Overview

Dataset statistics

Number of variables8
Number of observations103
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 KiB
Average record size in memory66.3 B

Variable types

Categorical6
Text2

Dataset

Description수도권 1호선에 포함된 도시광역철도 역들의 철도운영기관명,선명,역명,지상지하구분,역층,게이트내외,출구번호,상세위치,화장실개수,기저귀교환대개수 입니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041222/fileData.do

Alerts

선명 has constant value ""Constant
철도운영기관명 is highly overall correlated with 지상지하구분High correlation
지상지하구분 is highly overall correlated with 철도운영기관명High correlation
상세위치 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:03:49.340741
Analysis finished2023-12-12 16:03:50.107519
Duration0.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size956.0 B
코레일
89 
서울교통공사
14 

Length

Max length6
Median length3
Mean length3.407767
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
코레일 89
86.4%
서울교통공사 14
 
13.6%

Length

2023-12-13T01:03:50.521919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:03:50.655548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
코레일 89
86.4%
서울교통공사 14
 
13.6%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size956.0 B
1호선
103 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1호선
2nd row1호선
3rd row1호선
4th row1호선
5th row1호선

Common Values

ValueCountFrequency (%)
1호선 103
100.0%

Length

2023-12-13T01:03:50.786299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:03:50.898388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1호선 103
100.0%

역명
Text

Distinct87
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Memory size956.0 B
2023-12-13T01:03:51.139169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.5825243
Min length2

Characters and Unicode

Total characters266
Distinct characters112
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

Unique73 ?
Unique (%)70.9%

Sample

1st row제기동
2nd row신설동
3rd row동묘앞
4th row동묘앞
5th row동묘앞
ValueCountFrequency (%)
동묘앞 4
 
3.9%
주안 2
 
1.9%
망월사 2
 
1.9%
영등포 2
 
1.9%
수원 2
 
1.9%
종로3가 2
 
1.9%
종각 2
 
1.9%
석수 2
 
1.9%
용산 2
 
1.9%
부평 2
 
1.9%
Other values (77) 81
78.6%
2023-12-13T01:03:51.566365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 14
 
5.3%
산 11
 
4.1%
천 10
 
3.8%
대 7
 
2.6%
정 7
 
2.6%
부 6
 
2.3%
도 5
 
1.9%
수 5
 
1.9%
종 5
 
1.9%
안 5
 
1.9%
Other values (102) 191
71.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 259
97.4%
Decimal Number 3
 
1.1%
Open Punctuation 2
 
0.8%
Close Punctuation 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 14
 
5.4%
산 11
 
4.2%
천 10
 
3.9%
대 7
 
2.7%
정 7
 
2.7%
부 6
 
2.3%
도 5
 
1.9%
수 5
 
1.9%
종 5
 
1.9%
안 5
 
1.9%
Other values (98) 184
71.0%
Decimal Number
ValueCountFrequency (%)
3 2
66.7%
5 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 259
97.4%
Common 7
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 14
 
5.4%
산 11
 
4.2%
천 10
 
3.9%
대 7
 
2.7%
정 7
 
2.7%
부 6
 
2.3%
도 5
 
1.9%
수 5
 
1.9%
종 5
 
1.9%
안 5
 
1.9%
Other values (98) 184
71.0%
Common
ValueCountFrequency (%)
( 2
28.6%
) 2
28.6%
3 2
28.6%
5 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 259
97.4%
ASCII 7
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 14
 
5.4%
산 11
 
4.2%
천 10
 
3.9%
대 7
 
2.7%
정 7
 
2.7%
부 6
 
2.3%
도 5
 
1.9%
수 5
 
1.9%
종 5
 
1.9%
안 5
 
1.9%
Other values (98) 184
71.0%
ASCII
ValueCountFrequency (%)
( 2
28.6%
) 2
28.6%
3 2
28.6%
5 1
14.3%

지상지하구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size956.0 B
지상
86 
지하
17 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지상 86
83.5%
지하 17
 
16.5%

Length

2023-12-13T01:03:51.716628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:03:51.829620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지상 86
83.5%
지하 17
 
16.5%

역층
Categorical

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size956.0 B
1
43 
2
43 
3
15 
4
 
1
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0291262
Min length1

Unique

Unique2 ?
Unique (%)1.9%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 43
41.7%
2 43
41.7%
3 15
 
14.6%
4 1
 
1.0%
<NA> 1
 
1.0%

Length

2023-12-13T01:03:51.937339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:03:52.050656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 43
41.7%
2 43
41.7%
3 15
 
14.6%
4 1
 
1.0%
na 1
 
1.0%

게이트내외
Categorical

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size956.0 B
외
84 
내
19 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row외
2nd row내
3rd row내
4th row외
5th row외

Common Values

ValueCountFrequency (%)
외 84
81.6%
내 19
 
18.4%

Length

2023-12-13T01:03:52.148888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:03:52.245273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
외 84
81.6%
내 19
 
18.4%

출구번호
Categorical

Distinct18
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size956.0 B
1
52 
2
15 
3
9 
<NA>
 
4
1 /2
 
4
Other values (13)
19 

Length

Max length7
Median length1
Mean length1.6504854
Min length1

Unique

Unique10 ?
Unique (%)9.7%

Sample

1st row2
2nd row1
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
1 52
50.5%
2 15
 
14.6%
3 9
 
8.7%
<NA> 4
 
3.9%
1 /2 4
 
3.9%
4 4
 
3.9%
2 /3 3
 
2.9%
7 2
 
1.9%
4 /5 /6 1
 
1.0%
6 /7 /8 1
 
1.0%
Other values (8) 8
 
7.8%

Length

2023-12-13T01:03:52.348276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 60
49.6%
2 23
 
19.0%
3 13
 
10.7%
4 7
 
5.8%
na 4
 
3.3%
7 4
 
3.3%
5 3
 
2.5%
6 3
 
2.5%
8 3
 
2.5%
11 1
 
0.8%

상세위치
Text

UNIQUE 

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size956.0 B
2023-12-13T01:03:52.666723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length23
Mean length15.291262
Min length6

Characters and Unicode

Total characters1575
Distinct characters145
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)100.0%

Sample

1st row2번출구우측
2nd row대합실(지하1층) 역무실 앞
3rd row상선승강장 4-3지점
4th row하선승강장 7-3지점
5th row지하1층 3번출입구 부근
ValueCountFrequency (%)
옆 31
 
7.3%
방향 31
 
7.3%
출입구 28
 
6.6%
맞이방 23
 
5.4%
2f 16
 
3.8%
1번 15
 
3.5%
앞 10
 
2.4%
게이트 10
 
2.4%
부근 10
 
2.4%
지하1층 9
 
2.1%
Other values (132) 242
56.9%
2023-12-13T01:03:53.177780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
349
22.2%
방 62
 
3.9%
1 59
 
3.7%
구 57
 
3.6%
) 56
 
3.6%
( 56
 
3.6%
출 53
 
3.4%
번 52
 
3.3%
이 50
 
3.2%
2 47
 
3.0%
Other values (135) 734
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 915
58.1%
Space Separator 349
 
22.2%
Decimal Number 139
 
8.8%
Close Punctuation 57
 
3.6%
Open Punctuation 57
 
3.6%
Uppercase Letter 41
 
2.6%
Other Punctuation 8
 
0.5%
Dash Punctuation 4
 
0.3%
Lowercase Letter 4
 
0.3%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
방 62
 
6.8%
구 57
 
6.2%
출 53
 
5.8%
번 52
 
5.7%
이 50
 
5.5%
입 37
 
4.0%
향 33
 
3.6%
옆 32
 
3.5%
층 32
 
3.5%
맞 31
 
3.4%
Other values (111) 476
52.0%
Decimal Number
ValueCountFrequency (%)
1 59
42.4%
2 47
33.8%
3 23
 
16.5%
4 5
 
3.6%
7 2
 
1.4%
5 2
 
1.4%
8 1
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
F 37
90.2%
B 2
 
4.9%
C 1
 
2.4%
U 1
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
e 1
25.0%
v 1
25.0%
r 1
25.0%
o 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 56
98.2%
] 1
 
1.8%
Open Punctuation
ValueCountFrequency (%)
( 56
98.2%
[ 1
 
1.8%
Other Punctuation
ValueCountFrequency (%)
/ 7
87.5%
. 1
 
12.5%
Space Separator
ValueCountFrequency (%)
349
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
→ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 915
58.1%
Common 615
39.0%
Latin 45
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
방 62
 
6.8%
구 57
 
6.2%
출 53
 
5.8%
번 52
 
5.7%
이 50
 
5.5%
입 37
 
4.0%
향 33
 
3.6%
옆 32
 
3.5%
층 32
 
3.5%
맞 31
 
3.4%
Other values (111) 476
52.0%
Common
ValueCountFrequency (%)
349
56.7%
1 59
 
9.6%
) 56
 
9.1%
( 56
 
9.1%
2 47
 
7.6%
3 23
 
3.7%
/ 7
 
1.1%
4 5
 
0.8%
- 4
 
0.7%
7 2
 
0.3%
Other values (6) 7
 
1.1%
Latin
ValueCountFrequency (%)
F 37
82.2%
B 2
 
4.4%
e 1
 
2.2%
v 1
 
2.2%
r 1
 
2.2%
C 1
 
2.2%
U 1
 
2.2%
o 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 915
58.1%
ASCII 659
41.8%
Arrows 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
349
53.0%
1 59
 
9.0%
) 56
 
8.5%
( 56
 
8.5%
2 47
 
7.1%
F 37
 
5.6%
3 23
 
3.5%
/ 7
 
1.1%
4 5
 
0.8%
- 4
 
0.6%
Other values (13) 16
 
2.4%
Hangul
ValueCountFrequency (%)
방 62
 
6.8%
구 57
 
6.2%
출 53
 
5.8%
번 52
 
5.7%
이 50
 
5.5%
입 37
 
4.0%
향 33
 
3.6%
옆 32
 
3.5%
층 32
 
3.5%
맞 31
 
3.4%
Other values (111) 476
52.0%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

Correlations

2023-12-13T01:03:53.273798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
철도운영기관명역명지상지하구분역층게이트내외출구번호
철도운영기관명1.0001.0000.9380.6340.0000.464
역명1.0001.0000.6090.0000.0000.000
지상지하구분0.9380.6091.0000.6120.1970.496
역층0.6340.0000.6121.0000.0000.000
게이트내외0.0000.0000.1970.0001.0000.000
출구번호0.4640.0000.4960.0000.0001.000
2023-12-13T01:03:53.394671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출구번호역층지상지하구분게이트내외철도운영기관명
출구번호1.0000.0000.4100.0000.383
역층0.0001.0000.4190.0000.436
지상지하구분0.4100.4191.0000.1260.775
게이트내외0.0000.0000.1261.0000.000
철도운영기관명0.3830.4360.7750.0001.000
2023-12-13T01:03:53.505826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
철도운영기관명지상지하구분역층게이트내외출구번호
철도운영기관명1.0000.7750.4360.0000.383
지상지하구분0.7751.0000.4190.1260.410
역층0.4360.4191.0000.0000.000
게이트내외0.0000.1260.0001.0000.000
출구번호0.3830.4100.0000.0001.000

Missing values

2023-12-13T01:03:49.863160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:03:50.036723image/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

철도운영기관명선명역명지상지하구분역층게이트내외출구번호상세위치
0서울교통공사1호선제기동지하1외22번출구우측
1서울교통공사1호선신설동지하1내1대합실(지하1층) 역무실 앞
2서울교통공사1호선동묘앞지하1내3상선승강장 4-3지점
3서울교통공사1호선동묘앞지하1외2하선승강장 7-3지점
4서울교통공사1호선동묘앞지하1외3지하1층 3번출입구 부근
5서울교통공사1호선동묘앞지상1외21층 역무실 부근
6서울교통공사1호선동대문지하1외4지하1층 4번출입구 부근
7서울교통공사1호선종로5가지하1외8지하1층 지하상가 연결통로앞
8서울교통공사1호선종로3가지하1내11지하1층 라게이트 부근 (역무실앞) [게이트 내]
9서울교통공사1호선종로3가지하1외1지하1층 가게이트 부근
철도운영기관명선명역명지상지하구분역층게이트내외출구번호상세위치
93코레일1호선금정지상2외6 /7 /8(2)개찰구 옆
94코레일1호선군포지상2외1(2F) 비상게이트 옆
95코레일1호선구일지상1내1(1) 1번출입구
96코레일1호선구로지상3내1(3F)1번 출구 게이트 옆
97코레일1호선광운대지상1외1(1층) 1번 출입구 방향 옆
98코레일1호선개봉지상2외1(2F)맞이방 역무실 맞은편
99코레일1호선간석지상2내<NA>개찰구 안쪽 정면
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