Overview

Dataset statistics

Number of variables16
Number of observations879
Missing cells911
Missing cells (%)6.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory112.6 KiB
Average record size in memory131.2 B

Variable types

Text5
Categorical7
Numeric3
DateTime1

Dataset

Description관리번호,자치구,와이파이명,도로명주소,상세주소,설치위치(층),설치유형,설치기관,서비스구분,망종류,설치년도,실내외구분,wifi접속환경,X좌표,Y좌표,작업일자
Author영등포구
URLhttps://data.seoul.go.kr/dataList/OA-20907/S/1/datasetView.do

Alerts

자치구 has constant value ""Constant
실내외구분 is highly overall correlated with 설치유형 and 2 other fieldsHigh correlation
설치유형 is highly overall correlated with 설치년도 and 4 other fieldsHigh correlation
서비스구분 is highly overall correlated with 설치년도 and 4 other fieldsHigh correlation
설치년도 is highly overall correlated with 설치위치(층) and 4 other fieldsHigh correlation
설치위치(층) is highly overall correlated with 설치년도High correlation
설치기관 is highly overall correlated with 설치년도 and 4 other fieldsHigh correlation
망종류 is highly overall correlated with 설치년도 and 3 other fieldsHigh correlation
설치위치(층) is highly imbalanced (94.0%)Imbalance
도로명주소 has 41 (4.7%) missing valuesMissing
상세주소 has 18 (2.0%) missing valuesMissing
wifi접속환경 has 852 (96.9%) missing valuesMissing
관리번호 has unique valuesUnique

Reproduction

Analysis started2024-05-17 22:44:48.675641
Analysis finished2024-05-17 22:44:57.259558
Duration8.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Text

UNIQUE 

Distinct879
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
2024-05-18T07:44:57.810443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length8.523322
Min length7

Characters and Unicode

Total characters7492
Distinct characters25
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

Unique879 ?
Unique (%)100.0%

Sample

1st rowBS100984
2nd rowBS100985
3rd rowBS100986
4th rowBS100987
5th rowBS100988
ValueCountFrequency (%)
bs100984 1
 
0.1%
서울-2836 1
 
0.1%
서울-2842-1 1
 
0.1%
서울-2832-1 1
 
0.1%
서울-2832-2 1
 
0.1%
서울-2833 1
 
0.1%
서울-2833-1 1
 
0.1%
서울-2833-2 1
 
0.1%
서울-2834 1
 
0.1%
서울-2834-1 1
 
0.1%
Other values (869) 869
98.9%
2024-05-18T07:44:59.249633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1299
17.3%
1 719
 
9.6%
4 649
 
8.7%
2 541
 
7.2%
- 503
 
6.7%
3 379
 
5.1%
울 378
 
5.0%
서 378
 
5.0%
9 280
 
3.7%
6 263
 
3.5%
Other values (15) 2103
28.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4806
64.1%
Uppercase Letter 1264
 
16.9%
Other Letter 919
 
12.3%
Dash Punctuation 503
 
6.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 221
17.5%
P 221
17.5%
Y 221
17.5%
W 171
13.5%
S 145
11.5%
F 119
9.4%
B 104
8.2%
N 52
 
4.1%
H 5
 
0.4%
G 4
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 1299
27.0%
1 719
15.0%
4 649
13.5%
2 541
11.3%
3 379
 
7.9%
9 280
 
5.8%
6 263
 
5.5%
8 262
 
5.5%
5 215
 
4.5%
7 199
 
4.1%
Other Letter
ValueCountFrequency (%)
울 378
41.1%
서 378
41.1%
차 163
17.7%
Dash Punctuation
ValueCountFrequency (%)
- 503
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5309
70.9%
Latin 1264
 
16.9%
Hangul 919
 
12.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1299
24.5%
1 719
13.5%
4 649
12.2%
2 541
10.2%
- 503
 
9.5%
3 379
 
7.1%
9 280
 
5.3%
6 263
 
5.0%
8 262
 
4.9%
5 215
 
4.0%
Latin
ValueCountFrequency (%)
D 221
17.5%
P 221
17.5%
Y 221
17.5%
W 171
13.5%
S 145
11.5%
F 119
9.4%
B 104
8.2%
N 52
 
4.1%
H 5
 
0.4%
G 4
 
0.3%
Hangul
ValueCountFrequency (%)
울 378
41.1%
서 378
41.1%
차 163
17.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6573
87.7%
Hangul 919
 
12.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1299
19.8%
1 719
10.9%
4 649
9.9%
2 541
 
8.2%
- 503
 
7.7%
3 379
 
5.8%
9 280
 
4.3%
6 263
 
4.0%
8 262
 
4.0%
D 221
 
3.4%
Other values (12) 1457
22.2%
Hangul
ValueCountFrequency (%)
울 378
41.1%
서 378
41.1%
차 163
17.7%

자치구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
영등포구
879 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영등포구 879
100.0%

Length

2024-05-18T07:44:59.646082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T07:45:00.067680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영등포구 879
100.0%

와이파이명
Text

Distinct200
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
2024-05-18T07:45:00.713296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length22
Mean length8.7519909
Min length3

Characters and Unicode

Total characters7693
Distinct characters266
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

Unique96 ?
Unique (%)10.9%

Sample

1st row버스정류소_KBS
2nd row버스정류소_강남성심병원.대림성모병원
3rd row버스정류소_경남아너스빌아파트
4th row버스정류소_당산초등학교앞
5th row버스정류소_대림3동주민센터
ValueCountFrequency (%)
여의도한강공원 127
 
13.8%
여의도공원 51
 
5.6%
신길종합사회복지관 34
 
3.7%
구립영등포노인케어센터 32
 
3.5%
영등포노인종합복지관 30
 
3.3%
양화한강공원 26
 
2.8%
영등포구청-본관 23
 
2.5%
영등포역및영등포시장 21
 
2.3%
영등포구청_별관 19
 
2.1%
선유도공원 19
 
2.1%
Other values (199) 536
58.4%
2024-05-18T07:45:02.043329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
등 320
 
4.2%
영 309
 
4.0%
포 301
 
3.9%
원 270
 
3.5%
공 259
 
3.4%
도 218
 
2.8%
의 215
 
2.8%
터 203
 
2.6%
센 200
 
2.6%
여 199
 
2.6%
Other values (256) 5199
67.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7360
95.7%
Connector Punctuation 127
 
1.7%
Decimal Number 88
 
1.1%
Space Separator 39
 
0.5%
Dash Punctuation 26
 
0.3%
Other Punctuation 16
 
0.2%
Close Punctuation 14
 
0.2%
Open Punctuation 14
 
0.2%
Uppercase Letter 9
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
등 320
 
4.3%
영 309
 
4.2%
포 301
 
4.1%
원 270
 
3.7%
공 259
 
3.5%
도 218
 
3.0%
의 215
 
2.9%
터 203
 
2.8%
센 200
 
2.7%
여 199
 
2.7%
Other values (238) 4866
66.1%
Decimal Number
ValueCountFrequency (%)
1 26
29.5%
2 24
27.3%
3 13
14.8%
5 9
 
10.2%
7 6
 
6.8%
6 5
 
5.7%
4 5
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
G 4
44.4%
K 2
22.2%
B 1
 
11.1%
T 1
 
11.1%
S 1
 
11.1%
Connector Punctuation
ValueCountFrequency (%)
_ 127
100.0%
Space Separator
ValueCountFrequency (%)
39
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Other Punctuation
ValueCountFrequency (%)
. 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7360
95.7%
Common 324
 
4.2%
Latin 9
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
등 320
 
4.3%
영 309
 
4.2%
포 301
 
4.1%
원 270
 
3.7%
공 259
 
3.5%
도 218
 
3.0%
의 215
 
2.9%
터 203
 
2.8%
센 200
 
2.7%
여 199
 
2.7%
Other values (238) 4866
66.1%
Common
ValueCountFrequency (%)
_ 127
39.2%
39
 
12.0%
1 26
 
8.0%
- 26
 
8.0%
2 24
 
7.4%
. 16
 
4.9%
) 14
 
4.3%
( 14
 
4.3%
3 13
 
4.0%
5 9
 
2.8%
Other values (3) 16
 
4.9%
Latin
ValueCountFrequency (%)
G 4
44.4%
K 2
22.2%
B 1
 
11.1%
T 1
 
11.1%
S 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7360
95.7%
ASCII 333
 
4.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
등 320
 
4.3%
영 309
 
4.2%
포 301
 
4.1%
원 270
 
3.7%
공 259
 
3.5%
도 218
 
3.0%
의 215
 
2.9%
터 203
 
2.8%
센 200
 
2.7%
여 199
 
2.7%
Other values (238) 4866
66.1%
ASCII
ValueCountFrequency (%)
_ 127
38.1%
39
 
11.7%
1 26
 
7.8%
- 26
 
7.8%
2 24
 
7.2%
. 16
 
4.8%
) 14
 
4.2%
( 14
 
4.2%
3 13
 
3.9%
5 9
 
2.7%
Other values (8) 25
 
7.5%

도로명주소
Text

MISSING 

Distinct241
Distinct (%)28.8%
Missing41
Missing (%)4.7%
Memory size7.0 KiB
2024-05-18T07:45:03.152041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length30
Mean length14.282816
Min length3

Characters and Unicode

Total characters11969
Distinct characters161
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

Unique132 ?
Unique (%)15.8%

Sample

1st row여의공원로 13
2nd row대림동 960-8
3rd row신길제6동 4780
4th row양평동5가 88-4
5th row대림로 195
ValueCountFrequency (%)
영등포구 402
 
16.1%
서울특별시 293
 
11.7%
여의도동 122
 
4.9%
서울 81
 
3.2%
도림로482 56
 
2.2%
당산로 55
 
2.2%
여의공원로 54
 
2.2%
120 53
 
2.1%
영등포로 45
 
1.8%
123 41
 
1.6%
Other values (334) 1299
51.9%
2024-05-18T07:45:04.563923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1663
 
13.9%
로 630
 
5.3%
영 529
 
4.4%
등 494
 
4.1%
포 489
 
4.1%
1 477
 
4.0%
2 477
 
4.0%
구 409
 
3.4%
서 388
 
3.2%
울 387
 
3.2%
Other values (151) 6026
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7078
59.1%
Decimal Number 2866
23.9%
Space Separator 1663
 
13.9%
Dash Punctuation 241
 
2.0%
Close Punctuation 40
 
0.3%
Open Punctuation 40
 
0.3%
Other Punctuation 29
 
0.2%
Uppercase Letter 12
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 630
 
8.9%
영 529
 
7.5%
등 494
 
7.0%
포 489
 
6.9%
구 409
 
5.8%
서 388
 
5.5%
울 387
 
5.5%
시 306
 
4.3%
별 293
 
4.1%
특 293
 
4.1%
Other values (132) 2860
40.4%
Decimal Number
ValueCountFrequency (%)
1 477
16.6%
2 477
16.6%
4 382
13.3%
8 328
11.4%
3 280
9.8%
5 241
8.4%
0 235
8.2%
6 184
 
6.4%
9 153
 
5.3%
7 109
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
C 6
50.0%
V 3
25.0%
T 3
25.0%
Other Punctuation
ValueCountFrequency (%)
, 27
93.1%
. 2
 
6.9%
Space Separator
ValueCountFrequency (%)
1663
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 241
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7078
59.1%
Common 4879
40.8%
Latin 12
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 630
 
8.9%
영 529
 
7.5%
등 494
 
7.0%
포 489
 
6.9%
구 409
 
5.8%
서 388
 
5.5%
울 387
 
5.5%
시 306
 
4.3%
별 293
 
4.1%
특 293
 
4.1%
Other values (132) 2860
40.4%
Common
ValueCountFrequency (%)
1663
34.1%
1 477
 
9.8%
2 477
 
9.8%
4 382
 
7.8%
8 328
 
6.7%
3 280
 
5.7%
5 241
 
4.9%
- 241
 
4.9%
0 235
 
4.8%
6 184
 
3.8%
Other values (6) 371
 
7.6%
Latin
ValueCountFrequency (%)
C 6
50.0%
V 3
25.0%
T 3
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7078
59.1%
ASCII 4891
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1663
34.0%
1 477
 
9.8%
2 477
 
9.8%
4 382
 
7.8%
8 328
 
6.7%
3 280
 
5.7%
5 241
 
4.9%
- 241
 
4.9%
0 235
 
4.8%
6 184
 
3.8%
Other values (9) 383
 
7.8%
Hangul
ValueCountFrequency (%)
로 630
 
8.9%
영 529
 
7.5%
등 494
 
7.0%
포 489
 
6.9%
구 409
 
5.8%
서 388
 
5.5%
울 387
 
5.5%
시 306
 
4.3%
별 293
 
4.1%
특 293
 
4.1%
Other values (132) 2860
40.4%

상세주소
Text

MISSING 

Distinct564
Distinct (%)65.5%
Missing18
Missing (%)2.0%
Memory size7.0 KiB
2024-05-18T07:45:05.215574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length29
Mean length13.963995
Min length2

Characters and Unicode

Total characters12023
Distinct characters331
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique399 ?
Unique (%)46.3%

Sample

1st row19-130
2nd row19-010
3rd row19-011
4th row19-220
5th row19-458
ValueCountFrequency (%)
앞)_1 66
 
3.4%
옥내1 65
 
3.3%
2층 53
 
2.7%
1층 53
 
2.7%
3층 44
 
2.2%
cctv기둥 33
 
1.7%
구립영등포노인케어센터 32
 
1.6%
영등포노인종합복지관 30
 
1.5%
신길종합사회복지관 30
 
1.5%
4층 28
 
1.4%
Other values (564) 1532
77.9%
2024-05-18T07:45:06.323938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1109
 
9.2%
1 660
 
5.5%
) 391
 
3.3%
( 383
 
3.2%
- 342
 
2.8%
2 327
 
2.7%
등 290
 
2.4%
옥 232
 
1.9%
층 220
 
1.8%
3 210
 
1.7%
Other values (321) 7859
65.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7192
59.8%
Decimal Number 1976
 
16.4%
Space Separator 1109
 
9.2%
Close Punctuation 391
 
3.3%
Open Punctuation 383
 
3.2%
Dash Punctuation 342
 
2.8%
Uppercase Letter 284
 
2.4%
Connector Punctuation 168
 
1.4%
Lowercase Letter 149
 
1.2%
Other Punctuation 21
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
등 290
 
4.0%
옥 232
 
3.2%
층 220
 
3.1%
내 204
 
2.8%
관 193
 
2.7%
터 193
 
2.7%
센 176
 
2.4%
로 162
 
2.3%
영 153
 
2.1%
포 147
 
2.0%
Other values (285) 5222
72.6%
Decimal Number
ValueCountFrequency (%)
1 660
33.4%
2 327
16.5%
3 210
 
10.6%
9 153
 
7.7%
4 132
 
6.7%
0 119
 
6.0%
7 110
 
5.6%
5 103
 
5.2%
6 90
 
4.6%
8 72
 
3.6%
Lowercase Letter
ValueCountFrequency (%)
c 38
25.5%
u 24
16.1%
v 20
13.4%
t 19
12.8%
e 12
 
8.1%
l 12
 
8.1%
o 12
 
8.1%
b 9
 
6.0%
a 3
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
C 124
43.7%
T 62
21.8%
V 62
21.8%
I 12
 
4.2%
S 12
 
4.2%
F 7
 
2.5%
G 4
 
1.4%
E 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
, 9
42.9%
# 8
38.1%
/ 4
19.0%
Space Separator
ValueCountFrequency (%)
1109
100.0%
Close Punctuation
ValueCountFrequency (%)
) 391
100.0%
Open Punctuation
ValueCountFrequency (%)
( 383
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 342
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 168
100.0%
Math Symbol
ValueCountFrequency (%)
~ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7192
59.8%
Common 4398
36.6%
Latin 433
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
등 290
 
4.0%
옥 232
 
3.2%
층 220
 
3.1%
내 204
 
2.8%
관 193
 
2.7%
터 193
 
2.7%
센 176
 
2.4%
로 162
 
2.3%
영 153
 
2.1%
포 147
 
2.0%
Other values (285) 5222
72.6%
Common
ValueCountFrequency (%)
1109
25.2%
1 660
15.0%
) 391
 
8.9%
( 383
 
8.7%
- 342
 
7.8%
2 327
 
7.4%
3 210
 
4.8%
_ 168
 
3.8%
9 153
 
3.5%
4 132
 
3.0%
Other values (9) 523
11.9%
Latin
ValueCountFrequency (%)
C 124
28.6%
T 62
14.3%
V 62
14.3%
c 38
 
8.8%
u 24
 
5.5%
v 20
 
4.6%
t 19
 
4.4%
e 12
 
2.8%
I 12
 
2.8%
l 12
 
2.8%
Other values (7) 48
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7192
59.8%
ASCII 4831
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1109
23.0%
1 660
13.7%
) 391
 
8.1%
( 383
 
7.9%
- 342
 
7.1%
2 327
 
6.8%
3 210
 
4.3%
_ 168
 
3.5%
9 153
 
3.2%
4 132
 
2.7%
Other values (26) 956
19.8%
Hangul
ValueCountFrequency (%)
등 290
 
4.0%
옥 232
 
3.2%
층 220
 
3.1%
내 204
 
2.8%
관 193
 
2.7%
터 193
 
2.7%
센 176
 
2.4%
로 162
 
2.3%
영 153
 
2.1%
포 147
 
2.0%
Other values (285) 5222
72.6%

설치위치(층)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
<NA>
869 
3층
 
9
영등포본-공원-14
 
1

Length

Max length10
Median length4
Mean length3.9863481
Min length2

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> 869
98.9%
3층 9
 
1.0%
영등포본-공원-14 1
 
0.1%

Length

2024-05-18T07:45:06.650182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T07:45:06.886605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 869
98.9%
3층 9
 
1.0%
영등포본-공원-14 1
 
0.1%

설치유형
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
3. 공원(하천)
248 
6-2. 복지 - 노인
81 
7-2-3. 공공 - 동주민센터
69 
6-1. 복지 - 사회
65 
5-1. 버스정류소(국비)
63 
Other values (14)
353 

Length

Max length21
Median length17
Mean length12.464164
Min length7

Unique

Unique0 ?
Unique (%)0.0%

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 (%)
3. 공원(하천) 248
28.2%
6-2. 복지 - 노인 81
 
9.2%
7-2-3. 공공 - 동주민센터 69
 
7.8%
6-1. 복지 - 사회 65
 
7.4%
5-1. 버스정류소(국비) 63
 
7.2%
6-4. 복지 - 아동청소년 59
 
6.7%
7-2-1. 공공 - 구청사 및 별관 51
 
5.8%
1. 주요거리 43
 
4.9%
5-2. 버스정류소(시비) 41
 
4.7%
4. 문화관광 28
 
3.2%
Other values (9) 131
14.9%

Length

2024-05-18T07:45:07.177020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
433
15.6%
3 263
 
9.5%
복지 250
 
9.0%
공원(하천 248
 
8.9%
공공 183
 
6.6%
6-2 81
 
2.9%
노인 81
 
2.9%
및 78
 
2.8%
7-2-3 69
 
2.5%
동주민센터 69
 
2.5%
Other values (33) 1025
36.9%

설치기관
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
자치구
221 
디지털뉴딜(KT)
211 
디지털뉴딜(LG U+)
167 
서울시(AP)
165 
버스정류소(국비)
63 
Other values (3)
52 

Length

Max length12
Median length9
Mean length7.6734926
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 (%)
자치구 221
25.1%
디지털뉴딜(KT) 211
24.0%
디지털뉴딜(LG U+) 167
19.0%
서울시(AP) 165
18.8%
버스정류소(국비) 63
 
7.2%
버스정류소(시비) 41
 
4.7%
서울시(공유기) 7
 
0.8%
서울시(LTE) 4
 
0.5%

Length

2024-05-18T07:45:07.672034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T07:45:08.219734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자치구 221
21.1%
디지털뉴딜(kt 211
20.2%
디지털뉴딜(lg 167
16.0%
u 167
16.0%
서울시(ap 165
15.8%
버스정류소(국비 63
 
6.0%
버스정류소(시비 41
 
3.9%
서울시(공유기 7
 
0.7%
서울시(lte 4
 
0.4%

서비스구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
공공WiFi
592 
과기부WiFi(복지시설)
179 
과기부WiFi
63 
과기부WiFi(핫플레이스)
 
36
<NA>
 
9

Length

Max length14
Median length6
Mean length7.8043231
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공WiFi 592
67.3%
과기부WiFi(복지시설) 179
 
20.4%
과기부WiFi 63
 
7.2%
과기부WiFi(핫플레이스) 36
 
4.1%
<NA> 9
 
1.0%

Length

2024-05-18T07:45:08.571815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T07:45:08.868883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공wifi 592
67.3%
과기부wifi(복지시설 179
 
20.4%
과기부wifi 63
 
7.2%
과기부wifi(핫플레이스 36
 
4.1%
na 9
 
1.0%

망종류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
인터넷망_뉴딜용
378 
자가망_U무선망
248 
임대망
212 
<NA>
41 

Length

Max length8
Median length8
Mean length6.6075085
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인터넷망_뉴딜용 378
43.0%
자가망_U무선망 248
28.2%
임대망 212
24.1%
<NA> 41
 
4.7%

Length

2024-05-18T07:45:09.275780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T07:45:09.624917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인터넷망_뉴딜용 378
43.0%
자가망_u무선망 248
28.2%
임대망 212
24.1%
na 41
 
4.7%

설치년도
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.8089
Minimum2013
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-05-18T07:45:09.912003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2018
Q12019
median2022
Q32022
95-th percentile2023
Maximum2024
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.609302
Coefficient of variation (CV)0.00079636525
Kurtosis0.55810785
Mean2020.8089
Median Absolute Deviation (MAD)1
Skewness-0.85237442
Sum1776291
Variance2.5898528
MonotonicityNot monotonic
2024-05-18T07:45:10.269871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2022 400
45.5%
2019 201
22.9%
2021 114
 
13.0%
2018 56
 
6.4%
2023 50
 
5.7%
2020 44
 
5.0%
2017 9
 
1.0%
2013 3
 
0.3%
2024 2
 
0.2%
ValueCountFrequency (%)
2013 3
 
0.3%
2017 9
 
1.0%
2018 56
 
6.4%
2019 201
22.9%
2020 44
 
5.0%
2021 114
 
13.0%
2022 400
45.5%
2023 50
 
5.7%
2024 2
 
0.2%
ValueCountFrequency (%)
2024 2
 
0.2%
2023 50
 
5.7%
2022 400
45.5%
2021 114
 
13.0%
2020 44
 
5.0%
2019 201
22.9%
2018 56
 
6.4%
2017 9
 
1.0%
2013 3
 
0.3%

실내외구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
실내
457 
실외
422 

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 (%)
실내 457
52.0%
실외 422
48.0%

Length

2024-05-18T07:45:10.647999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T07:45:10.974431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
실내 457
52.0%
실외 422
48.0%

wifi접속환경
Text

MISSING 

Distinct22
Distinct (%)81.5%
Missing852
Missing (%)96.9%
Memory size7.0 KiB
2024-05-18T07:45:11.337335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length4
Mean length9.4814815
Min length4

Characters and Unicode

Total characters256
Distinct characters43
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

Unique21 ?
Unique (%)77.8%

Sample

1st rowH148
2nd rowH191
3rd rowH192
4th rowH193
5th rowH194
ValueCountFrequency (%)
보안접속 6
 
11.3%
proxy 6
 
11.3%
서버 6
 
11.3%
개발중 6
 
11.3%
임시적용(머큐리 6
 
11.3%
h202 1
 
1.9%
백홀 1
 
1.9%
10g 1
 
1.9%
h201 1
 
1.9%
h210 1
 
1.9%
Other values (18) 18
34.0%
2024-05-18T07:45:12.075951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26
 
10.2%
H 20
 
7.8%
1 14
 
5.5%
0 12
 
4.7%
2 12
 
4.7%
9 11
 
4.3%
보 6
 
2.3%
x 6
 
2.3%
) 6
 
2.3%
중 6
 
2.3%
Other values (33) 137
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 98
38.3%
Decimal Number 63
24.6%
Uppercase Letter 32
 
12.5%
Space Separator 26
 
10.2%
Lowercase Letter 24
 
9.4%
Close Punctuation 6
 
2.3%
Open Punctuation 6
 
2.3%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
보 6
 
6.1%
중 6
 
6.1%
안 6
 
6.1%
개 6
 
6.1%
버 6
 
6.1%
서 6
 
6.1%
발 6
 
6.1%
용 6
 
6.1%
접 6
 
6.1%
임 6
 
6.1%
Other values (8) 38
38.8%
Decimal Number
ValueCountFrequency (%)
1 14
22.2%
0 12
19.0%
2 12
19.0%
9 11
17.5%
6 3
 
4.8%
8 3
 
4.8%
7 2
 
3.2%
5 2
 
3.2%
3 2
 
3.2%
4 2
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
H 20
62.5%
P 6
 
18.8%
I 2
 
6.2%
G 1
 
3.1%
W 1
 
3.1%
F 1
 
3.1%
E 1
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
x 6
25.0%
y 6
25.0%
o 6
25.0%
r 6
25.0%
Space Separator
ValueCountFrequency (%)
26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 102
39.8%
Hangul 98
38.3%
Latin 56
21.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
보 6
 
6.1%
중 6
 
6.1%
안 6
 
6.1%
개 6
 
6.1%
버 6
 
6.1%
서 6
 
6.1%
발 6
 
6.1%
용 6
 
6.1%
접 6
 
6.1%
임 6
 
6.1%
Other values (8) 38
38.8%
Common
ValueCountFrequency (%)
26
25.5%
1 14
13.7%
0 12
11.8%
2 12
11.8%
9 11
10.8%
) 6
 
5.9%
( 6
 
5.9%
6 3
 
2.9%
8 3
 
2.9%
7 2
 
2.0%
Other values (4) 7
 
6.9%
Latin
ValueCountFrequency (%)
H 20
35.7%
x 6
 
10.7%
y 6
 
10.7%
o 6
 
10.7%
r 6
 
10.7%
P 6
 
10.7%
I 2
 
3.6%
G 1
 
1.8%
W 1
 
1.8%
F 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158
61.7%
Hangul 98
38.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26
16.5%
H 20
12.7%
1 14
 
8.9%
0 12
 
7.6%
2 12
 
7.6%
9 11
 
7.0%
x 6
 
3.8%
) 6
 
3.8%
y 6
 
3.8%
o 6
 
3.8%
Other values (15) 39
24.7%
Hangul
ValueCountFrequency (%)
보 6
 
6.1%
중 6
 
6.1%
안 6
 
6.1%
개 6
 
6.1%
버 6
 
6.1%
서 6
 
6.1%
발 6
 
6.1%
용 6
 
6.1%
접 6
 
6.1%
임 6
 
6.1%
Other values (8) 38
38.8%

X좌표
Real number (ℝ)

Distinct384
Distinct (%)43.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.515705
Minimum37.462288
Maximum37.54696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-05-18T07:45:12.497823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.462288
5-th percentile37.481206
Q137.50941
median37.518963
Q337.526314
95-th percentile37.538335
Maximum37.54696
Range0.084672
Interquartile range (IQR)0.016904

Descriptive statistics

Standard deviation0.016420292
Coefficient of variation (CV)0.00043769114
Kurtosis1.2076818
Mean37.515705
Median Absolute Deviation (MAD)0.007797
Skewness-1.0496158
Sum32976.305
Variance0.00026962598
MonotonicityNot monotonic
2024-05-18T07:45:13.071898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.516857 62
 
7.1%
37.511166 30
 
3.4%
37.526314 24
 
2.7%
37.52861 23
 
2.6%
37.526333 19
 
2.2%
37.526035 17
 
1.9%
37.49736 14
 
1.6%
37.49723 12
 
1.4%
37.525608 12
 
1.4%
37.50168 9
 
1.0%
Other values (374) 657
74.7%
ValueCountFrequency (%)
37.462288 1
 
0.1%
37.46304 1
 
0.1%
37.463654 1
 
0.1%
37.4639 1
 
0.1%
37.464264 2
0.2%
37.464535 3
0.3%
37.46478 3
0.3%
37.464836 2
0.2%
37.46656 1
 
0.1%
37.467113 1
 
0.1%
ValueCountFrequency (%)
37.54696 1
 
0.1%
37.544506 4
0.5%
37.544403 4
0.5%
37.5444 2
 
0.2%
37.544277 3
0.3%
37.544025 1
 
0.1%
37.54381 5
0.6%
37.543476 3
0.3%
37.54322 1
 
0.1%
37.543144 4
0.5%

Y좌표
Real number (ℝ)

Distinct377
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.90529
Minimum126.53178
Maximum126.94196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-05-18T07:45:13.486695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.53178
5-th percentile126.88384
Q1126.89447
median126.90256
Q3126.91695
95-th percentile126.9357
Maximum126.94196
Range0.410176
Interquartile range (IQR)0.02248

Descriptive statistics

Standard deviation0.019886066
Coefficient of variation (CV)0.00015670006
Kurtosis140.68603
Mean126.90529
Median Absolute Deviation (MAD)0.00883
Skewness-7.2897259
Sum111549.75
Variance0.00039545563
MonotonicityNot monotonic
2024-05-18T07:45:13.939847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.89054 62
 
7.1%
126.92144 30
 
3.4%
126.896355 24
 
2.7%
126.89447 23
 
2.6%
126.90018 17
 
1.9%
126.895485 17
 
1.9%
126.913155 14
 
1.6%
126.90755 12
 
1.4%
126.90324 12
 
1.4%
126.905266 10
 
1.1%
Other values (367) 658
74.9%
ValueCountFrequency (%)
126.53178 1
0.1%
126.87339 2
0.2%
126.87409 2
0.2%
126.8741 2
0.2%
126.874115 1
0.1%
126.8742 1
0.1%
126.874756 1
0.1%
126.874825 1
0.1%
126.87499 1
0.1%
126.8758 1
0.1%
ValueCountFrequency (%)
126.941956 3
0.3%
126.941864 3
0.3%
126.94155 1
 
0.1%
126.94129 2
0.2%
126.940994 3
0.3%
126.94067 3
0.3%
126.94037 1
 
0.1%
126.9401 1
 
0.1%
126.93989 3
0.3%
126.93904 3
0.3%

작업일자
Date

Distinct13
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
Minimum2024-05-17 11:12:52
Maximum2024-05-17 11:13:06
2024-05-18T07:45:14.287990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:45:14.644248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)

Interactions

2024-05-18T07:44:54.260784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:44:51.388288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:44:53.080911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:44:54.580230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:44:52.054965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:44:53.449590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:44:55.020882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:44:52.604152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T07:44:53.855044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T07:45:14.942906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치위치(층)설치유형설치기관서비스구분망종류설치년도실내외구분wifi접속환경X좌표Y좌표작업일자
설치위치(층)1.0000.4940.494NaN0.494NaN0.494NaN0.4940.4940.494
설치유형0.4941.0000.9270.9780.9350.8570.9801.0000.7000.5540.911
설치기관0.4940.9271.0000.9800.8880.7920.8561.0000.5050.3560.961
서비스구분NaN0.9780.9801.0000.5550.6720.7761.0000.4410.0580.958
망종류0.4940.9350.8880.5551.0000.7780.1141.0000.4020.5020.981
설치년도NaN0.8570.7920.6720.7781.0000.4061.0000.4220.2660.861
실내외구분0.4940.9800.8560.7760.1140.4061.0001.0000.5210.1300.755
wifi접속환경NaN1.0001.0001.0001.0001.0001.0001.0000.0000.0000.587
X좌표0.4940.7000.5050.4410.4020.4220.5210.0001.0000.4250.608
Y좌표0.4940.5540.3560.0580.5020.2660.1300.0000.4251.0000.324
작업일자0.4940.9110.9610.9580.9810.8610.7550.5870.6080.3241.000
2024-05-18T07:45:15.313567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치기관실내외구분설치위치(층)설치유형서비스구분망종류
설치기관1.0000.6750.3120.7330.8080.883
실내외구분0.6751.0000.3120.9730.5670.188
설치위치(층)0.3120.3121.0000.312NaN0.312
설치유형0.7330.9730.3121.0000.9250.725
서비스구분0.8080.567NaN0.9251.0000.562
망종류0.8830.1880.3120.7250.5621.000
2024-05-18T07:45:15.627416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치년도X좌표Y좌표설치위치(층)설치유형설치기관서비스구분망종류실내외구분
설치년도1.000-0.288-0.0921.0000.5680.5380.5330.7200.435
X좌표-0.2881.0000.1280.3120.3470.2690.2770.2630.400
Y좌표-0.0920.1281.0000.3120.3500.2420.0540.2040.215
설치위치(층)1.0000.3120.3121.0000.3120.312NaN0.3120.312
설치유형0.5680.3470.3500.3121.0000.7330.9250.7250.973
설치기관0.5380.2690.2420.3120.7331.0000.8080.8830.675
서비스구분0.5330.2770.054NaN0.9250.8081.0000.5620.567
망종류0.7200.2630.2040.3120.7250.8830.5621.0000.188
실내외구분0.4350.4000.2150.3120.9730.6750.5670.1881.000

Missing values

2024-05-18T07:44:55.516998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T07:44:56.316370image/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.
2024-05-18T07:44:56.926348image/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
0BS100984영등포구버스정류소_KBS여의공원로 1319-130<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.524727126.918232024-05-17 11:12:52.0
1BS100985영등포구버스정류소_강남성심병원.대림성모병원대림동 960-819-010<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.49087126.90792024-05-17 11:12:52.0
2BS100986영등포구버스정류소_경남아너스빌아파트신길제6동 478019-011<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.495586126.9115752024-05-17 11:12:52.0
3BS100987영등포구버스정류소_당산초등학교앞양평동5가 88-419-220<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.538918126.8945852024-05-17 11:12:52.0
4BS100988영등포구버스정류소_대림3동주민센터대림로 19519-458<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.49808126.898312024-05-17 11:12:52.0
5BS100989영등포구버스정류소_대림동코오롱아파트대림동 630-919-357<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.507614126.894352024-05-17 11:12:52.0
6BS100990영등포구버스정류소_대림코오롱아파트대림동 607-119-312<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.507107126.8943252024-05-17 11:12:52.0
7BS100991영등포구버스정류소_대방역신길동 141319-307<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.51347126.925822024-05-17 11:12:52.0
8BS100992영등포구버스정류소_두산위브진주아파트선유로9나길 1219-427<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.516422126.885312024-05-17 11:12:52.0
9BS100993영등포구버스정류소_문래동남성아파트경인로19-001<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.51183126.893712024-05-17 11:12:52.0
관리번호자치구와이파이명도로명주소상세주소설치위치(층)설치유형설치기관서비스구분망종류설치년도실내외구분wifi접속환경X좌표Y좌표작업일자
869서울4차-6655영등포구푸른공부방지역아동센터서울특별시 영등포구 양산로 138-1(당산동1가)4F_공부방_1<NA>6-4. 복지 - 아동청소년디지털뉴딜(LG U+)공공WiFi인터넷망_뉴딜용2022실내<NA>37.52361126.898282024-05-17 11:13:06.0
870서울5차-1111영등포구영등포디지털동행플라자서울특별시 영등포구 디지털로37나길 211번 위치 사무실 천장 내부3층7-4. 공공 - 경제/커뮤니티디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.491608126.899942024-05-17 11:13:06.0
871서울5차-1111-1영등포구영등포디지털동행플라자서울특별시 영등포구 디지털로37나길 212번 위치 사무실 천장 내부3층7-4. 공공 - 경제/커뮤니티디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.491608126.899942024-05-17 11:13:06.0
872서울5차-1111-2영등포구영등포디지털동행플라자서울특별시 영등포구 디지털로37나길 213번 위치 사무실 천장 내부3층7-4. 공공 - 경제/커뮤니티디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.491608126.899942024-05-17 11:13:06.0
873서울5차-1112영등포구영등포디지털동행플라자서울특별시 영등포구 디지털로37나길 214번 위치 사무실 천장 내부3층7-4. 공공 - 경제/커뮤니티디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.491608126.899942024-05-17 11:13:06.0
874서울5차-1112-1영등포구영등포디지털동행플라자서울특별시 영등포구 디지털로37나길 215번 위치 사무실 천장 내부3층7-4. 공공 - 경제/커뮤니티디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.491608126.899942024-05-17 11:13:06.0
875서울5차-1112-2영등포구영등포디지털동행플라자서울특별시 영등포구 디지털로37나길 216번 위치 사무실 천장 내부3층7-4. 공공 - 경제/커뮤니티디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.491608126.899942024-05-17 11:13:06.0
876서울5차-1113영등포구영등포디지털동행플라자서울특별시 영등포구 디지털로37나길 217번 위치 사무실 천장 내부3층7-4. 공공 - 경제/커뮤니티디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.491608126.899942024-05-17 11:13:06.0
877서울5차-1113-1영등포구영등포디지털동행플라자서울특별시 영등포구 디지털로37나길 218번 위치 사무실 천장 내부3층7-4. 공공 - 경제/커뮤니티디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.491608126.899942024-05-17 11:13:06.0
878서울5차-1113-2영등포구영등포디지털동행플라자서울특별시 영등포구 디지털로37나길 219번 위치 사무실 천장 내부3층7-4. 공공 - 경제/커뮤니티디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실내<NA>37.491608126.899942024-05-17 11:13:06.0