Overview

Dataset statistics

Number of variables6
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory52.1 B

Variable types

Text6

Dataset

Description대구1호선에 포함된 도시광역철도역들의 역명,역명(영문),역명(로마자),역명(일본어),역명(중국어간체),역명(중국어번체) 등의 정보 입니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15068942/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 19:12:05.102800
Analysis finished2023-12-12 19:12:05.888799
Duration0.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

역명
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T04:12:06.006740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length4.1875
Min length2

Characters and Unicode

Total characters134
Distinct characters79
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row설화명곡
2nd row화원
3rd row대곡(정부대구청사)
4th row진천
5th row월배
ValueCountFrequency (%)
설화명곡 1
 
3.1%
화원 1
 
3.1%
각산 1
 
3.1%
반야월 1
 
3.1%
신기 1
 
3.1%
율하 1
 
3.1%
용계 1
 
3.1%
방촌 1
 
3.1%
해안 1
 
3.1%
동촌 1
 
3.1%
Other values (22) 22
68.8%
2023-12-13T04:12:06.305578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 8
 
6.0%
( 6
 
4.5%
) 6
 
4.5%
구 5
 
3.7%
월 4
 
3.0%
동 4
 
3.0%
안 3
 
2.2%
장 3
 
2.2%
시 3
 
2.2%
신 3
 
2.2%
Other values (69) 89
66.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 117
87.3%
Open Punctuation 6
 
4.5%
Close Punctuation 6
 
4.5%
Decimal Number 3
 
2.2%
Other Punctuation 2
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 8
 
6.8%
구 5
 
4.3%
월 4
 
3.4%
동 4
 
3.4%
안 3
 
2.6%
장 3
 
2.6%
시 3
 
2.6%
신 3
 
2.6%
촌 3
 
2.6%
명 3
 
2.6%
Other values (63) 78
66.7%
Decimal Number
ValueCountFrequency (%)
2 2
66.7%
8 1
33.3%
Other Punctuation
ValueCountFrequency (%)
· 1
50.0%
. 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 117
87.3%
Common 17
 
12.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 8
 
6.8%
구 5
 
4.3%
월 4
 
3.4%
동 4
 
3.4%
안 3
 
2.6%
장 3
 
2.6%
시 3
 
2.6%
신 3
 
2.6%
촌 3
 
2.6%
명 3
 
2.6%
Other values (63) 78
66.7%
Common
ValueCountFrequency (%)
( 6
35.3%
) 6
35.3%
2 2
 
11.8%
· 1
 
5.9%
8 1
 
5.9%
. 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 117
87.3%
ASCII 16
 
11.9%
None 1
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 8
 
6.8%
구 5
 
4.3%
월 4
 
3.4%
동 4
 
3.4%
안 3
 
2.6%
장 3
 
2.6%
시 3
 
2.6%
신 3
 
2.6%
촌 3
 
2.6%
명 3
 
2.6%
Other values (63) 78
66.7%
ASCII
ValueCountFrequency (%)
( 6
37.5%
) 6
37.5%
2 2
 
12.5%
8 1
 
6.2%
. 1
 
6.2%
None
ValueCountFrequency (%)
· 1
100.0%

역명(영문)
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T04:12:06.498534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length31.5
Mean length15.78125
Min length5

Characters and Unicode

Total characters505
Distinct characters52
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st rowSeolhwa·Myeonggok
2nd rowHwawon
3rd rowDaegok(Central Gov't Office-Daegu)
4th rowJincheon
5th rowWolbae
ValueCountFrequency (%)
station 3
 
5.2%
university 2
 
3.4%
market 2
 
3.4%
seolhwa·myeonggok 1
 
1.7%
ayanggyo 1
 
1.7%
jungangno 1
 
1.7%
daegu 1
 
1.7%
chilseong 1
 
1.7%
sincheon(kyungpook 1
 
1.7%
nat'l 1
 
1.7%
Other values (44) 44
75.9%
2023-12-13T04:12:06.803868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 57
 
11.3%
o 45
 
8.9%
a 40
 
7.9%
e 38
 
7.5%
g 31
 
6.1%
27
 
5.3%
i 26
 
5.1%
t 20
 
4.0%
u 16
 
3.2%
c 14
 
2.8%
Other values (42) 191
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 393
77.8%
Uppercase Letter 61
 
12.1%
Space Separator 27
 
5.3%
Open Punctuation 6
 
1.2%
Close Punctuation 6
 
1.2%
Other Punctuation 6
 
1.2%
Dash Punctuation 3
 
0.6%
Decimal Number 3
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 57
14.5%
o 45
11.5%
a 40
10.2%
e 38
9.7%
g 31
 
7.9%
i 26
 
6.6%
t 20
 
5.1%
u 16
 
4.1%
c 14
 
3.6%
l 14
 
3.6%
Other values (14) 92
23.4%
Uppercase Letter
ValueCountFrequency (%)
S 8
13.1%
D 8
13.1%
M 6
 
9.8%
H 5
 
8.2%
B 4
 
6.6%
A 3
 
4.9%
C 3
 
4.9%
Y 3
 
4.9%
O 3
 
4.9%
G 3
 
4.9%
Other values (8) 15
24.6%
Other Punctuation
ValueCountFrequency (%)
· 2
33.3%
' 2
33.3%
‧ 1
16.7%
. 1
16.7%
Decimal Number
ValueCountFrequency (%)
2 2
66.7%
8 1
33.3%
Space Separator
ValueCountFrequency (%)
27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 454
89.9%
Common 51
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 57
 
12.6%
o 45
 
9.9%
a 40
 
8.8%
e 38
 
8.4%
g 31
 
6.8%
i 26
 
5.7%
t 20
 
4.4%
u 16
 
3.5%
c 14
 
3.1%
l 14
 
3.1%
Other values (32) 153
33.7%
Common
ValueCountFrequency (%)
27
52.9%
( 6
 
11.8%
) 6
 
11.8%
- 3
 
5.9%
2 2
 
3.9%
· 2
 
3.9%
' 2
 
3.9%
‧ 1
 
2.0%
8 1
 
2.0%
. 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 502
99.4%
None 2
 
0.4%
Punctuation 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 57
 
11.4%
o 45
 
9.0%
a 40
 
8.0%
e 38
 
7.6%
g 31
 
6.2%
27
 
5.4%
i 26
 
5.2%
t 20
 
4.0%
u 16
 
3.2%
c 14
 
2.8%
Other values (40) 188
37.5%
None
ValueCountFrequency (%)
· 2
100.0%
Punctuation
ValueCountFrequency (%)
‧ 1
100.0%

역명(로마자)
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T04:12:07.010140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length26
Mean length13.21875
Min length5

Characters and Unicode

Total characters423
Distinct characters48
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st rowSeolhwa·Myeonggok
2nd rowHwawon
3rd rowDaegok(Jeongbudaegucheongsa)
4th rowJincheon
5th rowWolbae
ValueCountFrequency (%)
station 2
 
5.0%
seolhwa·myeonggok 1
 
2.5%
ayanggyo 1
 
2.5%
chilseong 1
 
2.5%
market 1
 
2.5%
sincheon(gyeongbukdaeip-gu 1
 
2.5%
dongdaegu 1
 
2.5%
dong-gu 1
 
2.5%
office(keungogae 1
 
2.5%
dongchon 1
 
2.5%
Other values (29) 29
72.5%
2023-12-13T04:12:07.323510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 53
 
12.5%
o 47
 
11.1%
g 38
 
9.0%
a 35
 
8.3%
e 33
 
7.8%
i 20
 
4.7%
u 14
 
3.3%
h 13
 
3.1%
y 12
 
2.8%
d 11
 
2.6%
Other values (38) 147
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 349
82.5%
Uppercase Letter 45
 
10.6%
Space Separator 8
 
1.9%
Open Punctuation 5
 
1.2%
Close Punctuation 5
 
1.2%
Dash Punctuation 4
 
0.9%
Other Punctuation 4
 
0.9%
Decimal Number 3
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 53
15.2%
o 47
13.5%
g 38
10.9%
a 35
10.0%
e 33
9.5%
i 20
 
5.7%
u 14
 
4.0%
h 13
 
3.7%
y 12
 
3.4%
d 11
 
3.2%
Other values (13) 73
20.9%
Uppercase Letter
ValueCountFrequency (%)
S 8
17.8%
D 6
13.3%
H 5
11.1%
M 4
8.9%
B 3
 
6.7%
Y 3
 
6.7%
A 3
 
6.7%
J 3
 
6.7%
G 2
 
4.4%
W 2
 
4.4%
Other values (6) 6
13.3%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
' 1
25.0%
· 1
25.0%
Decimal Number
ValueCountFrequency (%)
2 2
66.7%
8 1
33.3%
Space Separator
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 394
93.1%
Common 29
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 53
13.5%
o 47
 
11.9%
g 38
 
9.6%
a 35
 
8.9%
e 33
 
8.4%
i 20
 
5.1%
u 14
 
3.6%
h 13
 
3.3%
y 12
 
3.0%
d 11
 
2.8%
Other values (29) 118
29.9%
Common
ValueCountFrequency (%)
8
27.6%
( 5
17.2%
) 5
17.2%
- 4
13.8%
. 2
 
6.9%
2 2
 
6.9%
' 1
 
3.4%
8 1
 
3.4%
· 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 422
99.8%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 53
 
12.6%
o 47
 
11.1%
g 38
 
9.0%
a 35
 
8.3%
e 33
 
7.8%
i 20
 
4.7%
u 14
 
3.3%
h 13
 
3.1%
y 12
 
2.8%
d 11
 
2.6%
Other values (37) 146
34.6%
None
ValueCountFrequency (%)
· 1
100.0%

역명(일본어)
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T04:12:07.510852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length2
Mean length4.3125
Min length2

Characters and Unicode

Total characters138
Distinct characters84
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st rowソルァミョンゴク
2nd rowファウォン
3rd row大谷
4th row辰泉
5th row月背
ValueCountFrequency (%)
ソルァミョンゴク 1
 
3.0%
大邱驛 1
 
3.0%
角山 1
 
3.0%
半夜月 1
 
3.0%
新基 1
 
3.0%
栗下 1
 
3.0%
龍溪 1
 
3.0%
芳村 1
 
3.0%
解顔 1
 
3.0%
東村 1
 
3.0%
Other values (23) 23
69.7%
2023-12-13T04:12:07.823451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ン 13
 
9.4%
大 7
 
5.1%
ジ 4
 
2.9%
( 4
 
2.9%
ョ 4
 
2.9%
) 4
 
2.9%
月 4
 
2.9%
村 3
 
2.2%
ソ 3
 
2.2%
ク 3
 
2.2%
Other values (74) 89
64.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 129
93.5%
Open Punctuation 4
 
2.9%
Close Punctuation 4
 
2.9%
Space Separator 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ン 13
 
10.1%
大 7
 
5.4%
ジ 4
 
3.1%
ョ 4
 
3.1%
月 4
 
3.1%
村 3
 
2.3%
ソ 3
 
2.3%
ク 3
 
2.3%
ル 2
 
1.6%
ト 2
 
1.6%
Other values (71) 84
65.1%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 72
52.2%
Katakana 57
41.3%
Common 9
 
6.5%

Most frequent character per script

Han
ValueCountFrequency (%)
大 7
 
9.7%
月 4
 
5.6%
村 3
 
4.2%
口 2
 
2.8%
路 2
 
2.8%
明 2
 
2.8%
半 2
 
2.8%
入 2
 
2.8%
邱 2
 
2.8%
新 2
 
2.8%
Other values (44) 44
61.1%
Katakana
ValueCountFrequency (%)
ン 13
22.8%
ジ 4
 
7.0%
ョ 4
 
7.0%
ソ 3
 
5.3%
ク 3
 
5.3%
ル 2
 
3.5%
ト 2
 
3.5%
グ 2
 
3.5%
ア 2
 
3.5%
ャ 2
 
3.5%
Other values (17) 20
35.1%
Common
ValueCountFrequency (%)
( 4
44.4%
) 4
44.4%
1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
CJK 69
50.0%
Katakana 57
41.3%
ASCII 9
 
6.5%
CJK Compat Ideographs 3
 
2.2%

Most frequent character per block

Katakana
ValueCountFrequency (%)
ン 13
22.8%
ジ 4
 
7.0%
ョ 4
 
7.0%
ソ 3
 
5.3%
ク 3
 
5.3%
ル 2
 
3.5%
ト 2
 
3.5%
グ 2
 
3.5%
ア 2
 
3.5%
ャ 2
 
3.5%
Other values (17) 20
35.1%
CJK
ValueCountFrequency (%)
大 7
 
10.1%
月 4
 
5.8%
村 3
 
4.3%
口 2
 
2.9%
路 2
 
2.9%
明 2
 
2.9%
半 2
 
2.9%
入 2
 
2.9%
邱 2
 
2.9%
新 2
 
2.9%
Other values (41) 41
59.4%
ASCII
ValueCountFrequency (%)
( 4
44.4%
) 4
44.4%
1
 
11.1%
CJK Compat Ideographs
ValueCountFrequency (%)
栗 1
33.3%
龍 1
33.3%
嶺 1
33.3%

역명(중국어 간체)
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T04:12:08.017198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length2
Mean length3.8125
Min length2

Characters and Unicode

Total characters122
Distinct characters80
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

Unique32 ?
Unique (%)100.0%

Sample

1st row舌化椧谷
2nd row花園
3rd row大谷
4th row辰泉
5th row月背
ValueCountFrequency (%)
舌化椧谷 1
 
2.8%
花園 1
 
2.8%
新川 1
 
2.8%
慶北大入口 1
 
2.8%
东大邱站 1
 
2.8%
东区厅(肯高盖 1
 
2.8%
峨洋桥 1
 
2.8%
大邱国际机场门口 1
 
2.8%
东村 1
 
2.8%
东村游园地 1
 
2.8%
Other values (26) 26
72.2%
2023-12-13T04:12:08.349285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
大 8
 
6.6%
) 5
 
4.1%
( 5
 
4.1%
东 4
 
3.3%
月 4
 
3.3%
村 4
 
3.3%
4
 
3.3%
站 3
 
2.5%
邱 3
 
2.5%
场 3
 
2.5%
Other values (70) 79
64.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 108
88.5%
Close Punctuation 5
 
4.1%
Open Punctuation 5
 
4.1%
Space Separator 4
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
大 8
 
7.4%
东 4
 
3.7%
月 4
 
3.7%
村 4
 
3.7%
站 3
 
2.8%
邱 3
 
2.8%
场 3
 
2.8%
半 2
 
1.9%
口 2
 
1.9%
路 2
 
1.9%
Other values (67) 73
67.6%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 108
88.5%
Common 14
 
11.5%

Most frequent character per script

Han
ValueCountFrequency (%)
大 8
 
7.4%
东 4
 
3.7%
月 4
 
3.7%
村 4
 
3.7%
站 3
 
2.8%
邱 3
 
2.8%
场 3
 
2.8%
半 2
 
1.9%
口 2
 
1.9%
路 2
 
1.9%
Other values (67) 73
67.6%
Common
ValueCountFrequency (%)
) 5
35.7%
( 5
35.7%
4
28.6%

Most occurring blocks

ValueCountFrequency (%)
CJK 105
86.1%
ASCII 14
 
11.5%
CJK Compat Ideographs 3
 
2.5%

Most frequent character per block

CJK
ValueCountFrequency (%)
大 8
 
7.6%
东 4
 
3.8%
月 4
 
3.8%
村 4
 
3.8%
站 3
 
2.9%
邱 3
 
2.9%
场 3
 
2.9%
半 2
 
1.9%
口 2
 
1.9%
路 2
 
1.9%
Other values (64) 70
66.7%
ASCII
ValueCountFrequency (%)
) 5
35.7%
( 5
35.7%
4
28.6%
CJK Compat Ideographs
ValueCountFrequency (%)
栗 1
33.3%
龍 1
33.3%
嶺 1
33.3%

역명(중국어 번체)
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T04:12:08.637816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length4.15625
Min length1

Characters and Unicode

Total characters133
Distinct characters91
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row舌化椧谷
2nd row花園
3rd row大谷(政府大邱廳舍)
4th row辰泉
5th row月背
ValueCountFrequency (%)
舌化椧谷 1
 
2.9%
東村 1
 
2.9%
七星市場 1
 
2.9%
新川 1
 
2.9%
慶北大入口 1
 
2.9%
東大邱驛 1
 
2.9%
東區廳 1
 
2.9%
峨洋橋 1
 
2.9%
解顔 1
 
2.9%
花園 1
 
2.9%
Other values (24) 24
70.6%
2023-12-13T04:12:09.067455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
大 8
 
6.0%
5
 
3.8%
( 5
 
3.8%
) 5
 
3.8%
月 4
 
3.0%
市 3
 
2.3%
驛 3
 
2.3%
東 3
 
2.3%
邱 3
 
2.3%
村 3
 
2.3%
Other values (81) 91
68.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 112
84.2%
Space Separator 5
 
3.8%
Open Punctuation 5
 
3.8%
Close Punctuation 5
 
3.8%
Decimal Number 3
 
2.3%
Other Punctuation 2
 
1.5%
Dash Punctuation 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
大 8
 
7.1%
月 4
 
3.6%
市 3
 
2.7%
驛 3
 
2.7%
東 3
 
2.7%
邱 3
 
2.7%
村 3
 
2.7%
新 3
 
2.7%
明 2
 
1.8%
半 2
 
1.8%
Other values (73) 78
69.6%
Decimal Number
ValueCountFrequency (%)
2 2
66.7%
8 1
33.3%
Other Punctuation
ValueCountFrequency (%)
· 1
50.0%
‧ 1
50.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 112
84.2%
Common 21
 
15.8%

Most frequent character per script

Han
ValueCountFrequency (%)
大 8
 
7.1%
月 4
 
3.6%
市 3
 
2.7%
驛 3
 
2.7%
東 3
 
2.7%
邱 3
 
2.7%
村 3
 
2.7%
新 3
 
2.7%
明 2
 
1.8%
半 2
 
1.8%
Other values (73) 78
69.6%
Common
ValueCountFrequency (%)
5
23.8%
( 5
23.8%
) 5
23.8%
2 2
 
9.5%
· 1
 
4.8%
8 1
 
4.8%
‧ 1
 
4.8%
- 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
CJK 112
84.2%
ASCII 19
 
14.3%
None 1
 
0.8%
Punctuation 1
 
0.8%

Most frequent character per block

CJK
ValueCountFrequency (%)
大 8
 
7.1%
月 4
 
3.6%
市 3
 
2.7%
驛 3
 
2.7%
東 3
 
2.7%
邱 3
 
2.7%
村 3
 
2.7%
新 3
 
2.7%
明 2
 
1.8%
半 2
 
1.8%
Other values (73) 78
69.6%
ASCII
ValueCountFrequency (%)
5
26.3%
( 5
26.3%
) 5
26.3%
2 2
 
10.5%
8 1
 
5.3%
- 1
 
5.3%
None
ValueCountFrequency (%)
· 1
100.0%
Punctuation
ValueCountFrequency (%)
‧ 1
100.0%

Correlations

2023-12-13T04:12:09.178710image/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

2023-12-13T04:12:05.748085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:12:05.849528image/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)
0설화명곡Seolhwa·MyeonggokSeolhwa·Myeonggokソルァミョンゴク舌化椧谷舌化椧谷
1화원HwawonHwawonファウォン花園花園
2대곡(정부대구청사)Daegok(Central Gov't Office-Daegu)Daegok(Jeongbudaegucheongsa)大谷大谷大谷(政府大邱廳舍)
3진천JincheonJincheon辰泉辰泉辰泉
4월배WolbaeWolbae月背月背月背
5상인SanginSangin上仁上仁上仁
6월촌WolchonWolchon月村月村月村
7송현SonghyeonSonghyeon松峴松岘松峴
8서부정류장(관문시장)Seobu Bus Terminal(Gwanmun Market)Seongdangmotソブジョンニュジャン(関門市場)西部客运站 (关门市场)西部客運站(關門市場)
9대명DaemyeongDaemyeong大明大明大明
역명역명(영문)역명(로마자)역명(일본어)역명(중국어 간체)역명(중국어 번체)
22아양교AyanggyoAyanggyoアヤンギョ (大邱国際空港入口)峨洋桥 (大邱国际机场门口)峨洋橋
23동촌DongchonDongchon東村东村 (东村游园地)東村
24해안HaeanHaean解顔解顔解顔
25방촌BangchonBangchon芳村芳村芳村
26용계YonggyeYonggye龍溪龍溪龍溪
27율하YulhaYulha栗下栗下栗下
28신기SingiSingi新基新基新基
29반야월BanyawolBanyawol半夜月半夜月半夜月
30각산GaksanGaksan角山角山角山
31안심(혁신도시·첨복단지)Ansim(Innovation city · High-techMedical Complex)Ansim(Hyeoksindositpcheombokdanji)安心安心安心(革新都市·尖複團地驛)