Overview

Dataset statistics

Number of variables9
Number of observations81
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 KiB
Average record size in memory76.6 B

Variable types

Text2
Categorical5
Numeric2

Dataset

Description관리번호,구분코드(01:전력구,02:통신구),관리기관,관리부서,시설물명,집수정위치,자치구,X좌표,Y좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21116/S/1/datasetView.do

Alerts

구분코드(01:전력구,02:통신구) has constant value ""Constant
관리기관 has constant value ""Constant
시설물명 has constant value ""Constant
X좌표 is highly overall correlated with 관리부서 and 1 other fieldsHigh correlation
Y좌표 is highly overall correlated with 관리부서 and 1 other fieldsHigh correlation
관리부서 is highly overall correlated with X좌표 and 2 other fieldsHigh correlation
자치구 is highly overall correlated with X좌표 and 2 other fieldsHigh correlation
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-11 05:21:13.809007
Analysis finished2023-12-11 05:21:15.461707
Duration1.65 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Text

UNIQUE 

Distinct81
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-12-11T14:21:15.836646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters891
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

Unique81 ?
Unique (%)100.0%

Sample

1st row2015_2_0211
2nd row2015_2_0212
3rd row2015_2_0213
4th row2015_2_0214
5th row2015_2_0215
ValueCountFrequency (%)
2015_2_0211 1
 
1.2%
2015_2_0261 1
 
1.2%
2015_2_0280 1
 
1.2%
2015_2_0279 1
 
1.2%
2015_2_0278 1
 
1.2%
2015_2_0277 1
 
1.2%
2015_2_0276 1
 
1.2%
2015_2_0275 1
 
1.2%
2015_2_0274 1
 
1.2%
2015_2_0273 1
 
1.2%
Other values (71) 71
87.7%
2023-12-11T14:21:16.526847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 253
28.4%
0 175
19.6%
_ 162
18.2%
1 97
 
10.9%
5 97
 
10.9%
3 24
 
2.7%
4 18
 
2.0%
6 18
 
2.0%
9 16
 
1.8%
7 16
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 729
81.8%
Connector Punctuation 162
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 253
34.7%
0 175
24.0%
1 97
 
13.3%
5 97
 
13.3%
3 24
 
3.3%
4 18
 
2.5%
6 18
 
2.5%
9 16
 
2.2%
7 16
 
2.2%
8 15
 
2.1%
Connector Punctuation
ValueCountFrequency (%)
_ 162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 891
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 253
28.4%
0 175
19.6%
_ 162
18.2%
1 97
 
10.9%
5 97
 
10.9%
3 24
 
2.7%
4 18
 
2.0%
6 18
 
2.0%
9 16
 
1.8%
7 16
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 891
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 253
28.4%
0 175
19.6%
_ 162
18.2%
1 97
 
10.9%
5 97
 
10.9%
3 24
 
2.7%
4 18
 
2.0%
6 18
 
2.0%
9 16
 
1.8%
7 16
 
1.8%

구분코드(01:전력구,02:통신구)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size780.0 B
2
81 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 81
100.0%

Length

2023-12-11T14:21:16.745520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:21:16.898587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 81
100.0%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size780.0 B
KT 강북, 서부, 중부
81 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKT 강북, 서부, 중부
2nd rowKT 강북, 서부, 중부
3rd rowKT 강북, 서부, 중부
4th rowKT 강북, 서부, 중부
5th rowKT 강북, 서부, 중부

Common Values

ValueCountFrequency (%)
KT 강북, 서부, 중부 81
100.0%

Length

2023-12-11T14:21:17.082958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:21:17.250203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kt 81
25.0%
강북 81
25.0%
서부 81
25.0%
중부 81
25.0%

관리부서
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size780.0 B
KT서울중부
41 
kt 신촌지점
7 
kt 성수지점
6 
kt 광화문지사(혜화)
6 
KT구로지사
6 
Other values (4)
15 

Length

Max length12
Median length6
Mean length7.3703704
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkt 성수지점
2nd rowkt 성수지점
3rd rowkt 성수지점
4th rowkt 성수지점
5th rowkt 성수지점

Common Values

ValueCountFrequency (%)
KT서울중부 41
50.6%
kt 신촌지점 7
 
8.6%
kt 성수지점 6
 
7.4%
kt 광화문지사(혜화) 6
 
7.4%
KT구로지사 6
 
7.4%
kt 서대문지사(가좌) 5
 
6.2%
kt 원효지점 5
 
6.2%
kt 도봉지점(방학) 3
 
3.7%
kt 서대문지사(홍제) 2
 
2.5%

Length

2023-12-11T14:21:17.446576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:21:17.809998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kt서울중부 41
35.7%
kt 34
29.6%
신촌지점 7
 
6.1%
성수지점 6
 
5.2%
광화문지사(혜화 6
 
5.2%
kt구로지사 6
 
5.2%
서대문지사(가좌 5
 
4.3%
원효지점 5
 
4.3%
도봉지점(방학 3
 
2.6%
서대문지사(홍제 2
 
1.7%

시설물명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size780.0 B
통신구
81 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
통신구 81
100.0%

Length

2023-12-11T14:21:18.036627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:21:18.187784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통신구 81
100.0%

집수정위치
Text

Distinct80
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size780.0 B
2023-12-11T14:21:18.526491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length29
Mean length21.259259
Min length9

Characters and Unicode

Total characters1722
Distinct characters194
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)97.5%

Sample

1st row성동구 아차산로 13길47 (KT성수지점앞)
2nd row성동구 뚝섬로 17가길21(경수초등앞)
3rd row성동구 아차산로 13길54(신한은행앞)
4th row광진구 동일로 190(화양사거리)
5th row성동구 아차산로 13길47 (KT 성수지점앞)
ValueCountFrequency (%)
성북구 12
 
4.2%
종로구 10
 
3.5%
서대문구 9
 
3.1%
앞 9
 
3.1%
중구 8
 
2.8%
동대문구 8
 
2.8%
마포구 7
 
2.4%
서울특별시 6
 
2.1%
용산구 6
 
2.1%
성동구 5
 
1.7%
Other values (179) 206
72.0%
2023-12-11T14:21:19.098334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
211
 
12.3%
구 96
 
5.6%
로 87
 
5.1%
) 80
 
4.6%
( 80
 
4.6%
동 58
 
3.4%
1 57
 
3.3%
2 41
 
2.4%
문 39
 
2.3%
앞 38
 
2.2%
Other values (184) 935
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1031
59.9%
Decimal Number 283
 
16.4%
Space Separator 211
 
12.3%
Close Punctuation 80
 
4.6%
Open Punctuation 80
 
4.6%
Dash Punctuation 19
 
1.1%
Lowercase Letter 10
 
0.6%
Uppercase Letter 8
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 96
 
9.3%
로 87
 
8.4%
동 58
 
5.6%
문 39
 
3.8%
앞 38
 
3.7%
대 28
 
2.7%
성 24
 
2.3%
길 19
 
1.8%
서 19
 
1.8%
북 18
 
1.7%
Other values (166) 605
58.7%
Decimal Number
ValueCountFrequency (%)
1 57
20.1%
2 41
14.5%
4 31
11.0%
3 29
10.2%
6 26
9.2%
7 24
8.5%
5 23
8.1%
8 22
 
7.8%
9 16
 
5.7%
0 14
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
k 5
50.0%
t 5
50.0%
Uppercase Letter
ValueCountFrequency (%)
T 4
50.0%
K 4
50.0%
Space Separator
ValueCountFrequency (%)
211
100.0%
Close Punctuation
ValueCountFrequency (%)
) 80
100.0%
Open Punctuation
ValueCountFrequency (%)
( 80
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1031
59.9%
Common 673
39.1%
Latin 18
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 96
 
9.3%
로 87
 
8.4%
동 58
 
5.6%
문 39
 
3.8%
앞 38
 
3.7%
대 28
 
2.7%
성 24
 
2.3%
길 19
 
1.8%
서 19
 
1.8%
북 18
 
1.7%
Other values (166) 605
58.7%
Common
ValueCountFrequency (%)
211
31.4%
) 80
 
11.9%
( 80
 
11.9%
1 57
 
8.5%
2 41
 
6.1%
4 31
 
4.6%
3 29
 
4.3%
6 26
 
3.9%
7 24
 
3.6%
5 23
 
3.4%
Other values (4) 71
 
10.5%
Latin
ValueCountFrequency (%)
k 5
27.8%
t 5
27.8%
T 4
22.2%
K 4
22.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1031
59.9%
ASCII 691
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
211
30.5%
) 80
 
11.6%
( 80
 
11.6%
1 57
 
8.2%
2 41
 
5.9%
4 31
 
4.5%
3 29
 
4.2%
6 26
 
3.8%
7 24
 
3.5%
5 23
 
3.3%
Other values (8) 89
12.9%
Hangul
ValueCountFrequency (%)
구 96
 
9.3%
로 87
 
8.4%
동 58
 
5.6%
문 39
 
3.8%
앞 38
 
3.7%
대 28
 
2.7%
성 24
 
2.3%
길 19
 
1.8%
서 19
 
1.8%
북 18
 
1.7%
Other values (166) 605
58.7%

자치구
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size780.0 B
성북구
13 
종로구
11 
서대문구
8 
동대문구
8 
중 구
8 
Other values (10)
33 

Length

Max length4
Median length3
Mean length3.2962963
Min length3

Unique

Unique2 ?
Unique (%)2.5%

Sample

1st row성동구
2nd row성동구
3rd row성동구
4th row광진구
5th row성동구

Common Values

ValueCountFrequency (%)
성북구 13
16.0%
종로구 11
13.6%
서대문구 8
9.9%
동대문구 8
9.9%
중 구 8
9.9%
마포구 7
8.6%
용산구 6
7.4%
성동구 5
 
6.2%
관악구 4
 
4.9%
도봉구 3
 
3.7%
Other values (5) 8
9.9%

Length

2023-12-11T14:21:19.307945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성북구 13
14.6%
종로구 11
12.4%
서대문구 8
9.0%
동대문구 8
9.0%
중 8
9.0%
구 8
9.0%
마포구 7
7.9%
용산구 6
6.7%
성동구 5
 
5.6%
관악구 4
 
4.5%
Other values (6) 11
12.4%

X좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199196.81
Minimum189980.8
Maximum206020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-11T14:21:19.915930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum189980.8
5-th percentile191759.6
Q1195332.4
median200172
Q3203174.4
95-th percentile205462.8
Maximum206020
Range16039.2
Interquartile range (IQR)7842.0001

Descriptive statistics

Standard deviation4720.6013
Coefficient of variation (CV)0.023698176
Kurtosis-1.2150298
Mean199196.81
Median Absolute Deviation (MAD)3666
Skewness-0.29555385
Sum16134942
Variance22284076
MonotonicityNot monotonic
2023-12-11T14:21:20.119619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205462.800004 3
 
3.7%
195332.399967 2
 
2.5%
200129.199987 2
 
2.5%
194744.000003 1
 
1.2%
205462.000024 1
 
1.2%
197226.00002 1
 
1.2%
197292.800002 1
 
1.2%
197523.600001 1
 
1.2%
196477.999971 1
 
1.2%
197382.800027 1
 
1.2%
Other values (67) 67
82.7%
ValueCountFrequency (%)
189980.800033 1
1.2%
190323.600004 1
1.2%
191186.800036 1
1.2%
191433.199959 1
1.2%
191759.599964 1
1.2%
192139.599967 1
1.2%
192332.800001 1
1.2%
192342.800029 1
1.2%
192372.399992 1
1.2%
192416.40003 1
1.2%
ValueCountFrequency (%)
206019.999998 1
 
1.2%
205553.600001 1
 
1.2%
205462.800004 3
3.7%
205462.000024 1
 
1.2%
205348.799998 1
 
1.2%
205292.399956 1
 
1.2%
205191.599997 1
 
1.2%
205046.400023 1
 
1.2%
204990.400042 1
 
1.2%
204941.200005 1
 
1.2%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452220.04
Minimum442231.2
Maximum463484.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2023-12-11T14:21:20.382641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum442231.2
5-th percentile442532
Q1450888
median452453.6
Q3454434.8
95-th percentile456942.8
Maximum463484.8
Range21253.6
Interquartile range (IQR)3546.8

Descriptive statistics

Standard deviation4115.9753
Coefficient of variation (CV)0.0091017092
Kurtosis1.7501478
Mean452220.04
Median Absolute Deviation (MAD)1781.2
Skewness-0.24670242
Sum36629824
Variance16941253
MonotonicityNot monotonic
2023-12-11T14:21:20.561877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
449785.599999 3
 
3.7%
455198.800005 2
 
2.5%
452999.2 2
 
2.5%
454637.600003 1
 
1.2%
455881.600004 1
 
1.2%
451169.599998 1
 
1.2%
451356.399995 1
 
1.2%
451019.599996 1
 
1.2%
452453.599997 1
 
1.2%
452329.199995 1
 
1.2%
Other values (67) 67
82.7%
ValueCountFrequency (%)
442231.199999 1
1.2%
442267.599999 1
1.2%
442390.799999 1
1.2%
442520.799999 1
1.2%
442531.999998 1
1.2%
442724.400005 1
1.2%
447359.2 1
1.2%
447431.599998 1
1.2%
448431.999999 1
1.2%
448483.200003 1
1.2%
ValueCountFrequency (%)
463484.800005 1
1.2%
463025.999995 1
1.2%
462863.199996 1
1.2%
458286.0 1
1.2%
456942.800001 1
1.2%
456660.400004 1
1.2%
456114.4 1
1.2%
455988.000004 1
1.2%
455947.999997 1
1.2%
455881.600004 1
1.2%

Interactions

2023-12-11T14:21:14.734977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:21:14.420894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:21:14.912891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:21:14.583626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T14:21:20.708981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호관리부서집수정위치자치구X좌표Y좌표
관리번호1.0001.0001.0001.0001.0001.000
관리부서1.0001.0001.0000.9660.8680.884
집수정위치1.0001.0001.0001.0001.0001.000
자치구1.0000.9661.0001.0000.9080.945
X좌표1.0000.8681.0000.9081.0000.661
Y좌표1.0000.8841.0000.9450.6611.000
2023-12-11T14:21:20.852731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구관리부서
자치구1.0000.810
관리부서0.8101.000
2023-12-11T14:21:20.969176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
X좌표Y좌표관리부서자치구
X좌표1.0000.3610.6400.618
Y좌표0.3611.0000.6910.755
관리부서0.6400.6911.0000.810
자치구0.6180.7550.8101.000

Missing values

2023-12-11T14:21:15.102888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T14:21:15.360301image/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.

Sample

관리번호구분코드(01:전력구,02:통신구)관리기관관리부서시설물명집수정위치자치구X좌표Y좌표
02015_2_02112KT 강북, 서부, 중부kt 성수지점통신구성동구 아차산로 13길47 (KT성수지점앞)성동구205462.800004449785.599999
12015_2_02122KT 강북, 서부, 중부kt 성수지점통신구성동구 뚝섬로 17가길21(경수초등앞)성동구205292.399956448919.999998
22015_2_02132KT 강북, 서부, 중부kt 성수지점통신구성동구 아차산로 13길54(신한은행앞)성동구205553.600001449796.399996
32015_2_02142KT 강북, 서부, 중부kt 성수지점통신구광진구 동일로 190(화양사거리)광진구206019.999998449917.6
42015_2_02152KT 강북, 서부, 중부kt 성수지점통신구성동구 아차산로 13길47 (KT 성수지점앞)성동구205462.800004449785.599999
52015_2_02162KT 강북, 서부, 중부kt 성수지점통신구성동구 아차산로 13길47 (KT 성수지점앞)성동구205462.800004449785.599999
62015_2_02192KT 강북, 서부, 중부kt 서대문지사(가좌)통신구서대문구 응암로121 (kt가좌지사 우측편)서대문구192372.399992453800.800002
72015_2_02202KT 강북, 서부, 중부kt 서대문지사(가좌)통신구서대문구 응암로113 (중소기업은행앞)서대문구192332.800001453691.600002
82015_2_02212KT 강북, 서부, 중부kt 서대문지사(가좌)통신구서대문구 증가로30길 25 (kt가좌지사 후면)서대문구192342.800029453836.400002
92015_2_02222KT 강북, 서부, 중부kt 서대문지사(가좌)통신구서대문구 증가로 261(증산2교 북단 우측)은평구192139.599967453813.999996
관리번호구분코드(01:전력구,02:통신구)관리기관관리부서시설물명집수정위치자치구X좌표Y좌표
712015_2_02932KT 강북, 서부, 중부KT서울중부통신구강북구 도봉로34 (미아사거리 대지극장)강북구202712.400025456942.800001
722015_2_02942KT 강북, 서부, 중부KT서울중부통신구강북구 도봉로173 (미아역사 5번출구)강북구202353.999957458286.0
732015_2_02952KT 강북, 서부, 중부KT서울중부통신구강남구 논현로872 (논현동 압구정사거리)강남구202548.800023447431.599998
742015_2_02962KT 강북, 서부, 중부KT서울중부통신구용산구 서빙고로4-12 (용산병원앞)용산구196920.799996447359.2
752015_2_02982KT 강북, 서부, 중부KT서울중부통신구중구 서소문로131 (시청앞수직구)중 구197826.000024451585.600005
762015_2_03002KT 강북, 서부, 중부KT서울중부통신구중구 을지로79 (분기구)중 구198813.200003451884.000001
772015_2_03012KT 강북, 서부, 중부KT서울중부통신구중구 을지로264중 구200623.999964451800.800002
782015_2_03022KT 강북, 서부, 중부KT서울중부통신구중구 다산로248 (율원파출소)중 구201447.600042451723.200003
792015_2_03032KT 강북, 서부, 중부KT서울중부통신구종로구종로266(청계6가수직구)종로구200647.600034452351.599997
802015_2_03042KT 강북, 서부, 중부KT서울중부통신구종로구 종로275-1(동대문수직구)종로구200742.800027452440.400004