Overview

Dataset statistics

Number of variables6
Number of observations151
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory51.9 B

Variable types

Numeric2
Text2
Categorical2

Dataset

Description수소유통 전담기관에서 조사한 전국 수소 충전소별 충전압력과 디스펜서 타입(싱글, 듀얼) 및 개수를 제공하는 공공데이터입니다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15102868/fileData.do

Alerts

디스펜서갯수 is highly imbalanced (52.6%)Imbalance
구분 has unique valuesUnique
충전소 코드 has unique valuesUnique
충전소 명 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:54:25.429854
Analysis finished2024-04-06 08:54:26.858948
Duration1.43 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

UNIQUE 

Distinct151
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76
Minimum1
Maximum151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-06T17:54:26.978222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.5
Q138.5
median76
Q3113.5
95-th percentile143.5
Maximum151
Range150
Interquartile range (IQR)75

Descriptive statistics

Standard deviation43.734045
Coefficient of variation (CV)0.57544796
Kurtosis-1.2
Mean76
Median Absolute Deviation (MAD)38
Skewness0
Sum11476
Variance1912.6667
MonotonicityStrictly increasing
2024-04-06T17:54:27.202735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
105 1
 
0.7%
98 1
 
0.7%
99 1
 
0.7%
100 1
 
0.7%
101 1
 
0.7%
102 1
 
0.7%
103 1
 
0.7%
104 1
 
0.7%
106 1
 
0.7%
Other values (141) 141
93.4%
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 (%)
151 1
0.7%
150 1
0.7%
149 1
0.7%
148 1
0.7%
147 1
0.7%
146 1
0.7%
145 1
0.7%
144 1
0.7%
143 1
0.7%
142 1
0.7%

충전소 코드
Text

UNIQUE 

Distinct151
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-04-06T17:54:27.530451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2869
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)100.0%

Sample

1st row2920020121HS2014001
2nd row4480020121HS2015001
3rd row3114020121HS2017001
4th row4812120121HS2017002
5th row2771020121HS2017003
ValueCountFrequency (%)
2920020121hs2014001 1
 
0.7%
4686020121hs2022035 1
 
0.7%
4372020121hs2022018 1
 
0.7%
4157020121hs2022019 1
 
0.7%
2671020121hs2022020 1
 
0.7%
2917020121hs2022021 1
 
0.7%
4281020121hs2022022 1
 
0.7%
4128120121hs2022023 1
 
0.7%
4623020121hs2022024 1
 
0.7%
1150020121hs2022025 1
 
0.7%
Other values (141) 141
93.4%
2024-04-06T17:54:28.252409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 757
26.4%
0 679
23.7%
1 587
20.5%
4 161
 
5.6%
H 151
 
5.3%
S 151
 
5.3%
3 123
 
4.3%
5 65
 
2.3%
7 60
 
2.1%
8 49
 
1.7%
Other values (2) 86
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2567
89.5%
Uppercase Letter 302
 
10.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 757
29.5%
0 679
26.5%
1 587
22.9%
4 161
 
6.3%
3 123
 
4.8%
5 65
 
2.5%
7 60
 
2.3%
8 49
 
1.9%
9 45
 
1.8%
6 41
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
H 151
50.0%
S 151
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2567
89.5%
Latin 302
 
10.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 757
29.5%
0 679
26.5%
1 587
22.9%
4 161
 
6.3%
3 123
 
4.8%
5 65
 
2.5%
7 60
 
2.3%
8 49
 
1.9%
9 45
 
1.8%
6 41
 
1.6%
Latin
ValueCountFrequency (%)
H 151
50.0%
S 151
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2869
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 757
26.4%
0 679
23.7%
1 587
20.5%
4 161
 
5.6%
H 151
 
5.3%
S 151
 
5.3%
3 123
 
4.3%
5 65
 
2.3%
7 60
 
2.1%
8 49
 
1.7%
Other values (2) 86
 
3.0%

충전소 명
Text

UNIQUE 

Distinct151
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-04-06T17:54:28.765815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length12.682119
Min length6

Characters and Unicode

Total characters1915
Distinct characters209
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

Unique151 ?
Unique (%)100.0%

Sample

1st row광주 진곡수소충전소
2nd row내포수소충전소
3rd row옥동LPG 수소 복합충전소
4th row창원팔룡 수소충전소
5th row대구 주행시험장 수소충전소
ValueCountFrequency (%)
수소충전소 99
27.8%
하이넷 40
 
11.2%
광주 4
 
1.1%
충전소 4
 
1.1%
서울특별시 3
 
0.8%
e1 3
 
0.8%
수소 3
 
0.8%
린데 3
 
0.8%
서울 2
 
0.6%
버스 2
 
0.6%
Other values (188) 193
54.2%
2024-04-06T17:54:29.549049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 321
16.8%
213
 
11.1%
전 158
 
8.3%
충 153
 
8.0%
수 151
 
7.9%
하 48
 
2.5%
이 48
 
2.5%
넷 41
 
2.1%
게 27
 
1.4%
) 27
 
1.4%
Other values (199) 728
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1584
82.7%
Space Separator 213
 
11.1%
Uppercase Letter 36
 
1.9%
Close Punctuation 27
 
1.4%
Open Punctuation 27
 
1.4%
Lowercase Letter 12
 
0.6%
Decimal Number 11
 
0.6%
Other Punctuation 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 321
20.3%
전 158
 
10.0%
충 153
 
9.7%
수 151
 
9.5%
하 48
 
3.0%
이 48
 
3.0%
넷 41
 
2.6%
게 27
 
1.7%
산 26
 
1.6%
휴 26
 
1.6%
Other values (173) 585
36.9%
Uppercase Letter
ValueCountFrequency (%)
H 13
36.1%
E 4
 
11.1%
S 3
 
8.3%
G 3
 
8.3%
P 3
 
8.3%
K 2
 
5.6%
L 2
 
5.6%
T 2
 
5.6%
M 1
 
2.8%
C 1
 
2.8%
Other values (2) 2
 
5.6%
Lowercase Letter
ValueCountFrequency (%)
o 2
16.7%
n 2
16.7%
i 2
16.7%
t 2
16.7%
g 1
8.3%
a 1
8.3%
v 1
8.3%
e 1
8.3%
Decimal Number
ValueCountFrequency (%)
1 6
54.5%
2 5
45.5%
Space Separator
ValueCountFrequency (%)
213
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1584
82.7%
Common 283
 
14.8%
Latin 48
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 321
20.3%
전 158
 
10.0%
충 153
 
9.7%
수 151
 
9.5%
하 48
 
3.0%
이 48
 
3.0%
넷 41
 
2.6%
게 27
 
1.7%
산 26
 
1.6%
휴 26
 
1.6%
Other values (173) 585
36.9%
Latin
ValueCountFrequency (%)
H 13
27.1%
E 4
 
8.3%
S 3
 
6.2%
G 3
 
6.2%
P 3
 
6.2%
K 2
 
4.2%
o 2
 
4.2%
n 2
 
4.2%
i 2
 
4.2%
L 2
 
4.2%
Other values (10) 12
25.0%
Common
ValueCountFrequency (%)
213
75.3%
) 27
 
9.5%
( 27
 
9.5%
1 6
 
2.1%
/ 5
 
1.8%
2 5
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1584
82.7%
ASCII 331
 
17.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 321
20.3%
전 158
 
10.0%
충 153
 
9.7%
수 151
 
9.5%
하 48
 
3.0%
이 48
 
3.0%
넷 41
 
2.6%
게 27
 
1.7%
산 26
 
1.6%
휴 26
 
1.6%
Other values (173) 585
36.9%
ASCII
ValueCountFrequency (%)
213
64.4%
) 27
 
8.2%
( 27
 
8.2%
H 13
 
3.9%
1 6
 
1.8%
/ 5
 
1.5%
2 5
 
1.5%
E 4
 
1.2%
S 3
 
0.9%
G 3
 
0.9%
Other values (16) 25
 
7.6%

디스펜서갯수
Categorical

IMBALANCE 

Distinct4
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1
118 
2
26 
3
 
6
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 118
78.1%
2 26
 
17.2%
3 6
 
4.0%
4 1
 
0.7%

Length

2024-04-06T17:54:29.805044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:54:30.019942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 118
78.1%
2 26
 
17.2%
3 6
 
4.0%
4 1
 
0.7%

디스펜서 타입
Categorical

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
싱글
92 
듀얼
56 
듀얼+듀얼
 
3

Length

Max length5
Median length2
Mean length2.0596026
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row듀얼
2nd row싱글
3rd row듀얼
4th row듀얼
5th row싱글

Common Values

ValueCountFrequency (%)
싱글 92
60.9%
듀얼 56
37.1%
듀얼+듀얼 3
 
2.0%

Length

2024-04-06T17:54:30.354971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:54:30.562168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
싱글 92
60.9%
듀얼 56
37.1%
듀얼+듀얼 3
 
2.0%

압축기충전압력(mpa)
Real number (ℝ)

Distinct11
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.986755
Minimum35
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-06T17:54:30.745943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile70
Q170
median70
Q370
95-th percentile84.5
Maximum90
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.1762845
Coefficient of variation (CV)0.087006153
Kurtosis13.536245
Mean70.986755
Median Absolute Deviation (MAD)0
Skewness-1.2194336
Sum10719
Variance38.14649
MonotonicityNot monotonic
2024-04-06T17:54:30.952093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
70 129
85.4%
80 7
 
4.6%
85 4
 
2.6%
90 3
 
2.0%
75 2
 
1.3%
40 1
 
0.7%
73 1
 
0.7%
84 1
 
0.7%
50 1
 
0.7%
35 1
 
0.7%
ValueCountFrequency (%)
35 1
 
0.7%
40 1
 
0.7%
50 1
 
0.7%
70 129
85.4%
73 1
 
0.7%
75 2
 
1.3%
80 7
 
4.6%
84 1
 
0.7%
85 4
 
2.6%
87 1
 
0.7%
ValueCountFrequency (%)
90 3
 
2.0%
87 1
 
0.7%
85 4
 
2.6%
84 1
 
0.7%
80 7
 
4.6%
75 2
 
1.3%
73 1
 
0.7%
70 129
85.4%
50 1
 
0.7%
40 1
 
0.7%

Interactions

2024-04-06T17:54:26.248423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:25.929702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:26.401798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:54:26.094518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:54:31.095281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분디스펜서갯수디스펜서 타입압축기충전압력(mpa)
구분1.0000.2440.3760.000
디스펜서갯수0.2441.0000.2180.412
디스펜서 타입0.3760.2181.0000.505
압축기충전압력(mpa)0.0000.4120.5051.000
2024-04-06T17:54:31.260663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
디스펜서 타입디스펜서갯수
디스펜서 타입1.0000.207
디스펜서갯수0.2071.000
2024-04-06T17:54:31.412823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분압축기충전압력(mpa)디스펜서갯수디스펜서 타입
구분1.0000.0590.1470.259
압축기충전압력(mpa)0.0591.0000.2750.238
디스펜서갯수0.1470.2751.0000.207
디스펜서 타입0.2590.2380.2071.000

Missing values

2024-04-06T17:54:26.597682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:54:26.777823image/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

구분충전소 코드충전소 명디스펜서갯수디스펜서 타입압축기충전압력(mpa)
012920020121HS2014001광주 진곡수소충전소1듀얼70
124480020121HS2015001내포수소충전소1싱글70
233114020121HS2017001옥동LPG 수소 복합충전소1듀얼70
344812120121HS2017002창원팔룡 수소충전소1듀얼70
452771020121HS2017003대구 주행시험장 수소충전소1싱글70
562920020121HS2018001광주 동곡수소충전소1싱글70
673120020121HS2018002경동수소복합충전소2싱글70
784812320121HS2018003창원성주수소충전소2듀얼70
893171020121HS2019001신일복합 수소충전소2싱글70
9104155020121HS2019002H안성휴게소 수소충전소 (서울 상행)1싱글70
구분충전소 코드충전소 명디스펜서갯수디스펜서 타입압축기충전압력(mpa)
1411424812720121HS2023009마산자유무역지역 수소충전소2듀얼+듀얼80
1421434215020121HS2022056하이넷 강릉시청 수소충전소2싱글70
1431444155020121HS2023010안성맞춤(제천)휴게소 수소충전소2싱글70
1441454427020121HS2023011현대제철 H수소충전소2싱글80
1451463114020121HS2023012울산상개 SK수소충전소4듀얼87
1461474143020121HS2023013하이넷 의왕왕곡 수소충전소1싱글70
1471484146320121HS2023014기흥휴게소(부산방향) 수소충전소2싱글75
1481493011020121HS2023015하이넷 대전삼정 수소충전소1싱글70
1491504418020121HS2023016보령1호 수소충전소3듀얼70
1501514580020121HS2023017부안 곰소 수소충전소1듀얼70