Overview

Dataset statistics

Number of variables10
Number of observations63
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory82.1 B

Variable types

Categorical2
Text6
Boolean1
Unsupported1

Dataset

Description수인분당선에 포함된 도시광역철도역들의 철도운영기관명, 선명, 역명, 영어명, 로마자, 일본어, 중국어간체, 중국어번체, 환승역여부, 신설일자에 대한 데이터가 있습니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041812/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 started2023-12-12 21:35:44.403837
Analysis finished2023-12-12 21:35:45.163088
Duration0.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size636.0 B
코레일
63 

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 63
100.0%

Length

2023-12-13T06:35:45.224065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:35:45.310307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
코레일 63
100.0%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size636.0 B
수인분당
63 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수인분당 63
100.0%

Length

2023-12-13T06:35:45.401498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:35:45.499362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수인분당 63
100.0%

역명
Text

UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size636.0 B
2023-12-13T06:35:45.719483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length2
Mean length3.9047619
Min length2

Characters and Unicode

Total characters246
Distinct characters119
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

Unique63 ?
Unique (%)100.0%

Sample

1st row수내(한국잡월드)
2nd row오목천
3rd row송도
4th row인천
5th row청량리
ValueCountFrequency (%)
수내(한국잡월드 1
 
1.6%
고잔 1
 
1.6%
안산 1
 
1.6%
신길온천 1
 
1.6%
정왕 1
 
1.6%
오이도 1
 
1.6%
월곶 1
 
1.6%
소래포구 1
 
1.6%
남동인더스파크 1
 
1.6%
원인재 1
 
1.6%
Other values (53) 53
84.1%
2023-12-13T06:35:46.147320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 11
 
4.5%
) 11
 
4.5%
대 10
 
4.1%
구 7
 
2.8%
정 6
 
2.4%
천 6
 
2.4%
인 6
 
2.4%
포 5
 
2.0%
원 5
 
2.0%
수 5
 
2.0%
Other values (109) 174
70.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 224
91.1%
Open Punctuation 11
 
4.5%
Close Punctuation 11
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 10
 
4.5%
구 7
 
3.1%
정 6
 
2.7%
천 6
 
2.7%
인 6
 
2.7%
포 5
 
2.2%
원 5
 
2.2%
수 5
 
2.2%
서 5
 
2.2%
리 4
 
1.8%
Other values (107) 165
73.7%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 224
91.1%
Common 22
 
8.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 10
 
4.5%
구 7
 
3.1%
정 6
 
2.7%
천 6
 
2.7%
인 6
 
2.7%
포 5
 
2.2%
원 5
 
2.2%
수 5
 
2.2%
서 5
 
2.2%
리 4
 
1.8%
Other values (107) 165
73.7%
Common
ValueCountFrequency (%)
( 11
50.0%
) 11
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 224
91.1%
ASCII 22
 
8.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 11
50.0%
) 11
50.0%
Hangul
ValueCountFrequency (%)
대 10
 
4.5%
구 7
 
3.1%
정 6
 
2.7%
천 6
 
2.7%
인 6
 
2.7%
포 5
 
2.2%
원 5
 
2.2%
수 5
 
2.2%
서 5
 
2.2%
리 4
 
1.8%
Other values (107) 165
73.7%

영어명
Text

UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size636.0 B
2023-12-13T06:35:46.410198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length17
Mean length8.4761905
Min length3

Characters and Unicode

Total characters534
Distinct characters45
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

Unique63 ?
Unique (%)100.0%

Sample

1st rowSunae
2nd rowOmokcheon
3rd rowsongdo
4th rowIncheon
5th rowCheongnyangni
ValueCountFrequency (%)
univ 3
 
3.9%
suwon 2
 
2.6%
incheon 2
 
2.6%
ansan 2
 
2.6%
oido 1
 
1.3%
wolgot 1
 
1.3%
soraepogu 1
 
1.3%
namdong 1
 
1.3%
induspark 1
 
1.3%
woninjae 1
 
1.3%
Other values (61) 61
80.3%
2023-12-13T06:35:46.849094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 76
14.2%
o 68
 
12.7%
e 44
 
8.2%
a 42
 
7.9%
g 42
 
7.9%
i 22
 
4.1%
u 21
 
3.9%
S 16
 
3.0%
h 13
 
2.4%
13
 
2.4%
Other values (35) 177
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 435
81.5%
Uppercase Letter 74
 
13.9%
Space Separator 20
 
3.7%
Other Punctuation 3
 
0.6%
Dash Punctuation 2
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 76
17.5%
o 68
15.6%
e 44
10.1%
a 42
9.7%
g 42
9.7%
i 22
 
5.1%
u 21
 
4.8%
h 13
 
3.0%
s 12
 
2.8%
t 10
 
2.3%
Other values (12) 85
19.5%
Uppercase Letter
ValueCountFrequency (%)
S 16
21.6%
G 9
12.2%
I 5
 
6.8%
M 5
 
6.8%
Y 4
 
5.4%
C 4
 
5.4%
O 4
 
5.4%
H 4
 
5.4%
J 4
 
5.4%
D 3
 
4.1%
Other values (9) 16
21.6%
Space Separator
ValueCountFrequency (%)
13
65.0%
  7
35.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 509
95.3%
Common 25
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 76
14.9%
o 68
13.4%
e 44
 
8.6%
a 42
 
8.3%
g 42
 
8.3%
i 22
 
4.3%
u 21
 
4.1%
S 16
 
3.1%
h 13
 
2.6%
s 12
 
2.4%
Other values (31) 153
30.1%
Common
ValueCountFrequency (%)
13
52.0%
  7
28.0%
. 3
 
12.0%
- 2
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 527
98.7%
None 7
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 76
14.4%
o 68
 
12.9%
e 44
 
8.3%
a 42
 
8.0%
g 42
 
8.0%
i 22
 
4.2%
u 21
 
4.0%
S 16
 
3.0%
h 13
 
2.5%
13
 
2.5%
Other values (34) 170
32.3%
None
ValueCountFrequency (%)
  7
100.0%

로마자
Text

UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size636.0 B
2023-12-13T06:35:47.156121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length8.2380952
Min length1

Characters and Unicode

Total characters519
Distinct characters43
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

Unique63 ?
Unique (%)100.0%

Sample

1st rowSunae
2nd rowOmokcheon
3rd rowsongdo
4th rowIncheon
5th rowCheongnyangni
ValueCountFrequency (%)
incheon 2
 
2.9%
sunae 1
 
1.4%
sinpo 1
 
1.4%
gangnam-gucheong 1
 
1.4%
seoul 1
 
1.4%
di 1
 
1.4%
foresta 1
 
1.4%
la 1
 
1.4%
wangsimni 1
 
1.4%
woninjae 1
 
1.4%
Other values (59) 59
84.3%
2023-12-13T06:35:47.622341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 72
13.9%
o 70
13.5%
e 48
 
9.2%
a 43
 
8.3%
g 40
 
7.7%
i 21
 
4.0%
u 20
 
3.9%
S 14
 
2.7%
h 14
 
2.7%
s 12
 
2.3%
Other values (33) 165
31.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 434
83.6%
Uppercase Letter 67
 
12.9%
Space Separator 14
 
2.7%
Other Punctuation 2
 
0.4%
Dash Punctuation 2
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 72
16.6%
o 70
16.1%
e 48
11.1%
a 43
9.9%
g 40
9.2%
i 21
 
4.8%
u 20
 
4.6%
h 14
 
3.2%
s 12
 
2.8%
l 11
 
2.5%
Other values (12) 83
19.1%
Uppercase Letter
ValueCountFrequency (%)
S 14
20.9%
G 9
13.4%
M 5
 
7.5%
Y 4
 
6.0%
D 4
 
6.0%
C 4
 
6.0%
J 4
 
6.0%
H 4
 
6.0%
I 4
 
6.0%
O 3
 
4.5%
Other values (7) 12
17.9%
Space Separator
ValueCountFrequency (%)
7
50.0%
  7
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 501
96.5%
Common 18
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 72
14.4%
o 70
14.0%
e 48
 
9.6%
a 43
 
8.6%
g 40
 
8.0%
i 21
 
4.2%
u 20
 
4.0%
S 14
 
2.8%
h 14
 
2.8%
s 12
 
2.4%
Other values (29) 147
29.3%
Common
ValueCountFrequency (%)
7
38.9%
  7
38.9%
. 2
 
11.1%
- 2
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 512
98.7%
None 7
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 72
14.1%
o 70
13.7%
e 48
 
9.4%
a 43
 
8.4%
g 40
 
7.8%
i 21
 
4.1%
u 20
 
3.9%
S 14
 
2.7%
h 14
 
2.7%
s 12
 
2.3%
Other values (32) 158
30.9%
None
ValueCountFrequency (%)
  7
100.0%

일본어
Text

UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size636.0 B
2023-12-13T06:35:47.890703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length4.4920635
Min length2

Characters and Unicode

Total characters283
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)100.0%

Sample

1st rowスネ
2nd rowオモクチョン
3rd rowソンド
4th rowインチョン
5th rowチョンニャンニ
ValueCountFrequency (%)
スネ 1
 
1.6%
コジャン 1
 
1.6%
アンサン 1
 
1.6%
シンギルオンチョン 1
 
1.6%
チョンワン 1
 
1.6%
オイド 1
 
1.6%
ウォルゴッ 1
 
1.6%
ソレポグ 1
 
1.6%
ナムドン・インダスパーク 1
 
1.6%
ウォニンジェ 1
 
1.6%
Other values (53) 53
84.1%
2023-12-13T06:35:48.308867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ン 62
21.9%
ョ 24
 
8.5%
チ 17
 
6.0%
ク 11
 
3.9%
ソ 9
 
3.2%
ス 8
 
2.8%
ル 8
 
2.8%
ポ 7
 
2.5%
イ 7
 
2.5%
ジ 7
 
2.5%
Other values (52) 123
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 281
99.3%
Modifier Letter 1
 
0.4%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ン 62
22.1%
ョ 24
 
8.5%
チ 17
 
6.0%
ク 11
 
3.9%
ソ 9
 
3.2%
ス 8
 
2.8%
ル 8
 
2.8%
ポ 7
 
2.5%
イ 7
 
2.5%
ジ 7
 
2.5%
Other values (50) 121
43.1%
Modifier Letter
ValueCountFrequency (%)
ー 1
100.0%
Other Punctuation
ValueCountFrequency (%)
・ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana 281
99.3%
Common 2
 
0.7%

Most frequent character per script

Katakana
ValueCountFrequency (%)
ン 62
22.1%
ョ 24
 
8.5%
チ 17
 
6.0%
ク 11
 
3.9%
ソ 9
 
3.2%
ス 8
 
2.8%
ル 8
 
2.8%
ポ 7
 
2.5%
イ 7
 
2.5%
ジ 7
 
2.5%
Other values (50) 121
43.1%
Common
ValueCountFrequency (%)
ー 1
50.0%
・ 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Katakana 283
100.0%

Most frequent character per block

Katakana
ValueCountFrequency (%)
ン 62
21.9%
ョ 24
 
8.5%
チ 17
 
6.0%
ク 11
 
3.9%
ソ 9
 
3.2%
ス 8
 
2.8%
ル 8
 
2.8%
ポ 7
 
2.5%
イ 7
 
2.5%
ジ 7
 
2.5%
Other values (52) 123
43.5%

중국어간체
Text

UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size636.0 B
2023-12-13T06:35:48.559247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length2
Mean length2.7619048
Min length2

Characters and Unicode

Total characters174
Distinct characters121
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

Unique63 ?
Unique (%)100.0%

Sample

1st row薮内
2nd row梧木川
3rd row松岛
4th row仁川
5th row清凉里(首尔市立大学)
ValueCountFrequency (%)
薮内 1
 
1.6%
古栈 1
 
1.6%
安山 1
 
1.6%
新吉溫泉 1
 
1.6%
正往 1
 
1.6%
烏耳島 1
 
1.6%
月串 1
 
1.6%
苏莱浦口 1
 
1.6%
南洞产业园地 1
 
1.6%
源仁斋 1
 
1.6%
Other values (53) 53
84.1%
2023-12-13T06:35:49.163061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
大 5
 
2.9%
浦 5
 
2.9%
里 4
 
2.3%
仁 4
 
2.3%
川 4
 
2.3%
学 4
 
2.3%
新 3
 
1.7%
山 3
 
1.7%
亭 3
 
1.7%
水 3
 
1.7%
Other values (111) 136
78.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 170
97.7%
Open Punctuation 2
 
1.1%
Close Punctuation 2
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
大 5
 
2.9%
浦 5
 
2.9%
里 4
 
2.4%
仁 4
 
2.4%
川 4
 
2.4%
学 4
 
2.4%
新 3
 
1.8%
山 3
 
1.8%
亭 3
 
1.8%
水 3
 
1.8%
Other values (109) 132
77.6%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 170
97.7%
Common 4
 
2.3%

Most frequent character per script

Han
ValueCountFrequency (%)
大 5
 
2.9%
浦 5
 
2.9%
里 4
 
2.4%
仁 4
 
2.4%
川 4
 
2.4%
学 4
 
2.4%
新 3
 
1.8%
山 3
 
1.8%
亭 3
 
1.8%
水 3
 
1.8%
Other values (109) 132
77.6%
Common
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 170
97.7%
ASCII 4
 
2.3%

Most frequent character per block

CJK
ValueCountFrequency (%)
大 5
 
2.9%
浦 5
 
2.9%
里 4
 
2.4%
仁 4
 
2.4%
川 4
 
2.4%
学 4
 
2.4%
新 3
 
1.8%
山 3
 
1.8%
亭 3
 
1.8%
水 3
 
1.8%
Other values (109) 132
77.6%
ASCII
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

중국어번체
Text

UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size636.0 B
2023-12-13T06:35:49.391129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.5238095
Min length1

Characters and Unicode

Total characters159
Distinct characters118
Distinct categories2 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)100.0%

Sample

1st row藪內
2nd row梧木川
3rd row松島
4th row仁川
5th row淸凉里
ValueCountFrequency (%)
藪內 1
 
1.6%
古棧 1
 
1.6%
安山 1
 
1.6%
新吉溫泉 1
 
1.6%
正往 1
 
1.6%
烏耳島 1
 
1.6%
月串 1
 
1.6%
蘇萊浦口 1
 
1.6%
南洞産業團地 1
 
1.6%
源仁齋 1
 
1.6%
Other values (53) 53
84.1%
2023-12-13T06:35:49.747828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
浦 5
 
3.1%
仁 4
 
2.5%
川 4
 
2.5%
大 4
 
2.5%
口 3
 
1.9%
亭 3
 
1.9%
里 3
 
1.9%
新 3
 
1.9%
水 3
 
1.9%
梅 3
 
1.9%
Other values (108) 124
78.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 158
99.4%
Dash Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
浦 5
 
3.2%
仁 4
 
2.5%
川 4
 
2.5%
大 4
 
2.5%
口 3
 
1.9%
亭 3
 
1.9%
里 3
 
1.9%
新 3
 
1.9%
水 3
 
1.9%
梅 3
 
1.9%
Other values (107) 123
77.8%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 154
96.9%
Hangul 4
 
2.5%
Common 1
 
0.6%

Most frequent character per script

Han
ValueCountFrequency (%)
浦 5
 
3.2%
仁 4
 
2.6%
川 4
 
2.6%
大 4
 
2.6%
口 3
 
1.9%
亭 3
 
1.9%
里 3
 
1.9%
新 3
 
1.9%
水 3
 
1.9%
梅 3
 
1.9%
Other values (103) 119
77.3%
Hangul
ValueCountFrequency (%)
앞 1
25.0%
오 1
25.0%
데 1
25.0%
로 1
25.0%
Common
ValueCountFrequency (%)
- 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 150
94.3%
CJK Compat Ideographs 4
 
2.5%
Hangul 4
 
2.5%
ASCII 1
 
0.6%

Most frequent character per block

CJK
ValueCountFrequency (%)
浦 5
 
3.3%
仁 4
 
2.7%
川 4
 
2.7%
大 4
 
2.7%
口 3
 
2.0%
亭 3
 
2.0%
里 3
 
2.0%
新 3
 
2.0%
水 3
 
2.0%
梅 3
 
2.0%
Other values (99) 115
76.7%
ASCII
ValueCountFrequency (%)
- 1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
串 1
25.0%
論 1
25.0%
靖 1
25.0%
靈 1
25.0%
Hangul
ValueCountFrequency (%)
앞 1
25.0%
오 1
25.0%
데 1
25.0%
로 1
25.0%

환승역여부
Boolean

Distinct2
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size195.0 B
False
39 
True
24 
ValueCountFrequency (%)
False 39
61.9%
True 24
38.1%
2023-12-13T06:35:49.861615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

신설일자
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size636.0 B

Correlations

2023-12-13T06:35:49.923049image/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

2023-12-13T06:35:44.928437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:35:45.100054image/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
0코레일수인분당수내(한국잡월드)SunaeSunaeスネ薮内藪內N19940901
1코레일수인분당오목천OmokcheonOmokcheonオモクチョン梧木川梧木川N20190912
2코레일수인분당송도songdosongdoソンド松岛松島N20120630
3코레일수인분당인천IncheonIncheonインチョン仁川仁川Y20160227
4코레일수인분당청량리CheongnyangniCheongnyangniチョンニャンニ清凉里(首尔市立大学)淸凉里Y-
5코레일수인분당압구정로데오Apgujeong RodeoApgujeongrodeoアプクジョソロデオ狎鸥亭罗德奥狎鷗亭로데오N20121006
6코레일수인분당선정릉(한국과학창의재단)SeonjeongneungSeonjeongneungソンジョンヌン宣靖陵宣靖陵Y20121006
7코레일수인분당선릉SeolleungSeolleungソンルン宣陵宣陵Y20030903
8코레일수인분당개포동Gaepo-dongGaepodongケポドン开浦洞開浦洞N20030903
9코레일수인분당복정(동서울대학)BokjeongBokjeongポクチョン福井福井Y19961123
철도운영기관명선명역명영어명로마자일본어중국어간체중국어번체환승역여부신설일자
53코레일수인분당구성GuseongGuseongクソン驹城駒城N20111228
54코레일수인분당기흥(백남준아트센터)GiheungGiheungキフン器兴器興Y20111228
55코레일수인분당매탄권선MaetanGwonseonMaetanGwonseonメタン梅滩劝善梅灘勸善N20131130
56코레일수인분당고색GosaekGosaekコセク古索古索N20200912
57코레일수인분당달월DarwolDarwolタルォル达月達月N20141227
58코레일수인분당인천논현Incheon NonhyeonIncheon Nonhyeonインチョンノンヒョン仁川论岘仁川論峴N20120630
59코레일수인분당호구포HogupoHogupoホグポ虎口浦虎口浦N20120630
60코레일수인분당연수YeonsuYeonsu Stationヨンス延寿延壽N20120630
61코레일수인분당인하대Inha Univ.InhaUniv.インハデ仁荷大学仁荷大N20160227
62코레일수인분당숭의(인하대병원)Sungui-スンイ崇义崇義N20160227