Overview

Dataset statistics

Number of variables5
Number of observations386
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.6 KiB
Average record size in memory41.3 B

Variable types

Categorical3
Numeric1
Text1

Dataset

Description수도권6호선에 포함된 도시광역철도역들의 철도운영기관명,선명,역명,출구번호,출구별 주요시설명, 주소 등의 데이터 입니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15073458/fileData.do

Alerts

철도운영기관명 has constant value ""Constant
선명 has constant value ""Constant

Reproduction

Analysis started2023-12-12 23:19:48.548286
Analysis finished2023-12-12 23:19:49.015800
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
서울교통공사
386 

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 386
100.0%

Length

2023-12-13T08:19:49.075502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:19:49.174549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울교통공사 386
100.0%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
6호선
386 

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 386
100.0%

Length

2023-12-13T08:19:49.276791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:19:49.379576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6호선 386
100.0%

역명
Categorical

Distinct28
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
효창공원앞
30 
대흥(서강대앞)
 
25
마포구청
 
25
보문
 
24
월드컵경기장(성산)
 
18
Other values (23)
264 

Length

Max length14
Median length10
Mean length5.8963731
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
효창공원앞 30
 
7.8%
대흥(서강대앞) 25
 
6.5%
마포구청 25
 
6.5%
보문 24
 
6.2%
월드컵경기장(성산) 18
 
4.7%
고려대(종암) 18
 
4.7%
광흥창(서강) 17
 
4.4%
상수 17
 
4.4%
태릉입구 16
 
4.1%
디지털미디어시티 15
 
3.9%
Other values (18) 181
46.9%

Length

2023-12-13T08:19:49.504117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
효창공원앞 30
 
7.8%
대흥(서강대앞 25
 
6.5%
마포구청 25
 
6.5%
보문 24
 
6.2%
월드컵경기장(성산 18
 
4.7%
고려대(종암 18
 
4.7%
광흥창(서강 17
 
4.4%
상수 17
 
4.4%
태릉입구 16
 
4.1%
디지털미디어시티 15
 
3.9%
Other values (18) 181
46.9%

출구번호
Real number (ℝ)

Distinct9
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0595855
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-13T08:19:49.602381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7972775
Coefficient of variation (CV)0.58742518
Kurtosis0.19588276
Mean3.0595855
Median Absolute Deviation (MAD)1
Skewness0.87094837
Sum1181
Variance3.2302066
MonotonicityNot monotonic
2023-12-13T08:19:49.698542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 89
23.1%
1 86
22.3%
2 83
21.5%
4 56
14.5%
5 27
 
7.0%
6 20
 
5.2%
7 18
 
4.7%
8 6
 
1.6%
9 1
 
0.3%
ValueCountFrequency (%)
1 86
22.3%
2 83
21.5%
3 89
23.1%
4 56
14.5%
5 27
 
7.0%
6 20
 
5.2%
7 18
 
4.7%
8 6
 
1.6%
9 1
 
0.3%
ValueCountFrequency (%)
9 1
 
0.3%
8 6
 
1.6%
7 18
 
4.7%
6 20
 
5.2%
5 27
 
7.0%
4 56
14.5%
3 89
23.1%
2 83
21.5%
1 86
22.3%

출구별 주요시설명
Text

Distinct336
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-13T08:19:49.970079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length6.4404145
Min length2

Characters and Unicode

Total characters2486
Distinct characters237
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique287 ?
Unique (%)74.4%

Sample

1st row신사1동사무소
2nd row역촌초등학교
3rd row서울기독대학교
4th row은평우체국
5th row서부경찰서
ValueCountFrequency (%)
고등학교 4
 
1.0%
방면 4
 
1.0%
성북구 3
 
0.7%
신북초등학교 3
 
0.7%
성산2동 2
 
0.5%
임시폐쇄 2
 
0.5%
성신여자대학교 2
 
0.5%
연서중학교 2
 
0.5%
상신중학교 2
 
0.5%
성산시장 2
 
0.5%
Other values (344) 391
93.8%
2023-12-13T08:19:50.404826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 124
 
5.0%
소 112
 
4.5%
학 100
 
4.0%
동 97
 
3.9%
사 72
 
2.9%
등 67
 
2.7%
파 65
 
2.6%
서 57
 
2.3%
무 56
 
2.3%
초 52
 
2.1%
Other values (227) 1684
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2351
94.6%
Decimal Number 75
 
3.0%
Space Separator 31
 
1.2%
Other Punctuation 11
 
0.4%
Open Punctuation 8
 
0.3%
Close Punctuation 8
 
0.3%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 124
 
5.3%
소 112
 
4.8%
학 100
 
4.3%
동 97
 
4.1%
사 72
 
3.1%
등 67
 
2.8%
파 65
 
2.8%
서 57
 
2.4%
무 56
 
2.4%
초 52
 
2.2%
Other values (213) 1549
65.9%
Decimal Number
ValueCountFrequency (%)
2 33
44.0%
1 28
37.3%
3 7
 
9.3%
5 2
 
2.7%
4 2
 
2.7%
9 1
 
1.3%
7 1
 
1.3%
0 1
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
G 1
50.0%
Space Separator
ValueCountFrequency (%)
31
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2351
94.6%
Common 133
 
5.3%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 124
 
5.3%
소 112
 
4.8%
학 100
 
4.3%
동 97
 
4.1%
사 72
 
3.1%
등 67
 
2.8%
파 65
 
2.8%
서 57
 
2.4%
무 56
 
2.4%
초 52
 
2.2%
Other values (213) 1549
65.9%
Common
ValueCountFrequency (%)
2 33
24.8%
31
23.3%
1 28
21.1%
/ 11
 
8.3%
( 8
 
6.0%
) 8
 
6.0%
3 7
 
5.3%
5 2
 
1.5%
4 2
 
1.5%
9 1
 
0.8%
Other values (2) 2
 
1.5%
Latin
ValueCountFrequency (%)
S 1
50.0%
G 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2351
94.6%
ASCII 135
 
5.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 124
 
5.3%
소 112
 
4.8%
학 100
 
4.3%
동 97
 
4.1%
사 72
 
3.1%
등 67
 
2.8%
파 65
 
2.8%
서 57
 
2.4%
무 56
 
2.4%
초 52
 
2.2%
Other values (213) 1549
65.9%
ASCII
ValueCountFrequency (%)
2 33
24.4%
31
23.0%
1 28
20.7%
/ 11
 
8.1%
( 8
 
5.9%
) 8
 
5.9%
3 7
 
5.2%
5 2
 
1.5%
4 2
 
1.5%
9 1
 
0.7%
Other values (4) 4
 
3.0%

Interactions

2023-12-13T08:19:48.745311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:19:50.493320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명출구번호
역명1.0000.403
출구번호0.4031.000
2023-12-13T08:19:50.568196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출구번호역명
출구번호1.0000.157
역명0.1571.000

Missing values

2023-12-13T08:19:48.875950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:19:48.970873image/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

철도운영기관명선명역명출구번호출구별 주요시설명
0서울교통공사6호선응암1신사1동사무소
1서울교통공사6호선응암2역촌초등학교
2서울교통공사6호선응암2서울기독대학교
3서울교통공사6호선응암3은평우체국
4서울교통공사6호선응암3서부경찰서
5서울교통공사6호선응암3서부종합시장
6서울교통공사6호선응암4응암3동사무소
7서울교통공사6호선역촌1역촌1파출소
8서울교통공사6호선역촌1서부종합시장
9서울교통공사6호선역촌1역촌1동사무소
철도운영기관명선명역명출구번호출구별 주요시설명
376서울교통공사6호선봉화산(서울의료원)2서울특별시서울의료원
377서울교통공사6호선봉화산(서울의료원)2봉화산역환승 주차장
378서울교통공사6호선봉화산(서울의료원)3중랑소방서
379서울교통공사6호선봉화산(서울의료원)3봉화지구대(중랑경찰서)
380서울교통공사6호선봉화산(서울의료원)3봉화초등학교
381서울교통공사6호선봉화산(서울의료원)4중랑구립정보도서관
382서울교통공사6호선봉화산(서울의료원)4신내동우체국
383서울교통공사6호선봉화산(서울의료원)4중랑구민체육센터
384서울교통공사6호선봉화산(서울의료원)4금성초등학교
385서울교통공사6호선봉화산(서울의료원)5대림두산아파트