Overview

Dataset statistics

Number of variables5
Number of observations198
Missing cells204
Missing cells (%)20.6%
Duplicate rows5
Duplicate rows (%)2.5%
Total size in memory7.9 KiB
Average record size in memory40.7 B

Variable types

DateTime1
Categorical2
Text2

Dataset

Description전라남도 광양시의 업소행정처분정보에 대한 데이터를 국민들에게 제공(처분확정일자, 업종명, 업소명, 소재지, 처분사항 등에 대한 자료)합니다.
Author전라남도 광양시
URLhttps://www.data.go.kr/data/3079450/fileData.do

Alerts

Dataset has 5 (2.5%) duplicate rowsDuplicates
처분확정일자 has 68 (34.3%) missing valuesMissing
업소명 has 68 (34.3%) missing valuesMissing
소재지 has 68 (34.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 03:46:15.627652
Analysis finished2023-12-12 03:46:16.495747
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

처분확정일자
Date

MISSING 

Distinct80
Distinct (%)61.5%
Missing68
Missing (%)34.3%
Memory size1.7 KiB
Minimum2021-01-05 00:00:00
Maximum2021-12-31 00:00:00
2023-12-12T12:46:16.593776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:46:16.800920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업종명
Categorical

Distinct14
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
일반음식점
86 
<NA>
68 
유흥주점영업
 
7
휴게음식점
 
7
식품제조가공업
 
7
Other values (9)
23 

Length

Max length11
Median length9
Mean length4.9848485
Min length4

Unique

Unique3 ?
Unique (%)1.5%

Sample

1st row일반음식점
2nd row일반음식점
3rd row일반음식점
4th row유흥주점영업
5th row일반음식점

Common Values

ValueCountFrequency (%)
일반음식점 86
43.4%
<NA> 68
34.3%
유흥주점영업 7
 
3.5%
휴게음식점 7
 
3.5%
식품제조가공업 7
 
3.5%
목욕장업 4
 
2.0%
즉석판매제조가공업 4
 
2.0%
건강기능식품일반판매업 4
 
2.0%
숙박업(일반) 4
 
2.0%
피부미용업 2
 
1.0%
Other values (4) 5
 
2.5%

Length

2023-12-12T12:46:17.025829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반음식점 86
43.4%
na 68
34.3%
유흥주점영업 7
 
3.5%
휴게음식점 7
 
3.5%
식품제조가공업 7
 
3.5%
목욕장업 4
 
2.0%
즉석판매제조가공업 4
 
2.0%
건강기능식품일반판매업 4
 
2.0%
숙박업(일반 4
 
2.0%
피부미용업 2
 
1.0%
Other values (4) 5
 
2.5%

업소명
Text

MISSING 

Distinct117
Distinct (%)90.0%
Missing68
Missing (%)34.3%
Memory size1.7 KiB
2023-12-12T12:46:17.445293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length5.8
Min length2

Characters and Unicode

Total characters754
Distinct characters276
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique105 ?
Unique (%)80.8%

Sample

1st row목원동네파전
2nd row야키토리영
3rd row코코비어
4th row코뿔
5th row솔마루
ValueCountFrequency (%)
광양중마점 5
 
3.3%
부영원앙뷔페 3
 
2.0%
한성반찬 2
 
1.3%
황금식당 2
 
1.3%
풍성복집 2
 
1.3%
마동국밥 2
 
1.3%
비키니 2
 
1.3%
광양점 2
 
1.3%
청주본가 2
 
1.3%
고깃집진가 2
 
1.3%
Other values (125) 129
84.3%
2023-12-12T12:46:18.085010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 23
 
3.1%
23
 
3.1%
양 18
 
2.4%
광 17
 
2.3%
마 14
 
1.9%
동 13
 
1.7%
중 11
 
1.5%
이 11
 
1.5%
탕 9
 
1.2%
( 9
 
1.2%
Other values (266) 606
80.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 681
90.3%
Space Separator 23
 
3.1%
Lowercase Letter 18
 
2.4%
Uppercase Letter 10
 
1.3%
Open Punctuation 9
 
1.2%
Close Punctuation 9
 
1.2%
Decimal Number 4
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 23
 
3.4%
양 18
 
2.6%
광 17
 
2.5%
마 14
 
2.1%
동 13
 
1.9%
중 11
 
1.6%
이 11
 
1.6%
탕 9
 
1.3%
고 8
 
1.2%
원 8
 
1.2%
Other values (240) 549
80.6%
Lowercase Letter
ValueCountFrequency (%)
e 4
22.2%
a 3
16.7%
f 2
11.1%
o 2
11.1%
p 1
 
5.6%
c 1
 
5.6%
l 1
 
5.6%
i 1
 
5.6%
t 1
 
5.6%
r 1
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
D 2
20.0%
B 1
10.0%
S 1
10.0%
C 1
10.0%
L 1
10.0%
F 1
10.0%
P 1
10.0%
K 1
10.0%
M 1
10.0%
Decimal Number
ValueCountFrequency (%)
9 2
50.0%
5 1
25.0%
1 1
25.0%
Space Separator
ValueCountFrequency (%)
23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 681
90.3%
Common 45
 
6.0%
Latin 28
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 23
 
3.4%
양 18
 
2.6%
광 17
 
2.5%
마 14
 
2.1%
동 13
 
1.9%
중 11
 
1.6%
이 11
 
1.6%
탕 9
 
1.3%
고 8
 
1.2%
원 8
 
1.2%
Other values (240) 549
80.6%
Latin
ValueCountFrequency (%)
e 4
 
14.3%
a 3
 
10.7%
D 2
 
7.1%
f 2
 
7.1%
o 2
 
7.1%
B 1
 
3.6%
p 1
 
3.6%
c 1
 
3.6%
S 1
 
3.6%
l 1
 
3.6%
Other values (10) 10
35.7%
Common
ValueCountFrequency (%)
23
51.1%
( 9
 
20.0%
) 9
 
20.0%
9 2
 
4.4%
5 1
 
2.2%
1 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 681
90.3%
ASCII 73
 
9.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 23
 
3.4%
양 18
 
2.6%
광 17
 
2.5%
마 14
 
2.1%
동 13
 
1.9%
중 11
 
1.6%
이 11
 
1.6%
탕 9
 
1.3%
고 8
 
1.2%
원 8
 
1.2%
Other values (240) 549
80.6%
ASCII
ValueCountFrequency (%)
23
31.5%
( 9
 
12.3%
) 9
 
12.3%
e 4
 
5.5%
a 3
 
4.1%
9 2
 
2.7%
D 2
 
2.7%
f 2
 
2.7%
o 2
 
2.7%
5 1
 
1.4%
Other values (16) 16
21.9%

소재지
Text

MISSING 

Distinct118
Distinct (%)90.8%
Missing68
Missing (%)34.3%
Memory size1.7 KiB
2023-12-12T12:46:18.439811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length37
Mean length28.992308
Min length19

Characters and Unicode

Total characters3769
Distinct characters148
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

Unique107 ?
Unique (%)82.3%

Sample

1st row전라남도 광양시 구마4길 7 (중동, 1층중1층일부)
2nd row전라남도 광양시 광양읍 대림오성로 46-1, 1층중일부
3rd row전라남도 광양시 광양읍 와룡길 12 (3층중1층일부)
4th row전라남도 광양시 오류2길 5 (중동, (2층중2층))
5th row전라남도 광양시 광양읍 제철로 515
ValueCountFrequency (%)
전라남도 130
 
16.8%
광양시 130
 
16.8%
광양읍 34
 
4.4%
중동 32
 
4.1%
1층중1층전부 12
 
1.6%
마동 10
 
1.3%
1층 10
 
1.3%
1층중1층일부 9
 
1.2%
광영동 8
 
1.0%
중마중앙로 7
 
0.9%
Other values (237) 392
50.6%
2023-12-12T12:46:19.057135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
644
 
17.1%
1 219
 
5.8%
광 189
 
5.0%
층 171
 
4.5%
전 167
 
4.4%
양 167
 
4.4%
중 149
 
4.0%
시 137
 
3.6%
( 134
 
3.6%
) 133
 
3.5%
Other values (138) 1659
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2159
57.3%
Space Separator 644
 
17.1%
Decimal Number 580
 
15.4%
Open Punctuation 134
 
3.6%
Close Punctuation 133
 
3.5%
Other Punctuation 81
 
2.1%
Dash Punctuation 34
 
0.9%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 189
 
8.8%
층 171
 
7.9%
전 167
 
7.7%
양 167
 
7.7%
중 149
 
6.9%
시 137
 
6.3%
라 132
 
6.1%
도 131
 
6.1%
남 130
 
6.0%
동 89
 
4.1%
Other values (119) 697
32.3%
Decimal Number
ValueCountFrequency (%)
1 219
37.8%
2 88
15.2%
3 73
 
12.6%
4 40
 
6.9%
5 36
 
6.2%
6 34
 
5.9%
0 29
 
5.0%
7 24
 
4.1%
9 20
 
3.4%
8 17
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
A 2
50.0%
L 1
25.0%
F 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 80
98.8%
. 1
 
1.2%
Space Separator
ValueCountFrequency (%)
644
100.0%
Open Punctuation
ValueCountFrequency (%)
( 134
100.0%
Close Punctuation
ValueCountFrequency (%)
) 133
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2159
57.3%
Common 1606
42.6%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 189
 
8.8%
층 171
 
7.9%
전 167
 
7.7%
양 167
 
7.7%
중 149
 
6.9%
시 137
 
6.3%
라 132
 
6.1%
도 131
 
6.1%
남 130
 
6.0%
동 89
 
4.1%
Other values (119) 697
32.3%
Common
ValueCountFrequency (%)
644
40.1%
1 219
 
13.6%
( 134
 
8.3%
) 133
 
8.3%
2 88
 
5.5%
, 80
 
5.0%
3 73
 
4.5%
4 40
 
2.5%
5 36
 
2.2%
- 34
 
2.1%
Other values (6) 125
 
7.8%
Latin
ValueCountFrequency (%)
A 2
50.0%
L 1
25.0%
F 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2159
57.3%
ASCII 1610
42.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
644
40.0%
1 219
 
13.6%
( 134
 
8.3%
) 133
 
8.3%
2 88
 
5.5%
, 80
 
5.0%
3 73
 
4.5%
4 40
 
2.5%
5 36
 
2.2%
- 34
 
2.1%
Other values (9) 129
 
8.0%
Hangul
ValueCountFrequency (%)
광 189
 
8.8%
층 171
 
7.9%
전 167
 
7.7%
양 167
 
7.7%
중 149
 
6.9%
시 137
 
6.3%
라 132
 
6.1%
도 131
 
6.1%
남 130
 
6.0%
동 89
 
4.1%
Other values (119) 697
32.3%

처분사항
Categorical

Distinct11
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
68 
과태료부과
51 
영업허가ㆍ등록취소
20 
시정명령
19 
영업정지
18 
Other values (6)
22 

Length

Max length9
Median length4
Mean length4.8888889
Min length2

Unique

Unique3 ?
Unique (%)1.5%

Sample

1st row과태료부과
2nd row영업정지
3rd row영업정지
4th row영업정지
5th row시설개수명령

Common Values

ValueCountFrequency (%)
<NA> 68
34.3%
과태료부과 51
25.8%
영업허가ㆍ등록취소 20
 
10.1%
시정명령 19
 
9.6%
영업정지 18
 
9.1%
영업소폐쇄 10
 
5.1%
시설개수명령 6
 
3.0%
과징금부과 3
 
1.5%
경고 1
 
0.5%
품목제조정지 1
 
0.5%

Length

2023-12-12T12:46:19.259315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 68
34.3%
과태료부과 51
25.8%
영업허가ㆍ등록취소 20
 
10.1%
시정명령 19
 
9.6%
영업정지 18
 
9.1%
영업소폐쇄 10
 
5.1%
시설개수명령 6
 
3.0%
과징금부과 3
 
1.5%
경고 1
 
0.5%
품목제조정지 1
 
0.5%

Correlations

2023-12-12T12:46:19.409723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분확정일자업종명처분사항
처분확정일자1.0000.8230.984
업종명0.8231.0000.593
처분사항0.9840.5931.000
2023-12-12T12:46:19.539814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분사항업종명
처분사항1.0000.289
업종명0.2891.000
2023-12-12T12:46:19.633812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명처분사항
업종명1.0000.289
처분사항0.2891.000

Missing values

2023-12-12T12:46:16.125327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:46:16.252239image/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.
2023-12-12T12:46:16.388789image/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

처분확정일자업종명업소명소재지처분사항
02021-01-05일반음식점목원동네파전전라남도 광양시 구마4길 7 (중동, 1층중1층일부)과태료부과
12021-01-05일반음식점야키토리영전라남도 광양시 광양읍 대림오성로 46-1, 1층중일부영업정지
22021-01-06일반음식점코코비어전라남도 광양시 광양읍 와룡길 12 (3층중1층일부)영업정지
32021-01-06유흥주점영업코뿔전라남도 광양시 오류2길 5 (중동, (2층중2층))영업정지
42021-01-11일반음식점솔마루전라남도 광양시 광양읍 제철로 515시설개수명령
52021-01-11휴게음식점비폴릿커피(BePoliteCoffee)전라남도 광양시 공영로 94 (중동, 1층중1층)과태료부과
62021-01-11휴게음식점버거킹광양중동점전라남도 광양시 중마중앙로 65 (중동, 1,2층전부)과태료부과
72021-01-26휴게음식점상운길전라남도 광양시 옥룡면 상운길 81-17, 농업회사법인샛터주식회사(3층중1층전부)과태료부과
82021-01-27일반음식점벼리벼리전라남도 광양시 발섬길 33 (중동,(1층))과태료부과
92021-02-01일반음식점금상첨화전라남도 광양시 봉강면 성불로 1150-24 (1층중1층전부)영업정지
처분확정일자업종명업소명소재지처분사항
188<NA><NA><NA><NA><NA>
189<NA><NA><NA><NA><NA>
190<NA><NA><NA><NA><NA>
191<NA><NA><NA><NA><NA>
192<NA><NA><NA><NA><NA>
193<NA><NA><NA><NA><NA>
194<NA><NA><NA><NA><NA>
195<NA><NA><NA><NA><NA>
196<NA><NA><NA><NA><NA>
197<NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

처분확정일자업종명업소명소재지처분사항# duplicates
4<NA><NA><NA><NA><NA>68
12021-08-11일반음식점부영원앙뷔페전라남도 광양시 광양읍 개성3길 30, 1층중1층전부과태료부과3
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