Overview

Dataset statistics

Number of variables16
Number of observations1119
Missing cells33
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory143.3 KiB
Average record size in memory131.1 B

Variable types

Text4
Categorical8
Numeric3
DateTime1

Dataset

Description관리번호,자치구,와이파이명,도로명주소,상세주소,설치위치(층),설치유형,설치기관,서비스구분,망종류,설치년도,실내외구분,wifi접속환경,X좌표,Y좌표,작업일자
Author마포구
URLhttps://data.seoul.go.kr/dataList/OA-20902/S/1/datasetView.do

Alerts

자치구 has constant value ""Constant
실내외구분 is highly overall correlated with 설치년도 and 4 other fieldsHigh correlation
설치기관 is highly overall correlated with 설치년도 and 6 other fieldsHigh correlation
망종류 is highly overall correlated with 설치년도 and 5 other fieldsHigh correlation
설치유형 is highly overall correlated with 설치년도 and 6 other fieldsHigh correlation
wifi접속환경 is highly overall correlated with 설치년도 and 5 other fieldsHigh correlation
설치위치(층) is highly overall correlated with 설치년도 and 7 other fieldsHigh correlation
서비스구분 is highly overall correlated with 설치위치(층) and 3 other fieldsHigh correlation
설치년도 is highly overall correlated with X좌표 and 6 other fieldsHigh correlation
X좌표 is highly overall correlated with 설치년도 and 3 other fieldsHigh correlation
Y좌표 is highly overall correlated with X좌표 and 1 other fieldsHigh correlation
설치위치(층) is highly imbalanced (89.9%)Imbalance
서비스구분 is highly imbalanced (52.6%)Imbalance
wifi접속환경 is highly imbalanced (90.2%)Imbalance
도로명주소 has 26 (2.3%) missing valuesMissing
관리번호 has unique valuesUnique

Reproduction

Analysis started2024-05-18 02:36:16.825302
Analysis finished2024-05-18 02:36:25.189286
Duration8.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Text

UNIQUE 

Distinct1119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2024-05-18T11:36:25.924504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length8.2680965
Min length7

Characters and Unicode

Total characters9252
Distinct characters24
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

Unique1119 ?
Unique (%)100.0%

Sample

1st rowBS100590
2nd rowBS100591
3rd rowBS100592
4th rowBS100593
5th rowBS100594
ValueCountFrequency (%)
bs100590 1
 
0.1%
서울-1848 1
 
0.1%
서울-1883 1
 
0.1%
서울-1882 1
 
0.1%
서울-1878 1
 
0.1%
서울-1874 1
 
0.1%
서울-1873 1
 
0.1%
서울-1872 1
 
0.1%
서울-1865 1
 
0.1%
서울-1921 1
 
0.1%
Other values (1109) 1109
99.1%
2024-05-18T11:36:27.416206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1699
18.4%
1 1186
12.8%
2 623
 
6.7%
6 597
 
6.5%
4 554
 
6.0%
- 459
 
5.0%
5 406
 
4.4%
울 392
 
4.2%
서 392
 
4.2%
W 387
 
4.2%
Other values (14) 2557
27.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6263
67.7%
Uppercase Letter 1480
 
16.0%
Other Letter 1050
 
11.3%
Dash Punctuation 459
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1699
27.1%
1 1186
18.9%
2 623
 
9.9%
6 597
 
9.5%
4 554
 
8.8%
5 406
 
6.5%
3 372
 
5.9%
9 294
 
4.7%
7 266
 
4.2%
8 266
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
W 387
26.1%
F 363
24.5%
M 252
17.0%
P 252
17.0%
S 103
 
7.0%
B 77
 
5.2%
N 24
 
1.6%
H 11
 
0.7%
T 6
 
0.4%
G 5
 
0.3%
Other Letter
ValueCountFrequency (%)
울 392
37.3%
서 392
37.3%
차 266
25.3%
Dash Punctuation
ValueCountFrequency (%)
- 459
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6722
72.7%
Latin 1480
 
16.0%
Hangul 1050
 
11.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1699
25.3%
1 1186
17.6%
2 623
 
9.3%
6 597
 
8.9%
4 554
 
8.2%
- 459
 
6.8%
5 406
 
6.0%
3 372
 
5.5%
9 294
 
4.4%
7 266
 
4.0%
Latin
ValueCountFrequency (%)
W 387
26.1%
F 363
24.5%
M 252
17.0%
P 252
17.0%
S 103
 
7.0%
B 77
 
5.2%
N 24
 
1.6%
H 11
 
0.7%
T 6
 
0.4%
G 5
 
0.3%
Hangul
ValueCountFrequency (%)
울 392
37.3%
서 392
37.3%
차 266
25.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8202
88.7%
Hangul 1050
 
11.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1699
20.7%
1 1186
14.5%
2 623
 
7.6%
6 597
 
7.3%
4 554
 
6.8%
- 459
 
5.6%
5 406
 
5.0%
W 387
 
4.7%
3 372
 
4.5%
F 363
 
4.4%
Other values (11) 1556
19.0%
Hangul
ValueCountFrequency (%)
울 392
37.3%
서 392
37.3%
차 266
25.3%

자치구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
마포구
1119 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
마포구 1119
100.0%

Length

2024-05-18T11:36:27.843837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:36:28.170844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마포구 1119
100.0%

와이파이명
Text

Distinct166
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2024-05-18T11:36:28.664934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length7.0205541
Min length3

Characters and Unicode

Total characters7856
Distinct characters253
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

Unique72 ?
Unique (%)6.4%

Sample

1st row버스정류소_DMC첨단산업센터
2nd row버스정류소_공덕역
3rd row버스정류소_공덕역
4th row버스정류소_공덕역
5th row버스정류소_공덕오거리
ValueCountFrequency (%)
에스플렉스센터 181
 
16.1%
난지한강공원 92
 
8.2%
마포중앙도서관 85
 
7.6%
마포구청 81
 
7.2%
경의선숲길 38
 
3.4%
평화공원 37
 
3.3%
망원한강공원 35
 
3.1%
홍대인근 34
 
3.0%
서울특별시중부여성발전센터 21
 
1.9%
마포구구의회 19
 
1.7%
Other values (157) 498
44.4%
2024-05-18T11:36:29.813077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 451
 
5.7%
마 302
 
3.8%
포 299
 
3.8%
터 277
 
3.5%
원 273
 
3.5%
센 272
 
3.5%
공 242
 
3.1%
지 236
 
3.0%
서 193
 
2.5%
관 192
 
2.4%
Other values (243) 5119
65.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7697
98.0%
Connector Punctuation 77
 
1.0%
Decimal Number 46
 
0.6%
Other Punctuation 19
 
0.2%
Uppercase Letter 7
 
0.1%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Dash Punctuation 2
 
< 0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 451
 
5.9%
마 302
 
3.9%
포 299
 
3.9%
터 277
 
3.6%
원 273
 
3.5%
센 272
 
3.5%
공 242
 
3.1%
지 236
 
3.1%
서 193
 
2.5%
관 192
 
2.5%
Other values (224) 4960
64.4%
Decimal Number
ValueCountFrequency (%)
1 21
45.7%
2 10
21.7%
3 6
 
13.0%
9 3
 
6.5%
0 2
 
4.3%
4 2
 
4.3%
7 2
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
C 2
28.6%
D 1
14.3%
M 1
14.3%
J 1
14.3%
T 1
14.3%
B 1
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 77
100.0%
Other Punctuation
ValueCountFrequency (%)
. 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7697
98.0%
Common 152
 
1.9%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 451
 
5.9%
마 302
 
3.9%
포 299
 
3.9%
터 277
 
3.6%
원 273
 
3.5%
센 272
 
3.5%
공 242
 
3.1%
지 236
 
3.1%
서 193
 
2.5%
관 192
 
2.5%
Other values (224) 4960
64.4%
Common
ValueCountFrequency (%)
_ 77
50.7%
1 21
 
13.8%
. 19
 
12.5%
2 10
 
6.6%
3 6
 
3.9%
) 3
 
2.0%
( 3
 
2.0%
9 3
 
2.0%
0 2
 
1.3%
4 2
 
1.3%
Other values (3) 6
 
3.9%
Latin
ValueCountFrequency (%)
C 2
28.6%
D 1
14.3%
M 1
14.3%
J 1
14.3%
T 1
14.3%
B 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7697
98.0%
ASCII 159
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 451
 
5.9%
마 302
 
3.9%
포 299
 
3.9%
터 277
 
3.6%
원 273
 
3.5%
센 272
 
3.5%
공 242
 
3.1%
지 236
 
3.1%
서 193
 
2.5%
관 192
 
2.5%
Other values (224) 4960
64.4%
ASCII
ValueCountFrequency (%)
_ 77
48.4%
1 21
 
13.2%
. 19
 
11.9%
2 10
 
6.3%
3 6
 
3.8%
) 3
 
1.9%
( 3
 
1.9%
9 3
 
1.9%
0 2
 
1.3%
4 2
 
1.3%
Other values (9) 13
 
8.2%

도로명주소
Text

MISSING 

Distinct328
Distinct (%)30.0%
Missing26
Missing (%)2.3%
Memory size8.9 KiB
2024-05-18T11:36:30.554466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length44
Mean length12.768527
Min length4

Characters and Unicode

Total characters13956
Distinct characters211
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

Unique234 ?
Unique (%)21.4%

Sample

1st row상암동 857-2
2nd row마포대로
3rd row마포대로
4th row백범로 192
5th row백범로 199
ValueCountFrequency (%)
마포구 406
 
13.3%
서울특별시 383
 
12.6%
매봉산로 192
 
6.3%
31 184
 
6.0%
월드컵로 174
 
5.7%
212 117
 
3.8%
성산로 85
 
2.8%
128 85
 
2.8%
한강난지로 59
 
1.9%
162 59
 
1.9%
Other values (450) 1301
42.7%
2024-05-18T11:36:31.827009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1952
 
14.0%
1 841
 
6.0%
로 785
 
5.6%
2 664
 
4.8%
3 495
 
3.5%
포 483
 
3.5%
마 483
 
3.5%
서 456
 
3.3%
구 413
 
3.0%
울 407
 
2.9%
Other values (201) 6977
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7947
56.9%
Decimal Number 3568
25.6%
Space Separator 1952
 
14.0%
Dash Punctuation 216
 
1.5%
Uppercase Letter 111
 
0.8%
Other Punctuation 57
 
0.4%
Close Punctuation 38
 
0.3%
Open Punctuation 38
 
0.3%
Lowercase Letter 24
 
0.2%
Math Symbol 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 785
 
9.9%
포 483
 
6.1%
마 483
 
6.1%
서 456
 
5.7%
구 413
 
5.2%
울 407
 
5.1%
시 398
 
5.0%
별 383
 
4.8%
특 383
 
4.8%
산 338
 
4.3%
Other values (160) 3418
43.0%
Lowercase Letter
ValueCountFrequency (%)
c 4
16.7%
i 3
12.5%
e 3
12.5%
s 2
 
8.3%
m 2
 
8.3%
r 1
 
4.2%
k 1
 
4.2%
h 1
 
4.2%
t 1
 
4.2%
v 1
 
4.2%
Other values (5) 5
20.8%
Decimal Number
ValueCountFrequency (%)
1 841
23.6%
2 664
18.6%
3 495
13.9%
6 293
 
8.2%
4 274
 
7.7%
8 250
 
7.0%
0 246
 
6.9%
5 232
 
6.5%
7 180
 
5.0%
9 93
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
A 54
48.6%
P 53
47.7%
C 1
 
0.9%
B 1
 
0.9%
U 1
 
0.9%
F 1
 
0.9%
Other Punctuation
ValueCountFrequency (%)
, 29
50.9%
. 21
36.8%
& 3
 
5.3%
/ 2
 
3.5%
; 2
 
3.5%
Space Separator
ValueCountFrequency (%)
1952
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 216
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7947
56.9%
Common 5874
42.1%
Latin 135
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 785
 
9.9%
포 483
 
6.1%
마 483
 
6.1%
서 456
 
5.7%
구 413
 
5.2%
울 407
 
5.1%
시 398
 
5.0%
별 383
 
4.8%
특 383
 
4.8%
산 338
 
4.3%
Other values (160) 3418
43.0%
Latin
ValueCountFrequency (%)
A 54
40.0%
P 53
39.3%
c 4
 
3.0%
i 3
 
2.2%
e 3
 
2.2%
s 2
 
1.5%
m 2
 
1.5%
C 1
 
0.7%
B 1
 
0.7%
r 1
 
0.7%
Other values (11) 11
 
8.1%
Common
ValueCountFrequency (%)
1952
33.2%
1 841
14.3%
2 664
 
11.3%
3 495
 
8.4%
6 293
 
5.0%
4 274
 
4.7%
8 250
 
4.3%
0 246
 
4.2%
5 232
 
3.9%
- 216
 
3.7%
Other values (10) 411
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7947
56.9%
ASCII 6009
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1952
32.5%
1 841
14.0%
2 664
 
11.1%
3 495
 
8.2%
6 293
 
4.9%
4 274
 
4.6%
8 250
 
4.2%
0 246
 
4.1%
5 232
 
3.9%
- 216
 
3.6%
Other values (31) 546
 
9.1%
Hangul
ValueCountFrequency (%)
로 785
 
9.9%
포 483
 
6.1%
마 483
 
6.1%
서 456
 
5.7%
구 413
 
5.2%
울 407
 
5.1%
시 398
 
5.0%
별 383
 
4.8%
특 383
 
4.8%
산 338
 
4.3%
Other values (160) 3418
43.0%

상세주소
Text

Distinct858
Distinct (%)77.2%
Missing7
Missing (%)0.6%
Memory size8.9 KiB
2024-05-18T11:36:32.712974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length31
Mean length10.128597
Min length2

Characters and Unicode

Total characters11263
Distinct characters358
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique750 ?
Unique (%)67.4%

Sample

1st row14-112
2nd row14-003
3rd row14-004
4th row14-153
5th row14-149
ValueCountFrequency (%)
시너지움 95
 
4.0%
스마티움 82
 
3.4%
cctv 46
 
1.9%
앞 40
 
1.7%
마포구 39
 
1.6%
2층 37
 
1.5%
구간 34
 
1.4%
3층 33
 
1.4%
4층 27
 
1.1%
가로등 25
 
1.0%
Other values (869) 1936
80.9%
2024-05-18T11:36:34.243523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1290
 
11.5%
1 709
 
6.3%
2 388
 
3.4%
층 379
 
3.4%
4 292
 
2.6%
_ 282
 
2.5%
3 267
 
2.4%
) 255
 
2.3%
( 255
 
2.3%
지 237
 
2.1%
Other values (348) 6909
61.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5979
53.1%
Decimal Number 2453
21.8%
Space Separator 1290
 
11.5%
Uppercase Letter 493
 
4.4%
Connector Punctuation 282
 
2.5%
Close Punctuation 255
 
2.3%
Open Punctuation 255
 
2.3%
Dash Punctuation 155
 
1.4%
Lowercase Letter 52
 
0.5%
Other Punctuation 47
 
0.4%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
층 379
 
6.3%
지 237
 
4.0%
움 181
 
3.0%
실 174
 
2.9%
마 152
 
2.5%
장 139
 
2.3%
옥 139
 
2.3%
동 126
 
2.1%
방 113
 
1.9%
관 110
 
1.8%
Other values (306) 4229
70.7%
Uppercase Letter
ValueCountFrequency (%)
F 171
34.7%
C 121
24.5%
T 62
 
12.6%
B 61
 
12.4%
V 60
 
12.2%
S 4
 
0.8%
A 3
 
0.6%
P 3
 
0.6%
U 2
 
0.4%
D 2
 
0.4%
Other values (3) 4
 
0.8%
Decimal Number
ValueCountFrequency (%)
1 709
28.9%
2 388
15.8%
4 292
11.9%
3 267
 
10.9%
0 212
 
8.6%
5 207
 
8.4%
6 114
 
4.6%
7 101
 
4.1%
9 86
 
3.5%
8 77
 
3.1%
Other Punctuation
ValueCountFrequency (%)
, 23
48.9%
# 17
36.2%
/ 3
 
6.4%
. 2
 
4.3%
* 1
 
2.1%
: 1
 
2.1%
Lowercase Letter
ValueCountFrequency (%)
c 20
38.5%
b 12
23.1%
t 9
17.3%
v 8
 
15.4%
k 2
 
3.8%
e 1
 
1.9%
Space Separator
ValueCountFrequency (%)
1290
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 282
100.0%
Close Punctuation
ValueCountFrequency (%)
) 255
100.0%
Open Punctuation
ValueCountFrequency (%)
( 255
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 155
100.0%
Other Symbol
ValueCountFrequency (%)
▲ 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5979
53.1%
Common 4739
42.1%
Latin 545
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
층 379
 
6.3%
지 237
 
4.0%
움 181
 
3.0%
실 174
 
2.9%
마 152
 
2.5%
장 139
 
2.3%
옥 139
 
2.3%
동 126
 
2.1%
방 113
 
1.9%
관 110
 
1.8%
Other values (306) 4229
70.7%
Common
ValueCountFrequency (%)
1290
27.2%
1 709
15.0%
2 388
 
8.2%
4 292
 
6.2%
_ 282
 
6.0%
3 267
 
5.6%
) 255
 
5.4%
( 255
 
5.4%
0 212
 
4.5%
5 207
 
4.4%
Other values (13) 582
12.3%
Latin
ValueCountFrequency (%)
F 171
31.4%
C 121
22.2%
T 62
 
11.4%
B 61
 
11.2%
V 60
 
11.0%
c 20
 
3.7%
b 12
 
2.2%
t 9
 
1.7%
v 8
 
1.5%
S 4
 
0.7%
Other values (9) 17
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5979
53.1%
ASCII 5283
46.9%
Geometric Shapes 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1290
24.4%
1 709
13.4%
2 388
 
7.3%
4 292
 
5.5%
_ 282
 
5.3%
3 267
 
5.1%
) 255
 
4.8%
( 255
 
4.8%
0 212
 
4.0%
5 207
 
3.9%
Other values (31) 1126
21.3%
Hangul
ValueCountFrequency (%)
층 379
 
6.3%
지 237
 
4.0%
움 181
 
3.0%
실 174
 
2.9%
마 152
 
2.5%
장 139
 
2.3%
옥 139
 
2.3%
동 126
 
2.1%
방 113
 
1.9%
관 110
 
1.8%
Other values (306) 4229
70.7%
Geometric Shapes
ValueCountFrequency (%)
▲ 1
100.0%

설치위치(층)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
<NA>
1071 
-
 
35
2층
 
5
1층
 
3
1
 
1
Other values (4)
 
4

Length

Max length4
Median length4
Mean length3.8784629
Min length1

Unique

Unique5 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1071
95.7%
- 35
 
3.1%
2층 5
 
0.4%
1층 3
 
0.3%
1 1
 
0.1%
8 1
 
0.1%
6 1
 
0.1%
3 1
 
0.1%
2 1
 
0.1%

Length

2024-05-18T11:36:34.769929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:36:35.169973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1071
95.7%
35
 
3.1%
2층 5
 
0.4%
1층 3
 
0.3%
1 1
 
0.1%
8 1
 
0.1%
6 1
 
0.1%
3 1
 
0.1%
2 1
 
0.1%

설치유형
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
3. 공원(하천)
251 
7-1-3. 공공 - 시산하기관
234 
4. 문화관광
138 
1. 주요거리
103 
7-2-1. 공공 - 구청사 및 별관
81 
Other values (13)
312 

Length

Max length21
Median length17
Mean length12.456658
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) 251
22.4%
7-1-3. 공공 - 시산하기관 234
20.9%
4. 문화관광 138
12.3%
1. 주요거리 103
9.2%
7-2-1. 공공 - 구청사 및 별관 81
 
7.2%
5-1. 버스정류소(국비) 51
 
4.6%
7-2-3. 공공 - 동주민센터 43
 
3.8%
6-1. 복지 - 사회 40
 
3.6%
6-2. 복지 - 노인 39
 
3.5%
7-2-2. 공공 - 구의회 및 보건소 32
 
2.9%
Other values (8) 107
9.6%

Length

2024-05-18T11:36:35.687059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
518
14.8%
공공 410
 
11.7%
3 259
 
7.4%
공원(하천 251
 
7.2%
7-1-3 234
 
6.7%
시산하기관 234
 
6.7%
4 138
 
3.9%
문화관광 138
 
3.9%
및 113
 
3.2%
복지 108
 
3.1%
Other values (31) 1097
31.3%

설치기관
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
서울시(AP)
386 
디지털뉴딜(LG U+)
274 
자치구
252 
디지털뉴딜(KT)
118 
버스정류소(국비)
51 
Other values (3)
 
38

Length

Max length12
Median length9
Mean length7.6827525
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 (%)
서울시(AP) 386
34.5%
디지털뉴딜(LG U+) 274
24.5%
자치구 252
22.5%
디지털뉴딜(KT) 118
 
10.5%
버스정류소(국비) 51
 
4.6%
버스정류소(시비) 26
 
2.3%
서울시(공유기) 7
 
0.6%
서울시(LTE) 5
 
0.4%

Length

2024-05-18T11:36:36.092245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:36:36.564050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울시(ap 386
27.7%
디지털뉴딜(lg 274
19.7%
u 274
19.7%
자치구 252
18.1%
디지털뉴딜(kt 118
 
8.5%
버스정류소(국비 51
 
3.7%
버스정류소(시비 26
 
1.9%
서울시(공유기 7
 
0.5%
서울시(lte 5
 
0.4%

서비스구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
공공WiFi
899 
과기부WiFi(핫플레이스)
 
77
과기부WiFi
 
51
과기부WiFi(복지시설)
 
49
<NA>
 
43

Length

Max length14
Median length6
Mean length6.8257373
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공WiFi 899
80.3%
과기부WiFi(핫플레이스) 77
 
6.9%
과기부WiFi 51
 
4.6%
과기부WiFi(복지시설) 49
 
4.4%
<NA> 43
 
3.8%

Length

2024-05-18T11:36:37.165126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:36:37.569373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공wifi 899
80.3%
과기부wifi(핫플레이스 77
 
6.9%
과기부wifi 51
 
4.6%
과기부wifi(복지시설 49
 
4.4%
na 43
 
3.8%

망종류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
인터넷망_뉴딜용
392 
임대망
351 
자가망_U무선망
350 
<NA>
 
26

Length

Max length8
Median length8
Mean length6.3386953
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인터넷망_뉴딜용 392
35.0%
임대망 351
31.4%
자가망_U무선망 350
31.3%
<NA> 26
 
2.3%

Length

2024-05-18T11:36:38.075310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:36:38.607135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인터넷망_뉴딜용 392
35.0%
임대망 351
31.4%
자가망_u무선망 350
31.3%
na 26
 
2.3%

설치년도
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.462
Minimum2016
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-05-18T11:36:39.082891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12016
median2020
Q32022
95-th percentile2022
Maximum2024
Range8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.5612594
Coefficient of variation (CV)0.001268288
Kurtosis-1.5738008
Mean2019.462
Median Absolute Deviation (MAD)2
Skewness-0.24782103
Sum2259778
Variance6.5600499
MonotonicityNot monotonic
2024-05-18T11:36:39.513240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2022 354
31.6%
2016 283
25.3%
2021 123
 
11.0%
2017 95
 
8.5%
2020 88
 
7.9%
2019 82
 
7.3%
2023 46
 
4.1%
2018 44
 
3.9%
2024 4
 
0.4%
ValueCountFrequency (%)
2016 283
25.3%
2017 95
 
8.5%
2018 44
 
3.9%
2019 82
 
7.3%
2020 88
 
7.9%
2021 123
 
11.0%
2022 354
31.6%
2023 46
 
4.1%
2024 4
 
0.4%
ValueCountFrequency (%)
2024 4
 
0.4%
2023 46
 
4.1%
2022 354
31.6%
2021 123
 
11.0%
2020 88
 
7.9%
2019 82
 
7.3%
2018 44
 
3.9%
2017 95
 
8.5%
2016 283
25.3%

실내외구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
실내
611 
실외
508 

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 611
54.6%
실외 508
45.4%

Length

2024-05-18T11:36:39.935042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:36:40.278427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
실내 611
54.6%
실외 508
45.4%

wifi접속환경
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
<NA>
1071 
보안접속 임시적용(머큐리 Proxy 서버 개발중)
 
42
H128
 
1
H129
 
1
H130
 
1
Other values (3)
 
3

Length

Max length27
Median length4
Mean length4.8632708
Min length4

Unique

Unique6 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1071
95.7%
보안접속 임시적용(머큐리 Proxy 서버 개발중) 42
 
3.8%
H128 1
 
0.1%
H129 1
 
0.1%
H130 1
 
0.1%
H131 1
 
0.1%
H132 1
 
0.1%
H133 1
 
0.1%

Length

2024-05-18T11:36:40.727616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T11:36:41.162356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1071
83.2%
보안접속 42
 
3.3%
임시적용(머큐리 42
 
3.3%
proxy 42
 
3.3%
서버 42
 
3.3%
개발중 42
 
3.3%
h128 1
 
0.1%
h129 1
 
0.1%
h130 1
 
0.1%
h131 1
 
0.1%
Other values (2) 2
 
0.2%

X좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct395
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.562253
Minimum37.524445
Maximum37.58627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-05-18T11:36:41.704542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.524445
5-th percentile37.541331
Q137.553671
median37.56388
Q337.571252
95-th percentile37.57596
Maximum37.58627
Range0.061825
Interquartile range (IQR)0.0175805

Descriptive statistics

Standard deviation0.011503494
Coefficient of variation (CV)0.00030625143
Kurtosis-0.58534364
Mean37.562253
Median Absolute Deviation (MAD)0.009536
Skewness-0.27799549
Sum42032.162
Variance0.00013233037
MonotonicityNot monotonic
2024-05-18T11:36:42.638017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.57596 177
 
15.8%
37.566166 114
 
10.2%
37.56388 85
 
7.6%
37.56284 59
 
5.3%
37.552345 30
 
2.7%
37.540737 20
 
1.8%
37.585938 19
 
1.7%
37.554844 14
 
1.3%
37.5655 14
 
1.3%
37.574173 11
 
1.0%
Other values (385) 576
51.5%
ValueCountFrequency (%)
37.524445 3
0.3%
37.53756 1
 
0.1%
37.538334 1
 
0.1%
37.53892 1
 
0.1%
37.538937 1
 
0.1%
37.539032 1
 
0.1%
37.53921 1
 
0.1%
37.53948 3
0.3%
37.539555 1
 
0.1%
37.53969 1
 
0.1%
ValueCountFrequency (%)
37.58627 1
 
0.1%
37.585938 19
1.7%
37.585598 1
 
0.1%
37.58533 1
 
0.1%
37.585323 1
 
0.1%
37.584427 1
 
0.1%
37.58115 1
 
0.1%
37.58041 3
 
0.3%
37.580296 6
 
0.5%
37.58 2
 
0.2%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct392
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.90958
Minimum126.39462
Maximum127.12225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2024-05-18T11:36:43.149554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.39462
5-th percentile126.883
Q1126.89058
median126.90166
Q3126.9256
95-th percentile126.95361
Maximum127.12225
Range0.727626
Interquartile range (IQR)0.035023

Descriptive statistics

Standard deviation0.031267053
Coefficient of variation (CV)0.00024637268
Kurtosis73.770846
Mean126.90958
Median Absolute Deviation (MAD)0.011085
Skewness-2.2633827
Sum142011.82
Variance0.00097762861
MonotonicityNot monotonic
2024-05-18T11:36:43.742329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.89058 177
 
15.8%
126.901665 114
 
10.2%
126.908264 85
 
7.6%
126.88542 59
 
5.3%
126.89989 30
 
2.7%
126.94408 20
 
1.8%
126.88477 19
 
1.7%
126.889946 14
 
1.3%
126.94864 14
 
1.3%
126.90037 14
 
1.3%
Other values (382) 573
51.2%
ValueCountFrequency (%)
126.39462 1
0.1%
126.870674 2
0.2%
126.870865 2
0.2%
126.870964 2
0.2%
126.87104 2
0.2%
126.871185 2
0.2%
126.871376 2
0.2%
126.87143 2
0.2%
126.87187 2
0.2%
126.87189 2
0.2%
ValueCountFrequency (%)
127.122246 5
0.4%
127.01593 3
0.3%
126.96081 1
 
0.1%
126.960724 1
 
0.1%
126.960304 4
0.4%
126.95826 1
 
0.1%
126.95739 1
 
0.1%
126.95734 1
 
0.1%
126.957214 1
 
0.1%
126.9571 1
 
0.1%

작업일자
Date

Distinct13
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
Minimum2024-05-18 11:12:52
Maximum2024-05-18 11:13:06
2024-05-18T11:36:44.172336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:36:44.737431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)

Interactions

2024-05-18T11:36:22.356294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:36:19.954365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:36:21.118209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:36:22.833333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:36:20.297110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:36:21.540816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:36:23.278203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:36:20.718184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T11:36:21.935595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T11:36:45.245792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치위치(층)설치유형설치기관서비스구분망종류설치년도실내외구분wifi접속환경X좌표Y좌표작업일자
설치위치(층)1.0000.9111.000NaN1.0001.0001.000NaN0.7640.7451.000
설치유형0.9111.0000.9490.9520.8410.8200.9940.7300.8080.5930.911
설치기관1.0000.9491.0000.9780.8460.9170.7621.0000.5840.2800.961
서비스구분NaN0.9520.9781.0000.4310.7690.6351.0000.3370.3370.990
망종류1.0000.8410.8460.4311.0000.8540.2721.0000.8280.2230.991
설치년도1.0000.8200.9170.7690.8541.0000.5911.0000.5120.3410.886
실내외구분1.0000.9940.7620.6350.2720.5911.0001.0000.4950.1390.634
wifi접속환경NaN0.7301.0001.0001.0001.0001.0001.0000.5660.0000.730
X좌표0.7640.8080.5840.3370.8280.5120.4950.5661.0000.7970.695
Y좌표0.7450.5930.2800.3370.2230.3410.1390.0000.7971.0000.425
작업일자1.0000.9110.9610.9900.9910.8860.6340.7300.6950.4251.000
2024-05-18T11:36:45.786586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
실내외구분설치기관망종류설치유형wifi접속환경설치위치(층)서비스구분
실내외구분1.0000.5860.4410.9270.9440.9330.442
설치기관0.5861.0000.8190.7960.9440.9330.800
망종류0.4410.8191.0000.6850.9440.9330.424
설치유형0.9270.7960.6851.0000.6240.7700.850
wifi접속환경0.9440.9440.9440.6241.000NaN0.944
설치위치(층)0.9330.9330.9330.770NaN1.0001.000
서비스구분0.4420.8000.4240.8500.9441.0001.000
2024-05-18T11:36:46.165162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치년도X좌표Y좌표설치위치(층)설치유형설치기관서비스구분망종류실내외구분wifi접속환경
설치년도1.000-0.5590.2750.9330.5660.5330.4570.7730.6250.944
X좌표-0.5591.000-0.7800.5360.4090.3350.2210.5370.4950.396
Y좌표0.275-0.7801.0000.6070.3500.1750.1370.1720.1700.000
설치위치(층)0.9330.5360.6071.0000.7700.9331.0000.9330.9330.000
설치유형0.5660.4090.3500.7701.0000.7960.8500.6850.9270.624
설치기관0.5330.3350.1750.9330.7961.0000.8000.8190.5860.944
서비스구분0.4570.2210.1371.0000.8500.8001.0000.4240.4420.944
망종류0.7730.5370.1720.9330.6850.8190.4241.0000.4410.944
실내외구분0.6250.4950.1700.9330.9270.5860.4420.4411.0000.944
wifi접속환경0.9440.3960.0000.0000.6240.9440.9440.9440.9441.000

Missing values

2024-05-18T11:36:23.787248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T11:36:24.447663image/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.
2024-05-18T11:36:24.885768image/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
0BS100590마포구버스정류소_DMC첨단산업센터상암동 857-214-112<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.585598126.884792024-05-18 11:12:52.0
1BS100591마포구버스정류소_공덕역마포대로14-003<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.543606126.9509052024-05-18 11:12:52.0
2BS100592마포구버스정류소_공덕역마포대로14-004<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.544502126.9513552024-05-18 11:12:52.0
3BS100593마포구버스정류소_공덕역백범로 19214-153<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.54348126.951432024-05-18 11:12:52.0
4BS100594마포구버스정류소_공덕오거리백범로 19914-149<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.54307126.953162024-05-18 11:12:52.0
5BS100595마포구버스정류소_기업은행서교동지점(최규하대통령가옥)월드컵로 6214-248<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.55479126.911462024-05-18 11:12:52.0
6BS100596마포구버스정류소_마포경찰서마포대로14-007<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.5502126.9552152024-05-18 11:12:52.0
7BS100597마포구버스정류소_마포경찰서마포대로14-008<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.55077126.955312024-05-18 11:12:52.0
8BS100598마포구버스정류소_마포구청역성산동 342-114-195<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.562244126.902232024-05-18 11:12:52.0
9BS100599마포구버스정류소_마포역마포대로14-001<NA>5-1. 버스정류소(국비)버스정류소(국비)과기부WiFi임대망2021실외<NA>37.5409126.947962024-05-18 11:12:52.0
관리번호자치구와이파이명도로명주소상세주소설치위치(층)설치유형설치기관서비스구분망종류설치년도실내외구분wifi접속환경X좌표Y좌표작업일자
1109서울5차-1087마포구마포시장서울특별시 마포구 공덕동 256-5장터생선구이 우측-2. 전통시장디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실외<NA>37.545105126.953462024-05-18 11:13:06.0
1110서울5차-1088마포구마포시장서울특별시 마포구 공덕동 256-21건어물-2. 전통시장디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실외<NA>37.545185126.9530942024-05-18 11:13:06.0
1111서울5차-1089마포구마포시장서울특별시 마포구 공덕동 256-11마포소문난족발-2. 전통시장디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실외<NA>37.54472126.953062024-05-18 11:13:06.0
1112서울5차-1090마포구마포시장서울특별시 마포구 공덕동 256-43유하의상실 간판위-2. 전통시장디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실외<NA>37.54487126.95372024-05-18 11:13:06.0
1113서울5차-1091마포구마포시장서울특별시 마포구 공덕동 256-43마포왕족발-2. 전통시장디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실외<NA>37.544575126.953292024-05-18 11:13:06.0
1114서울5차-1092마포구마포시장서울특별시 마포구 공덕동 256-43인천젓갈(공덕요거트건너)-2. 전통시장디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실외<NA>37.54479126.953242024-05-18 11:13:06.0
1115서울5차-1093마포구마포시장서울특별시 마포구 공덕동 256-30보경고전의상 좌측-2. 전통시장디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실외<NA>37.544792126.953942024-05-18 11:13:06.0
1116서울5차-1094마포구마포시장서울특별시 마포구 공덕동 256-30마포할머니빈대떡-2. 전통시장디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실외<NA>37.54455126.95362024-05-18 11:13:06.0
1117서울5차-1095마포구마포시장서울특별시 마포구 공덕동 256-10장수족발-2. 전통시장디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실외<NA>37.544807126.953412024-05-18 11:13:06.0
1118서울5차-1096마포구마포시장서울특별시 마포구 공덕동 256-11별빛달빛한잔옥상-2. 전통시장디지털뉴딜(LG U+)<NA>인터넷망_뉴딜용2023실외<NA>37.544884126.9528662024-05-18 11:13:06.0