Overview

Dataset statistics

Number of variables10
Number of observations86
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 KiB
Average record size in memory83.5 B

Variable types

Categorical6
Text1
Boolean3

Dataset

Description부산교통공사에서 운영하는 부산2호선의 승강장에 대한 데이터로 철도운영기관명,선명,역명,승강장번호,상하행,지상구분,역층,승강장연결 여부,스크린도어 유무,안전발판 유무 등이 있습니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041173/fileData.do

Alerts

철도운영기관명 has constant value ""Constant
선명 has constant value ""Constant
스크린도어 유무 has constant value ""Constant
안전발판 유무 has constant value ""Constant
상하행 is highly overall correlated with 승강장번호High correlation
승강장번호 is highly overall correlated with 상하행High correlation
지상구분 is highly overall correlated with 역층High correlation
역층 is highly overall correlated with 지상구분 and 1 other fieldsHigh correlation
승강장연결 여부 is highly overall correlated with 역층High correlation

Reproduction

Analysis started2023-12-12 22:41:11.213494
Analysis finished2023-12-12 22:41:11.959009
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size820.0 B
부산교통공사
86 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산교통공사 86
100.0%

Length

2023-12-13T07:41:12.014318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:41:12.109602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산교통공사 86
100.0%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size820.0 B
2호선
86 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2호선
2nd row2호선
3rd row2호선
4th row2호선
5th row2호선

Common Values

ValueCountFrequency (%)
2호선 86
100.0%

Length

2023-12-13T07:41:12.208996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:41:12.290322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2호선 86
100.0%

역명
Text

Distinct43
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size820.0 B
2023-12-13T07:41:12.472835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length2
Mean length5.1162791
Min length2

Characters and Unicode

Total characters440
Distinct characters106
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row장산(해운대백병원)
2nd row장산(해운대백병원)
3rd row중동
4th row중동
5th row해운대
ValueCountFrequency (%)
장산(해운대백병원 2
 
2.3%
개금 2
 
2.3%
주례 2
 
2.3%
감전(사상구청 2
 
2.3%
사상(서부터미널 2
 
2.3%
덕포 2
 
2.3%
모덕 2
 
2.3%
모라 2
 
2.3%
구남 2
 
2.3%
구명 2
 
2.3%
Other values (33) 66
76.7%
2023-12-13T07:41:12.804593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 28
 
6.4%
) 28
 
6.4%
대 22
 
5.0%
산 18
 
4.1%
부 12
 
2.7%
동 12
 
2.7%
원 10
 
2.3%
구 10
 
2.3%
· 10
 
2.3%
남 8
 
1.8%
Other values (96) 282
64.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 358
81.4%
Open Punctuation 28
 
6.4%
Close Punctuation 28
 
6.4%
Uppercase Letter 16
 
3.6%
Other Punctuation 10
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 22
 
6.1%
산 18
 
5.0%
부 12
 
3.4%
동 12
 
3.4%
원 10
 
2.8%
구 10
 
2.8%
남 8
 
2.2%
금 8
 
2.2%
청 8
 
2.2%
학 6
 
1.7%
Other values (86) 244
68.2%
Uppercase Letter
ValueCountFrequency (%)
B 4
25.0%
S 2
12.5%
K 2
12.5%
O 2
12.5%
C 2
12.5%
X 2
12.5%
E 2
12.5%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Other Punctuation
ValueCountFrequency (%)
· 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 358
81.4%
Common 66
 
15.0%
Latin 16
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 22
 
6.1%
산 18
 
5.0%
부 12
 
3.4%
동 12
 
3.4%
원 10
 
2.8%
구 10
 
2.8%
남 8
 
2.2%
금 8
 
2.2%
청 8
 
2.2%
학 6
 
1.7%
Other values (86) 244
68.2%
Latin
ValueCountFrequency (%)
B 4
25.0%
S 2
12.5%
K 2
12.5%
O 2
12.5%
C 2
12.5%
X 2
12.5%
E 2
12.5%
Common
ValueCountFrequency (%)
( 28
42.4%
) 28
42.4%
· 10
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 358
81.4%
ASCII 72
 
16.4%
None 10
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 28
38.9%
) 28
38.9%
B 4
 
5.6%
S 2
 
2.8%
K 2
 
2.8%
O 2
 
2.8%
C 2
 
2.8%
X 2
 
2.8%
E 2
 
2.8%
Hangul
ValueCountFrequency (%)
대 22
 
6.1%
산 18
 
5.0%
부 12
 
3.4%
동 12
 
3.4%
원 10
 
2.8%
구 10
 
2.8%
남 8
 
2.2%
금 8
 
2.2%
청 8
 
2.2%
학 6
 
1.7%
Other values (86) 244
68.2%
None
ValueCountFrequency (%)
· 10
100.0%

승강장번호
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size820.0 B
1
43 
2
43 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 43
50.0%
2 43
50.0%

Length

2023-12-13T07:41:12.913610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:41:12.996990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 43
50.0%
2 43
50.0%

상하행
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size820.0 B
상행
43 
하행
43 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상행 43
50.0%
하행 43
50.0%

Length

2023-12-13T07:41:13.120261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:41:13.237182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상행 43
50.0%
하행 43
50.0%

지상구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size820.0 B
지하
72 
지상
14 

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 (%)
지하 72
83.7%
지상 14
 
16.3%

Length

2023-12-13T07:41:13.338080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:41:13.432025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지하 72
83.7%
지상 14
 
16.3%

역층
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size820.0 B
2
40 
3
34 
4
6 
1
 
4
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 40
46.5%
3 34
39.5%
4 6
 
7.0%
1 4
 
4.7%
5 2
 
2.3%

Length

2023-12-13T07:41:13.534897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:41:13.703542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 40
46.5%
3 34
39.5%
4 6
 
7.0%
1 4
 
4.7%
5 2
 
2.3%

승강장연결 여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size218.0 B
True
48 
False
38 
ValueCountFrequency (%)
True 48
55.8%
False 38
44.2%
2023-12-13T07:41:13.867580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

스크린도어 유무
Boolean

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size218.0 B
True
86 
ValueCountFrequency (%)
True 86
100.0%
2023-12-13T07:41:13.986140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

안전발판 유무
Boolean

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size218.0 B
True
86 
ValueCountFrequency (%)
True 86
100.0%
2023-12-13T07:41:14.068566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:41:14.140081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명승강장번호상하행지상구분역층승강장연결 여부
역명1.0000.0000.0001.0001.0001.000
승강장번호0.0001.0000.9990.0000.0000.000
상하행0.0000.9991.0000.0000.0000.000
지상구분1.0000.0000.0001.0000.4960.000
역층1.0000.0000.0000.4961.0000.547
승강장연결 여부1.0000.0000.0000.0000.5471.000
2023-12-13T07:41:14.253568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상하행승강장번호역층승강장연결 여부지상구분
상하행1.0000.9760.0000.0000.000
승강장번호0.9761.0000.0000.0000.000
역층0.0000.0001.0000.6510.591
승강장연결 여부0.0000.0000.6511.0000.000
지상구분0.0000.0000.5910.0001.000
2023-12-13T07:41:14.375167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승강장번호상하행지상구분역층승강장연결 여부
승강장번호1.0000.9760.0000.0000.000
상하행0.9761.0000.0000.0000.000
지상구분0.0000.0001.0000.5910.000
역층0.0000.0000.5911.0000.651
승강장연결 여부0.0000.0000.0000.6511.000

Missing values

2023-12-13T07:41:11.776780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:41:11.908034image/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

철도운영기관명선명역명승강장번호상하행지상구분역층승강장연결 여부스크린도어 유무안전발판 유무
0부산교통공사2호선장산(해운대백병원)1상행지하2YYY
1부산교통공사2호선장산(해운대백병원)2하행지하2YYY
2부산교통공사2호선중동1상행지하2NYY
3부산교통공사2호선중동2하행지하2NYY
4부산교통공사2호선해운대1상행지하2YYY
5부산교통공사2호선해운대2하행지하2YYY
6부산교통공사2호선동백1상행지하2NYY
7부산교통공사2호선동백2하행지하2NYY
8부산교통공사2호선벡스코(시립미술관)1상행지하2NYY
9부산교통공사2호선벡스코(시립미술관)2하행지하2NYY
철도운영기관명선명역명승강장번호상하행지상구분역층승강장연결 여부스크린도어 유무안전발판 유무
76부산교통공사2호선호포1상행지상5YYY
77부산교통공사2호선호포2하행지상5YYY
78부산교통공사2호선증산1상행지상2NYY
79부산교통공사2호선증산2하행지상2NYY
80부산교통공사2호선부산대양산캠퍼스1상행지상1YYY
81부산교통공사2호선부산대양산캠퍼스2하행지상1YYY
82부산교통공사2호선남양산(범어)1상행지상3YYY
83부산교통공사2호선남양산(범어)2하행지상3YYY
84부산교통공사2호선양산(시청·동원과학기술대학교)1상행지상3YYY
85부산교통공사2호선양산(시청·동원과학기술대학교)2하행지상3YYY