Overview

Dataset statistics

Number of variables7
Number of observations336
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.8 KiB
Average record size in memory60.4 B

Variable types

Categorical5
Numeric2

Dataset

Description부산교통공사에서 운영하는 부산4호선의 승강장 이격거리에 대한 데이터로 철도운영기관명, 선명, 역명, 승강장이격거리에 대한 승강장번호, 차량순서, 차량출입문번호, 안전거리의 데이터가 있습니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041497/fileData.do

Alerts

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

Reproduction

Analysis started2023-12-13 00:54:12.247935
Analysis finished2023-12-13 00:54:12.859791
Duration0.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
부산교통공사
336 

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 336
100.0%

Length

2023-12-13T09:54:12.908331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:54:12.980067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산교통공사 336
100.0%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
4호선
336 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4호선
2nd row4호선
3rd row4호선
4th row4호선
5th row4호선

Common Values

ValueCountFrequency (%)
4호선 336
100.0%

Length

2023-12-13T09:54:13.051037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:54:13.134055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4호선 336
100.0%

역명
Categorical

Distinct14
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
고촌
24 
금사
24 
낙민
24 
동래
24 
동부산대학(윗반송)
24 
Other values (9)
216 

Length

Max length10
Median length2
Mean length4.3571429
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고촌 24
 
7.1%
금사 24
 
7.1%
낙민 24
 
7.1%
동래 24
 
7.1%
동부산대학(윗반송) 24
 
7.1%
명장 24
 
7.1%
미남 24
 
7.1%
반여농산물시장 24
 
7.1%
서동 24
 
7.1%
석대 24
 
7.1%
Other values (4) 96
28.6%

Length

2023-12-13T09:54:13.220452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고촌 24
 
7.1%
금사 24
 
7.1%
낙민 24
 
7.1%
동래 24
 
7.1%
동부산대학(윗반송 24
 
7.1%
명장 24
 
7.1%
미남 24
 
7.1%
반여농산물시장 24
 
7.1%
서동 24
 
7.1%
석대 24
 
7.1%
Other values (4) 96
28.6%

승강장번호
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
1
168 
2
168 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 168
50.0%
2 168
50.0%

Length

2023-12-13T09:54:13.323125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:54:13.413697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 168
50.0%
2 168
50.0%

차량순서
Real number (ℝ)

Distinct6
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-13T09:54:13.494550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3.5
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7103722
Coefficient of variation (CV)0.48867778
Kurtosis-1.2695813
Mean3.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum1176
Variance2.9253731
MonotonicityNot monotonic
2023-12-13T09:54:13.581590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 56
16.7%
2 56
16.7%
3 56
16.7%
4 56
16.7%
5 56
16.7%
6 56
16.7%
ValueCountFrequency (%)
1 56
16.7%
2 56
16.7%
3 56
16.7%
4 56
16.7%
5 56
16.7%
6 56
16.7%
ValueCountFrequency (%)
6 56
16.7%
5 56
16.7%
4 56
16.7%
3 56
16.7%
2 56
16.7%
1 56
16.7%
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
1
168 
2
168 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 168
50.0%
2 168
50.0%

Length

2023-12-13T09:54:13.675496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:54:13.754180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 168
50.0%
2 168
50.0%

안전거리
Real number (ℝ)

Distinct30
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3877976
Minimum7.9
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-13T09:54:13.834574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.9
5-th percentile8.4
Q19
median9.4
Q39.8
95-th percentile10.5
Maximum11
Range3.1
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.62571743
Coefficient of variation (CV)0.066652207
Kurtosis-0.42750152
Mean9.3877976
Median Absolute Deviation (MAD)0.4
Skewness0.10763595
Sum3154.3
Variance0.3915223
MonotonicityNot monotonic
2023-12-13T09:54:13.926860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9.0 56
16.7%
9.5 44
13.1%
10.0 26
 
7.7%
8.5 23
 
6.8%
9.3 22
 
6.5%
9.7 16
 
4.8%
10.5 14
 
4.2%
10.2 14
 
4.2%
9.8 14
 
4.2%
9.2 14
 
4.2%
Other values (20) 93
27.7%
ValueCountFrequency (%)
7.9 1
 
0.3%
8.0 2
 
0.6%
8.1 3
 
0.9%
8.2 2
 
0.6%
8.3 6
 
1.8%
8.4 6
 
1.8%
8.5 23
6.8%
8.6 4
 
1.2%
8.7 6
 
1.8%
8.8 7
 
2.1%
ValueCountFrequency (%)
11.0 3
 
0.9%
10.9 1
 
0.3%
10.7 1
 
0.3%
10.5 14
4.2%
10.4 5
 
1.5%
10.3 10
 
3.0%
10.2 14
4.2%
10.1 1
 
0.3%
10.0 26
7.7%
9.9 3
 
0.9%

Interactions

2023-12-13T09:54:12.564909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:54:12.437441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:54:12.647211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:54:12.499994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:54:13.992525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명승강장번호차량순서차량출입문번호안전거리
역명1.0000.0000.0000.0000.322
승강장번호0.0001.0000.0000.0000.000
차량순서0.0000.0001.0000.0000.293
차량출입문번호0.0000.0000.0001.0000.000
안전거리0.3220.0000.2930.0001.000
2023-12-13T09:54:14.077012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차량출입문번호역명승강장번호
차량출입문번호1.0000.0000.000
역명0.0001.0000.000
승강장번호0.0000.0001.000
2023-12-13T09:54:14.162148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차량순서안전거리역명승강장번호차량출입문번호
차량순서1.000-0.0360.0000.0000.000
안전거리-0.0361.0000.1340.0000.000
역명0.0000.1341.0000.0000.000
승강장번호0.0000.0000.0001.0000.000
차량출입문번호0.0000.0000.0000.0001.000

Missing values

2023-12-13T09:54:12.739634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:54:12.822574image/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

철도운영기관명선명역명승강장번호차량순서차량출입문번호안전거리
0부산교통공사4호선고촌1119.3
1부산교통공사4호선고촌1129.8
2부산교통공사4호선고촌1218.8
3부산교통공사4호선고촌1229.5
4부산교통공사4호선고촌1329.5
5부산교통공사4호선고촌13110.0
6부산교통공사4호선고촌1428.5
7부산교통공사4호선고촌1419.3
8부산교통공사4호선고촌1518.4
9부산교통공사4호선고촌1528.6
철도운영기관명선명역명승강장번호차량순서차량출입문번호안전거리
326부산교통공사4호선충렬사(안락)2229.0
327부산교통공사4호선충렬사(안락)2219.1
328부산교통공사4호선충렬사(안락)2329.3
329부산교통공사4호선충렬사(안락)2319.5
330부산교통공사4호선충렬사(안락)24210.0
331부산교통공사4호선충렬사(안락)2419.0
332부산교통공사4호선충렬사(안락)2529.4
333부산교통공사4호선충렬사(안락)2518.9
334부산교통공사4호선충렬사(안락)2619.0
335부산교통공사4호선충렬사(안락)2629.3