Overview

Dataset statistics

Number of variables7
Number of observations34
Missing cells1
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory62.9 B

Variable types

Numeric3
Categorical1
Text3

Dataset

Description인천광역시 미추홀구의 가스사업자에 현황에 대한 데이터로 상호, 도로명 주소, 전화번호, 좌표값 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15016230/fileData.do

Alerts

연번 is highly overall correlated with 인허가 구분High correlation
인허가 구분 is highly overall correlated with 연번High correlation
전화번호 has 1 (2.9%) missing valuesMissing
연번 has unique valuesUnique
상호 has unique valuesUnique
도로명주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:19:32.649202
Analysis finished2023-12-12 22:19:34.084008
Duration1.43 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.5
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T07:19:34.193970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.65
Q19.25
median17.5
Q325.75
95-th percentile32.35
Maximum34
Range33
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation9.9582462
Coefficient of variation (CV)0.56904264
Kurtosis-1.2
Mean17.5
Median Absolute Deviation (MAD)8.5
Skewness0
Sum595
Variance99.166667
MonotonicityStrictly increasing
2023-12-13T07:19:34.318916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 1
 
2.9%
27 1
 
2.9%
21 1
 
2.9%
22 1
 
2.9%
23 1
 
2.9%
24 1
 
2.9%
25 1
 
2.9%
26 1
 
2.9%
28 1
 
2.9%
19 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
1 1
2.9%
2 1
2.9%
3 1
2.9%
4 1
2.9%
5 1
2.9%
6 1
2.9%
7 1
2.9%
8 1
2.9%
9 1
2.9%
10 1
2.9%
ValueCountFrequency (%)
34 1
2.9%
33 1
2.9%
32 1
2.9%
31 1
2.9%
30 1
2.9%
29 1
2.9%
28 1
2.9%
27 1
2.9%
26 1
2.9%
25 1
2.9%

인허가 구분
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Memory size404.0 B
LPG충전
8 
고압가스냉동제조
7 
고압가스판매
4 
LPG판매
3 
가스용품제조
3 
Other values (5)
9 

Length

Max length8
Median length7
Mean length6.1764706
Min length5

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row고압가스제조
2nd row고압가스제조
3rd row고압가스 충전
4th row고압가스 충전
5th row고압가스냉동제조

Common Values

ValueCountFrequency (%)
LPG충전 8
23.5%
고압가스냉동제조 7
20.6%
고압가스판매 4
11.8%
LPG판매 3
 
8.8%
가스용품제조 3
 
8.8%
고압가스제조 2
 
5.9%
고압가스 충전 2
 
5.9%
고압가스저장 2
 
5.9%
용기등 제조 2
 
5.9%
도시가스충전소 1
 
2.9%

Length

2023-12-13T07:19:34.474613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:19:34.620984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
lpg충전 8
21.1%
고압가스냉동제조 7
18.4%
고압가스판매 4
10.5%
lpg판매 3
 
7.9%
가스용품제조 3
 
7.9%
고압가스제조 2
 
5.3%
고압가스 2
 
5.3%
충전 2
 
5.3%
고압가스저장 2
 
5.3%
용기등 2
 
5.3%
Other values (2) 3
 
7.9%

상호
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-13T07:19:34.827471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10.5
Mean length7.7352941
Min length3

Characters and Unicode

Total characters263
Distinct characters121
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

Unique34 ?
Unique (%)100.0%

Sample

1st row월드인덕션
2nd row롯데알미늄㈜
3rd row인천남부소방서
4th row인천문학다이빙(인천네이스)
5th row홈플러스㈜ 인하점
ValueCountFrequency (%)
1,2호기 3
 
7.0%
롯데알미늄㈜ 2
 
4.7%
도화충전소 2
 
4.7%
월드인덕션 1
 
2.3%
평화가스 1
 
2.3%
인천학익cng충전소 1
 
2.3%
㈜코리프냉동공조 1
 
2.3%
거송로스타mfg 1
 
2.3%
유진기업 1
 
2.3%
민솔(ms)가스 1
 
2.3%
Other values (29) 29
67.4%
2023-12-13T07:19:35.183110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 14
 
5.3%
인 10
 
3.8%
소 10
 
3.8%
㈜ 9
 
3.4%
전 9
 
3.4%
충 9
 
3.4%
가 9
 
3.4%
9
 
3.4%
천 8
 
3.0%
사 6
 
2.3%
Other values (111) 170
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 219
83.3%
Uppercase Letter 13
 
4.9%
Other Symbol 9
 
3.4%
Space Separator 9
 
3.4%
Decimal Number 6
 
2.3%
Other Punctuation 3
 
1.1%
Close Punctuation 2
 
0.8%
Open Punctuation 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 14
 
6.4%
인 10
 
4.6%
소 10
 
4.6%
전 9
 
4.1%
충 9
 
4.1%
가 9
 
4.1%
천 8
 
3.7%
사 6
 
2.7%
기 6
 
2.7%
동 3
 
1.4%
Other values (94) 135
61.6%
Uppercase Letter
ValueCountFrequency (%)
G 3
23.1%
M 2
15.4%
C 1
 
7.7%
N 1
 
7.7%
P 1
 
7.7%
L 1
 
7.7%
S 1
 
7.7%
F 1
 
7.7%
T 1
 
7.7%
I 1
 
7.7%
Decimal Number
ValueCountFrequency (%)
1 3
50.0%
2 3
50.0%
Other Symbol
ValueCountFrequency (%)
㈜ 9
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 228
86.7%
Common 22
 
8.4%
Latin 13
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 14
 
6.1%
인 10
 
4.4%
소 10
 
4.4%
㈜ 9
 
3.9%
전 9
 
3.9%
충 9
 
3.9%
가 9
 
3.9%
천 8
 
3.5%
사 6
 
2.6%
기 6
 
2.6%
Other values (95) 138
60.5%
Latin
ValueCountFrequency (%)
G 3
23.1%
M 2
15.4%
C 1
 
7.7%
N 1
 
7.7%
P 1
 
7.7%
L 1
 
7.7%
S 1
 
7.7%
F 1
 
7.7%
T 1
 
7.7%
I 1
 
7.7%
Common
ValueCountFrequency (%)
9
40.9%
1 3
 
13.6%
2 3
 
13.6%
, 3
 
13.6%
) 2
 
9.1%
( 2
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 219
83.3%
ASCII 35
 
13.3%
None 9
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 14
 
6.4%
인 10
 
4.6%
소 10
 
4.6%
전 9
 
4.1%
충 9
 
4.1%
가 9
 
4.1%
천 8
 
3.7%
사 6
 
2.7%
기 6
 
2.7%
동 3
 
1.4%
Other values (94) 135
61.6%
None
ValueCountFrequency (%)
㈜ 9
100.0%
ASCII
ValueCountFrequency (%)
9
25.7%
1 3
 
8.6%
2 3
 
8.6%
, 3
 
8.6%
G 3
 
8.6%
) 2
 
5.7%
( 2
 
5.7%
M 2
 
5.7%
C 1
 
2.9%
N 1
 
2.9%
Other values (6) 6
17.1%

도로명주소
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-13T07:19:35.443725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length26
Mean length23.352941
Min length17

Characters and Unicode

Total characters794
Distinct characters68
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)100.0%

Sample

1st row인천광역시 미추홀구 염전로165번길 38-38 (도화동)
2nd row인천광역시 미추홀구 염전로333번길 8
3rd row인천광역시 미추홀구 인하로 190
4th row인천광역시 미추홀구 매소홀로 618 1층 115호
5th row인천광역시 미추홀구 소성로 6 (용현동)
ValueCountFrequency (%)
인천광역시 34
22.4%
미추홀구 33
21.7%
도화동 5
 
3.3%
용현동 3
 
2.0%
주안동 3
 
2.0%
송림로 2
 
1.3%
인하로 2
 
1.3%
경인로 2
 
1.3%
소성로 2
 
1.3%
염전로 2
 
1.3%
Other values (61) 64
42.1%
2023-12-13T07:19:35.886261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
118
 
14.9%
인 40
 
5.0%
홀 36
 
4.5%
광 34
 
4.3%
역 34
 
4.3%
시 34
 
4.3%
천 34
 
4.3%
로 34
 
4.3%
구 33
 
4.2%
추 33
 
4.2%
Other values (58) 364
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 507
63.9%
Decimal Number 125
 
15.7%
Space Separator 118
 
14.9%
Close Punctuation 20
 
2.5%
Open Punctuation 20
 
2.5%
Dash Punctuation 3
 
0.4%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 40
 
7.9%
홀 36
 
7.1%
광 34
 
6.7%
역 34
 
6.7%
시 34
 
6.7%
천 34
 
6.7%
로 34
 
6.7%
구 33
 
6.5%
추 33
 
6.5%
미 33
 
6.5%
Other values (43) 162
32.0%
Decimal Number
ValueCountFrequency (%)
2 27
21.6%
3 20
16.0%
1 18
14.4%
8 11
8.8%
6 10
 
8.0%
5 9
 
7.2%
9 9
 
7.2%
0 9
 
7.2%
7 8
 
6.4%
4 4
 
3.2%
Space Separator
ValueCountFrequency (%)
118
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 507
63.9%
Common 287
36.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 40
 
7.9%
홀 36
 
7.1%
광 34
 
6.7%
역 34
 
6.7%
시 34
 
6.7%
천 34
 
6.7%
로 34
 
6.7%
구 33
 
6.5%
추 33
 
6.5%
미 33
 
6.5%
Other values (43) 162
32.0%
Common
ValueCountFrequency (%)
118
41.1%
2 27
 
9.4%
3 20
 
7.0%
) 20
 
7.0%
( 20
 
7.0%
1 18
 
6.3%
8 11
 
3.8%
6 10
 
3.5%
5 9
 
3.1%
9 9
 
3.1%
Other values (5) 25
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 507
63.9%
ASCII 287
36.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
118
41.1%
2 27
 
9.4%
3 20
 
7.0%
) 20
 
7.0%
( 20
 
7.0%
1 18
 
6.3%
8 11
 
3.8%
6 10
 
3.5%
5 9
 
3.1%
9 9
 
3.1%
Other values (5) 25
 
8.7%
Hangul
ValueCountFrequency (%)
인 40
 
7.9%
홀 36
 
7.1%
광 34
 
6.7%
역 34
 
6.7%
시 34
 
6.7%
천 34
 
6.7%
로 34
 
6.7%
구 33
 
6.5%
추 33
 
6.5%
미 33
 
6.5%
Other values (43) 162
32.0%

전화번호
Text

MISSING 

Distinct32
Distinct (%)97.0%
Missing1
Missing (%)2.9%
Memory size404.0 B
2023-12-13T07:19:36.087181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.969697
Min length11

Characters and Unicode

Total characters395
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)93.9%

Sample

1st row032-543-2770
2nd row032-870-7337
3rd row032-870-3254
4th row032-876-0100
5th row032-880-2174
ValueCountFrequency (%)
02-788-5226 2
 
6.1%
032-834-3002 1
 
3.0%
032-873-8284 1
 
3.0%
032-422-3301 1
 
3.0%
032-867-8181 1
 
3.0%
032-883-8844 1
 
3.0%
032-875-6398 1
 
3.0%
032-866-2560 1
 
3.0%
032-715-5129 1
 
3.0%
032-865-0082 1
 
3.0%
Other values (22) 22
66.7%
2023-12-13T07:19:36.469414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 66
16.7%
0 55
13.9%
2 55
13.9%
3 52
13.2%
8 44
11.1%
7 30
7.6%
5 23
 
5.8%
1 23
 
5.8%
6 22
 
5.6%
4 15
 
3.8%
Other values (2) 10
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 328
83.0%
Dash Punctuation 66
 
16.7%
Space Separator 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55
16.8%
2 55
16.8%
3 52
15.9%
8 44
13.4%
7 30
9.1%
5 23
7.0%
1 23
7.0%
6 22
 
6.7%
4 15
 
4.6%
9 9
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 66
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 395
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 66
16.7%
0 55
13.9%
2 55
13.9%
3 52
13.2%
8 44
11.1%
7 30
7.6%
5 23
 
5.8%
1 23
 
5.8%
6 22
 
5.6%
4 15
 
3.8%
Other values (2) 10
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 395
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 66
16.7%
0 55
13.9%
2 55
13.9%
3 52
13.2%
8 44
11.1%
7 30
7.6%
5 23
 
5.8%
1 23
 
5.8%
6 22
 
5.6%
4 15
 
3.8%
Other values (2) 10
 
2.5%

위도
Real number (ℝ)

Distinct32
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.460479
Minimum37.436545
Maximum37.479173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T07:19:36.604146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.436545
5-th percentile37.439162
Q137.44707
median37.464663
Q337.473331
95-th percentile37.478855
Maximum37.479173
Range0.04262826
Interquartile range (IQR)0.026260305

Descriptive statistics

Standard deviation0.014418288
Coefficient of variation (CV)0.00038489331
Kurtosis-1.426362
Mean37.460479
Median Absolute Deviation (MAD)0.013738325
Skewness-0.2662682
Sum1273.6563
Variance0.00020788702
MonotonicityNot monotonic
2023-12-13T07:19:36.715935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
37.43654516 2
 
5.9%
37.47333063 2
 
5.9%
37.47881162 1
 
2.9%
37.4605039 1
 
2.9%
37.46553572 1
 
2.9%
37.44829894 1
 
2.9%
37.45052676 1
 
2.9%
37.46479777 1
 
2.9%
37.44707898 1
 
2.9%
37.4457774 1
 
2.9%
Other values (22) 22
64.7%
ValueCountFrequency (%)
37.43654516 2
5.9%
37.44057139 1
2.9%
37.44095356 1
2.9%
37.44104173 1
2.9%
37.44140211 1
2.9%
37.4457774 1
2.9%
37.44657047 1
2.9%
37.44706744 1
2.9%
37.44707898 1
2.9%
37.44829894 1
2.9%
ValueCountFrequency (%)
37.47917342 1
2.9%
37.47893678 1
2.9%
37.47881162 1
2.9%
37.47858317 1
2.9%
37.4782196 1
2.9%
37.47661328 1
2.9%
37.47528703 1
2.9%
37.47340597 1
2.9%
37.47333063 2
5.9%
37.47112984 1
2.9%

경도
Real number (ℝ)

Distinct32
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.66358
Minimum126.63066
Maximum126.69116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T07:19:36.828000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.63066
5-th percentile126.6367
Q1126.65327
median126.66298
Q3126.67882
95-th percentile126.68789
Maximum126.69116
Range0.0605061
Interquartile range (IQR)0.02555255

Descriptive statistics

Standard deviation0.016901341
Coefficient of variation (CV)0.00013343488
Kurtosis-0.85631636
Mean126.66358
Median Absolute Deviation (MAD)0.01304195
Skewness-0.20340438
Sum4306.5619
Variance0.00028565531
MonotonicityNot monotonic
2023-12-13T07:19:36.948078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
126.6864606 2
 
5.9%
126.6813248 2
 
5.9%
126.6627208 1
 
2.9%
126.6730812 1
 
2.9%
126.6905326 1
 
2.9%
126.6788739 1
 
2.9%
126.6306578 1
 
2.9%
126.6645412 1
 
2.9%
126.6524107 1
 
2.9%
126.6531828 1
 
2.9%
Other values (22) 22
64.7%
ValueCountFrequency (%)
126.6306578 1
2.9%
126.6366048 1
2.9%
126.6367454 1
2.9%
126.6376224 1
2.9%
126.6387588 1
2.9%
126.641964 1
2.9%
126.6508493 1
2.9%
126.6524107 1
2.9%
126.6531828 1
2.9%
126.6535295 1
2.9%
ValueCountFrequency (%)
126.6911639 1
2.9%
126.6905326 1
2.9%
126.6864606 2
5.9%
126.6813248 2
5.9%
126.67963 1
2.9%
126.6788982 1
2.9%
126.6788739 1
2.9%
126.6786664 1
2.9%
126.6769332 1
2.9%
126.6730812 1
2.9%

Interactions

2023-12-13T07:19:33.517941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:19:32.971992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:19:33.256639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:19:33.637601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:19:33.068303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:19:33.340628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:19:33.727970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:19:33.144619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:19:33.417386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:19:37.075205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번인허가 구분상호도로명주소전화번호위도경도
연번1.0000.9641.0001.0000.9100.0000.185
인허가 구분0.9641.0001.0001.0001.0000.4070.384
상호1.0001.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.0001.000
전화번호0.9101.0001.0001.0001.0000.0000.906
위도0.0000.4071.0001.0000.0001.0000.842
경도0.1850.3841.0001.0000.9060.8421.000
2023-12-13T07:19:37.548927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도인허가 구분
연번1.0000.055-0.0560.671
위도0.0551.0000.2390.166
경도-0.0560.2391.0000.151
인허가 구분0.6710.1660.1511.000

Missing values

2023-12-13T07:19:33.900047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:19:34.031774image/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

연번인허가 구분상호도로명주소전화번호위도경도
01고압가스제조월드인덕션인천광역시 미추홀구 염전로165번길 38-38 (도화동)032-543-277037.478812126.662721
12고압가스제조롯데알미늄㈜인천광역시 미추홀구 염전로333번길 8032-870-733737.47113126.678898
23고압가스 충전인천남부소방서인천광역시 미추홀구 인하로 190032-870-325437.4484126.669004
34고압가스 충전인천문학다이빙(인천네이스)인천광역시 미추홀구 매소홀로 618 1층 115호<NA>37.436545126.686461
45고압가스냉동제조홈플러스㈜ 인하점인천광역시 미추홀구 소성로 6 (용현동)032-876-010037.447067126.650849
56고압가스냉동제조옹진군청사 1,2호기인천광역시 미추홀구 매소홀로 120 (용현동)032-880-217437.44657126.636745
67고압가스냉동제조인하대역사 1,2호기인천광역시 미추홀구 독배로 315 (용현동)02-788-522637.436545126.686461
78고압가스냉동제조숭의역사 1,2호기인천광역시 미추홀구 인주대로 지하 6 (숭의동)02-788-522637.460381126.638759
89고압가스냉동제조하늘 꿈 교회인천광역시 미추홀구 송림로 235 (도화동)032-762-665537.475287126.659284
910고압가스냉동제조인천IT타워관리위원회인천광역시 미추홀구 경인로 229 (도화동)032-255-699537.464567126.667954
연번인허가 구분상호도로명주소전화번호위도경도
2425LPG충전송강충전소인천광역시 미추홀구 소성로 24032-866-256037.447079126.652411
2526LPG충전한일가스산업㈜ 도화충전소인천광역시 미추홀구 한나루로 612032-715-512937.460504126.673081
2627LPG충전주안역충전소인천광역시 미추홀구 주안로 81032-873-828437.464509126.678666
2728LPG충전인천LPG충전소인천광역시 매소홀로271번길 19032-865-008237.445777126.653183
2829LPG판매동성가스인천광역시 미추홀구 염전로 201번길 62032-865-711137.476613126.667816
2930LPG판매평화가스인천광역시 미추홀구 아암대로253번길 75032-833-105737.440954126.637622
3031LPG판매민솔(MS)가스인천광역시 미추홀구 노적산로54번길 2032-876-720037.441042126.655324
3132가스용품제조유진기업인천광역시 미추홀구 길파로 86 (주안동)032-864-733037.473331126.681325
3233가스용품제조거송로스타MFG인천광역시 미추홀구 길파로86 (주안동,2층)032-561-052137.473331126.681325
3334가스용품제조코푸렉스인천광역시 미추홀구 염전로128번길23 (도화동)032-762-911137.478583126.656606