Overview

Dataset statistics

Number of variables7
Number of observations68
Missing cells4
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory59.9 B

Variable types

Categorical3
Numeric2
Text2

Dataset

Description광주도시철도공사에서 관리하는 도시광역철도역들의 철도운영기관명,선명,역명,출구번호,출구별 주요시설명,거리,주소 입니다.
Author국가철도공단
URLhttps://www.data.go.kr/data/15068948/fileData.do

Alerts

철도운영기관명 has constant value ""Constant
선명 has constant value ""Constant
거리 has 2 (2.9%) missing valuesMissing
주소 has 2 (2.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 17:22:21.612701
Analysis finished2023-12-12 17:22:22.905405
Duration1.29 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

철도운영기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
광주도시철도
68 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광주도시철도 68
100.0%

Length

2023-12-13T02:22:22.993218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:22:23.120970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주도시철도 68
100.0%

선명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
1호선
68 

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 (%)
1호선 68
100.0%

Length

2023-12-13T02:22:23.232991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:22:23.354513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1호선 68
100.0%

역명
Categorical

Distinct17
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size676.0 B
금남로4가
11 
농성
7 
문화전당
7 
운천
6 
남광주
6 
Other values (12)
31 

Length

Max length8
Median length7
Mean length3.7794118
Min length2

Unique

Unique2 ?
Unique (%)2.9%

Sample

1st row공항
2nd row광주송정역
3rd row광주송정역
4th row광주송정역
5th row광주송정역

Common Values

ValueCountFrequency (%)
금남로4가 11
16.2%
농성 7
10.3%
문화전당 7
10.3%
운천 6
8.8%
남광주 6
8.8%
광주송정역 5
7.4%
돌고개 4
 
5.9%
송정공원 4
 
5.9%
김대중컨벤션센터 3
 
4.4%
금남로5가 3
 
4.4%
Other values (7) 12
17.6%

Length

2023-12-13T02:22:23.485887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
금남로4가 11
16.2%
문화전당 7
10.3%
농성 7
10.3%
운천 6
8.8%
남광주 6
8.8%
광주송정역 5
7.4%
돌고개 4
 
5.9%
송정공원 4
 
5.9%
금남로5가 3
 
4.4%
김대중컨벤션센터 3
 
4.4%
Other values (7) 12
17.6%

출구번호
Real number (ℝ)

Distinct7
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2058824
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T02:22:23.613486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5887555
Coefficient of variation (CV)0.4955751
Kurtosis-0.21512853
Mean3.2058824
Median Absolute Deviation (MAD)1
Skewness0.59417576
Sum218
Variance2.524144
MonotonicityNot monotonic
2023-12-13T02:22:23.749643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 17
25.0%
3 16
23.5%
4 12
17.6%
1 9
13.2%
5 8
11.8%
6 3
 
4.4%
7 3
 
4.4%
ValueCountFrequency (%)
1 9
13.2%
2 17
25.0%
3 16
23.5%
4 12
17.6%
5 8
11.8%
6 3
 
4.4%
7 3
 
4.4%
ValueCountFrequency (%)
7 3
 
4.4%
6 3
 
4.4%
5 8
11.8%
4 12
17.6%
3 16
23.5%
2 17
25.0%
1 9
13.2%

출구별 주요시설명
Text

Distinct67
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
2023-12-13T02:22:24.005016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length5.9117647
Min length3

Characters and Unicode

Total characters402
Distinct characters150
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)97.1%

Sample

1st row광주공항
2nd row광산구청
3rd row송정매일시장
4th rowKTX 광주송정역
5th row송정떡갈비거리
ValueCountFrequency (%)
평동산업단지 2
 
2.8%
광주중앙도서관 1
 
1.4%
아시아창작스튜디오 1
 
1.4%
발산공원 1
 
1.4%
서구청 1
 
1.4%
광주영상복합문화관 1
 
1.4%
송정도서관 1
 
1.4%
광주ymca 1
 
1.4%
무등시네마 1
 
1.4%
송정공원 1
 
1.4%
Other values (60) 60
84.5%
2023-12-13T02:22:24.459695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
광 17
 
4.2%
주 15
 
3.7%
시 13
 
3.2%
원 13
 
3.2%
관 11
 
2.7%
공 10
 
2.5%
동 9
 
2.2%
화 9
 
2.2%
장 8
 
2.0%
교 7
 
1.7%
Other values (140) 290
72.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 378
94.0%
Uppercase Letter 12
 
3.0%
Decimal Number 7
 
1.7%
Space Separator 3
 
0.7%
Other Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 17
 
4.5%
주 15
 
4.0%
시 13
 
3.4%
원 13
 
3.4%
관 11
 
2.9%
공 10
 
2.6%
동 9
 
2.4%
화 9
 
2.4%
장 8
 
2.1%
교 7
 
1.9%
Other values (125) 266
70.4%
Uppercase Letter
ValueCountFrequency (%)
C 3
25.0%
Y 1
 
8.3%
A 1
 
8.3%
M 1
 
8.3%
N 1
 
8.3%
X 1
 
8.3%
T 1
 
8.3%
K 1
 
8.3%
V 1
 
8.3%
G 1
 
8.3%
Decimal Number
ValueCountFrequency (%)
5 3
42.9%
1 2
28.6%
8 2
28.6%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 378
94.0%
Latin 12
 
3.0%
Common 12
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 17
 
4.5%
주 15
 
4.0%
시 13
 
3.4%
원 13
 
3.4%
관 11
 
2.9%
공 10
 
2.6%
동 9
 
2.4%
화 9
 
2.4%
장 8
 
2.1%
교 7
 
1.9%
Other values (125) 266
70.4%
Latin
ValueCountFrequency (%)
C 3
25.0%
Y 1
 
8.3%
A 1
 
8.3%
M 1
 
8.3%
N 1
 
8.3%
X 1
 
8.3%
T 1
 
8.3%
K 1
 
8.3%
V 1
 
8.3%
G 1
 
8.3%
Common
ValueCountFrequency (%)
3
25.0%
5 3
25.0%
. 2
16.7%
1 2
16.7%
8 2
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 378
94.0%
ASCII 24
 
6.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
광 17
 
4.5%
주 15
 
4.0%
시 13
 
3.4%
원 13
 
3.4%
관 11
 
2.9%
공 10
 
2.6%
동 9
 
2.4%
화 9
 
2.4%
장 8
 
2.1%
교 7
 
1.9%
Other values (125) 266
70.4%
ASCII
ValueCountFrequency (%)
C 3
12.5%
3
12.5%
5 3
12.5%
. 2
 
8.3%
1 2
 
8.3%
8 2
 
8.3%
Y 1
 
4.2%
A 1
 
4.2%
M 1
 
4.2%
N 1
 
4.2%
Other values (5) 5
20.8%

거리
Real number (ℝ)

MISSING 

Distinct27
Distinct (%)40.9%
Missing2
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean483.33333
Minimum20
Maximum1100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T02:22:24.613583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q1300
median500
Q3700
95-th percentile887.5
Maximum1100
Range1080
Interquartile range (IQR)400

Descriptive statistics

Standard deviation272.69218
Coefficient of variation (CV)0.56419072
Kurtosis-0.74553923
Mean483.33333
Median Absolute Deviation (MAD)200
Skewness0.061062842
Sum31900
Variance74361.026
MonotonicityNot monotonic
2023-12-13T02:22:24.780843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
500 8
 
11.8%
700 7
 
10.3%
350 5
 
7.4%
800 5
 
7.4%
600 4
 
5.9%
300 4
 
5.9%
150 3
 
4.4%
450 2
 
2.9%
1000 2
 
2.9%
40 2
 
2.9%
Other values (17) 24
35.3%
ValueCountFrequency (%)
20 2
2.9%
30 1
 
1.5%
40 2
2.9%
50 1
 
1.5%
100 2
2.9%
150 3
4.4%
180 1
 
1.5%
200 1
 
1.5%
220 1
 
1.5%
300 4
5.9%
ValueCountFrequency (%)
1100 1
 
1.5%
1000 2
 
2.9%
900 1
 
1.5%
850 2
 
2.9%
800 5
7.4%
750 2
 
2.9%
700 7
10.3%
650 2
 
2.9%
600 4
5.9%
550 1
 
1.5%

주소
Text

MISSING 

Distinct62
Distinct (%)93.9%
Missing2
Missing (%)2.9%
Memory size676.0 B
2023-12-13T02:22:25.063052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length17.181818
Min length11

Characters and Unicode

Total characters1134
Distinct characters77
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)90.9%

Sample

1st row광주광역시 광산구 신촌동 698-9
2nd row광주광역시 광산구 송정동 833-8
3rd row광주광역시 광산구 송정동 854-1
4th row광주광역시 광산구 송정동 1003-1
5th row광주광역시 광산구 송정동
ValueCountFrequency (%)
광주광역시 66
25.5%
서구 24
 
9.3%
동구 23
 
8.9%
광산구 11
 
4.2%
치평동 7
 
2.7%
송정동 6
 
2.3%
남구 6
 
2.3%
농성동 5
 
1.9%
학동 4
 
1.5%
구동 4
 
1.5%
Other values (87) 103
39.8%
2023-12-13T02:22:25.497846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
193
17.0%
광 149
13.1%
동 82
 
7.2%
구 70
 
6.2%
주 69
 
6.1%
역 66
 
5.8%
시 66
 
5.8%
1 55
 
4.9%
- 37
 
3.3%
2 28
 
2.5%
Other values (67) 319
28.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 709
62.5%
Decimal Number 194
 
17.1%
Space Separator 193
 
17.0%
Dash Punctuation 37
 
3.3%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 149
21.0%
동 82
11.6%
구 70
9.9%
주 69
9.7%
역 66
9.3%
시 66
9.3%
서 27
 
3.8%
산 16
 
2.3%
남 9
 
1.3%
정 8
 
1.1%
Other values (54) 147
20.7%
Decimal Number
ValueCountFrequency (%)
1 55
28.4%
2 28
14.4%
3 22
 
11.3%
5 17
 
8.8%
9 15
 
7.7%
4 14
 
7.2%
7 12
 
6.2%
8 12
 
6.2%
6 12
 
6.2%
0 7
 
3.6%
Space Separator
ValueCountFrequency (%)
193
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%
Uppercase Letter
ValueCountFrequency (%)
F 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 709
62.5%
Common 424
37.4%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 149
21.0%
동 82
11.6%
구 70
9.9%
주 69
9.7%
역 66
9.3%
시 66
9.3%
서 27
 
3.8%
산 16
 
2.3%
남 9
 
1.3%
정 8
 
1.1%
Other values (54) 147
20.7%
Common
ValueCountFrequency (%)
193
45.5%
1 55
 
13.0%
- 37
 
8.7%
2 28
 
6.6%
3 22
 
5.2%
5 17
 
4.0%
9 15
 
3.5%
4 14
 
3.3%
7 12
 
2.8%
8 12
 
2.8%
Other values (2) 19
 
4.5%
Latin
ValueCountFrequency (%)
F 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 709
62.5%
ASCII 425
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
193
45.4%
1 55
 
12.9%
- 37
 
8.7%
2 28
 
6.6%
3 22
 
5.2%
5 17
 
4.0%
9 15
 
3.5%
4 14
 
3.3%
7 12
 
2.8%
8 12
 
2.8%
Other values (3) 20
 
4.7%
Hangul
ValueCountFrequency (%)
광 149
21.0%
동 82
11.6%
구 70
9.9%
주 69
9.7%
역 66
9.3%
시 66
9.3%
서 27
 
3.8%
산 16
 
2.3%
남 9
 
1.3%
정 8
 
1.1%
Other values (54) 147
20.7%

Interactions

2023-12-13T02:22:22.256453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:22:21.990223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:22:22.369046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:22:22.140630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:22:25.595759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역명출구번호출구별 주요시설명거리주소
역명1.0000.7501.0000.3980.984
출구번호0.7501.0000.9680.4080.335
출구별 주요시설명1.0000.9681.0001.0001.000
거리0.3980.4081.0001.0000.949
주소0.9840.3351.0000.9491.000
2023-12-13T02:22:25.715968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출구번호거리역명
출구번호1.0000.2210.419
거리0.2211.0000.145
역명0.4190.1451.000

Missing values

2023-12-13T02:22:22.521837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:22:22.710702image/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.
2023-12-13T02:22:22.849601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

철도운영기관명선명역명출구번호출구별 주요시설명거리주소
0광주도시철도1호선공항2광주공항450광주광역시 광산구 신촌동 698-9
1광주도시철도1호선광주송정역1광산구청300광주광역시 광산구 송정동 833-8
2광주도시철도1호선광주송정역2송정매일시장200광주광역시 광산구 송정동 854-1
3광주도시철도1호선광주송정역4KTX 광주송정역20광주광역시 광산구 송정동 1003-1
4광주도시철도1호선광주송정역1송정떡갈비거리370광주광역시 광산구 송정동
5광주도시철도1호선광주송정역1송정5일시장600광주광역시 광산구 송정동 884-1
6광주도시철도1호선금남로4가1충장로 쇼핑거리100광주광역시 동구 충장로
7광주도시철도1호선금남로4가2광주극장350광주광역시 동구 충장로5가 62
8광주도시철도1호선금남로4가4원각사180광주광역시 동구 금남로4가 51
9광주도시철도1호선금남로4가1금남로공원100광주광역시 동구 금남로3가
철도운영기관명선명역명출구번호출구별 주요시설명거리주소
58광주도시철도1호선운천35.18기념공원700광주광역시 서구 쌍촌동 1268
59광주도시철도1호선운천3광주학생교육문화회관700광주광역시 서구 상무1동 1268
60광주도시철도1호선운천3롯데마트800광주광역시 서구 치평동 1239
61광주도시철도1호선운천3메가박스800광주광역시 서구 치평동 1223-3
62광주도시철도1호선평동2평동산업단지<NA><NA>
63광주도시철도1호선평동3평동산업단지<NA><NA>
64광주도시철도1호선학동증심사입구3동구청소년수련관700광주광역시 동구 학동 724-9
65광주도시철도1호선학동증심사입구4동구문화센터500광주광역시 동구 소태동 524-5
66광주도시철도1호선화정1광주시교육청600광주광역시 서구 화정동 657-37
67광주도시철도1호선화정4광주종합버스터미널1100광주광역시 서구 광천동 49-1