Overview

Dataset statistics

Number of variables19
Number of observations107
Missing cells876
Missing cells (%)43.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.1 KiB
Average record size in memory163.2 B

Variable types

Categorical4
Text5
DateTime1
Unsupported4
Numeric5

Dataset

Description담배 수입 판매업체 현황_인허가
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=E1U7D9CL0OPKXGWVQ0B214052390&infSeq=1

Alerts

영업상태명 is highly overall correlated with X좌표값 and 3 other fieldsHigh correlation
소재지시설전화번호 is highly overall correlated with X좌표값 and 4 other fieldsHigh correlation
시군명 is highly overall correlated with WGS84위도 and 5 other fieldsHigh correlation
영업상태구분코드 is highly overall correlated with 소재지우편번호 and 5 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with X좌표값 and 2 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 시군명High correlation
WGS84경도 is highly overall correlated with 시군명High correlation
X좌표값 is highly overall correlated with 소재지우편번호 and 5 other fieldsHigh correlation
Y좌표값 is highly overall correlated with 소재지우편번호 and 5 other fieldsHigh correlation
영업상태구분코드 is highly imbalanced (68.8%)Imbalance
영업상태명 is highly imbalanced (66.0%)Imbalance
소재지시설전화번호 is highly imbalanced (90.4%)Imbalance
인허가취소일자 has 107 (100.0%) missing valuesMissing
소재지면적정보 has 107 (100.0%) missing valuesMissing
도로명우편번호 has 107 (100.0%) missing valuesMissing
소재지도로명주소 has 10 (9.3%) missing valuesMissing
소재지우편번호 has 2 (1.9%) missing valuesMissing
WGS84위도 has 16 (15.0%) missing valuesMissing
WGS84경도 has 16 (15.0%) missing valuesMissing
업태구분명정보 has 107 (100.0%) missing valuesMissing
X좌표값 has 101 (94.4%) missing valuesMissing
Y좌표값 has 101 (94.4%) missing valuesMissing
취급제품명정보 has 101 (94.4%) missing valuesMissing
담배공급업체명 has 101 (94.4%) missing valuesMissing
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
소재지면적정보 is an unsupported type, check if it needs cleaning or further analysisUnsupported
도로명우편번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
업태구분명정보 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 21:54:45.665676
Analysis finished2023-12-10 21:54:49.873572
Duration4.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size988.0 B
성남시
16 
용인시
15 
수원시
15 
부천시
11 
남양주시
9 
Other values (16)
41 

Length

Max length4
Median length3
Mean length3.1121495
Min length3

Unique

Unique7 ?
Unique (%)6.5%

Sample

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

Common Values

ValueCountFrequency (%)
성남시 16
15.0%
용인시 15
14.0%
수원시 15
14.0%
부천시 11
10.3%
남양주시 9
8.4%
고양시 7
6.5%
화성시 6
 
5.6%
하남시 4
 
3.7%
평택시 4
 
3.7%
안산시 4
 
3.7%
Other values (11) 16
15.0%

Length

2023-12-11T06:54:49.955986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성남시 16
15.0%
용인시 15
14.0%
수원시 15
14.0%
부천시 11
10.3%
남양주시 9
8.4%
고양시 7
6.5%
화성시 6
 
5.6%
하남시 4
 
3.7%
평택시 4
 
3.7%
안산시 4
 
3.7%
Other values (11) 16
15.0%

사업장명
Text

Distinct104
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size988.0 B
2023-12-11T06:54:50.223544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length10
Mean length7.271028
Min length2

Characters and Unicode

Total characters778
Distinct characters194
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)94.4%

Sample

1st row주식회사 지와이코리아
2nd row(주)허쉬
3rd row(주)페로젠코리아
4th row스퀘어코리아
5th row주식회사츄리온더쇼어
ValueCountFrequency (%)
주식회사 16
 
12.2%
디아이와이굿 2
 
1.5%
임팩트코리아타바코 2
 
1.5%
라미야 2
 
1.5%
주)유엔아이로직스 1
 
0.8%
dou 1
 
0.8%
지와이코리아 1
 
0.8%
베스트유통 1
 
0.8%
럭스 1
 
0.8%
주식회사전자담배이지스 1
 
0.8%
Other values (103) 103
78.6%
2023-12-11T06:54:50.676679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 50
 
6.4%
이 36
 
4.6%
( 29
 
3.7%
) 29
 
3.7%
코 26
 
3.3%
아 26
 
3.3%
사 26
 
3.3%
리 24
 
3.1%
24
 
3.1%
식 23
 
3.0%
Other values (184) 485
62.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 634
81.5%
Uppercase Letter 30
 
3.9%
Open Punctuation 29
 
3.7%
Close Punctuation 29
 
3.7%
Lowercase Letter 29
 
3.7%
Space Separator 24
 
3.1%
Other Punctuation 2
 
0.3%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 50
 
7.9%
이 36
 
5.7%
코 26
 
4.1%
아 26
 
4.1%
사 26
 
4.1%
리 24
 
3.8%
식 23
 
3.6%
회 23
 
3.6%
스 22
 
3.5%
라 11
 
1.7%
Other values (150) 367
57.9%
Uppercase Letter
ValueCountFrequency (%)
H 4
13.3%
T 3
 
10.0%
D 3
 
10.0%
I 2
 
6.7%
P 2
 
6.7%
O 2
 
6.7%
L 2
 
6.7%
E 2
 
6.7%
U 1
 
3.3%
R 1
 
3.3%
Other values (8) 8
26.7%
Lowercase Letter
ValueCountFrequency (%)
e 5
17.2%
c 4
13.8%
s 4
13.8%
r 3
10.3%
o 3
10.3%
a 2
 
6.9%
u 2
 
6.9%
n 2
 
6.9%
i 2
 
6.9%
x 1
 
3.4%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Space Separator
ValueCountFrequency (%)
24
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 635
81.6%
Common 84
 
10.8%
Latin 59
 
7.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 50
 
7.9%
이 36
 
5.7%
코 26
 
4.1%
아 26
 
4.1%
사 26
 
4.1%
리 24
 
3.8%
식 23
 
3.6%
회 23
 
3.6%
스 22
 
3.5%
라 11
 
1.7%
Other values (151) 368
58.0%
Latin
ValueCountFrequency (%)
e 5
 
8.5%
c 4
 
6.8%
H 4
 
6.8%
s 4
 
6.8%
T 3
 
5.1%
r 3
 
5.1%
o 3
 
5.1%
D 3
 
5.1%
I 2
 
3.4%
a 2
 
3.4%
Other values (19) 26
44.1%
Common
ValueCountFrequency (%)
( 29
34.5%
) 29
34.5%
24
28.6%
. 2
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 634
81.5%
ASCII 143
 
18.4%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 50
 
7.9%
이 36
 
5.7%
코 26
 
4.1%
아 26
 
4.1%
사 26
 
4.1%
리 24
 
3.8%
식 23
 
3.6%
회 23
 
3.6%
스 22
 
3.5%
라 11
 
1.7%
Other values (150) 367
57.9%
ASCII
ValueCountFrequency (%)
( 29
20.3%
) 29
20.3%
24
16.8%
e 5
 
3.5%
c 4
 
2.8%
H 4
 
2.8%
s 4
 
2.8%
T 3
 
2.1%
r 3
 
2.1%
o 3
 
2.1%
Other values (23) 35
24.5%
None
ValueCountFrequency (%)
㈜ 1
100.0%

인허가일자
Date

Distinct85
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Memory size988.0 B
Minimum2004-07-14 00:00:00
Maximum2023-10-04 00:00:00
2023-12-11T06:54:50.838144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:51.286832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107
Missing (%)100.0%
Memory size1.1 KiB

영업상태구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size988.0 B
<NA>
101 
BBBB
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBBBB
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 101
94.4%
BBBB 6
 
5.6%

Length

2023-12-11T06:54:51.417282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:54:51.517420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 101
94.4%
bbbb 6
 
5.6%

영업상태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size988.0 B
운영중
97 
영업
 
6
휴업 등
 
4

Length

Max length4
Median length3
Mean length2.9813084
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영업
2nd row운영중
3rd row운영중
4th row운영중
5th row운영중

Common Values

ValueCountFrequency (%)
운영중 97
90.7%
영업 6
 
5.6%
휴업 등 4
 
3.7%

Length

2023-12-11T06:54:51.632754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:54:51.792479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 97
87.4%
영업 6
 
5.4%
휴업 4
 
3.6%
등 4
 
3.6%

소재지시설전화번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size988.0 B
<NA>
105 
319025262
 
1
7086456864
 
1

Length

Max length10
Median length4
Mean length4.1028037
Min length4

Unique

Unique2 ?
Unique (%)1.9%

Sample

1st row319025262
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 105
98.1%
319025262 1
 
0.9%
7086456864 1
 
0.9%

Length

2023-12-11T06:54:51.941980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:54:52.057531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 105
98.1%
319025262 1
 
0.9%
7086456864 1
 
0.9%

소재지면적정보
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107
Missing (%)100.0%
Memory size1.1 KiB

도로명우편번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107
Missing (%)100.0%
Memory size1.1 KiB

소재지도로명주소
Text

MISSING 

Distinct94
Distinct (%)96.9%
Missing10
Missing (%)9.3%
Memory size988.0 B
2023-12-11T06:54:52.308957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length41
Mean length31.783505
Min length15

Characters and Unicode

Total characters3083
Distinct characters228
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

Unique91 ?
Unique (%)93.8%

Sample

1st row경기도 고양시 일산서구 킨텍스로 ***, 일산디엠시티스카이뷰 사무동 ****호 (대화동)
2nd row경기도 고양시 일산동구 일산로***번길 **-* (정발산동)
3rd row경기도 고양시 일산동구 중앙로****번길 **-**, A동 *층 ***호 (장항동,라페스타)
4th row경기도 고양시 일산동구 장백로 **, ***동 ***호 (백석동,백석역동문굿모닝힐*)
5th row경기도 고양시 일산동구 호수로 ***-**, ***동 *호 (장항동,신풍플로스타)
ValueCountFrequency (%)
97
 
15.8%
경기도 95
 
15.5%
호 45
 
7.4%
동 25
 
4.1%
성남시 16
 
2.6%
용인시 12
 
2.0%
부천시 11
 
1.8%
분당구 11
 
1.8%
수원시 10
 
1.6%
층 8
 
1.3%
Other values (206) 282
46.1%
2023-12-11T06:54:52.734337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 601
19.5%
515
16.7%
동 107
 
3.5%
경 102
 
3.3%
기 100
 
3.2%
시 100
 
3.2%
도 97
 
3.1%
로 96
 
3.1%
, 83
 
2.7%
( 60
 
1.9%
Other values (218) 1222
39.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1728
56.0%
Other Punctuation 684
 
22.2%
Space Separator 515
 
16.7%
Open Punctuation 60
 
1.9%
Close Punctuation 60
 
1.9%
Dash Punctuation 24
 
0.8%
Uppercase Letter 10
 
0.3%
Lowercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 107
 
6.2%
경 102
 
5.9%
기 100
 
5.8%
시 100
 
5.8%
도 97
 
5.6%
로 96
 
5.6%
구 59
 
3.4%
호 47
 
2.7%
남 36
 
2.1%
길 34
 
2.0%
Other values (204) 950
55.0%
Uppercase Letter
ValueCountFrequency (%)
B 4
40.0%
A 2
20.0%
T 1
 
10.0%
I 1
 
10.0%
K 1
 
10.0%
S 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
* 601
87.9%
, 83
 
12.1%
Lowercase Letter
ValueCountFrequency (%)
e 1
50.0%
n 1
50.0%
Space Separator
ValueCountFrequency (%)
515
100.0%
Open Punctuation
ValueCountFrequency (%)
( 60
100.0%
Close Punctuation
ValueCountFrequency (%)
) 60
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1728
56.0%
Common 1343
43.6%
Latin 12
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 107
 
6.2%
경 102
 
5.9%
기 100
 
5.8%
시 100
 
5.8%
도 97
 
5.6%
로 96
 
5.6%
구 59
 
3.4%
호 47
 
2.7%
남 36
 
2.1%
길 34
 
2.0%
Other values (204) 950
55.0%
Latin
ValueCountFrequency (%)
B 4
33.3%
A 2
16.7%
T 1
 
8.3%
I 1
 
8.3%
e 1
 
8.3%
n 1
 
8.3%
K 1
 
8.3%
S 1
 
8.3%
Common
ValueCountFrequency (%)
* 601
44.8%
515
38.3%
, 83
 
6.2%
( 60
 
4.5%
) 60
 
4.5%
- 24
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1728
56.0%
ASCII 1355
44.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 601
44.4%
515
38.0%
, 83
 
6.1%
( 60
 
4.4%
) 60
 
4.4%
- 24
 
1.8%
B 4
 
0.3%
A 2
 
0.1%
T 1
 
0.1%
I 1
 
0.1%
Other values (4) 4
 
0.3%
Hangul
ValueCountFrequency (%)
동 107
 
6.2%
경 102
 
5.9%
기 100
 
5.8%
시 100
 
5.8%
도 97
 
5.6%
로 96
 
5.6%
구 59
 
3.4%
호 47
 
2.7%
남 36
 
2.1%
길 34
 
2.0%
Other values (204) 950
55.0%

소재지지번주소
Text

Distinct102
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Memory size988.0 B
2023-12-11T06:54:52.973680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length41
Mean length32.327103
Min length1

Characters and Unicode

Total characters3459
Distinct characters197
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

Unique98 ?
Unique (%)91.6%

Sample

1st row경기도 고양시 일산서구 대화동 ****번지 **호 일산디엠시티스카이뷰 사무동 ****호
2nd row경기도 고양시 일산동구 정발산동 ****번지 *호 **통 *반
3rd row경기도 평택시 장당동 ***번지 **통 *반 제일하이빌(아) ***동 ***호
4th row경기도 고양시 일산동구 백석동 ****번지
5th row경기도 고양시 일산동구 장항동 ***번지 *호 ***동 *호
ValueCountFrequency (%)
호 116
 
14.9%
경기도 102
 
13.1%
번지 86
 
11.1%
통 46
 
5.9%
반 36
 
4.6%
동 31
 
4.0%
용인시 15
 
1.9%
수원시 15
 
1.9%
성남시 15
 
1.9%
분당구 11
 
1.4%
Other values (184) 303
39.0%
2023-12-11T06:54:53.325274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 779
22.5%
761
22.0%
동 139
 
4.0%
호 117
 
3.4%
시 109
 
3.2%
기 108
 
3.1%
도 106
 
3.1%
경 102
 
2.9%
지 98
 
2.8%
번 86
 
2.5%
Other values (187) 1054
30.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1898
54.9%
Other Punctuation 779
22.5%
Space Separator 761
22.0%
Uppercase Letter 13
 
0.4%
Dash Punctuation 6
 
0.2%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 139
 
7.3%
호 117
 
6.2%
시 109
 
5.7%
기 108
 
5.7%
도 106
 
5.6%
경 102
 
5.4%
지 98
 
5.2%
번 86
 
4.5%
구 71
 
3.7%
통 49
 
2.6%
Other values (177) 913
48.1%
Uppercase Letter
ValueCountFrequency (%)
A 4
30.8%
B 4
30.8%
I 3
23.1%
T 1
 
7.7%
C 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
* 779
100.0%
Space Separator
ValueCountFrequency (%)
761
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1898
54.9%
Common 1548
44.8%
Latin 13
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 139
 
7.3%
호 117
 
6.2%
시 109
 
5.7%
기 108
 
5.7%
도 106
 
5.6%
경 102
 
5.4%
지 98
 
5.2%
번 86
 
4.5%
구 71
 
3.7%
통 49
 
2.6%
Other values (177) 913
48.1%
Common
ValueCountFrequency (%)
* 779
50.3%
761
49.2%
- 6
 
0.4%
) 1
 
0.1%
( 1
 
0.1%
Latin
ValueCountFrequency (%)
A 4
30.8%
B 4
30.8%
I 3
23.1%
T 1
 
7.7%
C 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1898
54.9%
ASCII 1561
45.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 779
49.9%
761
48.8%
- 6
 
0.4%
A 4
 
0.3%
B 4
 
0.3%
I 3
 
0.2%
T 1
 
0.1%
) 1
 
0.1%
( 1
 
0.1%
C 1
 
0.1%
Hangul
ValueCountFrequency (%)
동 139
 
7.3%
호 117
 
6.2%
시 109
 
5.7%
기 108
 
5.7%
도 106
 
5.6%
경 102
 
5.4%
지 98
 
5.2%
번 86
 
4.5%
구 71
 
3.7%
통 49
 
2.6%
Other values (177) 913
48.1%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct99
Distinct (%)94.3%
Missing2
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean196785.09
Minimum10118
Maximum480070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:54:53.450885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10118
5-th percentile10412.2
Q114546
median17009
Q3443370
95-th percentile471278.8
Maximum480070
Range469952
Interquartile range (IQR)428824

Descriptive statistics

Standard deviation215829.28
Coefficient of variation (CV)1.0967766
Kurtosis-1.9091276
Mean196785.09
Median Absolute Deviation (MAD)6155
Skewness0.34092726
Sum20662434
Variance4.6582276 × 1010
MonotonicityNot monotonic
2023-12-11T06:54:53.576365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
472927 3
 
2.8%
463010 2
 
1.9%
14546 2
 
1.9%
442070 2
 
1.9%
446913 2
 
1.9%
449873 1
 
0.9%
16872 1
 
0.9%
16943 1
 
0.9%
16839 1
 
0.9%
16868 1
 
0.9%
Other values (89) 89
83.2%
(Missing) 2
 
1.9%
ValueCountFrequency (%)
10118 1
0.9%
10126 1
0.9%
10359 1
0.9%
10390 1
0.9%
10401 1
0.9%
10403 1
0.9%
10449 1
0.9%
10550 1
0.9%
10854 1
0.9%
11023 1
0.9%
ValueCountFrequency (%)
480070 1
 
0.9%
472934 1
 
0.9%
472927 3
2.8%
472846 1
 
0.9%
465010 1
 
0.9%
464880 1
 
0.9%
463857 1
 
0.9%
463847 1
 
0.9%
463828 1
 
0.9%
463808 1
 
0.9%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct89
Distinct (%)97.8%
Missing16
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean37.403745
Minimum36.945818
Maximum37.767817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:54:53.693391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.945818
5-th percentile37.140211
Q137.281026
median37.366142
Q337.502589
95-th percentile37.667131
Maximum37.767817
Range0.82199924
Interquartile range (IQR)0.22156275

Descriptive statistics

Standard deviation0.17138901
Coefficient of variation (CV)0.004582135
Kurtosis-0.16510909
Mean37.403745
Median Absolute Deviation (MAD)0.10875641
Skewness0.022643699
Sum3403.7408
Variance0.029374192
MonotonicityNot monotonic
2023-12-11T06:54:53.813840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.366142335 2
 
1.9%
37.6073923814 2
 
1.9%
37.3302905537 1
 
0.9%
37.3282448787 1
 
0.9%
37.2971223825 1
 
0.9%
37.323089473 1
 
0.9%
37.3286590642 1
 
0.9%
37.3273993013 1
 
0.9%
37.3166461202 1
 
0.9%
37.3218177681 1
 
0.9%
Other values (79) 79
73.8%
(Missing) 16
 
15.0%
ValueCountFrequency (%)
36.9458180686 1
0.9%
36.9911820631 1
0.9%
37.0509104053 1
0.9%
37.1156765419 1
0.9%
37.1270028568 1
0.9%
37.1534184881 1
0.9%
37.1640605469 1
0.9%
37.2464729414 1
0.9%
37.2498837147 1
0.9%
37.2563658009 1
0.9%
ValueCountFrequency (%)
37.7678173135 1
0.9%
37.7490646179 1
0.9%
37.7135713836 1
0.9%
37.7088125196 1
0.9%
37.6718479934 1
0.9%
37.6624141274 1
0.9%
37.6604752616 1
0.9%
37.6599073399 1
0.9%
37.6564853341 1
0.9%
37.6535603133 1
0.9%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct89
Distinct (%)97.8%
Missing16
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean126.9964
Minimum126.71055
Maximum127.49187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:54:53.939250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.71055
5-th percentile126.75677
Q1126.81386
median127.03111
Q3127.11224
95-th percentile127.2161
Maximum127.49187
Range0.78132511
Interquartile range (IQR)0.29838585

Descriptive statistics

Standard deviation0.17058794
Coefficient of variation (CV)0.0013432502
Kurtosis-0.69916109
Mean126.9964
Median Absolute Deviation (MAD)0.1281296
Skewness0.029072204
Sum11556.672
Variance0.029100247
MonotonicityNot monotonic
2023-12-11T06:54:54.072400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1053883207 2
 
1.9%
127.1934518075 2
 
1.9%
126.8143267315 1
 
0.9%
127.1125849262 1
 
0.9%
127.0739650825 1
 
0.9%
127.0887058668 1
 
0.9%
127.109704442 1
 
0.9%
127.1022386424 1
 
0.9%
127.0943839135 1
 
0.9%
126.7883996266 1
 
0.9%
Other values (79) 79
73.8%
(Missing) 16
 
15.0%
ValueCountFrequency (%)
126.7105452972 1
0.9%
126.7143191095 1
0.9%
126.7535235277 1
0.9%
126.7536983644 1
0.9%
126.753722682 1
0.9%
126.7598225524 1
0.9%
126.7608015036 1
0.9%
126.7612660446 1
0.9%
126.7681446464 1
0.9%
126.7696129537 1
0.9%
ValueCountFrequency (%)
127.4918704119 1
0.9%
127.3055972241 1
0.9%
127.2873337541 1
0.9%
127.2691273928 1
0.9%
127.2215364881 1
0.9%
127.2106575066 1
0.9%
127.2086340681 1
0.9%
127.2056171572 1
0.9%
127.1934518075 2
1.9%
127.190496118 1
0.9%

업태구분명정보
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107
Missing (%)100.0%
Memory size1.1 KiB

X좌표값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing101
Missing (%)94.4%
Infinite0
Infinite (%)0.0%
Mean223150.25
Minimum177849.2
Maximum339760.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:54:54.170758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum177849.2
5-th percentile178766.59
Q1187361.79
median209476.07
Q3219131.16
95-th percentile310025.48
Maximum339760.27
Range161911.07
Interquartile range (IQR)31769.369

Descriptive statistics

Standard deviation59673.614
Coefficient of variation (CV)0.26741451
Kurtosis4.3621599
Mean223150.25
Median Absolute Deviation (MAD)19651.17
Skewness1.9958364
Sum1338901.5
Variance3.5609402 × 109
MonotonicityNot monotonic
2023-12-11T06:54:54.261395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
177849.195871901 1
 
0.9%
220821.11917728 1
 
0.9%
181518.779976173 1
 
0.9%
214061.297420127 1
 
0.9%
339760.267060268 1
 
0.9%
204890.839649191 1
 
0.9%
(Missing) 101
94.4%
ValueCountFrequency (%)
177849.195871901 1
0.9%
181518.779976173 1
0.9%
204890.839649191 1
0.9%
214061.297420127 1
0.9%
220821.11917728 1
0.9%
339760.267060268 1
0.9%
ValueCountFrequency (%)
339760.267060268 1
0.9%
220821.11917728 1
0.9%
214061.297420127 1
0.9%
204890.839649191 1
0.9%
181518.779976173 1
0.9%
177849.195871901 1
0.9%

Y좌표값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing101
Missing (%)94.4%
Infinite0
Infinite (%)0.0%
Mean415866.98
Minimum263341.14
Maximum462962.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:54:54.360207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum263341.14
5-th percentile302248.72
Q1424827.43
median444672.05
Q3457174.31
95-th percentile462367.44
Maximum462962.31
Range199621.17
Interquartile range (IQR)32346.879

Descriptive statistics

Standard deviation76368.944
Coefficient of variation (CV)0.18363791
Kurtosis5.1008471
Mean415866.98
Median Absolute Deviation (MAD)17100.512
Skewness-2.2266192
Sum2495201.9
Variance5.8322156 × 109
MonotonicityNot monotonic
2023-12-11T06:54:54.462143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
462962.312594668 1
 
0.9%
460582.820741139 1
 
0.9%
442395.316651737 1
 
0.9%
418971.473947893 1
 
0.9%
263341.140528904 1
 
0.9%
446948.792207971 1
 
0.9%
(Missing) 101
94.4%
ValueCountFrequency (%)
263341.140528904 1
0.9%
418971.473947893 1
0.9%
442395.316651737 1
0.9%
446948.792207971 1
0.9%
460582.820741139 1
0.9%
462962.312594668 1
0.9%
ValueCountFrequency (%)
462962.312594668 1
0.9%
460582.820741139 1
0.9%
446948.792207971 1
0.9%
442395.316651737 1
0.9%
418971.473947893 1
0.9%
263341.140528904 1
0.9%

취급제품명정보
Text

MISSING 

Distinct5
Distinct (%)83.3%
Missing101
Missing (%)94.4%
Memory size988.0 B
2023-12-11T06:54:54.617570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length4.6666667
Min length4

Characters and Unicode

Total characters28
Distinct characters13
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

Unique4 ?
Unique (%)66.7%

Sample

1st row1회용 전자담배
2nd row전자담배
3rd rowTS담배
4th row궐련담배
5th row궐련담배
ValueCountFrequency (%)
궐련담배 2
28.6%
전자담배 2
28.6%
1회용 1
14.3%
ts담배 1
14.3%
권련담배 1
14.3%
2023-12-11T06:54:54.933753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
담 6
21.4%
배 6
21.4%
련 3
10.7%
궐 2
 
7.1%
전 2
 
7.1%
자 2
 
7.1%
1 1
 
3.6%
회 1
 
3.6%
용 1
 
3.6%
1
 
3.6%
Other values (3) 3
10.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24
85.7%
Uppercase Letter 2
 
7.1%
Decimal Number 1
 
3.6%
Space Separator 1
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
담 6
25.0%
배 6
25.0%
련 3
12.5%
궐 2
 
8.3%
전 2
 
8.3%
자 2
 
8.3%
회 1
 
4.2%
용 1
 
4.2%
권 1
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
T 1
50.0%
S 1
50.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24
85.7%
Common 2
 
7.1%
Latin 2
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
담 6
25.0%
배 6
25.0%
련 3
12.5%
궐 2
 
8.3%
전 2
 
8.3%
자 2
 
8.3%
회 1
 
4.2%
용 1
 
4.2%
권 1
 
4.2%
Common
ValueCountFrequency (%)
1 1
50.0%
1
50.0%
Latin
ValueCountFrequency (%)
T 1
50.0%
S 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24
85.7%
ASCII 4
 
14.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
담 6
25.0%
배 6
25.0%
련 3
12.5%
궐 2
 
8.3%
전 2
 
8.3%
자 2
 
8.3%
회 1
 
4.2%
용 1
 
4.2%
권 1
 
4.2%
ASCII
ValueCountFrequency (%)
1 1
25.0%
1
25.0%
T 1
25.0%
S 1
25.0%

담배공급업체명
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing101
Missing (%)94.4%
Memory size988.0 B
2023-12-11T06:54:55.114649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length81
Median length27.5
Mean length31.333333
Min length8

Characters and Unicode

Total characters188
Distinct characters54
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st rowSHENZEN TINOO INDUSTRIAL CO.,LTD
2nd rowvaporever Biotech Co.,Ltd
3rd row중국산동중연공업유한공사
4th rowJ..C newman Cigar Co, SWI-DE, LLC d/b/c/ Drew Estate, Tabacalera del Orente S.A.C
5th rowPT.Djarm
ValueCountFrequency (%)
co.,ltd 2
 
7.4%
co 2
 
7.4%
shenzen 1
 
3.7%
drew 1
 
3.7%
tobacco 1
 
3.7%
valladares 1
 
3.7%
oscar 1
 
3.7%
pt.djarm 1
 
3.7%
s.a.c 1
 
3.7%
orente 1
 
3.7%
Other values (15) 15
55.6%
2023-12-11T06:54:55.414001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21
 
11.2%
a 14
 
7.4%
e 11
 
5.9%
r 9
 
4.8%
C 8
 
4.3%
. 8
 
4.3%
o 7
 
3.7%
c 6
 
3.2%
T 6
 
3.2%
, 5
 
2.7%
Other values (44) 93
49.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 81
43.1%
Uppercase Letter 56
29.8%
Space Separator 21
 
11.2%
Other Punctuation 17
 
9.0%
Other Letter 12
 
6.4%
Dash Punctuation 1
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14
17.3%
e 11
13.6%
r 9
11.1%
o 7
8.6%
c 6
 
7.4%
t 5
 
6.2%
d 4
 
4.9%
l 4
 
4.9%
b 3
 
3.7%
s 3
 
3.7%
Other values (9) 15
18.5%
Uppercase Letter
ValueCountFrequency (%)
C 8
14.3%
T 6
10.7%
O 5
8.9%
D 5
8.9%
L 5
8.9%
S 4
7.1%
E 4
7.1%
N 4
7.1%
I 4
7.1%
A 2
 
3.6%
Other values (9) 9
16.1%
Other Letter
ValueCountFrequency (%)
중 2
16.7%
공 2
16.7%
사 1
8.3%
한 1
8.3%
유 1
8.3%
업 1
8.3%
연 1
8.3%
동 1
8.3%
산 1
8.3%
국 1
8.3%
Other Punctuation
ValueCountFrequency (%)
. 8
47.1%
, 5
29.4%
/ 3
 
17.6%
& 1
 
5.9%
Space Separator
ValueCountFrequency (%)
21
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 137
72.9%
Common 39
 
20.7%
Hangul 12
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14
 
10.2%
e 11
 
8.0%
r 9
 
6.6%
C 8
 
5.8%
o 7
 
5.1%
c 6
 
4.4%
T 6
 
4.4%
t 5
 
3.6%
O 5
 
3.6%
D 5
 
3.6%
Other values (28) 61
44.5%
Hangul
ValueCountFrequency (%)
중 2
16.7%
공 2
16.7%
사 1
8.3%
한 1
8.3%
유 1
8.3%
업 1
8.3%
연 1
8.3%
동 1
8.3%
산 1
8.3%
국 1
8.3%
Common
ValueCountFrequency (%)
21
53.8%
. 8
 
20.5%
, 5
 
12.8%
/ 3
 
7.7%
- 1
 
2.6%
& 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176
93.6%
Hangul 12
 
6.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21
 
11.9%
a 14
 
8.0%
e 11
 
6.2%
r 9
 
5.1%
C 8
 
4.5%
. 8
 
4.5%
o 7
 
4.0%
c 6
 
3.4%
T 6
 
3.4%
, 5
 
2.8%
Other values (34) 81
46.0%
Hangul
ValueCountFrequency (%)
중 2
16.7%
공 2
16.7%
사 1
8.3%
한 1
8.3%
유 1
8.3%
업 1
8.3%
연 1
8.3%
동 1
8.3%
산 1
8.3%
국 1
8.3%

Interactions

2023-12-11T06:54:48.708209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:46.688792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:47.168565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:47.613791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:48.169949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:48.809280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:46.760269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:47.234750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:47.731892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:48.295318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:48.895586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:46.851557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:47.312097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:47.841669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:48.390400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:48.967663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:46.986437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:47.423621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:47.959642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:48.480676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:49.096362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:47.076393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:47.511201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:48.076593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:54:48.592605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:54:55.530833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명소재지시설전화번호소재지도로명주소소재지우편번호WGS84위도WGS84경도X좌표값Y좌표값취급제품명정보담배공급업체명
시군명1.0000.7950.0000.0001.0000.6910.9550.9301.0001.0001.0001.000
인허가일자0.7951.0001.0000.0000.9590.9760.0000.0000.4160.4161.0001.000
영업상태명0.0001.0001.000NaN1.0000.0000.1700.101NaNNaNNaNNaN
소재지시설전화번호0.0000.000NaN1.0000.000NaNNaNNaNNaNNaN0.0000.000
소재지도로명주소1.0000.9591.0000.0001.0000.9581.0001.0001.0001.0001.0001.000
소재지우편번호0.6910.9760.000NaN0.9581.0000.3510.668NaNNaNNaNNaN
WGS84위도0.9550.0000.170NaN1.0000.3511.0000.684NaNNaNNaNNaN
WGS84경도0.9300.0000.101NaN1.0000.6680.6841.000NaNNaNNaNNaN
X좌표값1.0000.416NaNNaN1.000NaNNaNNaN1.0000.8270.9131.000
Y좌표값1.0000.416NaNNaN1.000NaNNaNNaN0.8271.0001.0001.000
취급제품명정보1.0001.000NaN0.0001.000NaNNaNNaN0.9131.0001.0001.000
담배공급업체명1.0001.000NaN0.0001.000NaNNaNNaN1.0001.0001.0001.000
2023-12-11T06:54:55.685025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태명소재지시설전화번호시군명영업상태구분코드
영업상태명1.0001.0000.0001.000
소재지시설전화번호1.0001.0001.0001.000
시군명0.0001.0001.0001.000
영업상태구분코드1.0001.0001.0001.000
2023-12-11T06:54:55.787193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도X좌표값Y좌표값시군명영업상태구분코드영업상태명소재지시설전화번호
소재지우편번호1.000-0.3760.2710.800-1.0000.4301.0000.000NaN
WGS84위도-0.3761.000-0.095NaNNaN0.7350.0000.1200.000
WGS84경도0.271-0.0951.000NaNNaN0.6710.0000.0910.000
X좌표값0.800NaNNaN1.000-0.6001.0001.0001.0001.000
Y좌표값-1.000NaNNaN-0.6001.0001.0001.0001.0001.000
시군명0.4300.7350.6711.0001.0001.0001.0000.0001.000
영업상태구분코드1.0000.0000.0001.0001.0001.0001.0001.0001.000
영업상태명0.0000.1200.0911.0001.0000.0001.0001.0001.000
소재지시설전화번호NaN0.0000.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-11T06:54:49.269813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:54:49.527579image/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.
2023-12-11T06:54:49.704104image/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

시군명사업장명인허가일자인허가취소일자영업상태구분코드영업상태명소재지시설전화번호소재지면적정보도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도업태구분명정보X좌표값Y좌표값취급제품명정보담배공급업체명
0고양시주식회사 지와이코리아2023-08-18<NA>BBBB영업319025262<NA><NA>경기도 고양시 일산서구 킨텍스로 ***, 일산디엠시티스카이뷰 사무동 ****호 (대화동)경기도 고양시 일산서구 대화동 ****번지 **호 일산디엠시티스카이뷰 사무동 ****호10390<NA><NA><NA>177849.195872462962.3125951회용 전자담배SHENZEN TINOO INDUSTRIAL CO.,LTD
1고양시(주)허쉬20141204<NA><NA>운영중<NA><NA><NA>경기도 고양시 일산동구 일산로***번길 **-* (정발산동)경기도 고양시 일산동구 정발산동 ****번지 *호 **통 *반1035937.671848126.773674<NA><NA><NA><NA><NA>
2고양시(주)페로젠코리아20120302<NA><NA>운영중<NA><NA><NA>경기도 고양시 일산동구 중앙로****번길 **-**, A동 *층 ***호 (장항동,라페스타)경기도 평택시 장당동 ***번지 **통 *반 제일하이빌(아) ***동 ***호41038037.660475126.769613<NA><NA><NA><NA><NA>
3고양시스퀘어코리아20130130<NA><NA>운영중<NA><NA><NA>경기도 고양시 일산동구 장백로 **, ***동 ***호 (백석동,백석역동문굿모닝힐*)경기도 고양시 일산동구 백석동 ****번지1044937.640214126.790417<NA><NA><NA><NA><NA>
4고양시주식회사츄리온더쇼어20100616<NA><NA>운영중<NA><NA><NA>경기도 고양시 일산동구 호수로 ***-**, ***동 *호 (장항동,신풍플로스타)경기도 고양시 일산동구 장항동 ***번지 *호 ***동 *호1040137.659907126.768145<NA><NA><NA><NA><NA>
5고양시츄리온더쇼어20070913<NA><NA>운영중<NA><NA><NA>경기도 고양시 일산동구 중앙로 ****경기도 고양시 일산동구 장항동 ***번지 *호 **통 *반 현대타운빌 ****호1040337.656485126.77447<NA><NA><NA><NA><NA>
6고양시명문플레버(주)20160414<NA><NA>휴업 등<NA><NA><NA>경기도 고양시 덕양구 삼원로 *, ***호 (삼송동)경기도 고양시 덕양구 삼송동 ***-*번지 ***호1055037.645158126.877482<NA><NA><NA><NA><NA>
7광명시이스모크20101119<NA><NA>운영중<NA><NA><NA>경기도 광명시 광명로 ***경기도 광명시 광명동 ***번지 *호 **통 *반 ***동 ***호1428737.478145126.851647<NA><NA><NA><NA><NA>
8광주시(주)무코20140922<NA><NA>운영중<NA><NA><NA>경기도 광주시 추곡길 **-**경기도 광주시 도척면 ***번지46488037.282446127.287334<NA><NA><NA><NA><NA>
9군포시DHT20141031<NA><NA>운영중<NA><NA><NA>경기도 군포시 군포로 ***, ***동 ****호 (대야미동,군포대야미 e-편한세상)경기도 군포시 대야미동 **통 **반 ***동 ****호1588837.325642126.915468<NA><NA><NA><NA><NA>
시군명사업장명인허가일자인허가취소일자영업상태구분코드영업상태명소재지시설전화번호소재지면적정보도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도업태구분명정보X좌표값Y좌표값취급제품명정보담배공급업체명
97하남시케이앤제이와이20150105<NA><NA>운영중<NA><NA><NA>경기도 하남시 대청로***번길 **, ***동 ****호 (창우동,은행한신아파트)경기도 하남시 창우동 ***번지 **통 *반 은행한신아파트 ***동 ****호1295437.537878127.221536<NA><NA><NA><NA><NA>
98하남시디아이와이20130808<NA><NA>운영중<NA><NA><NA>경기도 하남시 하남대로***번길 **, ***동 ****호 (신장동,에코타운)경기도 하남시 신장동 ***번지 **통 *반 에코타운 ***동 ****호1294937.541132127.210658<NA><NA><NA><NA><NA>
99화성시주식회사 선보20141016<NA><NA>운영중<NA><NA><NA>경기도 화성시 비봉면 푸른들판로 ****경기도 화성시 비봉면 구포리 ***번지 *호 **통1828337.249884126.872179<NA><NA><NA><NA><NA>
100화성시극동수입상사20040714<NA><NA>운영중<NA><NA><NA>경기도 화성시 정남면 서봉로 ***경기도 화성시 정남면 문학리 ***-*1852237.153418126.964488<NA><NA><NA><NA><NA>
101화성시디아이와이굿20101208<NA><NA>운영중<NA><NA><NA>경기도 화성시 정남면 서봉로 ****경기도 화성시 정남면 신리 ***번지 *호44596737.164061126.979878<NA><NA><NA><NA><NA>
102화성시디아이와이굿20100211<NA><NA>운영중<NA><NA><NA>경기도 화성시 향남읍 행정동로 **경기도 화성시 향남읍 행정리 ***번지 **통 *반 향남시범살구꽃마을화성파크드림 ****동 ***호1860137.127003126.928808<NA><NA><NA><NA><NA>
103화성시(주)유니온라이즈20120904<NA><NA>운영중<NA><NA><NA>경기도 화성시 남양로****번길 ** (신외동)경기도 화성시 신외동 ***번지 **통1825237.276481126.81177<NA><NA><NA><NA><NA>
104화성시(주)태화20140225<NA><NA>휴업 등<NA><NA><NA>경기도 화성시 장안면 장안공단로 ***-**경기도 화성시 장안면 금의리 **-*번지1858037.115677126.836201<NA><NA><NA><NA><NA>
105<NA>제이비케이 인터네셔널2023-10-04<NA>BBBB영업<NA><NA><NA>대구광역시 서구 평리로 ***, ***동 ****호 (내당동,삼익뉴타운)대구광역시 서구 내당동 ***번지 **통 *반 삼익뉴타운 ***동 ****호<NA><NA><NA><NA>339760.26706263341.140529궐련담배PT.Djarm
106<NA>토로스(Toros)2023-10-04<NA>BBBB영업<NA><NA><NA>서울특별시 강남구 영동대로***길 **, ***동 ***호 (청담동,래미안청담로이뷰)서울특별시 강남구 청담동 **통 *반 래미안청담로이뷰 ***동 ***호13443<NA><NA><NA>204890.839649446948.792208권련담배Oscar Valladares Tobacco & Co.