Overview

Dataset statistics

Number of variables7
Number of observations9360
Missing cells24
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory548.6 KiB
Average record size in memory60.0 B

Variable types

Categorical4
Text1
Numeric2

Dataset

Description서울교통공사에서 운영하는 도시광역철도역들의 철도운영기관명,선명,역명,승강장번호,차량순서,차량출입문번호,안전거리 에 대한 데이터 입니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041805/fileData.do

Alerts

철도운영기관명 has constant value ""Constant

Reproduction

Analysis started2023-12-12 04:09:59.189993
Analysis finished2023-12-12 04:10:00.547024
Duration1.36 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.3 KiB
서울교통공사
9360 

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 9360
100.0%

Length

2023-12-12T13:10:00.639917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:10:00.760823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울교통공사 9360
100.0%

선명
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.3 KiB
2호선
3760 
3호선
2720 
4호선
2080 
1호선
800 

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 3760
40.2%
3호선 2720
29.1%
4호선 2080
22.2%
1호선 800
 
8.5%

Length

2023-12-12T13:10:00.913965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:10:01.044081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2호선 3760
40.2%
3호선 2720
29.1%
4호선 2080
22.2%
1호선 800
 
8.5%

역명
Text

Distinct111
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size73.3 KiB
2023-12-12T13:10:01.438511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length4.157265
Min length2

Characters and Unicode

Total characters38912
Distinct characters164
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

Unique0 ?
Unique (%)0.0%

Sample

1st row동대문
2nd row동대문
3rd row동대문
4th row동대문
5th row동대문
ValueCountFrequency (%)
동대문 160
 
1.7%
종로3가 160
 
1.7%
을지로3가 160
 
1.7%
교대(법원·검찰청 160
 
1.7%
사당 160
 
1.7%
동대문역사문화공원 160
 
1.7%
충무로 160
 
1.7%
시청 160
 
1.7%
서울역 160
 
1.7%
신설동 112
 
1.2%
Other values (101) 7808
83.4%
2023-12-12T13:10:02.045305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 1952
 
5.0%
구 1920
 
4.9%
) 1712
 
4.4%
( 1712
 
4.4%
청 1200
 
3.1%
동 1104
 
2.8%
신 992
 
2.5%
로 960
 
2.5%
입 880
 
2.3%
문 832
 
2.1%
Other values (154) 25648
65.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34848
89.6%
Close Punctuation 1712
 
4.4%
Open Punctuation 1712
 
4.4%
Decimal Number 480
 
1.2%
Other Punctuation 160
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 1952
 
5.6%
구 1920
 
5.5%
청 1200
 
3.4%
동 1104
 
3.2%
신 992
 
2.8%
로 960
 
2.8%
입 880
 
2.5%
문 832
 
2.4%
원 720
 
2.1%
서 720
 
2.1%
Other values (148) 23568
67.6%
Decimal Number
ValueCountFrequency (%)
3 320
66.7%
5 80
 
16.7%
4 80
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 1712
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1712
100.0%
Other Punctuation
ValueCountFrequency (%)
· 160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34848
89.6%
Common 4064
 
10.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 1952
 
5.6%
구 1920
 
5.5%
청 1200
 
3.4%
동 1104
 
3.2%
신 992
 
2.8%
로 960
 
2.8%
입 880
 
2.5%
문 832
 
2.4%
원 720
 
2.1%
서 720
 
2.1%
Other values (148) 23568
67.6%
Common
ValueCountFrequency (%)
) 1712
42.1%
( 1712
42.1%
3 320
 
7.9%
· 160
 
3.9%
5 80
 
2.0%
4 80
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34848
89.6%
ASCII 3904
 
10.0%
None 160
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 1952
 
5.6%
구 1920
 
5.5%
청 1200
 
3.4%
동 1104
 
3.2%
신 992
 
2.8%
로 960
 
2.8%
입 880
 
2.5%
문 832
 
2.4%
원 720
 
2.1%
서 720
 
2.1%
Other values (148) 23568
67.6%
ASCII
ValueCountFrequency (%)
) 1712
43.9%
( 1712
43.9%
3 320
 
8.2%
5 80
 
2.0%
4 80
 
2.0%
None
ValueCountFrequency (%)
· 160
100.0%

승강장번호
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.3 KiB
1
4680 
2
4680 

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 4680
50.0%
2 4680
50.0%

Length

2023-12-12T13:10:02.198869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:10:02.300691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4680
50.0%
2 4680
50.0%

차량순서
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4179487
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2023-12-12T13:10:02.404970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation2.8712625
Coefficient of variation (CV)0.5299538
Kurtosis-1.2215027
Mean5.4179487
Median Absolute Deviation (MAD)2
Skewness0.041562297
Sum50712
Variance8.2441484
MonotonicityNot monotonic
2023-12-12T13:10:02.542014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 968
10.3%
2 968
10.3%
3 968
10.3%
4 968
10.3%
5 936
10.0%
6 936
10.0%
7 904
9.7%
8 904
9.7%
9 904
9.7%
10 904
9.7%
ValueCountFrequency (%)
1 968
10.3%
2 968
10.3%
3 968
10.3%
4 968
10.3%
5 936
10.0%
6 936
10.0%
7 904
9.7%
8 904
9.7%
9 904
9.7%
10 904
9.7%
ValueCountFrequency (%)
10 904
9.7%
9 904
9.7%
8 904
9.7%
7 904
9.7%
6 936
10.0%
5 936
10.0%
4 968
10.3%
3 968
10.3%
2 968
10.3%
1 968
10.3%

차량출입문번호
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.3 KiB
1
2340 
2
2340 
3
2340 
4
2340 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 2340
25.0%
2 2340
25.0%
3 2340
25.0%
4 2340
25.0%

Length

2023-12-12T13:10:02.673381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:10:02.803381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2340
25.0%
2 2340
25.0%
3 2340
25.0%
4 2340
25.0%

안전거리
Real number (ℝ)

Distinct121
Distinct (%)1.3%
Missing24
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean9.1484897
Minimum0
Maximum28
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size82.4 KiB
2023-12-12T13:10:02.942268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q17.5
median9
Q310.5
95-th percentile15
Maximum28
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.1137083
Coefficient of variation (CV)0.34035217
Kurtosis2.0771386
Mean9.1484897
Median Absolute Deviation (MAD)1.5
Skewness0.70661488
Sum85410.3
Variance9.6951793
MonotonicityNot monotonic
2023-12-12T13:10:03.134101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.0 1082
 
11.6%
10.0 1001
 
10.7%
8.0 823
 
8.8%
11.0 649
 
6.9%
9.5 569
 
6.1%
8.5 528
 
5.6%
7.0 433
 
4.6%
6.0 336
 
3.6%
5.0 332
 
3.5%
10.5 327
 
3.5%
Other values (111) 3256
34.8%
ValueCountFrequency (%)
0.0 1
 
< 0.1%
0.5 1
 
< 0.1%
0.7 3
 
< 0.1%
1.0 5
 
0.1%
1.5 6
 
0.1%
2.0 45
0.5%
2.3 1
 
< 0.1%
2.5 32
0.3%
2.6 1
 
< 0.1%
2.8 3
 
< 0.1%
ValueCountFrequency (%)
28.0 1
 
< 0.1%
26.0 1
 
< 0.1%
25.0 3
 
< 0.1%
24.0 3
 
< 0.1%
23.5 1
 
< 0.1%
23.0 8
0.1%
22.5 2
 
< 0.1%
22.0 4
 
< 0.1%
21.0 14
0.1%
20.5 2
 
< 0.1%

Interactions

2023-12-12T13:09:59.993805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:59.764116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:10:00.131065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:59.887423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:10:03.273808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
선명승강장번호차량순서차량출입문번호안전거리
선명1.0000.0000.0000.0000.195
승강장번호0.0001.0000.0000.0000.071
차량순서0.0000.0001.0000.0000.053
차량출입문번호0.0000.0000.0001.0000.077
안전거리0.1950.0710.0530.0771.000
2023-12-12T13:10:03.401263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승강장번호선명차량출입문번호
승강장번호1.0000.0000.000
선명0.0001.0000.000
차량출입문번호0.0000.0001.000
2023-12-12T13:10:03.506537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차량순서안전거리선명승강장번호차량출입문번호
차량순서1.0000.0100.0000.0000.000
안전거리0.0101.0000.1150.0540.048
선명0.0000.1151.0000.0000.000
승강장번호0.0000.0540.0001.0000.000
차량출입문번호0.0000.0480.0000.0001.000

Missing values

2023-12-12T13:10:00.297582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:10:00.463421image/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
0서울교통공사1호선동대문11116.0
1서울교통공사1호선동대문11216.0
2서울교통공사1호선동대문11314.0
3서울교통공사1호선동대문11416.0
4서울교통공사1호선동대문12116.0
5서울교통공사1호선동대문12214.0
6서울교통공사1호선동대문12313.0
7서울교통공사1호선동대문12413.0
8서울교통공사1호선동대문13313.0
9서울교통공사1호선동대문13213.0
철도운영기관명선명역명승강장번호차량순서차량출입문번호안전거리
9350서울교통공사4호선회현(남대문시장)2824.0
9351서울교통공사4호선회현(남대문시장)28110.0
9352서울교통공사4호선회현(남대문시장)29410.0
9353서울교통공사4호선회현(남대문시장)29311.0
9354서울교통공사4호선회현(남대문시장)2925.0
9355서울교통공사4호선회현(남대문시장)29112.0
9356서울교통공사4호선회현(남대문시장)21035.0
9357서울교통공사4호선회현(남대문시장)21024.5
9358서울교통공사4호선회현(남대문시장)210112.0
9359서울교통공사4호선회현(남대문시장)210412.0