Overview

Dataset statistics

Number of variables7
Number of observations3440
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory201.7 KiB
Average record size in memory60.0 B

Variable types

Categorical5
Numeric2

Dataset

Description서울교통공사에서 운영하는 3호선의 승강장 이격거리에 대한 데이터로 철도운영기관명, 선명, 역명, 승강장이격거리에 대한 승강장번호, 차량순서, 차량출입문번호, 안전거리의 데이터가 있습니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041516/fileData.do

Alerts

선명 has constant value ""Constant
역명 is highly overall correlated with 철도운영기관명High correlation
철도운영기관명 is highly overall correlated with 안전거리 and 1 other fieldsHigh correlation
안전거리 is highly overall correlated with 철도운영기관명High correlation

Reproduction

Analysis started2023-12-12 02:03:00.724881
Analysis finished2023-12-12 02:03:01.832406
Duration1.11 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.0 KiB
서울교통공사
2720 
코레일
720 

Length

Max length6
Median length6
Mean length5.372093
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울교통공사 2720
79.1%
코레일 720
 
20.9%

Length

2023-12-12T11:03:01.949647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:03:02.088000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울교통공사 2720
79.1%
코레일 720
 
20.9%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.0 KiB
3호선
3440 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3호선
2nd row3호선
3rd row3호선
4th row3호선
5th row3호선

Common Values

ValueCountFrequency (%)
3호선 3440
100.0%

Length

2023-12-12T11:03:02.265184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:03:02.423511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3호선 3440
100.0%

역명
Categorical

HIGH CORRELATION 

Distinct43
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size27.0 KiB
가락시장
 
80
도곡
 
80
경찰병원
 
80
고속터미널
 
80
교대(법원·검찰청)
 
80
Other values (38)
3040 

Length

Max length12
Median length2
Mean length3.2790698
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가락시장 80
 
2.3%
도곡 80
 
2.3%
경찰병원 80
 
2.3%
고속터미널 80
 
2.3%
교대(법원·검찰청) 80
 
2.3%
구파발 80
 
2.3%
금호 80
 
2.3%
남부터미널(예술의전당) 80
 
2.3%
녹번 80
 
2.3%
대청 80
 
2.3%
Other values (33) 2640
76.7%

Length

2023-12-12T11:03:02.587649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가락시장 80
 
2.3%
양재(서초구청 80
 
2.3%
오금 80
 
2.3%
옥수 80
 
2.3%
을지로3가 80
 
2.3%
일원 80
 
2.3%
잠원 80
 
2.3%
종로3가 80
 
2.3%
지축 80
 
2.3%
충무로 80
 
2.3%
Other values (33) 2640
76.7%

승강장번호
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.0 KiB
1
1720 
2
1720 

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 1720
50.0%
2 1720
50.0%

Length

2023-12-12T11:03:02.814961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:03:02.928259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1720
50.0%
2 1720
50.0%

차량순서
Real number (ℝ)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-12T11:03:03.058661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5.5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8726989
Coefficient of variation (CV)0.52230889
Kurtosis-1.2242775
Mean5.5
Median Absolute Deviation (MAD)2.5
Skewness0
Sum18920
Variance8.252399
MonotonicityNot monotonic
2023-12-12T11:03:03.231485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 344
10.0%
2 344
10.0%
3 344
10.0%
4 344
10.0%
5 344
10.0%
6 344
10.0%
7 344
10.0%
8 344
10.0%
9 344
10.0%
10 344
10.0%
ValueCountFrequency (%)
1 344
10.0%
2 344
10.0%
3 344
10.0%
4 344
10.0%
5 344
10.0%
6 344
10.0%
7 344
10.0%
8 344
10.0%
9 344
10.0%
10 344
10.0%
ValueCountFrequency (%)
10 344
10.0%
9 344
10.0%
8 344
10.0%
7 344
10.0%
6 344
10.0%
5 344
10.0%
4 344
10.0%
3 344
10.0%
2 344
10.0%
1 344
10.0%

차량출입문번호
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.0 KiB
1
860 
2
860 
3
860 
4
860 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row4

Common Values

ValueCountFrequency (%)
1 860
25.0%
2 860
25.0%
3 860
25.0%
4 860
25.0%

Length

2023-12-12T11:03:03.417170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:03:03.569106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 860
25.0%
2 860
25.0%
3 860
25.0%
4 860
25.0%

안전거리
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7838081
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-12T11:03:03.746988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16.5
median9
Q310.5
95-th percentile14.21
Maximum26
Range25
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1607062
Coefficient of variation (CV)0.35983324
Kurtosis0.97034336
Mean8.7838081
Median Absolute Deviation (MAD)2
Skewness0.62515186
Sum30216.3
Variance9.9900634
MonotonicityNot monotonic
2023-12-12T11:03:03.942369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 355
 
10.3%
11.0 263
 
7.6%
9.0 239
 
6.9%
8.0 210
 
6.1%
6.0 208
 
6.0%
5.0 204
 
5.9%
7.0 186
 
5.4%
8.5 164
 
4.8%
9.5 160
 
4.7%
5.5 154
 
4.5%
Other values (72) 1297
37.7%
ValueCountFrequency (%)
1.0 1
 
< 0.1%
2.0 5
 
0.1%
2.5 13
 
0.4%
3.0 43
 
1.2%
3.5 37
 
1.1%
4.0 102
3.0%
4.5 78
 
2.3%
4.6 1
 
< 0.1%
5.0 204
5.9%
5.2 2
 
0.1%
ValueCountFrequency (%)
26.0 1
 
< 0.1%
25.0 1
 
< 0.1%
23.0 2
 
0.1%
22.0 2
 
0.1%
20.0 4
 
0.1%
19.0 6
 
0.2%
18.5 7
 
0.2%
18.0 23
0.7%
17.5 3
 
0.1%
17.2 1
 
< 0.1%

Interactions

2023-12-12T11:03:01.343458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:03:01.138023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:03:01.477246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:03:01.250686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:03:04.086343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
철도운영기관명역명승강장번호차량순서차량출입문번호안전거리
철도운영기관명1.0001.0000.0000.0000.0000.656
역명1.0001.0000.0000.0000.0000.769
승강장번호0.0000.0001.0000.0000.0000.063
차량순서0.0000.0000.0001.0000.0000.112
차량출입문번호0.0000.0000.0000.0001.0000.067
안전거리0.6560.7690.0630.1120.0671.000
2023-12-12T11:03:04.252287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명철도운영기관명승강장번호차량출입문번호
역명1.0000.9940.0000.000
철도운영기관명0.9941.0000.0000.000
승강장번호0.0000.0001.0000.000
차량출입문번호0.0000.0000.0001.000
2023-12-12T11:03:04.425519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차량순서안전거리철도운영기관명역명승강장번호차량출입문번호
차량순서1.0000.0210.0000.0000.0000.000
안전거리0.0211.0000.5110.3880.0480.043
철도운영기관명0.0000.5111.0000.9940.0000.000
역명0.0000.3880.9941.0000.0000.000
승강장번호0.0000.0480.0000.0001.0000.000
차량출입문번호0.0000.0430.0000.0000.0001.000

Missing values

2023-12-12T11:03:01.616677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:03:01.759259image/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
0서울교통공사3호선가락시장1119.0
1서울교통공사3호선가락시장11214.5
2서울교통공사3호선가락시장11314.0
3서울교통공사3호선가락시장11414.0
4서울교통공사3호선가락시장1248.0
5서울교통공사3호선가락시장1218.0
6서울교통공사3호선가락시장1228.5
7서울교통공사3호선가락시장1238.5
8서울교통공사3호선가락시장1327.0
9서울교통공사3호선가락시장1317.0
철도운영기관명선명역명승강장번호차량순서차량출입문번호안전거리
3430코레일3호선화정2825.5
3431코레일3호선화정2835.5
3432코레일3호선화정2935.0
3433코레일3호선화정2945.0
3434코레일3호선화정2925.5
3435코레일3호선화정2915.5
3436코레일3호선화정21046.5
3437코레일3호선화정21018.0
3438코레일3호선화정21036.0
3439코레일3호선화정21027.5