Overview

Dataset statistics

Number of variables7
Number of observations3392
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory198.9 KiB
Average record size in memory60.0 B

Variable types

Categorical4
Text1
Numeric2

Dataset

Description서울교통공사에서 운영하는 7호선의 승강장이격거리에 대한 데이터로 철도운영기관명,선명,역명,승강장번호,차량순서,차량출입문번호,안전거리 등이 있습니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15041520/fileData.do

Alerts

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

Reproduction

Analysis started2023-12-12 09:28:46.593358
Analysis finished2023-12-12 09:28:47.708197
Duration1.11 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.6 KiB
서울교통공사
3392 

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 3392
100.0%

Length

2023-12-12T18:28:47.814053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:28:47.917028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울교통공사 3392
100.0%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.6 KiB
7호선
3392 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7호선
2nd row7호선
3rd row7호선
4th row7호선
5th row7호선

Common Values

ValueCountFrequency (%)
7호선 3392
100.0%

Length

2023-12-12T18:28:48.044807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:28:48.171014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7호선 3392
100.0%

역명
Text

Distinct51
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size26.6 KiB
2023-12-12T18:28:48.458691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.1981132
Min length2

Characters and Unicode

Total characters14240
Distinct characters113
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 row가산디지털단지
2nd row가산디지털단지
3rd row가산디지털단지
4th row가산디지털단지
5th row가산디지털단지
ValueCountFrequency (%)
청담 128
 
3.8%
온수(성공회대입구 128
 
3.8%
광명사거리 96
 
2.8%
하계 64
 
1.9%
상도 64
 
1.9%
가산디지털단지 64
 
1.9%
용마산 64
 
1.9%
상동 64
 
1.9%
상봉(시외버스터미널 64
 
1.9%
수락산 64
 
1.9%
Other values (41) 2592
76.4%
2023-12-12T18:28:48.990126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 704
 
4.9%
대 640
 
4.5%
( 576
 
4.0%
) 576
 
4.0%
청 384
 
2.7%
입 384
 
2.7%
산 384
 
2.7%
동 320
 
2.2%
중 256
 
1.8%
신 256
 
1.8%
Other values (103) 9760
68.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13088
91.9%
Open Punctuation 576
 
4.0%
Close Punctuation 576
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 704
 
5.4%
대 640
 
4.9%
청 384
 
2.9%
입 384
 
2.9%
산 384
 
2.9%
동 320
 
2.4%
중 256
 
2.0%
신 256
 
2.0%
천 256
 
2.0%
공 256
 
2.0%
Other values (101) 9248
70.7%
Open Punctuation
ValueCountFrequency (%)
( 576
100.0%
Close Punctuation
ValueCountFrequency (%)
) 576
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13088
91.9%
Common 1152
 
8.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 704
 
5.4%
대 640
 
4.9%
청 384
 
2.9%
입 384
 
2.9%
산 384
 
2.9%
동 320
 
2.4%
중 256
 
2.0%
신 256
 
2.0%
천 256
 
2.0%
공 256
 
2.0%
Other values (101) 9248
70.7%
Common
ValueCountFrequency (%)
( 576
50.0%
) 576
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13088
91.9%
ASCII 1152
 
8.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 704
 
5.4%
대 640
 
4.9%
청 384
 
2.9%
입 384
 
2.9%
산 384
 
2.9%
동 320
 
2.4%
중 256
 
2.0%
신 256
 
2.0%
천 256
 
2.0%
공 256
 
2.0%
Other values (101) 9248
70.7%
ASCII
ValueCountFrequency (%)
( 576
50.0%
) 576
50.0%

승강장번호
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.6 KiB
1
1632 
2
1600 
4
 
96
3
 
64

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 1632
48.1%
2 1600
47.2%
4 96
 
2.8%
3 64
 
1.9%

Length

2023-12-12T18:28:49.195158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:28:49.352815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1632
48.1%
2 1600
47.2%
4 96
 
2.8%
3 64
 
1.9%

차량순서
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2023-12-12T18:28:49.480920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.75
median4.5
Q36.25
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.2916257
Coefficient of variation (CV)0.50925015
Kurtosis-1.2381512
Mean4.5
Median Absolute Deviation (MAD)2
Skewness0
Sum15264
Variance5.2515482
MonotonicityNot monotonic
2023-12-12T18:28:49.613946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 424
12.5%
2 424
12.5%
3 424
12.5%
4 424
12.5%
5 424
12.5%
6 424
12.5%
7 424
12.5%
8 424
12.5%
ValueCountFrequency (%)
1 424
12.5%
2 424
12.5%
3 424
12.5%
4 424
12.5%
5 424
12.5%
6 424
12.5%
7 424
12.5%
8 424
12.5%
ValueCountFrequency (%)
8 424
12.5%
7 424
12.5%
6 424
12.5%
5 424
12.5%
4 424
12.5%
3 424
12.5%
2 424
12.5%
1 424
12.5%

차량출입문번호
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.6 KiB
1
848 
3
848 
4
848 
2
848 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 848
25.0%
3 848
25.0%
4 848
25.0%
2 848
25.0%

Length

2023-12-12T18:28:49.804272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:28:49.969332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 848
25.0%
3 848
25.0%
4 848
25.0%
2 848
25.0%

안전거리
Real number (ℝ)

Distinct20
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0990566
Minimum2
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2023-12-12T18:28:50.099910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q39
95-th percentile12
Maximum21
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6605186
Coefficient of variation (CV)0.37477073
Kurtosis1.7890492
Mean7.0990566
Median Absolute Deviation (MAD)2
Skewness1.0903105
Sum24080
Variance7.0783595
MonotonicityNot monotonic
2023-12-12T18:28:50.270550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
5 980
28.9%
9 663
19.5%
6 356
 
10.5%
4 313
 
9.2%
8 297
 
8.8%
7 269
 
7.9%
10 171
 
5.0%
11 88
 
2.6%
12 68
 
2.0%
3 47
 
1.4%
Other values (10) 140
 
4.1%
ValueCountFrequency (%)
2 11
 
0.3%
3 47
 
1.4%
4 313
 
9.2%
5 980
28.9%
6 356
 
10.5%
7 269
 
7.9%
8 297
 
8.8%
9 663
19.5%
10 171
 
5.0%
11 88
 
2.6%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 2
 
0.1%
19 7
 
0.2%
18 8
 
0.2%
17 3
 
0.1%
16 11
 
0.3%
15 24
 
0.7%
14 35
1.0%
13 38
1.1%
12 68
2.0%

Interactions

2023-12-12T18:28:47.160638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:28:46.896176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:28:47.319662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:28:47.013772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:28:50.393596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명승강장번호차량순서차량출입문번호안전거리
역명1.0000.6650.0000.0000.853
승강장번호0.6651.0000.0000.0000.197
차량순서0.0000.0001.0000.0000.000
차량출입문번호0.0000.0000.0001.0000.073
안전거리0.8530.1970.0000.0731.000
2023-12-12T18:28:50.547991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차량출입문번호승강장번호
차량출입문번호1.0000.000
승강장번호0.0001.000
2023-12-12T18:28:50.658238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차량순서안전거리승강장번호차량출입문번호
차량순서1.000-0.0090.0000.000
안전거리-0.0091.0000.1190.044
승강장번호0.0000.1191.0000.000
차량출입문번호0.0000.0440.0001.000

Missing values

2023-12-12T18:28:47.492937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:28:47.628008image/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
0서울교통공사7호선가산디지털단지11111
1서울교통공사7호선가산디지털단지11316
2서울교통공사7호선가산디지털단지11410
3서울교통공사7호선가산디지털단지11216
4서울교통공사7호선가산디지털단지12110
5서울교통공사7호선가산디지털단지12215
6서울교통공사7호선가산디지털단지12315
7서울교통공사7호선가산디지털단지1249
8서울교통공사7호선가산디지털단지1319
9서울교통공사7호선가산디지털단지13212
철도운영기관명선명역명승강장번호차량순서차량출입문번호안전거리
3382서울교통공사7호선학동2625
3383서울교통공사7호선학동2637
3384서울교통공사7호선학동2747
3385서울교통공사7호선학동2716
3386서울교통공사7호선학동2726
3387서울교통공사7호선학동2738
3388서울교통공사7호선학동2847
3389서울교통공사7호선학동2837
3390서울교통공사7호선학동2827
3391서울교통공사7호선학동2816