38.12120935 1
< 0.1%
38.12114632 1
< 0.1%
38.12103625 1
< 0.1%
38.12055259 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8741
Distinct (%)92.6%
Missing558
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean127.73905
Minimum125.97882
Maximum129.41112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T17:55:07.323786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.97882
5-th percentile126.58257
Q1127.00694
median127.54969
Q3128.58457
95-th percentile128.96837
Maximum129.41112
Range3.4323016
Interquartile range (IQR)1.5776294

Descriptive statistics

Standard deviation0.8235042
Coefficient of variation (CV)0.0064467692
Kurtosis-1.4866319
Mean127.73905
Median Absolute Deviation (MAD)0.7047988
Skewness0.12885377
Sum1206112.1
Variance0.67815917
MonotonicityNot monotonic
2024-05-18T17:55:07.938290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.002457 17
 
0.2%
127.0414302 16
 
0.2%
126.5866707 13
 
0.1%
127.0423363 12
 
0.1%
127.0380713 10
 
0.1%
127.0039168 10
 
0.1%
127.0154245 8
 
0.1%
126.708557 8
 
0.1%
126.718169 7
 
0.1%
128.3268058 7
 
0.1%
Other values (8731) 9334
93.3%
(Missing) 558
 
5.6%
ValueCountFrequency (%)
125.978821 1
< 0.1%
125.979838 1
< 0.1%
125.979983 1
< 0.1%
125.980352 1
< 0.1%
125.980393 1
< 0.1%
125.981859 1
< 0.1%
125.983142 1
< 0.1%
125.983267 1
< 0.1%
125.983387 1
< 0.1%
125.990348 1
< 0.1%
ValueCountFrequency (%)
129.4111226 1
< 0.1%
129.4088093 1
< 0.1%
129.4084246 1
< 0.1%
129.4049828 1
< 0.1%
129.401866 1
< 0.1%
129.4017905 1
< 0.1%
129.4009777 1
< 0.1%
129.4001446 1
< 0.1%
129.4001431 1
< 0.1%
129.3997551 1
< 0.1%

설치연도
Real number (ℝ)

MISSING 

Distinct34
Distinct (%)0.8%
Missing5874
Missing (%)58.7%
Infinite0
Infinite (%)0.0%
Mean2013.6716
Minimum1905
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T17:55:08.790531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1905
5-th percentile2005
Q12010
median2013
Q32019
95-th percentile2021
Maximum2023
Range118
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.0458052
Coefficient of variation (CV)0.0030023789
Kurtosis24.88348
Mean2013.6716
Median Absolute Deviation (MAD)6
Skewness-1.8448163
Sum8308409
Variance36.55176
MonotonicityNot monotonic
2024-05-18T17:55:09.262727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2007 731
 
7.3%
2021 655
 
6.6%
2013 592
 
5.9%
2010 279
 
2.8%
2017 262
 
2.6%
2020 246
 
2.5%
2014 198
 
2.0%
2015 179
 
1.8%
2019 142
 
1.4%
2016 142
 
1.4%
Other values (24) 700
 
7.0%
(Missing) 5874
58.7%
ValueCountFrequency (%)
1905 1
 
< 0.1%
1989 4
 
< 0.1%
1990 3
 
< 0.1%
1991 1
 
< 0.1%
1992 1
 
< 0.1%
1993 7
0.1%
1994 4
 
< 0.1%
1997 8
0.1%
1998 15
0.1%
1999 17
0.2%
ValueCountFrequency (%)
2023 8
 
0.1%
2022 82
 
0.8%
2021 655
6.6%
2020 246
 
2.5%
2019 142
 
1.4%
2018 126
 
1.3%
2017 262
 
2.6%
2016 142
 
1.4%
2015 179
 
1.8%
2014 198
 
2.0%

설치형태
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
한전주
4956 
전용주
2930 
<NA>
1514 
건축물
600 

Length

Max length4
Median length3
Mean length3.1514
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건축물
2nd row전용주
3rd row건축물
4th row전용주
5th row한전주

Common Values

ValueCountFrequency (%)
한전주 4956
49.6%
전용주 2930
29.3%
<NA> 1514
 
15.1%
건축물 600
 
6.0%

Length

2024-05-18T17:55:09.626996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T17:55:10.043142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한전주 4956
49.6%
전용주 2930
29.3%
na 1514
 
15.1%
건축물 600
 
6.0%

관리기관전화번호
Text

Distinct122
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T17:55:10.838660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.9609
Min length7

Characters and Unicode

Total characters119609
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

Unique10 ?
Unique (%)0.1%

Sample

1st row055-220-4644
2nd row02-2155-6958
3rd row055-220-4644
4th row055-212-5441
5th row02-2155-6958
ValueCountFrequency (%)
02-2155-6958 1636
 
16.4%
055-220-4644 1331
 
13.3%
063-320-2483 472
 
4.7%
063-430-2858 437
 
4.4%
041-660-2389 340
 
3.4%
02-860-2407 328
 
3.3%
063-454-3620 302
 
3.0%
031-324-5433 247
 
2.5%
055-212-5241 217
 
2.2%
055-212-5131 203
 
2.0%
Other values (112) 4487
44.9%
2024-05-18T17:55:12.245554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 19964
16.7%
5 17330
14.5%
0 16273
13.6%
2 15417
12.9%
4 12343
10.3%
6 9320
7.8%
3 8915
7.5%
1 7711
 
6.4%
8 5596
 
4.7%
9 3700
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99645
83.3%
Dash Punctuation 19964
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 17330
17.4%
0 16273
16.3%
2 15417
15.5%
4 12343
12.4%
6 9320
9.4%
3 8915
8.9%
1 7711
7.7%
8 5596
 
5.6%
9 3700
 
3.7%
7 3040
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 19964
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 19964
16.7%
5 17330
14.5%
0 16273
13.6%
2 15417
12.9%
4 12343
10.3%
6 9320
7.8%
3 8915
7.5%
1 7711
 
6.4%
8 5596
 
4.7%
9 3700
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 19964
16.7%
5 17330
14.5%
0 16273
13.6%
2 15417
12.9%
4 12343
10.3%
6 9320
7.8%
3 8915
7.5%
1 7711
 
6.4%
8 5596
 
4.7%
9 3700
 
3.1%

관리기관명
Text

Distinct124
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T17:55:13.048315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length10.8883
Min length5

Characters and Unicode

Total characters108883
Distinct characters144
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

Unique10 ?
Unique (%)0.1%

Sample

1st row창원시 마산합포구청 안전건설과
2nd row서울특별시 서초구청
3rd row창원시 마산합포구청 안전건설과
4th row의창동행정복지센터
5th row서울특별시 서초구청
ValueCountFrequency (%)
서울특별시 2161
 
10.2%
서초구청 1636
 
7.7%
안전건설과 1417
 
6.7%
창원시 1335
 
6.3%
마산합포구청 1331
 
6.3%
전라북도 1016
 
4.8%
충청남도 708
 
3.3%
건설과 600
 
2.8%
강원도 563
 
2.6%
경기도 522
 
2.5%
Other values (149) 9965
46.9%
2024-05-18T17:55:14.491866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11254
 
10.3%
청 8852
 
8.1%
시 6367
 
5.8%
도 4643
 
4.3%
구 4547
 
4.2%
서 4340
 
4.0%
전 3411
 
3.1%
산 2742
 
2.5%
특 2700
 
2.5%
별 2700
 
2.5%
Other values (134) 57327
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 97502
89.5%
Space Separator 11254
 
10.3%
Close Punctuation 58
 
0.1%
Open Punctuation 58
 
0.1%
Decimal Number 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 8852
 
9.1%
시 6367
 
6.5%
도 4643
 
4.8%
구 4547
 
4.7%
서 4340
 
4.5%
전 3411
 
3.5%
산 2742
 
2.8%
특 2700
 
2.8%
별 2700
 
2.8%
과 2498
 
2.6%
Other values (130) 54702
56.1%
Space Separator
ValueCountFrequency (%)
11254
100.0%
Close Punctuation
ValueCountFrequency (%)
) 58
100.0%
Open Punctuation
ValueCountFrequency (%)
( 58
100.0%
Decimal Number
ValueCountFrequency (%)
1 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 97502
89.5%
Common 11381
 
10.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 8852
 
9.1%
시 6367
 
6.5%
도 4643
 
4.8%
구 4547
 
4.7%
서 4340
 
4.5%
전 3411
 
3.5%
산 2742
 
2.8%
특 2700
 
2.8%
별 2700
 
2.8%
과 2498
 
2.6%
Other values (130) 54702
56.1%
Common
ValueCountFrequency (%)
11254
98.9%
) 58
 
0.5%
( 58
 
0.5%
1 11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 97502
89.5%
ASCII 11381
 
10.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11254
98.9%
) 58
 
0.5%
( 58
 
0.5%
1 11
 
0.1%
Hangul
ValueCountFrequency (%)
청 8852
 
9.1%
시 6367
 
6.5%
도 4643
 
4.8%
구 4547
 
4.7%
서 4340
 
4.5%
전 3411
 
3.5%
산 2742
 
2.8%
특 2700
 
2.8%
별 2700
 
2.8%
과 2498
 
2.6%
Other values (130) 54702
56.1%

데이터기준일자
Date

Distinct80
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-11-16 00:00:00
Maximum2024-04-26 00:00:00
2024-05-18T17:55:14.941923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:55:15.389430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

제공기관코드
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4600291
Minimum3010000
Maximum6520000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T17:55:15.941822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3010000
5-th percentile3210000
Q13720000
median4681000
Q35670000
95-th percentile5670000
Maximum6520000
Range3510000
Interquartile range (IQR)1950000

Descriptive statistics

Standard deviation958044.92
Coefficient of variation (CV)0.20825746
Kurtosis-1.2484533
Mean4600291
Median Absolute Deviation (MAD)989000
Skewness-0.23342298
Sum4.600291 × 1010
Variance9.1785007 × 1011
MonotonicityNot monotonic
2024-05-18T17:55:16.558555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5670000 2949
29.5%
3210000 1636
16.4%
4740000 472
 
4.7%
4731000 432
 
4.3%
4530000 340
 
3.4%
3160000 328
 
3.3%
4050000 248
 
2.5%
4220000 183
 
1.8%
3340000 181
 
1.8%
4420000 179
 
1.8%
Other values (84) 3052
30.5%
ValueCountFrequency (%)
3010000 10
 
0.1%
3140000 13
 
0.1%
3150000 49
 
0.5%
3160000 328
 
3.3%
3170000 69
 
0.7%
3190000 6
 
0.1%
3210000 1636
16.4%
3220000 38
 
0.4%
3230000 12
 
0.1%
3280000 40
 
0.4%
ValueCountFrequency (%)
6520000 30
 
0.3%
6510000 53
 
0.5%
6300000 36
 
0.4%
5690000 104
 
1.0%
5680000 7
 
0.1%
5670000 2949
29.5%
5590000 40
 
0.4%
5480000 43
 
0.4%
5430000 2
 
< 0.1%
5360000 14
 
0.1%

제공기관명
Text

Distinct94
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T17:55:17.358006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.4996
Min length5

Characters and Unicode

Total characters84996
Distinct characters97
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

Unique9 ?
Unique (%)0.1%

Sample

1st row경상남도 창원시
2nd row서울특별시 서초구
3rd row경상남도 창원시
4th row경상남도 창원시
5th row서울특별시 서초구
ValueCountFrequency (%)
경상남도 3101
15.6%
창원시 2949
14.8%
서울특별시 2161
 
10.9%
서초구 1636
 
8.2%
전북특별자치도 890
 
4.5%
충청남도 708
 
3.6%
전라북도 635
 
3.2%
경기도 522
 
2.6%
무주군 472
 
2.4%
진안군 437
 
2.2%
Other values (92) 6349
32.0%
2024-05-18T17:55:18.707841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9860
 
11.6%
시 7976
 
9.4%
도 7381
 
8.7%
서 4278
 
5.0%
남 4219
 
5.0%
경 3859
 
4.5%
원 3512
 
4.1%
특 3478
 
4.1%
별 3478
 
4.1%
상 3300
 
3.9%
Other values (87) 33655
39.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75136
88.4%
Space Separator 9860
 
11.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 7976
 
10.6%
도 7381
 
9.8%
서 4278
 
5.7%
남 4219
 
5.6%
경 3859
 
5.1%
원 3512
 
4.7%
특 3478
 
4.6%
별 3478
 
4.6%
상 3300
 
4.4%
창 3004
 
4.0%
Other values (86) 30651
40.8%
Space Separator
ValueCountFrequency (%)
9860
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 75136
88.4%
Common 9860
 
11.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 7976
 
10.6%
도 7381
 
9.8%
서 4278
 
5.7%
남 4219
 
5.6%
경 3859
 
5.1%
원 3512
 
4.7%
특 3478
 
4.6%
별 3478
 
4.6%
상 3300
 
4.4%
창 3004
 
4.0%
Other values (86) 30651
40.8%
Common
ValueCountFrequency (%)
9860
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 75136
88.4%
ASCII 9860
 
11.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9860
100.0%
Hangul
ValueCountFrequency (%)
시 7976
 
10.6%
도 7381
 
9.8%
서 4278
 
5.7%
남 4219
 
5.6%
경 3859
 
5.1%
원 3512
 
4.7%
특 3478
 
4.6%
별 3478
 
4.6%
상 3300
 
4.4%
창 3004
 
4.0%
Other values (86) 30651
40.8%

Interactions

2024-05-18T17:54:53.344268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:47.996228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:49.313771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:50.629880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:52.095379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:53.536767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:48.354238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:49.569810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:50.918940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:52.374051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:53.712486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:48.558230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:49.823904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:51.180990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:52.546930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:53.992565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:48.843050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:50.090886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:51.535731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:52.795074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:54.267141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:49.119460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:50.353917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:51.805498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T17:54:53.061798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T17:55:19.058369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치개수위도경도설치연도설치형태데이터기준일자제공기관코드제공기관명
설치개수1.0000.0780.0670.0590.1940.5920.0720.598
위도0.0781.0000.7530.4500.4870.9840.9270.993
경도0.0670.7531.0000.3770.3480.9770.7960.989
설치연도0.0590.4500.3771.0000.3270.7100.6740.757
설치형태0.1940.4870.3480.3271.0000.6230.4230.631
데이터기준일자0.5920.9840.9770.7100.6231.0000.9951.000
제공기관코드0.0720.9270.7960.6740.4230.9951.0001.000
제공기관명0.5980.9930.9890.7570.6311.0001.0001.000
2024-05-18T17:55:19.511269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치개수위도경도설치연도제공기관코드설치형태
설치개수1.0000.043-0.040-0.116-0.0380.061
위도0.0431.000-0.567-0.237-0.8070.246
경도-0.040-0.5671.0000.2070.5690.222
설치연도-0.116-0.2370.2071.0000.0990.112
제공기관코드-0.038-0.8070.5690.0991.0000.206
설치형태0.0610.2460.2220.1120.2061.000

Missing values

2024-05-18T17:54:54.652279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T17:54:55.106701image/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.
2024-05-18T17:54:55.541647image/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

보안등위치명설치개수소재지도로명주소소재지지번주소위도경도설치연도설치형태관리기관전화번호관리기관명데이터기준일자제공기관코드제공기관명
45764월영동0741<NA>경상남도 창원시 마산합포구 월영동 614-30335.176147128.562683<NA>건축물055-220-4644창원시 마산합포구청 안전건설과2021-10-205670000경상남도 창원시
28707방배2동0561<NA>서울특별시 서초구 방배동 산 73-15737.48046126.9898662007전용주02-2155-6958서울특별시 서초구청2022-11-303210000서울특별시 서초구
48304자산동0401<NA>경상남도 창원시 마산합포구 자산동 39-835.205528128.566299<NA>건축물055-220-4644창원시 마산합포구청 안전건설과2021-10-205670000경상남도 창원시
39663소답동1051<NA>경상남도 창원시 의창구 소답동 16935.261702128.629855<NA>전용주055-212-5441의창동행정복지센터2021-10-205670000경상남도 창원시
24476논현로31길 24-71<NA>서울특별시 서초구 양재동 6-2737.48177127.0414682007한전주02-2155-6958서울특별시 서초구청2022-11-303210000서울특별시 서초구
31506방배로 234-91<NA>서울특별시 서초구 방배동 773-1837.494251126.9902182021한전주02-2155-6958서울특별시 서초구청2022-11-303210000서울특별시 서초구
43492구산면9711<NA>경상남도 창원시 마산합포구 구산면 내포리 산 293-235.099867128.549326<NA>한전주055-220-4644창원시 마산합포구청 안전건설과2021-10-205670000경상남도 창원시
7031강원도 태백시 상장동2611강원도 태백시 장성1길 66강원도 태백시 장성동 150-5437.100974129.0059582018전용주033-550-2128강원도 태백시청2022-08-034220000강원도 태백시
34215서초대로30길 11-91<NA>서울특별시 서초구 방배동 141-2637.488012126.9965292012전용주02-2155-6958서울특별시 서초구청2022-11-303210000서울특별시 서초구
41334중앙동1971<NA>경상남도 창원시 성산구 중앙동 501-5735.226714128.677969<NA>한전주055-272-5219중앙동행정복지센터2021-10-205670000경상남도 창원시
보안등위치명설치개수소재지도로명주소소재지지번주소위도경도설치연도설치형태관리기관전화번호관리기관명데이터기준일자제공기관코드제공기관명
12366운산면M0141<NA>충청남도 서산시 운산면 여미리 43-636.821922126.5830362013전용주041-660-2389충청남도 서산시청2023-07-144530000충청남도 서산시
35548방배로28길 71<NA>서울특별시 서초구 방배동 837-1237.49036126.992672007한전주02-2155-6958서울특별시 서초구청2022-11-303210000서울특별시 서초구
48374월영동3371<NA>경상남도 창원시 마산합포구 월영동 620-2435.173618128.556137<NA>건축물055-220-4644창원시 마산합포구청 안전건설과2021-10-205670000경상남도 창원시
5659삼학동2141<NA>전라북도 군산시 삼학동 323-2835.975893126.7135952013전용주063-454-3620전라북도 군산시청2024-01-024671000전북특별자치도 군산시
7775가리봉동1서울특별시 구로구 남부순환로105나길 3-4서울특별시 구로구 가리봉동 135-4937.479632126.8915452017건축물02-860-2407서울특별시 구로구청2023-05-313160000서울특별시 구로구
48007자산동1921<NA>경상남도 창원시 마산합포구 자산동 28235.202347128.562683<NA>건축물055-220-4644창원시 마산합포구청 안전건설과2021-10-205670000경상남도 창원시
40484서상동1421<NA>경상남도 창원시 의창구 서상동 641-135.264178128.619264<NA>한전주055-212-5441의창동행정복지센터2021-10-205670000경상남도 창원시
35069동정동0661<NA>경상남도 창원시 의창구 동정동 349-135.265002128.614924<NA>한전주055-212-5441의창동행정복지센터2021-10-205670000경상남도 창원시
1676마방로6길 7-631<NA>서울특별시 서초구 양재동 273-1037.475644127.0442962007한전주02-2155-6958서울특별시 서초구청2022-11-303210000서울특별시 서초구
2371대야면5151<NA>전라북도 군산시 대야면 산월리 98535.947212126.8146942013한전주063-454-3620전라북도 군산시청2024-01-024671000전북특별자치도 군산시

Duplicate rows

Most frequently occurring

보안등위치명설치개수소재지도로명주소소재지지번주소위도경도설치연도설치형태관리기관전화번호관리기관명데이터기준일자제공기관코드제공기관명# duplicates
0노온사동 409-101경기도 광명시 광명로 482경기도 광명시 노온사동 409-837.44367126.8473251990한전주02-2680-6618경기도 광명시 학온동 행정복지센터2022-10-313900000경기도 광명시2
1노온사동 6331경기도 광명시 광명로 352경기도 광명시 노온사동 63337.432295126.847375<NA>한전주02-2680-6618경기도 광명시 학온동 행정복지센터2022-10-313900000경기도 광명시2