Overview

Dataset statistics

Number of variables7
Number of observations310
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.4 KiB
Average record size in memory57.4 B

Variable types

Numeric1
Text4
Categorical1
DateTime1

Dataset

Description부산광역시_서구_담배소매인지정현황_20220805
Author부산광역시 서구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15029599

Alerts

소매인구분 is highly imbalanced (76.4%)Imbalance
연번 has unique valuesUnique
지정번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 17:01:58.423992
Analysis finished2023-12-10 17:01:59.396230
Duration0.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct310
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.5
Minimum1
Maximum310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-11T02:01:59.527716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16.45
Q178.25
median155.5
Q3232.75
95-th percentile294.55
Maximum310
Range309
Interquartile range (IQR)154.5

Descriptive statistics

Standard deviation89.633513
Coefficient of variation (CV)0.57642131
Kurtosis-1.2
Mean155.5
Median Absolute Deviation (MAD)77.5
Skewness0
Sum48205
Variance8034.1667
MonotonicityStrictly increasing
2023-12-11T02:01:59.795941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
206 1
 
0.3%
213 1
 
0.3%
212 1
 
0.3%
211 1
 
0.3%
210 1
 
0.3%
209 1
 
0.3%
208 1
 
0.3%
207 1
 
0.3%
205 1
 
0.3%
Other values (300) 300
96.8%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
310 1
0.3%
309 1
0.3%
308 1
0.3%
307 1
0.3%
306 1
0.3%
305 1
0.3%
304 1
0.3%
303 1
0.3%
302 1
0.3%
301 1
0.3%

지정번호
Text

UNIQUE 

Distinct310
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-11T02:02:00.201599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters7130
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

Unique310 ?
Unique (%)100.0%

Sample

1st row2023-3260107-05-6-00021
2nd row2023-3260107-05-6-00020
3rd row2023-3260107-05-6-00019
4th row2023-3260107-05-6-00018
5th row2023-3260107-05-6-00017
ValueCountFrequency (%)
2023-3260107-05-6-00021 1
 
0.3%
2008-3260066-05-6-01662 1
 
0.3%
2006-3260049-05-6-12361 1
 
0.3%
2006-3260066-05-6-11124 1
 
0.3%
2007-3260066-05-6-00023 1
 
0.3%
2007-3260097-05-6-00079 1
 
0.3%
2008-3260066-05-6-00001 1
 
0.3%
2008-3260066-05-6-00003 1
 
0.3%
2009-3260066-05-6-00013 1
 
0.3%
2008-3260066-05-6-01666 1
 
0.3%
Other values (300) 300
96.8%
2023-12-11T02:02:01.013587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2328
32.7%
- 1240
17.4%
2 853
 
12.0%
6 711
 
10.0%
1 446
 
6.3%
3 433
 
6.1%
5 381
 
5.3%
7 250
 
3.5%
9 242
 
3.4%
4 181
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5890
82.6%
Dash Punctuation 1240
 
17.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2328
39.5%
2 853
 
14.5%
6 711
 
12.1%
1 446
 
7.6%
3 433
 
7.4%
5 381
 
6.5%
7 250
 
4.2%
9 242
 
4.1%
4 181
 
3.1%
8 65
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 1240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2328
32.7%
- 1240
17.4%
2 853
 
12.0%
6 711
 
10.0%
1 446
 
6.3%
3 433
 
6.1%
5 381
 
5.3%
7 250
 
3.5%
9 242
 
3.4%
4 181
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2328
32.7%
- 1240
17.4%
2 853
 
12.0%
6 711
 
10.0%
1 446
 
6.3%
3 433
 
6.1%
5 381
 
5.3%
7 250
 
3.5%
9 242
 
3.4%
4 181
 
2.5%

대표자명
Text

Distinct302
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-11T02:02:01.667794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length3
Mean length3.383871
Min length2

Characters and Unicode

Total characters1049
Distinct characters185
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique295 ?
Unique (%)95.2%

Sample

1st row김세련
2nd row박점숙
3rd row김지영
4th row소현숙
5th row장미경
ValueCountFrequency (%)
주)코리아세븐 4
 
1.2%
정승인 4
 
1.2%
박만성 3
 
0.9%
한주형 2
 
0.6%
김경숙 2
 
0.6%
김영미 2
 
0.6%
김순자 2
 
0.6%
송상철 2
 
0.6%
li 2
 
0.6%
조순현 1
 
0.3%
Other values (300) 300
92.6%
2023-12-11T02:02:02.470157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 81
 
7.7%
이 39
 
3.7%
정 36
 
3.4%
자 33
 
3.1%
순 30
 
2.9%
영 29
 
2.8%
경 24
 
2.3%
박 23
 
2.2%
성 21
 
2.0%
숙 20
 
1.9%
Other values (175) 713
68.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1008
96.1%
Space Separator 14
 
1.3%
Close Punctuation 8
 
0.8%
Open Punctuation 8
 
0.8%
Uppercase Letter 7
 
0.7%
Other Punctuation 3
 
0.3%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 81
 
8.0%
이 39
 
3.9%
정 36
 
3.6%
자 33
 
3.3%
순 30
 
3.0%
영 29
 
2.9%
경 24
 
2.4%
박 23
 
2.3%
성 21
 
2.1%
숙 20
 
2.0%
Other values (165) 672
66.7%
Uppercase Letter
ValueCountFrequency (%)
L 2
28.6%
I 2
28.6%
H 1
14.3%
U 1
14.3%
A 1
14.3%
Space Separator
ValueCountFrequency (%)
14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Other Punctuation
ValueCountFrequency (%)
， 3
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1008
96.1%
Common 34
 
3.2%
Latin 7
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 81
 
8.0%
이 39
 
3.9%
정 36
 
3.6%
자 33
 
3.3%
순 30
 
3.0%
영 29
 
2.9%
경 24
 
2.4%
박 23
 
2.3%
성 21
 
2.1%
숙 20
 
2.0%
Other values (165) 672
66.7%
Common
ValueCountFrequency (%)
14
41.2%
) 8
23.5%
( 8
23.5%
， 3
 
8.8%
1 1
 
2.9%
Latin
ValueCountFrequency (%)
L 2
28.6%
I 2
28.6%
H 1
14.3%
U 1
14.3%
A 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1008
96.1%
ASCII 38
 
3.6%
None 3
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 81
 
8.0%
이 39
 
3.9%
정 36
 
3.6%
자 33
 
3.3%
순 30
 
3.0%
영 29
 
2.9%
경 24
 
2.4%
박 23
 
2.3%
성 21
 
2.1%
숙 20
 
2.0%
Other values (165) 672
66.7%
ASCII
ValueCountFrequency (%)
14
36.8%
) 8
21.1%
( 8
21.1%
L 2
 
5.3%
I 2
 
5.3%
H 1
 
2.6%
U 1
 
2.6%
A 1
 
2.6%
1 1
 
2.6%
None
ValueCountFrequency (%)
， 3
100.0%

소매인구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
제7조의3제2항에따른경우
298 
제7조의3제3항에따른경우
 
12

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제7조의3제2항에따른경우
2nd row제7조의3제2항에따른경우
3rd row제7조의3제2항에따른경우
4th row제7조의3제2항에따른경우
5th row제7조의3제2항에따른경우

Common Values

ValueCountFrequency (%)
제7조의3제2항에따른경우 298
96.1%
제7조의3제3항에따른경우 12
 
3.9%

Length

2023-12-11T02:02:02.684912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:02:02.819131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제7조의3제2항에따른경우 298
96.1%
제7조의3제3항에따른경우 12
 
3.9%

업소명
Text

Distinct265
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-11T02:02:03.124329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length18
Mean length6.7967742
Min length1

Characters and Unicode

Total characters2107
Distinct characters286
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

Unique261 ?
Unique (%)84.2%

Sample

1st row다올잡화
2nd row토성나루
3rd row일월일일 송도점
4th row지에스25 부민드림점
5th row씨유 부산송도혜성점
ValueCountFrequency (%)
무 43
 
10.4%
씨유 22
 
5.3%
이마트24 11
 
2.7%
세븐일레븐 11
 
2.7%
지에스25 8
 
1.9%
gs25 7
 
1.7%
지에스(gs)25 6
 
1.5%
주)코리아세븐 4
 
1.0%
서대신점 4
 
1.0%
대신푸르지오점 2
 
0.5%
Other values (282) 295
71.4%
2023-12-11T02:02:03.759251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 113
 
5.4%
103
 
4.9%
대 67
 
3.2%
마 60
 
2.8%
트 54
 
2.6%
무 53
 
2.5%
부 49
 
2.3%
2 44
 
2.1%
유 41
 
1.9%
신 41
 
1.9%
Other values (276) 1482
70.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1818
86.3%
Space Separator 103
 
4.9%
Decimal Number 87
 
4.1%
Uppercase Letter 44
 
2.1%
Open Punctuation 22
 
1.0%
Close Punctuation 22
 
1.0%
Lowercase Letter 8
 
0.4%
Dash Punctuation 2
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 113
 
6.2%
대 67
 
3.7%
마 60
 
3.3%
트 54
 
3.0%
무 53
 
2.9%
부 49
 
2.7%
유 41
 
2.3%
신 41
 
2.3%
스 41
 
2.3%
서 40
 
2.2%
Other values (254) 1259
69.3%
Uppercase Letter
ValueCountFrequency (%)
S 19
43.2%
G 18
40.9%
C 3
 
6.8%
U 2
 
4.5%
L 1
 
2.3%
M 1
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
g 2
25.0%
s 2
25.0%
i 1
12.5%
k 1
12.5%
c 1
12.5%
u 1
12.5%
Decimal Number
ValueCountFrequency (%)
2 44
50.6%
5 30
34.5%
4 11
 
12.6%
7 1
 
1.1%
1 1
 
1.1%
Space Separator
ValueCountFrequency (%)
103
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1818
86.3%
Common 237
 
11.2%
Latin 52
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 113
 
6.2%
대 67
 
3.7%
마 60
 
3.3%
트 54
 
3.0%
무 53
 
2.9%
부 49
 
2.7%
유 41
 
2.3%
신 41
 
2.3%
스 41
 
2.3%
서 40
 
2.2%
Other values (254) 1259
69.3%
Latin
ValueCountFrequency (%)
S 19
36.5%
G 18
34.6%
C 3
 
5.8%
U 2
 
3.8%
g 2
 
3.8%
s 2
 
3.8%
L 1
 
1.9%
i 1
 
1.9%
k 1
 
1.9%
c 1
 
1.9%
Other values (2) 2
 
3.8%
Common
ValueCountFrequency (%)
103
43.5%
2 44
18.6%
5 30
 
12.7%
( 22
 
9.3%
) 22
 
9.3%
4 11
 
4.6%
- 2
 
0.8%
7 1
 
0.4%
1 1
 
0.4%
& 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1818
86.3%
ASCII 289
 
13.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 113
 
6.2%
대 67
 
3.7%
마 60
 
3.3%
트 54
 
3.0%
무 53
 
2.9%
부 49
 
2.7%
유 41
 
2.3%
신 41
 
2.3%
스 41
 
2.3%
서 40
 
2.2%
Other values (254) 1259
69.3%
ASCII
ValueCountFrequency (%)
103
35.6%
2 44
15.2%
5 30
 
10.4%
( 22
 
7.6%
) 22
 
7.6%
S 19
 
6.6%
G 18
 
6.2%
4 11
 
3.8%
C 3
 
1.0%
U 2
 
0.7%
Other values (12) 15
 
5.2%

업소도로명주소
Text

Distinct309
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-11T02:02:04.143574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length48
Mean length29.870968
Min length16

Characters and Unicode

Total characters9260
Distinct characters220
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

Unique308 ?
Unique (%)99.4%

Sample

1st row부산광역시 서구 망양로 54. 1층 (서대신동3가)
2nd row부산광역시 서구 까치고개로245번길 23. 1층 (토성동1가)
3rd row부산광역시 서구 성산길 31 (암남동)
4th row부산광역시 서구 구덕로238번길 14 (부용동1가)
5th row부산광역시 서구 충무대로 24. 102.103호 (암남동. 송도혜성주상복합빌딩)
ValueCountFrequency (%)
부산광역시 310
 
17.3%
서구 310
 
17.3%
암남동 55
 
3.1%
1층 54
 
3.0%
남부민동 46
 
2.6%
충무대로 29
 
1.6%
아미동2가 23
 
1.3%
구덕로 21
 
1.2%
동대신동2가 20
 
1.1%
101호 20
 
1.1%
Other values (480) 901
50.4%
2023-12-11T02:02:04.740153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1494
 
16.1%
1 447
 
4.8%
동 401
 
4.3%
부 398
 
4.3%
서 362
 
3.9%
구 360
 
3.9%
시 330
 
3.6%
산 323
 
3.5%
광 312
 
3.4%
역 312
 
3.4%
Other values (210) 4521
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5423
58.6%
Decimal Number 1546
 
16.7%
Space Separator 1494
 
16.1%
Open Punctuation 287
 
3.1%
Close Punctuation 287
 
3.1%
Other Punctuation 155
 
1.7%
Dash Punctuation 57
 
0.6%
Uppercase Letter 11
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 401
 
7.4%
부 398
 
7.3%
서 362
 
6.7%
구 360
 
6.6%
시 330
 
6.1%
산 323
 
6.0%
광 312
 
5.8%
역 312
 
5.8%
로 275
 
5.1%
대 210
 
3.9%
Other values (189) 2140
39.5%
Decimal Number
ValueCountFrequency (%)
1 447
28.9%
2 286
18.5%
3 168
 
10.9%
0 123
 
8.0%
5 121
 
7.8%
4 105
 
6.8%
9 79
 
5.1%
6 78
 
5.0%
8 71
 
4.6%
7 68
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
B 5
45.5%
A 2
 
18.2%
G 1
 
9.1%
L 1
 
9.1%
C 1
 
9.1%
W 1
 
9.1%
Space Separator
ValueCountFrequency (%)
1494
100.0%
Open Punctuation
ValueCountFrequency (%)
( 287
100.0%
Close Punctuation
ValueCountFrequency (%)
) 287
100.0%
Other Punctuation
ValueCountFrequency (%)
. 155
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5423
58.6%
Common 3826
41.3%
Latin 11
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 401
 
7.4%
부 398
 
7.3%
서 362
 
6.7%
구 360
 
6.6%
시 330
 
6.1%
산 323
 
6.0%
광 312
 
5.8%
역 312
 
5.8%
로 275
 
5.1%
대 210
 
3.9%
Other values (189) 2140
39.5%
Common
ValueCountFrequency (%)
1494
39.0%
1 447
 
11.7%
( 287
 
7.5%
) 287
 
7.5%
2 286
 
7.5%
3 168
 
4.4%
. 155
 
4.1%
0 123
 
3.2%
5 121
 
3.2%
4 105
 
2.7%
Other values (5) 353
 
9.2%
Latin
ValueCountFrequency (%)
B 5
45.5%
A 2
 
18.2%
G 1
 
9.1%
L 1
 
9.1%
C 1
 
9.1%
W 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5423
58.6%
ASCII 3837
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1494
38.9%
1 447
 
11.6%
( 287
 
7.5%
) 287
 
7.5%
2 286
 
7.5%
3 168
 
4.4%
. 155
 
4.0%
0 123
 
3.2%
5 121
 
3.2%
4 105
 
2.7%
Other values (11) 364
 
9.5%
Hangul
ValueCountFrequency (%)
동 401
 
7.4%
부 398
 
7.3%
서 362
 
6.7%
구 360
 
6.6%
시 330
 
6.1%
산 323
 
6.0%
광 312
 
5.8%
역 312
 
5.8%
로 275
 
5.1%
대 210
 
3.9%
Other values (189) 2140
39.5%

지정일자
Date

Distinct232
Distinct (%)74.8%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
Minimum2001-01-02 00:00:00
Maximum2023-07-03 00:00:00
2023-12-11T02:02:05.016270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:05.278658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T02:01:58.986939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:02:05.447402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번소매인구분
연번1.0000.126
소매인구분0.1261.000
2023-12-11T02:02:05.590103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번소매인구분
연번1.0000.095
소매인구분0.0951.000

Missing values

2023-12-11T02:01:59.150632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:01:59.317592image/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

연번지정번호대표자명소매인구분업소명업소도로명주소지정일자
012023-3260107-05-6-00021김세련제7조의3제2항에따른경우다올잡화부산광역시 서구 망양로 54. 1층 (서대신동3가)2023-07-03
122023-3260107-05-6-00020박점숙제7조의3제2항에따른경우토성나루부산광역시 서구 까치고개로245번길 23. 1층 (토성동1가)2023-06-20
232023-3260107-05-6-00019김지영제7조의3제2항에따른경우일월일일 송도점부산광역시 서구 성산길 31 (암남동)2023-06-07
342023-3260107-05-6-00018소현숙제7조의3제2항에따른경우지에스25 부민드림점부산광역시 서구 구덕로238번길 14 (부용동1가)2023-06-02
452023-3260107-05-6-00017장미경제7조의3제2항에따른경우씨유 부산송도혜성점부산광역시 서구 충무대로 24. 102.103호 (암남동. 송도혜성주상복합빌딩)2023-06-01
562023-3260107-05-6-00016김칠성제7조의3제2항에따른경우이마트24 부산동대신점부산광역시 서구 구덕로346번길 28. 1층 103호 (동대신동3가. 리버스12차)2023-05-15
672023-3260107-05-6-00015박근아제7조의3제2항에따른경우그나케이크부산광역시 서구 구덕로249번길 23 (부용동2가)2023-05-15
782023-3260107-05-6-00014권동하제7조의3제2항에따른경우지에스(GS)25 인터불고점부산광역시 서구 원양로 125. 2동 1층 (암남동)2023-04-04
892023-3260107-05-6-00012김나영， 김민정제7조의3제2항에따른경우씨유부산송도베스트점부산광역시 서구 송도해변로 97. 베스트웨스턴플러스 부산송도호텔 104호 (암남동)2023-03-27
9102023-3260107-05-6-00011김영미제7조의3제2항에따른경우굿피트코리아 영미대리점부산광역시 서구 대영로74번길 15. 1층 (동대신동1가)2023-03-15
연번지정번호대표자명소매인구분업소명업소도로명주소지정일자
3003012001-3260049-05-6-00532황인구제7조의3제2항에따른경우현대약국부산광역시 서구 구덕로295번길 47 (서대신동2가)2001-01-02
3013022001-3260049-05-6-00515정태욱제7조의3제2항에따른경우부성마트부산광역시 서구 서대신동2가 282번지 6호 15통 5반2001-01-02
3023032001-3260049-05-6-00501곽오순제7조의3제2항에따른경우햇님상회부산광역시 서구 해돋이로383번길 2 (서대신동2가)2001-01-02
3033042001-3260049-05-6-00411정화언제7조의3제2항에따른경우선미편의할인마트부산광역시 서구 부용로 20 (서대신동1가)2001-01-02
3043052001-3260049-05-6-00323유혜련제7조의3제2항에따른경우파워C-마트부산광역시 서구 보수대로258번길 15. 1층 102호 (동대신동3가. 리버스원룸)2001-01-02
3053062001-3260049-05-6-00312염상모제7조의3제2항에따른경우대선전기철물부산광역시 서구 보수대로230번길 1 (동대신동3가)2001-01-02
3063072001-3260049-05-6-00244도학봉제7조의3제2항에따른경우한아름슈퍼마켓부산광역시 서구 동대신동2가 79번지 17호 15통 3반2001-01-02
3073082001-3260049-05-6-00236유상회제7조의3제2항에따른경우밀양상회부산광역시 서구 대영로111번길 29 (동대신동2가)2001-01-02
3083092001-3260049-05-6-00215김형옥제7조의3제2항에따른경우새마을구판장부산광역시 서구 동대신동2가 79번지 27호 15통 2반 우익아파트 A동 02호2001-01-02
3093102001-3260049-05-6-00139유순자제7조의3제2항에따른경우무부산광역시 서구 대영로86번길 24-1 (동대신동1가)2001-12-27