Overview

Dataset statistics

Number of variables6
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory52.9 B

Variable types

Text6

Dataset

Description수도권 인천2호선의 도시광역철도역들에 대한 한글,영문,로마자,일본어,중국어(간체,번체) 등의 정보입니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15064663/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

Reproduction

Analysis started2023-12-12 22:41:33.255976
Analysis finished2023-12-12 22:41:33.731410
Duration0.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

역명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T07:41:33.868807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.3703704
Min length2

Characters and Unicode

Total characters145
Distinct characters71
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row검단오류(검단산업단지)
2nd row왕길
3rd row검단사거리
4th row마전
5th row완정
ValueCountFrequency (%)
검단오류(검단산업단지 1
 
3.7%
인천가좌 1
 
3.7%
인천대공원 1
 
3.7%
남동구청 1
 
3.7%
만수 1
 
3.7%
모래내시장 1
 
3.7%
석천사거리 1
 
3.7%
인천시청 1
 
3.7%
석바위시장 1
 
3.7%
시민공원(문화창작지대 1
 
3.7%
Other values (17) 17
63.0%
2023-12-13T07:41:34.530777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 8
 
5.5%
( 7
 
4.8%
) 7
 
4.8%
검 5
 
3.4%
천 5
 
3.4%
단 5
 
3.4%
가 5
 
3.4%
장 5
 
3.4%
정 4
 
2.8%
인 4
 
2.8%
Other values (61) 90
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 130
89.7%
Open Punctuation 7
 
4.8%
Close Punctuation 7
 
4.8%
Uppercase Letter 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 8
 
6.2%
검 5
 
3.8%
천 5
 
3.8%
단 5
 
3.8%
가 5
 
3.8%
장 5
 
3.8%
정 4
 
3.1%
인 4
 
3.1%
거 4
 
3.1%
리 4
 
3.1%
Other values (58) 81
62.3%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 130
89.7%
Common 14
 
9.7%
Latin 1
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 8
 
6.2%
검 5
 
3.8%
천 5
 
3.8%
단 5
 
3.8%
가 5
 
3.8%
장 5
 
3.8%
정 4
 
3.1%
인 4
 
3.1%
거 4
 
3.1%
리 4
 
3.1%
Other values (58) 81
62.3%
Common
ValueCountFrequency (%)
( 7
50.0%
) 7
50.0%
Latin
ValueCountFrequency (%)
J 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 130
89.7%
ASCII 15
 
10.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 8
 
6.2%
검 5
 
3.8%
천 5
 
3.8%
단 5
 
3.8%
가 5
 
3.8%
장 5
 
3.8%
정 4
 
3.1%
인 4
 
3.1%
거 4
 
3.1%
리 4
 
3.1%
Other values (58) 81
62.3%
ASCII
ValueCountFrequency (%)
( 7
46.7%
) 7
46.7%
J 1
 
6.7%

역명(영문)
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T07:41:34.743122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length29
Mean length17.148148
Min length4

Characters and Unicode

Total characters463
Distinct characters47
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

Unique27 ?
Unique (%)100.0%

Sample

1st rowGeomdan Oryu(Geomdan Industrial Complex)
2nd rowWanggil
3rd rowGeomdan Sageori
4th rowMajeon
5th rowWanjeong
ValueCountFrequency (%)
market 4
 
6.9%
incheon 3
 
5.2%
sageori 3
 
5.2%
office 2
 
3.4%
juan 2
 
3.4%
city 2
 
3.4%
geomdan 2
 
3.4%
industrial 2
 
3.4%
mansu 1
 
1.7%
citizens 1
 
1.7%
Other values (36) 36
62.1%
2023-12-13T07:41:35.115330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 44
 
9.5%
a 44
 
9.5%
n 43
 
9.3%
o 37
 
8.0%
31
 
6.7%
i 19
 
4.1%
g 17
 
3.7%
r 17
 
3.7%
t 16
 
3.5%
u 16
 
3.5%
Other values (37) 179
38.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 349
75.4%
Uppercase Letter 65
 
14.0%
Space Separator 31
 
6.7%
Open Punctuation 7
 
1.5%
Close Punctuation 7
 
1.5%
Dash Punctuation 2
 
0.4%
Decimal Number 1
 
0.2%
Final Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 44
12.6%
a 44
12.6%
n 43
12.3%
o 37
10.6%
i 19
 
5.4%
g 17
 
4.9%
r 17
 
4.9%
t 16
 
4.6%
u 16
 
4.6%
m 14
 
4.0%
Other values (14) 82
23.5%
Uppercase Letter
ValueCountFrequency (%)
G 12
18.5%
C 9
13.8%
S 9
13.8%
M 7
10.8%
I 6
9.2%
J 4
 
6.2%
W 4
 
6.2%
O 3
 
4.6%
N 2
 
3.1%
P 2
 
3.1%
Other values (7) 7
10.8%
Space Separator
ValueCountFrequency (%)
31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 414
89.4%
Common 49
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 44
 
10.6%
a 44
 
10.6%
n 43
 
10.4%
o 37
 
8.9%
i 19
 
4.6%
g 17
 
4.1%
r 17
 
4.1%
t 16
 
3.9%
u 16
 
3.9%
m 14
 
3.4%
Other values (31) 147
35.5%
Common
ValueCountFrequency (%)
31
63.3%
( 7
 
14.3%
) 7
 
14.3%
- 2
 
4.1%
1 1
 
2.0%
’ 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 462
99.8%
Punctuation 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 44
 
9.5%
a 44
 
9.5%
n 43
 
9.3%
o 37
 
8.0%
31
 
6.7%
i 19
 
4.1%
g 17
 
3.7%
r 17
 
3.7%
t 16
 
3.5%
u 16
 
3.5%
Other values (36) 178
38.5%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%

역명(로마자)
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T07:41:35.329151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length18
Mean length12.62963
Min length4

Characters and Unicode

Total characters341
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st rowGeomdan Oryu
2nd rowWanggil
3rd rowGeomdan Sageori
4th rowMajeon
5th rowWanjeong
ValueCountFrequency (%)
incheon 3
 
6.1%
market 3
 
6.1%
office 2
 
4.1%
juan 2
 
4.1%
gajeong 2
 
4.1%
geomdan 2
 
4.1%
sageori 2
 
4.1%
park 2
 
4.1%
moraenae 1
 
2.0%
seokcheon 1
 
2.0%
Other values (29) 29
59.2%
2023-12-13T07:41:35.721998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 37
 
10.9%
n 34
 
10.0%
e 33
 
9.7%
o 27
 
7.9%
22
 
6.5%
i 15
 
4.4%
g 14
 
4.1%
m 12
 
3.5%
r 12
 
3.5%
t 11
 
3.2%
Other values (31) 124
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 267
78.3%
Uppercase Letter 49
 
14.4%
Space Separator 22
 
6.5%
Dash Punctuation 2
 
0.6%
Final Punctuation 1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 37
13.9%
n 34
12.7%
e 33
12.4%
o 27
10.1%
i 15
 
5.6%
g 14
 
5.2%
m 12
 
4.5%
r 12
 
4.5%
t 11
 
4.1%
u 11
 
4.1%
Other values (14) 61
22.8%
Uppercase Letter
ValueCountFrequency (%)
G 9
18.4%
S 7
14.3%
M 6
12.2%
C 5
10.2%
W 4
8.2%
I 4
8.2%
J 3
 
6.1%
O 3
 
6.1%
N 2
 
4.1%
P 2
 
4.1%
Other values (4) 4
8.2%
Space Separator
ValueCountFrequency (%)
22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 316
92.7%
Common 25
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 37
 
11.7%
n 34
 
10.8%
e 33
 
10.4%
o 27
 
8.5%
i 15
 
4.7%
g 14
 
4.4%
m 12
 
3.8%
r 12
 
3.8%
t 11
 
3.5%
u 11
 
3.5%
Other values (28) 110
34.8%
Common
ValueCountFrequency (%)
22
88.0%
- 2
 
8.0%
’ 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340
99.7%
Punctuation 1
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 37
 
10.9%
n 34
 
10.0%
e 33
 
9.7%
o 27
 
7.9%
22
 
6.5%
i 15
 
4.4%
g 14
 
4.1%
m 12
 
3.5%
r 12
 
3.5%
t 11
 
3.2%
Other values (30) 123
36.2%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%

역명(일본어)
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T07:41:35.918890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length6.8148148
Min length3

Characters and Unicode

Total characters184
Distinct characters45
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

Unique27 ?
Unique (%)100.0%

Sample

1st rowコムダンオリュ
2nd rowワンギル
3rd rowコムダンサゴリ
4th rowマジョン
5th rowワンジョン
ValueCountFrequency (%)
コムダンオリュ 1
 
3.7%
インチョンカジュア 1
 
3.7%
インチョンテゴンウォン 1
 
3.7%
ナムドングチョン 1
 
3.7%
マンス 1
 
3.7%
モレネシジャン 1
 
3.7%
ソクチョンサゴリ 1
 
3.7%
インチョンシチョン 1
 
3.7%
ソッバウィシジャン 1
 
3.7%
シミンゴンウォン 1
 
3.7%
Other values (17) 17
63.0%
2023-12-13T07:41:36.251674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ン 41
22.3%
ョ 14
 
7.6%
チ 11
 
6.0%
ジ 10
 
5.4%
シ 6
 
3.3%
ソ 6
 
3.3%
ム 6
 
3.3%
ア 6
 
3.3%
ウ 6
 
3.3%
カ 5
 
2.7%
Other values (35) 73
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 183
99.5%
Dash Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ン 41
22.4%
ョ 14
 
7.7%
チ 11
 
6.0%
ジ 10
 
5.5%
シ 6
 
3.3%
ソ 6
 
3.3%
ム 6
 
3.3%
ア 6
 
3.3%
ウ 6
 
3.3%
カ 5
 
2.7%
Other values (34) 72
39.3%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana 183
99.5%
Common 1
 
0.5%

Most frequent character per script

Katakana
ValueCountFrequency (%)
ン 41
22.4%
ョ 14
 
7.7%
チ 11
 
6.0%
ジ 10
 
5.5%
シ 6
 
3.3%
ソ 6
 
3.3%
ム 6
 
3.3%
ア 6
 
3.3%
ウ 6
 
3.3%
カ 5
 
2.7%
Other values (34) 72
39.3%
Common
ValueCountFrequency (%)
- 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Katakana 183
99.5%
ASCII 1
 
0.5%

Most frequent character per block

Katakana
ValueCountFrequency (%)
ン 41
22.4%
ョ 14
 
7.7%
チ 11
 
6.0%
ジ 10
 
5.5%
シ 6
 
3.3%
ソ 6
 
3.3%
ム 6
 
3.3%
ア 6
 
3.3%
ウ 6
 
3.3%
カ 5
 
2.7%
Other values (34) 72
39.3%
ASCII
ValueCountFrequency (%)
- 1
100.0%

역명(중국어 간체)
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T07:41:36.538337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length5.037037
Min length2

Characters and Unicode

Total characters136
Distinct characters75
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row黔丹梧柳(黔丹产业园区)
2nd row旺吉
3rd row黔丹十字路口
4th row麻田
5th row完井
ValueCountFrequency (%)
黔丹梧柳(黔丹产业园区 1
 
3.7%
仁川佳佐 1
 
3.7%
仁川大公园 1
 
3.7%
南洞区厅 1
 
3.7%
万寿 1
 
3.7%
沙丘市场 1
 
3.7%
石泉十字路口 1
 
3.7%
仁川市厅 1
 
3.7%
石岩市场 1
 
3.7%
市民公园(文化创作地带 1
 
3.7%
Other values (17) 17
63.0%
2023-12-13T07:41:36.880898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 6
 
4.4%
) 6
 
4.4%
市 6
 
4.4%
黔 5
 
3.7%
场 5
 
3.7%
川 4
 
2.9%
仁 4
 
2.9%
石 4
 
2.9%
路 3
 
2.2%
公 3
 
2.2%
Other values (65) 90
66.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 123
90.4%
Open Punctuation 6
 
4.4%
Close Punctuation 6
 
4.4%
Uppercase Letter 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
市 6
 
4.9%
黔 5
 
4.1%
场 5
 
4.1%
川 4
 
3.3%
仁 4
 
3.3%
石 4
 
3.3%
路 3
 
2.4%
公 3
 
2.4%
丹 3
 
2.4%
亭 3
 
2.4%
Other values (62) 83
67.5%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 123
90.4%
Common 12
 
8.8%
Latin 1
 
0.7%

Most frequent character per script

Han
ValueCountFrequency (%)
市 6
 
4.9%
黔 5
 
4.1%
场 5
 
4.1%
川 4
 
3.3%
仁 4
 
3.3%
石 4
 
3.3%
路 3
 
2.4%
公 3
 
2.4%
丹 3
 
2.4%
亭 3
 
2.4%
Other values (62) 83
67.5%
Common
ValueCountFrequency (%)
( 6
50.0%
) 6
50.0%
Latin
ValueCountFrequency (%)
J 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 123
90.4%
ASCII 13
 
9.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 6
46.2%
) 6
46.2%
J 1
 
7.7%
CJK
ValueCountFrequency (%)
市 6
 
4.9%
黔 5
 
4.1%
场 5
 
4.1%
川 4
 
3.3%
仁 4
 
3.3%
石 4
 
3.3%
路 3
 
2.4%
公 3
 
2.4%
丹 3
 
2.4%
亭 3
 
2.4%
Other values (62) 83
67.5%

역명(중국어 번체)
Text

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T07:41:37.072215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5
Min length1

Characters and Unicode

Total characters135
Distinct characters76
Distinct categories5 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)92.6%

Sample

1st row黔丹梧柳(黔丹産業團地)
2nd row旺吉
3rd row黔丹四거리
4th row麻田
5th row完井
ValueCountFrequency (%)
2
 
7.4%
黔丹梧柳(黔丹産業團地 1
 
3.7%
仁川佳佐 1
 
3.7%
仁川大公園 1
 
3.7%
南洞區廳 1
 
3.7%
萬壽 1
 
3.7%
모래내市場 1
 
3.7%
石泉四거리 1
 
3.7%
仁川市廳 1
 
3.7%
석바위市場 1
 
3.7%
Other values (16) 16
59.3%
2023-12-13T07:41:37.422996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 6
 
4.4%
市 6
 
4.4%
( 6
 
4.4%
場 5
 
3.7%
리 4
 
3.0%
거 4
 
3.0%
仁 4
 
3.0%
川 4
 
3.0%
黔 4
 
3.0%
丹 3
 
2.2%
Other values (66) 89
65.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 120
88.9%
Close Punctuation 6
 
4.4%
Open Punctuation 6
 
4.4%
Dash Punctuation 2
 
1.5%
Uppercase Letter 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
市 6
 
5.0%
場 5
 
4.2%
리 4
 
3.3%
거 4
 
3.3%
仁 4
 
3.3%
川 4
 
3.3%
黔 4
 
3.3%
丹 3
 
2.5%
佳 3
 
2.5%
廳 3
 
2.5%
Other values (62) 80
66.7%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 100
74.1%
Hangul 20
 
14.8%
Common 14
 
10.4%
Latin 1
 
0.7%

Most frequent character per script

Han
ValueCountFrequency (%)
市 6
 
6.0%
場 5
 
5.0%
仁 4
 
4.0%
川 4
 
4.0%
黔 4
 
4.0%
丹 3
 
3.0%
佳 3
 
3.0%
廳 3
 
3.0%
西 3
 
3.0%
亭 3
 
3.0%
Other values (49) 62
62.0%
Hangul
ValueCountFrequency (%)
리 4
20.0%
거 4
20.0%
아 2
10.0%
밸 1
 
5.0%
모 1
 
5.0%
내 1
 
5.0%
석 1
 
5.0%
래 1
 
5.0%
바 1
 
5.0%
위 1
 
5.0%
Other values (3) 3
15.0%
Common
ValueCountFrequency (%)
) 6
42.9%
( 6
42.9%
- 2
 
14.3%
Latin
ValueCountFrequency (%)
J 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 99
73.3%
Hangul 20
 
14.8%
ASCII 15
 
11.1%
CJK Compat Ideographs 1
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 6
40.0%
( 6
40.0%
- 2
 
13.3%
J 1
 
6.7%
CJK
ValueCountFrequency (%)
市 6
 
6.1%
場 5
 
5.1%
仁 4
 
4.0%
川 4
 
4.0%
黔 4
 
4.0%
丹 3
 
3.0%
佳 3
 
3.0%
廳 3
 
3.0%
西 3
 
3.0%
亭 3
 
3.0%
Other values (48) 61
61.6%
Hangul
ValueCountFrequency (%)
리 4
20.0%
거 4
20.0%
아 2
10.0%
밸 1
 
5.0%
모 1
 
5.0%
내 1
 
5.0%
석 1
 
5.0%
래 1
 
5.0%
바 1
 
5.0%
위 1
 
5.0%
Other values (3) 3
15.0%
CJK Compat Ideographs
ValueCountFrequency (%)
女 1
100.0%

Correlations

2023-12-13T07:41:37.530002image/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

2023-12-13T07:41:33.597294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:41:33.694226image/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)
0검단오류(검단산업단지)Geomdan Oryu(Geomdan Industrial Complex)Geomdan Oryuコムダンオリュ黔丹梧柳(黔丹产业园区)黔丹梧柳(黔丹産業團地)
1왕길WanggilWanggilワンギル旺吉旺吉
2검단사거리Geomdan SageoriGeomdan Sageoriコムダンサゴリ黔丹十字路口黔丹四거리
3마전MajeonMajeonマジョン麻田麻田
4완정WanjeongWanjeongワンジョン完井完井
5독정DokjeongDokjeongドクチョン篤亭篤亭
6검암GeomamGeomamコマム黔岩黔岩
7검바위GeombawiGeombawiコムバウィ黔石-
8아시아드경기장(공촌사거리)Asiad Stadium(Gongchon Sageori)Asiad Stadiumアシア-ドキョンギジャン亚运会赛场(公村十字路口)아시아드競技場(公村四거리)
9서구청Seo-gu OfficeSeo-gu Officeソグチョン西区厅西區廳
역명역명(영문)역명(로마자)역명(일본어)역명(중국어 간체)역명(중국어 번체)
17주안JuanJuanチュアン朱安朱安
18시민공원(문화창작지대)Citizens Park(Culture Creation Zone)Citizens Parkシミンゴンウォン市民公园(文化创作地带)市民公園(文化創作地帶)
19석바위시장Seokbawi MarketSeokbawi Marketソッバウィシジャン石岩市场석바위市場
20인천시청Incheon City HallIncheon City Hallインチョンシチョン仁川市厅仁川市廳
21석천사거리Seokcheon SageoriSeokcheon Sageoriソクチョンサゴリ石泉十字路口石泉四거리
22모래내시장Moraenae MarketMoraenae Marketモレネシジャン沙丘市场모래내市場
23만수MansuMansuマンス万寿萬壽
24남동구청Namdong-gu OfficeNamdong-gu Officeナムドングチョン南洞区厅南洞區廳
25인천대공원Incheon Grand ParkIncheon Grand Parkインチョンテゴンウォン仁川大公园仁川大公園
26운연(서창)Unyeon(Seochang)Unyeonウニョン云宴雲宴(西昌)