Overview

Dataset statistics

Number of variables8
Number of observations591
Missing cells241
Missing cells (%)5.1%
Duplicate rows39
Duplicate rows (%)6.6%
Total size in memory37.6 KiB
Average record size in memory65.2 B

Variable types

Categorical5
Text2
Numeric1

Dataset

Description서울메트로에서 관리하는 도시광역철도역들의 철도운영기관명, 선명, 역명, 상하행구분, 출입구번호, 상세위치, 시작층, 종료층의 데이터가 있습니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041363/fileData.do

Alerts

철도운영기관 has constant value ""Constant
Dataset has 39 (6.6%) duplicate rowsDuplicates
상하행구분 is highly overall correlated with 시작층 and 1 other fieldsHigh correlation
시작층 is highly overall correlated with 상하행구분 and 1 other fieldsHigh correlation
종료층 is highly overall correlated with 상하행구분 and 1 other fieldsHigh correlation
출입구번호 has 241 (40.8%) missing valuesMissing

Reproduction

Analysis started2023-12-12 08:16:57.360556
Analysis finished2023-12-12 08:16:58.260626
Duration0.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
서울교통공사
591 

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 591
100.0%

Length

2023-12-12T17:16:58.358373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:16:58.497830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울교통공사 591
100.0%

선명
Categorical

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2호선
233 
3호선
202 
4호선
123 
1호선
33 

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 (%)
2호선 233
39.4%
3호선 202
34.2%
4호선 123
20.8%
1호선 33
 
5.6%

Length

2023-12-12T17:16:58.631552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:16:58.772886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2호선 233
39.4%
3호선 202
34.2%
4호선 123
20.8%
1호선 33
 
5.6%

역명
Text

Distinct97
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2023-12-12T17:16:59.093153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length4.0947547
Min length2

Characters and Unicode

Total characters2420
Distinct characters154
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row동대문
2nd row동묘앞
3rd row동묘앞
4th row동묘앞
5th row동묘앞
ValueCountFrequency (%)
고속터미널 24
 
4.1%
가락시장 22
 
3.7%
충무로 21
 
3.6%
경찰병원 20
 
3.4%
사당 19
 
3.2%
오금 15
 
2.5%
서울역 12
 
2.0%
역삼 12
 
2.0%
용두(동대문구청 12
 
2.0%
동묘앞 12
 
2.0%
Other values (87) 422
71.4%
2023-12-12T17:16:59.571894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 123
 
5.1%
대 117
 
4.8%
) 94
 
3.9%
( 94
 
3.9%
청 74
 
3.1%
동 67
 
2.8%
문 61
 
2.5%
신 59
 
2.4%
서 58
 
2.4%
로 55
 
2.3%
Other values (144) 1618
66.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2214
91.5%
Close Punctuation 94
 
3.9%
Open Punctuation 94
 
3.9%
Decimal Number 14
 
0.6%
Other Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 123
 
5.6%
대 117
 
5.3%
청 74
 
3.3%
동 67
 
3.0%
문 61
 
2.8%
신 59
 
2.7%
서 58
 
2.6%
로 55
 
2.5%
입 46
 
2.1%
사 45
 
2.0%
Other values (139) 1509
68.2%
Decimal Number
ValueCountFrequency (%)
3 12
85.7%
4 2
 
14.3%
Close Punctuation
ValueCountFrequency (%)
) 94
100.0%
Open Punctuation
ValueCountFrequency (%)
( 94
100.0%
Other Punctuation
ValueCountFrequency (%)
· 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2214
91.5%
Common 206
 
8.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 123
 
5.6%
대 117
 
5.3%
청 74
 
3.3%
동 67
 
3.0%
문 61
 
2.8%
신 59
 
2.7%
서 58
 
2.6%
로 55
 
2.5%
입 46
 
2.1%
사 45
 
2.0%
Other values (139) 1509
68.2%
Common
ValueCountFrequency (%)
) 94
45.6%
( 94
45.6%
3 12
 
5.8%
· 4
 
1.9%
4 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2214
91.5%
ASCII 202
 
8.3%
None 4
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 123
 
5.6%
대 117
 
5.3%
청 74
 
3.3%
동 67
 
3.0%
문 61
 
2.8%
신 59
 
2.7%
서 58
 
2.6%
로 55
 
2.5%
입 46
 
2.1%
사 45
 
2.0%
Other values (139) 1509
68.2%
ASCII
ValueCountFrequency (%)
) 94
46.5%
( 94
46.5%
3 12
 
5.9%
4 2
 
1.0%
None
ValueCountFrequency (%)
· 4
100.0%

상하행구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
상행
312 
하행
279 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row상행
2nd row상행
3rd row상행
4th row상행
5th row하행

Common Values

ValueCountFrequency (%)
상행 312
52.8%
하행 279
47.2%

Length

2023-12-12T17:16:59.789864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:17:00.001595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상행 312
52.8%
하행 279
47.2%

출입구번호
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)2.9%
Missing241
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean3.6542857
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-12-12T17:17:00.212464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5577393
Coefficient of variation (CV)0.69992866
Kurtosis2.9346396
Mean3.6542857
Median Absolute Deviation (MAD)2
Skewness1.4166199
Sum1279
Variance6.5420303
MonotonicityNot monotonic
2023-12-12T17:17:00.378846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 82
 
13.9%
3 60
 
10.2%
2 55
 
9.3%
5 42
 
7.1%
4 42
 
7.1%
6 29
 
4.9%
8 21
 
3.6%
7 7
 
1.2%
14 6
 
1.0%
9 6
 
1.0%
(Missing) 241
40.8%
ValueCountFrequency (%)
1 82
13.9%
2 55
9.3%
3 60
10.2%
4 42
7.1%
5 42
7.1%
6 29
 
4.9%
7 7
 
1.2%
8 21
 
3.6%
9 6
 
1.0%
14 6
 
1.0%
ValueCountFrequency (%)
14 6
 
1.0%
9 6
 
1.0%
8 21
 
3.6%
7 7
 
1.2%
6 29
 
4.9%
5 42
7.1%
4 42
7.1%
3 60
10.2%
2 55
9.3%
1 82
13.9%

상세위치
Text

Distinct196
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
2023-12-12T17:17:00.702206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length13
Mean length11.485618
Min length4

Characters and Unicode

Total characters6788
Distinct characters82
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)22.3%

Sample

1st row(B1)1번 출입구 방면
2nd row(B2)시청방향 5-4
3rd row(B1)청량리방향 6-2
4th row(B3)청량리방향 9-1
5th row(B2)청량리방향
ValueCountFrequency (%)
출입구 362
24.5%
방면 352
23.8%
b2 53
 
3.6%
f1)1번 38
 
2.6%
b3 32
 
2.2%
b1)1번 31
 
2.1%
f1)3번 28
 
1.9%
f1)2번 24
 
1.6%
b1)3번 22
 
1.5%
기둥 22
 
1.5%
Other values (160) 512
34.7%
2023-12-12T17:17:01.130180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
974
14.3%
( 608
 
9.0%
) 608
 
9.0%
1 515
 
7.6%
방 377
 
5.6%
B 375
 
5.5%
구 362
 
5.3%
면 362
 
5.3%
입 362
 
5.3%
출 362
 
5.3%
Other values (72) 1883
27.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2611
38.5%
Decimal Number 1226
18.1%
Space Separator 974
 
14.3%
Uppercase Letter 614
 
9.0%
Open Punctuation 608
 
9.0%
Close Punctuation 608
 
9.0%
Dash Punctuation 127
 
1.9%
Other Punctuation 14
 
0.2%
Math Symbol 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
방 377
14.4%
구 362
13.9%
면 362
13.9%
입 362
13.9%
출 362
13.9%
번 362
13.9%
선 36
 
1.4%
기 22
 
0.8%
통 22
 
0.8%
둥 22
 
0.8%
Other values (50) 322
12.3%
Decimal Number
ValueCountFrequency (%)
1 515
42.0%
2 255
20.8%
3 167
 
13.6%
4 112
 
9.1%
5 57
 
4.6%
6 35
 
2.9%
7 29
 
2.4%
8 29
 
2.4%
9 18
 
1.5%
0 9
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
B 375
61.1%
F 210
34.2%
X 22
 
3.6%
A 3
 
0.5%
E 2
 
0.3%
H 2
 
0.3%
Space Separator
ValueCountFrequency (%)
974
100.0%
Open Punctuation
ValueCountFrequency (%)
( 608
100.0%
Close Punctuation
ValueCountFrequency (%)
) 608
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 127
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 14
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3563
52.5%
Hangul 2611
38.5%
Latin 614
 
9.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
방 377
14.4%
구 362
13.9%
면 362
13.9%
입 362
13.9%
출 362
13.9%
번 362
13.9%
선 36
 
1.4%
기 22
 
0.8%
통 22
 
0.8%
둥 22
 
0.8%
Other values (50) 322
12.3%
Common
ValueCountFrequency (%)
974
27.3%
( 608
17.1%
) 608
17.1%
1 515
14.5%
2 255
 
7.2%
3 167
 
4.7%
- 127
 
3.6%
4 112
 
3.1%
5 57
 
1.6%
6 35
 
1.0%
Other values (6) 105
 
2.9%
Latin
ValueCountFrequency (%)
B 375
61.1%
F 210
34.2%
X 22
 
3.6%
A 3
 
0.5%
E 2
 
0.3%
H 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4177
61.5%
Hangul 2611
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
974
23.3%
( 608
14.6%
) 608
14.6%
1 515
12.3%
B 375
 
9.0%
2 255
 
6.1%
F 210
 
5.0%
3 167
 
4.0%
- 127
 
3.0%
4 112
 
2.7%
Other values (12) 226
 
5.4%
Hangul
ValueCountFrequency (%)
방 377
14.4%
구 362
13.9%
면 362
13.9%
입 362
13.9%
출 362
13.9%
번 362
13.9%
선 36
 
1.4%
기 22
 
0.8%
통 22
 
0.8%
둥 22
 
0.8%
Other values (50) 322
12.3%

시작층
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
지하1층
187 
지상1층
174 
지하2층
105 
지하3층
54 
지상2층
31 
Other values (3)
40 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지하1층
2nd row지하2층
3rd row지하1층
4th row지하3층
5th row지하2층

Common Values

ValueCountFrequency (%)
지하1층 187
31.6%
지상1층 174
29.4%
지하2층 105
17.8%
지하3층 54
 
9.1%
지상2층 31
 
5.2%
지하4층 21
 
3.6%
<NA> 14
 
2.4%
지상3층 5
 
0.8%

Length

2023-12-12T17:17:01.283436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:17:01.425307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지하1층 187
31.6%
지상1층 174
29.4%
지하2층 105
17.8%
지하3층 54
 
9.1%
지상2층 31
 
5.2%
지하4층 21
 
3.6%
na 14
 
2.4%
지상3층 5
 
0.8%

종료층
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
지하1층
182 
지상1층
169 
지하2층
125 
지하3층
49 
지상3층
20 
Other values (3)
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지상1층
2nd row지하1층
3rd row지하2층
4th row지하2층
5th row지하3층

Common Values

ValueCountFrequency (%)
지하1층 182
30.8%
지상1층 169
28.6%
지하2층 125
21.2%
지하3층 49
 
8.3%
지상3층 20
 
3.4%
지상2층 17
 
2.9%
지하4층 15
 
2.5%
<NA> 14
 
2.4%

Length

2023-12-12T17:17:01.574614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:17:01.695265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지하1층 182
30.8%
지상1층 169
28.6%
지하2층 125
21.2%
지하3층 49
 
8.3%
지상3층 20
 
3.4%
지상2층 17
 
2.9%
지하4층 15
 
2.5%
na 14
 
2.4%

Interactions

2023-12-12T17:16:57.854010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:17:01.809118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
선명역명상하행구분출입구번호시작층종료층
선명1.0000.9930.0000.3940.2690.275
역명0.9931.0000.0000.8270.7660.767
상하행구분0.0000.0001.0000.0000.6320.601
출입구번호0.3940.8270.0001.0000.0000.000
시작층0.2690.7660.6320.0001.0000.938
종료층0.2750.7670.6010.0000.9381.000
2023-12-12T17:17:01.941195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
선명종료층상하행구분시작층
선명1.0000.1910.0000.187
종료층0.1911.0000.6450.624
상하행구분0.0000.6451.0000.678
시작층0.1870.6240.6781.000
2023-12-12T17:17:02.063614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출입구번호선명상하행구분시작층종료층
출입구번호1.0000.1840.0000.0000.000
선명0.1841.0000.0000.1870.191
상하행구분0.0000.0001.0000.6780.645
시작층0.0000.1870.6781.0000.624
종료층0.0000.1910.6450.6241.000

Missing values

2023-12-12T17:16:58.012680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:16:58.176688image/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

철도운영기관선명역명상하행구분출입구번호상세위치시작층종료층
0서울교통공사1호선동대문상행1(B1)1번 출입구 방면지하1층지상1층
1서울교통공사1호선동묘앞상행<NA>(B2)시청방향 5-4지하2층지하1층
2서울교통공사1호선동묘앞상행<NA>(B1)청량리방향 6-2지하1층지하2층
3서울교통공사1호선동묘앞상행<NA>(B3)청량리방향 9-1지하3층지하2층
4서울교통공사1호선동묘앞하행<NA>(B2)청량리방향지하2층지하3층
5서울교통공사1호선동묘앞하행1(F1)1번 출입구 방면지상1층지하1층
6서울교통공사1호선동묘앞상행3(B1)3번 출입구 방면지하1층지상1층
7서울교통공사1호선동묘앞하행3(F1)3번 출입구 방면지상1층지하1층
8서울교통공사1호선동묘앞상행2(B1)2번 출입구 방면지하1층지상1층
9서울교통공사1호선동묘앞하행2(F1)2번 출입구 방면지상1층지하1층
철도운영기관선명역명상하행구분출입구번호상세위치시작층종료층
581서울교통공사4호선혜화하행3(F1)3번 출입구 방면지상1층지하1층
582서울교통공사4호선혜화상행1(B1)1번 출입구 방면지하1층지상1층
583서울교통공사4호선혜화하행1(F1)1번 출입구 방면지상1층지하1층
584서울교통공사4호선혜화상행3(B1)3번 출입구 방면지하1층지상1층
585서울교통공사4호선회현(남대문시장)하행<NA>(B2)지하2층지하4층
586서울교통공사4호선회현(남대문시장)상행<NA>(B4) 4-4지하4층지하2층
587서울교통공사4호선회현(남대문시장)하행<NA>(B2)지하2층지하4층
588서울교통공사4호선회현(남대문시장)상행<NA>(B3)A계단 옆지하3층지하2층
589서울교통공사4호선회현(남대문시장)하행<NA>(B2)A계단 옆지하2층지하3층
590서울교통공사4호선회현(남대문시장)상행<NA>(B4) 9-4지하4층지하2층

Duplicate rows

Most frequently occurring

철도운영기관선명역명상하행구분출입구번호상세위치시작층종료층# duplicates
19서울교통공사3호선고속터미널하행<NA>(B2)지하2층지하3층8
14서울교통공사3호선경찰병원하행<NA>(B1)지하1층지하2층6
0서울교통공사2호선사당하행<NA>(B1)지하1층지하2층4
1서울교통공사2호선신대방하행<NA>(F3)지상3층지상2층4
13서울교통공사3호선가락시장하행<NA>(B2)지하2층지하4층4
22서울교통공사3호선도곡하행<NA>(B2)지하2층지하4층4
2서울교통공사2호선신도림상행<NA>(B3)외선지하3층지하2층3
4서울교통공사2호선신도림하행<NA>(B2)지하2층지하3층3
7서울교통공사2호선왕십리상행<NA>(환승통로)<NA><NA>3
8서울교통공사2호선왕십리하행<NA>(환승통로)<NA><NA>3