Overview

Dataset statistics

Number of variables12
Number of observations999
Missing cells2006
Missing cells (%)16.7%
Duplicate rows78
Duplicate rows (%)7.8%
Total size in memory93.8 KiB
Average record size in memory96.1 B

Variable types

Unsupported5
Text4
Categorical3

Dataset

Description파일 다운로드
Author서울교통공사
URLhttps://data.seoul.go.kr/dataList/OA-12926/F/1/datasetView.do

Alerts

Dataset has 78 (7.8%) duplicate rowsDuplicates
Unnamed: 3 is highly overall correlated with Unnamed: 4High correlation
Unnamed: 4 is highly overall correlated with Unnamed: 3 and 1 other fieldsHigh correlation
Unnamed: 5 is highly overall correlated with Unnamed: 4High correlation
Unnamed: 3 is highly imbalanced (56.3%)Imbalance
Unnamed: 5 is highly imbalanced (52.3%)Imbalance
Unnamed: 10 has 996 (99.7%) missing valuesMissing
Unnamed: 11 has 992 (99.3%) missing valuesMissing
환 기 구 현 황(1~4호선) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 1 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 started2024-04-29 16:38:18.773682
Analysis finished2024-04-29 16:38:20.979296
Duration2.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

환 기 구 현 황(1~4호선)
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size7.9 KiB

Unnamed: 1
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size7.9 KiB

Unnamed: 2
Text

Distinct189
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2024-04-30T01:38:21.136898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length4.8498498
Min length2

Characters and Unicode

Total characters4845
Distinct characters135
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

Unique6 ?
Unique (%)0.6%

Sample

1st row구 간
2nd row서울역
3rd row서울역
4th row서울역
5th row서울역
ValueCountFrequency (%)
신설동~용두 14
 
1.4%
종로3가 14
 
1.4%
사당 14
 
1.4%
시청 13
 
1.3%
신설동 12
 
1.2%
영등포구청 12
 
1.2%
잠원~고속터미널 12
 
1.2%
을지로3가 11
 
1.1%
삼성~선릉 11
 
1.1%
봉천~신림 11
 
1.1%
Other values (181) 877
87.6%
2024-04-30T01:38:21.507215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
~ 475
 
9.8%
대 262
 
5.4%
신 220
 
4.5%
동 183
 
3.8%
로 131
 
2.7%
문 125
 
2.6%
구 124
 
2.6%
청 94
 
1.9%
가 92
 
1.9%
종 87
 
1.8%
Other values (125) 3052
63.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4273
88.2%
Math Symbol 475
 
9.8%
Decimal Number 82
 
1.7%
Uppercase Letter 6
 
0.1%
Space Separator 5
 
0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 262
 
6.1%
신 220
 
5.1%
동 183
 
4.3%
로 131
 
3.1%
문 125
 
2.9%
구 124
 
2.9%
청 94
 
2.2%
가 92
 
2.2%
종 87
 
2.0%
역 86
 
2.0%
Other values (117) 2869
67.1%
Decimal Number
ValueCountFrequency (%)
3 49
59.8%
5 19
 
23.2%
4 14
 
17.1%
Math Symbol
ValueCountFrequency (%)
~ 475
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 6
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4273
88.2%
Common 566
 
11.7%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 262
 
6.1%
신 220
 
5.1%
동 183
 
4.3%
로 131
 
3.1%
문 125
 
2.9%
구 124
 
2.9%
청 94
 
2.2%
가 92
 
2.2%
종 87
 
2.0%
역 86
 
2.0%
Other values (117) 2869
67.1%
Common
ValueCountFrequency (%)
~ 475
83.9%
3 49
 
8.7%
5 19
 
3.4%
4 14
 
2.5%
5
 
0.9%
) 2
 
0.4%
( 2
 
0.4%
Latin
ValueCountFrequency (%)
U 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4273
88.2%
ASCII 572
 
11.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
~ 475
83.0%
3 49
 
8.6%
5 19
 
3.3%
4 14
 
2.4%
U 6
 
1.0%
5
 
0.9%
) 2
 
0.3%
( 2
 
0.3%
Hangul
ValueCountFrequency (%)
대 262
 
6.1%
신 220
 
5.1%
동 183
 
4.3%
로 131
 
3.1%
문 125
 
2.9%
구 124
 
2.9%
청 94
 
2.2%
가 92
 
2.2%
종 87
 
2.0%
역 86
 
2.0%
Other values (117) 2869
67.1%

Unnamed: 3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
역사
467 
본선
465 
역사(변전실)
48 
변전실
 
9
본선(유치선)
 
4
Other values (4)
 
6

Length

Max length7
Median length2
Mean length2.2872873
Min length2

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st row용도
2nd row역사
3rd row역사
4th row역사
5th row역사

Common Values

ValueCountFrequency (%)
역사 467
46.7%
본선 465
46.5%
역사(변전실) 48
 
4.8%
변전실 9
 
0.9%
본선(유치선) 4
 
0.4%
본선(변전실) 3
 
0.3%
용도 1
 
0.1%
<NA> 1
 
0.1%
출고선 1
 
0.1%

Length

2024-04-30T01:38:21.631072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:38:21.727897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
역사 467
46.7%
본선 465
46.5%
역사(변전실 48
 
4.8%
변전실 9
 
0.9%
본선(유치선 4
 
0.4%
본선(변전실 3
 
0.3%
용도 1
 
0.1%
na 1
 
0.1%
출고선 1
 
0.1%

Unnamed: 4
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
배기
375 
급기
309 
자연
305 
급배기
 
8
기능
 
1

Length

Max length4
Median length2
Mean length2.01001
Min length2

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row기능
2nd row급기
3rd row배기
4th row배기
5th row급기

Common Values

ValueCountFrequency (%)
배기 375
37.5%
급기 309
30.9%
자연 305
30.5%
급배기 8
 
0.8%
기능 1
 
0.1%
<NA> 1
 
0.1%

Length

2024-04-30T01:38:21.844679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:38:21.943786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
배기 375
37.5%
급기 309
30.9%
자연 305
30.5%
급배기 8
 
0.8%
기능 1
 
0.1%
na 1
 
0.1%

Unnamed: 5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
보도
713 
중앙
123 
녹지
110 
기타
 
44
차도
 
7
Other values (2)
 
2

Length

Max length4
Median length2
Mean length2.002002
Min length2

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row위치
2nd row기타
3rd row기타
4th row녹지
5th row녹지

Common Values

ValueCountFrequency (%)
보도 713
71.4%
중앙 123
 
12.3%
녹지 110
 
11.0%
기타 44
 
4.4%
차도 7
 
0.7%
위치 1
 
0.1%
<NA> 1
 
0.1%

Length

2024-04-30T01:38:22.060340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:38:22.161703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보도 713
71.4%
중앙 123
 
12.3%
녹지 110
 
11.0%
기타 44
 
4.4%
차도 7
 
0.7%
위치 1
 
0.1%
na 1
 
0.1%

Unnamed: 6
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.1%
Memory size7.9 KiB

Unnamed: 7
Unsupported

REJECTED  UNSUPPORTED 

Missing5
Missing (%)0.5%
Memory size7.9 KiB

Unnamed: 8
Unsupported

REJECTED  UNSUPPORTED 

Missing5
Missing (%)0.5%
Memory size7.9 KiB

Unnamed: 9
Text

Distinct689
Distinct (%)69.5%
Missing7
Missing (%)0.7%
Memory size7.9 KiB
2024-04-30T01:38:22.461100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length6.0050403
Min length2

Characters and Unicode

Total characters5957
Distinct characters530
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique495 ?
Unique (%)49.9%

Sample

1st row인접건물
2nd row서울역 버스정류장
3rd row서울역 버스정류장
4th row서울역 중앙분리대
5th row서울역 중앙분리대
ValueCountFrequency (%)
우리은행 13
 
1.1%
국철 12
 
1.1%
국민은행 10
 
0.9%
신도림역(외부 8
 
0.7%
하나은행 6
 
0.5%
gs25 6
 
0.5%
앞 6
 
0.5%
고속터미널역 6
 
0.5%
대한항공 6
 
0.5%
은평뉴타운사업부지 5
 
0.4%
Other values (751) 1058
93.1%
2024-04-30T01:38:22.924372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
146
 
2.5%
스 121
 
2.0%
대 110
 
1.8%
동 97
 
1.6%
원 94
 
1.6%
리 91
 
1.5%
국 87
 
1.5%
은 83
 
1.4%
사 73
 
1.2%
공 71
 
1.2%
Other values (520) 4984
83.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5244
88.0%
Uppercase Letter 264
 
4.4%
Space Separator 146
 
2.5%
Decimal Number 92
 
1.5%
Close Punctuation 61
 
1.0%
Open Punctuation 61
 
1.0%
Lowercase Letter 46
 
0.8%
Other Punctuation 27
 
0.5%
Dash Punctuation 8
 
0.1%
Math Symbol 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 121
 
2.3%
대 110
 
2.1%
동 97
 
1.8%
원 94
 
1.8%
리 91
 
1.7%
국 87
 
1.7%
은 83
 
1.6%
사 73
 
1.4%
공 71
 
1.4%
행 69
 
1.3%
Other values (454) 4348
82.9%
Uppercase Letter
ValueCountFrequency (%)
S 33
12.5%
G 29
11.0%
L 29
11.0%
K 27
 
10.2%
T 18
 
6.8%
A 14
 
5.3%
B 12
 
4.5%
P 12
 
4.5%
O 11
 
4.2%
I 10
 
3.8%
Other values (14) 69
26.1%
Lowercase Letter
ValueCountFrequency (%)
e 10
21.7%
n 4
 
8.7%
y 3
 
6.5%
a 3
 
6.5%
f 3
 
6.5%
l 3
 
6.5%
w 2
 
4.3%
i 2
 
4.3%
s 2
 
4.3%
r 2
 
4.3%
Other values (11) 12
26.1%
Decimal Number
ValueCountFrequency (%)
2 20
21.7%
1 19
20.7%
5 14
15.2%
3 11
12.0%
0 9
9.8%
4 6
 
6.5%
8 6
 
6.5%
6 3
 
3.3%
7 3
 
3.3%
9 1
 
1.1%
Other Punctuation
ValueCountFrequency (%)
, 13
48.1%
. 8
29.6%
/ 3
 
11.1%
# 2
 
7.4%
& 1
 
3.7%
Space Separator
ValueCountFrequency (%)
146
100.0%
Close Punctuation
ValueCountFrequency (%)
) 61
100.0%
Open Punctuation
ValueCountFrequency (%)
( 61
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Math Symbol
ValueCountFrequency (%)
+ 6
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5246
88.1%
Common 401
 
6.7%
Latin 310
 
5.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 121
 
2.3%
대 110
 
2.1%
동 97
 
1.8%
원 94
 
1.8%
리 91
 
1.7%
국 87
 
1.7%
은 83
 
1.6%
사 73
 
1.4%
공 71
 
1.4%
행 69
 
1.3%
Other values (455) 4350
82.9%
Latin
ValueCountFrequency (%)
S 33
 
10.6%
G 29
 
9.4%
L 29
 
9.4%
K 27
 
8.7%
T 18
 
5.8%
A 14
 
4.5%
B 12
 
3.9%
P 12
 
3.9%
O 11
 
3.5%
I 10
 
3.2%
Other values (35) 115
37.1%
Common
ValueCountFrequency (%)
146
36.4%
) 61
15.2%
( 61
15.2%
2 20
 
5.0%
1 19
 
4.7%
5 14
 
3.5%
, 13
 
3.2%
3 11
 
2.7%
0 9
 
2.2%
- 8
 
2.0%
Other values (10) 39
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5244
88.0%
ASCII 711
 
11.9%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
146
20.5%
) 61
 
8.6%
( 61
 
8.6%
S 33
 
4.6%
G 29
 
4.1%
L 29
 
4.1%
K 27
 
3.8%
2 20
 
2.8%
1 19
 
2.7%
T 18
 
2.5%
Other values (55) 268
37.7%
Hangul
ValueCountFrequency (%)
스 121
 
2.3%
대 110
 
2.1%
동 97
 
1.8%
원 94
 
1.8%
리 91
 
1.7%
국 87
 
1.7%
은 83
 
1.6%
사 73
 
1.4%
공 71
 
1.4%
행 69
 
1.3%
Other values (454) 4348
82.9%
None
ValueCountFrequency (%)
㈜ 2
100.0%

Unnamed: 10
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing996
Missing (%)99.7%
Memory size7.9 KiB
2024-04-30T01:38:23.055886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length3
Min length2

Characters and Unicode

Total characters9
Distinct characters7
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

Unique3 ?
Unique (%)100.0%

Sample

1st row비고
2nd row3칸 폐쇄
3rd row폐쇄
ValueCountFrequency (%)
폐쇄 2
50.0%
비고 1
25.0%
3칸 1
25.0%
2024-04-30T01:38:23.260542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
폐 2
22.2%
쇄 2
22.2%
비 1
11.1%
고 1
11.1%
3 1
11.1%
칸 1
11.1%
1
11.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7
77.8%
Decimal Number 1
 
11.1%
Space Separator 1
 
11.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
폐 2
28.6%
쇄 2
28.6%
비 1
14.3%
고 1
14.3%
칸 1
14.3%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7
77.8%
Common 2
 
22.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
폐 2
28.6%
쇄 2
28.6%
비 1
14.3%
고 1
14.3%
칸 1
14.3%
Common
ValueCountFrequency (%)
3 1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7
77.8%
ASCII 2
 
22.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
폐 2
28.6%
쇄 2
28.6%
비 1
14.3%
고 1
14.3%
칸 1
14.3%
ASCII
ValueCountFrequency (%)
3 1
50.0%
1
50.0%

Unnamed: 11
Text

MISSING 

Distinct4
Distinct (%)57.1%
Missing992
Missing (%)99.3%
Memory size7.9 KiB
2024-04-30T01:38:23.381786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length2
Mean length5.7142857
Min length2

Characters and Unicode

Total characters40
Distinct characters16
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

Unique3 ?
Unique (%)42.9%

Sample

1st row136,137 일체형
2nd row139,140 일체형
3rd row281,282일체형
4th row추가
5th row추가
ValueCountFrequency (%)
추가 4
44.4%
일체형 2
22.2%
136,137 1
 
11.1%
139,140 1
 
11.1%
281,282일체형 1
 
11.1%
2024-04-30T01:38:23.606770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5
12.5%
추 4
10.0%
가 4
10.0%
3 3
7.5%
, 3
7.5%
일 3
7.5%
체 3
7.5%
형 3
7.5%
2 3
7.5%
2
 
5.0%
Other values (6) 7
17.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18
45.0%
Other Letter 17
42.5%
Other Punctuation 3
 
7.5%
Space Separator 2
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5
27.8%
3 3
16.7%
2 3
16.7%
8 2
 
11.1%
6 1
 
5.6%
7 1
 
5.6%
9 1
 
5.6%
4 1
 
5.6%
0 1
 
5.6%
Other Letter
ValueCountFrequency (%)
추 4
23.5%
가 4
23.5%
일 3
17.6%
체 3
17.6%
형 3
17.6%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23
57.5%
Hangul 17
42.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5
21.7%
3 3
13.0%
, 3
13.0%
2 3
13.0%
2
 
8.7%
8 2
 
8.7%
6 1
 
4.3%
7 1
 
4.3%
9 1
 
4.3%
4 1
 
4.3%
Hangul
ValueCountFrequency (%)
추 4
23.5%
가 4
23.5%
일 3
17.6%
체 3
17.6%
형 3
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23
57.5%
Hangul 17
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5
21.7%
3 3
13.0%
, 3
13.0%
2 3
13.0%
2
 
8.7%
8 2
 
8.7%
6 1
 
4.3%
7 1
 
4.3%
9 1
 
4.3%
4 1
 
4.3%
Hangul
ValueCountFrequency (%)
추 4
23.5%
가 4
23.5%
일 3
17.6%
체 3
17.6%
형 3
17.6%

Correlations

2024-04-30T01:38:23.698676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 10Unnamed: 11
Unnamed: 31.0000.7700.6720.0001.000
Unnamed: 40.7701.0000.6530.0001.000
Unnamed: 50.6720.6531.0000.0001.000
Unnamed: 100.0000.0000.0001.000NaN
Unnamed: 111.0001.0001.000NaN1.000
2024-04-30T01:38:23.801937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 3Unnamed: 5Unnamed: 4
Unnamed: 31.0000.4550.615
Unnamed: 50.4551.0000.513
Unnamed: 40.6150.5131.000
2024-04-30T01:38:23.883394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 3Unnamed: 4Unnamed: 5
Unnamed: 31.0000.6150.455
Unnamed: 40.6151.0000.513
Unnamed: 50.4550.5131.000

Missing values

2024-04-30T01:38:20.585195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T01:38:20.759025image/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.
2024-04-30T01:38:20.889909image/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

환 기 구 현 황(1~4호선)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
0호선번호구 간용도기능위치높이가로세로인접건물비고<NA>
113서울역역사급기기타486.22.6서울역 버스정류장<NA><NA>
214서울역역사배기기타505.62.6서울역 버스정류장<NA><NA>
315서울역역사배기녹지245.32.6서울역 중앙분리대<NA><NA>
416서울역역사급기녹지555.62.7서울역 중앙분리대<NA><NA>
517서울역~시청본선자연보도644.91.7한음보청기<NA><NA>
618서울역~시청본선자연보도17451.7YTN TOWER<NA><NA>
719서울역~시청본선자연보도674.81.6허브치과의원<NA><NA>
8110서울역~시청본선자연녹지1104.91.9대한서울상공회의소<NA><NA>
9111서울역~시청본선자연보도1534.62.6신한은행<NA><NA>
환 기 구 현 황(1~4호선)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
9894172사당역사급기보도1582.68.4한국전력<NA><NA>
9904173사당역사급기보도982.796.23코코 부리니<NA><NA>
9914174사당역사배기보도342.616.71뚜레쮸르<NA><NA>
9924175사당역사급기중앙1902.627.9우리안경<NA><NA>
9934176사당역사배기중앙1902.627.9우리안경<NA><NA>
9944177사당~남태령본선배기보도621.63.65S-OIL<NA><NA>
9954178사당~남태령본선급기중앙3903.19.84중앙분리대<NA><NA>
9964179사당~남태령본선급기중앙2333.410.1중앙분리대<NA><NA>
9974180남태령역사급기녹지2406.947.22남태령역<NA><NA>
9984181남태령역사배기녹지117317.11남태령역<NA><NA>

Duplicate rows

Most frequently occurring

Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 9Unnamed: 10Unnamed: 11# duplicates
36신도림역사배기녹지국철 신도림역(외부)<NA><NA>4
58이촌~동작본선자연보도국립중앙박물관<NA><NA>4
37신도림U~신도림본선자연녹지구로구민생활관<NA><NA>3
38신도림U~신도림본선자연녹지한국문화예술위원회<NA><NA>3
39신도림~문래본선자연보도문래공원<NA><NA>3
45신천~종합운동장본선자연보도잠실1단지<NA><NA>3
57을지로입구~을지로3가본선배기보도SK본사 사옥<NA><NA>3
62잠원~고속터미널본선자연보도경원중학교<NA><NA>3
68청량리~회기본선자연기타국철지상<NA><NA>3
0강남~교대본선자연중앙F.R.I<NA><NA>2