Overview

Dataset statistics

Number of variables19
Number of observations92
Missing cells13
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.2 KiB
Average record size in memory158.4 B

Variable types

Numeric4
Categorical11
Text4

Dataset

Description서울특별시 관악구 관내에 여름철 무더위 대비 횡단보도 등 주변에 설치한 그늘막 설치 현황(설치장소, 위도, 경도, 상세위치, 시설물의 길이 및 높이 정보 포함)
Author서울특별시 관악구
URLhttps://www.data.go.kr/data/15113656/fileData.do

Alerts

시도 has constant value ""Constant
시군구 has constant value ""Constant
면적(제곱미터) has constant value ""Constant
길이(미터) has constant value ""Constant
분사압(제곱센티미터 당 kgf) has constant value ""Constant
분사량(분당 L) has constant value ""Constant
1회 분사시간(분) has constant value ""Constant
원단명 is highly overall correlated with 연번 and 6 other fieldsHigh correlation
행정동 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
펼침지름(M) is highly overall correlated with 전체높이(M) and 2 other fieldsHigh correlation
종류 is highly overall correlated with 전체높이(M) and 2 other fieldsHigh correlation
연번 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 행정동 and 1 other fieldsHigh correlation
전체높이(M) is highly overall correlated with 종류 and 2 other fieldsHigh correlation
원단명 is highly imbalanced (79.3%)Imbalance
설치장소명 has 13 (14.1%) missing valuesMissing
연번 has unique valuesUnique
관리번호 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2024-05-04 07:37:16.531175
Analysis finished2024-05-04 07:37:26.388682
Duration9.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct92
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.5
Minimum1
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-05-04T07:37:26.725718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.55
Q123.75
median46.5
Q369.25
95-th percentile87.45
Maximum92
Range91
Interquartile range (IQR)45.5

Descriptive statistics

Standard deviation26.70206
Coefficient of variation (CV)0.57423785
Kurtosis-1.2
Mean46.5
Median Absolute Deviation (MAD)23
Skewness0
Sum4278
Variance713
MonotonicityStrictly increasing
2024-05-04T07:37:27.378433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
60 1
 
1.1%
69 1
 
1.1%
68 1
 
1.1%
67 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
62 1
 
1.1%
Other values (82) 82
89.1%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
92 1
1.1%
91 1
1.1%
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%
84 1
1.1%
83 1
1.1%

종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size868.0 B
그늘막(스마트형)
75 
그늘막(고정형)
17 

Length

Max length9
Median length9
Mean length8.8152174
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row그늘막(스마트형)
2nd row그늘막(스마트형)
3rd row그늘막(스마트형)
4th row그늘막(스마트형)
5th row그늘막(스마트형)

Common Values

ValueCountFrequency (%)
그늘막(스마트형) 75
81.5%
그늘막(고정형) 17
 
18.5%

Length

2024-05-04T07:37:27.919645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:37:28.406098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
그늘막(스마트형 75
81.5%
그늘막(고정형 17
 
18.5%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
서울특별시
92 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 92
100.0%

Length

2024-05-04T07:37:28.814906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:37:29.109746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 92
100.0%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
관악구
92 

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 92
100.0%

Length

2024-05-04T07:37:29.404215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:37:29.677682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관악구 92
100.0%

행정동
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Memory size868.0 B
행운동
12 
조원동
10 
서원동
9 
중앙동
8 
신림동
8 
Other values (14)
45 

Length

Max length4
Median length3
Mean length3.1304348
Min length3

Unique

Unique5 ?
Unique (%)5.4%

Sample

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

Common Values

ValueCountFrequency (%)
행운동 12
13.0%
조원동 10
10.9%
서원동 9
9.8%
중앙동 8
8.7%
신림동 8
8.7%
보라매동 8
8.7%
은천동 8
8.7%
낙성대동 4
 
4.3%
청룡동 4
 
4.3%
서림동 4
 
4.3%
Other values (9) 17
18.5%

Length

2024-05-04T07:37:30.024065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
행운동 12
13.0%
조원동 10
10.9%
서원동 9
9.8%
중앙동 8
8.7%
신림동 8
8.7%
보라매동 8
8.7%
은천동 8
8.7%
남현동 4
 
4.3%
서림동 4
 
4.3%
청룡동 4
 
4.3%
Other values (9) 17
18.5%

관리번호
Text

UNIQUE 

Distinct92
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size868.0 B
2024-05-04T07:37:30.793334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.0217391
Min length5

Characters and Unicode

Total characters554
Distinct characters47
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

Unique92 ?
Unique (%)100.0%

Sample

1st row보라매동-S1
2nd row보라매동-S2
3rd row보라매동-S3
4th row보라매동-S4
5th row보라매동-S5
ValueCountFrequency (%)
보라매동-s1 1
 
1.1%
서원동-s7 1
 
1.1%
신림동-1 1
 
1.1%
신사동-s1 1
 
1.1%
신사동-4 1
 
1.1%
신사동-3 1
 
1.1%
서림동-s3 1
 
1.1%
서림동-s2 1
 
1.1%
서림동-s1 1
 
1.1%
서림동-1 1
 
1.1%
Other values (82) 82
89.1%
2024-05-04T07:37:32.272573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 92
16.6%
동 89
16.1%
S 75
13.5%
1 26
 
4.7%
원 20
 
3.6%
2 17
 
3.1%
서 13
 
2.3%
3 13
 
2.3%
운 12
 
2.2%
행 12
 
2.2%
Other values (37) 185
33.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 294
53.1%
Decimal Number 93
 
16.8%
Dash Punctuation 92
 
16.6%
Uppercase Letter 75
 
13.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 89
30.3%
원 20
 
6.8%
서 13
 
4.4%
운 12
 
4.1%
행 12
 
4.1%
신 12
 
4.1%
림 12
 
4.1%
조 10
 
3.4%
은 8
 
2.7%
천 8
 
2.7%
Other values (25) 98
33.3%
Decimal Number
ValueCountFrequency (%)
1 26
28.0%
2 17
18.3%
3 13
14.0%
4 9
 
9.7%
5 8
 
8.6%
6 7
 
7.5%
7 6
 
6.5%
8 5
 
5.4%
9 1
 
1.1%
0 1
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 92
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 75
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 294
53.1%
Common 185
33.4%
Latin 75
 
13.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 89
30.3%
원 20
 
6.8%
서 13
 
4.4%
운 12
 
4.1%
행 12
 
4.1%
신 12
 
4.1%
림 12
 
4.1%
조 10
 
3.4%
은 8
 
2.7%
천 8
 
2.7%
Other values (25) 98
33.3%
Common
ValueCountFrequency (%)
- 92
49.7%
1 26
 
14.1%
2 17
 
9.2%
3 13
 
7.0%
4 9
 
4.9%
5 8
 
4.3%
6 7
 
3.8%
7 6
 
3.2%
8 5
 
2.7%
9 1
 
0.5%
Latin
ValueCountFrequency (%)
S 75
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 294
53.1%
ASCII 260
46.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 92
35.4%
S 75
28.8%
1 26
 
10.0%
2 17
 
6.5%
3 13
 
5.0%
4 9
 
3.5%
5 8
 
3.1%
6 7
 
2.7%
7 6
 
2.3%
8 5
 
1.9%
Other values (2) 2
 
0.8%
Hangul
ValueCountFrequency (%)
동 89
30.3%
원 20
 
6.8%
서 13
 
4.4%
운 12
 
4.1%
행 12
 
4.1%
신 12
 
4.1%
림 12
 
4.1%
조 10
 
3.4%
은 8
 
2.7%
천 8
 
2.7%
Other values (25) 98
33.3%

설치장소명
Text

MISSING 

Distinct79
Distinct (%)100.0%
Missing13
Missing (%)14.1%
Memory size868.0 B
2024-05-04T07:37:33.060519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length24
Mean length11.493671
Min length3

Characters and Unicode

Total characters908
Distinct characters228
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)100.0%

Sample

1st rowSK텔레콤 앞 교통섬
2nd row대박집 앞
3rd row크린토피아 앞
4th rowCU 앞
5th row황군집
ValueCountFrequency (%)
앞 50
 
23.0%
교통섬 9
 
4.1%
횡단보도 8
 
3.7%
옆 4
 
1.8%
서울대입구역 4
 
1.8%
그늘막 4
 
1.8%
이설 3
 
1.4%
세븐일레븐 3
 
1.4%
파리바게트 3
 
1.4%
편의점 2
 
0.9%
Other values (112) 127
58.5%
2024-05-04T07:37:34.663398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
152
 
16.7%
앞 57
 
6.3%
구 19
 
2.1%
교 17
 
1.9%
대 13
 
1.4%
역 13
 
1.4%
2 13
 
1.4%
보 12
 
1.3%
( 12
 
1.3%
) 11
 
1.2%
Other values (218) 589
64.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 662
72.9%
Space Separator 152
 
16.7%
Decimal Number 41
 
4.5%
Uppercase Letter 24
 
2.6%
Open Punctuation 12
 
1.3%
Close Punctuation 11
 
1.2%
Other Punctuation 4
 
0.4%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 57
 
8.6%
구 19
 
2.9%
교 17
 
2.6%
대 13
 
2.0%
역 13
 
2.0%
보 12
 
1.8%
출 11
 
1.7%
도 10
 
1.5%
통 10
 
1.5%
리 10
 
1.5%
Other values (191) 490
74.0%
Uppercase Letter
ValueCountFrequency (%)
K 4
16.7%
S 4
16.7%
C 3
12.5%
T 3
12.5%
U 2
8.3%
L 2
8.3%
G 2
8.3%
B 1
 
4.2%
H 1
 
4.2%
O 1
 
4.2%
Decimal Number
ValueCountFrequency (%)
2 13
31.7%
0 8
19.5%
1 6
14.6%
6 3
 
7.3%
7 3
 
7.3%
5 2
 
4.9%
4 2
 
4.9%
3 2
 
4.9%
9 1
 
2.4%
8 1
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
, 2
50.0%
Space Separator
ValueCountFrequency (%)
152
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 662
72.9%
Common 222
 
24.4%
Latin 24
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 57
 
8.6%
구 19
 
2.9%
교 17
 
2.6%
대 13
 
2.0%
역 13
 
2.0%
보 12
 
1.8%
출 11
 
1.7%
도 10
 
1.5%
통 10
 
1.5%
리 10
 
1.5%
Other values (191) 490
74.0%
Common
ValueCountFrequency (%)
152
68.5%
2 13
 
5.9%
( 12
 
5.4%
) 11
 
5.0%
0 8
 
3.6%
1 6
 
2.7%
6 3
 
1.4%
7 3
 
1.4%
. 2
 
0.9%
, 2
 
0.9%
Other values (6) 10
 
4.5%
Latin
ValueCountFrequency (%)
K 4
16.7%
S 4
16.7%
C 3
12.5%
T 3
12.5%
U 2
8.3%
L 2
8.3%
G 2
8.3%
B 1
 
4.2%
H 1
 
4.2%
O 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 662
72.9%
ASCII 246
 
27.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
152
61.8%
2 13
 
5.3%
( 12
 
4.9%
) 11
 
4.5%
0 8
 
3.3%
1 6
 
2.4%
K 4
 
1.6%
S 4
 
1.6%
C 3
 
1.2%
T 3
 
1.2%
Other values (17) 30
 
12.2%
Hangul
ValueCountFrequency (%)
앞 57
 
8.6%
구 19
 
2.9%
교 17
 
2.6%
대 13
 
2.0%
역 13
 
2.0%
보 12
 
1.8%
출 11
 
1.7%
도 10
 
1.5%
통 10
 
1.5%
리 10
 
1.5%
Other values (191) 490
74.0%

도로명주소
Text

Distinct84
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size868.0 B
2024-05-04T07:37:35.523638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length36
Mean length27
Min length23

Characters and Unicode

Total characters2484
Distinct characters96
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)84.8%

Sample

1st row서울특별시 관악구 봉천로 239 (봉천동)
2nd row서울특별시 관악구 보라매로 3 (봉천동)
3rd row서울특별시 관악구 봉천로 209 (봉천동)
4th row서울특별시 관악구 보라매로5길 1 (봉천동)
5th row서울특별시 관악구 보라매로 2 (봉천동)
ValueCountFrequency (%)
서울특별시 92
19.7%
관악구 92
19.7%
봉천동 46
 
9.9%
신림동 42
 
9.0%
남부순환로 37
 
7.9%
신림로 11
 
2.4%
관악로 7
 
1.5%
봉천로 7
 
1.5%
은천로 5
 
1.1%
남현동 4
 
0.9%
Other values (99) 124
26.6%
2024-05-04T07:37:37.544892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
562
22.6%
관 100
 
4.0%
악 100
 
4.0%
시 96
 
3.9%
서 93
 
3.7%
특 92
 
3.7%
별 92
 
3.7%
구 92
 
3.7%
울 92
 
3.7%
) 92
 
3.7%
Other values (86) 1073
43.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1437
57.9%
Space Separator 562
 
22.6%
Decimal Number 291
 
11.7%
Close Punctuation 92
 
3.7%
Open Punctuation 92
 
3.7%
Other Punctuation 7
 
0.3%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 100
 
7.0%
악 100
 
7.0%
시 96
 
6.7%
서 93
 
6.5%
특 92
 
6.4%
별 92
 
6.4%
구 92
 
6.4%
울 92
 
6.4%
동 92
 
6.4%
로 92
 
6.4%
Other values (71) 496
34.5%
Decimal Number
ValueCountFrequency (%)
1 68
23.4%
2 33
11.3%
6 28
9.6%
5 27
 
9.3%
8 26
 
8.9%
4 24
 
8.2%
3 23
 
7.9%
9 22
 
7.6%
7 21
 
7.2%
0 19
 
6.5%
Space Separator
ValueCountFrequency (%)
562
100.0%
Close Punctuation
ValueCountFrequency (%)
) 92
100.0%
Open Punctuation
ValueCountFrequency (%)
( 92
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1437
57.9%
Common 1047
42.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 100
 
7.0%
악 100
 
7.0%
시 96
 
6.7%
서 93
 
6.5%
특 92
 
6.4%
별 92
 
6.4%
구 92
 
6.4%
울 92
 
6.4%
동 92
 
6.4%
로 92
 
6.4%
Other values (71) 496
34.5%
Common
ValueCountFrequency (%)
562
53.7%
) 92
 
8.8%
( 92
 
8.8%
1 68
 
6.5%
2 33
 
3.2%
6 28
 
2.7%
5 27
 
2.6%
8 26
 
2.5%
4 24
 
2.3%
3 23
 
2.2%
Other values (5) 72
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1437
57.9%
ASCII 1047
42.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
562
53.7%
) 92
 
8.8%
( 92
 
8.8%
1 68
 
6.5%
2 33
 
3.2%
6 28
 
2.7%
5 27
 
2.6%
8 26
 
2.5%
4 24
 
2.3%
3 23
 
2.2%
Other values (5) 72
 
6.9%
Hangul
ValueCountFrequency (%)
관 100
 
7.0%
악 100
 
7.0%
시 96
 
6.7%
서 93
 
6.5%
특 92
 
6.4%
별 92
 
6.4%
구 92
 
6.4%
울 92
 
6.4%
동 92
 
6.4%
로 92
 
6.4%
Other values (71) 496
34.5%

지번주소
Text

Distinct85
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Memory size868.0 B
2024-05-04T07:37:38.326217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length20.934783
Min length18

Characters and Unicode

Total characters1926
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)87.0%

Sample

1st row서울특별시 관악구 봉천동 971-14
2nd row서울특별시 관악구 봉천동 972-3
3rd row서울특별시 관악구 봉천동 729-22
4th row서울특별시 관악구 봉천동 729-1
5th row서울특별시 관악구 봉천동 971-3
ValueCountFrequency (%)
서울특별시 92
25.0%
관악구 92
25.0%
봉천동 46
12.5%
신림동 42
11.4%
남현동 4
 
1.1%
1597-1 3
 
0.8%
979-2 3
 
0.8%
1422-42 2
 
0.5%
1668-15 2
 
0.5%
1446-2 2
 
0.5%
Other values (80) 80
21.7%
2024-05-04T07:37:39.585631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
368
19.1%
1 99
 
5.1%
서 92
 
4.8%
악 92
 
4.8%
울 92
 
4.8%
구 92
 
4.8%
동 92
 
4.8%
관 92
 
4.8%
시 92
 
4.8%
별 92
 
4.8%
Other values (17) 723
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1012
52.5%
Decimal Number 458
23.8%
Space Separator 368
 
19.1%
Dash Punctuation 88
 
4.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 92
9.1%
악 92
9.1%
울 92
9.1%
구 92
9.1%
동 92
9.1%
관 92
9.1%
시 92
9.1%
별 92
9.1%
특 92
9.1%
봉 46
 
4.5%
Other values (5) 138
13.6%
Decimal Number
ValueCountFrequency (%)
1 99
21.6%
2 55
12.0%
4 52
11.4%
6 52
11.4%
9 40
8.7%
5 40
8.7%
3 36
 
7.9%
7 33
 
7.2%
8 26
 
5.7%
0 25
 
5.5%
Space Separator
ValueCountFrequency (%)
368
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1012
52.5%
Common 914
47.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 92
9.1%
악 92
9.1%
울 92
9.1%
구 92
9.1%
동 92
9.1%
관 92
9.1%
시 92
9.1%
별 92
9.1%
특 92
9.1%
봉 46
 
4.5%
Other values (5) 138
13.6%
Common
ValueCountFrequency (%)
368
40.3%
1 99
 
10.8%
- 88
 
9.6%
2 55
 
6.0%
4 52
 
5.7%
6 52
 
5.7%
9 40
 
4.4%
5 40
 
4.4%
3 36
 
3.9%
7 33
 
3.6%
Other values (2) 51
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1012
52.5%
ASCII 914
47.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
368
40.3%
1 99
 
10.8%
- 88
 
9.6%
2 55
 
6.0%
4 52
 
5.7%
6 52
 
5.7%
9 40
 
4.4%
5 40
 
4.4%
3 36
 
3.9%
7 33
 
3.6%
Other values (2) 51
 
5.6%
Hangul
ValueCountFrequency (%)
서 92
9.1%
악 92
9.1%
울 92
9.1%
구 92
9.1%
동 92
9.1%
관 92
9.1%
시 92
9.1%
별 92
9.1%
특 92
9.1%
봉 46
 
4.5%
Other values (5) 138
13.6%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct92
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.93797
Minimum126.90127
Maximum126.9827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-05-04T07:37:39.990032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.90127
5-th percentile126.90378
Q1126.92691
median126.93425
Q3126.95253
95-th percentile126.96588
Maximum126.9827
Range0.0814253
Interquartile range (IQR)0.025623425

Descriptive statistics

Standard deviation0.019123459
Coefficient of variation (CV)0.00015065199
Kurtosis-0.4921016
Mean126.93797
Median Absolute Deviation (MAD)0.0147656
Skewness0.056315336
Sum11678.293
Variance0.00036570667
MonotonicityNot monotonic
2024-05-04T07:37:40.463027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9283397 1
 
1.1%
126.9315851 1
 
1.1%
126.9220274 1
 
1.1%
126.9268746 1
 
1.1%
126.9147036 1
 
1.1%
126.9213972 1
 
1.1%
126.9254394 1
 
1.1%
126.933699 1
 
1.1%
126.9340894 1
 
1.1%
126.9376555 1
 
1.1%
Other values (82) 82
89.1%
ValueCountFrequency (%)
126.9012737 1
1.1%
126.9017082 1
1.1%
126.9027436 1
1.1%
126.9031092 1
1.1%
126.9037591 1
1.1%
126.9037883 1
1.1%
126.9066811 1
1.1%
126.9080406 1
1.1%
126.9091997 1
1.1%
126.9143278 1
1.1%
ValueCountFrequency (%)
126.982699 1
1.1%
126.9814515 1
1.1%
126.9775206 1
1.1%
126.9733636 1
1.1%
126.9661147 1
1.1%
126.9656948 1
1.1%
126.9638497 1
1.1%
126.9615242 1
1.1%
126.9613273 1
1.1%
126.9609222 1
1.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.481231
Minimum37.460552
Maximum37.492034
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-05-04T07:37:40.890456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.460552
5-th percentile37.470521
Q137.478241
median37.482066
Q337.484867
95-th percentile37.489822
Maximum37.492034
Range0.0314823
Interquartile range (IQR)0.006625975

Descriptive statistics

Standard deviation0.00643699
Coefficient of variation (CV)0.00017173903
Kurtosis2.4912565
Mean37.481231
Median Absolute Deviation (MAD)0.00339625
Skewness-1.238482
Sum3448.2732
Variance4.1434841 × 10-5
MonotonicityNot monotonic
2024-05-04T07:37:41.320870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.48433 2
 
2.2%
37.4896018 1
 
1.1%
37.4788128 1
 
1.1%
37.4898536 1
 
1.1%
37.4842014 1
 
1.1%
37.4821585 1
 
1.1%
37.4832536 1
 
1.1%
37.4840729 1
 
1.1%
37.4724356 1
 
1.1%
37.472164 1
 
1.1%
Other values (81) 81
88.0%
ValueCountFrequency (%)
37.4605517 1
1.1%
37.4606961 1
1.1%
37.4610678 1
1.1%
37.4614326 1
1.1%
37.4699412 1
1.1%
37.4709945 1
1.1%
37.471886 1
1.1%
37.472164 1
1.1%
37.4724356 1
1.1%
37.4757907 1
1.1%
ValueCountFrequency (%)
37.492034 1
1.1%
37.491215 1
1.1%
37.4911341 1
1.1%
37.4903236 1
1.1%
37.4898536 1
1.1%
37.4897962 1
1.1%
37.4897423 1
1.1%
37.4896356 1
1.1%
37.489609 1
1.1%
37.4896018 1
1.1%

원단명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size868.0 B
매쉬
89 
<NA>
 
3

Length

Max length4
Median length2
Mean length2.0652174
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
매쉬 89
96.7%
<NA> 3
 
3.3%

Length

2024-05-04T07:37:41.756406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:37:42.073848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
매쉬 89
96.7%
na 3
 
3.3%

전체높이(M)
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0311957
Minimum2.3
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-05-04T07:37:42.354784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile3
Q13
median3
Q33
95-th percentile3.2
Maximum3.5
Range1.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.14889219
Coefficient of variation (CV)0.049119954
Kurtosis11.312925
Mean3.0311957
Median Absolute Deviation (MAD)0
Skewness-1.2205235
Sum278.87
Variance0.022168884
MonotonicityNot monotonic
2024-05-04T07:37:42.673204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3.0 73
79.3%
3.2 14
 
15.2%
3.5 2
 
2.2%
3.4 1
 
1.1%
2.3 1
 
1.1%
2.37 1
 
1.1%
ValueCountFrequency (%)
2.3 1
 
1.1%
2.37 1
 
1.1%
3.0 73
79.3%
3.2 14
 
15.2%
3.4 1
 
1.1%
3.5 2
 
2.2%
ValueCountFrequency (%)
3.5 2
 
2.2%
3.4 1
 
1.1%
3.2 14
 
15.2%
3.0 73
79.3%
2.37 1
 
1.1%
2.3 1
 
1.1%

펼침지름(M)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size868.0 B
5.4
42 
5.0
30 
4.0
15 
3.0
 
3
4.4
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.4
3rd row5.4
4th row5.4
5th row5.4

Common Values

ValueCountFrequency (%)
5.4 42
45.7%
5.0 30
32.6%
4.0 15
 
16.3%
3.0 3
 
3.3%
4.4 2
 
2.2%

Length

2024-05-04T07:37:42.995789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:37:43.264330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5.4 42
45.7%
5.0 30
32.6%
4.0 15
 
16.3%
3.0 3
 
3.3%
4.4 2
 
2.2%

면적(제곱미터)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
해당 항목 X
92 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row해당 항목 X
2nd row해당 항목 X
3rd row해당 항목 X
4th row해당 항목 X
5th row해당 항목 X

Common Values

ValueCountFrequency (%)
해당 항목 X 92
100.0%

Length

2024-05-04T07:37:43.660758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:37:43.872674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해당 92
33.3%
항목 92
33.3%
x 92
33.3%

길이(미터)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
해당 항목 X
92 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row해당 항목 X
2nd row해당 항목 X
3rd row해당 항목 X
4th row해당 항목 X
5th row해당 항목 X

Common Values

ValueCountFrequency (%)
해당 항목 X 92
100.0%

Length

2024-05-04T07:37:44.058128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:37:44.267392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해당 92
33.3%
항목 92
33.3%
x 92
33.3%

분사압(제곱센티미터 당 kgf)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
해당 항목 X
92 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row해당 항목 X
2nd row해당 항목 X
3rd row해당 항목 X
4th row해당 항목 X
5th row해당 항목 X

Common Values

ValueCountFrequency (%)
해당 항목 X 92
100.0%

Length

2024-05-04T07:37:44.467541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:37:44.658968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해당 92
33.3%
항목 92
33.3%
x 92
33.3%

분사량(분당 L)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
해당 항목 X
92 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row해당 항목 X
2nd row해당 항목 X
3rd row해당 항목 X
4th row해당 항목 X
5th row해당 항목 X

Common Values

ValueCountFrequency (%)
해당 항목 X 92
100.0%

Length

2024-05-04T07:37:45.033578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:37:45.311050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해당 92
33.3%
항목 92
33.3%
x 92
33.3%

1회 분사시간(분)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
해당 항목 X
92 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row해당 항목 X
2nd row해당 항목 X
3rd row해당 항목 X
4th row해당 항목 X
5th row해당 항목 X

Common Values

ValueCountFrequency (%)
해당 항목 X 92
100.0%

Length

2024-05-04T07:37:45.600069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:37:45.873134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해당 92
33.3%
항목 92
33.3%
x 92
33.3%

Interactions

2024-05-04T07:37:23.049588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:37:19.480402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:37:20.498447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:37:21.761700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:37:23.426316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:37:19.747373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:37:20.751313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:37:22.097368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:37:23.977868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:37:20.017353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:37:21.067724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:37:22.384213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:37:24.302501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:37:20.267490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:37:21.355302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:37:22.643838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T07:37:46.122696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번종류행정동관리번호설치장소명도로명주소지번주소경도위도전체높이(M)펼침지름(M)
연번1.0000.3090.9721.0001.0000.9590.9500.8820.6460.2600.326
종류0.3091.0000.3501.0001.0000.9150.8770.4170.1421.0000.846
행정동0.9720.3501.0001.0001.0000.9940.9930.9240.9200.2950.314
관리번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
설치장소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명주소0.9590.9150.9941.0001.0001.0001.0001.0001.0000.9950.867
지번주소0.9500.8770.9931.0001.0001.0001.0001.0001.0000.9890.831
경도0.8820.4170.9241.0001.0001.0001.0001.0000.6890.3640.222
위도0.6460.1420.9201.0001.0001.0001.0000.6891.0000.2130.121
전체높이(M)0.2601.0000.2951.0001.0000.9950.9890.3640.2131.0000.761
펼침지름(M)0.3260.8460.3141.0001.0000.8670.8310.2220.1210.7611.000
2024-05-04T07:37:46.584589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
원단명행정동펼침지름(M)종류
원단명1.0001.0001.0001.000
행정동1.0001.0000.1410.276
펼침지름(M)1.0000.1411.0000.948
종류1.0000.2760.9481.000
2024-05-04T07:37:46.874465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번경도위도전체높이(M)종류행정동원단명펼침지름(M)
연번1.000-0.599-0.2410.1220.2240.8001.0000.133
경도-0.5991.000-0.3130.0100.3040.6451.0000.085
위도-0.241-0.3131.000-0.1040.1320.6451.0000.060
전체높이(M)0.1220.010-0.1041.0000.9890.1431.0000.704
종류0.2240.3040.1320.9891.0000.2761.0000.948
행정동0.8000.6450.6450.1430.2761.0001.0000.141
원단명1.0001.0001.0001.0001.0001.0001.0001.000
펼침지름(M)0.1330.0850.0600.7040.9480.1411.0001.000

Missing values

2024-05-04T07:37:24.886706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T07:37:25.981633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

연번종류시도시군구행정동관리번호설치장소명도로명주소지번주소경도위도원단명전체높이(M)펼침지름(M)면적(제곱미터)길이(미터)분사압(제곱센티미터 당 kgf)분사량(분당 L)1회 분사시간(분)
01그늘막(스마트형)서울특별시관악구보라매동보라매동-S1<NA>서울특별시 관악구 봉천로 239 (봉천동)서울특별시 관악구 봉천동 971-14126.9283437.489602매쉬3.05.0해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
12그늘막(스마트형)서울특별시관악구보라매동보라매동-S2<NA>서울특별시 관악구 보라매로 3 (봉천동)서울특별시 관악구 봉천동 972-3126.92749137.489796매쉬3.05.4해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
23그늘막(스마트형)서울특별시관악구보라매동보라매동-S3<NA>서울특별시 관악구 봉천로 209 (봉천동)서울특별시 관악구 봉천동 729-22126.9251637.490324매쉬3.05.4해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
34그늘막(스마트형)서울특별시관악구보라매동보라매동-S4SK텔레콤 앞 교통섬서울특별시 관악구 보라매로5길 1 (봉천동)서울특별시 관악구 봉천동 729-1126.92610437.492034매쉬3.05.4해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
45그늘막(스마트형)서울특별시관악구보라매동보라매동-S5대박집 앞서울특별시 관악구 보라매로 2 (봉천동)서울특별시 관악구 봉천동 971-3126.92796237.489742매쉬3.05.4해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
56그늘막(스마트형)서울특별시관악구보라매동보라매동-S6크린토피아 앞서울특별시 관악구 봉천로 259 (봉천동)서울특별시 관악구 봉천동 969-42126.9302537.488678매쉬3.05.4해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
67그늘막(스마트형)서울특별시관악구보라매동보라매동-S7CU 앞서울특별시 관악구 보라매로 20 (봉천동)서울특별시 관악구 봉천동 702-36126.92731537.491215매쉬3.05.4해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
78그늘막(스마트형)서울특별시관악구보라매동보라매동-S8황군집서울특별시 관악구 보라매로 17 (봉천동)서울특별시 관악구 봉천동 702-102126.92691837.491134매쉬3.05.4해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
89그늘막(스마트형)서울특별시관악구성현동성현동-S1로잔동물병원 앞서울특별시 관악구 관악로29길 3 (봉천동)서울특별시 관악구 봉천동 40-29126.95646537.486431<NA>3.05.4해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
910그늘막(고정형)서울특별시관악구행운동행운동-5오렌지와 연필 앞서울특별시 관악구 남부순환로 1895 (봉천동)서울특별시 관악구 봉천동 1680-11126.96017537.478655매쉬3.24.0해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
연번종류시도시군구행정동관리번호설치장소명도로명주소지번주소경도위도원단명전체높이(M)펼침지름(M)면적(제곱미터)길이(미터)분사압(제곱센티미터 당 kgf)분사량(분당 L)1회 분사시간(분)
8283그늘막(스마트형)서울특별시관악구조원동조원동-S3올리브영 앞 횡단보도서울특별시 관악구 남부순환로 1377 (신림동)서울특별시 관악구 신림동 1668-15126.90375937.480217매쉬3.05.0해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
8384그늘막(스마트형)서울특별시관악구조원동조원동-S4카마스터 앞 교통섬서울특별시 관악구 신사로 54 (신림동)서울특별시 관악구 신림동 551-26126.909237.485034매쉬3.05.4해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
8485그늘막(스마트형)서울특별시관악구조원동조원동-S5이디야커피 앞서울특별시 관악구 남부순환로 1369 (신림동)서울특별시 관악구 신림동 1668126.90274437.480001매쉬3.05.4해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
8586그늘막(스마트형)서울특별시관악구조원동조원동-S6와다닥숯불닭갈비 앞서울특별시 관악구 시흥대로 558 (신림동)서울특별시 관악구 신림동 1655-19126.90170837.482742매쉬3.05.4해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
8687그늘막(스마트형)서울특별시관악구조원동조원동-S7노래클럽 앞서울특별시 관악구 시흥대로 552 (신림동)서울특별시 관악구 신림동 1655-24126.90127437.482197매쉬3.05.4해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
8788그늘막(스마트형)서울특별시관악구조원동조원동-S8보라매운수 옆서울특별시 관악구 조원중앙로 45 (신림동)서울특별시 관악구 신림동 1650-11126.90668137.483328매쉬3.05.4해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
8889그늘막(고정형)서울특별시관악구대학동대학동-1삼성고등학교, 나들목 공원 힝단보도 앞서울특별시 관악구 신림로 41 (신림동)서울특별시 관악구 신림동 222126.94399737.469941매쉬3.24.0해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
8990그늘막(스마트형)서울특별시관악구삼성동삼성동-S1<NA>서울특별시 관악구 호암로 417 (신림동, 국제산장아파트)서울특별시 관악구 신림동 1693126.9277637.460696매쉬3.05.4해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
9091그늘막(스마트형)서울특별시관악구미성동미성동-S1안경공장 앞서울특별시 관악구 문성로 103 (신림동)서울특별시 관악구 신림동 1477-2126.91507337.477832매쉬3.05.0해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X
9192그늘막(스마트형)서울특별시관악구미성동미성동-S2난곡사거리서울특별시 관악구 남부순환로 1474 (신림동)서울특별시 관악구 신림동 1474-23126.91432837.481519매쉬3.05.4해당 항목 X해당 항목 X해당 항목 X해당 항목 X해당 항목 X