Overview

Dataset statistics

Number of variables6
Number of observations69
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory50.9 B

Variable types

Numeric1
Categorical2
Text2
DateTime1

Dataset

Description전라남도 광양시의 교통카드 가맹점 정보(상호명, 대표자, 주소 및 위치,데이터기준일)에 대한 데이터를 전 국민들과 무료로 공유합니다.
Author전라남도 광양시
URLhttps://www.data.go.kr/data/3079472/fileData.do

Alerts

데이터기준일 has constant value ""Constant
연번 is highly overall correlated with 형태High correlation
형태 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2024-04-21 02:52:44.810024
Analysis finished2024-04-21 02:52:46.755125
Duration1.95 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct69
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35
Minimum1
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2024-04-21T11:52:46.818953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.4
Q118
median35
Q352
95-th percentile65.6
Maximum69
Range68
Interquartile range (IQR)34

Descriptive statistics

Standard deviation20.062403
Coefficient of variation (CV)0.5732115
Kurtosis-1.2
Mean35
Median Absolute Deviation (MAD)17
Skewness0
Sum2415
Variance402.5
MonotonicityStrictly increasing
2024-04-21T11:52:46.950242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
45 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
46 1
 
1.4%
44 1
 
1.4%
53 1
 
1.4%
Other values (59) 59
85.5%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%
64 1
1.4%
63 1
1.4%
62 1
1.4%
61 1
1.4%
60 1
1.4%

읍면동
Categorical

Distinct10
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Memory size684.0 B
광양읍
29 
중마동
23 
광영동
6 
태인동
3 
금호동
 
2
Other values (5)
6 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique4 ?
Unique (%)5.8%

Sample

1st row광양읍
2nd row광양읍
3rd row광양읍
4th row광영동
5th row진월면

Common Values

ValueCountFrequency (%)
광양읍 29
42.0%
중마동 23
33.3%
광영동 6
 
8.7%
태인동 3
 
4.3%
금호동 2
 
2.9%
옥곡면 2
 
2.9%
진월면 1
 
1.4%
진상면 1
 
1.4%
다압면 1
 
1.4%
옥룡면 1
 
1.4%

Length

2024-04-21T11:52:47.073026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:52:47.177575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광양읍 29
42.0%
중마동 23
33.3%
광영동 6
 
8.7%
태인동 3
 
4.3%
금호동 2
 
2.9%
옥곡면 2
 
2.9%
진월면 1
 
1.4%
진상면 1
 
1.4%
다압면 1
 
1.4%
옥룡면 1
 
1.4%

형태
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size684.0 B
GS25
23 
코리아세븐
18 
CU
15 
전남 가두충전(일반충전상)
13 

Length

Max length14
Median length5
Mean length5.7101449
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전남 가두충전(일반충전상)
2nd row전남 가두충전(일반충전상)
3rd row전남 가두충전(일반충전상)
4th row전남 가두충전(일반충전상)
5th row전남 가두충전(일반충전상)

Common Values

ValueCountFrequency (%)
GS25 23
33.3%
코리아세븐 18
26.1%
CU 15
21.7%
전남 가두충전(일반충전상) 13
18.8%

Length

2024-04-21T11:52:47.301054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:52:47.400246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
gs25 23
28.0%
코리아세븐 18
22.0%
cu 15
18.3%
전남 13
15.9%
가두충전(일반충전상 13
15.9%

상호명
Text

Distinct67
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size684.0 B
2024-04-21T11:52:47.625034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length7.8985507
Min length4

Characters and Unicode

Total characters545
Distinct characters101
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)94.2%

Sample

1st row네거리로또명당
2nd row부영할인마트
3rd row종이장미문구
4th row동광할인마트
5th row금복슈퍼
ValueCountFrequency (%)
gs25광양목성점 2
 
2.8%
알리바이 2
 
2.8%
gs25광양이편한점 2
 
2.8%
포스코태인1 1
 
1.4%
광양금광1 1
 
1.4%
광양마동점2 1
 
1.4%
중동이편한점1 1
 
1.4%
네거리로또명당 1
 
1.4%
광양주공점1 1
 
1.4%
k7광양옥룡점2 1
 
1.4%
Other values (58) 58
81.7%
2024-04-21T11:52:48.007208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 57
 
10.5%
광 53
 
9.7%
양 47
 
8.6%
2 37
 
6.8%
1 30
 
5.5%
G 20
 
3.7%
S 20
 
3.7%
5 20
 
3.7%
마 16
 
2.9%
7 14
 
2.6%
Other values (91) 231
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 374
68.6%
Decimal Number 101
 
18.5%
Uppercase Letter 68
 
12.5%
Space Separator 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 57
 
15.2%
광 53
 
14.2%
양 47
 
12.6%
마 16
 
4.3%
중 13
 
3.5%
영 8
 
2.1%
동 8
 
2.1%
인 6
 
1.6%
이 6
 
1.6%
공 5
 
1.3%
Other values (81) 155
41.4%
Uppercase Letter
ValueCountFrequency (%)
G 20
29.4%
S 20
29.4%
K 14
20.6%
C 7
 
10.3%
U 7
 
10.3%
Decimal Number
ValueCountFrequency (%)
2 37
36.6%
1 30
29.7%
5 20
19.8%
7 14
 
13.9%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 374
68.6%
Common 103
 
18.9%
Latin 68
 
12.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 57
 
15.2%
광 53
 
14.2%
양 47
 
12.6%
마 16
 
4.3%
중 13
 
3.5%
영 8
 
2.1%
동 8
 
2.1%
인 6
 
1.6%
이 6
 
1.6%
공 5
 
1.3%
Other values (81) 155
41.4%
Common
ValueCountFrequency (%)
2 37
35.9%
1 30
29.1%
5 20
19.4%
7 14
 
13.6%
2
 
1.9%
Latin
ValueCountFrequency (%)
G 20
29.4%
S 20
29.4%
K 14
20.6%
C 7
 
10.3%
U 7
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 374
68.6%
ASCII 171
31.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 57
 
15.2%
광 53
 
14.2%
양 47
 
12.6%
마 16
 
4.3%
중 13
 
3.5%
영 8
 
2.1%
동 8
 
2.1%
인 6
 
1.6%
이 6
 
1.6%
공 5
 
1.3%
Other values (81) 155
41.4%
ASCII
ValueCountFrequency (%)
2 37
21.6%
1 30
17.5%
G 20
11.7%
S 20
11.7%
5 20
11.7%
7 14
 
8.2%
K 14
 
8.2%
C 7
 
4.1%
U 7
 
4.1%
2
 
1.2%

주소 및 위치
Text

Distinct59
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Memory size684.0 B
2024-04-21T11:52:48.302409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length35
Mean length22.347826
Min length15

Characters and Unicode

Total characters1542
Distinct characters109
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)71.0%

Sample

1st row전남 광양시 광양읍 목성리 921-1
2nd row전남 광양시 광양읍 인서리 부영상가 107
3rd row전라남도 광양시 광양읍 대림오성로 139, 오성APT상가 101호
4th row전남 광양시 광영동 733-1동광아파트상가 113
5th row전라남도 광양시 진월면 선소중앙길 28
ValueCountFrequency (%)
광양시 69
18.8%
전라남도 58
 
15.8%
광양읍 28
 
7.6%
중동 16
 
4.3%
전남 11
 
3.0%
광영동 6
 
1.6%
신재로 5
 
1.4%
8 4
 
1.1%
26 3
 
0.8%
20 3
 
0.8%
Other values (123) 165
44.8%
2024-04-21T11:52:48.719970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
300
19.5%
광 110
 
7.1%
양 98
 
6.4%
남 70
 
4.5%
시 70
 
4.5%
전 69
 
4.5%
1 67
 
4.3%
도 58
 
3.8%
라 58
 
3.8%
로 41
 
2.7%
Other values (99) 601
39.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 926
60.1%
Space Separator 300
 
19.5%
Decimal Number 242
 
15.7%
Close Punctuation 25
 
1.6%
Open Punctuation 25
 
1.6%
Other Punctuation 12
 
0.8%
Dash Punctuation 8
 
0.5%
Uppercase Letter 3
 
0.2%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 110
 
11.9%
양 98
 
10.6%
남 70
 
7.6%
시 70
 
7.6%
전 69
 
7.5%
도 58
 
6.3%
라 58
 
6.3%
로 41
 
4.4%
동 38
 
4.1%
읍 28
 
3.0%
Other values (80) 286
30.9%
Decimal Number
ValueCountFrequency (%)
1 67
27.7%
2 33
13.6%
0 28
11.6%
3 23
 
9.5%
6 21
 
8.7%
8 19
 
7.9%
5 18
 
7.4%
4 14
 
5.8%
9 12
 
5.0%
7 7
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
A 1
33.3%
T 1
33.3%
P 1
33.3%
Space Separator
ValueCountFrequency (%)
300
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 926
60.1%
Common 613
39.8%
Latin 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 110
 
11.9%
양 98
 
10.6%
남 70
 
7.6%
시 70
 
7.6%
전 69
 
7.5%
도 58
 
6.3%
라 58
 
6.3%
로 41
 
4.4%
동 38
 
4.1%
읍 28
 
3.0%
Other values (80) 286
30.9%
Common
ValueCountFrequency (%)
300
48.9%
1 67
 
10.9%
2 33
 
5.4%
0 28
 
4.6%
) 25
 
4.1%
( 25
 
4.1%
3 23
 
3.8%
6 21
 
3.4%
8 19
 
3.1%
5 18
 
2.9%
Other values (6) 54
 
8.8%
Latin
ValueCountFrequency (%)
A 1
33.3%
T 1
33.3%
P 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 926
60.1%
ASCII 616
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
300
48.7%
1 67
 
10.9%
2 33
 
5.4%
0 28
 
4.5%
) 25
 
4.1%
( 25
 
4.1%
3 23
 
3.7%
6 21
 
3.4%
8 19
 
3.1%
5 18
 
2.9%
Other values (9) 57
 
9.3%
Hangul
ValueCountFrequency (%)
광 110
 
11.9%
양 98
 
10.6%
남 70
 
7.6%
시 70
 
7.6%
전 69
 
7.5%
도 58
 
6.3%
라 58
 
6.3%
로 41
 
4.4%
동 38
 
4.1%
읍 28
 
3.0%
Other values (80) 286
30.9%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
Minimum2024-04-02 00:00:00
Maximum2024-04-02 00:00:00
2024-04-21T11:52:48.820505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:52:48.898038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-21T11:52:46.469015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:52:48.960965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번읍면동형태상호명주소 및 위치
연번1.0000.4820.7421.0000.988
읍면동0.4821.0000.3551.0001.000
형태0.7420.3551.0001.0001.000
상호명1.0001.0001.0001.0001.000
주소 및 위치0.9881.0001.0001.0001.000
2024-04-21T11:52:49.056195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동형태
읍면동1.0000.204
형태0.2041.000
2024-04-21T11:52:49.131833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번읍면동형태
연번1.0000.1590.523
읍면동0.1591.0000.204
형태0.5230.2041.000

Missing values

2024-04-21T11:52:46.626531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:52:46.715407image/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

연번읍면동형태상호명주소 및 위치데이터기준일
01광양읍전남 가두충전(일반충전상)네거리로또명당전남 광양시 광양읍 목성리 921-12024-04-02
12광양읍전남 가두충전(일반충전상)부영할인마트전남 광양시 광양읍 인서리 부영상가 1072024-04-02
23광양읍전남 가두충전(일반충전상)종이장미문구전라남도 광양시 광양읍 대림오성로 139, 오성APT상가 101호2024-04-02
34광영동전남 가두충전(일반충전상)동광할인마트전남 광양시 광영동 733-1동광아파트상가 1132024-04-02
45진월면전남 가두충전(일반충전상)금복슈퍼전라남도 광양시 진월면 선소중앙길 282024-04-02
56진상면전남 가두충전(일반충전상)진상정류소전라남도 광양시 진상면 학연로 122024-04-02
67광양읍전남 가두충전(일반충전상)북부상회전라남도 광양시 광양읍 인덕로 10822024-04-02
78다압면전남 가두충전(일반충전상)다압농협전라남도 광양시 다압면 항동3길 112024-04-02
89광영동전남 가두충전(일반충전상)한남코사마트전라남도 광양시 금영로 162 (광영동)2024-04-02
910광양읍GS25GS25광양목성점전라남도 광양시 광양읍 신재로 82, 목성아파트 상가2024-04-02
연번읍면동형태상호명주소 및 위치데이터기준일
5960광양읍코리아세븐광양터미널점2전남 광양시 광양읍 순광로 6882024-04-02
6061광양읍코리아세븐광양터미널점1전남 광양시 광양읍 순광로 6882024-04-02
6162중마동CU광양백운점1전라남도 광양시 신재로 1052024-04-02
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