Overview

Dataset statistics

Number of variables10
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory85.3 B

Variable types

Categorical2
Text6
Boolean1
Unsupported1

Dataset

Description경춘선에 포함된 도시광역철도역들의 철도운영기관명, 선명, 역명, 영어명, 로마자, 일본어, 중국어간체, 중국어번체, 환승역여부, 신설일자에 대한 데이터가 있습니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041813/fileData.do

Alerts

철도운영기관명 has constant value ""Constant
선명 has constant value ""Constant
역명 has unique valuesUnique
영어명 has unique valuesUnique
로마자 has unique valuesUnique
일본어 has unique valuesUnique
중국어간체 has unique valuesUnique
중국어번체 has unique valuesUnique
신설일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-16 06:46:59.102749
Analysis finished2024-03-16 06:47:03.477303
Duration4.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
코레일
25 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row코레일
2nd row코레일
3rd row코레일
4th row코레일
5th row코레일

Common Values

ValueCountFrequency (%)
코레일 25
100.0%

Length

2024-03-16T06:47:03.597723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T06:47:03.841548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
코레일 25
100.0%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
경춘
25 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경춘
2nd row경춘
3rd row경춘
4th row경춘
5th row경춘

Common Values

ValueCountFrequency (%)
경춘 25
100.0%

Length

2024-03-16T06:47:04.146965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T06:47:04.409044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경춘 25
100.0%

역명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-03-16T06:47:04.763477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.32
Min length2

Characters and Unicode

Total characters108
Distinct characters65
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

Unique25 ?
Unique (%)100.0%

Sample

1st row광운대
2nd row대성리
3rd row청량리
4th row회기
5th row중랑
ValueCountFrequency (%)
광운대 1
 
4.0%
평내호평 1
 
4.0%
춘천(한림대 1
 
4.0%
김유정 1
 
4.0%
강촌 1
 
4.0%
백양리(엘리시안강촌 1
 
4.0%
굴봉산(제이드가든 1
 
4.0%
가평(자라섬·남이섬 1
 
4.0%
상천(호명호수 1
 
4.0%
청평 1
 
4.0%
Other values (15) 15
60.0%
2024-03-16T06:47:05.551078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 7
 
6.5%
) 7
 
6.5%
대 5
 
4.6%
리 4
 
3.7%
천 4
 
3.7%
평 4
 
3.7%
강 3
 
2.8%
호 3
 
2.8%
내 3
 
2.8%
가 2
 
1.9%
Other values (55) 66
61.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
86.1%
Open Punctuation 7
 
6.5%
Close Punctuation 7
 
6.5%
Other Punctuation 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 5
 
5.4%
리 4
 
4.3%
천 4
 
4.3%
평 4
 
4.3%
강 3
 
3.2%
호 3
 
3.2%
내 3
 
3.2%
가 2
 
2.2%
남 2
 
2.2%
원 2
 
2.2%
Other values (52) 61
65.6%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
86.1%
Common 15
 
13.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 5
 
5.4%
리 4
 
4.3%
천 4
 
4.3%
평 4
 
4.3%
강 3
 
3.2%
호 3
 
3.2%
내 3
 
3.2%
가 2
 
2.2%
남 2
 
2.2%
원 2
 
2.2%
Other values (52) 61
65.6%
Common
ValueCountFrequency (%)
( 7
46.7%
) 7
46.7%
· 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
86.1%
ASCII 14
 
13.0%
None 1
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 7
50.0%
) 7
50.0%
Hangul
ValueCountFrequency (%)
대 5
 
5.4%
리 4
 
4.3%
천 4
 
4.3%
평 4
 
4.3%
강 3
 
3.2%
호 3
 
3.2%
내 3
 
3.2%
가 2
 
2.2%
남 2
 
2.2%
원 2
 
2.2%
Other values (52) 61
65.6%
None
ValueCountFrequency (%)
· 1
100.0%

영어명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-03-16T06:47:06.172755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length13
Mean length10.2
Min length5

Characters and Unicode

Total characters255
Distinct characters40
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st rowKwangwoon Univ.
2nd rowDaeseong-ri
3rd rowCheongnyangni
4th rowHoegi
5th rowJungnang
ValueCountFrequency (%)
kwangwoon 1
 
3.4%
geumgok 1
 
3.4%
chuncheon 1
 
3.4%
gimyujeong 1
 
3.4%
gangchon 1
 
3.4%
baegyang-ri 1
 
3.4%
gulbongsan 1
 
3.4%
gapyeong 1
 
3.4%
sangcheon 1
 
3.4%
cheongpyeong 1
 
3.4%
Other values (19) 19
65.5%
2024-03-16T06:47:07.196401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 40
15.7%
e 27
 
10.6%
g 24
 
9.4%
o 24
 
9.4%
a 23
 
9.0%
y 10
 
3.9%
i 9
 
3.5%
u 9
 
3.5%
h 9
 
3.5%
m 6
 
2.4%
Other values (30) 74
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 217
85.1%
Uppercase Letter 29
 
11.4%
Space Separator 4
 
1.6%
Dash Punctuation 2
 
0.8%
Close Punctuation 1
 
0.4%
Open Punctuation 1
 
0.4%
Other Punctuation 1
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 40
18.4%
e 27
12.4%
g 24
11.1%
o 24
11.1%
a 23
10.6%
y 10
 
4.6%
i 9
 
4.1%
u 9
 
4.1%
h 9
 
4.1%
m 6
 
2.8%
Other values (11) 36
16.6%
Uppercase Letter
ValueCountFrequency (%)
G 6
20.7%
C 4
13.8%
S 3
10.3%
B 3
10.3%
H 2
 
6.9%
M 2
 
6.9%
T 2
 
6.9%
P 1
 
3.4%
K 1
 
3.4%
J 1
 
3.4%
Other values (4) 4
13.8%
Space Separator
ValueCountFrequency (%)
4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 246
96.5%
Common 9
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 40
16.3%
e 27
11.0%
g 24
 
9.8%
o 24
 
9.8%
a 23
 
9.3%
y 10
 
4.1%
i 9
 
3.7%
u 9
 
3.7%
h 9
 
3.7%
m 6
 
2.4%
Other values (25) 65
26.4%
Common
ValueCountFrequency (%)
4
44.4%
- 2
22.2%
) 1
 
11.1%
( 1
 
11.1%
. 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 255
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 40
15.7%
e 27
 
10.6%
g 24
 
9.4%
o 24
 
9.4%
a 23
 
9.0%
y 10
 
3.9%
i 9
 
3.5%
u 9
 
3.5%
h 9
 
3.5%
m 6
 
2.4%
Other values (30) 74
29.0%

로마자
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-03-16T06:47:07.741760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length13
Mean length10.2
Min length5

Characters and Unicode

Total characters255
Distinct characters40
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st rowKwangwoon Univ.
2nd rowDaeseong-ri
3rd rowCheongnyangni
4th rowHoegi
5th rowJungnang
ValueCountFrequency (%)
kwangwoon 1
 
3.4%
geumgok 1
 
3.4%
chuncheon 1
 
3.4%
gimyujeong 1
 
3.4%
gangchon 1
 
3.4%
baegyang-ri 1
 
3.4%
gulbongsan 1
 
3.4%
gapyeong 1
 
3.4%
sangcheon 1
 
3.4%
cheongpyeong 1
 
3.4%
Other values (19) 19
65.5%
2024-03-16T06:47:08.681388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 40
15.7%
e 27
 
10.6%
g 24
 
9.4%
o 24
 
9.4%
a 23
 
9.0%
y 10
 
3.9%
h 9
 
3.5%
i 9
 
3.5%
u 9
 
3.5%
m 6
 
2.4%
Other values (30) 74
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 216
84.7%
Uppercase Letter 30
 
11.8%
Space Separator 4
 
1.6%
Dash Punctuation 2
 
0.8%
Close Punctuation 1
 
0.4%
Open Punctuation 1
 
0.4%
Other Punctuation 1
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 40
18.5%
e 27
12.5%
g 24
11.1%
o 24
11.1%
a 23
10.6%
y 10
 
4.6%
h 9
 
4.2%
i 9
 
4.2%
u 9
 
4.2%
m 6
 
2.8%
Other values (11) 35
16.2%
Uppercase Letter
ValueCountFrequency (%)
G 6
20.0%
S 4
13.3%
C 4
13.3%
B 3
10.0%
H 2
 
6.7%
M 2
 
6.7%
T 2
 
6.7%
P 1
 
3.3%
K 1
 
3.3%
J 1
 
3.3%
Other values (4) 4
13.3%
Space Separator
ValueCountFrequency (%)
4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 246
96.5%
Common 9
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 40
16.3%
e 27
11.0%
g 24
 
9.8%
o 24
 
9.8%
a 23
 
9.3%
y 10
 
4.1%
h 9
 
3.7%
i 9
 
3.7%
u 9
 
3.7%
m 6
 
2.4%
Other values (25) 65
26.4%
Common
ValueCountFrequency (%)
4
44.4%
- 2
22.2%
) 1
 
11.1%
( 1
 
11.1%
. 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 255
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 40
15.7%
e 27
 
10.6%
g 24
 
9.4%
o 24
 
9.4%
a 23
 
9.0%
y 10
 
3.9%
h 9
 
3.5%
i 9
 
3.5%
u 9
 
3.5%
m 6
 
2.4%
Other values (30) 74
29.0%

일본어
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-03-16T06:47:09.180155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.92
Min length3

Characters and Unicode

Total characters123
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st rowクァンウンデ
2nd rowテソンニ
3rd rowチョンニャンニ
4th rowフェギ
5th rowチュンナン
ValueCountFrequency (%)
クァンウンデ 1
 
4.0%
ピョンネホピョン 1
 
4.0%
チュンチョン 1
 
4.0%
キミュジョン 1
 
4.0%
カンチョン 1
 
4.0%
ペギャンニ 1
 
4.0%
クルボンサン 1
 
4.0%
カピョン 1
 
4.0%
サンチョン 1
 
4.0%
チョンピョン 1
 
4.0%
Other values (15) 15
60.0%
2024-03-16T06:47:09.781672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ン 32
26.0%
ョ 13
 
10.6%
チ 10
 
8.1%
ク 5
 
4.1%
ピ 5
 
4.1%
サ 5
 
4.1%
ル 4
 
3.3%
ニ 4
 
3.3%
ュ 4
 
3.3%
ウ 3
 
2.4%
Other values (25) 38
30.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 123
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ン 32
26.0%
ョ 13
 
10.6%
チ 10
 
8.1%
ク 5
 
4.1%
ピ 5
 
4.1%
サ 5
 
4.1%
ル 4
 
3.3%
ニ 4
 
3.3%
ュ 4
 
3.3%
ウ 3
 
2.4%
Other values (25) 38
30.9%

Most occurring scripts

ValueCountFrequency (%)
Katakana 123
100.0%

Most frequent character per script

Katakana
ValueCountFrequency (%)
ン 32
26.0%
ョ 13
 
10.6%
チ 10
 
8.1%
ク 5
 
4.1%
ピ 5
 
4.1%
サ 5
 
4.1%
ル 4
 
3.3%
ニ 4
 
3.3%
ュ 4
 
3.3%
ウ 3
 
2.4%
Other values (25) 38
30.9%

Most occurring blocks

ValueCountFrequency (%)
Katakana 123
100.0%

Most frequent character per block

Katakana
ValueCountFrequency (%)
ン 32
26.0%
ョ 13
 
10.6%
チ 10
 
8.1%
ク 5
 
4.1%
ピ 5
 
4.1%
サ 5
 
4.1%
ル 4
 
3.3%
ニ 4
 
3.3%
ュ 4
 
3.3%
ウ 3
 
2.4%
Other values (25) 38
30.9%

중국어간체
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-03-16T06:47:10.159073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length2
Mean length3.16
Min length2

Characters and Unicode

Total characters79
Distinct characters61
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row光云大学
2nd row大成里
3rd row清凉里(首尔市立大学)
4th row回基
5th row中浪
ValueCountFrequency (%)
光云大学 1
 
4.0%
坪内好坪 1
 
4.0%
春川 1
 
4.0%
金裕贞 1
 
4.0%
江村 1
 
4.0%
白杨里 1
 
4.0%
屈峰山 1
 
4.0%
加平 1
 
4.0%
上泉 1
 
4.0%
清平 1
 
4.0%
Other values (15) 15
60.0%
2024-03-16T06:47:11.042012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
大 3
 
3.8%
里 3
 
3.8%
内 3
 
3.8%
市 2
 
2.5%
金 2
 
2.5%
山 2
 
2.5%
上 2
 
2.5%
坪 2
 
2.5%
) 2
 
2.5%
平 2
 
2.5%
Other values (51) 56
70.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75
94.9%
Close Punctuation 2
 
2.5%
Open Punctuation 2
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
大 3
 
4.0%
里 3
 
4.0%
内 3
 
4.0%
市 2
 
2.7%
金 2
 
2.7%
山 2
 
2.7%
上 2
 
2.7%
坪 2
 
2.7%
平 2
 
2.7%
春 2
 
2.7%
Other values (49) 52
69.3%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 75
94.9%
Common 4
 
5.1%

Most frequent character per script

Han
ValueCountFrequency (%)
大 3
 
4.0%
里 3
 
4.0%
内 3
 
4.0%
市 2
 
2.7%
金 2
 
2.7%
山 2
 
2.7%
上 2
 
2.7%
坪 2
 
2.7%
平 2
 
2.7%
春 2
 
2.7%
Other values (49) 52
69.3%
Common
ValueCountFrequency (%)
) 2
50.0%
( 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 75
94.9%
ASCII 4
 
5.1%

Most frequent character per block

CJK
ValueCountFrequency (%)
大 3
 
4.0%
里 3
 
4.0%
内 3
 
4.0%
市 2
 
2.7%
金 2
 
2.7%
山 2
 
2.7%
上 2
 
2.7%
坪 2
 
2.7%
平 2
 
2.7%
春 2
 
2.7%
Other values (49) 52
69.3%
ASCII
ValueCountFrequency (%)
) 2
50.0%
( 2
50.0%

중국어번체
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-03-16T06:47:11.430687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.44
Min length2

Characters and Unicode

Total characters61
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row光云大
2nd row大成里
3rd row淸凉里
4th row回基
5th row中浪
ValueCountFrequency (%)
光云大 1
 
4.0%
坪內好坪 1
 
4.0%
春川 1
 
4.0%
金裕貞 1
 
4.0%
江村 1
 
4.0%
白楊里 1
 
4.0%
屈峰山 1
 
4.0%
加平 1
 
4.0%
上泉 1
 
4.0%
清平 1
 
4.0%
Other values (15) 15
60.0%
2024-03-16T06:47:12.269714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
內 3
 
4.9%
里 3
 
4.9%
大 2
 
3.3%
川 2
 
3.3%
春 2
 
3.3%
金 2
 
3.3%
平 2
 
3.3%
山 2
 
3.3%
坪 2
 
3.3%
上 2
 
3.3%
Other values (39) 39
63.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 61
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
內 3
 
4.9%
里 3
 
4.9%
大 2
 
3.3%
川 2
 
3.3%
春 2
 
3.3%
金 2
 
3.3%
平 2
 
3.3%
山 2
 
3.3%
坪 2
 
3.3%
上 2
 
3.3%
Other values (39) 39
63.9%

Most occurring scripts

ValueCountFrequency (%)
Han 61
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
內 3
 
4.9%
里 3
 
4.9%
大 2
 
3.3%
川 2
 
3.3%
春 2
 
3.3%
金 2
 
3.3%
平 2
 
3.3%
山 2
 
3.3%
坪 2
 
3.3%
上 2
 
3.3%
Other values (39) 39
63.9%

Most occurring blocks

ValueCountFrequency (%)
CJK 61
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
內 3
 
4.9%
里 3
 
4.9%
大 2
 
3.3%
川 2
 
3.3%
春 2
 
3.3%
金 2
 
3.3%
平 2
 
3.3%
山 2
 
3.3%
坪 2
 
3.3%
上 2
 
3.3%
Other values (39) 39
63.9%

환승역여부
Boolean

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size157.0 B
False
18 
True
7 
ValueCountFrequency (%)
False 18
72.0%
True 7
 
28.0%
2024-03-16T06:47:12.604614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

신설일자
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size332.0 B

Correlations

2024-03-16T06:47:12.769255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명영어명로마자일본어중국어간체중국어번체환승역여부
역명1.0001.0001.0001.0001.0001.0001.000
영어명1.0001.0001.0001.0001.0001.0001.000
로마자1.0001.0001.0001.0001.0001.0001.000
일본어1.0001.0001.0001.0001.0001.0001.000
중국어간체1.0001.0001.0001.0001.0001.0001.000
중국어번체1.0001.0001.0001.0001.0001.0001.000
환승역여부1.0001.0001.0001.0001.0001.0001.000

Missing values

2024-03-16T06:47:03.037584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T06:47:03.358727image/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

철도운영기관명선명역명영어명로마자일본어중국어간체중국어번체환승역여부신설일자
0코레일경춘광운대Kwangwoon Univ.Kwangwoon Univ.クァンウンデ光云大学光云大Y-
1코레일경춘대성리Daeseong-riDaeseong-riテソンニ大成里大成里N20101221
2코레일경춘청량리CheongnyangniCheongnyangniチョンニャンニ清凉里(首尔市立大学)淸凉里Y20160926
3코레일경춘회기HoegiHoegiフェギ回基回基Y20160926
4코레일경춘중랑JungnangJungnangチュンナン中浪中浪Y20160926
5코레일경춘상봉Sangbong(Intercity Bus Terminal)Sangbong(Intercity Bus Terminal)サンボン上凤(市外巴士客运站)上鳳Y-
6코레일경춘망우ManguManguマンウ忘忧忘憂Y-
7코레일경춘신내sinnaeSinnaeシンネ新内新內Y20131228
8코레일경춘갈매GalmaeGalmaeカルメ葛梅葛梅N20101221
9코레일경춘별내(삼육대학교)ByeollaeByeollaeピョルネ别内別內N20121215
철도운영기관명선명역명영어명로마자일본어중국어간체중국어번체환승역여부신설일자
15코레일경춘마석MaseokMaseokマソク磨石磨石N20101221
16코레일경춘청평CheongpyeongCheongpyeongチョンピョン清平清平N20101221
17코레일경춘상천(호명호수)SangcheonSangcheonサンチョン上泉上泉N20101221
18코레일경춘가평(자라섬·남이섬)GapyeongGapyeongカピョン加平加平N20101221
19코레일경춘굴봉산(제이드가든)GulbongsanGulbongsanクルボンサン屈峰山屈峰山N20101221
20코레일경춘백양리(엘리시안강촌)Baegyang-riBaegyang-riペギャンニ白杨里白楊里N20101221
21코레일경춘강촌GangchonGangchonカンチョン江村江村N20101221
22코레일경춘김유정GimyujeongGimyujeongキミュジョン金裕贞金裕貞N20101221
23코레일경춘춘천(한림대)ChuncheonChuncheonチュンチョン春川春川N20101221
24코레일경춘남춘천(강원대)NamchuncheonNamchuncheonナムチュンチョン南春川南春川N20101221