Overview

Dataset statistics

Number of variables10
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory89.1 B

Variable types

Categorical8
Text2

Dataset

Description수도권6호선에 포함된 도시광역철도역들의 철도운영기관명, 선명, 역명, 휠체어리프트의 관리번호, 출입구번호, 상세위치, 길이, 폭, 시작층, 종료층의 데이터가 있습니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041433/fileData.do

Alerts

철도운영기관명 has constant value ""Constant
선명 has constant value ""Constant
길이 has constant value ""Constant
폭 is highly overall correlated with 출입구번호High correlation
종료층 is highly overall correlated with 출입구번호 and 1 other fieldsHigh correlation
시작층 is highly overall correlated with 출입구번호 and 1 other fieldsHigh correlation
출입구번호 is highly overall correlated with 휠체어리프트의 관리번호 and 3 other fieldsHigh correlation
휠체어리프트의 관리번호 is highly overall correlated with 출입구번호High correlation
출입구번호 is highly imbalanced (55.4%)Imbalance
폭 is highly imbalanced (76.5%)Imbalance

Reproduction

Analysis started2023-12-12 15:02:28.586846
Analysis finished2023-12-12 15:02:29.493614
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
서울교통공사
26 

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 26
100.0%

Length

2023-12-13T00:02:29.566852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:02:29.656621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울교통공사 26
100.0%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
6호선
26 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6호선
2nd row6호선
3rd row6호선
4th row6호선
5th row6호선

Common Values

ValueCountFrequency (%)
6호선 26
100.0%

Length

2023-12-13T00:02:29.763077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:02:29.853693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6호선 26
100.0%

역명
Text

Distinct14
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-13T00:02:29.981080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length5.1923077
Min length2

Characters and Unicode

Total characters135
Distinct characters50
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

Unique6 ?
Unique (%)23.1%

Sample

1st row불광
2nd row불광
3rd row구산
4th row새절(신사)
5th row디지털미디어시티
ValueCountFrequency (%)
신당 5
19.2%
디지털미디어시티 3
11.5%
불광 2
 
7.7%
삼각지 2
 
7.7%
상월곡(한국과학기술연구원 2
 
7.7%
석계 2
 
7.7%
봉화산(서울의료원 2
 
7.7%
동묘앞 2
 
7.7%
구산 1
 
3.8%
새절(신사 1
 
3.8%
Other values (4) 4
15.4%
2023-12-13T00:02:30.301919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 8
 
5.9%
) 8
 
5.9%
신 6
 
4.4%
디 6
 
4.4%
지 5
 
3.7%
당 5
 
3.7%
서 4
 
3.0%
원 4
 
3.0%
티 3
 
2.2%
광 3
 
2.2%
Other values (40) 83
61.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 119
88.1%
Open Punctuation 8
 
5.9%
Close Punctuation 8
 
5.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 6
 
5.0%
디 6
 
5.0%
지 5
 
4.2%
당 5
 
4.2%
서 4
 
3.4%
원 4
 
3.4%
티 3
 
2.5%
광 3
 
2.5%
대 3
 
2.5%
산 3
 
2.5%
Other values (38) 77
64.7%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 119
88.1%
Common 16
 
11.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 6
 
5.0%
디 6
 
5.0%
지 5
 
4.2%
당 5
 
4.2%
서 4
 
3.4%
원 4
 
3.4%
티 3
 
2.5%
광 3
 
2.5%
대 3
 
2.5%
산 3
 
2.5%
Other values (38) 77
64.7%
Common
ValueCountFrequency (%)
( 8
50.0%
) 8
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 119
88.1%
ASCII 16
 
11.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 8
50.0%
) 8
50.0%
Hangul
ValueCountFrequency (%)
신 6
 
5.0%
디 6
 
5.0%
지 5
 
4.2%
당 5
 
4.2%
서 4
 
3.4%
원 4
 
3.4%
티 3
 
2.5%
광 3
 
2.5%
대 3
 
2.5%
산 3
 
2.5%
Other values (38) 77
64.7%

휠체어리프트의 관리번호
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Memory size340.0 B
1
14 
2
8 
3
2 
4
 
1
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)7.7%

Sample

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

Common Values

ValueCountFrequency (%)
1 14
53.8%
2 8
30.8%
3 2
 
7.7%
4 1
 
3.8%
5 1
 
3.8%

Length

2023-12-13T00:02:30.418499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:02:30.510688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 14
53.8%
2 8
30.8%
3 2
 
7.7%
4 1
 
3.8%
5 1
 
3.8%

출입구번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size340.0 B
<NA>
21 
3
 
1
2
 
1
7
 
1
9
 
1

Length

Max length4
Median length4
Mean length3.4230769
Min length1

Unique

Unique5 ?
Unique (%)19.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 21
80.8%
3 1
 
3.8%
2 1
 
3.8%
7 1
 
3.8%
9 1
 
3.8%
5 1
 
3.8%

Length

2023-12-13T00:02:30.613059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:02:30.729373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 21
80.8%
3 1
 
3.8%
2 1
 
3.8%
7 1
 
3.8%
9 1
 
3.8%
5 1
 
3.8%

상세위치
Text

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-13T00:02:30.892410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length22
Mean length20.769231
Min length14

Characters and Unicode

Total characters540
Distinct characters56
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)92.3%

Sample

1st row(B1)종점3환승통로(B4-B1)-종3가측
2nd row(B1)시점3환승통로(B2-B1)-연신내측
3rd row(B2)승-대(B4-B1)
4th row(B1)하선 승/시-대(B2-B1)
5th row(B2)공항철도 환승 상선 승/종-대(B2-B1)
ValueCountFrequency (%)
환승 6
 
11.3%
b1)대-대(b2-b1 2
 
3.8%
대(b3-b2 2
 
3.8%
시/승-대(b3-b2 2
 
3.8%
b2)1호선 2
 
3.8%
시/승 2
 
3.8%
2
 
3.8%
b1)2호선 2
 
3.8%
하선 2
 
3.8%
승/종-대(b2-b1 2
 
3.8%
Other values (27) 29
54.7%
2023-12-13T00:02:31.226427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 68
12.6%
( 60
11.1%
) 60
11.1%
- 44
 
8.1%
1 42
 
7.8%
2 30
 
5.6%
27
 
5.0%
승 25
 
4.6%
대 23
 
4.3%
3 13
 
2.4%
Other values (46) 148
27.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 170
31.5%
Decimal Number 92
17.0%
Uppercase Letter 77
14.3%
Open Punctuation 60
 
11.1%
Close Punctuation 60
 
11.1%
Dash Punctuation 44
 
8.1%
Space Separator 27
 
5.0%
Other Punctuation 10
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
승 25
 
14.7%
대 23
 
13.5%
환 11
 
6.5%
선 10
 
5.9%
종 8
 
4.7%
시 7
 
4.1%
향 5
 
2.9%
호 5
 
2.9%
방 5
 
2.9%
입 4
 
2.4%
Other values (33) 67
39.4%
Decimal Number
ValueCountFrequency (%)
1 42
45.7%
2 30
32.6%
3 13
 
14.1%
4 4
 
4.3%
5 2
 
2.2%
9 1
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
B 68
88.3%
F 9
 
11.7%
Open Punctuation
ValueCountFrequency (%)
( 60
100.0%
Close Punctuation
ValueCountFrequency (%)
) 60
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%
Space Separator
ValueCountFrequency (%)
27
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 293
54.3%
Hangul 170
31.5%
Latin 77
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
승 25
 
14.7%
대 23
 
13.5%
환 11
 
6.5%
선 10
 
5.9%
종 8
 
4.7%
시 7
 
4.1%
향 5
 
2.9%
호 5
 
2.9%
방 5
 
2.9%
입 4
 
2.4%
Other values (33) 67
39.4%
Common
ValueCountFrequency (%)
( 60
20.5%
) 60
20.5%
- 44
15.0%
1 42
14.3%
2 30
10.2%
27
9.2%
3 13
 
4.4%
/ 10
 
3.4%
4 4
 
1.4%
5 2
 
0.7%
Latin
ValueCountFrequency (%)
B 68
88.3%
F 9
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 370
68.5%
Hangul 170
31.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 68
18.4%
( 60
16.2%
) 60
16.2%
- 44
11.9%
1 42
11.4%
2 30
8.1%
27
 
7.3%
3 13
 
3.5%
/ 10
 
2.7%
F 9
 
2.4%
Other values (3) 7
 
1.9%
Hangul
ValueCountFrequency (%)
승 25
 
14.7%
대 23
 
13.5%
환 11
 
6.5%
선 10
 
5.9%
종 8
 
4.7%
시 7
 
4.1%
향 5
 
2.9%
호 5
 
2.9%
방 5
 
2.9%
입 4
 
2.4%
Other values (33) 67
39.4%

길이
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
125
26 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row125
2nd row125
3rd row125
4th row125
5th row125

Common Values

ValueCountFrequency (%)
125 26
100.0%

Length

2023-12-13T00:02:31.342143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:02:31.432210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
125 26
100.0%

폭
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
80
25 
90
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)3.8%

Sample

1st row80
2nd row80
3rd row80
4th row80
5th row80

Common Values

ValueCountFrequency (%)
80 25
96.2%
90 1
 
3.8%

Length

2023-12-13T00:02:31.515162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:02:31.620831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
80 25
96.2%
90 1
 
3.8%

시작층
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size340.0 B
지하1
12 
지하2
8 
지상1
5 
지하3
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)3.8%

Sample

1st row지하1
2nd row지하1
3rd row지하2
4th row지하1
5th row지하2

Common Values

ValueCountFrequency (%)
지하1 12
46.2%
지하2 8
30.8%
지상1 5
19.2%
지하3 1
 
3.8%

Length

2023-12-13T00:02:31.718349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:02:31.818086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지하1 12
46.2%
지하2 8
30.8%
지상1 5
19.2%
지하3 1
 
3.8%

종료층
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size340.0 B
지하2
11 
지하3
9 
지하1
5 
지하4
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)3.8%

Sample

1st row지하2
2nd row지하2
3rd row지하3
4th row지하2
5th row지하3

Common Values

ValueCountFrequency (%)
지하2 11
42.3%
지하3 9
34.6%
지하1 5
19.2%
지하4 1
 
3.8%

Length

2023-12-13T00:02:31.991653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:02:32.101055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지하2 11
42.3%
지하3 9
34.6%
지하1 5
19.2%
지하4 1
 
3.8%

Correlations

2023-12-13T00:02:32.192592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명휠체어리프트의 관리번호출입구번호상세위치폭시작층종료층
역명1.0000.0001.0000.8721.0000.4450.645
휠체어리프트의 관리번호0.0001.0001.0001.0000.0000.0000.084
출입구번호1.0001.0001.0001.000NaNNaN1.000
상세위치0.8721.0001.0001.0001.0001.0001.000
폭1.0000.000NaN1.0001.0000.0000.000
시작층0.4450.000NaN1.0000.0001.0000.936
종료층0.6450.0841.0001.0000.0000.9361.000
2023-12-13T00:02:32.327445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폭종료층시작층출입구번호휠체어리프트의 관리번호
폭1.0000.0000.0001.0000.000
종료층0.0001.0000.6621.0000.000
시작층0.0000.6621.0001.0000.000
출입구번호1.0001.0001.0001.0001.000
휠체어리프트의 관리번호0.0000.0000.0001.0001.000
2023-12-13T00:02:32.432022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
휠체어리프트의 관리번호출입구번호폭시작층종료층
휠체어리프트의 관리번호1.0001.0000.0000.0000.000
출입구번호1.0001.0001.0001.0001.000
폭0.0001.0001.0000.0000.000
시작층0.0001.0000.0001.0000.662
종료층0.0001.0000.0000.6621.000

Missing values

2023-12-13T00:02:29.274014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:02:29.418329image/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

철도운영기관명선명역명휠체어리프트의 관리번호출입구번호상세위치길이폭시작층종료층
0서울교통공사6호선불광1<NA>(B1)종점3환승통로(B4-B1)-종3가측12580지하1지하2
1서울교통공사6호선불광2<NA>(B1)시점3환승통로(B2-B1)-연신내측12580지하1지하2
2서울교통공사6호선구산1<NA>(B2)승-대(B4-B1)12580지하2지하3
3서울교통공사6호선새절(신사)1<NA>(B1)하선 승/시-대(B2-B1)12580지하1지하2
4서울교통공사6호선디지털미디어시티1<NA>(B2)공항철도 환승 상선 승/종-대(B2-B1)12580지하2지하3
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