Overview

Dataset statistics

Number of variables5
Number of observations183
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 KiB
Average record size in memory41.7 B

Variable types

Categorical3
Numeric1
Text1

Dataset

Description수도권8호선에 포함된 도시광역철도역들의 철도운영기관명,선명,역명,출구번호,출구별 주요시설명 등의 데이터 입니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15073456/fileData.do

Alerts

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

Reproduction

Analysis started2023-12-12 09:47:13.406251
Analysis finished2023-12-12 09:47:13.898249
Duration0.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
서울교통공사
183 

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 183
100.0%

Length

2023-12-12T18:47:13.990477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:47:14.117514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울교통공사 183
100.0%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
8호선
183 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8호선
2nd row8호선
3rd row8호선
4th row8호선
5th row8호선

Common Values

ValueCountFrequency (%)
8호선 183
100.0%

Length

2023-12-12T18:47:14.235248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:47:14.360431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
8호선 183
100.0%

역명
Categorical

Distinct14
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
송파
18 
석촌
17 
남한산성입구(성남법원·검찰청)
17 
모란
17 
단대오거리
16 
Other values (9)
98 

Length

Max length16
Median length2
Mean length4.284153
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
송파 18
9.8%
석촌 17
9.3%
남한산성입구(성남법원·검찰청) 17
9.3%
모란 17
9.3%
단대오거리 16
8.7%
강동구청 14
 
7.7%
몽촌토성(평화의문) 13
 
7.1%
신흥 13
 
7.1%
장지 12
 
6.6%
산성 11
 
6.0%
Other values (4) 35
19.1%

Length

2023-12-12T18:47:14.501385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파 18
9.8%
석촌 17
9.3%
남한산성입구(성남법원·검찰청 17
9.3%
모란 17
9.3%
단대오거리 16
8.7%
강동구청 14
 
7.7%
몽촌토성(평화의문 13
 
7.1%
신흥 13
 
7.1%
장지 12
 
6.6%
산성 11
 
6.0%
Other values (4) 35
19.1%

출구번호
Real number (ℝ)

Distinct12
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2349727
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T18:47:14.629069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile7.9
Maximum12
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2396234
Coefficient of variation (CV)0.69231602
Kurtosis3.6973232
Mean3.2349727
Median Absolute Deviation (MAD)1
Skewness1.707601
Sum592
Variance5.015913
MonotonicityNot monotonic
2023-12-12T18:47:14.764961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 43
23.5%
3 37
20.2%
4 36
19.7%
2 35
19.1%
5 12
 
6.6%
6 6
 
3.3%
7 4
 
2.2%
8 3
 
1.6%
11 3
 
1.6%
12 2
 
1.1%
Other values (2) 2
 
1.1%
ValueCountFrequency (%)
1 43
23.5%
2 35
19.1%
3 37
20.2%
4 36
19.7%
5 12
 
6.6%
6 6
 
3.3%
7 4
 
2.2%
8 3
 
1.6%
9 1
 
0.5%
10 1
 
0.5%
ValueCountFrequency (%)
12 2
 
1.1%
11 3
 
1.6%
10 1
 
0.5%
9 1
 
0.5%
8 3
 
1.6%
7 4
 
2.2%
6 6
 
3.3%
5 12
 
6.6%
4 36
19.7%
3 37
20.2%

출구별 주요시설명
Text

Distinct170
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T18:47:15.356377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length6.3224044
Min length2

Characters and Unicode

Total characters1157
Distinct characters163
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

Unique158 ?
Unique (%)86.3%

Sample

1st row암사종합시장
2nd row암사1동사무소
3rd row암사4동사무소
4th row천일중학교
5th row강동초등학교
ValueCountFrequency (%)
방면 4
 
2.0%
성남종합운동장 3
 
1.5%
성남서중학교 2
 
1.0%
성남지방노동사무소 2
 
1.0%
성남 2
 
1.0%
근로복지공단 2
 
1.0%
가락동 2
 
1.0%
고등학교 2
 
1.0%
올림픽공원 2
 
1.0%
성남시 2
 
1.0%
Other values (172) 180
88.7%
2023-12-12T18:47:15.761563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 54
 
4.7%
학 49
 
4.2%
교 49
 
4.2%
소 48
 
4.1%
성 46
 
4.0%
파 42
 
3.6%
남 35
 
3.0%
사 32
 
2.8%
등 31
 
2.7%
무 25
 
2.2%
Other values (153) 746
64.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1095
94.6%
Decimal Number 29
 
2.5%
Space Separator 20
 
1.7%
Other Punctuation 5
 
0.4%
Open Punctuation 3
 
0.3%
Close Punctuation 3
 
0.3%
Uppercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 54
 
4.9%
학 49
 
4.5%
교 49
 
4.5%
소 48
 
4.4%
성 46
 
4.2%
파 42
 
3.8%
남 35
 
3.2%
사 32
 
2.9%
등 31
 
2.8%
무 25
 
2.3%
Other values (143) 684
62.5%
Decimal Number
ValueCountFrequency (%)
2 13
44.8%
1 12
41.4%
3 2
 
6.9%
4 2
 
6.9%
Uppercase Letter
ValueCountFrequency (%)
I 1
50.0%
C 1
50.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1095
94.6%
Common 60
 
5.2%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 54
 
4.9%
학 49
 
4.5%
교 49
 
4.5%
소 48
 
4.4%
성 46
 
4.2%
파 42
 
3.8%
남 35
 
3.2%
사 32
 
2.9%
등 31
 
2.8%
무 25
 
2.3%
Other values (143) 684
62.5%
Common
ValueCountFrequency (%)
20
33.3%
2 13
21.7%
1 12
20.0%
/ 5
 
8.3%
( 3
 
5.0%
) 3
 
5.0%
3 2
 
3.3%
4 2
 
3.3%
Latin
ValueCountFrequency (%)
I 1
50.0%
C 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1095
94.6%
ASCII 62
 
5.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 54
 
4.9%
학 49
 
4.5%
교 49
 
4.5%
소 48
 
4.4%
성 46
 
4.2%
파 42
 
3.8%
남 35
 
3.2%
사 32
 
2.9%
등 31
 
2.8%
무 25
 
2.3%
Other values (143) 684
62.5%
ASCII
ValueCountFrequency (%)
20
32.3%
2 13
21.0%
1 12
19.4%
/ 5
 
8.1%
( 3
 
4.8%
) 3
 
4.8%
3 2
 
3.2%
4 2
 
3.2%
I 1
 
1.6%
C 1
 
1.6%

Interactions

2023-12-12T18:47:13.599168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:47:15.856134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명출구번호
역명1.0000.500
출구번호0.5001.000
2023-12-12T18:47:15.932281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출구번호역명
출구번호1.0000.224
역명0.2241.000

Missing values

2023-12-12T18:47:13.730860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:47:13.844869image/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
0서울교통공사8호선암사1암사종합시장
1서울교통공사8호선암사1암사1동사무소
2서울교통공사8호선암사1암사4동사무소
3서울교통공사8호선암사2천일중학교
4서울교통공사8호선암사2강동초등학교
5서울교통공사8호선암사3한국점자도서관
6서울교통공사8호선암사3신암초등학교
7서울교통공사8호선암사4암사소방파출소
8서울교통공사8호선암사4암사2동사무소
9서울교통공사8호선암사4암사2파출소
철도운영기관명선명역명출구번호출구별 주요시설명
173서울교통공사8호선모란6모란시장
174서울교통공사8호선모란7성남IC방면
175서울교통공사8호선모란8탄천방면
176서울교통공사8호선모란9성수초등학교
177서울교통공사8호선모란10근로복지공단 성남지사
178서울교통공사8호선모란11수진동우체국
179서울교통공사8호선모란11성남소방서
180서울교통공사8호선모란11풍생중/ 고등학교
181서울교통공사8호선모란12중앙로
182서울교통공사8호선모란12성남종합운동장