Overview

Dataset statistics

Number of variables6
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory51.3 B

Variable types

Text6

Dataset

Description부산에 위치한 부산1호선의 도시광역철도역들의 한글,영문,로마자,일본어,중국어(간체,번체) 등의 정보입니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15064687/fileData.do

Alerts

역명 has unique valuesUnique
역명(영문) has unique valuesUnique
역명(로마자) has unique valuesUnique
역명(일본어) has unique valuesUnique
역명(중국어 간체) has unique valuesUnique
역명(중국어 번체) has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:17:42.112647
Analysis finished2023-12-12 02:17:42.744054
Duration0.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

역명
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T11:17:42.941082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length2
Mean length4.575
Min length2

Characters and Unicode

Total characters183
Distinct characters96
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row다대포해수욕장(몰운대)
2nd row다대포항
3rd row낫개
4th row신장림(하나병원)
5th row장림
ValueCountFrequency (%)
다대포해수욕장(몰운대 1
 
2.5%
다대포항 1
 
2.5%
교대 1
 
2.5%
범일 1
 
2.5%
범내골 1
 
2.5%
서면 1
 
2.5%
부전(부산시민공원·송상현광장 1
 
2.5%
양정 1
 
2.5%
시청(연제 1
 
2.5%
연산 1
 
2.5%
Other values (30) 30
75.0%
2023-12-12T11:17:43.463399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 12
 
6.6%
) 12
 
6.6%
대 8
 
4.4%
산 7
 
3.8%
장 6
 
3.3%
부 6
 
3.3%
동 5
 
2.7%
원 5
 
2.7%
포 4
 
2.2%
병 4
 
2.2%
Other values (86) 114
62.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 158
86.3%
Open Punctuation 12
 
6.6%
Close Punctuation 12
 
6.6%
Other Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 8
 
5.1%
산 7
 
4.4%
장 6
 
3.8%
부 6
 
3.8%
동 5
 
3.2%
원 5
 
3.2%
포 4
 
2.5%
병 4
 
2.5%
신 4
 
2.5%
하 4
 
2.5%
Other values (83) 105
66.5%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 158
86.3%
Common 25
 
13.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 8
 
5.1%
산 7
 
4.4%
장 6
 
3.8%
부 6
 
3.8%
동 5
 
3.2%
원 5
 
3.2%
포 4
 
2.5%
병 4
 
2.5%
신 4
 
2.5%
하 4
 
2.5%
Other values (83) 105
66.5%
Common
ValueCountFrequency (%)
( 12
48.0%
) 12
48.0%
· 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 158
86.3%
ASCII 24
 
13.1%
None 1
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 12
50.0%
) 12
50.0%
Hangul
ValueCountFrequency (%)
대 8
 
5.1%
산 7
 
4.4%
장 6
 
3.8%
부 6
 
3.8%
동 5
 
3.2%
원 5
 
3.2%
포 4
 
2.5%
병 4
 
2.5%
신 4
 
2.5%
하 4
 
2.5%
Other values (83) 105
66.5%
None
ValueCountFrequency (%)
· 1
100.0%

역명(영문)
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T11:17:43.955564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length13
Mean length8.4
Min length4

Characters and Unicode

Total characters336
Distinct characters40
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

Unique40 ?
Unique (%)100.0%

Sample

1st rowDadaepo Beach
2nd rowDadaepo Harbor
3rd rowNatgae
4th rowSinjangnim
5th rowJangnim
ValueCountFrequency (%)
dadaepo 2
 
4.2%
dongnae 1
 
2.1%
edu 1
 
2.1%
beomil 1
 
2.1%
beomnaegol 1
 
2.1%
seomyeon 1
 
2.1%
bujeon 1
 
2.1%
yangjeong 1
 
2.1%
city 1
 
2.1%
hall 1
 
2.1%
Other values (37) 37
77.1%
2023-12-12T11:17:44.737546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 46
13.7%
a 42
 
12.5%
o 33
 
9.8%
e 29
 
8.6%
g 20
 
6.0%
i 16
 
4.8%
s 12
 
3.6%
u 10
 
3.0%
m 9
 
2.7%
l 8
 
2.4%
Other values (30) 111
33.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 274
81.5%
Uppercase Letter 49
 
14.6%
Space Separator 8
 
2.4%
Other Punctuation 5
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 46
16.8%
a 42
15.3%
o 33
12.0%
e 29
10.6%
g 20
7.3%
i 16
 
5.8%
s 12
 
4.4%
u 10
 
3.6%
m 9
 
3.3%
l 8
 
2.9%
Other values (12) 49
17.9%
Uppercase Letter
ValueCountFrequency (%)
D 8
16.3%
B 8
16.3%
S 6
12.2%
N 6
12.2%
J 5
10.2%
H 3
 
6.1%
G 2
 
4.1%
C 2
 
4.1%
U 2
 
4.1%
Y 2
 
4.1%
Other values (5) 5
10.2%
Other Punctuation
ValueCountFrequency (%)
. 3
60.0%
' 2
40.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 323
96.1%
Common 13
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 46
14.2%
a 42
13.0%
o 33
 
10.2%
e 29
 
9.0%
g 20
 
6.2%
i 16
 
5.0%
s 12
 
3.7%
u 10
 
3.1%
m 9
 
2.8%
l 8
 
2.5%
Other values (27) 98
30.3%
Common
ValueCountFrequency (%)
8
61.5%
. 3
 
23.1%
' 2
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 46
13.7%
a 42
 
12.5%
o 33
 
9.8%
e 29
 
8.6%
g 20
 
6.0%
i 16
 
4.8%
s 12
 
3.6%
u 10
 
3.0%
m 9
 
2.7%
l 8
 
2.4%
Other values (30) 111
33.0%

역명(로마자)
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T11:17:45.079822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length24
Mean length10.75
Min length1

Characters and Unicode

Total characters430
Distinct characters37
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

Unique40 ?
Unique (%)100.0%

Sample

1st rowdadaepohaesuyokjang
2nd rowdadaepohang
3rd rowNatgae
4th rowSinjangnim
5th rowJangnim
ValueCountFrequency (%)
dadaepohaesuyokjang 1
 
2.4%
busanjin 1
 
2.4%
gyodae 1
 
2.4%
beomil 1
 
2.4%
beomnaegol 1
 
2.4%
seomyeon 1
 
2.4%
bujeon 1
 
2.4%
yangjeong 1
 
2.4%
sicheong(yeonje 1
 
2.4%
yeonsan 1
 
2.4%
Other values (31) 31
75.6%
2023-12-12T11:17:45.604559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 55
12.8%
a 53
12.3%
o 47
 
10.9%
e 43
 
10.0%
g 31
 
7.2%
i 16
 
3.7%
s 16
 
3.7%
u 16
 
3.7%
h 14
 
3.3%
d 12
 
2.8%
Other values (27) 127
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 369
85.8%
Uppercase Letter 45
 
10.5%
Open Punctuation 7
 
1.6%
Close Punctuation 7
 
1.6%
Space Separator 1
 
0.2%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 55
14.9%
a 53
14.4%
o 47
12.7%
e 43
11.7%
g 31
8.4%
i 16
 
4.3%
s 16
 
4.3%
u 16
 
4.3%
h 14
 
3.8%
d 12
 
3.3%
Other values (11) 66
17.9%
Uppercase Letter
ValueCountFrequency (%)
B 10
22.2%
S 8
17.8%
D 6
13.3%
J 6
13.3%
N 4
 
8.9%
G 3
 
6.7%
Y 3
 
6.7%
M 1
 
2.2%
C 1
 
2.2%
H 1
 
2.2%
Other values (2) 2
 
4.4%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 414
96.3%
Common 16
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 55
13.3%
a 53
12.8%
o 47
11.4%
e 43
 
10.4%
g 31
 
7.5%
i 16
 
3.9%
s 16
 
3.9%
u 16
 
3.9%
h 14
 
3.4%
d 12
 
2.9%
Other values (23) 111
26.8%
Common
ValueCountFrequency (%)
( 7
43.8%
) 7
43.8%
1
 
6.2%
- 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 55
12.8%
a 53
12.3%
o 47
 
10.9%
e 43
 
10.0%
g 31
 
7.2%
i 16
 
3.7%
s 16
 
3.7%
u 16
 
3.7%
h 14
 
3.3%
d 12
 
2.8%
Other values (27) 127
29.5%

역명(일본어)
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T11:17:45.918509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.725
Min length2

Characters and Unicode

Total characters109
Distinct characters76
Distinct categories2 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row多大浦海水浴場
2nd row多大浦港
3rd rowナッケ
4th row新長林
5th row長 林
ValueCountFrequency (%)
多大浦海水浴場 1
 
2.4%
釜山鎮 1
 
2.4%
釜山教育大学 1
 
2.4%
凡一 1
 
2.4%
ポ厶ネゴル 1
 
2.4%
西面 1
 
2.4%
釜田 1
 
2.4%
楊亭 1
 
2.4%
市庁 1
 
2.4%
蓮山 1
 
2.4%
Other values (31) 31
75.6%
2023-12-12T11:17:46.313343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
大 7
 
6.4%
山 6
 
5.5%
釜 5
 
4.6%
新 4
 
3.7%
長 3
 
2.8%
浦 3
 
2.8%
多 2
 
1.8%
学 2
 
1.8%
ル 2
 
1.8%
チ 2
 
1.8%
Other values (66) 73
67.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 108
99.1%
Space Separator 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
大 7
 
6.5%
山 6
 
5.6%
釜 5
 
4.6%
新 4
 
3.7%
長 3
 
2.8%
浦 3
 
2.8%
多 2
 
1.9%
学 2
 
1.9%
ル 2
 
1.9%
チ 2
 
1.9%
Other values (65) 72
66.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 93
85.3%
Katakana 15
 
13.8%
Common 1
 
0.9%

Most frequent character per script

Han
ValueCountFrequency (%)
大 7
 
7.5%
山 6
 
6.5%
釜 5
 
5.4%
新 4
 
4.3%
長 3
 
3.2%
浦 3
 
3.2%
多 2
 
2.2%
学 2
 
2.2%
東 2
 
2.2%
西 2
 
2.2%
Other values (52) 57
61.3%
Katakana
ValueCountFrequency (%)
ル 2
13.3%
チ 2
13.3%
ケ 1
 
6.7%
ポ 1
 
6.7%
ネ 1
 
6.7%
ゴ 1
 
6.7%
ト 1
 
6.7%
ン 1
 
6.7%
メ 1
 
6.7%
ッ 1
 
6.7%
Other values (3) 3
20.0%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 93
85.3%
Katakana 15
 
13.8%
ASCII 1
 
0.9%

Most frequent character per block

CJK
ValueCountFrequency (%)
大 7
 
7.5%
山 6
 
6.5%
釜 5
 
5.4%
新 4
 
4.3%
長 3
 
3.2%
浦 3
 
3.2%
多 2
 
2.2%
学 2
 
2.2%
東 2
 
2.2%
西 2
 
2.2%
Other values (52) 57
61.3%
Katakana
ValueCountFrequency (%)
ル 2
13.3%
チ 2
13.3%
ケ 1
 
6.7%
ポ 1
 
6.7%
ネ 1
 
6.7%
ゴ 1
 
6.7%
ト 1
 
6.7%
ン 1
 
6.7%
メ 1
 
6.7%
ッ 1
 
6.7%
Other values (3) 3
20.0%
ASCII
ValueCountFrequency (%)
1
100.0%

역명(중국어 간체)
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T11:17:46.565267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.625
Min length2

Characters and Unicode

Total characters105
Distinct characters70
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row多大浦海水浴场
2nd row多大浦港
3rd row納 漑
4th row新长林
5th row长 林
ValueCountFrequency (%)
多大浦海水浴场 1
 
2.3%
莲山 1
 
2.3%
佐川 1
 
2.3%
釜山教育大学 1
 
2.3%
凡一 1
 
2.3%
凡内谷 1
 
2.3%
西面 1
 
2.3%
釜田 1
 
2.3%
杨亭 1
 
2.3%
市厅 1
 
2.3%
Other values (33) 33
76.7%
2023-12-12T11:17:46.912868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
大 7
 
6.7%
山 6
 
5.7%
釜 5
 
4.8%
新 4
 
3.8%
浦 3
 
2.9%
东 3
 
2.9%
长 3
 
2.9%
3
 
2.9%
凡 2
 
1.9%
南 2
 
1.9%
Other values (60) 67
63.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 102
97.1%
Space Separator 3
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
大 7
 
6.9%
山 6
 
5.9%
釜 5
 
4.9%
新 4
 
3.9%
浦 3
 
2.9%
东 3
 
2.9%
长 3
 
2.9%
凡 2
 
2.0%
南 2
 
2.0%
亭 2
 
2.0%
Other values (59) 65
63.7%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 102
97.1%
Common 3
 
2.9%

Most frequent character per script

Han
ValueCountFrequency (%)
大 7
 
6.9%
山 6
 
5.9%
釜 5
 
4.9%
新 4
 
3.9%
浦 3
 
2.9%
东 3
 
2.9%
长 3
 
2.9%
凡 2
 
2.0%
南 2
 
2.0%
亭 2
 
2.0%
Other values (59) 65
63.7%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 102
97.1%
ASCII 3
 
2.9%

Most frequent character per block

CJK
ValueCountFrequency (%)
大 7
 
6.9%
山 6
 
5.9%
釜 5
 
4.9%
新 4
 
3.9%
浦 3
 
2.9%
东 3
 
2.9%
长 3
 
2.9%
凡 2
 
2.0%
南 2
 
2.0%
亭 2
 
2.0%
Other values (59) 65
63.7%
ASCII
ValueCountFrequency (%)
3
100.0%

역명(중국어 번체)
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T11:17:47.136448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length2
Mean length2.7
Min length2

Characters and Unicode

Total characters108
Distinct characters73
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row多大浦海水浴場 (沒雲臺)
2nd row多大浦港
3rd row羅浦
4th row新長林
5th row長林
ValueCountFrequency (%)
多大浦海水浴場 1
 
2.4%
釜山鎭 1
 
2.4%
釜山敎育大學 1
 
2.4%
凡一 1
 
2.4%
凡內谷 1
 
2.4%
西面 1
 
2.4%
釜田 1
 
2.4%
楊亭 1
 
2.4%
市廳 1
 
2.4%
蓮山 1
 
2.4%
Other values (31) 31
75.6%
2023-12-12T11:17:47.482076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
山 7
 
6.5%
大 7
 
6.5%
釜 5
 
4.6%
浦 4
 
3.7%
新 4
 
3.7%
東 3
 
2.8%
長 3
 
2.8%
下 2
 
1.9%
學 2
 
1.9%
凡 2
 
1.9%
Other values (63) 69
63.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 105
97.2%
Space Separator 1
 
0.9%
Open Punctuation 1
 
0.9%
Close Punctuation 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
山 7
 
6.7%
大 7
 
6.7%
釜 5
 
4.8%
浦 4
 
3.8%
新 4
 
3.8%
東 3
 
2.9%
長 3
 
2.9%
下 2
 
1.9%
學 2
 
1.9%
凡 2
 
1.9%
Other values (60) 66
62.9%
Space Separator
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 105
97.2%
Common 3
 
2.8%

Most frequent character per script

Han
ValueCountFrequency (%)
山 7
 
6.7%
大 7
 
6.7%
釜 5
 
4.8%
浦 4
 
3.8%
新 4
 
3.8%
東 3
 
2.9%
長 3
 
2.9%
下 2
 
1.9%
學 2
 
1.9%
凡 2
 
1.9%
Other values (60) 66
62.9%
Common
ValueCountFrequency (%)
1
33.3%
( 1
33.3%
) 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
CJK 103
95.4%
ASCII 3
 
2.8%
CJK Compat Ideographs 2
 
1.9%

Most frequent character per block

CJK
ValueCountFrequency (%)
山 7
 
6.8%
大 7
 
6.8%
釜 5
 
4.9%
浦 4
 
3.9%
新 4
 
3.9%
東 3
 
2.9%
長 3
 
2.9%
下 2
 
1.9%
學 2
 
1.9%
凡 2
 
1.9%
Other values (58) 64
62.1%
CJK Compat Ideographs
ValueCountFrequency (%)
老 1
50.0%
羅 1
50.0%
ASCII
ValueCountFrequency (%)
1
33.3%
( 1
33.3%
) 1
33.3%

Correlations

2023-12-12T11:17:47.595751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명역명(영문)역명(로마자)역명(일본어)역명(중국어 간체)역명(중국어 번체)
역명1.0001.0001.0001.0001.0001.000
역명(영문)1.0001.0001.0001.0001.0001.000
역명(로마자)1.0001.0001.0001.0001.0001.000
역명(일본어)1.0001.0001.0001.0001.0001.000
역명(중국어 간체)1.0001.0001.0001.0001.0001.000
역명(중국어 번체)1.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T11:17:42.548895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:17:42.690351image/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

역명역명(영문)역명(로마자)역명(일본어)역명(중국어 간체)역명(중국어 번체)
0다대포해수욕장(몰운대)Dadaepo Beachdadaepohaesuyokjang多大浦海水浴場多大浦海水浴场多大浦海水浴場 (沒雲臺)
1다대포항Dadaepo Harbordadaepohang多大浦港多大浦港多大浦港
2낫개NatgaeNatgaeナッケ納 漑羅浦
3신장림(하나병원)SinjangnimSinjangnim新長林新长林新長林
4장림JangnimJangnim長 林长 林長林
5동매Dongmae-トンメ东 嵋東山
6신평SinpyeongSinpyeong新平新平新平
7하단(아트몰링)HadanHadan(Busanbonbyeongwon)下端下端下端
8당리(사하구청)DangniDangri(Sahagucheong)堂里堂里堂裏
9사하SahaSaha沙下沙下沙下
역명역명(영문)역명(로마자)역명(일본어)역명(중국어 간체)역명(중국어 번체)
30동래(한국건강관리협회)DongnaeDongnae東莱东莱東萊
31명륜MyeongnyunMyeongnyun明倫明伦明倫
32온천장(우리들병원)OncheonjangOncheonjang温泉場温泉场溫泉場
33부산대Pusan Nat'l Univ.Busandae釜山大学釜山大学釜山大學
34장전JangjeonJangjeon(Busangatolrikdaehakgyo)長箭长箭長箭
35구서GuseoGuseo久瑞久瑞久瑞
36두실DusilDusil斗実斗实斗實
37남산NamsanNamsan(Busanoegukdaehakgyo)南山南山南山
38범어사BeomeosaBeomeosa梵魚寺梵鱼寺梵魚寺
39노포(종합버스터미널)NopoNopo(Jonghabbeoseuteomineol)老圃老圃老圃