Overview

Dataset statistics

Number of variables10
Number of observations53
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory83.5 B

Variable types

Categorical2
Text6
Boolean1
Numeric1

Dataset

Description수도권7호선에 포함된 도시광역철도역들의 철도운영기관명, 선명, 역명, 영어명, 로마자, 일본어, 중국어간체, 중국어번체, 환승역여부, 신설일자 에 대한 데이터가 있습니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041808/fileData.do

Alerts

선명 has constant value ""Constant
신설일자 is highly overall correlated with 철도운영기관명High correlation
철도운영기관명 is highly overall correlated with 신설일자High correlation
역명 has unique valuesUnique
영어명 has unique valuesUnique
일본어 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:15:16.232078
Analysis finished2023-12-12 22:15:17.022494
Duration0.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size556.0 B
서울교통공사
42 
인천교통공사
11 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울교통공사 42
79.2%
인천교통공사 11
 
20.8%

Length

2023-12-13T07:15:17.079575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:15:17.162443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울교통공사 42
79.2%
인천교통공사 11
 
20.8%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
7호선
53 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7호선
2nd row7호선
3rd row7호선
4th row7호선
5th row7호선

Common Values

ValueCountFrequency (%)
7호선 53
100.0%

Length

2023-12-13T07:15:17.252998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:15:17.334363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7호선 53
100.0%

역명
Text

UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-13T07:15:17.523575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.1320755
Min length2

Characters and Unicode

Total characters219
Distinct characters115
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

Unique53 ?
Unique (%)100.0%

Sample

1st row장암
2nd row도봉산
3rd row수락산
4th row마들
5th row노원
ValueCountFrequency (%)
장암 1
 
1.9%
총신대입구(이수 1
 
1.9%
숭실대입구(살피재 1
 
1.9%
상도 1
 
1.9%
장승배기 1
 
1.9%
신대방삼거리 1
 
1.9%
보라매 1
 
1.9%
신풍 1
 
1.9%
대림(구로구청 1
 
1.9%
남구로 1
 
1.9%
Other values (43) 43
81.1%
2023-12-13T07:15:18.112630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 10
 
4.6%
( 9
 
4.1%
) 9
 
4.1%
대 9
 
4.1%
산 7
 
3.2%
입 5
 
2.3%
동 5
 
2.3%
청 5
 
2.3%
중 4
 
1.8%
천 4
 
1.8%
Other values (105) 152
69.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 201
91.8%
Open Punctuation 9
 
4.1%
Close Punctuation 9
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 10
 
5.0%
대 9
 
4.5%
산 7
 
3.5%
입 5
 
2.5%
동 5
 
2.5%
청 5
 
2.5%
중 4
 
2.0%
천 4
 
2.0%
장 4
 
2.0%
남 4
 
2.0%
Other values (103) 144
71.6%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 201
91.8%
Common 18
 
8.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 10
 
5.0%
대 9
 
4.5%
산 7
 
3.5%
입 5
 
2.5%
동 5
 
2.5%
청 5
 
2.5%
중 4
 
2.0%
천 4
 
2.0%
장 4
 
2.0%
남 4
 
2.0%
Other values (103) 144
71.6%
Common
ValueCountFrequency (%)
( 9
50.0%
) 9
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 201
91.8%
ASCII 18
 
8.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 10
 
5.0%
대 9
 
4.5%
산 7
 
3.5%
입 5
 
2.5%
동 5
 
2.5%
청 5
 
2.5%
중 4
 
2.0%
천 4
 
2.0%
장 4
 
2.0%
남 4
 
2.0%
Other values (103) 144
71.6%
ASCII
ValueCountFrequency (%)
( 9
50.0%
) 9
50.0%

영어명
Text

UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-13T07:15:18.315974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length24
Mean length13.075472
Min length3

Characters and Unicode

Total characters693
Distinct characters49
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

Unique53 ?
Unique (%)100.0%

Sample

1st rowJangam
2nd rowDobongsan
3rd rowSuraksan
4th rowMadeul
5th rowNowon
ValueCountFrequency (%)
univ 4
 
4.9%
office 3
 
3.7%
terminal 2
 
2.4%
bus 2
 
2.4%
park 2
 
2.4%
bucheon 2
 
2.4%
daerim(guro-gu 1
 
1.2%
complex 1
 
1.2%
digital 1
 
1.2%
gasan 1
 
1.2%
Other values (63) 63
76.8%
2023-12-13T07:15:18.622801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 78
 
11.3%
a 62
 
8.9%
o 54
 
7.8%
g 53
 
7.6%
e 48
 
6.9%
u 35
 
5.1%
i 30
 
4.3%
29
 
4.2%
m 21
 
3.0%
s 21
 
3.0%
Other values (39) 262
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 544
78.5%
Uppercase Letter 88
 
12.7%
Space Separator 29
 
4.2%
Open Punctuation 9
 
1.3%
Close Punctuation 9
 
1.3%
Dash Punctuation 7
 
1.0%
Other Punctuation 7
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 78
14.3%
a 62
11.4%
o 54
9.9%
g 53
9.7%
e 48
 
8.8%
u 35
 
6.4%
i 30
 
5.5%
m 21
 
3.9%
s 21
 
3.9%
l 19
 
3.5%
Other values (14) 123
22.6%
Uppercase Letter
ValueCountFrequency (%)
S 18
20.5%
G 10
11.4%
N 7
 
8.0%
C 7
 
8.0%
B 7
 
8.0%
T 5
 
5.7%
J 5
 
5.7%
U 5
 
5.7%
M 4
 
4.5%
P 3
 
3.4%
Other values (8) 17
19.3%
Other Punctuation
ValueCountFrequency (%)
. 5
71.4%
' 1
 
14.3%
& 1
 
14.3%
Space Separator
ValueCountFrequency (%)
29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 632
91.2%
Common 61
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 78
 
12.3%
a 62
 
9.8%
o 54
 
8.5%
g 53
 
8.4%
e 48
 
7.6%
u 35
 
5.5%
i 30
 
4.7%
m 21
 
3.3%
s 21
 
3.3%
l 19
 
3.0%
Other values (32) 211
33.4%
Common
ValueCountFrequency (%)
29
47.5%
( 9
 
14.8%
) 9
 
14.8%
- 7
 
11.5%
. 5
 
8.2%
' 1
 
1.6%
& 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 78
 
11.3%
a 62
 
8.9%
o 54
 
7.8%
g 53
 
7.6%
e 48
 
6.9%
u 35
 
5.1%
i 30
 
4.3%
29
 
4.2%
m 21
 
3.0%
s 21
 
3.0%
Other values (39) 262
37.8%

로마자
Text

Distinct38
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-13T07:15:18.778821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length9.2830189
Min length1

Characters and Unicode

Total characters492
Distinct characters43
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

Unique36 ?
Unique (%)67.9%

Sample

1st rowJangam
2nd rowDobongsan
3rd rowSuraksan
4th rowMadeul
5th row-
ValueCountFrequency (%)
15
23.4%
hagye 2
 
3.1%
namseong 2
 
3.1%
univ 2
 
3.1%
resort 1
 
1.6%
sang-do 1
 
1.6%
jangam 1
 
1.6%
sindaebangsamgeo-ri 1
 
1.6%
nonhyeon 1
 
1.6%
banpo 1
 
1.6%
Other values (37) 37
57.8%
2023-12-13T07:15:19.088302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 58
 
11.8%
a 47
 
9.6%
g 45
 
9.1%
o 37
 
7.5%
e 36
 
7.3%
- 22
 
4.5%
u 20
 
4.1%
i 20
 
4.1%
s 19
 
3.9%
r 13
 
2.6%
Other values (33) 175
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 386
78.5%
Uppercase Letter 54
 
11.0%
Dash Punctuation 22
 
4.5%
Space Separator 11
 
2.2%
Close Punctuation 7
 
1.4%
Open Punctuation 7
 
1.4%
Other Punctuation 5
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 58
15.0%
a 47
12.2%
g 45
11.7%
o 37
9.6%
e 36
9.3%
u 20
 
5.2%
i 20
 
5.2%
s 19
 
4.9%
r 13
 
3.4%
m 13
 
3.4%
Other values (13) 78
20.2%
Uppercase Letter
ValueCountFrequency (%)
S 11
20.4%
G 6
11.1%
N 5
9.3%
J 5
9.3%
C 5
9.3%
U 4
 
7.4%
H 3
 
5.6%
B 3
 
5.6%
T 3
 
5.6%
M 3
 
5.6%
Other values (4) 6
11.1%
Other Punctuation
ValueCountFrequency (%)
. 4
80.0%
' 1
 
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 440
89.4%
Common 52
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 58
13.2%
a 47
 
10.7%
g 45
 
10.2%
o 37
 
8.4%
e 36
 
8.2%
u 20
 
4.5%
i 20
 
4.5%
s 19
 
4.3%
r 13
 
3.0%
m 13
 
3.0%
Other values (27) 132
30.0%
Common
ValueCountFrequency (%)
- 22
42.3%
11
21.2%
) 7
 
13.5%
( 7
 
13.5%
. 4
 
7.7%
' 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 58
 
11.8%
a 47
 
9.6%
g 45
 
9.1%
o 37
 
7.5%
e 36
 
7.3%
- 22
 
4.5%
u 20
 
4.1%
i 20
 
4.1%
s 19
 
3.9%
r 13
 
2.6%
Other values (33) 175
35.6%

일본어
Text

UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-13T07:15:19.281791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length5.7735849
Min length2

Characters and Unicode

Total characters306
Distinct characters68
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

Unique53 ?
Unique (%)100.0%

Sample

1st rowチャンアム
2nd rowトボンサン
3rd rowスラッサン
4th rowマドゥル
5th rowノウォン
ValueCountFrequency (%)
チャンアム 1
 
1.9%
イス 1
 
1.9%
スンシルデイック 1
 
1.9%
サンド 1
 
1.9%
チャンスンベギ 1
 
1.9%
シンデバンサムゴリ 1
 
1.9%
ポラメ 1
 
1.9%
シンプン 1
 
1.9%
テリム 1
 
1.9%
ナムグロ 1
 
1.9%
Other values (43) 43
81.1%
2023-12-13T07:15:19.606942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ン 66
21.6%
チ 17
 
5.6%
ョ 15
 
4.9%
サ 14
 
4.6%
ク 13
 
4.2%
ム 10
 
3.3%
ル 9
 
2.9%
ジ 8
 
2.6%
ッ 7
 
2.3%
ゴ 5
 
1.6%
Other values (58) 142
46.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 297
97.1%
Other Punctuation 4
 
1.3%
Space Separator 2
 
0.7%
Close Punctuation 1
 
0.3%
Modifier Letter 1
 
0.3%
Open Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ン 66
22.2%
チ 17
 
5.7%
ョ 15
 
5.1%
サ 14
 
4.7%
ク 13
 
4.4%
ム 10
 
3.4%
ル 9
 
3.0%
ジ 8
 
2.7%
ッ 7
 
2.4%
ゴ 5
 
1.7%
Other values (52) 133
44.8%
Other Punctuation
ValueCountFrequency (%)
・ 3
75.0%
· 1
 
25.0%
Space Separator
ValueCountFrequency (%)
  2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Modifier Letter
ValueCountFrequency (%)
ー 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Katakana 295
96.4%
Common 9
 
2.9%
Han 2
 
0.7%

Most frequent character per script

Katakana
ValueCountFrequency (%)
ン 66
22.4%
チ 17
 
5.8%
ョ 15
 
5.1%
サ 14
 
4.7%
ク 13
 
4.4%
ム 10
 
3.4%
ル 9
 
3.1%
ジ 8
 
2.7%
ッ 7
 
2.4%
ゴ 5
 
1.7%
Other values (50) 131
44.4%
Common
ValueCountFrequency (%)
・ 3
33.3%
  2
22.2%
· 1
 
11.1%
) 1
 
11.1%
ー 1
 
11.1%
( 1
 
11.1%
Han
ValueCountFrequency (%)
園 1
50.0%
公 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Katakana 299
97.7%
None 3
 
1.0%
ASCII 2
 
0.7%
CJK 2
 
0.7%

Most frequent character per block

Katakana
ValueCountFrequency (%)
ン 66
22.1%
チ 17
 
5.7%
ョ 15
 
5.0%
サ 14
 
4.7%
ク 13
 
4.3%
ム 10
 
3.3%
ル 9
 
3.0%
ジ 8
 
2.7%
ッ 7
 
2.3%
ゴ 5
 
1.7%
Other values (52) 135
45.2%
None
ValueCountFrequency (%)
  2
66.7%
· 1
33.3%
ASCII
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%
CJK
ValueCountFrequency (%)
園 1
50.0%
公 1
50.0%

중국어간체
Text

Distinct43
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-13T07:15:19.775484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length3.5471698
Min length1

Characters and Unicode

Total characters188
Distinct characters114
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

Unique42 ?
Unique (%)79.2%

Sample

1st row长岩
2nd row道峰山
3rd row水落山
4th row马得
5th row芦原
ValueCountFrequency (%)
11
 
20.8%
鹤洞 1
 
1.9%
盘浦 1
 
1.9%
高速巴士客运站 1
 
1.9%
內方 1
 
1.9%
梨水 1
 
1.9%
南城 1
 
1.9%
崇实大学(赛毗岭 1
 
1.9%
上道 1
 
1.9%
长丞拜基 1
 
1.9%
Other values (33) 33
62.3%
2023-12-13T07:15:20.102141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 11
 
5.9%
) 8
 
4.3%
大 8
 
4.3%
( 8
 
4.3%
学 6
 
3.2%
山 5
 
2.7%
区 3
 
1.6%
中 3
 
1.6%
马 3
 
1.6%
公 3
 
1.6%
Other values (104) 130
69.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 161
85.6%
Dash Punctuation 11
 
5.9%
Close Punctuation 8
 
4.3%
Open Punctuation 8
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
大 8
 
5.0%
学 6
 
3.7%
山 5
 
3.1%
区 3
 
1.9%
中 3
 
1.9%
马 3
 
1.9%
公 3
 
1.9%
水 3
 
1.9%
南 3
 
1.9%
园 3
 
1.9%
Other values (101) 121
75.2%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 161
85.6%
Common 27
 
14.4%

Most frequent character per script

Han
ValueCountFrequency (%)
大 8
 
5.0%
学 6
 
3.7%
山 5
 
3.1%
区 3
 
1.9%
中 3
 
1.9%
马 3
 
1.9%
公 3
 
1.9%
水 3
 
1.9%
南 3
 
1.9%
园 3
 
1.9%
Other values (101) 121
75.2%
Common
ValueCountFrequency (%)
- 11
40.7%
) 8
29.6%
( 8
29.6%

Most occurring blocks

ValueCountFrequency (%)
CJK 158
84.0%
ASCII 27
 
14.4%
CJK Compat Ideographs 3
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 11
40.7%
) 8
29.6%
( 8
29.6%
CJK
ValueCountFrequency (%)
大 8
 
5.1%
学 6
 
3.8%
山 5
 
3.2%
区 3
 
1.9%
中 3
 
1.9%
马 3
 
1.9%
公 3
 
1.9%
水 3
 
1.9%
南 3
 
1.9%
园 3
 
1.9%
Other values (98) 118
74.7%
CJK Compat Ideographs
ValueCountFrequency (%)
梨 1
33.3%
老 1
33.3%
陵 1
33.3%

중국어번체
Text

Distinct45
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-13T07:15:20.319912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length3.7735849
Min length1

Characters and Unicode

Total characters200
Distinct characters115
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)83.0%

Sample

1st row長岩
2nd row道峰山
3rd row水落山
4th row-
5th row-
ValueCountFrequency (%)
9
 
13.8%
廳 2
 
3.1%
川 2
 
3.1%
富 2
 
3.1%
洞 2
 
3.1%
衣 1
 
1.5%
新豊 1
 
1.5%
南九老 1
 
1.5%
加山디지털團地 1
 
1.5%
鐵山 1
 
1.5%
Other values (43) 43
66.2%
2023-12-13T07:15:20.721796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
 
6.0%
- 9
 
4.5%
山 7
 
3.5%
( 7
 
3.5%
) 7
 
3.5%
大 6
 
3.0%
中 4
 
2.0%
洞 4
 
2.0%
口 4
 
2.0%
入 4
 
2.0%
Other values (105) 136
68.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 165
82.5%
Space Separator 12
 
6.0%
Dash Punctuation 9
 
4.5%
Open Punctuation 7
 
3.5%
Close Punctuation 7
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
山 7
 
4.2%
大 6
 
3.6%
中 4
 
2.4%
洞 4
 
2.4%
口 4
 
2.4%
入 4
 
2.4%
南 4
 
2.4%
廳 3
 
1.8%
川 3
 
1.8%
水 3
 
1.8%
Other values (101) 123
74.5%
Space Separator
ValueCountFrequency (%)
12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 139
69.5%
Common 35
 
17.5%
Hangul 26
 
13.0%

Most frequent character per script

Han
ValueCountFrequency (%)
山 7
 
5.0%
大 6
 
4.3%
中 4
 
2.9%
洞 4
 
2.9%
口 4
 
2.9%
入 4
 
2.9%
南 4
 
2.9%
廳 3
 
2.2%
川 3
 
2.2%
水 3
 
2.2%
Other values (78) 97
69.8%
Hangul
ValueCountFrequency (%)
거 3
 
11.5%
리 2
 
7.7%
재 1
 
3.8%
피 1
 
3.8%
살 1
 
3.8%
삼 1
 
3.8%
디 1
 
3.8%
지 1
 
3.8%
털 1
 
3.8%
사 1
 
3.8%
Other values (13) 13
50.0%
Common
ValueCountFrequency (%)
12
34.3%
- 9
25.7%
( 7
20.0%
) 7
20.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 134
67.0%
ASCII 35
 
17.5%
Hangul 26
 
13.0%
CJK Compat Ideographs 5
 
2.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12
34.3%
- 9
25.7%
( 7
20.0%
) 7
20.0%
CJK
ValueCountFrequency (%)
山 7
 
5.2%
大 6
 
4.5%
中 4
 
3.0%
洞 4
 
3.0%
口 4
 
3.0%
入 4
 
3.0%
南 4
 
3.0%
廳 3
 
2.2%
川 3
 
2.2%
水 3
 
2.2%
Other values (73) 92
68.7%
Hangul
ValueCountFrequency (%)
거 3
 
11.5%
리 2
 
7.7%
재 1
 
3.8%
피 1
 
3.8%
살 1
 
3.8%
삼 1
 
3.8%
디 1
 
3.8%
지 1
 
3.8%
털 1
 
3.8%
사 1
 
3.8%
Other values (13) 13
50.0%
CJK Compat Ideographs
ValueCountFrequency (%)
老 1
20.0%
梨 1
20.0%
龍 1
20.0%
論 1
20.0%
陵 1
20.0%

환승역여부
Boolean

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size185.0 B
False
37 
True
16 
ValueCountFrequency (%)
False 37
69.8%
True 16
30.2%
2023-12-13T07:15:20.865577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

신설일자
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20017113
Minimum19961130
Maximum20210522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T07:15:21.014109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19961130
5-th percentile19961130
Q119961223
median20001130
Q320010430
95-th percentile20121027
Maximum20210522
Range249392
Interquartile range (IQR)49207

Descriptive statistics

Standard deviation66680.428
Coefficient of variation (CV)0.0033311711
Kurtosis1.1969952
Mean20017113
Median Absolute Deviation (MAD)30905
Skewness1.4517847
Sum1.060907 × 109
Variance4.4462795 × 109
MonotonicityNot monotonic
2023-12-13T07:15:21.163550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
19961130 12
22.6%
20121027 9
17.0%
20001130 5
9.4%
20010228 4
 
7.5%
20010430 3
 
5.7%
20000429 3
 
5.7%
20001230 3
 
5.7%
20001211 2
 
3.8%
20210522 2
 
3.8%
20001020 2
 
3.8%
Other values (8) 8
15.1%
ValueCountFrequency (%)
19961130 12
22.6%
19961213 1
 
1.9%
19961223 1
 
1.9%
19970113 1
 
1.9%
19970225 1
 
1.9%
19970625 1
 
1.9%
19970701 1
 
1.9%
19970703 1
 
1.9%
20000429 3
 
5.7%
20001020 2
 
3.8%
ValueCountFrequency (%)
20210522 2
 
3.8%
20121027 9
17.0%
20010430 3
 
5.7%
20010228 4
7.5%
20001230 3
 
5.7%
20001211 2
 
3.8%
20001130 5
9.4%
20001030 1
 
1.9%
20001020 2
 
3.8%
20000429 3
 
5.7%

Interactions

2023-12-13T07:15:16.719445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:15:21.270960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
철도운영기관명역명영어명로마자일본어중국어간체중국어번체환승역여부신설일자
철도운영기관명1.0001.0001.0000.0001.0001.0000.7370.0001.000
역명1.0001.0001.0001.0001.0001.0001.0001.0001.000
영어명1.0001.0001.0001.0001.0001.0001.0001.0001.000
로마자0.0001.0001.0001.0001.0001.0000.9800.0000.000
일본어1.0001.0001.0001.0001.0001.0001.0001.0001.000
중국어간체1.0001.0001.0001.0001.0001.0000.0000.6390.000
중국어번체0.7371.0001.0000.9801.0000.0001.0000.0000.880
환승역여부0.0001.0001.0000.0001.0000.6390.0001.0000.000
신설일자1.0001.0001.0000.0001.0000.0000.8800.0001.000
2023-12-13T07:15:21.387012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
철도운영기관명환승역여부
철도운영기관명1.0000.000
환승역여부0.0001.000
2023-12-13T07:15:21.477747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신설일자철도운영기관명환승역여부
신설일자1.0000.9800.000
철도운영기관명0.9801.0000.000
환승역여부0.0000.0001.000

Missing values

2023-12-13T07:15:16.828678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:15:16.962480image/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
0서울교통공사7호선장암JangamJangamチャンアム长岩長岩N19970225
1서울교통공사7호선도봉산DobongsanDobongsanトボンサン道峰山道峰山Y19961130
2서울교통공사7호선수락산SuraksanSuraksanスラッサン水落山水落山N19970703
3서울교통공사7호선마들MadeulMadeulマドゥル马得-N19961130
4서울교통공사7호선노원Nowon-ノウォン芦原-Y19961130
5서울교통공사7호선중계JunggyeJunggye Hagyeチュンゲ中溪中溪N19961130
6서울교통공사7호선하계HagyeHagyeハゲ下溪下溪N19961130
7서울교통공사7호선공릉(서울과학기술대)Gongneung(Seoul National Univ. of Science & Technology)Gongneung(Seoulgwahakgisuldae)コンヌン孔陵(首尔科学技术大学)孔陵(서울産業大入口)N19961213
8서울교통공사7호선태릉입구TaereungTaereungテルンイック泰陵泰陵入口Y19961223
9서울교통공사7호선먹골MeokgolMeokgolモッコル墨谷-N19961130
철도운영기관명선명역명영어명로마자일본어중국어간체중국어번체환승역여부신설일자
43인천교통공사7호선부천종합운동장Bucheon Stadium-プチョンジョンハブンドンジャン-富川綜合運動場N20121027
44인천교통공사7호선춘의Chunui-チュニ-春 衣N20121027
45인천교통공사7호선신중동Sinjung-dong-シンジュンドン-新 中 洞N20121027
46인천교통공사7호선부천시청Bucheon City Hall-プチョンシチョン-富 川 市 廳N20121027
47인천교통공사7호선상동Sang-dong-サンドン-上 洞N20121027
48인천교통공사7호선삼산체육관Samsan Gymnasium-サムサン·チェユックァン-三山體育館N20121027
49인천교통공사7호선굴포천Gulpocheon-クルポチョン-掘 浦 川N20121027
50인천교통공사7호선부평구청Bupyeong-gu office-プピョングチョン-富 平 區 廳Y20121027
51인천교통공사7호선산곡Sangok-サンゴク-山谷N20210522
52인천교통공사7호선석남(거북시장)Seongnam(Geobuk Market)-ソンナム-石南(거북市場)Y20210522