Overview

Dataset statistics

Number of variables8
Number of observations355
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.0 KiB
Average record size in memory66.4 B

Variable types

Numeric2
Text3
DateTime1
Categorical2

Dataset

Description경상남도 밀양시 담배소매인 지정 현황 정보를 제공합니다. 밀양시 담배소매인 지정 연번, 업소명, 업소 도로명 주소 정보를 확인할 수 있습니다.
Author경상남도 밀양시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15021432

Alerts

데이터기준일자 has constant value ""Constant
법인구분 is highly imbalanced (62.2%)Imbalance
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:12:35.049206
Analysis finished2023-12-11 00:12:36.302248
Duration1.25 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct355
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178
Minimum1
Maximum355
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-11T09:12:36.396368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.7
Q189.5
median178
Q3266.5
95-th percentile337.3
Maximum355
Range354
Interquartile range (IQR)177

Descriptive statistics

Standard deviation102.62391
Coefficient of variation (CV)0.57653881
Kurtosis-1.2
Mean178
Median Absolute Deviation (MAD)89
Skewness0
Sum63190
Variance10531.667
MonotonicityStrictly increasing
2023-12-11T09:12:36.576489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
224 1
 
0.3%
244 1
 
0.3%
243 1
 
0.3%
242 1
 
0.3%
241 1
 
0.3%
240 1
 
0.3%
239 1
 
0.3%
238 1
 
0.3%
237 1
 
0.3%
Other values (345) 345
97.2%
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 (%)
355 1
0.3%
354 1
0.3%
353 1
0.3%
352 1
0.3%
351 1
0.3%
350 1
0.3%
349 1
0.3%
348 1
0.3%
347 1
0.3%
346 1
0.3%

업소명
Text

Distinct327
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-11T09:12:36.835246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length14
Mean length7.5690141
Min length2

Characters and Unicode

Total characters2687
Distinct characters331
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

Unique323 ?
Unique (%)91.0%

Sample

1st row페미리 25시
2nd row동밀양농협 하나로마트
3rd row카페 윤
4th row(주)코리아세븐 밀양원룸센타점
5th row지에스25 밀양무안명당점
ValueCountFrequency (%)
개인 26
 
5.6%
세븐일레븐 22
 
4.7%
씨유 15
 
3.2%
이마트24 13
 
2.8%
지에스25 8
 
1.7%
밀양점 4
 
0.9%
필프라이스 4
 
0.9%
밀양내이점 3
 
0.6%
gs25 3
 
0.6%
지에스(gs)25 3
 
0.6%
Other values (351) 365
78.3%
2023-12-11T09:12:37.272067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 154
 
5.7%
밀 138
 
5.1%
양 138
 
5.1%
112
 
4.2%
마 69
 
2.6%
트 65
 
2.4%
븐 59
 
2.2%
이 54
 
2.0%
2 48
 
1.8%
유 47
 
1.7%
Other values (321) 1803
67.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2338
87.0%
Space Separator 112
 
4.2%
Decimal Number 105
 
3.9%
Uppercase Letter 62
 
2.3%
Open Punctuation 22
 
0.8%
Close Punctuation 22
 
0.8%
Lowercase Letter 18
 
0.7%
Other Punctuation 5
 
0.2%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 154
 
6.6%
밀 138
 
5.9%
양 138
 
5.9%
마 69
 
3.0%
트 65
 
2.8%
븐 59
 
2.5%
이 54
 
2.3%
유 47
 
2.0%
일 39
 
1.7%
인 36
 
1.5%
Other values (283) 1539
65.8%
Uppercase Letter
ValueCountFrequency (%)
G 20
32.3%
S 19
30.6%
C 5
 
8.1%
D 3
 
4.8%
U 3
 
4.8%
I 2
 
3.2%
W 2
 
3.2%
R 2
 
3.2%
E 1
 
1.6%
T 1
 
1.6%
Other values (4) 4
 
6.5%
Lowercase Letter
ValueCountFrequency (%)
o 3
16.7%
a 2
11.1%
y 2
11.1%
p 2
11.1%
s 2
11.1%
e 2
11.1%
h 1
 
5.6%
u 1
 
5.6%
n 1
 
5.6%
d 1
 
5.6%
Decimal Number
ValueCountFrequency (%)
2 48
45.7%
5 27
25.7%
4 19
 
18.1%
1 5
 
4.8%
3 3
 
2.9%
0 1
 
1.0%
6 1
 
1.0%
9 1
 
1.0%
Space Separator
ValueCountFrequency (%)
112
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Other Punctuation
ValueCountFrequency (%)
& 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2338
87.0%
Common 269
 
10.0%
Latin 80
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 154
 
6.6%
밀 138
 
5.9%
양 138
 
5.9%
마 69
 
3.0%
트 65
 
2.8%
븐 59
 
2.5%
이 54
 
2.3%
유 47
 
2.0%
일 39
 
1.7%
인 36
 
1.5%
Other values (283) 1539
65.8%
Latin
ValueCountFrequency (%)
G 20
25.0%
S 19
23.8%
C 5
 
6.2%
o 3
 
3.8%
D 3
 
3.8%
U 3
 
3.8%
I 2
 
2.5%
a 2
 
2.5%
y 2
 
2.5%
p 2
 
2.5%
Other values (15) 19
23.8%
Common
ValueCountFrequency (%)
112
41.6%
2 48
17.8%
5 27
 
10.0%
( 22
 
8.2%
) 22
 
8.2%
4 19
 
7.1%
1 5
 
1.9%
& 5
 
1.9%
- 3
 
1.1%
3 3
 
1.1%
Other values (3) 3
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2338
87.0%
ASCII 349
 
13.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 154
 
6.6%
밀 138
 
5.9%
양 138
 
5.9%
마 69
 
3.0%
트 65
 
2.8%
븐 59
 
2.5%
이 54
 
2.3%
유 47
 
2.0%
일 39
 
1.7%
인 36
 
1.5%
Other values (283) 1539
65.8%
ASCII
ValueCountFrequency (%)
112
32.1%
2 48
13.8%
5 27
 
7.7%
( 22
 
6.3%
) 22
 
6.3%
G 20
 
5.7%
4 19
 
5.4%
S 19
 
5.4%
C 5
 
1.4%
1 5
 
1.4%
Other values (28) 50
14.3%

업소지번주소
Text

Distinct328
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-11T09:12:37.551582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length34
Mean length21.352113
Min length1

Characters and Unicode

Total characters7580
Distinct characters151
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

Unique326 ?
Unique (%)91.8%

Sample

1st row경상남도 밀양시 삼문동 301-13
2nd row경상남도 밀양시 산외면 남기리 1018-1
3rd row경상남도 밀양시 교동 250-2
4th row경상남도 밀양시 내이동 1583-5 1층
5th row경상남도 밀양시 무안면 무안리 767-15 1층 일부호
ValueCountFrequency (%)
경상남도 328
19.2%
밀양시 328
19.2%
내이동 59
 
3.5%
삼문동 47
 
2.8%
1호 45
 
2.6%
하남읍 27
 
1.6%
삼랑진읍 26
 
1.5%
상남면 26
 
1.5%
부북면 20
 
1.2%
3호 18
 
1.1%
Other values (453) 783
45.9%
2023-12-11T09:12:37.914751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1406
18.5%
남 387
 
5.1%
상 369
 
4.9%
1 345
 
4.6%
양 343
 
4.5%
도 337
 
4.4%
밀 332
 
4.4%
시 331
 
4.4%
경 328
 
4.3%
지 244
 
3.2%
Other values (141) 3158
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4653
61.4%
Decimal Number 1420
 
18.7%
Space Separator 1406
 
18.5%
Dash Punctuation 100
 
1.3%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 387
 
8.3%
상 369
 
7.9%
양 343
 
7.4%
도 337
 
7.2%
밀 332
 
7.1%
시 331
 
7.1%
경 328
 
7.0%
지 244
 
5.2%
번 229
 
4.9%
리 181
 
3.9%
Other values (128) 1572
33.8%
Decimal Number
ValueCountFrequency (%)
1 345
24.3%
2 161
11.3%
5 141
9.9%
3 138
 
9.7%
4 121
 
8.5%
8 121
 
8.5%
7 112
 
7.9%
0 97
 
6.8%
6 93
 
6.5%
9 91
 
6.4%
Space Separator
ValueCountFrequency (%)
1406
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4653
61.4%
Common 2926
38.6%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 387
 
8.3%
상 369
 
7.9%
양 343
 
7.4%
도 337
 
7.2%
밀 332
 
7.1%
시 331
 
7.1%
경 328
 
7.0%
지 244
 
5.2%
번 229
 
4.9%
리 181
 
3.9%
Other values (128) 1572
33.8%
Common
ValueCountFrequency (%)
1406
48.1%
1 345
 
11.8%
2 161
 
5.5%
5 141
 
4.8%
3 138
 
4.7%
4 121
 
4.1%
8 121
 
4.1%
7 112
 
3.8%
- 100
 
3.4%
0 97
 
3.3%
Other values (2) 184
 
6.3%
Latin
ValueCountFrequency (%)
e 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4653
61.4%
ASCII 2927
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1406
48.0%
1 345
 
11.8%
2 161
 
5.5%
5 141
 
4.8%
3 138
 
4.7%
4 121
 
4.1%
8 121
 
4.1%
7 112
 
3.8%
- 100
 
3.4%
0 97
 
3.3%
Other values (3) 185
 
6.3%
Hangul
ValueCountFrequency (%)
남 387
 
8.3%
상 369
 
7.9%
양 343
 
7.4%
도 337
 
7.2%
밀 332
 
7.1%
시 331
 
7.1%
경 328
 
7.0%
지 244
 
5.2%
번 229
 
4.9%
리 181
 
3.9%
Other values (128) 1572
33.8%

업소도로명주소
Text

Distinct351
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-11T09:12:38.135665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length44
Mean length24.104225
Min length1

Characters and Unicode

Total characters8557
Distinct characters209
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique348 ?
Unique (%)98.0%

Sample

1st row경상남도 밀양시 수월2길 12-1 (삼문동)
2nd row경상남도 밀양시 산외면 산외로 53
3rd row경상남도 밀양시 용평로 535 (교동)
4th row경상남도 밀양시 백민로8길 20. 1층 (내이동)
5th row경상남도 밀양시 무안면 사명로 479. 1층 일부호
ValueCountFrequency (%)
경상남도 352
 
18.4%
밀양시 352
 
18.4%
내이동 62
 
3.2%
삼문동 52
 
2.7%
1층 49
 
2.6%
하남읍 29
 
1.5%
상남면 28
 
1.5%
삼랑진읍 28
 
1.5%
중앙로 25
 
1.3%
밀양대로 20
 
1.0%
Other values (487) 917
47.9%
2023-12-11T09:12:38.502831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1565
18.3%
남 420
 
4.9%
상 418
 
4.9%
밀 395
 
4.6%
양 387
 
4.5%
시 370
 
4.3%
도 364
 
4.3%
경 352
 
4.1%
1 352
 
4.1%
로 252
 
2.9%
Other values (199) 3682
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5238
61.2%
Space Separator 1565
 
18.3%
Decimal Number 1205
 
14.1%
Close Punctuation 173
 
2.0%
Open Punctuation 173
 
2.0%
Other Punctuation 109
 
1.3%
Dash Punctuation 77
 
0.9%
Lowercase Letter 11
 
0.1%
Uppercase Letter 5
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 420
 
8.0%
상 418
 
8.0%
밀 395
 
7.5%
양 387
 
7.4%
시 370
 
7.1%
도 364
 
6.9%
경 352
 
6.7%
로 252
 
4.8%
동 222
 
4.2%
면 131
 
2.5%
Other values (176) 1927
36.8%
Decimal Number
ValueCountFrequency (%)
1 352
29.2%
2 148
12.3%
3 137
 
11.4%
4 103
 
8.5%
5 100
 
8.3%
0 89
 
7.4%
6 74
 
6.1%
8 72
 
6.0%
7 71
 
5.9%
9 59
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
l 4
36.4%
e 3
27.3%
u 2
18.2%
s 2
18.2%
Uppercase Letter
ValueCountFrequency (%)
B 2
40.0%
W 2
40.0%
D 1
20.0%
Space Separator
ValueCountFrequency (%)
1565
100.0%
Close Punctuation
ValueCountFrequency (%)
) 173
100.0%
Open Punctuation
ValueCountFrequency (%)
( 173
100.0%
Other Punctuation
ValueCountFrequency (%)
. 109
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5238
61.2%
Common 3303
38.6%
Latin 16
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 420
 
8.0%
상 418
 
8.0%
밀 395
 
7.5%
양 387
 
7.4%
시 370
 
7.1%
도 364
 
6.9%
경 352
 
6.7%
로 252
 
4.8%
동 222
 
4.2%
면 131
 
2.5%
Other values (176) 1927
36.8%
Common
ValueCountFrequency (%)
1565
47.4%
1 352
 
10.7%
) 173
 
5.2%
( 173
 
5.2%
2 148
 
4.5%
3 137
 
4.1%
. 109
 
3.3%
4 103
 
3.1%
5 100
 
3.0%
0 89
 
2.7%
Other values (6) 354
 
10.7%
Latin
ValueCountFrequency (%)
l 4
25.0%
e 3
18.8%
B 2
12.5%
W 2
12.5%
u 2
12.5%
s 2
12.5%
D 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5238
61.2%
ASCII 3319
38.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1565
47.2%
1 352
 
10.6%
) 173
 
5.2%
( 173
 
5.2%
2 148
 
4.5%
3 137
 
4.1%
. 109
 
3.3%
4 103
 
3.1%
5 100
 
3.0%
0 89
 
2.7%
Other values (13) 370
 
11.1%
Hangul
ValueCountFrequency (%)
남 420
 
8.0%
상 418
 
8.0%
밀 395
 
7.5%
양 387
 
7.4%
시 370
 
7.1%
도 364
 
6.9%
경 352
 
6.7%
로 252
 
4.8%
동 222
 
4.2%
면 131
 
2.5%
Other values (176) 1927
36.8%

업소주소우편번호
Real number (ℝ)

Distinct64
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53685.163
Minimum50400
Maximum627912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-11T09:12:38.643229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50400
5-th percentile50403.7
Q150418
median50429
Q350448
95-th percentile50467
Maximum627912
Range577512
Interquartile range (IQR)30

Descriptive statistics

Standard deviation43282.956
Coefficient of variation (CV)0.80623683
Kurtosis174.97722
Mean53685.163
Median Absolute Deviation (MAD)15
Skewness13.266176
Sum19058233
Variance1.8734143 × 109
MonotonicityNot monotonic
2023-12-11T09:12:38.763604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50467 17
 
4.8%
50423 16
 
4.5%
50408 13
 
3.7%
50418 12
 
3.4%
50438 12
 
3.4%
50422 11
 
3.1%
50460 11
 
3.1%
50419 10
 
2.8%
50445 10
 
2.8%
50439 10
 
2.8%
Other values (54) 233
65.6%
ValueCountFrequency (%)
50400 6
1.7%
50401 6
1.7%
50402 5
 
1.4%
50403 1
 
0.3%
50404 8
2.3%
50406 3
 
0.8%
50407 1
 
0.3%
50408 13
3.7%
50409 6
1.7%
50411 9
2.5%
ValueCountFrequency (%)
627912 1
 
0.3%
627894 1
 
0.3%
50467 17
4.8%
50466 2
 
0.6%
50465 4
 
1.1%
50464 1
 
0.3%
50463 2
 
0.6%
50462 3
 
0.8%
50461 6
 
1.7%
50460 11
3.1%

지정일자
Date

Distinct329
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
Minimum1900-01-01 00:00:00
Maximum2023-09-19 00:00:00
2023-12-11T09:12:38.886355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:12:39.019462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

법인구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
개인
329 
법인
 
26

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개인
2nd row법인
3rd row개인
4th row법인
5th row개인

Common Values

ValueCountFrequency (%)
개인 329
92.7%
법인 26
 
7.3%

Length

2023-12-11T09:12:39.146012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:12:39.258262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 329
92.7%
법인 26
 
7.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-09-26
355 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-26
2nd row2023-09-26
3rd row2023-09-26
4th row2023-09-26
5th row2023-09-26

Common Values

ValueCountFrequency (%)
2023-09-26 355
100.0%

Length

2023-12-11T09:12:39.362045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:12:39.467766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-26 355
100.0%

Interactions

2023-12-11T09:12:35.794990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:12:35.548452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:12:35.920331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:12:35.670847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:12:39.518856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업소주소우편번호법인구분
연번1.0000.2130.000
업소주소우편번호0.2131.0000.298
법인구분0.0000.2981.000
2023-12-11T09:12:39.596210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업소주소우편번호법인구분
연번1.0000.0220.000
업소주소우편번호0.0221.0000.188
법인구분0.0000.1881.000

Missing values

2023-12-11T09:12:36.072511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:12:36.236747image/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

연번업소명업소지번주소업소도로명주소업소주소우편번호지정일자법인구분데이터기준일자
01페미리 25시경상남도 밀양시 삼문동 301-13경상남도 밀양시 수월2길 12-1 (삼문동)504352023-09-19개인2023-09-26
12동밀양농협 하나로마트경상남도 밀양시 산외면 남기리 1018-1경상남도 밀양시 산외면 산외로 53504112023-09-19법인2023-09-26
23카페 윤경상남도 밀양시 교동 250-2경상남도 밀양시 용평로 535 (교동)504322023-08-22개인2023-09-26
34(주)코리아세븐 밀양원룸센타점경상남도 밀양시 내이동 1583-5 1층경상남도 밀양시 백민로8길 20. 1층 (내이동)504222023-08-17법인2023-09-26
45지에스25 밀양무안명당점경상남도 밀양시 무안면 무안리 767-15 1층 일부호경상남도 밀양시 무안면 사명로 479. 1층 일부호504082023-08-02개인2023-09-26
56세븐일레븐 밀양북성로점경상남도 밀양시 내이동 1185-8경상남도 밀양시 북성로 19 (내이동)504232023-07-18개인2023-09-26
67지정 오토바이경상남도 밀양시 무안면 무안리 767-20경상남도 밀양시 무안면 사명로 475504082023-06-19개인2023-09-26
78밀양얼음골종점계곡펜션편의점경상남도 밀양시 산내면 삼양리 169경상남도 밀양시 산내면 얼음골로 202504152023-06-12개인2023-09-26
89일등전자담배경상남도 밀양시 내이동 951-6경상남도 밀양시 노상하4길 44 (내이동)504272023-06-12개인2023-09-26
910도도이꾸경상남도 밀양시 삼랑진읍 검세리 817-1 102호경상남도 밀양시 삼랑진읍 천태로 98. 102호504672023-04-27개인2023-09-26
연번업소명업소지번주소업소도로명주소업소주소우편번호지정일자법인구분데이터기준일자
345346기산슈퍼경상남도 밀양시 상남면 기산리 1412번지경상남도 밀양시 상남면 상남로 1119504491984-05-21개인2023-09-26
346347개인경상남도 밀양시 상남면 예림리 430번지 1호경상남도 밀양시 상남면 양림동촌1길 126-2504521983-06-30개인2023-09-26
347348개인경상남도 밀양시 하남읍 수산리 342번지 3호경상남도 밀양시 하남읍 수산로 245 (대평동회관)504611982-05-17개인2023-09-26
348349심신슈퍼경상남도 밀양시 하남읍 수산리 475번지 9호경상남도 밀양시 하남읍 내동2길 2504592001-05-15개인2023-09-26
349350밀양 프랜차이즈편의점경상남도 밀양시 가곡동 462번지 3호경상남도 밀양시 중앙로 62 (가곡동. 밀양역)504452005-01-19법인2023-09-26
350351솔지방경상남도 밀양시 내일동 95번지 1호경상남도 밀양시 석정로 11 (내일동)504301993-01-04개인2023-09-26
351352제일이발관경상남도 밀양시 무안면 무안리 813번지 18호경상남도 밀양시 무안면 사명로 503504081989-04-20개인2023-09-26
352353개인경상남도 밀양시 초동면 신호리 18번지경상남도 밀양시 초동면 대구령길 3-4504551986-05-04개인2023-09-26
353354개인경상남도 밀양시 상남면 평촌리 694번지 1호경상남도 밀양시 상남면 평촌길 35504531900-01-01개인2023-09-26
354355개인경상남도 밀양시 산내면 용전리 160번지 3호경상남도 밀양시 산내면 산내용전3길 2504131900-01-01개인2023-09-26