Overview

Dataset statistics

Number of variables17
Number of observations257
Missing cells231
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.3 KiB
Average record size in memory136.5 B

Variable types

Unsupported4
Text9
Categorical4

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-22228/F/1/datasetView.do

Alerts

Unnamed: 9 is highly overall correlated with Unnamed: 4 and 2 other fieldsHigh correlation
Unnamed: 5 is highly overall correlated with Unnamed: 4 and 2 other fieldsHigh correlation
Unnamed: 4 is highly overall correlated with Unnamed: 5 and 2 other fieldsHigh correlation
Unnamed: 15 is highly overall correlated with Unnamed: 4 and 2 other fieldsHigh correlation
Unnamed: 4 is highly imbalanced (70.3%)Imbalance
Unnamed: 5 is highly imbalanced (65.4%)Imbalance
Unnamed: 15 is highly imbalanced (96.3%)Imbalance
Unnamed: 10 has 231 (89.9%) missing valuesMissing
Unnamed: 1 has unique valuesUnique
Unnamed: 2 has unique valuesUnique
Unnamed: 3 has unique valuesUnique
□ 서울시 택시승차대 운영목록 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-23 04:15:09.575641
Analysis finished2024-03-23 04:15:10.946821
Duration1.37 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

□ 서울시 택시승차대 운영목록
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size2.1 KiB

Unnamed: 1
Text

UNIQUE 

Distinct257
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-03-23T13:15:11.285459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length9.1011673
Min length9

Characters and Unicode

Total characters2339
Distinct characters24
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique257 ?
Unique (%)100.0%

Sample

1st rowID주소 (신관리번호)
2nd row01-011001
3rd row01-011002
4th row01-020203
5th row01-080204
ValueCountFrequency (%)
id주소 1
 
0.4%
22-011605p 1
 
0.4%
19-011101 1
 
0.4%
20-011501 1
 
0.4%
19-011102 1
 
0.4%
19-011103 1
 
0.4%
19-021104 1
 
0.4%
19-021105 1
 
0.4%
19-031706p 1
 
0.4%
19-041107 1
 
0.4%
Other values (248) 248
96.1%
2024-03-23T13:15:11.902397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 637
27.2%
0 580
24.8%
2 272
11.6%
- 256
10.9%
3 132
 
5.6%
4 102
 
4.4%
5 87
 
3.7%
6 83
 
3.5%
8 54
 
2.3%
7 52
 
2.2%
Other values (14) 84
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2048
87.6%
Dash Punctuation 256
 
10.9%
Uppercase Letter 25
 
1.1%
Other Letter 7
 
0.3%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 637
31.1%
0 580
28.3%
2 272
13.3%
3 132
 
6.4%
4 102
 
5.0%
5 87
 
4.2%
6 83
 
4.1%
8 54
 
2.6%
7 52
 
2.5%
9 49
 
2.4%
Other Letter
ValueCountFrequency (%)
호 1
14.3%
번 1
14.3%
리 1
14.3%
관 1
14.3%
신 1
14.3%
소 1
14.3%
주 1
14.3%
Uppercase Letter
ValueCountFrequency (%)
P 23
92.0%
D 1
 
4.0%
I 1
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 256
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2307
98.6%
Latin 25
 
1.1%
Hangul 7
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 637
27.6%
0 580
25.1%
2 272
11.8%
- 256
11.1%
3 132
 
5.7%
4 102
 
4.4%
5 87
 
3.8%
6 83
 
3.6%
8 54
 
2.3%
7 52
 
2.3%
Other values (4) 52
 
2.3%
Hangul
ValueCountFrequency (%)
호 1
14.3%
번 1
14.3%
리 1
14.3%
관 1
14.3%
신 1
14.3%
소 1
14.3%
주 1
14.3%
Latin
ValueCountFrequency (%)
P 23
92.0%
D 1
 
4.0%
I 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2332
99.7%
Hangul 7
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 637
27.3%
0 580
24.9%
2 272
11.7%
- 256
11.0%
3 132
 
5.7%
4 102
 
4.4%
5 87
 
3.7%
6 83
 
3.6%
8 54
 
2.3%
7 52
 
2.2%
Other values (7) 77
 
3.3%
Hangul
ValueCountFrequency (%)
호 1
14.3%
번 1
14.3%
리 1
14.3%
관 1
14.3%
신 1
14.3%
소 1
14.3%
주 1
14.3%

Unnamed: 2
Text

UNIQUE 

Distinct257
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-03-23T13:15:12.305185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.1050584
Min length4

Characters and Unicode

Total characters1312
Distinct characters51
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

Unique257 ?
Unique (%)100.0%

Sample

1st row구관리번호
2nd row종로-02
3rd row종로-01
4th row종로-19
5th row종로-20
ValueCountFrequency (%)
구관리번호 1
 
0.4%
마포-11 1
 
0.4%
동작-05 1
 
0.4%
영등포-09 1
 
0.4%
영등포-08 1
 
0.4%
영등포-02 1
 
0.4%
영등포-01 1
 
0.4%
영등포-14p 1
 
0.4%
영등포-11 1
 
0.4%
영등포-10 1
 
0.4%
Other values (247) 247
96.1%
2024-03-23T13:15:12.956643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 256
19.5%
0 188
 
14.3%
1 103
 
7.9%
2 51
 
3.9%
강 46
 
3.5%
3 33
 
2.5%
서 31
 
2.4%
중 31
 
2.4%
4 30
 
2.3%
로 30
 
2.3%
Other values (41) 513
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 521
39.7%
Decimal Number 512
39.0%
Dash Punctuation 256
19.5%
Uppercase Letter 23
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 46
 
8.8%
서 31
 
6.0%
중 31
 
6.0%
로 30
 
5.8%
남 26
 
5.0%
포 25
 
4.8%
동 24
 
4.6%
송 22
 
4.2%
파 22
 
4.2%
종 21
 
4.0%
Other values (29) 243
46.6%
Decimal Number
ValueCountFrequency (%)
0 188
36.7%
1 103
20.1%
2 51
 
10.0%
3 33
 
6.4%
4 30
 
5.9%
5 28
 
5.5%
6 24
 
4.7%
7 20
 
3.9%
8 18
 
3.5%
9 17
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 256
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 768
58.5%
Hangul 521
39.7%
Latin 23
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 46
 
8.8%
서 31
 
6.0%
중 31
 
6.0%
로 30
 
5.8%
남 26
 
5.0%
포 25
 
4.8%
동 24
 
4.6%
송 22
 
4.2%
파 22
 
4.2%
종 21
 
4.0%
Other values (29) 243
46.6%
Common
ValueCountFrequency (%)
- 256
33.3%
0 188
24.5%
1 103
13.4%
2 51
 
6.6%
3 33
 
4.3%
4 30
 
3.9%
5 28
 
3.6%
6 24
 
3.1%
7 20
 
2.6%
8 18
 
2.3%
Latin
ValueCountFrequency (%)
P 23
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 791
60.3%
Hangul 521
39.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 256
32.4%
0 188
23.8%
1 103
13.0%
2 51
 
6.4%
3 33
 
4.2%
4 30
 
3.8%
5 28
 
3.5%
6 24
 
3.0%
P 23
 
2.9%
7 20
 
2.5%
Other values (2) 35
 
4.4%
Hangul
ValueCountFrequency (%)
강 46
 
8.8%
서 31
 
6.0%
중 31
 
6.0%
로 30
 
5.8%
남 26
 
5.0%
포 25
 
4.8%
동 24
 
4.6%
송 22
 
4.2%
파 22
 
4.2%
종 21
 
4.0%
Other values (29) 243
46.6%

Unnamed: 3
Text

UNIQUE 

Distinct257
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-03-23T13:15:13.515313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.2801556
Min length4

Characters and Unicode

Total characters1100
Distinct characters43
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique257 ?
Unique (%)100.0%

Sample

1st rowJCD관리번호
2nd rowX-19
3rd rowX-16
4th rowX-27
5th rowX-28
ValueCountFrequency (%)
jcd관리번호 1
 
0.4%
m-05 1
 
0.4%
l-17 1
 
0.4%
u-38 1
 
0.4%
u-23 1
 
0.4%
u-08 1
 
0.4%
u-11 1
 
0.4%
pole-22 1
 
0.4%
u-39 1
 
0.4%
u-15 1
 
0.4%
Other values (247) 247
96.1%
2024-03-23T13:15:14.291701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 256
23.3%
0 107
 
9.7%
1 105
 
9.5%
2 85
 
7.7%
3 54
 
4.9%
P 37
 
3.4%
4 34
 
3.1%
5 32
 
2.9%
7 31
 
2.8%
6 27
 
2.5%
Other values (33) 332
30.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 512
46.5%
Uppercase Letter 259
23.5%
Dash Punctuation 256
23.3%
Lowercase Letter 69
 
6.3%
Other Letter 4
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 37
14.3%
A 25
 
9.7%
Y 22
 
8.5%
X 21
 
8.1%
S 21
 
8.1%
V 16
 
6.2%
U 13
 
5.0%
T 11
 
4.2%
M 11
 
4.2%
D 9
 
3.5%
Other values (15) 73
28.2%
Decimal Number
ValueCountFrequency (%)
0 107
20.9%
1 105
20.5%
2 85
16.6%
3 54
10.5%
4 34
 
6.6%
5 32
 
6.2%
7 31
 
6.1%
6 27
 
5.3%
9 20
 
3.9%
8 17
 
3.3%
Other Letter
ValueCountFrequency (%)
호 1
25.0%
번 1
25.0%
리 1
25.0%
관 1
25.0%
Lowercase Letter
ValueCountFrequency (%)
e 23
33.3%
l 23
33.3%
o 23
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 768
69.8%
Latin 328
29.8%
Hangul 4
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 37
 
11.3%
A 25
 
7.6%
e 23
 
7.0%
l 23
 
7.0%
o 23
 
7.0%
Y 22
 
6.7%
X 21
 
6.4%
S 21
 
6.4%
V 16
 
4.9%
U 13
 
4.0%
Other values (18) 104
31.7%
Common
ValueCountFrequency (%)
- 256
33.3%
0 107
13.9%
1 105
13.7%
2 85
 
11.1%
3 54
 
7.0%
4 34
 
4.4%
5 32
 
4.2%
7 31
 
4.0%
6 27
 
3.5%
9 20
 
2.6%
Hangul
ValueCountFrequency (%)
호 1
25.0%
번 1
25.0%
리 1
25.0%
관 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1096
99.6%
Hangul 4
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 256
23.4%
0 107
 
9.8%
1 105
 
9.6%
2 85
 
7.8%
3 54
 
4.9%
P 37
 
3.4%
4 34
 
3.1%
5 32
 
2.9%
7 31
 
2.8%
6 27
 
2.5%
Other values (29) 328
29.9%
Hangul
ValueCountFrequency (%)
호 1
25.0%
번 1
25.0%
리 1
25.0%
관 1
25.0%

Unnamed: 4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
쉘터형
233 
폴형
 
23
타입
 
1

Length

Max length3
Median length3
Mean length2.9066148
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row타입
2nd row쉘터형
3rd row쉘터형
4th row쉘터형
5th row쉘터형

Common Values

ValueCountFrequency (%)
쉘터형 233
90.7%
폴형 23
 
8.9%
타입 1
 
0.4%

Length

2024-03-23T13:15:14.456323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T13:15:14.577374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
쉘터형 233
90.7%
폴형 23
 
8.9%
타입 1
 
0.4%

Unnamed: 5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
표준형
201 
폴형
23 
구형
 
20
표준형-시범
 
4
버스쉘터형①
 
2
Other values (7)
 
7

Length

Max length6
Median length3
Mean length2.9610895
Min length2

Unique

Unique7 ?
Unique (%)2.7%

Sample

1st row유형
2nd row표준형
3rd row표준형
4th row구형
5th row구형

Common Values

ValueCountFrequency (%)
표준형 201
78.2%
폴형 23
 
8.9%
구형 20
 
7.8%
표준형-시범 4
 
1.6%
버스쉘터형① 2
 
0.8%
유형 1
 
0.4%
버스쉘터형⑤ 1
 
0.4%
버스쉘터형③ 1
 
0.4%
버스쉘터형④ 1
 
0.4%
표준형-확장 1
 
0.4%
Other values (2) 2
 
0.8%

Length

2024-03-23T13:15:14.715600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
표준형 201
78.2%
폴형 23
 
8.9%
구형 20
 
7.8%
표준형-시범 4
 
1.6%
버스쉘터형① 2
 
0.8%
유형 1
 
0.4%
버스쉘터형⑤ 1
 
0.4%
버스쉘터형③ 1
 
0.4%
버스쉘터형④ 1
 
0.4%
표준형-확장 1
 
0.4%
Other values (2) 2
 
0.8%

Unnamed: 6
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size2.1 KiB

Unnamed: 7
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size2.1 KiB

Unnamed: 8
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size2.1 KiB

Unnamed: 9
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
OK
146 
-
110 
전원투입
 
1

Length

Max length4
Median length2
Mean length1.5797665
Min length1

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row전원투입
2nd rowOK
3rd row-
4th rowOK
5th rowOK

Common Values

ValueCountFrequency (%)
OK 146
56.8%
- 110
42.8%
전원투입 1
 
0.4%

Length

2024-03-23T13:15:14.846915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T13:15:14.996366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ok 146
56.8%
110
42.8%
전원투입 1
 
0.4%

Unnamed: 10
Text

MISSING 

Distinct26
Distinct (%)100.0%
Missing231
Missing (%)89.9%
Memory size2.1 KiB
2024-03-23T13:15:15.214092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0769231
Min length2

Characters and Unicode

Total characters80
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row행정구
2nd row종로구
3rd row중구
4th row용산구
5th row성동구
ValueCountFrequency (%)
은평구 1
 
3.8%
중구 1
 
3.8%
서대문구 1
 
3.8%
송파구 1
 
3.8%
강남구 1
 
3.8%
서초구 1
 
3.8%
관악구 1
 
3.8%
동작구 1
 
3.8%
영등포구 1
 
3.8%
금천구 1
 
3.8%
Other values (16) 16
61.5%
2024-03-23T13:15:15.595514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 27
33.8%
동 4
 
5.0%
강 4
 
5.0%
서 3
 
3.8%
중 2
 
2.5%
포 2
 
2.5%
천 2
 
2.5%
성 2
 
2.5%
로 2
 
2.5%
대 2
 
2.5%
Other values (28) 30
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 27
33.8%
동 4
 
5.0%
강 4
 
5.0%
서 3
 
3.8%
중 2
 
2.5%
포 2
 
2.5%
천 2
 
2.5%
성 2
 
2.5%
로 2
 
2.5%
대 2
 
2.5%
Other values (28) 30
37.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 27
33.8%
동 4
 
5.0%
강 4
 
5.0%
서 3
 
3.8%
중 2
 
2.5%
포 2
 
2.5%
천 2
 
2.5%
성 2
 
2.5%
로 2
 
2.5%
대 2
 
2.5%
Other values (28) 30
37.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 27
33.8%
동 4
 
5.0%
강 4
 
5.0%
서 3
 
3.8%
중 2
 
2.5%
포 2
 
2.5%
천 2
 
2.5%
성 2
 
2.5%
로 2
 
2.5%
대 2
 
2.5%
Other values (28) 30
37.5%

Unnamed: 11
Text

Distinct119
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-03-23T13:15:15.968085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.4513619
Min length2

Characters and Unicode

Total characters887
Distinct characters133
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)23.3%

Sample

1st row행정동
2nd row사직동
3rd row사직동
4th row삼청동
5th row종로1,2,3,4가동
ValueCountFrequency (%)
종로1,2,3,4가동 12
 
4.7%
잠실동 9
 
3.5%
명동 8
 
3.1%
구로동 7
 
2.7%
신정동 7
 
2.7%
서초동 6
 
2.3%
대치동 6
 
2.3%
잠원동 6
 
2.3%
상계동 5
 
1.9%
한강로동 5
 
1.9%
Other values (109) 186
72.4%
2024-03-23T13:15:16.486839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 257
29.0%
, 38
 
4.3%
로 29
 
3.3%
신 21
 
2.4%
가 17
 
1.9%
잠 17
 
1.9%
종 15
 
1.7%
현 14
 
1.6%
2 13
 
1.5%
4 12
 
1.4%
Other values (123) 454
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 796
89.7%
Decimal Number 53
 
6.0%
Other Punctuation 38
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 257
32.3%
로 29
 
3.6%
신 21
 
2.6%
가 17
 
2.1%
잠 17
 
2.1%
종 15
 
1.9%
현 14
 
1.8%
실 11
 
1.4%
삼 11
 
1.4%
대 11
 
1.4%
Other values (116) 393
49.4%
Decimal Number
ValueCountFrequency (%)
2 13
24.5%
4 12
22.6%
1 12
22.6%
3 12
22.6%
6 2
 
3.8%
5 2
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 796
89.7%
Common 91
 
10.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 257
32.3%
로 29
 
3.6%
신 21
 
2.6%
가 17
 
2.1%
잠 17
 
2.1%
종 15
 
1.9%
현 14
 
1.8%
실 11
 
1.4%
삼 11
 
1.4%
대 11
 
1.4%
Other values (116) 393
49.4%
Common
ValueCountFrequency (%)
, 38
41.8%
2 13
 
14.3%
4 12
 
13.2%
1 12
 
13.2%
3 12
 
13.2%
6 2
 
2.2%
5 2
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 796
89.7%
ASCII 91
 
10.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 257
32.3%
로 29
 
3.6%
신 21
 
2.6%
가 17
 
2.1%
잠 17
 
2.1%
종 15
 
1.9%
현 14
 
1.8%
실 11
 
1.4%
삼 11
 
1.4%
대 11
 
1.4%
Other values (116) 393
49.4%
ASCII
ValueCountFrequency (%)
, 38
41.8%
2 13
 
14.3%
4 12
 
13.2%
1 12
 
13.2%
3 12
 
13.2%
6 2
 
2.2%
5 2
 
2.2%

Unnamed: 12
Text

Distinct254
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-03-23T13:15:16.919344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length8.6420233
Min length4

Characters and Unicode

Total characters2221
Distinct characters140
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

Unique252 ?
Unique (%)98.1%

Sample

1st row지번주소
2nd row신문로1가 163
3rd row신문로1가 239
4th row안국동 138-2
5th row견지동 68-5
ValueCountFrequency (%)
신정동 7
 
1.4%
구로동 7
 
1.4%
서초동 6
 
1.2%
대치동 6
 
1.2%
방이동 6
 
1.2%
잠원동 6
 
1.2%
논현동 5
 
1.0%
잠실동 5
 
1.0%
역삼동 5
 
1.0%
상계동 5
 
1.0%
Other values (383) 456
88.7%
2024-03-23T13:15:17.594579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
259
 
11.7%
동 234
 
10.5%
1 191
 
8.6%
- 175
 
7.9%
2 119
 
5.4%
3 110
 
5.0%
4 90
 
4.1%
6 85
 
3.8%
5 78
 
3.5%
7 77
 
3.5%
Other values (130) 803
36.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 971
43.7%
Other Letter 816
36.7%
Space Separator 259
 
11.7%
Dash Punctuation 175
 
7.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 234
28.7%
가 42
 
5.1%
로 32
 
3.9%
신 27
 
3.3%
잠 13
 
1.6%
대 11
 
1.3%
정 11
 
1.3%
방 11
 
1.3%
서 11
 
1.3%
계 11
 
1.3%
Other values (118) 413
50.6%
Decimal Number
ValueCountFrequency (%)
1 191
19.7%
2 119
12.3%
3 110
11.3%
4 90
9.3%
6 85
8.8%
5 78
8.0%
7 77
7.9%
8 76
 
7.8%
9 75
 
7.7%
0 70
 
7.2%
Space Separator
ValueCountFrequency (%)
259
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 175
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1405
63.3%
Hangul 816
36.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 234
28.7%
가 42
 
5.1%
로 32
 
3.9%
신 27
 
3.3%
잠 13
 
1.6%
대 11
 
1.3%
정 11
 
1.3%
방 11
 
1.3%
서 11
 
1.3%
계 11
 
1.3%
Other values (118) 413
50.6%
Common
ValueCountFrequency (%)
259
18.4%
1 191
13.6%
- 175
12.5%
2 119
8.5%
3 110
7.8%
4 90
 
6.4%
6 85
 
6.0%
5 78
 
5.6%
7 77
 
5.5%
8 76
 
5.4%
Other values (2) 145
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1405
63.3%
Hangul 816
36.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
259
18.4%
1 191
13.6%
- 175
12.5%
2 119
8.5%
3 110
7.8%
4 90
 
6.4%
6 85
 
6.0%
5 78
 
5.6%
7 77
 
5.5%
8 76
 
5.4%
Other values (2) 145
10.3%
Hangul
ValueCountFrequency (%)
동 234
28.7%
가 42
 
5.1%
로 32
 
3.9%
신 27
 
3.3%
잠 13
 
1.6%
대 11
 
1.3%
정 11
 
1.3%
방 11
 
1.3%
서 11
 
1.3%
계 11
 
1.3%
Other values (118) 413
50.6%

Unnamed: 13
Text

Distinct249
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-03-23T13:15:17.965960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length14
Mean length7.688716
Min length5

Characters and Unicode

Total characters1976
Distinct characters168
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

Unique243 ?
Unique (%)94.6%

Sample

1st row도로명주소
2nd row새문안로 92
3rd row새문안로 97
4th row율곡로 55
5th row우정국로 48
ValueCountFrequency (%)
올림픽로 11
 
2.1%
한강대로 8
 
1.5%
남부순환로 8
 
1.5%
종로 7
 
1.4%
강남대로 7
 
1.4%
을지로 6
 
1.2%
405 5
 
1.0%
동일로 5
 
1.0%
마포대로 5
 
1.0%
목동서로 4
 
0.8%
Other values (338) 452
87.3%
2024-03-23T13:15:18.516944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
262
 
13.3%
로 257
 
13.0%
1 151
 
7.6%
2 124
 
6.3%
3 95
 
4.8%
0 71
 
3.6%
5 69
 
3.5%
4 67
 
3.4%
대 59
 
3.0%
6 55
 
2.8%
Other values (158) 766
38.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 931
47.1%
Decimal Number 759
38.4%
Space Separator 262
 
13.3%
Dash Punctuation 21
 
1.1%
Control 1
 
0.1%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 257
27.6%
대 59
 
6.3%
동 29
 
3.1%
강 24
 
2.6%
남 20
 
2.1%
길 19
 
2.0%
한 15
 
1.6%
림 14
 
1.5%
서 13
 
1.4%
올 12
 
1.3%
Other values (143) 469
50.4%
Decimal Number
ValueCountFrequency (%)
1 151
19.9%
2 124
16.3%
3 95
12.5%
0 71
9.4%
5 69
9.1%
4 67
8.8%
6 55
 
7.2%
7 44
 
5.8%
9 42
 
5.5%
8 41
 
5.4%
Space Separator
ValueCountFrequency (%)
262
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Control
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1045
52.9%
Hangul 931
47.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 257
27.6%
대 59
 
6.3%
동 29
 
3.1%
강 24
 
2.6%
남 20
 
2.1%
길 19
 
2.0%
한 15
 
1.6%
림 14
 
1.5%
서 13
 
1.4%
올 12
 
1.3%
Other values (143) 469
50.4%
Common
ValueCountFrequency (%)
262
25.1%
1 151
14.4%
2 124
11.9%
3 95
 
9.1%
0 71
 
6.8%
5 69
 
6.6%
4 67
 
6.4%
6 55
 
5.3%
7 44
 
4.2%
9 42
 
4.0%
Other values (5) 65
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1045
52.9%
Hangul 931
47.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
262
25.1%
1 151
14.4%
2 124
11.9%
3 95
 
9.1%
0 71
 
6.8%
5 69
 
6.6%
4 67
 
6.4%
6 55
 
5.3%
7 44
 
4.2%
9 42
 
4.0%
Other values (5) 65
 
6.2%
Hangul
ValueCountFrequency (%)
로 257
27.6%
대 59
 
6.3%
동 29
 
3.1%
강 24
 
2.6%
남 20
 
2.1%
길 19
 
2.0%
한 15
 
1.6%
림 14
 
1.5%
서 13
 
1.4%
올 12
 
1.3%
Other values (143) 469
50.4%

Unnamed: 14
Text

Distinct128
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-03-23T13:15:18.900155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.6108949
Min length2

Characters and Unicode

Total characters928
Distinct characters152
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)25.7%

Sample

1st row추가인접도로
2nd row새문안로
3rd row새문안로
4th row율곡로
5th row우정국로
ValueCountFrequency (%)
남부순환로 10
 
3.8%
종로 9
 
3.5%
올림픽로 8
 
3.1%
강남대로 8
 
3.1%
청파로 7
 
2.7%
동일로 5
 
1.9%
을지로 5
 
1.9%
천호대로 5
 
1.9%
마포대로 4
 
1.5%
경인로 4
 
1.5%
Other values (118) 195
75.0%
2024-03-23T13:15:20.001840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 255
27.5%
대 60
 
6.5%
동 25
 
2.7%
남 22
 
2.4%
강 20
 
2.2%
산 15
 
1.6%
한 12
 
1.3%
서 12
 
1.3%
도 11
 
1.2%
환 10
 
1.1%
Other values (142) 486
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 910
98.1%
Decimal Number 12
 
1.3%
Space Separator 6
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 255
28.0%
대 60
 
6.6%
동 25
 
2.7%
남 22
 
2.4%
강 20
 
2.2%
산 15
 
1.6%
한 12
 
1.3%
서 12
 
1.3%
도 11
 
1.2%
환 10
 
1.1%
Other values (136) 468
51.4%
Decimal Number
ValueCountFrequency (%)
3 4
33.3%
6 3
25.0%
1 2
16.7%
2 2
16.7%
5 1
 
8.3%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 910
98.1%
Common 18
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 255
28.0%
대 60
 
6.6%
동 25
 
2.7%
남 22
 
2.4%
강 20
 
2.2%
산 15
 
1.6%
한 12
 
1.3%
서 12
 
1.3%
도 11
 
1.2%
환 10
 
1.1%
Other values (136) 468
51.4%
Common
ValueCountFrequency (%)
6
33.3%
3 4
22.2%
6 3
16.7%
1 2
 
11.1%
2 2
 
11.1%
5 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 910
98.1%
ASCII 18
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 255
28.0%
대 60
 
6.6%
동 25
 
2.7%
남 22
 
2.4%
강 20
 
2.2%
산 15
 
1.6%
한 12
 
1.3%
서 12
 
1.3%
도 11
 
1.2%
환 10
 
1.1%
Other values (136) 468
51.4%
ASCII
ValueCountFrequency (%)
6
33.3%
3 4
22.2%
6 3
16.7%
1 2
 
11.1%
2 2
 
11.1%
5 1
 
5.6%

Unnamed: 15
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
View
256 
링크
 
1

Length

Max length4
Median length4
Mean length3.9922179
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row링크
2nd rowView
3rd rowView
4th rowView
5th rowView

Common Values

ValueCountFrequency (%)
View 256
99.6%
링크 1
 
0.4%

Length

2024-03-23T13:15:20.369615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T13:15:20.556305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
view 256
99.6%
링크 1
 
0.4%

Unnamed: 16
Text

Distinct255
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-03-23T13:15:20.913924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length23
Mean length13.564202
Min length3

Characters and Unicode

Total characters3486
Distinct characters379
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

Unique254 ?
Unique (%)98.8%

Sample

1st row위치명
2nd row광화문오피시아빌딩 앞
3rd row포시즌호텔 앞(현재 공사중)
4th row우리은행 재동지점 앞
5th row신한은행 종로금융센터 앞
ValueCountFrequency (%)
앞 146
 
20.6%
건너 23
 
3.2%
옆 17
 
2.4%
출구 7
 
1.0%
5번출구 6
 
0.8%
101동 6
 
0.8%
1번출구 6
 
0.8%
좌측 6
 
0.8%
2번출구 5
 
0.7%
우측 5
 
0.7%
Other values (435) 482
68.0%
2024-03-23T13:15:21.565341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
453
 
13.0%
앞 226
 
6.5%
( 94
 
2.7%
) 94
 
2.7%
동 69
 
2.0%
구 64
 
1.8%
역 59
 
1.7%
빌 52
 
1.5%
딩 51
 
1.5%
출 47
 
1.3%
Other values (369) 2277
65.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2622
75.2%
Space Separator 453
 
13.0%
Decimal Number 147
 
4.2%
Open Punctuation 94
 
2.7%
Close Punctuation 94
 
2.7%
Uppercase Letter 55
 
1.6%
Lowercase Letter 9
 
0.3%
Other Punctuation 7
 
0.2%
Dash Punctuation 4
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 226
 
8.6%
동 69
 
2.6%
구 64
 
2.4%
역 59
 
2.3%
빌 52
 
2.0%
딩 51
 
1.9%
출 47
 
1.8%
번 46
 
1.8%
트 42
 
1.6%
점 39
 
1.5%
Other values (326) 1927
73.5%
Uppercase Letter
ValueCountFrequency (%)
K 10
18.2%
S 7
12.7%
C 5
9.1%
E 5
9.1%
G 4
 
7.3%
I 4
 
7.3%
B 3
 
5.5%
L 3
 
5.5%
T 2
 
3.6%
U 2
 
3.6%
Other values (8) 10
18.2%
Decimal Number
ValueCountFrequency (%)
1 45
30.6%
2 21
14.3%
0 19
12.9%
3 16
 
10.9%
4 12
 
8.2%
5 11
 
7.5%
7 7
 
4.8%
9 6
 
4.1%
8 5
 
3.4%
6 5
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
e 2
22.2%
w 1
11.1%
i 1
11.1%
v 1
11.1%
t 1
11.1%
n 1
11.1%
a 1
11.1%
s 1
11.1%
Other Punctuation
ValueCountFrequency (%)
, 5
71.4%
/ 2
 
28.6%
Space Separator
ValueCountFrequency (%)
453
100.0%
Open Punctuation
ValueCountFrequency (%)
( 94
100.0%
Close Punctuation
ValueCountFrequency (%)
) 94
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2622
75.2%
Common 800
 
22.9%
Latin 64
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 226
 
8.6%
동 69
 
2.6%
구 64
 
2.4%
역 59
 
2.3%
빌 52
 
2.0%
딩 51
 
1.9%
출 47
 
1.8%
번 46
 
1.8%
트 42
 
1.6%
점 39
 
1.5%
Other values (326) 1927
73.5%
Latin
ValueCountFrequency (%)
K 10
15.6%
S 7
 
10.9%
C 5
 
7.8%
E 5
 
7.8%
G 4
 
6.2%
I 4
 
6.2%
B 3
 
4.7%
L 3
 
4.7%
T 2
 
3.1%
U 2
 
3.1%
Other values (16) 19
29.7%
Common
ValueCountFrequency (%)
453
56.6%
( 94
 
11.8%
) 94
 
11.8%
1 45
 
5.6%
2 21
 
2.6%
0 19
 
2.4%
3 16
 
2.0%
4 12
 
1.5%
5 11
 
1.4%
7 7
 
0.9%
Other values (7) 28
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2622
75.2%
ASCII 864
 
24.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
453
52.4%
( 94
 
10.9%
) 94
 
10.9%
1 45
 
5.2%
2 21
 
2.4%
0 19
 
2.2%
3 16
 
1.9%
4 12
 
1.4%
5 11
 
1.3%
K 10
 
1.2%
Other values (33) 89
 
10.3%
Hangul
ValueCountFrequency (%)
앞 226
 
8.6%
동 69
 
2.6%
구 64
 
2.4%
역 59
 
2.3%
빌 52
 
2.0%
딩 51
 
1.9%
출 47
 
1.8%
번 46
 
1.8%
트 42
 
1.6%
점 39
 
1.5%
Other values (326) 1927
73.5%

Correlations

2024-03-23T13:15:21.711271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 4Unnamed: 5Unnamed: 9Unnamed: 10Unnamed: 15
Unnamed: 41.0001.0000.9581.0001.000
Unnamed: 51.0001.0000.9501.0001.000
Unnamed: 90.9580.9501.0001.0001.000
Unnamed: 101.0001.0001.0001.0001.000
Unnamed: 151.0001.0001.0001.0001.000
2024-03-23T13:15:21.864869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 9Unnamed: 5Unnamed: 4Unnamed: 15
Unnamed: 91.0000.7330.7500.998
Unnamed: 50.7331.0000.9820.980
Unnamed: 40.7500.9821.0000.998
Unnamed: 150.9980.9800.9981.000
2024-03-23T13:15:22.039900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 4Unnamed: 5Unnamed: 9Unnamed: 15
Unnamed: 41.0000.9820.7500.998
Unnamed: 50.9821.0000.7330.980
Unnamed: 90.7500.7331.0000.998
Unnamed: 150.9980.9800.9981.000

Missing values

2024-03-23T13:15:10.575812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T13:15:10.849769image/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

□ 서울시 택시승차대 운영목록Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16
0순번ID주소 (신관리번호)구관리번호JCD관리번호타입유형설치일좌표\n(Poi_X)좌표\n(Poi_Y)전원투입행정구행정동지번주소도로명주소추가인접도로링크위치명
1101-011001종로-02X-19쉘터형표준형2010-10-28 00:00:00126.9749837.57030OK종로구사직동신문로1가 163새문안로 92새문안로View광화문오피시아빌딩 앞
2201-011002종로-01X-16쉘터형표준형2010-10-28 00:00:00126.9752437.57042-<NA>사직동신문로1가 239새문안로 97새문안로View포시즌호텔 앞(현재 공사중)
3301-020203종로-19X-27쉘터형구형2002년126.9854937.57670OK<NA>삼청동안국동 138-2율곡로 55율곡로View우리은행 재동지점 앞
4401-080204종로-20X-28쉘터형구형2002년126.9829937.57318OK<NA>종로1,2,3,4가동견지동 68-5우정국로 48우정국로View신한은행 종로금융센터 앞
5501-080205종로-07X-39쉘터형구형2002년126.9877537.56944OK<NA>종로1,2,3,4가동관철동 5-7삼일대로 390삼일대로View코아아트홀 옆
6601-080206종로-06X-29쉘터형구형2002년126.9874637.56866OK<NA>종로1,2,3,4가동관철동 10-2청계천로 85삼일대로View한국산업은행 앞
7701-080207종로-14X-35쉘터형구형2002년126.9979137.57307OK<NA>종로1,2,3,4가동인의동 101-1창경궁로 119창경궁로View서울시재향군인회 앞(한국교직원공제회 옆)
8801-081008종로-08X-13쉘터형표준형2010-10-26 00:00:00126.9890337.57029OK<NA>종로1,2,3,4가동종로2가 40수표로 105종로View탑골공원삼일문 옆
9901-081009종로-04X-15쉘터형표준형2010-10-29 00:00:00126.9786937.57027OK<NA>종로1,2,3,4가동청진동 246종로3길 17종로View교보생명빌딩 옆(한국수출무역공사 건너)
□ 서울시 택시승차대 운영목록Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16
24724724-141619P송파-02PPole-13폴형폴형2016-09-01 00:00:00127.1042137.51952-<NA>잠실동신천동 15오금로 17-2오금로 3길View장미2차아파트 31동 옆
24824824-141620송파-16S-06쉘터형표준형2016-09-30 00:00:00127.1011737.51226-<NA>잠실동신천동 29올림픽로 300송파대로View롯데 너구리동상 건너(롯데월드타워 신축부지)
24924924-141621송파-15S-25쉘터형표준형2016-10-05 00:00:00127.1011137.51164-<NA>잠실동잠실동 40-1올림픽로 240송파대로View롯데호텔월드 앞(너구리동상)
25025024-142022송파-17S-37쉘터형표준형2020-06-11 00:00:00127.0971037.51227OK<NA>잠실동잠실동 40-1올림픽로 240올림픽로View롯데월드 정문 앞
25125125-021901강동-01B-03쉘터형표준형2019-10-24 00:00:00127.1637537.55663-강동구고덕동고덕동 299고덕로 391-1고덕로View고덕그라시움 107동 앞
25225225-040202강동-03B-16쉘터형구형2002년127.1425437.54616-<NA>천호동천호동 42양재대로 1571상암로View홈플러스 강동점 앞(굽은다리역 2번 출구 방향)
25325325-050203강동-05B-05쉘터형구형2002년127.1290137.52369-<NA>성내동성내동 448-11강동대로 207강동대로View삼성SDS 성내사옥 앞
25425425-052104강동-04B-07쉘터형표준형2021-05-21 00:00:00127.1369837.53029-<NA>성내동성내동 397-10양재대로 1393양재대로View삼진빌딩 앞
25525525-052105강동-06B-08쉘터형표준형2021-05-21 00:00:00127.1217237.52703OK<NA>성내동성내동 459-6강동대로 143-22강동대로View채선당 올림픽공원점 앞(청구빌라트 옆)
25625625-091206강동-02B-17쉘터형표준형2012-04-13 00:00:00127.1482637.55476-<NA>상일동상일동 149동남로 892동남로View강동경희대학교병원 앞(장례식장 건물 앞)