Overview

Dataset statistics

Number of variables16
Number of observations692
Missing cells743
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory89.3 KiB
Average record size in memory132.2 B

Variable types

Text4
Categorical8
Numeric3
Unsupported1

Dataset

Description관리번호,자치구,와이파이명,도로명주소,상세주소,설치위치(층),설치유형,설치기관,서비스구분,망종류,설치년도,실내외구분,wifi접속환경,X좌표,Y좌표,작업일자
Author관악구
URLhttps://data.seoul.go.kr/dataList/OA-20909/S/1/datasetView.do

Alerts

자치구 has constant value ""Constant
실내외구분 is highly overall correlated with 설치년도 and 5 other fieldsHigh correlation
설치위치(층) is highly overall correlated with 서비스구분 and 1 other fieldsHigh correlation
설치기관 is highly overall correlated with 설치년도 and 5 other fieldsHigh correlation
서비스구분 is highly overall correlated with 설치위치(층) and 4 other fieldsHigh correlation
설치유형 is highly overall correlated with 설치기관 and 4 other fieldsHigh correlation
작업일자 is highly overall correlated with 설치년도 and 5 other fieldsHigh correlation
설치년도 is highly overall correlated with 설치기관 and 3 other fieldsHigh correlation
망종류 is highly overall correlated with 설치년도 and 3 other fieldsHigh correlation
설치위치(층) is highly imbalanced (55.5%)Imbalance
도로명주소 has 47 (6.8%) missing valuesMissing
wifi접속환경 has 692 (100.0%) missing valuesMissing
관리번호 has unique valuesUnique
wifi접속환경 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-10 23:39:26.739665
Analysis finished2024-05-10 23:39:33.882364
Duration7.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Text

UNIQUE 

Distinct692
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
2024-05-10T23:39:34.555726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length8.2991329
Min length7

Characters and Unicode

Total characters5743
Distinct characters20
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique692 ?
Unique (%)100.0%

Sample

1st rowBS100242
2nd rowBS100243
3rd rowBS100244
4th rowBS100245
5th rowBS100246
ValueCountFrequency (%)
bs100242 1
 
0.1%
서울-4170 1
 
0.1%
서울-4180 1
 
0.1%
서울-3990 1
 
0.1%
서울-3990-1 1
 
0.1%
서울-3990-2 1
 
0.1%
서울-4046 1
 
0.1%
서울-4048 1
 
0.1%
서울-4168 1
 
0.1%
서울-4169 1
 
0.1%
Other values (682) 682
98.6%
2024-05-10T23:39:36.155452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 804
14.0%
1 643
11.2%
2 443
 
7.7%
- 418
 
7.3%
4 379
 
6.6%
서 352
 
6.1%
울 352
 
6.1%
6 278
 
4.8%
7 273
 
4.8%
5 259
 
4.5%
Other values (10) 1542
26.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3705
64.5%
Other Letter 895
 
15.6%
Uppercase Letter 725
 
12.6%
Dash Punctuation 418
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 804
21.7%
1 643
17.4%
2 443
12.0%
4 379
10.2%
6 278
 
7.5%
7 273
 
7.4%
5 259
 
7.0%
9 237
 
6.4%
3 213
 
5.7%
8 176
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
A 150
20.7%
G 150
20.7%
S 122
16.8%
W 113
15.6%
F 113
15.6%
B 77
10.6%
Other Letter
ValueCountFrequency (%)
서 352
39.3%
울 352
39.3%
차 191
21.3%
Dash Punctuation
ValueCountFrequency (%)
- 418
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4123
71.8%
Hangul 895
 
15.6%
Latin 725
 
12.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 804
19.5%
1 643
15.6%
2 443
10.7%
- 418
10.1%
4 379
9.2%
6 278
 
6.7%
7 273
 
6.6%
5 259
 
6.3%
9 237
 
5.7%
3 213
 
5.2%
Latin
ValueCountFrequency (%)
A 150
20.7%
G 150
20.7%
S 122
16.8%
W 113
15.6%
F 113
15.6%
B 77
10.6%
Hangul
ValueCountFrequency (%)
서 352
39.3%
울 352
39.3%
차 191
21.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4848
84.4%
Hangul 895
 
15.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 804
16.6%
1 643
13.3%
2 443
9.1%
- 418
8.6%
4 379
7.8%
6 278
 
5.7%
7 273
 
5.6%
5 259
 
5.3%
9 237
 
4.9%
3 213
 
4.4%
Other values (7) 901
18.6%
Hangul
ValueCountFrequency (%)
서 352
39.3%
울 352
39.3%
차 191
21.3%

자치구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
관악구
692 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
관악구 692
100.0%

Length

2024-05-10T23:39:36.818614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:39:37.216283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관악구 692
100.0%

와이파이명
Text

Distinct201
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
2024-05-10T23:39:37.768093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length7.1907514
Min length3

Characters and Unicode

Total characters4976
Distinct characters229
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)13.6%

Sample

1st row버스정류소_관악구청
2nd row버스정류소_구로디지털단지역
3rd row버스정류소_봉천사거리.봉천중앙시장
4th row버스정류소_봉현초등학교
5th row버스정류소_사당역
ValueCountFrequency (%)
관악구청 53
 
7.7%
도림천공원 30
 
4.3%
관악노인종합복지관 16
 
2.3%
사당역 16
 
2.3%
관악구육아종합지원센터 15
 
2.2%
환경공무관휴게실 14
 
2.0%
봉천역 14
 
2.0%
관악구보건소 14
 
2.0%
서울대입구역 14
 
2.0%
샤로수길 13
 
1.9%
Other values (191) 493
71.2%
2024-05-10T23:39:39.276015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
관 315
 
6.3%
악 197
 
4.0%
구 157
 
3.2%
원 154
 
3.1%
역 131
 
2.6%
지 127
 
2.6%
센 121
 
2.4%
터 120
 
2.4%
동 103
 
2.1%
소 101
 
2.0%
Other values (219) 3450
69.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4844
97.3%
Connector Punctuation 77
 
1.5%
Other Punctuation 26
 
0.5%
Uppercase Letter 21
 
0.4%
Open Punctuation 3
 
0.1%
Close Punctuation 3
 
0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 315
 
6.5%
악 197
 
4.1%
구 157
 
3.2%
원 154
 
3.2%
역 131
 
2.7%
지 127
 
2.6%
센 121
 
2.5%
터 120
 
2.5%
동 103
 
2.1%
소 101
 
2.1%
Other values (205) 3318
68.5%
Uppercase Letter
ValueCountFrequency (%)
A 4
19.0%
C 4
19.0%
W 4
19.0%
Y 4
19.0%
T 2
9.5%
K 2
9.5%
G 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 25
96.2%
& 1
 
3.8%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
5 1
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 77
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4844
97.3%
Common 111
 
2.2%
Latin 21
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 315
 
6.5%
악 197
 
4.1%
구 157
 
3.2%
원 154
 
3.2%
역 131
 
2.7%
지 127
 
2.6%
센 121
 
2.5%
터 120
 
2.5%
동 103
 
2.1%
소 101
 
2.1%
Other values (205) 3318
68.5%
Common
ValueCountFrequency (%)
_ 77
69.4%
. 25
 
22.5%
( 3
 
2.7%
) 3
 
2.7%
2 1
 
0.9%
& 1
 
0.9%
5 1
 
0.9%
Latin
ValueCountFrequency (%)
A 4
19.0%
C 4
19.0%
W 4
19.0%
Y 4
19.0%
T 2
9.5%
K 2
9.5%
G 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4844
97.3%
ASCII 132
 
2.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
관 315
 
6.5%
악 197
 
4.1%
구 157
 
3.2%
원 154
 
3.2%
역 131
 
2.7%
지 127
 
2.6%
센 121
 
2.5%
터 120
 
2.5%
동 103
 
2.1%
소 101
 
2.1%
Other values (205) 3318
68.5%
ASCII
ValueCountFrequency (%)
_ 77
58.3%
. 25
 
18.9%
A 4
 
3.0%
C 4
 
3.0%
W 4
 
3.0%
Y 4
 
3.0%
( 3
 
2.3%
) 3
 
2.3%
T 2
 
1.5%
K 2
 
1.5%
Other values (4) 4
 
3.0%

도로명주소
Text

MISSING 

Distinct309
Distinct (%)47.9%
Missing47
Missing (%)6.8%
Memory size5.5 KiB
2024-05-10T23:39:40.341021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length33
Mean length14.344186
Min length4

Characters and Unicode

Total characters9252
Distinct characters142
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)31.0%

Sample

1st row봉천동 1571-1
2nd row시흥대로
3rd row관악로 201
4th row성현로 117
5th row과천대로
ValueCountFrequency (%)
관악구 348
 
17.1%
서울특별시 325
 
16.0%
관악로 98
 
4.8%
145 86
 
4.2%
남부순환로 47
 
2.3%
신림동 42
 
2.1%
봉천동 41
 
2.0%
35 28
 
1.4%
신림로 28
 
1.4%
난곡로 21
 
1.0%
Other values (395) 971
47.7%
2024-05-10T23:39:41.677009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1391
 
15.0%
1 536
 
5.8%
관 484
 
5.2%
악 470
 
5.1%
로 449
 
4.9%
구 354
 
3.8%
서 354
 
3.8%
울 350
 
3.8%
시 337
 
3.6%
별 325
 
3.5%
Other values (132) 4202
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5271
57.0%
Decimal Number 2346
25.4%
Space Separator 1391
 
15.0%
Dash Punctuation 154
 
1.7%
Uppercase Letter 31
 
0.3%
Close Punctuation 17
 
0.2%
Open Punctuation 17
 
0.2%
Other Punctuation 16
 
0.2%
Lowercase Letter 8
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 484
 
9.2%
악 470
 
8.9%
로 449
 
8.5%
구 354
 
6.7%
서 354
 
6.7%
울 350
 
6.6%
시 337
 
6.4%
별 325
 
6.2%
특 325
 
6.2%
길 211
 
4.0%
Other values (101) 1612
30.6%
Decimal Number
ValueCountFrequency (%)
1 536
22.8%
4 314
13.4%
5 299
12.7%
3 231
9.8%
2 230
9.8%
6 176
 
7.5%
7 165
 
7.0%
9 148
 
6.3%
0 140
 
6.0%
8 107
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
C 13
41.9%
T 6
19.4%
V 6
19.4%
D 2
 
6.5%
J 1
 
3.2%
X 1
 
3.2%
B 1
 
3.2%
G 1
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
e 3
37.5%
f 2
25.0%
o 1
 
12.5%
a 1
 
12.5%
n 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
, 14
87.5%
. 1
 
6.2%
/ 1
 
6.2%
Space Separator
ValueCountFrequency (%)
1391
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 154
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5271
57.0%
Common 3942
42.6%
Latin 39
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 484
 
9.2%
악 470
 
8.9%
로 449
 
8.5%
구 354
 
6.7%
서 354
 
6.7%
울 350
 
6.6%
시 337
 
6.4%
별 325
 
6.2%
특 325
 
6.2%
길 211
 
4.0%
Other values (101) 1612
30.6%
Common
ValueCountFrequency (%)
1391
35.3%
1 536
 
13.6%
4 314
 
8.0%
5 299
 
7.6%
3 231
 
5.9%
2 230
 
5.8%
6 176
 
4.5%
7 165
 
4.2%
- 154
 
3.9%
9 148
 
3.8%
Other values (8) 298
 
7.6%
Latin
ValueCountFrequency (%)
C 13
33.3%
T 6
15.4%
V 6
15.4%
e 3
 
7.7%
f 2
 
5.1%
D 2
 
5.1%
o 1
 
2.6%
J 1
 
2.6%
X 1
 
2.6%
B 1
 
2.6%
Other values (3) 3
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5271
57.0%
ASCII 3981
43.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1391
34.9%
1 536
 
13.5%
4 314
 
7.9%
5 299
 
7.5%
3 231
 
5.8%
2 230
 
5.8%
6 176
 
4.4%
7 165
 
4.1%
- 154
 
3.9%
9 148
 
3.7%
Other values (21) 337
 
8.5%
Hangul
ValueCountFrequency (%)
관 484
 
9.2%
악 470
 
8.9%
로 449
 
8.5%
구 354
 
6.7%
서 354
 
6.7%
울 350
 
6.6%
시 337
 
6.4%
별 325
 
6.2%
특 325
 
6.2%
길 211
 
4.0%
Other values (101) 1612
30.6%

상세주소
Text

Distinct564
Distinct (%)82.0%
Missing4
Missing (%)0.6%
Memory size5.5 KiB
2024-05-10T23:39:42.501381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length34
Mean length11.954942
Min length2

Characters and Unicode

Total characters8225
Distinct characters300
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique534 ?
Unique (%)77.6%

Sample

1st row21-185
2nd row21-001
3rd row21-138
4th row21-236
5th row21-028
ValueCountFrequency (%)
cctv폴대상단_1 52
 
4.2%
cctv 29
 
2.3%
cctv폴대 24
 
1.9%
상단_1 23
 
1.8%
복도 23
 
1.8%
3층 19
 
1.5%
옥외1 15
 
1.2%
관악가족행복센터 15
 
1.2%
2층 15
 
1.2%
사당역 13
 
1.0%
Other values (590) 1020
81.7%
2024-05-10T23:39:43.927551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
562
 
6.8%
1 542
 
6.6%
_ 365
 
4.4%
2 288
 
3.5%
( 227
 
2.8%
) 226
 
2.7%
C 215
 
2.6%
층 206
 
2.5%
3 197
 
2.4%
- 166
 
2.0%
Other values (290) 5231
63.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4286
52.1%
Decimal Number 1703
 
20.7%
Uppercase Letter 578
 
7.0%
Space Separator 562
 
6.8%
Connector Punctuation 365
 
4.4%
Open Punctuation 227
 
2.8%
Close Punctuation 226
 
2.7%
Dash Punctuation 166
 
2.0%
Lowercase Letter 70
 
0.9%
Other Punctuation 42
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
층 206
 
4.8%
옥 163
 
3.8%
대 144
 
3.4%
동 123
 
2.9%
외 120
 
2.8%
실 110
 
2.6%
민 107
 
2.5%
폴 101
 
2.4%
관 100
 
2.3%
터 99
 
2.3%
Other values (247) 3013
70.3%
Uppercase Letter
ValueCountFrequency (%)
C 215
37.2%
V 116
20.1%
T 116
20.1%
F 51
 
8.8%
A 23
 
4.0%
B 16
 
2.8%
D 15
 
2.6%
N 5
 
0.9%
M 5
 
0.9%
P 4
 
0.7%
Other values (8) 12
 
2.1%
Decimal Number
ValueCountFrequency (%)
1 542
31.8%
2 288
16.9%
3 197
 
11.6%
0 162
 
9.5%
6 103
 
6.0%
5 102
 
6.0%
4 101
 
5.9%
8 85
 
5.0%
7 64
 
3.8%
9 59
 
3.5%
Lowercase Letter
ValueCountFrequency (%)
c 32
45.7%
t 16
22.9%
v 16
22.9%
d 2
 
2.9%
e 2
 
2.9%
f 2
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 28
66.7%
. 8
 
19.0%
# 4
 
9.5%
: 2
 
4.8%
Space Separator
ValueCountFrequency (%)
562
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 365
100.0%
Open Punctuation
ValueCountFrequency (%)
( 227
100.0%
Close Punctuation
ValueCountFrequency (%)
) 226
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 166
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4286
52.1%
Common 3291
40.0%
Latin 648
 
7.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
층 206
 
4.8%
옥 163
 
3.8%
대 144
 
3.4%
동 123
 
2.9%
외 120
 
2.8%
실 110
 
2.6%
민 107
 
2.5%
폴 101
 
2.4%
관 100
 
2.3%
터 99
 
2.3%
Other values (247) 3013
70.3%
Latin
ValueCountFrequency (%)
C 215
33.2%
V 116
17.9%
T 116
17.9%
F 51
 
7.9%
c 32
 
4.9%
A 23
 
3.5%
t 16
 
2.5%
v 16
 
2.5%
B 16
 
2.5%
D 15
 
2.3%
Other values (14) 32
 
4.9%
Common
ValueCountFrequency (%)
562
17.1%
1 542
16.5%
_ 365
11.1%
2 288
8.8%
( 227
6.9%
) 226
6.9%
3 197
 
6.0%
- 166
 
5.0%
0 162
 
4.9%
6 103
 
3.1%
Other values (9) 453
13.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4286
52.1%
ASCII 3939
47.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
562
14.3%
1 542
13.8%
_ 365
 
9.3%
2 288
 
7.3%
( 227
 
5.8%
) 226
 
5.7%
C 215
 
5.5%
3 197
 
5.0%
- 166
 
4.2%
0 162
 
4.1%
Other values (33) 989
25.1%
Hangul
ValueCountFrequency (%)
층 206
 
4.8%
옥 163
 
3.8%
대 144
 
3.4%
동 123
 
2.9%
외 120
 
2.8%
실 110
 
2.6%
민 107
 
2.5%
폴 101
 
2.4%
관 100
 
2.3%
터 99
 
2.3%
Other values (247) 3013
70.3%

설치위치(층)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
<NA>
503 
1층
 
43
2층
 
36
3층
 
22
4층
 
18
Other values (10)
70 

Length

Max length5
Median length4
Mean length3.4942197
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 503
72.7%
1층 43
 
6.2%
2층 36
 
5.2%
3층 22
 
3.2%
4층 18
 
2.6%
지하1층 14
 
2.0%
5층 13
 
1.9%
6층 10
 
1.4%
옥외 8
 
1.2%
- 8
 
1.2%
Other values (5) 17
 
2.5%

Length

2024-05-10T23:39:44.567214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 503
72.5%
1층 43
 
6.2%
2층 36
 
5.2%
3층 22
 
3.2%
4층 18
 
2.6%
지하1층 14
 
2.0%
5층 13
 
1.9%
6층 10
 
1.4%
옥외 8
 
1.2%
8
 
1.2%
Other values (5) 19
 
2.7%

설치유형
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
1. 주요거리
148 
3. 공원(하천)
81 
7-2-1. 공공 - 구청사 및 별관
75 
7-2-3. 공공 - 동주민센터
64 
6-1. 복지 - 사회
49 
Other values (14)
275 

Length

Max length21
Median length17
Mean length12.388728
Min length7

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row5-1. 버스정류소(국비)
2nd row5-1. 버스정류소(국비)
3rd row5-1. 버스정류소(국비)
4th row5-1. 버스정류소(국비)
5th row5-1. 버스정류소(국비)

Common Values

ValueCountFrequency (%)
1. 주요거리 148
21.4%
3. 공원(하천) 81
11.7%
7-2-1. 공공 - 구청사 및 별관 75
10.8%
7-2-3. 공공 - 동주민센터 64
9.2%
6-1. 복지 - 사회 49
 
7.1%
2. 전통시장 46
 
6.6%
5-2. 버스정류소(시비) 45
 
6.5%
6-2. 복지 - 노인 43
 
6.2%
5-1. 버스정류소(국비) 32
 
4.6%
7-2-2. 공공 - 구의회 및 보건소 30
 
4.3%
Other values (9) 79
11.4%

Length

2024-05-10T23:39:45.205423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
329
 
14.6%
공공 201
 
8.9%
1 148
 
6.6%
주요거리 148
 
6.6%
복지 128
 
5.7%
및 105
 
4.7%
3 81
 
3.6%
공원(하천 81
 
3.6%
별관 75
 
3.3%
7-2-1 75
 
3.3%
Other values (32) 881
39.1%

설치기관
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
디지털뉴딜(LG U+)
198 
디지털뉴딜(KT)
154 
자치구
151 
서울시(AP)
103 
버스정류소(시비)
45 
Other values (2)
41 

Length

Max length12
Median length9
Mean length8.2384393
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row버스정류소(국비)
2nd row버스정류소(국비)
3rd row버스정류소(국비)
4th row버스정류소(국비)
5th row버스정류소(국비)

Common Values

ValueCountFrequency (%)
디지털뉴딜(LG U+) 198
28.6%
디지털뉴딜(KT) 154
22.3%
자치구 151
21.8%
서울시(AP) 103
14.9%
버스정류소(시비) 45
 
6.5%
버스정류소(국비) 32
 
4.6%
서울시(공유기) 9
 
1.3%

Length

2024-05-10T23:39:45.746357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:39:46.238762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
디지털뉴딜(lg 198
22.2%
u 198
22.2%
디지털뉴딜(kt 154
17.3%
자치구 151
17.0%
서울시(ap 103
11.6%
버스정류소(시비 45
 
5.1%
버스정류소(국비 32
 
3.6%
서울시(공유기 9
 
1.0%

서비스구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
공공WiFi
461 
과기부WiFi(핫플레이스)
118 
과기부WiFi(복지시설)
 
43
<NA>
 
38
과기부WiFi
 
32

Length

Max length14
Median length6
Mean length7.7355491
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공WiFi 461
66.6%
과기부WiFi(핫플레이스) 118
 
17.1%
과기부WiFi(복지시설) 43
 
6.2%
<NA> 38
 
5.5%
과기부WiFi 32
 
4.6%

Length

2024-05-10T23:39:46.942182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:39:47.292741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공wifi 461
66.6%
과기부wifi(핫플레이스 118
 
17.1%
과기부wifi(복지시설 43
 
6.2%
na 38
 
5.5%
과기부wifi 32
 
4.6%

망종류
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
인터넷망_뉴딜용
352 
자가망U무선망
151 
임대망
80 
자가망_U무선망
64 
<NA>
45 

Length

Max length8
Median length8
Mean length6.9436416
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인터넷망_뉴딜용 352
50.9%
자가망U무선망 151
21.8%
임대망 80
 
11.6%
자가망_U무선망 64
 
9.2%
<NA> 45
 
6.5%

Length

2024-05-10T23:39:47.905451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:39:48.322423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인터넷망_뉴딜용 352
50.9%
자가망u무선망 151
21.8%
임대망 80
 
11.6%
자가망_u무선망 64
 
9.2%
na 45
 
6.5%

설치년도
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021.2254
Minimum2014
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-05-10T23:39:48.794407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2018
Q12021
median2022
Q32022
95-th percentile2023
Maximum2023
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6853984
Coefficient of variation (CV)0.0008338498
Kurtosis2.5404931
Mean2021.2254
Median Absolute Deviation (MAD)1
Skewness-1.5998918
Sum1398688
Variance2.8405678
MonotonicityNot monotonic
2024-05-10T23:39:49.183141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2022 323
46.7%
2021 123
 
17.8%
2023 105
 
15.2%
2019 60
 
8.7%
2018 31
 
4.5%
2017 22
 
3.2%
2020 19
 
2.7%
2014 4
 
0.6%
2015 4
 
0.6%
2016 1
 
0.1%
ValueCountFrequency (%)
2014 4
 
0.6%
2015 4
 
0.6%
2016 1
 
0.1%
2017 22
 
3.2%
2018 31
 
4.5%
2019 60
 
8.7%
2020 19
 
2.7%
2021 123
 
17.8%
2022 323
46.7%
2023 105
 
15.2%
ValueCountFrequency (%)
2023 105
 
15.2%
2022 323
46.7%
2021 123
 
17.8%
2020 19
 
2.7%
2019 60
 
8.7%
2018 31
 
4.5%
2017 22
 
3.2%
2016 1
 
0.1%
2015 4
 
0.6%
2014 4
 
0.6%

실내외구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
실외
356 
실내
336 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
실외 356
51.4%
실내 336
48.6%

Length

2024-05-10T23:39:49.691250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:39:50.088834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
실외 356
51.4%
실내 336
48.6%

wifi접속환경
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing692
Missing (%)100.0%
Memory size6.2 KiB

X좌표
Real number (ℝ)

Distinct377
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.479241
Minimum37.449474
Maximum37.494534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-05-10T23:39:50.617280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.449474
5-th percentile37.466651
Q137.475507
median37.479466
Q337.484313
95-th percentile37.489488
Maximum37.494534
Range0.04506
Interquartile range (IQR)0.0088065

Descriptive statistics

Standard deviation0.007294122
Coefficient of variation (CV)0.00019461765
Kurtosis0.19622818
Mean37.479241
Median Absolute Deviation (MAD)0.0044765
Skewness-0.5629484
Sum25935.635
Variance5.3204216 × 10-5
MonotonicityNot monotonic
2024-05-10T23:39:51.173774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.478127 54
 
7.8%
37.478436 18
 
2.6%
37.4929 16
 
2.3%
37.464836 12
 
1.7%
37.46678 12
 
1.7%
37.488525 11
 
1.6%
37.482464 9
 
1.3%
37.47794 8
 
1.2%
37.482456 6
 
0.9%
37.478626 6
 
0.9%
Other values (367) 540
78.0%
ValueCountFrequency (%)
37.449474 1
0.1%
37.455208 1
0.1%
37.457813 1
0.1%
37.458355 1
0.1%
37.45865 1
0.1%
37.458885 1
0.1%
37.459953 1
0.1%
37.46114 1
0.1%
37.461212 1
0.1%
37.461464 2
0.3%
ValueCountFrequency (%)
37.494534 1
 
0.1%
37.49294 1
 
0.1%
37.4929 16
2.3%
37.491817 2
 
0.3%
37.491802 1
 
0.1%
37.49161 1
 
0.1%
37.4915 1
 
0.1%
37.490715 1
 
0.1%
37.490665 1
 
0.1%
37.490494 1
 
0.1%

Y좌표
Real number (ℝ)

Distinct372
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.93856
Minimum126.90237
Maximum126.98278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-05-10T23:39:51.927946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.90237
5-th percentile126.91117
Q1126.92596
median126.93752
Q3126.95151
95-th percentile126.96616
Maximum126.98278
Range0.08041
Interquartile range (IQR)0.0255525

Descriptive statistics

Standard deviation0.017500223
Coefficient of variation (CV)0.00013786372
Kurtosis-0.472958
Mean126.93856
Median Absolute Deviation (MAD)0.01399
Skewness0.17756074
Sum87841.484
Variance0.00030625779
MonotonicityNot monotonic
2024-05-10T23:39:52.641220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.95151 54
 
7.8%
126.951126 18
 
2.6%
126.92629 16
 
2.3%
126.91986 14
 
2.0%
126.93124 12
 
1.7%
126.94666 11
 
1.6%
126.916405 9
 
1.3%
126.95181 8
 
1.2%
126.922424 6
 
0.9%
126.90937 6
 
0.9%
Other values (362) 538
77.7%
ValueCountFrequency (%)
126.90237 1
 
0.1%
126.90245 1
 
0.1%
126.902626 1
 
0.1%
126.90361 1
 
0.1%
126.90479 1
 
0.1%
126.90517 1
 
0.1%
126.9056 4
0.6%
126.90589 1
 
0.1%
126.9059 1
 
0.1%
126.90724 3
0.4%
ValueCountFrequency (%)
126.98278 1
0.1%
126.98176 2
0.3%
126.98167 1
0.1%
126.98128 1
0.1%
126.98124 1
0.1%
126.98109 1
0.1%
126.981 1
0.1%
126.98052 2
0.3%
126.97998 2
0.3%
126.97832 1
0.1%

작업일자
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
2024-05-10 11:12:53.0
150 
2024-05-10 11:13:04.0
101 
2024-05-10 11:13:02.0
84 
2024-05-10 11:13:01.0
70 
2024-05-10 11:12:58.0
60 
Other values (7)
227 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-05-10 11:12:51.0
2nd row2024-05-10 11:12:51.0
3rd row2024-05-10 11:12:51.0
4th row2024-05-10 11:12:51.0
5th row2024-05-10 11:12:51.0

Common Values

ValueCountFrequency (%)
2024-05-10 11:12:53.0 150
21.7%
2024-05-10 11:13:04.0 101
14.6%
2024-05-10 11:13:02.0 84
12.1%
2024-05-10 11:13:01.0 70
10.1%
2024-05-10 11:12:58.0 60
 
8.7%
2024-05-10 11:13:03.0 52
 
7.5%
2024-05-10 11:12:56.0 45
 
6.5%
2024-05-10 11:13:05.0 45
 
6.5%
2024-05-10 11:12:57.0 33
 
4.8%
2024-05-10 11:12:51.0 27
 
3.9%
Other values (2) 25
 
3.6%

Length

2024-05-10T23:39:53.903355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024-05-10 692
50.0%
11:12:53.0 150
 
10.8%
11:13:04.0 101
 
7.3%
11:13:02.0 84
 
6.1%
11:13:01.0 70
 
5.1%
11:12:58.0 60
 
4.3%
11:13:03.0 52
 
3.8%
11:12:56.0 45
 
3.3%
11:13:05.0 45
 
3.3%
11:12:57.0 33
 
2.4%
Other values (3) 52
 
3.8%

Interactions

2024-05-10T23:39:31.368480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:39:29.271590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:39:30.315113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:39:31.765589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:39:29.638982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:39:30.586331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:39:32.053126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:39:29.960887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:39:30.937249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T23:39:54.379971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치위치(층)설치유형설치기관서비스구분망종류설치년도실내외구분X좌표Y좌표작업일자
설치위치(층)1.0000.6880.570NaN0.5700.6851.0000.4040.2020.442
설치유형0.6881.0000.9270.9490.8750.8290.9880.6990.6930.909
설치기관0.5700.9271.0000.8680.9660.7880.5510.4240.5540.968
서비스구분NaN0.9490.8681.0000.8250.5750.7590.5570.4220.983
망종류0.5700.8750.9660.8251.0000.8310.6820.4650.5190.951
설치년도0.6850.8290.7880.5750.8311.0000.5170.2980.3720.879
실내외구분1.0000.9880.5510.7590.6820.5171.0000.3140.3250.886
X좌표0.4040.6990.4240.5570.4650.2980.3141.0000.7790.508
Y좌표0.2020.6930.5540.4220.5190.3720.3250.7791.0000.583
작업일자0.4420.9090.9680.9830.9510.8790.8860.5080.5831.000
2024-05-10T23:39:54.964987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
실내외구분설치위치(층)설치기관서비스구분설치유형망종류작업일자
실내외구분1.0000.9670.5900.5500.9820.4800.725
설치위치(층)0.9671.0000.4341.0000.3290.4340.267
설치기관0.5900.4341.0000.8010.7460.8770.910
서비스구분0.5501.0000.8011.0000.8380.4710.822
설치유형0.9820.3290.7460.8381.0000.6870.623
망종류0.4800.4340.8770.4710.6871.0000.900
작업일자0.7250.2670.9100.8220.6230.9001.000
2024-05-10T23:39:55.571274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치년도X좌표Y좌표설치위치(층)설치유형설치기관서비스구분망종류실내외구분작업일자
설치년도1.0000.135-0.1770.3500.4800.5770.4050.7050.5190.591
X좌표0.1351.000-0.2060.1790.3460.2300.3650.2940.2400.240
Y좌표-0.177-0.2061.0000.0790.3410.3200.2630.3350.2480.289
설치위치(층)0.3500.1790.0791.0000.3290.4341.0000.4340.9670.267
설치유형0.4800.3460.3410.3291.0000.7460.8380.6870.9820.623
설치기관0.5770.2300.3200.4340.7461.0000.8010.8770.5900.910
서비스구분0.4050.3650.2631.0000.8380.8011.0000.4710.5500.822
망종류0.7050.2940.3350.4340.6870.8770.4711.0000.4800.900
실내외구분0.5190.2400.2480.9670.9820.5900.5500.4801.0000.725
작업일자0.5910.2400.2890.2670.6230.9100.8220.9000.7251.000

Missing values

2024-05-10T23:39:32.512048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T23:39:33.230385image/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.
2024-05-10T23:39:33.651882image/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

관리번호자치구와이파이명도로명주소상세주소설치위치(층)설치유형설치기관서비스구분망종류설치년도실내외구분wifi접속환경X좌표Y좌표작업일자
0BS100242관악구버스정류소_관악구청봉천동 1571-121-185<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.478687126.950832024-05-10 11:12:51.0
1BS100243관악구버스정류소_구로디지털단지역시흥대로21-001<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.483936126.902452024-05-10 11:12:51.0
2BS100244관악구버스정류소_봉천사거리.봉천중앙시장관악로 20121-138<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.48291126.953672024-05-10 11:12:51.0
3BS100245관악구버스정류소_봉현초등학교성현로 11721-236<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.490715126.955472024-05-10 11:12:51.0
4BS100246관악구버스정류소_사당역과천대로21-028<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.47518126.981672024-05-10 11:12:51.0
5BS100247관악구버스정류소_사당자동차학원과천대로21-027<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.472237126.982782024-05-10 11:12:51.0
6BS100248관악구버스정류소_산복터널.관악산휴먼시아2단지신림동 산102-921-173<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.45865126.922342024-05-10 11:12:51.0
7BS100249관악구버스정류소_산장아파트신림동 산78-821-170<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.459953126.927732024-05-10 11:12:51.0
8BS100250관악구버스정류소_삼성산주공아파트신림동 1714-421-171<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.461212126.925362024-05-10 11:12:51.0
9BS100251관악구버스정류소_서울대학교신림동 154-321-127<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.46674126.947952024-05-10 11:12:51.0
관리번호자치구와이파이명도로명주소상세주소설치위치(층)설치유형설치기관서비스구분망종류설치년도실내외구분wifi접속환경X좌표Y좌표작업일자
682서울5차-1117-1관악구중앙사회복지관서울특별시 관악구 봉천로41길 33느티나무카페 무더위쉼터1층6-1. 복지 - 사회디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.484352126.948112024-05-10 11:13:05.0
683서울5차-1118관악구중앙사회복지관서울특별시 관악구 봉천로41길 33복도2층6-1. 복지 - 사회디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.484356126.948042024-05-10 11:13:05.0
684서울5차-1118-1관악구중앙사회복지관서울특별시 관악구 봉천로41길 33복도 안쪽2층6-1. 복지 - 사회디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.484356126.948042024-05-10 11:13:05.0
685서울5차-1119관악구중앙사회복지관서울특별시 관악구 봉천로41길 33복지사업팀 입구3층6-1. 복지 - 사회디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.48436126.94812024-05-10 11:13:05.0
686서울5차-1119-1관악구중앙사회복지관서울특별시 관악구 봉천로41길 33복지사업팀 안쪽3층6-1. 복지 - 사회디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.48436126.94812024-05-10 11:13:05.0
687서울5차-1120관악구중앙사회복지관서울특별시 관악구 봉천로41길 33복도4층6-1. 복지 - 사회디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.483734126.950242024-05-10 11:13:05.0
688서울5차-1120-1관악구중앙사회복지관서울특별시 관악구 봉천로41길 33대그룹 화장실 앞4층6-1. 복지 - 사회디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.483734126.950242024-05-10 11:13:05.0
689서울5차-1128관악구조원생활스포츠센터서울특별시 관악구 신림동 1646문화체육실A지하1층4. 문화관광디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.4823126.907242024-05-10 11:13:05.0
690서울5차-1128-1관악구조원생활스포츠센터서울특별시 관악구 신림동 1646안내데스크 앞지하1층4. 문화관광디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.4823126.907242024-05-10 11:13:05.0
691서울5차-1128-2관악구조원생활스포츠센터서울특별시 관악구 신림동 1646문화체육실C 앞지하1층4. 문화관광디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.4823126.907242024-05-10 11:13:05.0