Overview

Dataset statistics

Number of variables7
Number of observations136
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 KiB
Average record size in memory60.0 B

Variable types

Numeric3
Categorical1
Text3

Dataset

Description(주)한국가스기술공사에서 관리하는 수소충전소 판매가격 현황 데이터입니다. 지역, 상호, 판매가격, 지역별 평균가 등을 제공합니다.
URLhttps://www.data.go.kr/data/15118863/fileData.do

Alerts

순번 is highly overall correlated with 구분High correlation
판매가격 is highly overall correlated with 지역별 평균가High correlation
지역별 평균가 is highly overall correlated with 판매가격 and 1 other fieldsHigh correlation
구분 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
순번 has unique valuesUnique
상호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 01:11:02.280135
Analysis finished2023-12-12 01:11:03.916781
Duration1.64 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct136
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.5
Minimum1
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T10:11:04.021576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.75
Q134.75
median68.5
Q3102.25
95-th percentile129.25
Maximum136
Range135
Interquartile range (IQR)67.5

Descriptive statistics

Standard deviation39.403892
Coefficient of variation (CV)0.57523929
Kurtosis-1.2
Mean68.5
Median Absolute Deviation (MAD)34
Skewness0
Sum9316
Variance1552.6667
MonotonicityStrictly increasing
2023-12-12T10:11:04.245190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
95 1
 
0.7%
89 1
 
0.7%
90 1
 
0.7%
91 1
 
0.7%
92 1
 
0.7%
93 1
 
0.7%
94 1
 
0.7%
96 1
 
0.7%
70 1
 
0.7%
Other values (126) 126
92.6%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
136 1
0.7%
135 1
0.7%
134 1
0.7%
133 1
0.7%
132 1
0.7%
131 1
0.7%
130 1
0.7%
129 1
0.7%
128 1
0.7%
127 1
0.7%

구분
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
경기
24 
충북
14 
경남
14 
충남
10 
강원
10 
Other values (11)
64 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기 24
17.6%
충북 14
10.3%
경남 14
10.3%
충남 10
7.4%
강원 10
7.4%
서울 9
 
6.6%
울산 9
 
6.6%
대전 8
 
5.9%
전북 8
 
5.9%
인천 7
 
5.1%
Other values (6) 23
16.9%

Length

2023-12-12T10:11:04.392983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 24
17.6%
충북 14
10.3%
경남 14
10.3%
충남 10
7.4%
강원 10
7.4%
서울 9
 
6.6%
울산 9
 
6.6%
대전 8
 
5.9%
전북 8
 
5.9%
인천 7
 
5.1%
Other values (6) 23
16.9%

상호
Text

UNIQUE 

Distinct136
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T10:11:04.718509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length12.654412
Min length6

Characters and Unicode

Total characters1721
Distinct characters197
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

Unique136 ?
Unique (%)100.0%

Sample

1st row마곡 에코 수소충전소
2nd row서울 강서 공영차고지 버스 수소충전소
3rd row서울특별시 서소문청사 수소충전소
4th row서울특별시 상암수소스테이션
5th row서울특별시 양재그린카스테이션
ValueCountFrequency (%)
수소충전소 84
25.1%
하이넷 37
 
11.1%
충전소 6
 
1.8%
창원 5
 
1.5%
광주 4
 
1.2%
평택 3
 
0.9%
서울특별시 3
 
0.9%
e1 3
 
0.9%
울산 3
 
0.9%
삼척 2
 
0.6%
Other values (173) 184
55.1%
2023-12-12T10:11:05.243345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 282
16.4%
200
 
11.6%
전 139
 
8.1%
충 137
 
8.0%
수 131
 
7.6%
하 44
 
2.6%
이 43
 
2.5%
넷 37
 
2.1%
게 24
 
1.4%
휴 23
 
1.3%
Other values (187) 661
38.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1423
82.7%
Space Separator 200
 
11.6%
Uppercase Letter 28
 
1.6%
Close Punctuation 23
 
1.3%
Open Punctuation 23
 
1.3%
Lowercase Letter 12
 
0.7%
Decimal Number 8
 
0.5%
Other Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 282
19.8%
전 139
 
9.8%
충 137
 
9.6%
수 131
 
9.2%
하 44
 
3.1%
이 43
 
3.0%
넷 37
 
2.6%
게 24
 
1.7%
휴 23
 
1.6%
주 22
 
1.5%
Other values (161) 541
38.0%
Uppercase Letter
ValueCountFrequency (%)
H 11
39.3%
E 3
 
10.7%
P 2
 
7.1%
T 2
 
7.1%
S 2
 
7.1%
G 2
 
7.1%
N 1
 
3.6%
L 1
 
3.6%
M 1
 
3.6%
C 1
 
3.6%
Other values (2) 2
 
7.1%
Lowercase Letter
ValueCountFrequency (%)
t 2
16.7%
o 2
16.7%
i 2
16.7%
n 2
16.7%
a 1
8.3%
g 1
8.3%
v 1
8.3%
e 1
8.3%
Decimal Number
ValueCountFrequency (%)
2 4
50.0%
1 4
50.0%
Space Separator
ValueCountFrequency (%)
200
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1423
82.7%
Common 258
 
15.0%
Latin 40
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 282
19.8%
전 139
 
9.8%
충 137
 
9.6%
수 131
 
9.2%
하 44
 
3.1%
이 43
 
3.0%
넷 37
 
2.6%
게 24
 
1.7%
휴 23
 
1.6%
주 22
 
1.5%
Other values (161) 541
38.0%
Latin
ValueCountFrequency (%)
H 11
27.5%
E 3
 
7.5%
P 2
 
5.0%
t 2
 
5.0%
T 2
 
5.0%
o 2
 
5.0%
i 2
 
5.0%
n 2
 
5.0%
S 2
 
5.0%
G 2
 
5.0%
Other values (10) 10
25.0%
Common
ValueCountFrequency (%)
200
77.5%
) 23
 
8.9%
( 23
 
8.9%
2 4
 
1.6%
1 4
 
1.6%
/ 4
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1423
82.7%
ASCII 298
 
17.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 282
19.8%
전 139
 
9.8%
충 137
 
9.6%
수 131
 
9.2%
하 44
 
3.1%
이 43
 
3.0%
넷 37
 
2.6%
게 24
 
1.7%
휴 23
 
1.6%
주 22
 
1.5%
Other values (161) 541
38.0%
ASCII
ValueCountFrequency (%)
200
67.1%
) 23
 
7.7%
( 23
 
7.7%
H 11
 
3.7%
2 4
 
1.3%
1 4
 
1.3%
/ 4
 
1.3%
E 3
 
1.0%
P 2
 
0.7%
t 2
 
0.7%
Other values (16) 22
 
7.4%

주소
Text

Distinct135
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T10:11:05.700041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length28
Mean length19.801471
Min length10

Characters and Unicode

Total characters2693
Distinct characters206
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134 ?
Unique (%)98.5%

Sample

1st row서울 강서구 마곡동 61-1
2nd row서울 강서구 개화동 663
3rd row서울 중구 서소문동 37
4th row서울특별시 마포구 상암동 481-34
5th row서울 서초구 양재동 201-1
ValueCountFrequency (%)
경기 15
 
2.3%
경기도 9
 
1.4%
창원시 8
 
1.2%
충청북도 8
 
1.2%
서울 7
 
1.1%
경상남도 7
 
1.1%
울산광역시 7
 
1.1%
경남 7
 
1.1%
전북 6
 
0.9%
강원 6
 
0.9%
Other values (444) 570
87.7%
2023-12-12T10:11:06.345225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
518
 
19.2%
1 113
 
4.2%
동 103
 
3.8%
시 95
 
3.5%
- 94
 
3.5%
구 71
 
2.6%
3 59
 
2.2%
2 54
 
2.0%
6 52
 
1.9%
리 48
 
1.8%
Other values (196) 1486
55.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1562
58.0%
Space Separator 518
 
19.2%
Decimal Number 508
 
18.9%
Dash Punctuation 94
 
3.5%
Uppercase Letter 4
 
0.1%
Open Punctuation 3
 
0.1%
Close Punctuation 3
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 103
 
6.6%
시 95
 
6.1%
구 71
 
4.5%
리 48
 
3.1%
산 47
 
3.0%
남 45
 
2.9%
경 44
 
2.8%
도 42
 
2.7%
주 37
 
2.4%
북 37
 
2.4%
Other values (177) 993
63.6%
Decimal Number
ValueCountFrequency (%)
1 113
22.2%
3 59
11.6%
2 54
10.6%
6 52
10.2%
4 46
9.1%
7 44
 
8.7%
8 43
 
8.5%
9 34
 
6.7%
5 32
 
6.3%
0 31
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
L 1
25.0%
B 1
25.0%
P 1
25.0%
G 1
25.0%
Space Separator
ValueCountFrequency (%)
518
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 94
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1561
58.0%
Common 1126
41.8%
Latin 5
 
0.2%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 103
 
6.6%
시 95
 
6.1%
구 71
 
4.5%
리 48
 
3.1%
산 47
 
3.0%
남 45
 
2.9%
경 44
 
2.8%
도 42
 
2.7%
주 37
 
2.4%
북 37
 
2.4%
Other values (176) 992
63.5%
Common
ValueCountFrequency (%)
518
46.0%
1 113
 
10.0%
- 94
 
8.3%
3 59
 
5.2%
2 54
 
4.8%
6 52
 
4.6%
4 46
 
4.1%
7 44
 
3.9%
8 43
 
3.8%
9 34
 
3.0%
Other values (4) 69
 
6.1%
Latin
ValueCountFrequency (%)
L 1
20.0%
B 1
20.0%
e 1
20.0%
P 1
20.0%
G 1
20.0%
Han
ValueCountFrequency (%)
內 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1561
58.0%
ASCII 1131
42.0%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
518
45.8%
1 113
 
10.0%
- 94
 
8.3%
3 59
 
5.2%
2 54
 
4.8%
6 52
 
4.6%
4 46
 
4.1%
7 44
 
3.9%
8 43
 
3.8%
9 34
 
3.0%
Other values (9) 74
 
6.5%
Hangul
ValueCountFrequency (%)
동 103
 
6.6%
시 95
 
6.1%
구 71
 
4.5%
리 48
 
3.1%
산 47
 
3.0%
남 45
 
2.9%
경 44
 
2.8%
도 42
 
2.7%
주 37
 
2.4%
북 37
 
2.4%
Other values (176) 992
63.5%
CJK
ValueCountFrequency (%)
內 1
100.0%

전화번호
Text

Distinct129
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T10:11:06.708393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.220588
Min length11

Characters and Unicode

Total characters1662
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

Unique128 ?
Unique (%)94.1%

Sample

1st row02-6081-8883
2nd row000-000-0000
3rd row070-8648-1164
4th row02-3151-0336
5th row02-529-4250
ValueCountFrequency (%)
000-000-0000 8
 
5.9%
033-534-6648 1
 
0.7%
070-7780-2047 1
 
0.7%
033-734-0764 1
 
0.7%
033-735-5567 1
 
0.7%
033-638-3003 1
 
0.7%
033-575-5190 1
 
0.7%
033-333-4415 1
 
0.7%
033-940-1071 1
 
0.7%
033-262-6664 1
 
0.7%
Other values (119) 119
87.5%
2023-12-12T10:11:07.242892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 354
21.3%
- 272
16.4%
3 149
9.0%
7 138
 
8.3%
4 137
 
8.2%
5 132
 
7.9%
6 113
 
6.8%
1 109
 
6.6%
2 107
 
6.4%
8 86
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1390
83.6%
Dash Punctuation 272
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 354
25.5%
3 149
10.7%
7 138
 
9.9%
4 137
 
9.9%
5 132
 
9.5%
6 113
 
8.1%
1 109
 
7.8%
2 107
 
7.7%
8 86
 
6.2%
9 65
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 272
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1662
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 354
21.3%
- 272
16.4%
3 149
9.0%
7 138
 
8.3%
4 137
 
8.2%
5 132
 
7.9%
6 113
 
6.8%
1 109
 
6.6%
2 107
 
6.4%
8 86
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1662
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 354
21.3%
- 272
16.4%
3 149
9.0%
7 138
 
8.3%
4 137
 
8.2%
5 132
 
7.9%
6 113
 
6.8%
1 109
 
6.6%
2 107
 
6.4%
8 86
 
5.2%

판매가격
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9430.5882
Minimum7700
Maximum12400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T10:11:07.402166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7700
5-th percentile8000
Q18800
median9900
Q39900
95-th percentile10140
Maximum12400
Range4700
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation874.91101
Coefficient of variation (CV)0.092773748
Kurtosis0.28694914
Mean9430.5882
Median Absolute Deviation (MAD)0
Skewness-0.24900362
Sum1282560
Variance765469.28
MonotonicityNot monotonic
2023-12-12T10:11:07.548249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
9900 74
54.4%
8000 13
 
9.6%
8800 10
 
7.4%
8200 7
 
5.1%
8500 5
 
3.7%
9400 4
 
2.9%
9800 3
 
2.2%
7700 3
 
2.2%
9600 3
 
2.2%
8300 2
 
1.5%
Other values (11) 12
 
8.8%
ValueCountFrequency (%)
7700 3
 
2.2%
7800 1
 
0.7%
7900 1
 
0.7%
8000 13
9.6%
8200 7
5.1%
8300 2
 
1.5%
8500 5
 
3.7%
8800 10
7.4%
9200 1
 
0.7%
9400 4
 
2.9%
ValueCountFrequency (%)
12400 1
 
0.7%
11800 1
 
0.7%
11300 1
 
0.7%
10900 1
 
0.7%
10600 2
 
1.5%
10560 1
 
0.7%
10000 1
 
0.7%
9900 74
54.4%
9800 3
 
2.2%
9600 3
 
2.2%

지역별 평균가
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9430.5772
Minimum8200
Maximum10315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T10:11:07.711699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8200
5-th percentile8471.4
Q19277.8
median9583.3
Q39742.9
95-th percentile10250
Maximum10315
Range2115
Interquartile range (IQR)465.1

Descriptive statistics

Standard deviation551.24458
Coefficient of variation (CV)0.058452899
Kurtosis-0.14940808
Mean9430.5772
Median Absolute Deviation (MAD)226.7
Skewness-0.92884724
Sum1282558.5
Variance303870.59
MonotonicityNot monotonic
2023-12-12T10:11:07.818369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
9583.3 24
17.6%
9557.1 14
10.3%
8471.4 14
10.3%
9690.0 10
7.4%
9810.0 10
7.4%
9277.8 9
 
6.6%
8622.2 9
 
6.6%
9575.0 8
 
5.9%
9925.0 8
 
5.9%
9742.9 7
 
5.1%
Other values (5) 23
16.9%
ValueCountFrequency (%)
8200.0 6
 
4.4%
8471.4 14
10.3%
8622.2 9
 
6.6%
9277.8 9
 
6.6%
9557.1 14
10.3%
9575.0 8
 
5.9%
9583.3 24
17.6%
9690.0 10
7.4%
9725.0 4
 
2.9%
9742.9 7
 
5.1%
ValueCountFrequency (%)
10315.0 4
 
2.9%
10250.0 4
 
2.9%
9925.0 8
 
5.9%
9900.0 5
 
3.7%
9810.0 10
7.4%
9742.9 7
 
5.1%
9725.0 4
 
2.9%
9690.0 10
7.4%
9583.3 24
17.6%
9575.0 8
 
5.9%

Interactions

2023-12-12T10:11:03.414328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:11:02.747685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:11:03.096752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:11:03.512947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:11:02.904144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:11:03.222975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:11:03.596858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:11:02.991617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:11:03.310483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:11:07.920001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분판매가격지역별 평균가
순번1.0000.9650.6220.873
구분0.9651.0000.7671.000
판매가격0.6220.7671.0000.679
지역별 평균가0.8731.0000.6791.000
2023-12-12T10:11:08.049994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번판매가격지역별 평균가구분
순번1.000-0.164-0.0270.805
판매가격-0.1641.0000.5900.425
지역별 평균가-0.0270.5901.0000.964
구분0.8050.4250.9641.000

Missing values

2023-12-12T10:11:03.707307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:11:03.856248image/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

순번구분상호주소전화번호판매가격지역별 평균가
01서울마곡 에코 수소충전소서울 강서구 마곡동 61-102-6081-888399009277.8
12서울서울 강서 공영차고지 버스 수소충전소서울 강서구 개화동 663000-000-000088009277.8
23서울서울특별시 서소문청사 수소충전소서울 중구 서소문동 37070-8648-116488009277.8
34서울서울특별시 상암수소스테이션서울특별시 마포구 상암동 481-3402-3151-033688009277.8
45서울서울특별시 양재그린카스테이션서울 서초구 양재동 201-102-529-425088009277.8
56서울E1 오곡 수소충전소서울 강서구 오곡동 699-14032-684-906199009277.8
67서울H강동수소충전소서울특별시 강동구 상일동 443-5 복지상일충전소 B동02-426-537298009277.8
78서울H국회수소충전소서울 영등포구 여의도동 1070-8882-774299009277.8
89서울H 광진 Moving Station서울 광진구 중곡동 611-702-463-569488009277.8
910인천하이넷 인천연희 수소충전소인천 서구 연희동 211-3070-4035-156099009742.9
순번구분상호주소전화번호판매가격지역별 평균가
126127울산울산 덕하복합충전소울산 울주군 청량읍 상남리 1171052-911-544283008622.2
127128울산울산 투게더수소충전소울산광역시 남구 야음동 220-1052-700-585985008622.2
128129울산언양휴게소(서울) 수소충전소울산광역시 울주군 온산읍 학남리 9-1052-263-6146106008622.2
129130울산경수소충전소울산 북구 창평동 1098-4052-201-300085008622.2
130131울산에어프로덕츠 울산 수소충전소울산광역시 남구 용연동 581-8 한국산업가스(주)052-259-409079008622.2
131132울산그린복합충전소울산광역시 울주군 웅촌면 곡천리 1-11052-239-960083008622.2
132133울산신일복합충전소울산광역시 울주군 청량읍 상남리 1171 덕하 공영차고지 1동052-225-150085008622.2
133134부산하이넷 부산정관 수소충전소부산 기장군 정관읍 예림리 384-2051-727-786899009900.0
134135부산H부산수소충전소부산광역시 사상구 학장동 271-12051-317-277799009900.0
135136부산서부산NK수소충전소부산광역시 강서구 송정동 803-6051-711-058099009900.0