Overview

Dataset statistics

Number of variables6
Number of observations99
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory49.3 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/15064037/fileData.do

Alerts

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

Reproduction

Analysis started2023-12-12 16:02:50.011150
Analysis finished2023-12-12 16:02:50.713739
Duration0.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

역명
Text

UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-13T01:02:50.930339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length2
Mean length2.7272727
Min length2

Characters and Unicode

Total characters270
Distinct characters121
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

Unique99 ?
Unique (%)100.0%

Sample

1st row가능
2nd row가산디지털단지
3rd row간석
4th row개봉
5th row관악
ValueCountFrequency (%)
가능 1
 
1.0%
세류 1
 
1.0%
온수 1
 
1.0%
오산대 1
 
1.0%
오산 1
 
1.0%
오류동 1
 
1.0%
영등포 1
 
1.0%
역곡 1
 
1.0%
양주 1
 
1.0%
안양 1
 
1.0%
Other values (89) 89
89.9%
2023-12-13T01:02:51.387048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 12
 
4.4%
대 10
 
3.7%
산 10
 
3.7%
천 10
 
3.7%
정 7
 
2.6%
신 5
 
1.9%
서 5
 
1.9%
지 5
 
1.9%
도 5
 
1.9%
가 4
 
1.5%
Other values (111) 197
73.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 260
96.3%
Close Punctuation 4
 
1.5%
Open Punctuation 4
 
1.5%
Decimal Number 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12
 
4.6%
대 10
 
3.8%
산 10
 
3.8%
천 10
 
3.8%
정 7
 
2.7%
신 5
 
1.9%
서 5
 
1.9%
지 5
 
1.9%
도 5
 
1.9%
가 4
 
1.5%
Other values (107) 187
71.9%
Decimal Number
ValueCountFrequency (%)
5 1
50.0%
3 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 260
96.3%
Common 10
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12
 
4.6%
대 10
 
3.8%
산 10
 
3.8%
천 10
 
3.8%
정 7
 
2.7%
신 5
 
1.9%
서 5
 
1.9%
지 5
 
1.9%
도 5
 
1.9%
가 4
 
1.5%
Other values (107) 187
71.9%
Common
ValueCountFrequency (%)
) 4
40.0%
( 4
40.0%
5 1
 
10.0%
3 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 260
96.3%
ASCII 10
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 12
 
4.6%
대 10
 
3.8%
산 10
 
3.8%
천 10
 
3.8%
정 7
 
2.7%
신 5
 
1.9%
서 5
 
1.9%
지 5
 
1.9%
도 5
 
1.9%
가 4
 
1.5%
Other values (107) 187
71.9%
ASCII
ValueCountFrequency (%)
) 4
40.0%
( 4
40.0%
5 1
 
10.0%
3 1
 
10.0%

역명(영문)
Text

UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-13T01:02:52.046786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length21
Mean length9.1111111
Min length4

Characters and Unicode

Total characters902
Distinct characters51
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)100.0%

Sample

1st rowGaneung
2nd rowGasan Digital Complex
3rd rowGanseok
4th rowGaebong
5th rowGwanak
ValueCountFrequency (%)
univ 6
 
5.0%
of 2
 
1.7%
osan 2
 
1.7%
seoul 2
 
1.7%
jongno 2
 
1.7%
dongducheon 2
 
1.7%
sinchang 1
 
0.8%
oryudong 1
 
0.8%
yeongdeungpo 1
 
0.8%
yeokgok 1
 
0.8%
Other values (101) 101
83.5%
2023-12-13T01:02:52.556989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 136
15.1%
o 103
 
11.4%
g 84
 
9.3%
e 72
 
8.0%
a 71
 
7.9%
i 37
 
4.1%
u 35
 
3.9%
y 26
 
2.9%
S 24
 
2.7%
22
 
2.4%
Other values (41) 292
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 742
82.3%
Uppercase Letter 119
 
13.2%
Space Separator 22
 
2.4%
Other Punctuation 6
 
0.7%
Close Punctuation 5
 
0.6%
Open Punctuation 5
 
0.6%
Decimal Number 2
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 136
18.3%
o 103
13.9%
g 84
11.3%
e 72
9.7%
a 71
9.6%
i 37
 
5.0%
u 35
 
4.7%
y 26
 
3.5%
s 21
 
2.8%
k 20
 
2.7%
Other values (15) 137
18.5%
Uppercase Letter
ValueCountFrequency (%)
S 24
20.2%
D 17
14.3%
J 12
10.1%
G 11
9.2%
U 9
 
7.6%
B 9
 
7.6%
O 6
 
5.0%
H 5
 
4.2%
C 5
 
4.2%
N 5
 
4.2%
Other values (9) 16
13.4%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
5 1
50.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 861
95.5%
Common 41
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 136
15.8%
o 103
12.0%
g 84
 
9.8%
e 72
 
8.4%
a 71
 
8.2%
i 37
 
4.3%
u 35
 
4.1%
y 26
 
3.0%
S 24
 
2.8%
s 21
 
2.4%
Other values (34) 252
29.3%
Common
ValueCountFrequency (%)
22
53.7%
. 6
 
14.6%
) 5
 
12.2%
( 5
 
12.2%
3 1
 
2.4%
5 1
 
2.4%
- 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 136
15.1%
o 103
 
11.4%
g 84
 
9.3%
e 72
 
8.0%
a 71
 
7.9%
i 37
 
4.1%
u 35
 
3.9%
y 26
 
2.9%
S 24
 
2.7%
22
 
2.4%
Other values (41) 292
32.4%

역명(로마자)
Text

UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-13T01:02:52.867240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length24
Mean length8.9090909
Min length4

Characters and Unicode

Total characters882
Distinct characters49
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)100.0%

Sample

1st rowGaneung
2nd rowGasan Dijiteol Danji
3rd rowGanseok
4th rowGaebong
5th rowGwanak
ValueCountFrequency (%)
univ 5
 
4.4%
pyeongtaek 2
 
1.8%
dongducheon 2
 
1.8%
yongsan 1
 
0.9%
sinimun 1
 
0.9%
osandae 1
 
0.9%
osan 1
 
0.9%
oryu-dong 1
 
0.9%
yeongdeungpo 1
 
0.9%
yeokgok 1
 
0.9%
Other values (98) 98
86.0%
2023-12-13T01:02:53.340161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 133
15.1%
o 99
 
11.2%
g 84
 
9.5%
e 72
 
8.2%
a 70
 
7.9%
i 38
 
4.3%
u 35
 
4.0%
y 25
 
2.8%
S 25
 
2.8%
s 20
 
2.3%
Other values (39) 281
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 727
82.4%
Uppercase Letter 116
 
13.2%
Space Separator 15
 
1.7%
Dash Punctuation 11
 
1.2%
Other Punctuation 5
 
0.6%
Close Punctuation 3
 
0.3%
Open Punctuation 3
 
0.3%
Decimal Number 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 133
18.3%
o 99
13.6%
g 84
11.6%
e 72
9.9%
a 70
9.6%
i 38
 
5.2%
u 35
 
4.8%
y 25
 
3.4%
s 20
 
2.8%
h 19
 
2.6%
Other values (13) 132
18.2%
Uppercase Letter
ValueCountFrequency (%)
S 25
21.6%
D 18
15.5%
J 12
10.3%
G 11
9.5%
B 9
 
7.8%
U 7
 
6.0%
O 6
 
5.2%
N 5
 
4.3%
H 4
 
3.4%
C 4
 
3.4%
Other values (9) 15
12.9%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
5 1
50.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 843
95.6%
Common 39
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 133
15.8%
o 99
11.7%
g 84
 
10.0%
e 72
 
8.5%
a 70
 
8.3%
i 38
 
4.5%
u 35
 
4.2%
y 25
 
3.0%
S 25
 
3.0%
s 20
 
2.4%
Other values (32) 242
28.7%
Common
ValueCountFrequency (%)
15
38.5%
- 11
28.2%
. 5
 
12.8%
) 3
 
7.7%
( 3
 
7.7%
3 1
 
2.6%
5 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 882
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 133
15.1%
o 99
 
11.2%
g 84
 
9.5%
e 72
 
8.2%
a 70
 
7.9%
i 38
 
4.3%
u 35
 
4.0%
y 25
 
2.8%
S 25
 
2.8%
s 20
 
2.3%
Other values (39) 281
31.9%

역명(일본어)
Text

Distinct98
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-13T01:02:53.609644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length4.6262626
Min length2

Characters and Unicode

Total characters458
Distinct characters61
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

Unique97 ?
Unique (%)98.0%

Sample

1st rowカヌン
2nd rowカサン·デジタルダンジ
3rd rowカンソク
4th rowケボン
5th rowクァナク
ValueCountFrequency (%)
タンジョン 2
 
2.0%
ソサ 1
 
1.0%
オンス 1
 
1.0%
オサンデ 1
 
1.0%
オサン 1
 
1.0%
オリュドン 1
 
1.0%
ヨンドゥンポ 1
 
1.0%
ヨッコク 1
 
1.0%
ヤンジュ 1
 
1.0%
アニヤン 1
 
1.0%
Other values (88) 88
88.9%
2023-12-13T01:02:54.054260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ン 112
24.5%
ョ 33
 
7.2%
チ 28
 
6.1%
ク 19
 
4.1%
ト 15
 
3.3%
ソ 14
 
3.1%
サ 14
 
3.1%
ジ 11
 
2.4%
ド 9
 
2.0%
ウ 9
 
2.0%
Other values (51) 194
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 457
99.8%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ン 112
24.5%
ョ 33
 
7.2%
チ 28
 
6.1%
ク 19
 
4.2%
ト 15
 
3.3%
ソ 14
 
3.1%
サ 14
 
3.1%
ジ 11
 
2.4%
ド 9
 
2.0%
ウ 9
 
2.0%
Other values (50) 193
42.2%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana 457
99.8%
Common 1
 
0.2%

Most frequent character per script

Katakana
ValueCountFrequency (%)
ン 112
24.5%
ョ 33
 
7.2%
チ 28
 
6.1%
ク 19
 
4.2%
ト 15
 
3.3%
ソ 14
 
3.1%
サ 14
 
3.1%
ジ 11
 
2.4%
ド 9
 
2.0%
ウ 9
 
2.0%
Other values (50) 193
42.2%
Common
ValueCountFrequency (%)
· 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Katakana 457
99.8%
None 1
 
0.2%

Most frequent character per block

Katakana
ValueCountFrequency (%)
ン 112
24.5%
ョ 33
 
7.2%
チ 28
 
6.1%
ク 19
 
4.2%
ト 15
 
3.3%
ソ 14
 
3.1%
サ 14
 
3.1%
ジ 11
 
2.4%
ド 9
 
2.0%
ウ 9
 
2.0%
Other values (50) 193
42.2%
None
ValueCountFrequency (%)
· 1
100.0%

역명(중국어 간체)
Text

UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-13T01:02:54.405486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length2
Mean length2.6767677
Min length2

Characters and Unicode

Total characters265
Distinct characters161
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

Unique99 ?
Unique (%)100.0%

Sample

1st row佳陵
2nd row加山数码园区
3rd row间石
4th row开峰
5th row冠岳
ValueCountFrequency (%)
佳陵 1
 
1.0%
细柳 1
 
1.0%
温水 1
 
1.0%
乌山大学 1
 
1.0%
乌山 1
 
1.0%
梧柳洞 1
 
1.0%
永登浦 1
 
1.0%
驿谷 1
 
1.0%
杨州 1
 
1.0%
安养 1
 
1.0%
Other values (89) 89
89.9%
2023-12-13T01:02:54.929013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
山 10
 
3.8%
大 8
 
3.0%
川 7
 
2.6%
东 6
 
2.3%
学 6
 
2.3%
洞 5
 
1.9%
新 5
 
1.9%
井 5
 
1.9%
道 4
 
1.5%
西 3
 
1.1%
Other values (151) 206
77.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 257
97.0%
Open Punctuation 3
 
1.1%
Close Punctuation 3
 
1.1%
Space Separator 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
山 10
 
3.9%
大 8
 
3.1%
川 7
 
2.7%
东 6
 
2.3%
学 6
 
2.3%
洞 5
 
1.9%
新 5
 
1.9%
井 5
 
1.9%
道 4
 
1.6%
西 3
 
1.2%
Other values (147) 198
77.0%
Space Separator
ValueCountFrequency (%)
1
50.0%
  1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 257
97.0%
Common 8
 
3.0%

Most frequent character per script

Han
ValueCountFrequency (%)
山 10
 
3.9%
大 8
 
3.1%
川 7
 
2.7%
东 6
 
2.3%
学 6
 
2.3%
洞 5
 
1.9%
新 5
 
1.9%
井 5
 
1.9%
道 4
 
1.6%
西 3
 
1.2%
Other values (147) 198
77.0%
Common
ValueCountFrequency (%)
( 3
37.5%
) 3
37.5%
1
 
12.5%
  1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
CJK 257
97.0%
ASCII 7
 
2.6%
None 1
 
0.4%

Most frequent character per block

CJK
ValueCountFrequency (%)
山 10
 
3.9%
大 8
 
3.1%
川 7
 
2.7%
东 6
 
2.3%
学 6
 
2.3%
洞 5
 
1.9%
新 5
 
1.9%
井 5
 
1.9%
道 4
 
1.6%
西 3
 
1.2%
Other values (147) 198
77.0%
ASCII
ValueCountFrequency (%)
( 3
42.9%
) 3
42.9%
1
 
14.3%
None
ValueCountFrequency (%)
  1
100.0%

역명(중국어 번체)
Text

UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-13T01:02:55.272991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length2
Mean length2.6666667
Min length2

Characters and Unicode

Total characters264
Distinct characters166
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

Unique99 ?
Unique (%)100.0%

Sample

1st row佳陵
2nd row加山디지털團地
3rd row間石
4th row開峯
5th row冠岳
ValueCountFrequency (%)
佳陵 1
 
1.0%
細柳 1
 
1.0%
溫水 1
 
1.0%
烏山大 1
 
1.0%
烏山 1
 
1.0%
梧柳洞 1
 
1.0%
永登浦 1
 
1.0%
驛谷 1
 
1.0%
楊州 1
 
1.0%
安養 1
 
1.0%
Other values (89) 89
89.9%
2023-12-13T01:02:55.756042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
山 10
 
3.8%
大 9
 
3.4%
川 7
 
2.7%
東 6
 
2.3%
洞 5
 
1.9%
井 5
 
1.9%
新 5
 
1.9%
( 4
 
1.5%
道 4
 
1.5%
) 4
 
1.5%
Other values (156) 205
77.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 254
96.2%
Open Punctuation 4
 
1.5%
Close Punctuation 4
 
1.5%
Decimal Number 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
山 10
 
3.9%
大 9
 
3.5%
川 7
 
2.8%
東 6
 
2.4%
洞 5
 
2.0%
井 5
 
2.0%
新 5
 
2.0%
道 4
 
1.6%
安 3
 
1.2%
平 3
 
1.2%
Other values (152) 197
77.6%
Decimal Number
ValueCountFrequency (%)
5 1
50.0%
3 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 247
93.6%
Common 10
 
3.8%
Hangul 7
 
2.7%

Most frequent character per script

Han
ValueCountFrequency (%)
山 10
 
4.0%
大 9
 
3.6%
川 7
 
2.8%
東 6
 
2.4%
洞 5
 
2.0%
井 5
 
2.0%
新 5
 
2.0%
道 4
 
1.6%
安 3
 
1.2%
平 3
 
1.2%
Other values (146) 190
76.9%
Hangul
ValueCountFrequency (%)
앞 2
28.6%
울 1
14.3%
서 1
14.3%
털 1
14.3%
지 1
14.3%
디 1
14.3%
Common
ValueCountFrequency (%)
( 4
40.0%
) 4
40.0%
5 1
 
10.0%
3 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 240
90.9%
ASCII 10
 
3.8%
Hangul 7
 
2.7%
CJK Compat Ideographs 7
 
2.7%

Most frequent character per block

CJK
ValueCountFrequency (%)
山 10
 
4.2%
大 9
 
3.8%
川 7
 
2.9%
東 6
 
2.5%
洞 5
 
2.1%
井 5
 
2.1%
新 5
 
2.1%
道 4
 
1.7%
安 3
 
1.2%
平 3
 
1.2%
Other values (140) 183
76.2%
ASCII
ValueCountFrequency (%)
( 4
40.0%
) 4
40.0%
5 1
 
10.0%
3 1
 
10.0%
Hangul
ValueCountFrequency (%)
앞 2
28.6%
울 1
14.3%
서 1
14.3%
털 1
14.3%
지 1
14.3%
디 1
14.3%
CJK Compat Ideographs
ValueCountFrequency (%)
龍 2
28.6%
里 1
14.3%
鹿 1
14.3%
綠 1
14.3%
鷺 1
14.3%
陵 1
14.3%

Correlations

2023-12-13T01:02:55.865130image/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

2023-12-13T01:02:50.560214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:02:50.665674image/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)
0가능GaneungGaneungカヌン佳陵佳陵
1가산디지털단지Gasan Digital ComplexGasan Dijiteol Danjiカサン·デジタルダンジ加山数码园区加山디지털團地
2간석GanseokGanseokカンソク间石間石
3개봉GaebongGaebongケボン开峰開峯
4관악GwanakGwanakクァナク冠岳冠岳
5광명GwangmyeongGwangmyeongクァンミョン光明光明
6광운대Kwangwoon Univ.Kwangwoon Univ.クァンウンデ光云大学光云大
7구로GuroGu-roクロ九老九老
8구일GuilGuilクイル九一九一
9군포GunpoGunpoクンポ军浦軍浦
역명역명(영문)역명(로마자)역명(일본어)역명(중국어 간체)역명(중국어 번체)
89직산JiksanJiksanチッサン稷山稷山
90진위JinwiJinwiチヌィ振威振威
91창동ChangdongChang-dongチャンドン仓洞倉洞
92천안CheonanCheonanチョナン天安天安
93청량리(서울시립대입구)Cheongnyangni (University of Seoul)Cheongnyangni (SeoulSiripdaeip-gu)チョンニャンニ清凉里(首尔市立大学)淸凉里(서울市立大入口)
94탕정TangjeongTangjeongタンジョン汤井湯井
95평택PyeongtaekPyeongtaekピョンテク平泽平澤
96화서HwaseoHwaseoファソ华西華西
97회기HoegiHoegiフェギ回基回基
98회룡HoeryongHoeryongフェリョン回龙回龍