Overview

Dataset statistics

Number of variables6
Number of observations51
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory50.6 B

Variable types

Text6

Dataset

Description역명(한글),역명(영문),역명(로마자),역명(일본어),역명(중국어간체),역명(중국어번체) 등의 정보를 제공
URLhttps://www.data.go.kr/data/15064039/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 12:57:04.367243
Analysis finished2023-12-12 12:57:05.072927
Duration0.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

역명
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T21:57:05.259765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.2352941
Min length2

Characters and Unicode

Total characters216
Distinct characters101
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

Unique51 ?
Unique (%)100.0%

Sample

1st row시청
2nd row을지로입구
3rd row을지로3가
4th row을지로4가
5th row동대문역사문화공원
ValueCountFrequency (%)
시청 1
 
2.0%
낙성대 1
 
2.0%
봉천 1
 
2.0%
신림 1
 
2.0%
신대방 1
 
2.0%
구로디지털단지 1
 
2.0%
대림(구로구청 1
 
2.0%
신도림 1
 
2.0%
문래 1
 
2.0%
영등포구청 1
 
2.0%
Other values (41) 41
80.4%
2023-12-12T21:57:05.606808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 15
 
6.9%
대 12
 
5.6%
청 9
 
4.2%
) 9
 
4.2%
( 9
 
4.2%
신 8
 
3.7%
로 6
 
2.8%
지 5
 
2.3%
동 5
 
2.3%
입 5
 
2.3%
Other values (91) 133
61.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 195
90.3%
Close Punctuation 9
 
4.2%
Open Punctuation 9
 
4.2%
Decimal Number 2
 
0.9%
Other Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 15
 
7.7%
대 12
 
6.2%
청 9
 
4.6%
신 8
 
4.1%
로 6
 
3.1%
지 5
 
2.6%
동 5
 
2.6%
입 5
 
2.6%
림 4
 
2.1%
문 4
 
2.1%
Other values (86) 122
62.6%
Decimal Number
ValueCountFrequency (%)
4 1
50.0%
3 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 195
90.3%
Common 21
 
9.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 15
 
7.7%
대 12
 
6.2%
청 9
 
4.6%
신 8
 
4.1%
로 6
 
3.1%
지 5
 
2.6%
동 5
 
2.6%
입 5
 
2.6%
림 4
 
2.1%
문 4
 
2.1%
Other values (86) 122
62.6%
Common
ValueCountFrequency (%)
) 9
42.9%
( 9
42.9%
4 1
 
4.8%
3 1
 
4.8%
· 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 195
90.3%
ASCII 20
 
9.3%
None 1
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 15
 
7.7%
대 12
 
6.2%
청 9
 
4.6%
신 8
 
4.1%
로 6
 
3.1%
지 5
 
2.6%
동 5
 
2.6%
입 5
 
2.6%
림 4
 
2.1%
문 4
 
2.1%
Other values (86) 122
62.6%
ASCII
ValueCountFrequency (%)
) 9
45.0%
( 9
45.0%
4 1
 
5.0%
3 1
 
5.0%
None
ValueCountFrequency (%)
· 1
100.0%

역명(영문)
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T21:57:05.840572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length29
Mean length14.960784
Min length6

Characters and Unicode

Total characters763
Distinct characters53
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)100.0%

Sample

1st rowCity Hall
2nd rowEuljiro 1(il)ga
3rd rowEuljiro 3(sam)ga
4th rowEuljiro 4(sa)ga
5th rowDongdaemun History & Culture Park
ValueCountFrequency (%)
office 8
 
8.1%
univ 7
 
7.1%
euljiro 3
 
3.0%
seoul 3
 
3.0%
2
 
2.0%
complex 2
 
2.0%
sindaebang 1
 
1.0%
sindorim 1
 
1.0%
guro-gu 1
 
1.0%
daerim 1
 
1.0%
Other values (70) 70
70.7%
2023-12-12T21:57:06.210673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 71
 
9.3%
a 56
 
7.3%
o 55
 
7.2%
i 51
 
6.7%
e 50
 
6.6%
48
 
6.3%
g 46
 
6.0%
u 36
 
4.7%
l 30
 
3.9%
s 23
 
3.0%
Other values (43) 297
38.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 576
75.5%
Uppercase Letter 93
 
12.2%
Space Separator 48
 
6.3%
Close Punctuation 12
 
1.6%
Open Punctuation 12
 
1.6%
Other Punctuation 11
 
1.4%
Dash Punctuation 7
 
0.9%
Decimal Number 3
 
0.4%
Initial Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 71
12.3%
a 56
9.7%
o 55
9.5%
i 51
 
8.9%
e 50
 
8.7%
g 46
 
8.0%
u 36
 
6.2%
l 30
 
5.2%
s 23
 
4.0%
r 21
 
3.6%
Other values (14) 137
23.8%
Uppercase Letter
ValueCountFrequency (%)
S 19
20.4%
O 8
 
8.6%
U 7
 
7.5%
C 7
 
7.5%
D 7
 
7.5%
G 7
 
7.5%
Y 5
 
5.4%
E 5
 
5.4%
H 5
 
5.4%
P 3
 
3.2%
Other values (8) 20
21.5%
Other Punctuation
ValueCountFrequency (%)
. 7
63.6%
& 2
 
18.2%
' 2
 
18.2%
Decimal Number
ValueCountFrequency (%)
4 1
33.3%
1 1
33.3%
3 1
33.3%
Space Separator
ValueCountFrequency (%)
48
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 669
87.7%
Common 94
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 71
 
10.6%
a 56
 
8.4%
o 55
 
8.2%
i 51
 
7.6%
e 50
 
7.5%
g 46
 
6.9%
u 36
 
5.4%
l 30
 
4.5%
s 23
 
3.4%
r 21
 
3.1%
Other values (32) 230
34.4%
Common
ValueCountFrequency (%)
48
51.1%
) 12
 
12.8%
( 12
 
12.8%
. 7
 
7.4%
- 7
 
7.4%
& 2
 
2.1%
' 2
 
2.1%
‘ 1
 
1.1%
4 1
 
1.1%
1 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 762
99.9%
Punctuation 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 71
 
9.3%
a 56
 
7.3%
o 55
 
7.2%
i 51
 
6.7%
e 50
 
6.6%
48
 
6.3%
g 46
 
6.0%
u 36
 
4.7%
l 30
 
3.9%
s 23
 
3.0%
Other values (42) 296
38.8%
Punctuation
ValueCountFrequency (%)
‘ 1
100.0%

역명(로마자)
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T21:57:06.446382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length28
Mean length14.254902
Min length6

Characters and Unicode

Total characters727
Distinct characters51
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)100.0%

Sample

1st rowCity Hall
2nd rowEuljiroip-gu
3rd rowEuljiro3(sam)-ga
4th rowEuljiro4(sa)-ga
5th rowDongdaemun yeoksa munhwa park
ValueCountFrequency (%)
univ 7
 
8.0%
office 7
 
8.0%
seoul 2
 
2.3%
city 1
 
1.1%
gurodigiteoldanji 1
 
1.1%
yeongdeungpo-gu 1
 
1.1%
mullae 1
 
1.1%
sindorim 1
 
1.1%
guro-gu 1
 
1.1%
daerim 1
 
1.1%
Other values (64) 64
73.6%
2023-12-12T21:57:06.817503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 74
 
10.2%
a 58
 
8.0%
o 54
 
7.4%
i 49
 
6.7%
e 49
 
6.7%
g 46
 
6.3%
36
 
5.0%
u 32
 
4.4%
l 24
 
3.3%
s 22
 
3.0%
Other values (41) 283
38.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 562
77.3%
Uppercase Letter 83
 
11.4%
Space Separator 36
 
5.0%
Dash Punctuation 12
 
1.7%
Close Punctuation 11
 
1.5%
Open Punctuation 11
 
1.5%
Other Punctuation 9
 
1.2%
Decimal Number 2
 
0.3%
Initial Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 74
13.2%
a 58
10.3%
o 54
9.6%
i 49
 
8.7%
e 49
 
8.7%
g 46
 
8.2%
u 32
 
5.7%
l 24
 
4.3%
s 22
 
3.9%
m 21
 
3.7%
Other values (14) 133
23.7%
Uppercase Letter
ValueCountFrequency (%)
S 18
21.7%
G 8
9.6%
O 7
 
8.4%
U 7
 
8.4%
E 6
 
7.2%
D 5
 
6.0%
Y 5
 
6.0%
H 4
 
4.8%
B 4
 
4.8%
J 3
 
3.6%
Other values (7) 16
19.3%
Other Punctuation
ValueCountFrequency (%)
. 7
77.8%
' 1
 
11.1%
& 1
 
11.1%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
36
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 645
88.7%
Common 82
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 74
 
11.5%
a 58
 
9.0%
o 54
 
8.4%
i 49
 
7.6%
e 49
 
7.6%
g 46
 
7.1%
u 32
 
5.0%
l 24
 
3.7%
s 22
 
3.4%
m 21
 
3.3%
Other values (31) 216
33.5%
Common
ValueCountFrequency (%)
36
43.9%
- 12
 
14.6%
) 11
 
13.4%
( 11
 
13.4%
. 7
 
8.5%
‘ 1
 
1.2%
' 1
 
1.2%
& 1
 
1.2%
3 1
 
1.2%
4 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 726
99.9%
Punctuation 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 74
 
10.2%
a 58
 
8.0%
o 54
 
7.4%
i 49
 
6.7%
e 49
 
6.7%
g 46
 
6.3%
36
 
5.0%
u 32
 
4.4%
l 24
 
3.3%
s 22
 
3.0%
Other values (40) 282
38.8%
Punctuation
ValueCountFrequency (%)
‘ 1
100.0%

역명(일본어)
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T21:57:07.041417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length10
Mean length5.5882353
Min length2

Characters and Unicode

Total characters285
Distinct characters61
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

Unique51 ?
Unique (%)100.0%

Sample

1st rowシチョン
2nd rowウルチロイック
3rd rowウルチロサムガ
4th rowウルチロサガ
5th rowトンデムン·ヨッサムンファゴンウォン
ValueCountFrequency (%)
シチョン 1
 
2.0%
ナッソンデ 1
 
2.0%
ポンチョン 1
 
2.0%
シンリム 1
 
2.0%
シンデバン 1
 
2.0%
クロ·デジタルダンジ 1
 
2.0%
テリム 1
 
2.0%
シンドリム 1
 
2.0%
ムンレ 1
 
2.0%
ヨンドゥンポグチョン 1
 
2.0%
Other values (41) 41
80.4%
2023-12-12T21:57:07.358834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ン 59
20.7%
チ 18
 
6.3%
ム 17
 
6.0%
ョ 15
 
5.3%
シ 14
 
4.9%
ル 11
 
3.9%
デ 10
 
3.5%
サ 9
 
3.2%
ソ 8
 
2.8%
ッ 8
 
2.8%
Other values (51) 116
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 279
97.9%
Open Punctuation 2
 
0.7%
Close Punctuation 2
 
0.7%
Other Punctuation 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ン 59
21.1%
チ 18
 
6.5%
ム 17
 
6.1%
ョ 15
 
5.4%
シ 14
 
5.0%
ル 11
 
3.9%
デ 10
 
3.6%
サ 9
 
3.2%
ソ 8
 
2.9%
ッ 8
 
2.9%
Other values (48) 110
39.4%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana 275
96.5%
Common 6
 
2.1%
Han 4
 
1.4%

Most frequent character per script

Katakana
ValueCountFrequency (%)
ン 59
21.5%
チ 18
 
6.5%
ム 17
 
6.2%
ョ 15
 
5.5%
シ 14
 
5.1%
ル 11
 
4.0%
デ 10
 
3.6%
サ 9
 
3.3%
ソ 8
 
2.9%
ッ 8
 
2.9%
Other values (44) 106
38.5%
Han
ValueCountFrequency (%)
村 1
25.0%
新 1
25.0%
成 1
25.0%
三 1
25.0%
Common
ValueCountFrequency (%)
( 2
33.3%
) 2
33.3%
· 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Katakana 275
96.5%
ASCII 4
 
1.4%
CJK 4
 
1.4%
None 2
 
0.7%

Most frequent character per block

Katakana
ValueCountFrequency (%)
ン 59
21.5%
チ 18
 
6.5%
ム 17
 
6.2%
ョ 15
 
5.5%
シ 14
 
5.1%
ル 11
 
4.0%
デ 10
 
3.6%
サ 9
 
3.3%
ソ 8
 
2.9%
ッ 8
 
2.9%
Other values (44) 106
38.5%
ASCII
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%
None
ValueCountFrequency (%)
· 2
100.0%
CJK
ValueCountFrequency (%)
村 1
25.0%
新 1
25.0%
成 1
25.0%
三 1
25.0%

역명(중국어 간체)
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T21:57:07.559612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length3.8627451
Min length2

Characters and Unicode

Total characters197
Distinct characters111
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

Unique51 ?
Unique (%)100.0%

Sample

1st row市厅
2nd row乙支路入口
3rd row乙支路三街
4th row乙支路四街
5th row东大门历史文化公园
ValueCountFrequency (%)
市厅 1
 
2.0%
落星垈 1
 
2.0%
奉天 1
 
2.0%
新林 1
 
2.0%
新大方 1
 
2.0%
九老数码园区 1
 
2.0%
大林 1
 
2.0%
新道林 1
 
2.0%
文来 1
 
2.0%
永登浦区厅 1
 
2.0%
Other values (41) 41
80.4%
2023-12-12T21:57:07.862504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
大 10
 
5.1%
新 9
 
4.6%
区 6
 
3.0%
学 6
 
3.0%
厅 6
 
3.0%
路 5
 
2.5%
( 5
 
2.5%
) 5
 
2.5%
支 3
 
1.5%
十 3
 
1.5%
Other values (101) 139
70.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 187
94.9%
Open Punctuation 5
 
2.5%
Close Punctuation 5
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
大 10
 
5.3%
新 9
 
4.8%
区 6
 
3.2%
学 6
 
3.2%
厅 6
 
3.2%
路 5
 
2.7%
支 3
 
1.6%
十 3
 
1.6%
林 3
 
1.6%
三 3
 
1.6%
Other values (99) 133
71.1%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 187
94.9%
Common 10
 
5.1%

Most frequent character per script

Han
ValueCountFrequency (%)
大 10
 
5.3%
新 9
 
4.8%
区 6
 
3.2%
学 6
 
3.2%
厅 6
 
3.2%
路 5
 
2.7%
支 3
 
1.6%
十 3
 
1.6%
林 3
 
1.6%
三 3
 
1.6%
Other values (99) 133
71.1%
Common
ValueCountFrequency (%)
( 5
50.0%
) 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 185
93.9%
ASCII 10
 
5.1%
CJK Compat Ideographs 2
 
1.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
大 10
 
5.4%
新 9
 
4.9%
区 6
 
3.2%
学 6
 
3.2%
厅 6
 
3.2%
路 5
 
2.7%
支 3
 
1.6%
十 3
 
1.6%
林 3
 
1.6%
三 3
 
1.6%
Other values (97) 131
70.8%
ASCII
ValueCountFrequency (%)
( 5
50.0%
) 5
50.0%
CJK Compat Ideographs
ValueCountFrequency (%)
落 1
50.0%
林 1
50.0%

역명(중국어 번체)
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T21:57:08.093348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.2156863
Min length1

Characters and Unicode

Total characters215
Distinct characters118
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)100.0%

Sample

1st row市廳
2nd row乙支路入口
3rd row乙支路3街
4th row乙支路4街
5th row東大門歷史文化公園
ValueCountFrequency (%)
市廳 1
 
2.0%
落星垈 1
 
2.0%
奉天 1
 
2.0%
新林 1
 
2.0%
新大方 1
 
2.0%
九老디지털團地 1
 
2.0%
大林(九老區廳 1
 
2.0%
新道林 1
 
2.0%
文來 1
 
2.0%
永登浦區廳 1
 
2.0%
Other values (41) 41
80.4%
2023-12-12T21:57:08.390986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
大 11
 
5.1%
) 9
 
4.2%
( 9
 
4.2%
廳 9
 
4.2%
新 8
 
3.7%
區 7
 
3.3%
入 5
 
2.3%
口 5
 
2.3%
林 4
 
1.9%
路 4
 
1.9%
Other values (108) 144
67.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 192
89.3%
Close Punctuation 9
 
4.2%
Open Punctuation 9
 
4.2%
Dash Punctuation 2
 
0.9%
Decimal Number 2
 
0.9%
Other Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
大 11
 
5.7%
廳 9
 
4.7%
新 8
 
4.2%
區 7
 
3.6%
入 5
 
2.6%
口 5
 
2.6%
林 4
 
2.1%
路 4
 
2.1%
九 3
 
1.6%
蠶 3
 
1.6%
Other values (102) 133
69.3%
Decimal Number
ValueCountFrequency (%)
4 1
50.0%
3 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 173
80.5%
Common 23
 
10.7%
Hangul 19
 
8.8%

Most frequent character per script

Han
ValueCountFrequency (%)
大 11
 
6.4%
廳 9
 
5.2%
新 8
 
4.6%
區 7
 
4.0%
入 5
 
2.9%
口 5
 
2.9%
林 4
 
2.3%
路 4
 
2.3%
九 3
 
1.7%
蠶 3
 
1.7%
Other values (86) 114
65.9%
Hangul
ValueCountFrequency (%)
서 2
 
10.5%
울 2
 
10.5%
터 2
 
10.5%
디 1
 
5.3%
털 1
 
5.3%
지 1
 
5.3%
까 1
 
5.3%
리 1
 
5.3%
거 1
 
5.3%
네 1
 
5.3%
Other values (6) 6
31.6%
Common
ValueCountFrequency (%)
) 9
39.1%
( 9
39.1%
- 2
 
8.7%
4 1
 
4.3%
3 1
 
4.3%
? 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
CJK 168
78.1%
ASCII 23
 
10.7%
Hangul 19
 
8.8%
CJK Compat Ideographs 5
 
2.3%

Most frequent character per block

CJK
ValueCountFrequency (%)
大 11
 
6.5%
廳 9
 
5.4%
新 8
 
4.8%
區 7
 
4.2%
入 5
 
3.0%
口 5
 
3.0%
林 4
 
2.4%
路 4
 
2.4%
九 3
 
1.8%
蠶 3
 
1.8%
Other values (82) 109
64.9%
ASCII
ValueCountFrequency (%)
) 9
39.1%
( 9
39.1%
- 2
 
8.7%
4 1
 
4.3%
3 1
 
4.3%
? 1
 
4.3%
CJK Compat Ideographs
ValueCountFrequency (%)
龍 2
40.0%
落 1
20.0%
梨 1
20.0%
歷 1
20.0%
Hangul
ValueCountFrequency (%)
서 2
 
10.5%
울 2
 
10.5%
터 2
 
10.5%
디 1
 
5.3%
털 1
 
5.3%
지 1
 
5.3%
까 1
 
5.3%
리 1
 
5.3%
거 1
 
5.3%
네 1
 
5.3%
Other values (6) 6
31.6%

Correlations

2023-12-12T21:57:08.469901image/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

2023-12-12T21:57:04.852344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:57:05.012860image/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)
0시청City HallCity Hallシチョン市厅市廳
1을지로입구Euljiro 1(il)gaEuljiroip-guウルチロイック乙支路入口乙支路入口
2을지로3가Euljiro 3(sam)gaEuljiro3(sam)-gaウルチロサムガ乙支路三街乙支路3街
3을지로4가Euljiro 4(sa)gaEuljiro4(sa)-gaウルチロサガ乙支路四街乙支路4街
4동대문역사문화공원Dongdaemun History & Culture ParkDongdaemun yeoksa munhwa parkトンデムン·ヨッサムンファゴンウォン东大门历史文化公园東大門歷史文化公園
5신당SindangSindangシンダン新堂新堂
6상왕십리SangwangsimniSangwangsimniサンワンシムニ上往十里上往十里
7왕십리WangsimniWangsimniワンシムニ往十里往十里
8한양대Hanyang Univ.Hanyang Univ.ハニャンデ汉阳大学漢陽大
9뚝섬TtukseomTtukseomトゥッソム纛岛-
역명역명(영문)역명(로마자)역명(일본어)역명(중국어 간체)역명(중국어 번체)
41아현AhyeonAhyeonアヒョン阿岘阿峴
42충정로(경기대입구)Chungjeongno (Kyonggi Univ.)Chungjeongno (Kyonggi Univ.)チュンジョンノ忠正路忠正路(京畿大入口)
43용답YongdapYongdapヨンダプ龙踏龍踏
44신답SindapSindapシンダプ新踏新踏
45용두(동대문구청)Yongdu (Dongdaemun-gu Office)Yongdu (Dongdaemun-gu Office)ヨンドゥ龙头(东大门区厅)龍頭(東大門區廳)
46신설동SinseoldongSinseol-dongシンソルトン新设洞新設洞
47도림천DorimcheonDorimcheonトリムチョン道林川道林川
48양천구청Yangcheon-gu OfficeYangcheon-gu Officeヤンチョングチョン阳川区厅陽川區廳
49신정네거리SinjeongnegeoriSinjeongnegeo-riシンジョンネゴリ新亭十字路口新亭네거리
50까치산KkachisanKkachisanカチサン喜鹊山까치山