Overview

Dataset statistics

Number of variables5
Number of observations94
Missing cells3
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory41.4 B

Variable types

Categorical2
Text3

Dataset

Description대전광역시 유성구 관내에 있는 안경업소현황으로 시도명, 시군구명, 안경업소명, 소재지도로명주소, 전화번호 등의 데이터를 제공합니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15119287/fileData.do

Alerts

시군구명 is highly overall correlated with 시도명High correlation
시도명 is highly overall correlated with 시군구명High correlation
시도명 is highly imbalanced (91.5%)Imbalance
시군구명 is highly imbalanced (91.5%)Imbalance
안경업소명 has 1 (1.1%) missing valuesMissing
소재지도로명주소 has 1 (1.1%) missing valuesMissing
전화번호 has 1 (1.1%) missing valuesMissing

Reproduction

Analysis started2024-04-21 08:37:54.478588
Analysis finished2024-04-21 08:37:56.369037
Duration1.89 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size880.0 B
대전광역시
93 
<NA>
 
1

Length

Max length5
Median length5
Mean length4.9893617
Min length4

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 93
98.9%
<NA> 1
 
1.1%

Length

2024-04-21T17:37:56.575546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:37:56.828336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 93
98.9%
na 1
 
1.1%

시군구명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size880.0 B
유성구
93 
<NA>
 
1

Length

Max length4
Median length3
Mean length3.0106383
Min length3

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
유성구 93
98.9%
<NA> 1
 
1.1%

Length

2024-04-21T17:37:56.994768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:37:57.155959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유성구 93
98.9%
na 1
 
1.1%

안경업소명
Text

MISSING 

Distinct93
Distinct (%)100.0%
Missing1
Missing (%)1.1%
Memory size880.0 B
2024-04-21T17:37:57.838664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length14
Mean length8.9139785
Min length4

Characters and Unicode

Total characters829
Distinct characters177
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)100.0%

Sample

1st row1001안경콘택트노은점
2nd row1001안경콘택트반석점
3rd row7클리어 by 씨채널
4th row과기대안경원
5th row글라스 스토리 안경원
ValueCountFrequency (%)
안경원 5
 
3.9%
안경 4
 
3.1%
충남대점 3
 
2.3%
오렌즈 2
 
1.6%
다비치안경 2
 
1.6%
안경매니져 2
 
1.6%
일공공일안경콘택트 2
 
1.6%
장대점 1
 
0.8%
유성자이 1
 
0.8%
윤슬안경원 1
 
0.8%
Other values (106) 106
82.2%
2024-04-21T17:37:58.823680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
안 81
 
9.8%
경 76
 
9.2%
점 42
 
5.1%
36
 
4.3%
원 30
 
3.6%
트 25
 
3.0%
콘 23
 
2.8%
이 23
 
2.8%
대 22
 
2.7%
스 21
 
2.5%
Other values (167) 450
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 735
88.7%
Space Separator 36
 
4.3%
Lowercase Letter 19
 
2.3%
Decimal Number 13
 
1.6%
Uppercase Letter 11
 
1.3%
Open Punctuation 7
 
0.8%
Close Punctuation 7
 
0.8%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 81
 
11.0%
경 76
 
10.3%
점 42
 
5.7%
원 30
 
4.1%
트 25
 
3.4%
콘 23
 
3.1%
이 23
 
3.1%
대 22
 
3.0%
스 21
 
2.9%
택 19
 
2.6%
Other values (140) 373
50.7%
Lowercase Letter
ValueCountFrequency (%)
e 5
26.3%
r 2
 
10.5%
c 2
 
10.5%
i 2
 
10.5%
y 2
 
10.5%
n 1
 
5.3%
t 1
 
5.3%
w 1
 
5.3%
b 1
 
5.3%
a 1
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
O 3
27.3%
A 2
18.2%
N 2
18.2%
S 1
 
9.1%
V 1
 
9.1%
L 1
 
9.1%
E 1
 
9.1%
Decimal Number
ValueCountFrequency (%)
0 5
38.5%
1 4
30.8%
3 2
 
15.4%
5 1
 
7.7%
7 1
 
7.7%
Space Separator
ValueCountFrequency (%)
36
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 735
88.7%
Common 64
 
7.7%
Latin 30
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 81
 
11.0%
경 76
 
10.3%
점 42
 
5.7%
원 30
 
4.1%
트 25
 
3.4%
콘 23
 
3.1%
이 23
 
3.1%
대 22
 
3.0%
스 21
 
2.9%
택 19
 
2.6%
Other values (140) 373
50.7%
Latin
ValueCountFrequency (%)
e 5
16.7%
O 3
 
10.0%
A 2
 
6.7%
r 2
 
6.7%
N 2
 
6.7%
c 2
 
6.7%
i 2
 
6.7%
y 2
 
6.7%
S 1
 
3.3%
n 1
 
3.3%
Other values (8) 8
26.7%
Common
ValueCountFrequency (%)
36
56.2%
( 7
 
10.9%
) 7
 
10.9%
0 5
 
7.8%
1 4
 
6.2%
3 2
 
3.1%
5 1
 
1.6%
& 1
 
1.6%
7 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 735
88.7%
ASCII 94
 
11.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
안 81
 
11.0%
경 76
 
10.3%
점 42
 
5.7%
원 30
 
4.1%
트 25
 
3.4%
콘 23
 
3.1%
이 23
 
3.1%
대 22
 
3.0%
스 21
 
2.9%
택 19
 
2.6%
Other values (140) 373
50.7%
ASCII
ValueCountFrequency (%)
36
38.3%
( 7
 
7.4%
) 7
 
7.4%
0 5
 
5.3%
e 5
 
5.3%
1 4
 
4.3%
O 3
 
3.2%
3 2
 
2.1%
A 2
 
2.1%
r 2
 
2.1%
Other values (17) 21
22.3%

소재지도로명주소
Text

MISSING 

Distinct91
Distinct (%)97.8%
Missing1
Missing (%)1.1%
Memory size880.0 B
2024-04-21T17:37:59.724346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length38
Mean length30.16129
Min length21

Characters and Unicode

Total characters2805
Distinct characters159
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

Unique89 ?
Unique (%)95.7%

Sample

1st row대전광역시 유성구 은구비남로33번길 29 (지족동)
2nd row대전광역시 유성구 반석로 15, 101호 (반석동)
3rd row대전광역시 유성구 계룡로 114, 대전유성BYC빌딩 1층 104호 (봉명동)
4th row대전광역시 유성구 대학로 291 (구성동, N13)
5th row대전광역시 유성구 궁동로18번길 47 (궁동)
ValueCountFrequency (%)
대전광역시 93
 
16.7%
유성구 93
 
16.7%
1층 22
 
4.0%
지족동 14
 
2.5%
궁동 13
 
2.3%
봉명동 10
 
1.8%
101호 8
 
1.4%
관평동 7
 
1.3%
궁동로18번길 6
 
1.1%
노은로 6
 
1.1%
Other values (196) 284
51.1%
2024-04-21T17:38:00.972522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
463
 
16.5%
1 143
 
5.1%
대 128
 
4.6%
동 106
 
3.8%
성 105
 
3.7%
전 104
 
3.7%
구 102
 
3.6%
유 99
 
3.5%
) 93
 
3.3%
( 93
 
3.3%
Other values (149) 1369
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1615
57.6%
Space Separator 463
 
16.5%
Decimal Number 444
 
15.8%
Close Punctuation 93
 
3.3%
Open Punctuation 93
 
3.3%
Other Punctuation 65
 
2.3%
Dash Punctuation 14
 
0.5%
Uppercase Letter 10
 
0.4%
Lowercase Letter 8
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 128
 
7.9%
동 106
 
6.6%
성 105
 
6.5%
전 104
 
6.4%
구 102
 
6.3%
유 99
 
6.1%
시 93
 
5.8%
역 93
 
5.8%
광 93
 
5.8%
로 92
 
5.7%
Other values (119) 600
37.2%
Decimal Number
ValueCountFrequency (%)
1 143
32.2%
2 46
 
10.4%
0 45
 
10.1%
3 37
 
8.3%
8 36
 
8.1%
5 35
 
7.9%
4 33
 
7.4%
6 24
 
5.4%
9 23
 
5.2%
7 22
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
I 2
20.0%
Y 2
20.0%
C 1
10.0%
A 1
10.0%
S 1
10.0%
B 1
10.0%
P 1
10.0%
N 1
10.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
25.0%
c 2
25.0%
r 1
12.5%
t 1
12.5%
i 1
12.5%
n 1
12.5%
Other Punctuation
ValueCountFrequency (%)
, 64
98.5%
& 1
 
1.5%
Space Separator
ValueCountFrequency (%)
463
100.0%
Close Punctuation
ValueCountFrequency (%)
) 93
100.0%
Open Punctuation
ValueCountFrequency (%)
( 93
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1615
57.6%
Common 1172
41.8%
Latin 18
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 128
 
7.9%
동 106
 
6.6%
성 105
 
6.5%
전 104
 
6.4%
구 102
 
6.3%
유 99
 
6.1%
시 93
 
5.8%
역 93
 
5.8%
광 93
 
5.8%
로 92
 
5.7%
Other values (119) 600
37.2%
Common
ValueCountFrequency (%)
463
39.5%
1 143
 
12.2%
) 93
 
7.9%
( 93
 
7.9%
, 64
 
5.5%
2 46
 
3.9%
0 45
 
3.8%
3 37
 
3.2%
8 36
 
3.1%
5 35
 
3.0%
Other values (6) 117
 
10.0%
Latin
ValueCountFrequency (%)
e 2
11.1%
c 2
11.1%
I 2
11.1%
Y 2
11.1%
C 1
 
5.6%
A 1
 
5.6%
r 1
 
5.6%
t 1
 
5.6%
S 1
 
5.6%
i 1
 
5.6%
Other values (4) 4
22.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1615
57.6%
ASCII 1190
42.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
463
38.9%
1 143
 
12.0%
) 93
 
7.8%
( 93
 
7.8%
, 64
 
5.4%
2 46
 
3.9%
0 45
 
3.8%
3 37
 
3.1%
8 36
 
3.0%
5 35
 
2.9%
Other values (20) 135
 
11.3%
Hangul
ValueCountFrequency (%)
대 128
 
7.9%
동 106
 
6.6%
성 105
 
6.5%
전 104
 
6.4%
구 102
 
6.3%
유 99
 
6.1%
시 93
 
5.8%
역 93
 
5.8%
광 93
 
5.8%
로 92
 
5.7%
Other values (119) 600
37.2%

전화번호
Text

MISSING 

Distinct84
Distinct (%)90.3%
Missing1
Missing (%)1.1%
Memory size880.0 B
2024-04-21T17:38:01.863816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.258065
Min length12

Characters and Unicode

Total characters1140
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)89.2%

Sample

1st row042-477-7666
2nd row042-826-1101
3rd row042-000-0000
4th row042-862-3430
5th row042-000-0000
ValueCountFrequency (%)
042-000-0000 10
 
10.8%
0507-1355-5445 1
 
1.1%
042-931-7989 1
 
1.1%
042-861-0140 1
 
1.1%
042-935-4947 1
 
1.1%
042-826-8988 1
 
1.1%
042-824-8246 1
 
1.1%
0507-1418-5745 1
 
1.1%
042-936-7787 1
 
1.1%
042-822-1052 1
 
1.1%
Other values (74) 74
79.6%
2024-04-21T17:38:03.181478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 223
19.6%
- 186
16.3%
2 164
14.4%
4 137
12.0%
8 81
 
7.1%
5 69
 
6.1%
7 67
 
5.9%
3 63
 
5.5%
1 56
 
4.9%
6 55
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 954
83.7%
Dash Punctuation 186
 
16.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223
23.4%
2 164
17.2%
4 137
14.4%
8 81
 
8.5%
5 69
 
7.2%
7 67
 
7.0%
3 63
 
6.6%
1 56
 
5.9%
6 55
 
5.8%
9 39
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 223
19.6%
- 186
16.3%
2 164
14.4%
4 137
12.0%
8 81
 
7.1%
5 69
 
6.1%
7 67
 
5.9%
3 63
 
5.5%
1 56
 
4.9%
6 55
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 223
19.6%
- 186
16.3%
2 164
14.4%
4 137
12.0%
8 81
 
7.1%
5 69
 
6.1%
7 67
 
5.9%
3 63
 
5.5%
1 56
 
4.9%
6 55
 
4.8%

Correlations

2024-04-21T17:38:03.445048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
안경업소명소재지도로명주소전화번호
안경업소명1.0001.0001.000
소재지도로명주소1.0001.0000.995
전화번호1.0000.9951.000
2024-04-21T17:38:03.684701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명시도명
시군구명1.0001.000
시도명1.0001.000
2024-04-21T17:38:03.911962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명시군구명
시도명1.0001.000
시군구명1.0001.000

Missing values

2024-04-21T17:37:55.578446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T17:37:55.888459image/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.
2024-04-21T17:37:56.191153image/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

시도명시군구명안경업소명소재지도로명주소전화번호
0대전광역시유성구1001안경콘택트노은점대전광역시 유성구 은구비남로33번길 29 (지족동)042-477-7666
1대전광역시유성구1001안경콘택트반석점대전광역시 유성구 반석로 15, 101호 (반석동)042-826-1101
2대전광역시유성구7클리어 by 씨채널대전광역시 유성구 계룡로 114, 대전유성BYC빌딩 1층 104호 (봉명동)042-000-0000
3대전광역시유성구과기대안경원대전광역시 유성구 대학로 291 (구성동, N13)042-862-3430
4대전광역시유성구글라스 스토리 안경원대전광역시 유성구 궁동로18번길 47 (궁동)042-000-0000
5대전광역시유성구글로우안경원대전광역시 유성구 유성대로 1750, 지상1층 (전민동)0507-1425-3077
6대전광역시유성구금강안경원대전광역시 유성구 계룡로 47 (봉명동)042-824-1001
7대전광역시유성구노글라스안경원대전광역시 유성구 유성대로 1756, 1층 (전민동)042-863-0079
8대전광역시유성구눈에담은안경원대전광역시 유성구 관들1길 68 (관평동)042-933-9001
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