Overview

Dataset statistics

Number of variables10
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory83.5 B

Variable types

Categorical3
Text5
Boolean1
Unsupported1

Dataset

Description수도권9호선에 포함된 도시광역철도역들의 철도운영기관명, 선명, 역명, 영어명, 로마자, 일본어, 중국어간체, 중국어번체, 환승역여부, 신설일자 에 대한 데이터가 있습니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041811/fileData.do

Alerts

철도운영기관명 has constant value ""Constant
선명 has constant value ""Constant
중국어번체 is highly imbalanced (63.0%)Imbalance
역명 has unique valuesUnique
영어명 has unique valuesUnique
로마자 has unique valuesUnique
일본어 has unique valuesUnique
중국어간체 has unique valuesUnique
신설일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 23:38:19.040305
Analysis finished2023-12-12 23:38:20.216427
Duration1.18 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
서울시메트로9호선주식회사
38 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울시메트로9호선주식회사
2nd row서울시메트로9호선주식회사
3rd row서울시메트로9호선주식회사
4th row서울시메트로9호선주식회사
5th row서울시메트로9호선주식회사

Common Values

ValueCountFrequency (%)
서울시메트로9호선주식회사 38
100.0%

Length

2023-12-13T08:38:20.313581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:38:20.453471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울시메트로9호선주식회사 38
100.0%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
9호선
38 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9호선
2nd row9호선
3rd row9호선
4th row9호선
5th row9호선

Common Values

ValueCountFrequency (%)
9호선 38
100.0%

Length

2023-12-13T08:38:20.598335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:38:20.736768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
9호선 38
100.0%

역명
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-13T08:38:20.987745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length3.6315789
Min length2

Characters and Unicode

Total characters138
Distinct characters91
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

Unique38 ?
Unique (%)100.0%

Sample

1st row개화
2nd row김포공항
3rd row공항시장
4th row신방화
5th row마곡나루
ValueCountFrequency (%)
개화 1
 
2.6%
삼성중앙 1
 
2.6%
둔촌오륜 1
 
2.6%
신반포 1
 
2.6%
고속터미널 1
 
2.6%
사평 1
 
2.6%
신논현 1
 
2.6%
언주 1
 
2.6%
선정릉 1
 
2.6%
종합운동장 1
 
2.6%
Other values (28) 28
73.7%
2023-12-13T08:38:21.400084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
신 4
 
2.9%
촌 4
 
2.9%
( 3
 
2.2%
포 3
 
2.2%
원 3
 
2.2%
) 3
 
2.2%
앙 3
 
2.2%
중 3
 
2.2%
석 3
 
2.2%
사 3
 
2.2%
Other values (81) 106
76.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 132
95.7%
Open Punctuation 3
 
2.2%
Close Punctuation 3
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 4
 
3.0%
촌 4
 
3.0%
포 3
 
2.3%
원 3
 
2.3%
앙 3
 
2.3%
중 3
 
2.3%
석 3
 
2.3%
사 3
 
2.3%
동 3
 
2.3%
공 3
 
2.3%
Other values (79) 100
75.8%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 132
95.7%
Common 6
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 4
 
3.0%
촌 4
 
3.0%
포 3
 
2.3%
원 3
 
2.3%
앙 3
 
2.3%
중 3
 
2.3%
석 3
 
2.3%
사 3
 
2.3%
동 3
 
2.3%
공 3
 
2.3%
Other values (79) 100
75.8%
Common
ValueCountFrequency (%)
( 3
50.0%
) 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 132
95.7%
ASCII 6
 
4.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
신 4
 
3.0%
촌 4
 
3.0%
포 3
 
2.3%
원 3
 
2.3%
앙 3
 
2.3%
중 3
 
2.3%
석 3
 
2.3%
사 3
 
2.3%
동 3
 
2.3%
공 3
 
2.3%
Other values (79) 100
75.8%
ASCII
ValueCountFrequency (%)
( 3
50.0%
) 3
50.0%

영어명
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-13T08:38:21.680739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length10.842105
Min length5

Characters and Unicode

Total characters412
Distinct characters41
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st rowGaehwa
2nd rowGimpo Int'l Airport
3rd rowAirport Market
4th rowSinbanghwa Station
5th rowMagongnaru
ValueCountFrequency (%)
airport 2
 
3.8%
seokchon 2
 
3.8%
gaehwa 1
 
1.9%
gimpo 1
 
1.9%
express 1
 
1.9%
bus 1
 
1.9%
terminal 1
 
1.9%
sapyeong 1
 
1.9%
sinnonhyeon 1
 
1.9%
eonju 1
 
1.9%
Other values (41) 41
77.4%
2023-12-13T08:38:22.129556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 55
 
13.3%
o 41
 
10.0%
a 35
 
8.5%
e 32
 
7.8%
g 25
 
6.1%
u 17
 
4.1%
S 16
 
3.9%
15
 
3.6%
i 15
 
3.6%
r 14
 
3.4%
Other values (31) 147
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 341
82.8%
Uppercase Letter 55
 
13.3%
Space Separator 15
 
3.6%
Other Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 55
16.1%
o 41
12.0%
a 35
10.3%
e 32
 
9.4%
g 25
 
7.3%
u 17
 
5.0%
i 15
 
4.4%
r 14
 
4.1%
p 11
 
3.2%
s 11
 
3.2%
Other values (12) 85
24.9%
Uppercase Letter
ValueCountFrequency (%)
S 16
29.1%
G 5
 
9.1%
D 4
 
7.3%
H 4
 
7.3%
B 3
 
5.5%
M 3
 
5.5%
Y 3
 
5.5%
A 3
 
5.5%
N 3
 
5.5%
O 2
 
3.6%
Other values (7) 9
16.4%
Space Separator
ValueCountFrequency (%)
15
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 396
96.1%
Common 16
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 55
13.9%
o 41
 
10.4%
a 35
 
8.8%
e 32
 
8.1%
g 25
 
6.3%
u 17
 
4.3%
S 16
 
4.0%
i 15
 
3.8%
r 14
 
3.5%
p 11
 
2.8%
Other values (29) 135
34.1%
Common
ValueCountFrequency (%)
15
93.8%
' 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 55
 
13.3%
o 41
 
10.0%
a 35
 
8.5%
e 32
 
7.8%
g 25
 
6.1%
u 17
 
4.1%
S 16
 
3.9%
15
 
3.6%
i 15
 
3.6%
r 14
 
3.4%
Other values (31) 147
35.7%

로마자
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-13T08:38:22.403854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length20
Mean length12.236842
Min length5

Characters and Unicode

Total characters465
Distinct characters43
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st rowGaehwa
2nd rowGimpo Int'l Airport
3rd rowAirport Market
4th rowSinbanghwa Station
5th rowMagongnaru
ValueCountFrequency (%)
airport 2
 
3.7%
seokchon 2
 
3.7%
national 2
 
3.7%
gaehwa 1
 
1.9%
seonjeongneung 1
 
1.9%
dunchon 1
 
1.9%
oryun 1
 
1.9%
sinbanpo 1
 
1.9%
express 1
 
1.9%
bus 1
 
1.9%
Other values (41) 41
75.9%
2023-12-13T08:38:22.800031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 65
 
14.0%
o 50
 
10.8%
a 36
 
7.7%
e 35
 
7.5%
g 31
 
6.7%
u 20
 
4.3%
i 17
 
3.7%
16
 
3.4%
S 16
 
3.4%
r 14
 
3.0%
Other values (33) 165
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 388
83.4%
Uppercase Letter 56
 
12.0%
Space Separator 16
 
3.4%
Close Punctuation 2
 
0.4%
Open Punctuation 2
 
0.4%
Other Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 65
16.8%
o 50
12.9%
a 36
 
9.3%
e 35
 
9.0%
g 31
 
8.0%
u 20
 
5.2%
i 17
 
4.4%
r 14
 
3.6%
y 13
 
3.4%
s 12
 
3.1%
Other values (13) 95
24.5%
Uppercase Letter
ValueCountFrequency (%)
S 16
28.6%
G 6
 
10.7%
N 4
 
7.1%
D 4
 
7.1%
A 4
 
7.1%
Y 3
 
5.4%
H 3
 
5.4%
B 3
 
5.4%
C 3
 
5.4%
M 2
 
3.6%
Other values (6) 8
14.3%
Space Separator
ValueCountFrequency (%)
16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 444
95.5%
Common 21
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 65
14.6%
o 50
 
11.3%
a 36
 
8.1%
e 35
 
7.9%
g 31
 
7.0%
u 20
 
4.5%
i 17
 
3.8%
S 16
 
3.6%
r 14
 
3.2%
y 13
 
2.9%
Other values (29) 147
33.1%
Common
ValueCountFrequency (%)
16
76.2%
) 2
 
9.5%
( 2
 
9.5%
' 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 465
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 65
 
14.0%
o 50
 
10.8%
a 36
 
7.7%
e 35
 
7.5%
g 31
 
6.7%
u 20
 
4.3%
i 17
 
3.7%
16
 
3.4%
S 16
 
3.4%
r 14
 
3.0%
Other values (33) 165
35.5%

일본어
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-13T08:38:23.014539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.1578947
Min length3

Characters and Unicode

Total characters234
Distinct characters61
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st rowケファ
2nd rowキンポゴンハン
3rd rowコンハンシジャン
4th row新防火駅
5th rowマゴンナル
ValueCountFrequency (%)
ケファ 1
 
2.6%
サムソン·チュンアン 1
 
2.6%
トゥンチョノリュン 1
 
2.6%
シンバンポ 1
 
2.6%
コソクターミナル 1
 
2.6%
サピョン 1
 
2.6%
シンノンヒョン 1
 
2.6%
オンジュ 1
 
2.6%
ソンジョンヌン 1
 
2.6%
チョンハブンドンジャン 1
 
2.6%
Other values (28) 28
73.7%
2023-12-13T08:38:23.371110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ン 61
26.1%
ョ 12
 
5.1%
チ 11
 
4.7%
ク 11
 
4.7%
ソ 9
 
3.8%
ジ 7
 
3.0%
ュ 6
 
2.6%
サ 6
 
2.6%
ャ 6
 
2.6%
シ 4
 
1.7%
Other values (51) 101
43.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 231
98.7%
Other Punctuation 2
 
0.9%
Modifier Letter 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ン 61
26.4%
ョ 12
 
5.2%
チ 11
 
4.8%
ク 11
 
4.8%
ソ 9
 
3.9%
ジ 7
 
3.0%
ュ 6
 
2.6%
サ 6
 
2.6%
ャ 6
 
2.6%
シ 4
 
1.7%
Other values (48) 98
42.4%
Other Punctuation
ValueCountFrequency (%)
・ 1
50.0%
· 1
50.0%
Modifier Letter
ValueCountFrequency (%)
ー 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana 227
97.0%
Han 4
 
1.7%
Common 3
 
1.3%

Most frequent character per script

Katakana
ValueCountFrequency (%)
ン 61
26.9%
ョ 12
 
5.3%
チ 11
 
4.8%
ク 11
 
4.8%
ソ 9
 
4.0%
ジ 7
 
3.1%
ュ 6
 
2.6%
サ 6
 
2.6%
ャ 6
 
2.6%
シ 4
 
1.8%
Other values (44) 94
41.4%
Han
ValueCountFrequency (%)
駅 1
25.0%
火 1
25.0%
防 1
25.0%
新 1
25.0%
Common
ValueCountFrequency (%)
・ 1
33.3%
· 1
33.3%
ー 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Katakana 229
97.9%
CJK 4
 
1.7%
None 1
 
0.4%

Most frequent character per block

Katakana
ValueCountFrequency (%)
ン 61
26.6%
ョ 12
 
5.2%
チ 11
 
4.8%
ク 11
 
4.8%
ソ 9
 
3.9%
ジ 7
 
3.1%
ュ 6
 
2.6%
サ 6
 
2.6%
ャ 6
 
2.6%
シ 4
 
1.7%
Other values (46) 96
41.9%
None
ValueCountFrequency (%)
· 1
100.0%
CJK
ValueCountFrequency (%)
駅 1
25.0%
火 1
25.0%
防 1
25.0%
新 1
25.0%

중국어간체
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-13T08:38:23.622449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length7
Mean length3.4736842
Min length2

Characters and Unicode

Total characters132
Distinct characters107
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st row开花
2nd row金浦机场
3rd row空港市场
4th row新芳华站
5th row麻谷渡口
ValueCountFrequency (%)
开花 1
 
2.6%
三成中央 1
 
2.6%
遁村五轮 1
 
2.6%
新盘浦 1
 
2.6%
高速巴士客运站 1
 
2.6%
砂平 1
 
2.6%
新论岘 1
 
2.6%
彦州 1
 
2.6%
宣靖陵 1
 
2.6%
综合运动场 1
 
2.6%
Other values (28) 28
73.7%
2023-12-13T08:38:24.005311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
村 4
 
3.0%
新 4
 
3.0%
浦 3
 
2.3%
石 3
 
2.3%
场 3
 
2.3%
站 2
 
1.5%
国 2
 
1.5%
央 2
 
1.5%
中 2
 
1.5%
三 2
 
1.5%
Other values (97) 105
79.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 130
98.5%
Close Punctuation 1
 
0.8%
Open Punctuation 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
村 4
 
3.1%
新 4
 
3.1%
浦 3
 
2.3%
石 3
 
2.3%
场 3
 
2.3%
站 2
 
1.5%
国 2
 
1.5%
央 2
 
1.5%
中 2
 
1.5%
三 2
 
1.5%
Other values (95) 103
79.2%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 130
98.5%
Common 2
 
1.5%

Most frequent character per script

Han
ValueCountFrequency (%)
村 4
 
3.1%
新 4
 
3.1%
浦 3
 
2.3%
石 3
 
2.3%
场 3
 
2.3%
站 2
 
1.5%
国 2
 
1.5%
央 2
 
1.5%
中 2
 
1.5%
三 2
 
1.5%
Other values (95) 103
79.2%
Common
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 129
97.7%
ASCII 2
 
1.5%
CJK Compat Ideographs 1
 
0.8%

Most frequent character per block

CJK
ValueCountFrequency (%)
村 4
 
3.1%
新 4
 
3.1%
浦 3
 
2.3%
石 3
 
2.3%
场 3
 
2.3%
站 2
 
1.6%
国 2
 
1.6%
央 2
 
1.6%
中 2
 
1.6%
三 2
 
1.6%
Other values (94) 102
79.1%
CJK Compat Ideographs
ValueCountFrequency (%)
金 1
100.0%
ASCII
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

중국어번체
Categorical

IMBALANCE 

Distinct7
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size436.0 B
-
32 
開花
 
1
新芳華站
 
1
麻谷나루
 
1
堂山
 
1
Other values (2)
 
2

Length

Max length5
Median length1
Mean length1.3684211
Min length1

Unique

Unique6 ?
Unique (%)15.8%

Sample

1st row開花
2nd row-
3rd row-
4th row新芳華站
5th row麻谷나루

Common Values

ValueCountFrequency (%)
- 32
84.2%
開花 1
 
2.6%
新芳華站 1
 
2.6%
麻谷나루 1
 
2.6%
堂山 1
 
2.6%
鷺梁津 1
 
2.6%
高速터미널 1
 
2.6%

Length

2023-12-13T08:38:24.177186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:38:24.331651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
32
84.2%
開花 1
 
2.6%
新芳華站 1
 
2.6%
麻谷나루 1
 
2.6%
堂山 1
 
2.6%
鷺梁津 1
 
2.6%
高速터미널 1
 
2.6%

환승역여부
Boolean

Distinct2
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size170.0 B
False
25 
True
13 
ValueCountFrequency (%)
False 25
65.8%
True 13
34.2%
2023-12-13T08:38:24.446494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

신설일자
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size436.0 B

Correlations

2023-12-13T08:38:24.537180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명영어명로마자일본어중국어간체중국어번체환승역여부
역명1.0001.0001.0001.0001.0001.0001.000
영어명1.0001.0001.0001.0001.0001.0001.000
로마자1.0001.0001.0001.0001.0001.0001.000
일본어1.0001.0001.0001.0001.0001.0001.000
중국어간체1.0001.0001.0001.0001.0001.0001.000
중국어번체1.0001.0001.0001.0001.0001.0000.298
환승역여부1.0001.0001.0001.0001.0000.2981.000
2023-12-13T08:38:24.656622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환승역여부중국어번체
환승역여부1.0000.289
중국어번체0.2891.000
2023-12-13T08:38:24.739535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중국어번체환승역여부
중국어번체1.0000.289
환승역여부0.2891.000

Missing values

2023-12-13T08:38:19.949606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:38:20.144992image/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

철도운영기관명선명역명영어명로마자일본어중국어간체중국어번체환승역여부신설일자
0서울시메트로9호선주식회사9호선개화GaehwaGaehwaケファ开花開花N20090722
1서울시메트로9호선주식회사9호선김포공항Gimpo Int'l AirportGimpo Int'l Airportキンポゴンハン金浦机场-Y20070630
2서울시메트로9호선주식회사9호선공항시장Airport MarketAirport Marketコンハンシジャン空港市场-N20090722
3서울시메트로9호선주식회사9호선신방화Sinbanghwa StationSinbanghwa Station新防火駅新芳华站新芳華站N20090724
4서울시메트로9호선주식회사9호선마곡나루MagongnaruMagongnaruマゴンナル麻谷渡口麻谷나루Y20090722
5서울시메트로9호선주식회사9호선양천향교Yangcheon HyanggyoYangcheon Hyanggyoヤンチョンヒャンギョ阳川乡校-N20090722
6서울시메트로9호선주식회사9호선가양GayangGayangカヤン加阳-N20090722
7서울시메트로9호선주식회사9호선증미JeungmiJeungmiチュンミ曾米-N20090722
8서울시메트로9호선주식회사9호선등촌DeungchonDeungchonトゥンチョン登村-N20090722
9서울시메트로9호선주식회사9호선염창YeomchangYeomchangヨムチャン盐仓-N20090722
철도운영기관명선명역명영어명로마자일본어중국어간체중국어번체환승역여부신설일자
28서울시메트로9호선주식회사9호선봉은사BongeunsaBongeunsaポンウンサ奉恩寺-N-
29서울시메트로9호선주식회사9호선종합운동장Sports ComplexSports Complexチョンハブンドンジャン综合运动场-Y-
30서울시메트로9호선주식회사9호선삼전SamjeonSamjeonサムジョン三田-N20181201
31서울시메트로9호선주식회사9호선석촌고분Seokchon GobunSeokchon Gobunソクチョンゴブン石村古坟-N20181201
32서울시메트로9호선주식회사9호선석촌SeokchonSeokchonソクチョン石村-Y20181201
33서울시메트로9호선주식회사9호선송파나루SongpanaruSongpanaruソンパナル松坡渡口-N20181201
34서울시메트로9호선주식회사9호선한성백제Hanseong BaekjeHanseongBaekjeハンソンベクチェ汉城百济-N20181201
35서울시메트로9호선주식회사9호선올림픽공원(한국체대)Olympic ParkOlympicGongwonオリンピック・コンウォン奥林匹克公园(韩国体育大学)-Y20181201
36서울시메트로9호선주식회사9호선둔촌오륜Dunchon OryunDunchon Oryunトゥンチョノリュン遁村五轮-N20181201
37서울시메트로9호선주식회사9호선중앙보훈병원VHS Medical Centerjoongangbohunbyeongwonチュンアンボフンビョンウォン中央报勋医院-N20181201