Overview

Dataset statistics

Number of variables5
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 KiB
Average record size in memory48.0 B

Variable types

Numeric1
Text1
Categorical3

Dataset

Description대전 도시철도 역사의 승강장 내 비상계단 설치 현황에 대한 데이터로 역명 및 부역명, 승강장 형태, 승강장 선형, 수량 등의 항목을 제공합니다.
Author대전교통공사
URLhttps://www.data.go.kr/data/15056487/fileData.do

Alerts

승강장 형식 is highly overall correlated with 비상계단 수량High correlation
비상계단 수량 is highly overall correlated with 승강장 형식High correlation
역번호 has unique valuesUnique
역사명 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:14:15.386403
Analysis finished2023-12-11 23:14:15.858360
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

역번호
Real number (ℝ)

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.5
Minimum101
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T08:14:15.930474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102.05
Q1106.25
median111.5
Q3116.75
95-th percentile120.95
Maximum122
Range21
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.4935866
Coefficient of variation (CV)0.058238445
Kurtosis-1.2
Mean111.5
Median Absolute Deviation (MAD)5.5
Skewness0
Sum2453
Variance42.166667
MonotonicityStrictly increasing
2023-12-12T08:14:16.069143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
101 1
 
4.5%
113 1
 
4.5%
122 1
 
4.5%
121 1
 
4.5%
120 1
 
4.5%
119 1
 
4.5%
118 1
 
4.5%
117 1
 
4.5%
116 1
 
4.5%
115 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
101 1
4.5%
102 1
4.5%
103 1
4.5%
104 1
4.5%
105 1
4.5%
106 1
4.5%
107 1
4.5%
108 1
4.5%
109 1
4.5%
110 1
4.5%
ValueCountFrequency (%)
122 1
4.5%
121 1
4.5%
120 1
4.5%
119 1
4.5%
118 1
4.5%
117 1
4.5%
116 1
4.5%
115 1
4.5%
114 1
4.5%
113 1
4.5%

역사명
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T08:14:16.258031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length5.0454545
Min length2

Characters and Unicode

Total characters111
Distinct characters67
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row판암(대전대)
2nd row신흥
3rd row대동(우송대)
4th row대전역
5th row중앙로
ValueCountFrequency (%)
판암(대전대 1
 
4.5%
신흥 1
 
4.5%
지족(침신대 1
 
4.5%
노은 1
 
4.5%
월드컵경기장(노은도매시장 1
 
4.5%
현충원(한밭대 1
 
4.5%
구암 1
 
4.5%
유성온천(충남대·목원대 1
 
4.5%
갑천 1
 
4.5%
월평(한국과학기술원 1
 
4.5%
Other values (12) 12
54.5%
2023-12-12T08:14:16.649477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 11
 
9.9%
( 8
 
7.2%
) 8
 
7.2%
청 3
 
2.7%
전 3
 
2.7%
원 3
 
2.7%
월 2
 
1.8%
한 2
 
1.8%
천 2
 
1.8%
암 2
 
1.8%
Other values (57) 67
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 94
84.7%
Open Punctuation 8
 
7.2%
Close Punctuation 8
 
7.2%
Other Punctuation 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 11
 
11.7%
청 3
 
3.2%
전 3
 
3.2%
원 3
 
3.2%
월 2
 
2.1%
한 2
 
2.1%
천 2
 
2.1%
암 2
 
2.1%
성 2
 
2.1%
충 2
 
2.1%
Other values (54) 62
66.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 94
84.7%
Common 17
 
15.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 11
 
11.7%
청 3
 
3.2%
전 3
 
3.2%
원 3
 
3.2%
월 2
 
2.1%
한 2
 
2.1%
천 2
 
2.1%
암 2
 
2.1%
성 2
 
2.1%
충 2
 
2.1%
Other values (54) 62
66.0%
Common
ValueCountFrequency (%)
( 8
47.1%
) 8
47.1%
· 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 94
84.7%
ASCII 16
 
14.4%
None 1
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 11
 
11.7%
청 3
 
3.2%
전 3
 
3.2%
원 3
 
3.2%
월 2
 
2.1%
한 2
 
2.1%
천 2
 
2.1%
암 2
 
2.1%
성 2
 
2.1%
충 2
 
2.1%
Other values (54) 62
66.0%
ASCII
ValueCountFrequency (%)
( 8
50.0%
) 8
50.0%
None
ValueCountFrequency (%)
· 1
100.0%

승강장 형식
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size308.0 B
상대식
16 
섬식
4 
복합식
2 

Length

Max length3
Median length3
Mean length2.8181818
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row복합식
2nd row상대식
3rd row섬식
4th row섬식
5th row상대식

Common Values

ValueCountFrequency (%)
상대식 16
72.7%
섬식 4
 
18.2%
복합식 2
 
9.1%

Length

2023-12-12T08:14:16.808251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:14:16.923513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상대식 16
72.7%
섬식 4
 
18.2%
복합식 2
 
9.1%

승강장 선형
Categorical

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
직선형
19 
곡선형
3 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row곡선형
2nd row직선형
3rd row곡선형
4th row직선형
5th row직선형

Common Values

ValueCountFrequency (%)
직선형 19
86.4%
곡선형 3
 
13.6%

Length

2023-12-12T08:14:17.025913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:14:17.120141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
직선형 19
86.4%
곡선형 3
 
13.6%

비상계단 수량
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size308.0 B
4
17 
2
4 
8
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.5%

Sample

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

Common Values

ValueCountFrequency (%)
4 17
77.3%
2 4
 
18.2%
8 1
 
4.5%

Length

2023-12-12T08:14:17.224202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:14:17.335510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 17
77.3%
2 4
 
18.2%
8 1
 
4.5%

Interactions

2023-12-12T08:14:15.592703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:14:17.411643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역번호역사명승강장 형식승강장 선형비상계단 수량
역번호1.0001.0000.4500.4670.461
역사명1.0001.0001.0001.0001.000
승강장 형식0.4501.0001.0000.1620.984
승강장 선형0.4671.0000.1621.0000.000
비상계단 수량0.4611.0000.9840.0001.000
2023-12-12T08:14:17.508144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승강장 선형비상계단 수량승강장 형식
승강장 선형1.0000.0000.252
비상계단 수량0.0001.0000.841
승강장 형식0.2520.8411.000
2023-12-12T08:14:17.601185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역번호승강장 형식승강장 선형비상계단 수량
역번호1.0000.1970.2440.206
승강장 형식0.1971.0000.2520.841
승강장 선형0.2440.2521.0000.000
비상계단 수량0.2060.8410.0001.000

Missing values

2023-12-12T08:14:15.713744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:14:15.820746image/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

역번호역사명승강장 형식승강장 선형비상계단 수량
0101판암(대전대)복합식곡선형4
1102신흥상대식직선형4
2103대동(우송대)섬식곡선형2
3104대전역섬식직선형2
4105중앙로상대식직선형4
5106중구청섬식직선형2
6107서대전네거리섬식직선형2
7108오룡상대식직선형4
8109용문상대식직선형4
9110탄방상대식직선형4
역번호역사명승강장 형식승강장 선형비상계단 수량
12113갈마상대식직선형4
13114월평(한국과학기술원)상대식직선형4
14115갑천상대식직선형4
15116유성온천(충남대·목원대)상대식직선형4
16117구암상대식직선형4
17118현충원(한밭대)상대식직선형4
18119월드컵경기장(노은도매시장)상대식직선형4
19120노은상대식직선형4
20121지족(침신대)상대식곡선형4
21122반석(칠성대)복합식직선형8